From 9d6c4484b5b8574bfda96f697af147ba274de7b3 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Mon, 4 Aug 2025 14:02:42 -0500 Subject: [PATCH 01/26] Update backends --- docs/tutorials/hello-world.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/hello-world.ipynb b/docs/tutorials/hello-world.ipynb index 20feefb8aac..68f61c00808 100644 --- a/docs/tutorials/hello-world.ipynb +++ b/docs/tutorials/hello-world.ipynb @@ -327,8 +327,8 @@ "\n", " # Use the following code instead if you want to run on a simulator:\n", "\n", - " from qiskit_ibm_runtime.fake_provider import FakeAlmadenV2\n", - " backend = FakeAlmadenV2()\n", + " from qiskit_ibm_runtime.fake_provider import FakeFez\n", + " backend = FakeFez()\n", " estimator = Estimator(backend)\n", "\n", " # Convert to an ISA circuit and layout-mapped observables.\n", From e89b11a17a231b7f180ce8ec53a06ca76ab5adca Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Mon, 4 Aug 2025 16:09:28 -0500 Subject: [PATCH 02/26] start grover --- docs/guides/qpu-information.mdx | 7 +- docs/guides/visualize-results.ipynb | 16 +---- docs/tutorials/grovers-algorithm.ipynb | 88 +++++++++++++++++++++++--- 3 files changed, 88 insertions(+), 23 deletions(-) diff --git a/docs/guides/qpu-information.mdx b/docs/guides/qpu-information.mdx index 316a985b712..c6f231c9a12 100644 --- a/docs/guides/qpu-information.mdx +++ b/docs/guides/qpu-information.mdx @@ -81,7 +81,12 @@ To access the details page, click the **Compute resources** tab, then click the To find your available QPUs, open the [Compute resources](https://quantum.cloud.ibm.com/computers) page (make sure you are signed in). Note that your selected region might impact the QPUs listed. Click a QPU to view its details. -You can also view your available QPUs by using the [backends API.](/docs/api/qiskit-ibm-runtime/qiskit-runtime-service#backends) +You can also view your available QPUs by using the [backends API.](/docs/api/qiskit-ibm-runtime/qiskit-runtime-service#backends) For example, the following code will return all of the backends that the specified instance (`my_instance`) can access: + +```python + QiskitRuntimeService(instance="my_instance_CRN") + service.backend() +``` ## View QPU configuration diff --git a/docs/guides/visualize-results.ipynb b/docs/guides/visualize-results.ipynb index 22c0390ae63..c336f6d355e 100644 --- a/docs/guides/visualize-results.ipynb +++ b/docs/guides/visualize-results.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "1adb4ac8-f331-485b-89f5-e4ec20a6855f", "metadata": {}, "source": [ "# Visualize results" @@ -10,7 +9,6 @@ }, { "cell_type": "markdown", - "id": "6efd5b06-c754-4b34-a14d-e404c9e920cb", "metadata": { "tags": [ "version-info" @@ -32,7 +30,6 @@ }, { "cell_type": "markdown", - "id": "b446771a-caf4-4b59-8e8c-1d115249b818", "metadata": {}, "source": [ "## Plot histogram \n", @@ -53,7 +50,6 @@ { "cell_type": "code", "execution_count": 1, - "id": "03592c4f-8a0a-4c0b-81f6-658f57d33ee4", "metadata": {}, "outputs": [], "source": [ @@ -71,7 +67,6 @@ { "cell_type": "code", "execution_count": 2, - "id": "2ff2c2d1-185f-4183-b80a-b5b599411245", "metadata": {}, "outputs": [ { @@ -103,7 +98,6 @@ { "cell_type": "code", "execution_count": 3, - "id": "3b41d6da-1bca-4b82-ac84-b5b811b7d430", "metadata": {}, "outputs": [ { @@ -123,7 +117,6 @@ }, { "cell_type": "markdown", - "id": "330e0fcb-ce13-4ad2-855e-4e0b8665cf76", "metadata": {}, "source": [ "### Options when plotting a histogram\n", @@ -141,7 +134,6 @@ { "cell_type": "code", "execution_count": 4, - "id": "7f456bcc-94a8-4522-9be2-77ea91045c1f", "metadata": {}, "outputs": [ { @@ -178,7 +170,6 @@ }, { "cell_type": "markdown", - "id": "2a3d185d-96ce-4a91-890f-b8e3d7f31eb0", "metadata": {}, "source": [ "## Plotting estimator results\n", @@ -191,7 +182,6 @@ { "cell_type": "code", "execution_count": 5, - "id": "35e07a46-8423-44ff-abad-7c10c72291a5", "metadata": {}, "outputs": [ { @@ -264,7 +254,6 @@ }, { "cell_type": "markdown", - "id": "d0e52ff0-96d5-45fb-b5df-38f43fd19ecc", "metadata": {}, "source": [ "The following cell uses the estimated [standard error](https://en.wikipedia.org/wiki/Standard_error) of each result and adds them as error bars. See the [`bar` plot documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html) for a full description of the plot." @@ -273,7 +262,6 @@ { "cell_type": "code", "execution_count": 6, - "id": "6f122049-0b5a-49e8-bde4-d99d91f4b77c", "metadata": {}, "outputs": [ { @@ -313,7 +301,7 @@ "metadata": { "description": "Plot quantum circuit execution results using Qiskit", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -327,7 +315,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.2" }, "title": "Visualize results" }, diff --git a/docs/tutorials/grovers-algorithm.ipynb b/docs/tutorials/grovers-algorithm.ipynb index c56043d8528..90994633fa9 100644 --- a/docs/tutorials/grovers-algorithm.ipynb +++ b/docs/tutorials/grovers-algorithm.ipynb @@ -114,14 +114,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "6e419a8a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'ibm_cusco'" + "'ibm_torino'" ] }, "execution_count": 2, @@ -151,19 +151,91 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "c150298f", "metadata": {}, "outputs": [ + { + "ename": "TypeError", + "evalue": "__array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m marked_states = [\u001b[33m\"\u001b[39m\u001b[33m011\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m100\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 3\u001b[39m oracle = grover_oracle(marked_states)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[43moracle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43miqp\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\circuit\\quantumcircuit.py:3440\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 3437\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m 3438\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3440\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3441\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3442\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3443\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3444\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3445\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3446\u001b[39m \u001b[43m \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3447\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3448\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3449\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3450\u001b[39m \u001b[43m \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3451\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3452\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3453\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3454\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3455\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3456\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3457\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3458\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3459\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:331\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m 317\u001b[39m circuit,\n\u001b[32m 318\u001b[39m filename=filename,\n\u001b[32m (...)\u001b[39m\u001b[32m 328\u001b[39m wire_order=complete_wire_order,\n\u001b[32m 329\u001b[39m )\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[43m \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 334\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 335\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 339\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 340\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 342\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 343\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 344\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 345\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 346\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 347\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 348\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 349\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m 350\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 351\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 352\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:727\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 709\u001b[39m fold = \u001b[32m25\u001b[39m\n\u001b[32m 711\u001b[39m qcd = _matplotlib.MatplotlibDrawer(\n\u001b[32m 712\u001b[39m qubits,\n\u001b[32m 713\u001b[39m clbits,\n\u001b[32m (...)\u001b[39m\u001b[32m 725\u001b[39m expr_len=expr_len,\n\u001b[32m 726\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m727\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mqcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:375\u001b[39m, in \u001b[36mMatplotlibDrawer.draw\u001b[39m\u001b[34m(self, filename, verbose)\u001b[39m\n\u001b[32m 373\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._global_phase:\n\u001b[32m 374\u001b[39m plt_mod.text(xl, yt, \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mGlobal Phase: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpi_check(\u001b[38;5;28mself\u001b[39m._global_phase,\u001b[38;5;250m \u001b[39moutput=\u001b[33m'\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m'\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m375\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_regs_wires\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_folds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_x_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqubits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclbits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 376\u001b[39m \u001b[38;5;28mself\u001b[39m._draw_ops(\n\u001b[32m 377\u001b[39m \u001b[38;5;28mself\u001b[39m._nodes,\n\u001b[32m 378\u001b[39m node_data,\n\u001b[32m (...)\u001b[39m\u001b[32m 385\u001b[39m verbose,\n\u001b[32m 386\u001b[39m )\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m filename:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:980\u001b[39m, in \u001b[36mMatplotlibDrawer._draw_regs_wires\u001b[39m\u001b[34m(self, num_folds, xmax, max_x_index, qubits_dict, clbits_dict, glob_data)\u001b[39m\n\u001b[32m 969\u001b[39m \u001b[38;5;66;03m# Mask off any lines or boxes in the bit label area to clean up\u001b[39;00m\n\u001b[32m 970\u001b[39m \u001b[38;5;66;03m# from folding for ControlFlow and other wrapping gates\u001b[39;00m\n\u001b[32m 971\u001b[39m box = glob_data[\u001b[33m\"\u001b[39m\u001b[33mpatches_mod\u001b[39m\u001b[33m\"\u001b[39m].Rectangle(\n\u001b[32m 972\u001b[39m xy=(glob_data[\u001b[33m\"\u001b[39m\u001b[33mx_offset\u001b[39m\u001b[33m\"\u001b[39m] - \u001b[32m0.1\u001b[39m, -fold_num * (glob_data[\u001b[33m\"\u001b[39m\u001b[33mn_lines\u001b[39m\u001b[33m\"\u001b[39m] + \u001b[32m1\u001b[39m) + \u001b[32m0.5\u001b[39m),\n\u001b[32m 973\u001b[39m width=-\u001b[32m25.0\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 978\u001b[39m zorder=PORDER_MASK,\n\u001b[32m 979\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m980\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_ax\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd_patch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbox\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 982\u001b[39m \u001b[38;5;66;03m# draw index number\u001b[39;00m\n\u001b[32m 983\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._style[\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m]:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2384\u001b[39m, in \u001b[36m_AxesBase.add_patch\u001b[39m\u001b[34m(self, p)\u001b[39m\n\u001b[32m 2382\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.get_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 2383\u001b[39m p.set_clip_path(\u001b[38;5;28mself\u001b[39m.patch)\n\u001b[32m-> \u001b[39m\u001b[32m2384\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_update_patch_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2385\u001b[39m \u001b[38;5;28mself\u001b[39m._children.append(p)\n\u001b[32m 2386\u001b[39m p._remove_method = \u001b[38;5;28mself\u001b[39m._children.remove\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2406\u001b[39m, in \u001b[36m_AxesBase._update_patch_limits\u001b[39m\u001b[34m(self, patch)\u001b[39m\n\u001b[32m 2403\u001b[39m \u001b[38;5;66;03m# Get all vertices on the path\u001b[39;00m\n\u001b[32m 2404\u001b[39m \u001b[38;5;66;03m# Loop through each segment to get extrema for Bezier curve sections\u001b[39;00m\n\u001b[32m 2405\u001b[39m vertices = []\n\u001b[32m-> \u001b[39m\u001b[32m2406\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m.\u001b[49m\u001b[43miter_bezier\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimplify\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2407\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Get distance along the curve of any extrema\u001b[39;49;00m\n\u001b[32m 2408\u001b[39m \u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdzeros\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxis_aligned_extrema\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2409\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calculate vertices of start, end and any extrema in between\u001b[39;49;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:437\u001b[39m, in \u001b[36mPath.iter_bezier\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 435\u001b[39m first_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 436\u001b[39m prev_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43miter_segments\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfirst_vert\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m!=\u001b[49m\u001b[43m \u001b[49m\u001b[43mPath\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMOVETO\u001b[49m\u001b[43m:\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:404\u001b[39m, in \u001b[36mPath.iter_segments\u001b[39m\u001b[34m(self, transform, remove_nans, clip, snap, stroke_width, simplify, curves, sketch)\u001b[39m\n\u001b[32m 402\u001b[39m codes = \u001b[38;5;28miter\u001b[39m(cleaned.codes)\n\u001b[32m 403\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m curr_vertices, code \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(vertices, codes):\n\u001b[32m--> \u001b[39m\u001b[32m404\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mSTOP\u001b[49m:\n\u001b[32m 405\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 406\u001b[39m extra_vertices = NUM_VERTICES_FOR_CODE[code] - \u001b[32m1\u001b[39m\n", + "\u001b[31mTypeError\u001b[39m: __array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" + ] + }, + { + "ename": "ValueError", + "evalue": "object __array__ method not producing an array", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\pyplot.py:197\u001b[39m, in \u001b[36m_draw_all_if_interactive\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 195\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_draw_all_if_interactive\u001b[39m() -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 196\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m matplotlib.is_interactive():\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\_pylab_helpers.py:132\u001b[39m, in \u001b[36mGcf.draw_all\u001b[39m\u001b[34m(cls, force)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.get_all_fig_managers():\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager.canvas.figure.stale:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43mmanager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:1893\u001b[39m, in \u001b[36mFigureCanvasBase.draw_idle\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1891\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_idle_drawing:\n\u001b[32m 1892\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._idle_draw_cntx():\n\u001b[32m-> \u001b[39m\u001b[32m1893\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[39m, in \u001b[36mFigureCanvasAgg.draw\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 385\u001b[39m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m.toolbar._wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.toolbar\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[32m 390\u001b[39m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[32m 391\u001b[39m \u001b[38;5;28msuper\u001b[39m().draw()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3153\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3150\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3153\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpatch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3154\u001b[39m mimage._draw_list_compositing_images(\n\u001b[32m 3155\u001b[39m renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m.suppressComposite)\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:588\u001b[39m, in \u001b[36mPatch.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 586\u001b[39m tpath = transform.transform_path_non_affine(path)\n\u001b[32m 587\u001b[39m affine = transform.get_affine()\n\u001b[32m--> \u001b[39m\u001b[32m588\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_paths_with_artist_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 589\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 590\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 591\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Work around a bug in the PDF and SVG renderers, which\u001b[39;49;00m\n\u001b[32m 592\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# do not draw the hatches if the facecolor is fully\u001b[39;49;00m\n\u001b[32m 593\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# transparent, but do if it is None.\u001b[39;49;00m\n\u001b[32m 594\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:573\u001b[39m, in \u001b[36mPatch._draw_paths_with_artist_properties\u001b[39m\u001b[34m(self, renderer, draw_path_args_list)\u001b[39m\n\u001b[32m 570\u001b[39m renderer = PathEffectRenderer(\u001b[38;5;28mself\u001b[39m.get_path_effects(), renderer)\n\u001b[32m 572\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m draw_path_args \u001b[38;5;129;01min\u001b[39;00m draw_path_args_list:\n\u001b[32m--> \u001b[39m\u001b[32m573\u001b[39m \u001b[43mrenderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mdraw_path_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 575\u001b[39m gc.restore()\n\u001b[32m 576\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33mpatch\u001b[39m\u001b[33m'\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:132\u001b[39m, in \u001b[36mRendererAgg.draw_path\u001b[39m\u001b[34m(self, gc, path, transform, rgbFace)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_renderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrgbFace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[32m 134\u001b[39m cant_chunk = \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m\n", + "\u001b[31mValueError\u001b[39m: object __array__ method not producing an array" + ] + }, + { + "ename": "ImportError", + "evalue": "cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\formatters.py:402\u001b[39m, in \u001b[36mBaseFormatter.__call__\u001b[39m\u001b[34m(self, obj)\u001b[39m\n\u001b[32m 400\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 401\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[32m 404\u001b[39m method = get_real_method(obj, \u001b[38;5;28mself\u001b[39m.print_method)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170\u001b[39m, in \u001b[36mprint_figure\u001b[39m\u001b[34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[39m\n\u001b[32m 167\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[32m 168\u001b[39m FigureCanvasBase(fig)\n\u001b[32m--> \u001b[39m\u001b[32m170\u001b[39m \u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 171\u001b[39m data = bytes_io.getvalue()\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m fmt == \u001b[33m'\u001b[39m\u001b[33msvg\u001b[39m\u001b[33m'\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:2164\u001b[39m, in \u001b[36mFigureCanvasBase.print_figure\u001b[39m\u001b[34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[39m\n\u001b[32m 2161\u001b[39m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[32m 2162\u001b[39m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[32m 2163\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[33m\"\u001b[39m\u001b[33m_draw_disabled\u001b[39m\u001b[33m\"\u001b[39m, nullcontext)():\n\u001b[32m-> \u001b[39m\u001b[32m2164\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[32m 2166\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches == \u001b[33m\"\u001b[39m\u001b[33mtight\u001b[39m\u001b[33m\"\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m 3153\u001b[39m \u001b[38;5;28mself\u001b[39m.patch.draw(renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3154\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3155\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n\u001b[32m 3158\u001b[39m sfig.draw(renderer)\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3070\u001b[39m, in \u001b[36m_AxesBase.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3067\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[32m 3068\u001b[39m _draw_rasterized(\u001b[38;5;28mself\u001b[39m.figure, artists_rasterized, renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3070\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3071\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3073\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33maxes\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3074\u001b[39m \u001b[38;5;28mself\u001b[39m.stale = \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:748\u001b[39m, in \u001b[36mText.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 745\u001b[39m renderer.open_group(\u001b[33m'\u001b[39m\u001b[33mtext\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28mself\u001b[39m.get_gid())\n\u001b[32m 747\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._cm_set(text=\u001b[38;5;28mself\u001b[39m._get_wrapped_text()):\n\u001b[32m--> \u001b[39m\u001b[32m748\u001b[39m bbox, info, descent = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 749\u001b[39m trans = \u001b[38;5;28mself\u001b[39m.get_transform()\n\u001b[32m 751\u001b[39m \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[32m 752\u001b[39m \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:442\u001b[39m, in \u001b[36mText._get_layout\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 440\u001b[39m corners_rotated = M.transform(corners_horiz)\n\u001b[32m 441\u001b[39m \u001b[38;5;66;03m# compute the bounds of the rotated box\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m442\u001b[39m xmin = \u001b[43mcorners_rotated\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 443\u001b[39m xmax = corners_rotated[:, \u001b[32m0\u001b[39m].max()\n\u001b[32m 444\u001b[39m ymin = corners_rotated[:, \u001b[32m1\u001b[39m].min()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:14\u001b[39m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m numerictypes \u001b[38;5;28;01mas\u001b[39;00m nt\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _exceptions\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_ufunc_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _no_nep50_warning\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_globals\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _NoValue\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcompat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pickle, os_fspath\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_ufunc_config.py:11\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcontextvars\u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_utils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m set_module\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mumath\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 12\u001b[39m UFUNC_BUFSIZE_DEFAULT,\n\u001b[32m 13\u001b[39m ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,\n\u001b[32m 14\u001b[39m SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,\n\u001b[32m 15\u001b[39m )\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m umath\n\u001b[32m 18\u001b[39m __all__ = [\n\u001b[32m 19\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mseterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33msetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mseterrcall\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterrcall\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 20\u001b[39m \u001b[33m\"\u001b[39m\u001b[33merrstate\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m_no_nep50_warning\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 21\u001b[39m ]\n", + "\u001b[31mImportError\u001b[39m: cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)" + ] + }, { "data": { "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 3, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -373,7 +445,7 @@ "metadata": { "description": "Learn the basics of quantum computing, and how to use IBM Quantum services and systems to solve real-world problems.", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -387,7 +459,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.2" }, "title": "Grover's algorithm" }, From 5d485398bc7f059e5b9a3b95fafa615aa47e64ce Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Tue, 5 Aug 2025 13:09:03 -0500 Subject: [PATCH 03/26] Don't need to install rustworkx --- .../advanced-techniques-for-qaoa.ipynb | 1 - ...antum-compilation-for-time-evolution.ipynb | 3 +- docs/tutorials/grovers-algorithm.ipynb | 258 ++++++++++++++---- docs/tutorials/multi-product-formula.ipynb | 1 - .../tutorials/operator-back-propagation.ipynb | 3 +- .../pauli-correlation-encoding-for-qaoa.ipynb | 3 +- ...ime-benchmarking-for-qubit-selection.ipynb | 103 +++---- ...anspilation-optimizations-with-sabre.ipynb | 1 - 8 files changed, 250 insertions(+), 123 deletions(-) diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index bd7c300f6f3..be9537d1ed1 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -48,7 +48,6 @@ "Before starting this tutorial, be sure you have the following installed:\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", "- Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "- Rustworkx graph library (`pip install rustworkx`)\n", "- Python SAT (`pip install python-sat`)" ] }, diff --git a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb index b01b7343728..7c2cc6ff90a 100644 --- a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb +++ b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb @@ -34,8 +34,7 @@ "\n", "* Qiskit SDK v1.0 or later, with visualization support (`pip install 'qiskit[visualization]'`)\n", "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "* AQC-Tensor Qiskit addon (`pip install 'qiskit-addon-aqc-tensor[aer,quimb-jax]'`)\n", - "* rustworkx v0.15 or later (`pip install rustworkx`)" + "* AQC-Tensor Qiskit addon (`pip install 'qiskit-addon-aqc-tensor[aer,quimb-jax]'`)" ] }, { diff --git a/docs/tutorials/grovers-algorithm.ipynb b/docs/tutorials/grovers-algorithm.ipynb index 90994633fa9..2c6a09aab5d 100644 --- a/docs/tutorials/grovers-algorithm.ipynb +++ b/docs/tutorials/grovers-algorithm.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "id": "e2cb0472", "metadata": {}, "outputs": [], @@ -151,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "c150298f", "metadata": {}, "outputs": [ @@ -162,7 +162,7 @@ "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m marked_states = [\u001b[33m\"\u001b[39m\u001b[33m011\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m100\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 3\u001b[39m oracle = grover_oracle(marked_states)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[43moracle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43miqp\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m marked_states = [\u001b[33m\"\u001b[39m\u001b[33m011\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m100\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 3\u001b[39m oracle = grover_oracle(marked_states)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[43moracle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43miqp\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\circuit\\quantumcircuit.py:3440\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 3437\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m 3438\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3440\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3441\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3442\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3443\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3444\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3445\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3446\u001b[39m \u001b[43m \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3447\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3448\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3449\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3450\u001b[39m \u001b[43m \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3451\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3452\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3453\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3454\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3455\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3456\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3457\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3458\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3459\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:331\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m 317\u001b[39m circuit,\n\u001b[32m 318\u001b[39m filename=filename,\n\u001b[32m (...)\u001b[39m\u001b[32m 328\u001b[39m wire_order=complete_wire_order,\n\u001b[32m 329\u001b[39m )\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[43m \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 334\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 335\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 339\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 340\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 342\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 343\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 344\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 345\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 346\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 347\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 348\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 349\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m 350\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 351\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 352\u001b[39m )\n", "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:727\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 709\u001b[39m fold = \u001b[32m25\u001b[39m\n\u001b[32m 711\u001b[39m qcd = _matplotlib.MatplotlibDrawer(\n\u001b[32m 712\u001b[39m qubits,\n\u001b[32m 713\u001b[39m clbits,\n\u001b[32m (...)\u001b[39m\u001b[32m 725\u001b[39m expr_len=expr_len,\n\u001b[32m 726\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m727\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mqcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n", @@ -179,7 +179,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" + "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" ] }, { @@ -246,33 +246,214 @@ ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "25487b93", + "cell_type": "code", + "execution_count": 11, + "id": "7baca7e2-99fc-4089-b5d8-30da56816a6a", "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "__array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m marked_states = [\u001b[33m\"\u001b[39m\u001b[33m011\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m100\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 3\u001b[39m oracle = grover_oracle(marked_states)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[43moracle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43miqp\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\circuit\\quantumcircuit.py:3440\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 3437\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m 3438\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3440\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3441\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3442\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3443\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3444\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3445\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3446\u001b[39m \u001b[43m \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3447\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3448\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3449\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3450\u001b[39m \u001b[43m \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3451\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3452\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3453\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3454\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3455\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3456\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3457\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3458\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3459\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:331\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m 317\u001b[39m circuit,\n\u001b[32m 318\u001b[39m filename=filename,\n\u001b[32m (...)\u001b[39m\u001b[32m 328\u001b[39m wire_order=complete_wire_order,\n\u001b[32m 329\u001b[39m )\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[43m \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 334\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 335\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 339\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 340\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 342\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 343\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 344\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 345\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 346\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 347\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 348\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 349\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m 350\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 351\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 352\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:727\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 709\u001b[39m fold = \u001b[32m25\u001b[39m\n\u001b[32m 711\u001b[39m qcd = _matplotlib.MatplotlibDrawer(\n\u001b[32m 712\u001b[39m qubits,\n\u001b[32m 713\u001b[39m clbits,\n\u001b[32m (...)\u001b[39m\u001b[32m 725\u001b[39m expr_len=expr_len,\n\u001b[32m 726\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m727\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mqcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:375\u001b[39m, in \u001b[36mMatplotlibDrawer.draw\u001b[39m\u001b[34m(self, filename, verbose)\u001b[39m\n\u001b[32m 373\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._global_phase:\n\u001b[32m 374\u001b[39m plt_mod.text(xl, yt, \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mGlobal Phase: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpi_check(\u001b[38;5;28mself\u001b[39m._global_phase,\u001b[38;5;250m \u001b[39moutput=\u001b[33m'\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m'\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m375\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_regs_wires\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_folds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_x_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqubits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclbits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 376\u001b[39m \u001b[38;5;28mself\u001b[39m._draw_ops(\n\u001b[32m 377\u001b[39m \u001b[38;5;28mself\u001b[39m._nodes,\n\u001b[32m 378\u001b[39m node_data,\n\u001b[32m (...)\u001b[39m\u001b[32m 385\u001b[39m verbose,\n\u001b[32m 386\u001b[39m )\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m filename:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:980\u001b[39m, in \u001b[36mMatplotlibDrawer._draw_regs_wires\u001b[39m\u001b[34m(self, num_folds, xmax, max_x_index, qubits_dict, clbits_dict, glob_data)\u001b[39m\n\u001b[32m 969\u001b[39m \u001b[38;5;66;03m# Mask off any lines or boxes in the bit label area to clean up\u001b[39;00m\n\u001b[32m 970\u001b[39m \u001b[38;5;66;03m# from folding for ControlFlow and other wrapping gates\u001b[39;00m\n\u001b[32m 971\u001b[39m box = glob_data[\u001b[33m\"\u001b[39m\u001b[33mpatches_mod\u001b[39m\u001b[33m\"\u001b[39m].Rectangle(\n\u001b[32m 972\u001b[39m xy=(glob_data[\u001b[33m\"\u001b[39m\u001b[33mx_offset\u001b[39m\u001b[33m\"\u001b[39m] - \u001b[32m0.1\u001b[39m, -fold_num * (glob_data[\u001b[33m\"\u001b[39m\u001b[33mn_lines\u001b[39m\u001b[33m\"\u001b[39m] + \u001b[32m1\u001b[39m) + \u001b[32m0.5\u001b[39m),\n\u001b[32m 973\u001b[39m width=-\u001b[32m25.0\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 978\u001b[39m zorder=PORDER_MASK,\n\u001b[32m 979\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m980\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_ax\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd_patch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbox\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 982\u001b[39m \u001b[38;5;66;03m# draw index number\u001b[39;00m\n\u001b[32m 983\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._style[\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m]:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2384\u001b[39m, in \u001b[36m_AxesBase.add_patch\u001b[39m\u001b[34m(self, p)\u001b[39m\n\u001b[32m 2382\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.get_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 2383\u001b[39m p.set_clip_path(\u001b[38;5;28mself\u001b[39m.patch)\n\u001b[32m-> \u001b[39m\u001b[32m2384\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_update_patch_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2385\u001b[39m \u001b[38;5;28mself\u001b[39m._children.append(p)\n\u001b[32m 2386\u001b[39m p._remove_method = \u001b[38;5;28mself\u001b[39m._children.remove\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2406\u001b[39m, in \u001b[36m_AxesBase._update_patch_limits\u001b[39m\u001b[34m(self, patch)\u001b[39m\n\u001b[32m 2403\u001b[39m \u001b[38;5;66;03m# Get all vertices on the path\u001b[39;00m\n\u001b[32m 2404\u001b[39m \u001b[38;5;66;03m# Loop through each segment to get extrema for Bezier curve sections\u001b[39;00m\n\u001b[32m 2405\u001b[39m vertices = []\n\u001b[32m-> \u001b[39m\u001b[32m2406\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m.\u001b[49m\u001b[43miter_bezier\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimplify\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2407\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Get distance along the curve of any extrema\u001b[39;49;00m\n\u001b[32m 2408\u001b[39m \u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdzeros\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxis_aligned_extrema\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2409\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calculate vertices of start, end and any extrema in between\u001b[39;49;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:437\u001b[39m, in \u001b[36mPath.iter_bezier\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 435\u001b[39m first_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 436\u001b[39m prev_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43miter_segments\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfirst_vert\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m!=\u001b[49m\u001b[43m \u001b[49m\u001b[43mPath\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMOVETO\u001b[49m\u001b[43m:\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:404\u001b[39m, in \u001b[36mPath.iter_segments\u001b[39m\u001b[34m(self, transform, remove_nans, clip, snap, stroke_width, simplify, curves, sketch)\u001b[39m\n\u001b[32m 402\u001b[39m codes = \u001b[38;5;28miter\u001b[39m(cleaned.codes)\n\u001b[32m 403\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m curr_vertices, code \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(vertices, codes):\n\u001b[32m--> \u001b[39m\u001b[32m404\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mSTOP\u001b[49m:\n\u001b[32m 405\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 406\u001b[39m extra_vertices = NUM_VERTICES_FOR_CODE[code] - \u001b[32m1\u001b[39m\n", + "\u001b[31mTypeError\u001b[39m: __array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" + ] + }, + { + "ename": "ValueError", + "evalue": "object __array__ method not producing an array", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\pyplot.py:197\u001b[39m, in \u001b[36m_draw_all_if_interactive\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 195\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_draw_all_if_interactive\u001b[39m() -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 196\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m matplotlib.is_interactive():\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\_pylab_helpers.py:132\u001b[39m, in \u001b[36mGcf.draw_all\u001b[39m\u001b[34m(cls, force)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.get_all_fig_managers():\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager.canvas.figure.stale:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43mmanager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:1893\u001b[39m, in \u001b[36mFigureCanvasBase.draw_idle\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1891\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_idle_drawing:\n\u001b[32m 1892\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._idle_draw_cntx():\n\u001b[32m-> \u001b[39m\u001b[32m1893\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[39m, in \u001b[36mFigureCanvasAgg.draw\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 385\u001b[39m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m.toolbar._wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.toolbar\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[32m 390\u001b[39m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[32m 391\u001b[39m \u001b[38;5;28msuper\u001b[39m().draw()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3153\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3150\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3153\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpatch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3154\u001b[39m mimage._draw_list_compositing_images(\n\u001b[32m 3155\u001b[39m renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m.suppressComposite)\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:588\u001b[39m, in \u001b[36mPatch.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 586\u001b[39m tpath = transform.transform_path_non_affine(path)\n\u001b[32m 587\u001b[39m affine = transform.get_affine()\n\u001b[32m--> \u001b[39m\u001b[32m588\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_paths_with_artist_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 589\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 590\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 591\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Work around a bug in the PDF and SVG renderers, which\u001b[39;49;00m\n\u001b[32m 592\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# do not draw the hatches if the facecolor is fully\u001b[39;49;00m\n\u001b[32m 593\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# transparent, but do if it is None.\u001b[39;49;00m\n\u001b[32m 594\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:573\u001b[39m, in \u001b[36mPatch._draw_paths_with_artist_properties\u001b[39m\u001b[34m(self, renderer, draw_path_args_list)\u001b[39m\n\u001b[32m 570\u001b[39m renderer = PathEffectRenderer(\u001b[38;5;28mself\u001b[39m.get_path_effects(), renderer)\n\u001b[32m 572\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m draw_path_args \u001b[38;5;129;01min\u001b[39;00m draw_path_args_list:\n\u001b[32m--> \u001b[39m\u001b[32m573\u001b[39m \u001b[43mrenderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mdraw_path_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 575\u001b[39m gc.restore()\n\u001b[32m 576\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33mpatch\u001b[39m\u001b[33m'\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:132\u001b[39m, in \u001b[36mRendererAgg.draw_path\u001b[39m\u001b[34m(self, gc, path, transform, rgbFace)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_renderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrgbFace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[32m 134\u001b[39m cant_chunk = \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m\n", + "\u001b[31mValueError\u001b[39m: object __array__ method not producing an array" + ] + }, + { + "ename": "ImportError", + "evalue": "cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\formatters.py:402\u001b[39m, in \u001b[36mBaseFormatter.__call__\u001b[39m\u001b[34m(self, obj)\u001b[39m\n\u001b[32m 400\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 401\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[32m 404\u001b[39m method = get_real_method(obj, \u001b[38;5;28mself\u001b[39m.print_method)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170\u001b[39m, in \u001b[36mprint_figure\u001b[39m\u001b[34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[39m\n\u001b[32m 167\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[32m 168\u001b[39m FigureCanvasBase(fig)\n\u001b[32m--> \u001b[39m\u001b[32m170\u001b[39m \u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 171\u001b[39m data = bytes_io.getvalue()\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m fmt == \u001b[33m'\u001b[39m\u001b[33msvg\u001b[39m\u001b[33m'\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:2164\u001b[39m, in \u001b[36mFigureCanvasBase.print_figure\u001b[39m\u001b[34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[39m\n\u001b[32m 2161\u001b[39m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[32m 2162\u001b[39m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[32m 2163\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[33m\"\u001b[39m\u001b[33m_draw_disabled\u001b[39m\u001b[33m\"\u001b[39m, nullcontext)():\n\u001b[32m-> \u001b[39m\u001b[32m2164\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[32m 2166\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches == \u001b[33m\"\u001b[39m\u001b[33mtight\u001b[39m\u001b[33m\"\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m 3153\u001b[39m \u001b[38;5;28mself\u001b[39m.patch.draw(renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3154\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3155\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n\u001b[32m 3158\u001b[39m sfig.draw(renderer)\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3070\u001b[39m, in \u001b[36m_AxesBase.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3067\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[32m 3068\u001b[39m _draw_rasterized(\u001b[38;5;28mself\u001b[39m.figure, artists_rasterized, renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3070\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3071\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3073\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33maxes\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3074\u001b[39m \u001b[38;5;28mself\u001b[39m.stale = \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:748\u001b[39m, in \u001b[36mText.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 745\u001b[39m renderer.open_group(\u001b[33m'\u001b[39m\u001b[33mtext\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28mself\u001b[39m.get_gid())\n\u001b[32m 747\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._cm_set(text=\u001b[38;5;28mself\u001b[39m._get_wrapped_text()):\n\u001b[32m--> \u001b[39m\u001b[32m748\u001b[39m bbox, info, descent = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 749\u001b[39m trans = \u001b[38;5;28mself\u001b[39m.get_transform()\n\u001b[32m 751\u001b[39m \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[32m 752\u001b[39m \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:442\u001b[39m, in \u001b[36mText._get_layout\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 440\u001b[39m corners_rotated = M.transform(corners_horiz)\n\u001b[32m 441\u001b[39m \u001b[38;5;66;03m# compute the bounds of the rotated box\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m442\u001b[39m xmin = \u001b[43mcorners_rotated\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 443\u001b[39m xmax = corners_rotated[:, \u001b[32m0\u001b[39m].max()\n\u001b[32m 444\u001b[39m ymin = corners_rotated[:, \u001b[32m1\u001b[39m].min()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:14\u001b[39m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m numerictypes \u001b[38;5;28;01mas\u001b[39;00m nt\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _exceptions\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_ufunc_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _no_nep50_warning\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_globals\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _NoValue\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcompat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pickle, os_fspath\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_ufunc_config.py:11\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcontextvars\u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_utils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m set_module\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mumath\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 12\u001b[39m UFUNC_BUFSIZE_DEFAULT,\n\u001b[32m 13\u001b[39m ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,\n\u001b[32m 14\u001b[39m SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,\n\u001b[32m 15\u001b[39m )\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m umath\n\u001b[32m 18\u001b[39m __all__ = [\n\u001b[32m 19\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mseterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33msetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mseterrcall\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterrcall\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 20\u001b[39m \u001b[33m\"\u001b[39m\u001b[33merrstate\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m_no_nep50_warning\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 21\u001b[39m ]\n", + "\u001b[31mImportError\u001b[39m: cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "### Grover operator\n", + "marked_states = [\"011\", \"100\"]\n", "\n", - "The built-in Qiskit `grover_operator()` takes an oracle circuit and returns a circuit that is composed of the oracle circuit itself and a circuit that amplifies the states marked by the oracle. Here, we use the `decompose()` method the circuit to see the gates within the operator:" + "oracle = grover_oracle(marked_states)\n", + "oracle.draw(output=\"mpl\", style=\"iqp\")" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "283d5265", + "execution_count": 11, + "id": "d3a26fc9-9090-4527-a749-a412661260b6", "metadata": {}, "outputs": [ + { + "ename": "TypeError", + "evalue": "__array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m marked_states = [\u001b[33m\"\u001b[39m\u001b[33m011\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m100\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 3\u001b[39m oracle = grover_oracle(marked_states)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[43moracle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43miqp\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\circuit\\quantumcircuit.py:3440\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 3437\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m 3438\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3440\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3441\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3442\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3443\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3444\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3445\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3446\u001b[39m \u001b[43m \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3447\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3448\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3449\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3450\u001b[39m \u001b[43m \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3451\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3452\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3453\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3454\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3455\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3456\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3457\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3458\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3459\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:331\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m 317\u001b[39m circuit,\n\u001b[32m 318\u001b[39m filename=filename,\n\u001b[32m (...)\u001b[39m\u001b[32m 328\u001b[39m wire_order=complete_wire_order,\n\u001b[32m 329\u001b[39m )\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[43m \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 334\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 335\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 339\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 340\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 342\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 343\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 344\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 345\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 346\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 347\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 348\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 349\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m 350\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 351\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 352\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:727\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 709\u001b[39m fold = \u001b[32m25\u001b[39m\n\u001b[32m 711\u001b[39m qcd = _matplotlib.MatplotlibDrawer(\n\u001b[32m 712\u001b[39m qubits,\n\u001b[32m 713\u001b[39m clbits,\n\u001b[32m (...)\u001b[39m\u001b[32m 725\u001b[39m expr_len=expr_len,\n\u001b[32m 726\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m727\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mqcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:375\u001b[39m, in \u001b[36mMatplotlibDrawer.draw\u001b[39m\u001b[34m(self, filename, verbose)\u001b[39m\n\u001b[32m 373\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._global_phase:\n\u001b[32m 374\u001b[39m plt_mod.text(xl, yt, \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mGlobal Phase: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpi_check(\u001b[38;5;28mself\u001b[39m._global_phase,\u001b[38;5;250m \u001b[39moutput=\u001b[33m'\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m'\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m375\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_regs_wires\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_folds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_x_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqubits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclbits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 376\u001b[39m \u001b[38;5;28mself\u001b[39m._draw_ops(\n\u001b[32m 377\u001b[39m \u001b[38;5;28mself\u001b[39m._nodes,\n\u001b[32m 378\u001b[39m node_data,\n\u001b[32m (...)\u001b[39m\u001b[32m 385\u001b[39m verbose,\n\u001b[32m 386\u001b[39m )\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m filename:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:980\u001b[39m, in \u001b[36mMatplotlibDrawer._draw_regs_wires\u001b[39m\u001b[34m(self, num_folds, xmax, max_x_index, qubits_dict, clbits_dict, glob_data)\u001b[39m\n\u001b[32m 969\u001b[39m \u001b[38;5;66;03m# Mask off any lines or boxes in the bit label area to clean up\u001b[39;00m\n\u001b[32m 970\u001b[39m \u001b[38;5;66;03m# from folding for ControlFlow and other wrapping gates\u001b[39;00m\n\u001b[32m 971\u001b[39m box = glob_data[\u001b[33m\"\u001b[39m\u001b[33mpatches_mod\u001b[39m\u001b[33m\"\u001b[39m].Rectangle(\n\u001b[32m 972\u001b[39m xy=(glob_data[\u001b[33m\"\u001b[39m\u001b[33mx_offset\u001b[39m\u001b[33m\"\u001b[39m] - \u001b[32m0.1\u001b[39m, -fold_num * (glob_data[\u001b[33m\"\u001b[39m\u001b[33mn_lines\u001b[39m\u001b[33m\"\u001b[39m] + \u001b[32m1\u001b[39m) + \u001b[32m0.5\u001b[39m),\n\u001b[32m 973\u001b[39m width=-\u001b[32m25.0\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 978\u001b[39m zorder=PORDER_MASK,\n\u001b[32m 979\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m980\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_ax\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd_patch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbox\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 982\u001b[39m \u001b[38;5;66;03m# draw index number\u001b[39;00m\n\u001b[32m 983\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._style[\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m]:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2384\u001b[39m, in \u001b[36m_AxesBase.add_patch\u001b[39m\u001b[34m(self, p)\u001b[39m\n\u001b[32m 2382\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.get_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 2383\u001b[39m p.set_clip_path(\u001b[38;5;28mself\u001b[39m.patch)\n\u001b[32m-> \u001b[39m\u001b[32m2384\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_update_patch_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2385\u001b[39m \u001b[38;5;28mself\u001b[39m._children.append(p)\n\u001b[32m 2386\u001b[39m p._remove_method = \u001b[38;5;28mself\u001b[39m._children.remove\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2406\u001b[39m, in \u001b[36m_AxesBase._update_patch_limits\u001b[39m\u001b[34m(self, patch)\u001b[39m\n\u001b[32m 2403\u001b[39m \u001b[38;5;66;03m# Get all vertices on the path\u001b[39;00m\n\u001b[32m 2404\u001b[39m \u001b[38;5;66;03m# Loop through each segment to get extrema for Bezier curve sections\u001b[39;00m\n\u001b[32m 2405\u001b[39m vertices = []\n\u001b[32m-> \u001b[39m\u001b[32m2406\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m.\u001b[49m\u001b[43miter_bezier\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimplify\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2407\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Get distance along the curve of any extrema\u001b[39;49;00m\n\u001b[32m 2408\u001b[39m \u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdzeros\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxis_aligned_extrema\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2409\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calculate vertices of start, end and any extrema in between\u001b[39;49;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:437\u001b[39m, in \u001b[36mPath.iter_bezier\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 435\u001b[39m first_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 436\u001b[39m prev_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43miter_segments\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfirst_vert\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m!=\u001b[49m\u001b[43m \u001b[49m\u001b[43mPath\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMOVETO\u001b[49m\u001b[43m:\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:404\u001b[39m, in \u001b[36mPath.iter_segments\u001b[39m\u001b[34m(self, transform, remove_nans, clip, snap, stroke_width, simplify, curves, sketch)\u001b[39m\n\u001b[32m 402\u001b[39m codes = \u001b[38;5;28miter\u001b[39m(cleaned.codes)\n\u001b[32m 403\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m curr_vertices, code \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(vertices, codes):\n\u001b[32m--> \u001b[39m\u001b[32m404\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mSTOP\u001b[49m:\n\u001b[32m 405\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 406\u001b[39m extra_vertices = NUM_VERTICES_FOR_CODE[code] - \u001b[32m1\u001b[39m\n", + "\u001b[31mTypeError\u001b[39m: __array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" + ] + }, + { + "ename": "ValueError", + "evalue": "object __array__ method not producing an array", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\pyplot.py:197\u001b[39m, in \u001b[36m_draw_all_if_interactive\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 195\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_draw_all_if_interactive\u001b[39m() -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 196\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m matplotlib.is_interactive():\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\_pylab_helpers.py:132\u001b[39m, in \u001b[36mGcf.draw_all\u001b[39m\u001b[34m(cls, force)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.get_all_fig_managers():\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager.canvas.figure.stale:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43mmanager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:1893\u001b[39m, in \u001b[36mFigureCanvasBase.draw_idle\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1891\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_idle_drawing:\n\u001b[32m 1892\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._idle_draw_cntx():\n\u001b[32m-> \u001b[39m\u001b[32m1893\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[39m, in \u001b[36mFigureCanvasAgg.draw\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 385\u001b[39m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m.toolbar._wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.toolbar\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[32m 390\u001b[39m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[32m 391\u001b[39m \u001b[38;5;28msuper\u001b[39m().draw()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3153\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3150\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3153\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpatch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3154\u001b[39m mimage._draw_list_compositing_images(\n\u001b[32m 3155\u001b[39m renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m.suppressComposite)\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:588\u001b[39m, in \u001b[36mPatch.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 586\u001b[39m tpath = transform.transform_path_non_affine(path)\n\u001b[32m 587\u001b[39m affine = transform.get_affine()\n\u001b[32m--> \u001b[39m\u001b[32m588\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_paths_with_artist_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 589\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 590\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 591\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Work around a bug in the PDF and SVG renderers, which\u001b[39;49;00m\n\u001b[32m 592\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# do not draw the hatches if the facecolor is fully\u001b[39;49;00m\n\u001b[32m 593\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# transparent, but do if it is None.\u001b[39;49;00m\n\u001b[32m 594\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:573\u001b[39m, in \u001b[36mPatch._draw_paths_with_artist_properties\u001b[39m\u001b[34m(self, renderer, draw_path_args_list)\u001b[39m\n\u001b[32m 570\u001b[39m renderer = PathEffectRenderer(\u001b[38;5;28mself\u001b[39m.get_path_effects(), renderer)\n\u001b[32m 572\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m draw_path_args \u001b[38;5;129;01min\u001b[39;00m draw_path_args_list:\n\u001b[32m--> \u001b[39m\u001b[32m573\u001b[39m \u001b[43mrenderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mdraw_path_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 575\u001b[39m gc.restore()\n\u001b[32m 576\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33mpatch\u001b[39m\u001b[33m'\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:132\u001b[39m, in \u001b[36mRendererAgg.draw_path\u001b[39m\u001b[34m(self, gc, path, transform, rgbFace)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_renderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrgbFace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[32m 134\u001b[39m cant_chunk = \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m\n", + "\u001b[31mValueError\u001b[39m: object __array__ method not producing an array" + ] + }, + { + "ename": "ImportError", + "evalue": "cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\formatters.py:402\u001b[39m, in \u001b[36mBaseFormatter.__call__\u001b[39m\u001b[34m(self, obj)\u001b[39m\n\u001b[32m 400\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 401\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[32m 404\u001b[39m method = get_real_method(obj, \u001b[38;5;28mself\u001b[39m.print_method)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170\u001b[39m, in \u001b[36mprint_figure\u001b[39m\u001b[34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[39m\n\u001b[32m 167\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[32m 168\u001b[39m FigureCanvasBase(fig)\n\u001b[32m--> \u001b[39m\u001b[32m170\u001b[39m \u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 171\u001b[39m data = bytes_io.getvalue()\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m fmt == \u001b[33m'\u001b[39m\u001b[33msvg\u001b[39m\u001b[33m'\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:2164\u001b[39m, in \u001b[36mFigureCanvasBase.print_figure\u001b[39m\u001b[34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[39m\n\u001b[32m 2161\u001b[39m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[32m 2162\u001b[39m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[32m 2163\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[33m\"\u001b[39m\u001b[33m_draw_disabled\u001b[39m\u001b[33m\"\u001b[39m, nullcontext)():\n\u001b[32m-> \u001b[39m\u001b[32m2164\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[32m 2166\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches == \u001b[33m\"\u001b[39m\u001b[33mtight\u001b[39m\u001b[33m\"\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m 3153\u001b[39m \u001b[38;5;28mself\u001b[39m.patch.draw(renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3154\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3155\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n\u001b[32m 3158\u001b[39m sfig.draw(renderer)\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3070\u001b[39m, in \u001b[36m_AxesBase.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3067\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[32m 3068\u001b[39m _draw_rasterized(\u001b[38;5;28mself\u001b[39m.figure, artists_rasterized, renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3070\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3071\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3073\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33maxes\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3074\u001b[39m \u001b[38;5;28mself\u001b[39m.stale = \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:748\u001b[39m, in \u001b[36mText.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 745\u001b[39m renderer.open_group(\u001b[33m'\u001b[39m\u001b[33mtext\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28mself\u001b[39m.get_gid())\n\u001b[32m 747\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._cm_set(text=\u001b[38;5;28mself\u001b[39m._get_wrapped_text()):\n\u001b[32m--> \u001b[39m\u001b[32m748\u001b[39m bbox, info, descent = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 749\u001b[39m trans = \u001b[38;5;28mself\u001b[39m.get_transform()\n\u001b[32m 751\u001b[39m \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[32m 752\u001b[39m \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:442\u001b[39m, in \u001b[36mText._get_layout\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 440\u001b[39m corners_rotated = M.transform(corners_horiz)\n\u001b[32m 441\u001b[39m \u001b[38;5;66;03m# compute the bounds of the rotated box\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m442\u001b[39m xmin = \u001b[43mcorners_rotated\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 443\u001b[39m xmax = corners_rotated[:, \u001b[32m0\u001b[39m].max()\n\u001b[32m 444\u001b[39m ymin = corners_rotated[:, \u001b[32m1\u001b[39m].min()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:14\u001b[39m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m numerictypes \u001b[38;5;28;01mas\u001b[39;00m nt\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _exceptions\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_ufunc_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _no_nep50_warning\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_globals\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _NoValue\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcompat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pickle, os_fspath\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_ufunc_config.py:11\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcontextvars\u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_utils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m set_module\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mumath\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 12\u001b[39m UFUNC_BUFSIZE_DEFAULT,\n\u001b[32m 13\u001b[39m ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,\n\u001b[32m 14\u001b[39m SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,\n\u001b[32m 15\u001b[39m )\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m umath\n\u001b[32m 18\u001b[39m __all__ = [\n\u001b[32m 19\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mseterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33msetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mseterrcall\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterrcall\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 20\u001b[39m \u001b[33m\"\u001b[39m\u001b[33merrstate\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m_no_nep50_warning\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 21\u001b[39m ]\n", + "\u001b[31mImportError\u001b[39m: cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)" + ] + }, { "data": { "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 4, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], + "source": [ + "marked_states = [\"011\", \"100\"]\n", + "\n", + "oracle = grover_oracle(marked_states)\n", + "oracle.draw(output=\"mpl\", style=\"iqp\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "25487b93", + "metadata": {}, + "source": [ + "### Grover operator\n", + "\n", + "The built-in Qiskit `grover_operator()` takes an oracle circuit and returns a circuit that is composed of the oracle circuit itself and a circuit that amplifies the states marked by the oracle. Here, we use the `decompose()` method the circuit to see the gates within the operator:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "283d5265", + "metadata": {}, + "outputs": [], "source": [ "grover_op = grover_operator(oracle)\n", "grover_op.decompose().draw(output=\"mpl\", style=\"iqp\")" @@ -289,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "f4c3d4b5", "metadata": {}, "outputs": [], @@ -313,21 +494,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "4933ae44", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "qc = QuantumCircuit(grover_op.num_qubits)\n", "# Create even superposition of all basis states\n", @@ -349,21 +519,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "c9a3020e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "target = backend.target\n", "pm = generate_preset_pass_manager(target=target, optimization_level=3)\n", @@ -387,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "0eb154d4", "metadata": {}, "outputs": [], @@ -410,21 +569,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "a5ef9913", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "plot_distribution(dist)" ] diff --git a/docs/tutorials/multi-product-formula.ipynb b/docs/tutorials/multi-product-formula.ipynb index 9ddc0a9c301..f379a43befa 100644 --- a/docs/tutorials/multi-product-formula.ipynb +++ b/docs/tutorials/multi-product-formula.ipynb @@ -82,7 +82,6 @@ "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", "* MPF Qiskit addons (`pip install qiskit_addon_mpf`)\n", "* Qiskit addons utils (`pip install qiskit_addon_utils`)\n", - "* Rustworkx graph library (`pip install rustworkx`)\n", "* Quimb library (`pip install quimb`)\n", "* Qiskit Quimb library (`pip install qiskit-quimb`)\n", "* Numpy v0.21 for compatibility across packages (`pip install numpy==0.21`)" diff --git a/docs/tutorials/operator-back-propagation.ipynb b/docs/tutorials/operator-back-propagation.ipynb index 7d2472c4004..b96329d1402 100644 --- a/docs/tutorials/operator-back-propagation.ipynb +++ b/docs/tutorials/operator-back-propagation.ipynb @@ -68,8 +68,7 @@ "- Qiskit SDK 1.2 or later (`pip install qiskit`)\n", "- Qiskit Runtime 0.28 or later (`pip install qiskit-ibm-runtime`)\n", "- OBP Qiskit addon (`pip install qiskit-addon-obp`)\n", - "- Qiskit addon utils (`pip install qiskit-addon-utils`)\n", - "- rustworkx 0.15 or later (`pip install rustworkx`)" + "- Qiskit addon utils (`pip install qiskit-addon-utils`)" ] }, { diff --git a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb index fd8b3ee9ece..e900c015ad3 100644 --- a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb +++ b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb @@ -60,8 +60,7 @@ "\n", "Before starting this tutorial, be sure you have the following installed:\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "- Qiskit Runtime 0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "- Rustworkx graph library (`pip install rustworkx`)" + "- Qiskit Runtime 0.22 or later (`pip install qiskit-ibm-runtime`)" ] }, { diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 2f929bdeff9..66a6a8f0158 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "f69d5853-e815-4754-894d-833017217572", "metadata": {}, "source": [ "# Real-time benchmarking for qubit selection\n", @@ -11,8 +10,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "2797ee64-644f-40c9-8327-094867333a69", + "execution_count": 1, "metadata": { "tags": [ "remove-cell" @@ -26,7 +24,6 @@ }, { "cell_type": "markdown", - "id": "500dc8c9-a5d8-4ef1-932f-30e400d6bdde", "metadata": {}, "source": [ "## Background\n", @@ -37,7 +34,6 @@ }, { "cell_type": "markdown", - "id": "0babd413-d91f-4fd7-a0f5-bb46ae0bbf5b", "metadata": {}, "source": [ "## Requirements\n", @@ -51,7 +47,6 @@ }, { "cell_type": "markdown", - "id": "3df52d5f-806a-4846-849e-633706a96d0b", "metadata": {}, "source": [ "## Setup" @@ -59,8 +54,7 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "4766c18a-ba45-456b-8b78-6b6f1d214586", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +85,6 @@ }, { "cell_type": "markdown", - "id": "65d49ed2-0581-486e-9031-a08fa9bace92", "metadata": {}, "source": [ "## Step 1: Map classical inputs to a quantum problem\n", @@ -101,7 +94,6 @@ }, { "cell_type": "markdown", - "id": "16948f21-a39b-4444-bf02-5f81331825c4", "metadata": {}, "source": [ "### Setting up backend and coupling map" @@ -109,8 +101,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "f968acca-9131-4f5d-aa74-70befcdda4f5", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -125,8 +116,7 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "62b36ded-ab4e-414e-b146-ff522786a871", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +144,6 @@ }, { "cell_type": "markdown", - "id": "875117af-8a2c-4aea-92d9-ffeee7ff37d5", "metadata": {}, "source": [ "### Characterization experiments\n", @@ -184,8 +173,7 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "9d57c42d-7a91-4e79-bc6c-6e579da929f8", + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +247,6 @@ }, { "cell_type": "markdown", - "id": "cad4f8d3-c2d5-4bb5-92be-432a4573e14a", "metadata": {}, "source": [ "### QPU properties over time\n", @@ -268,8 +255,7 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "af1b6722-e77b-436a-bbcd-f272b95bb28c", + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -298,7 +284,6 @@ }, { "cell_type": "markdown", - "id": "ac75df1b-f689-475c-94a2-d70d85b1f8ca", "metadata": {}, "source": [ "Then, let's plot the values" @@ -306,14 +291,14 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "e0ba509d-e0e0-438b-aedf-5e01919c7d4f", + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, "metadata": {}, @@ -395,7 +380,6 @@ }, { "cell_type": "markdown", - "id": "a3343934-9273-457c-900b-2004b3aa9d0c", "metadata": {}, "source": [ "You can see that over several days some of the qubit properties can change considerably. This highlights the importance of having fresh information of the QPU status, to be able to select the best performing qubits for an experiment." @@ -403,17 +387,17 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "95571eca-02ba-4452-816a-c04822675be8", + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -440,7 +424,6 @@ }, { "cell_type": "markdown", - "id": "0e53f0a5-7713-4915-ae47-c1aa0a4f17cd", "metadata": {}, "source": [ "## Step 2: Optimize problem for quantum hardware execution\n", @@ -449,7 +432,6 @@ }, { "cell_type": "markdown", - "id": "862789c2-d17e-43ed-8e1b-2f37d7628ed2", "metadata": {}, "source": [ "## Step 3: Execute using Qiskit primitives" @@ -457,7 +439,6 @@ }, { "cell_type": "markdown", - "id": "d88f925f-3ffd-43de-ac1f-2c5ba3b8cd96", "metadata": {}, "source": [ "### Execute a quantum circuit with default qubit selection\n", @@ -466,8 +447,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "5c0d09ad-2e6a-4067-8034-8df92a475ff1", + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -485,7 +465,6 @@ }, { "cell_type": "markdown", - "id": "6d4fb04c-a5cf-48e8-a759-dde64fd751ac", "metadata": {}, "source": [ "### Execute a quantum circuit with real-time qubit selection\n", @@ -495,10 +474,32 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "a8266467-9d60-411f-89dd-8cad6558f588", + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RequestsApiError", + "evalue": "'400 Client Error: Bad Request for url: https://us-east.quantum-computing.cloud.ibm.com/sessions. {\"errors\":[{\"code\":1352,\"message\":\"You are not authorized to run a session when using the open plan.\",\"solution\":\"Create an instance of a different plan type or use a different execution mode .\",\"more_info\":\"https://cloud.ibm.com/apidocs/quantum-computing#error-handling\"}],\"trace\":\"d957770b-0f25-4b3d-974d-cf62562ecc4e\"}\\n'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mHTTPError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:328\u001b[39m, in \u001b[36mRetrySession.request\u001b[39m\u001b[34m(self, method, url, bare, **kwargs)\u001b[39m\n\u001b[32m 327\u001b[39m response = \u001b[38;5;28msuper\u001b[39m().request(method, final_url, headers=headers, **kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m328\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 329\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m RequestException \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[32m 330\u001b[39m \u001b[38;5;66;03m# Wrap the requests exceptions into a IBM Q custom one, for\u001b[39;00m\n\u001b[32m 331\u001b[39m \u001b[38;5;66;03m# compatibility.\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\requests\\models.py:1021\u001b[39m, in \u001b[36mResponse.raise_for_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1020\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[32m-> \u001b[39m\u001b[32m1021\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response=\u001b[38;5;28mself\u001b[39m)\n", + "\u001b[31mHTTPError\u001b[39m: 400 Client Error: Bad Request for url: https://us-east.quantum-computing.cloud.ibm.com/sessions", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[31mRequestsApiError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m batches_exp = BatchExperiment(batches, backend) \u001b[38;5;66;03m# , analysis=None)\u001b[39;00m\n\u001b[32m 4\u001b[39m run_options = {\u001b[33m\"\u001b[39m\u001b[33mshots\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m1e3\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mdynamic\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m}\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mSession\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[32m 7\u001b[39m sampler = SamplerV2(mode=session)\n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m# Run characterization experiments\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:132\u001b[39m, in \u001b[36mSession.__init__\u001b[39m\u001b[34m(self, backend, max_time, create_new)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28mself\u001b[39m._instance = \u001b[38;5;28mself\u001b[39m._backend._instance\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.configuration().simulator:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28mself\u001b[39m._session_id = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_create_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcreate_new\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcreate_new\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:137\u001b[39m, in \u001b[36mSession._create_session\u001b[39m\u001b[34m(self, create_new)\u001b[39m\n\u001b[32m 135\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Create a session.\"\"\"\u001b[39;00m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m._service, QiskitRuntimeService) \u001b[38;5;129;01mand\u001b[39;00m create_new:\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m session = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_service\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_api_client\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_instance\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate_session\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 138\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_instance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_max_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_service\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchannel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdedicated\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 139\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 140\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m session.get(\u001b[33m\"\u001b[39m\u001b[33mid\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 141\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\clients\\runtime.py:237\u001b[39m, in \u001b[36mRuntimeClient.create_session\u001b[39m\u001b[34m(self, backend, instance, max_time, channel, mode)\u001b[39m\n\u001b[32m 224\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate_session\u001b[39m(\n\u001b[32m 225\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 226\u001b[39m backend: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m (...)\u001b[39m\u001b[32m 230\u001b[39m mode: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 231\u001b[39m ) -> Dict[\u001b[38;5;28mstr\u001b[39m, Any]:\n\u001b[32m 232\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Create a session.\u001b[39;00m\n\u001b[32m 233\u001b[39m \n\u001b[32m 234\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m 235\u001b[39m \u001b[33;03m mode: Execution mode.\u001b[39;00m\n\u001b[32m 236\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m237\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_api\u001b[49m\u001b[43m.\u001b[49m\u001b[43mruntime_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43msession_id\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minstance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\n\u001b[32m 239\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\rest\\runtime_session.py:65\u001b[39m, in \u001b[36mRuntimeSession.create\u001b[39m\u001b[34m(self, backend, instance, max_time, channel, mode)\u001b[39m\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 64\u001b[39m payload[\u001b[33m\"\u001b[39m\u001b[33mmax_ttl\u001b[39m\u001b[33m\"\u001b[39m] = max_time \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m65\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msession\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpayload\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_HEADER_JSON_CONTENT\u001b[49m\u001b[43m)\u001b[49m.json()\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\requests\\sessions.py:637\u001b[39m, in \u001b[36mSession.post\u001b[39m\u001b[34m(self, url, data, json, **kwargs)\u001b[39m\n\u001b[32m 626\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\u001b[38;5;28mself\u001b[39m, url, data=\u001b[38;5;28;01mNone\u001b[39;00m, json=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 627\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33mr\u001b[39m\u001b[33;03m\"\"\"Sends a POST request. Returns :class:`Response` object.\u001b[39;00m\n\u001b[32m 628\u001b[39m \n\u001b[32m 629\u001b[39m \u001b[33;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 634\u001b[39m \u001b[33;03m :rtype: requests.Response\u001b[39;00m\n\u001b[32m 635\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m637\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPOST\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:356\u001b[39m, in \u001b[36mRetrySession.request\u001b[39m\u001b[34m(self, method, url, bare, **kwargs)\u001b[39m\n\u001b[32m 350\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m status_code == \u001b[32m503\u001b[39m: \u001b[38;5;66;03m# Planned maintenance outage\u001b[39;00m\n\u001b[32m 351\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RequestsApiError(\n\u001b[32m 352\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mUnexpected response received from server. Please check if the service \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mis in maintenance mode \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 354\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mhttps://docs.quantum.ibm.com/announcements/service-alerts \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmessage\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 355\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m356\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RequestsApiError(message, status_code) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mex\u001b[39;00m\n\u001b[32m 358\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m response\n", + "\u001b[31mRequestsApiError\u001b[39m: '400 Client Error: Bad Request for url: https://us-east.quantum-computing.cloud.ibm.com/sessions. {\"errors\":[{\"code\":1352,\"message\":\"You are not authorized to run a session when using the open plan.\",\"solution\":\"Create an instance of a different plan type or use a different execution mode .\",\"more_info\":\"https://cloud.ibm.com/apidocs/quantum-computing#error-handling\"}],\"trace\":\"d957770b-0f25-4b3d-974d-cf62562ecc4e\"}\\n'" + ] + } + ], "source": [ "# Prepare characterization experiments\n", "batches = [t1_exp, t2_exp, readout_exp, singleq_rb_exp, twoq_rb_exp_batched]\n", @@ -629,7 +630,6 @@ }, { "cell_type": "markdown", - "id": "d72e021a-3ea6-4ed2-829f-e59ce8017ef4", "metadata": {}, "source": [ "## Step 4: Post-process and return result in desired classical format\n", @@ -641,8 +641,7 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "5a19a5d1-5daf-4d65-9785-8f8724853821", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -674,20 +673,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "656ec97a-3fd9-4635-9a98-1c5589761689", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(8, 6))\n", "plt.errorbar(\n", @@ -722,7 +710,6 @@ }, { "cell_type": "markdown", - "id": "3705d85c-e11d-4a8f-bfae-20bd501124d8", "metadata": {}, "source": [ "With increasing chain length, and thus less freedom to choose physical qubits, the fact that we have updated information about qubit properties becomes less substantial, with there being no difference when all of the qubits are used." @@ -730,7 +717,6 @@ }, { "cell_type": "markdown", - "id": "cd81b208-b13b-4988-854e-1741408f36f3", "metadata": {}, "source": [ "
\n", @@ -740,7 +726,6 @@ }, { "cell_type": "markdown", - "id": "b94755c1-1c19-434a-96b9-b83922b5d63c", "metadata": {}, "source": [ "## Tutorial survey\n", @@ -754,7 +739,7 @@ "metadata": { "description": "Run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU.", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -768,7 +753,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.2" }, "title": "Real-time benchmarking for qubit selection\n" }, diff --git a/docs/tutorials/transpilation-optimizations-with-sabre.ipynb b/docs/tutorials/transpilation-optimizations-with-sabre.ipynb index fecbcfcb158..6b9b8c62cba 100644 --- a/docs/tutorials/transpilation-optimizations-with-sabre.ipynb +++ b/docs/tutorials/transpilation-optimizations-with-sabre.ipynb @@ -47,7 +47,6 @@ "Before starting this tutorial, be sure you have the following installed:\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", "- Qiskit Runtime 0.28 or later (`pip install qiskit-ibm-runtime`)\n", - "- Rustworkx graph library (`pip install rustworkx`)\n", "- Serverless (`pip install qiskit-ibm-catalog qiskit_serverless`)" ] }, From 8a4631ddc7d77dda9ee19c4e32bcc5fb5c9cd2a4 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Tue, 5 Aug 2025 13:21:48 -0500 Subject: [PATCH 04/26] rerun grover's --- docs/tutorials/grovers-algorithm.ipynb | 311 ++++++------------------- 1 file changed, 73 insertions(+), 238 deletions(-) diff --git a/docs/tutorials/grovers-algorithm.ipynb b/docs/tutorials/grovers-algorithm.ipynb index 2c6a09aab5d..8bd9c64b613 100644 --- a/docs/tutorials/grovers-algorithm.ipynb +++ b/docs/tutorials/grovers-algorithm.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Grover's algorithm\n", - "*Usage estimate: under one minute on ibm_nairobi (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: under one minute on ibm_brisbane (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "id": "e2cb0472", "metadata": {}, "outputs": [], @@ -121,7 +121,7 @@ { "data": { "text/plain": [ - "'ibm_torino'" + "'ibm_brisbane'" ] }, "execution_count": 2, @@ -151,91 +151,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "c150298f", "metadata": {}, "outputs": [ - { - "ename": "TypeError", - "evalue": "__array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m marked_states = [\u001b[33m\"\u001b[39m\u001b[33m011\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m100\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 3\u001b[39m oracle = grover_oracle(marked_states)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[43moracle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43miqp\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\circuit\\quantumcircuit.py:3440\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 3437\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m 3438\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3440\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3441\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3442\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3443\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3444\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3445\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3446\u001b[39m \u001b[43m \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3447\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3448\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3449\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3450\u001b[39m \u001b[43m \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3451\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3452\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3453\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3454\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3455\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3456\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3457\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3458\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3459\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:331\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m 317\u001b[39m circuit,\n\u001b[32m 318\u001b[39m filename=filename,\n\u001b[32m (...)\u001b[39m\u001b[32m 328\u001b[39m wire_order=complete_wire_order,\n\u001b[32m 329\u001b[39m )\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[43m \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 334\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 335\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 339\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 340\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 342\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 343\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 344\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 345\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 346\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 347\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 348\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 349\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m 350\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 351\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 352\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:727\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 709\u001b[39m fold = \u001b[32m25\u001b[39m\n\u001b[32m 711\u001b[39m qcd = _matplotlib.MatplotlibDrawer(\n\u001b[32m 712\u001b[39m qubits,\n\u001b[32m 713\u001b[39m clbits,\n\u001b[32m (...)\u001b[39m\u001b[32m 725\u001b[39m expr_len=expr_len,\n\u001b[32m 726\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m727\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mqcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:375\u001b[39m, in \u001b[36mMatplotlibDrawer.draw\u001b[39m\u001b[34m(self, filename, verbose)\u001b[39m\n\u001b[32m 373\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._global_phase:\n\u001b[32m 374\u001b[39m plt_mod.text(xl, yt, \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mGlobal Phase: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpi_check(\u001b[38;5;28mself\u001b[39m._global_phase,\u001b[38;5;250m \u001b[39moutput=\u001b[33m'\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m'\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m375\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_regs_wires\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_folds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_x_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqubits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclbits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 376\u001b[39m \u001b[38;5;28mself\u001b[39m._draw_ops(\n\u001b[32m 377\u001b[39m \u001b[38;5;28mself\u001b[39m._nodes,\n\u001b[32m 378\u001b[39m node_data,\n\u001b[32m (...)\u001b[39m\u001b[32m 385\u001b[39m verbose,\n\u001b[32m 386\u001b[39m )\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m filename:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:980\u001b[39m, in \u001b[36mMatplotlibDrawer._draw_regs_wires\u001b[39m\u001b[34m(self, num_folds, xmax, max_x_index, qubits_dict, clbits_dict, glob_data)\u001b[39m\n\u001b[32m 969\u001b[39m \u001b[38;5;66;03m# Mask off any lines or boxes in the bit label area to clean up\u001b[39;00m\n\u001b[32m 970\u001b[39m \u001b[38;5;66;03m# from folding for ControlFlow and other wrapping gates\u001b[39;00m\n\u001b[32m 971\u001b[39m box = glob_data[\u001b[33m\"\u001b[39m\u001b[33mpatches_mod\u001b[39m\u001b[33m\"\u001b[39m].Rectangle(\n\u001b[32m 972\u001b[39m xy=(glob_data[\u001b[33m\"\u001b[39m\u001b[33mx_offset\u001b[39m\u001b[33m\"\u001b[39m] - \u001b[32m0.1\u001b[39m, -fold_num * (glob_data[\u001b[33m\"\u001b[39m\u001b[33mn_lines\u001b[39m\u001b[33m\"\u001b[39m] + \u001b[32m1\u001b[39m) + \u001b[32m0.5\u001b[39m),\n\u001b[32m 973\u001b[39m width=-\u001b[32m25.0\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 978\u001b[39m zorder=PORDER_MASK,\n\u001b[32m 979\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m980\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_ax\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd_patch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbox\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 982\u001b[39m \u001b[38;5;66;03m# draw index number\u001b[39;00m\n\u001b[32m 983\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._style[\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m]:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2384\u001b[39m, in \u001b[36m_AxesBase.add_patch\u001b[39m\u001b[34m(self, p)\u001b[39m\n\u001b[32m 2382\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.get_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 2383\u001b[39m p.set_clip_path(\u001b[38;5;28mself\u001b[39m.patch)\n\u001b[32m-> \u001b[39m\u001b[32m2384\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_update_patch_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2385\u001b[39m \u001b[38;5;28mself\u001b[39m._children.append(p)\n\u001b[32m 2386\u001b[39m p._remove_method = \u001b[38;5;28mself\u001b[39m._children.remove\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2406\u001b[39m, in \u001b[36m_AxesBase._update_patch_limits\u001b[39m\u001b[34m(self, patch)\u001b[39m\n\u001b[32m 2403\u001b[39m \u001b[38;5;66;03m# Get all vertices on the path\u001b[39;00m\n\u001b[32m 2404\u001b[39m \u001b[38;5;66;03m# Loop through each segment to get extrema for Bezier curve sections\u001b[39;00m\n\u001b[32m 2405\u001b[39m vertices = []\n\u001b[32m-> \u001b[39m\u001b[32m2406\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m.\u001b[49m\u001b[43miter_bezier\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimplify\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2407\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Get distance along the curve of any extrema\u001b[39;49;00m\n\u001b[32m 2408\u001b[39m \u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdzeros\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxis_aligned_extrema\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2409\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calculate vertices of start, end and any extrema in between\u001b[39;49;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:437\u001b[39m, in \u001b[36mPath.iter_bezier\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 435\u001b[39m first_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 436\u001b[39m prev_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43miter_segments\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfirst_vert\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m!=\u001b[49m\u001b[43m \u001b[49m\u001b[43mPath\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMOVETO\u001b[49m\u001b[43m:\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:404\u001b[39m, in \u001b[36mPath.iter_segments\u001b[39m\u001b[34m(self, transform, remove_nans, clip, snap, stroke_width, simplify, curves, sketch)\u001b[39m\n\u001b[32m 402\u001b[39m codes = \u001b[38;5;28miter\u001b[39m(cleaned.codes)\n\u001b[32m 403\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m curr_vertices, code \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(vertices, codes):\n\u001b[32m--> \u001b[39m\u001b[32m404\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mSTOP\u001b[49m:\n\u001b[32m 405\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 406\u001b[39m extra_vertices = NUM_VERTICES_FOR_CODE[code] - \u001b[32m1\u001b[39m\n", - "\u001b[31mTypeError\u001b[39m: __array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" - ] - }, - { - "ename": "ValueError", - "evalue": "object __array__ method not producing an array", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\pyplot.py:197\u001b[39m, in \u001b[36m_draw_all_if_interactive\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 195\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_draw_all_if_interactive\u001b[39m() -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 196\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m matplotlib.is_interactive():\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\_pylab_helpers.py:132\u001b[39m, in \u001b[36mGcf.draw_all\u001b[39m\u001b[34m(cls, force)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.get_all_fig_managers():\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager.canvas.figure.stale:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43mmanager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:1893\u001b[39m, in \u001b[36mFigureCanvasBase.draw_idle\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1891\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_idle_drawing:\n\u001b[32m 1892\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._idle_draw_cntx():\n\u001b[32m-> \u001b[39m\u001b[32m1893\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[39m, in \u001b[36mFigureCanvasAgg.draw\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 385\u001b[39m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m.toolbar._wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.toolbar\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[32m 390\u001b[39m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[32m 391\u001b[39m \u001b[38;5;28msuper\u001b[39m().draw()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3153\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3150\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3153\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpatch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3154\u001b[39m mimage._draw_list_compositing_images(\n\u001b[32m 3155\u001b[39m renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m.suppressComposite)\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:588\u001b[39m, in \u001b[36mPatch.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 586\u001b[39m tpath = transform.transform_path_non_affine(path)\n\u001b[32m 587\u001b[39m affine = transform.get_affine()\n\u001b[32m--> \u001b[39m\u001b[32m588\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_paths_with_artist_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 589\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 590\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 591\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Work around a bug in the PDF and SVG renderers, which\u001b[39;49;00m\n\u001b[32m 592\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# do not draw the hatches if the facecolor is fully\u001b[39;49;00m\n\u001b[32m 593\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# transparent, but do if it is None.\u001b[39;49;00m\n\u001b[32m 594\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:573\u001b[39m, in \u001b[36mPatch._draw_paths_with_artist_properties\u001b[39m\u001b[34m(self, renderer, draw_path_args_list)\u001b[39m\n\u001b[32m 570\u001b[39m renderer = PathEffectRenderer(\u001b[38;5;28mself\u001b[39m.get_path_effects(), renderer)\n\u001b[32m 572\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m draw_path_args \u001b[38;5;129;01min\u001b[39;00m draw_path_args_list:\n\u001b[32m--> \u001b[39m\u001b[32m573\u001b[39m \u001b[43mrenderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mdraw_path_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 575\u001b[39m gc.restore()\n\u001b[32m 576\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33mpatch\u001b[39m\u001b[33m'\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:132\u001b[39m, in \u001b[36mRendererAgg.draw_path\u001b[39m\u001b[34m(self, gc, path, transform, rgbFace)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_renderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrgbFace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[32m 134\u001b[39m cant_chunk = \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m\n", - "\u001b[31mValueError\u001b[39m: object __array__ method not producing an array" - ] - }, - { - "ename": "ImportError", - "evalue": "cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\formatters.py:402\u001b[39m, in \u001b[36mBaseFormatter.__call__\u001b[39m\u001b[34m(self, obj)\u001b[39m\n\u001b[32m 400\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 401\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[32m 404\u001b[39m method = get_real_method(obj, \u001b[38;5;28mself\u001b[39m.print_method)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170\u001b[39m, in \u001b[36mprint_figure\u001b[39m\u001b[34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[39m\n\u001b[32m 167\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[32m 168\u001b[39m FigureCanvasBase(fig)\n\u001b[32m--> \u001b[39m\u001b[32m170\u001b[39m \u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 171\u001b[39m data = bytes_io.getvalue()\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m fmt == \u001b[33m'\u001b[39m\u001b[33msvg\u001b[39m\u001b[33m'\u001b[39m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:2164\u001b[39m, in \u001b[36mFigureCanvasBase.print_figure\u001b[39m\u001b[34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[39m\n\u001b[32m 2161\u001b[39m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[32m 2162\u001b[39m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[32m 2163\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[33m\"\u001b[39m\u001b[33m_draw_disabled\u001b[39m\u001b[33m\"\u001b[39m, nullcontext)():\n\u001b[32m-> \u001b[39m\u001b[32m2164\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[32m 2166\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches == \u001b[33m\"\u001b[39m\u001b[33mtight\u001b[39m\u001b[33m\"\u001b[39m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m 3153\u001b[39m \u001b[38;5;28mself\u001b[39m.patch.draw(renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3154\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3155\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n\u001b[32m 3158\u001b[39m sfig.draw(renderer)\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3070\u001b[39m, in \u001b[36m_AxesBase.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3067\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[32m 3068\u001b[39m _draw_rasterized(\u001b[38;5;28mself\u001b[39m.figure, artists_rasterized, renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3070\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3071\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3073\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33maxes\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3074\u001b[39m \u001b[38;5;28mself\u001b[39m.stale = \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:748\u001b[39m, in \u001b[36mText.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 745\u001b[39m renderer.open_group(\u001b[33m'\u001b[39m\u001b[33mtext\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28mself\u001b[39m.get_gid())\n\u001b[32m 747\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._cm_set(text=\u001b[38;5;28mself\u001b[39m._get_wrapped_text()):\n\u001b[32m--> \u001b[39m\u001b[32m748\u001b[39m bbox, info, descent = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 749\u001b[39m trans = \u001b[38;5;28mself\u001b[39m.get_transform()\n\u001b[32m 751\u001b[39m \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[32m 752\u001b[39m \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:442\u001b[39m, in \u001b[36mText._get_layout\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 440\u001b[39m corners_rotated = M.transform(corners_horiz)\n\u001b[32m 441\u001b[39m \u001b[38;5;66;03m# compute the bounds of the rotated box\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m442\u001b[39m xmin = \u001b[43mcorners_rotated\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 443\u001b[39m xmax = corners_rotated[:, \u001b[32m0\u001b[39m].max()\n\u001b[32m 444\u001b[39m ymin = corners_rotated[:, \u001b[32m1\u001b[39m].min()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:14\u001b[39m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m numerictypes \u001b[38;5;28;01mas\u001b[39;00m nt\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _exceptions\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_ufunc_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _no_nep50_warning\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_globals\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _NoValue\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcompat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pickle, os_fspath\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_ufunc_config.py:11\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcontextvars\u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_utils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m set_module\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mumath\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 12\u001b[39m UFUNC_BUFSIZE_DEFAULT,\n\u001b[32m 13\u001b[39m ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,\n\u001b[32m 14\u001b[39m SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,\n\u001b[32m 15\u001b[39m )\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m umath\n\u001b[32m 18\u001b[39m __all__ = [\n\u001b[32m 19\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mseterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33msetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mseterrcall\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterrcall\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 20\u001b[39m \u001b[33m\"\u001b[39m\u001b[33merrstate\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m_no_nep50_warning\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 21\u001b[39m ]\n", - "\u001b[31mImportError\u001b[39m: cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)" - ] - }, { "data": { + "image/png": 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", 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" ] }, + "execution_count": 3, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -247,91 +176,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "7baca7e2-99fc-4089-b5d8-30da56816a6a", "metadata": {}, "outputs": [ - { - "ename": "TypeError", - "evalue": "__array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m marked_states = [\u001b[33m\"\u001b[39m\u001b[33m011\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m100\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 3\u001b[39m oracle = grover_oracle(marked_states)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[43moracle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43miqp\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\circuit\\quantumcircuit.py:3440\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 3437\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m 3438\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3440\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3441\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3442\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3443\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3444\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3445\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3446\u001b[39m \u001b[43m \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3447\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3448\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3449\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3450\u001b[39m \u001b[43m \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3451\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3452\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3453\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3454\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3455\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3456\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3457\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3458\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3459\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:331\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m 317\u001b[39m circuit,\n\u001b[32m 318\u001b[39m filename=filename,\n\u001b[32m (...)\u001b[39m\u001b[32m 328\u001b[39m wire_order=complete_wire_order,\n\u001b[32m 329\u001b[39m )\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[43m \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 334\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 335\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 339\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 340\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 342\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 343\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 344\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 345\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 346\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 347\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 348\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 349\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m 350\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 351\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 352\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:727\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 709\u001b[39m fold = \u001b[32m25\u001b[39m\n\u001b[32m 711\u001b[39m qcd = _matplotlib.MatplotlibDrawer(\n\u001b[32m 712\u001b[39m qubits,\n\u001b[32m 713\u001b[39m clbits,\n\u001b[32m (...)\u001b[39m\u001b[32m 725\u001b[39m expr_len=expr_len,\n\u001b[32m 726\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m727\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mqcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:375\u001b[39m, in \u001b[36mMatplotlibDrawer.draw\u001b[39m\u001b[34m(self, filename, verbose)\u001b[39m\n\u001b[32m 373\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._global_phase:\n\u001b[32m 374\u001b[39m plt_mod.text(xl, yt, \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mGlobal Phase: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpi_check(\u001b[38;5;28mself\u001b[39m._global_phase,\u001b[38;5;250m \u001b[39moutput=\u001b[33m'\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m'\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m375\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_regs_wires\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_folds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_x_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqubits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclbits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 376\u001b[39m \u001b[38;5;28mself\u001b[39m._draw_ops(\n\u001b[32m 377\u001b[39m \u001b[38;5;28mself\u001b[39m._nodes,\n\u001b[32m 378\u001b[39m node_data,\n\u001b[32m (...)\u001b[39m\u001b[32m 385\u001b[39m verbose,\n\u001b[32m 386\u001b[39m )\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m filename:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:980\u001b[39m, in \u001b[36mMatplotlibDrawer._draw_regs_wires\u001b[39m\u001b[34m(self, num_folds, xmax, max_x_index, qubits_dict, clbits_dict, glob_data)\u001b[39m\n\u001b[32m 969\u001b[39m \u001b[38;5;66;03m# Mask off any lines or boxes in the bit label area to clean up\u001b[39;00m\n\u001b[32m 970\u001b[39m \u001b[38;5;66;03m# from folding for ControlFlow and other wrapping gates\u001b[39;00m\n\u001b[32m 971\u001b[39m box = glob_data[\u001b[33m\"\u001b[39m\u001b[33mpatches_mod\u001b[39m\u001b[33m\"\u001b[39m].Rectangle(\n\u001b[32m 972\u001b[39m xy=(glob_data[\u001b[33m\"\u001b[39m\u001b[33mx_offset\u001b[39m\u001b[33m\"\u001b[39m] - \u001b[32m0.1\u001b[39m, -fold_num * (glob_data[\u001b[33m\"\u001b[39m\u001b[33mn_lines\u001b[39m\u001b[33m\"\u001b[39m] + \u001b[32m1\u001b[39m) + \u001b[32m0.5\u001b[39m),\n\u001b[32m 973\u001b[39m width=-\u001b[32m25.0\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 978\u001b[39m zorder=PORDER_MASK,\n\u001b[32m 979\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m980\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_ax\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd_patch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbox\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 982\u001b[39m \u001b[38;5;66;03m# draw index number\u001b[39;00m\n\u001b[32m 983\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._style[\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m]:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2384\u001b[39m, in \u001b[36m_AxesBase.add_patch\u001b[39m\u001b[34m(self, p)\u001b[39m\n\u001b[32m 2382\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.get_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 2383\u001b[39m p.set_clip_path(\u001b[38;5;28mself\u001b[39m.patch)\n\u001b[32m-> \u001b[39m\u001b[32m2384\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_update_patch_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2385\u001b[39m \u001b[38;5;28mself\u001b[39m._children.append(p)\n\u001b[32m 2386\u001b[39m p._remove_method = \u001b[38;5;28mself\u001b[39m._children.remove\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2406\u001b[39m, in \u001b[36m_AxesBase._update_patch_limits\u001b[39m\u001b[34m(self, patch)\u001b[39m\n\u001b[32m 2403\u001b[39m \u001b[38;5;66;03m# Get all vertices on the path\u001b[39;00m\n\u001b[32m 2404\u001b[39m \u001b[38;5;66;03m# Loop through each segment to get extrema for Bezier curve sections\u001b[39;00m\n\u001b[32m 2405\u001b[39m vertices = []\n\u001b[32m-> \u001b[39m\u001b[32m2406\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m.\u001b[49m\u001b[43miter_bezier\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimplify\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2407\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Get distance along the curve of any extrema\u001b[39;49;00m\n\u001b[32m 2408\u001b[39m \u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdzeros\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxis_aligned_extrema\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2409\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calculate vertices of start, end and any extrema in between\u001b[39;49;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:437\u001b[39m, in \u001b[36mPath.iter_bezier\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 435\u001b[39m first_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 436\u001b[39m prev_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43miter_segments\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfirst_vert\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m!=\u001b[49m\u001b[43m \u001b[49m\u001b[43mPath\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMOVETO\u001b[49m\u001b[43m:\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:404\u001b[39m, in \u001b[36mPath.iter_segments\u001b[39m\u001b[34m(self, transform, remove_nans, clip, snap, stroke_width, simplify, curves, sketch)\u001b[39m\n\u001b[32m 402\u001b[39m codes = \u001b[38;5;28miter\u001b[39m(cleaned.codes)\n\u001b[32m 403\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m curr_vertices, code \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(vertices, codes):\n\u001b[32m--> \u001b[39m\u001b[32m404\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mSTOP\u001b[49m:\n\u001b[32m 405\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 406\u001b[39m extra_vertices = NUM_VERTICES_FOR_CODE[code] - \u001b[32m1\u001b[39m\n", - "\u001b[31mTypeError\u001b[39m: __array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" - ] - }, - { - "ename": "ValueError", - "evalue": "object __array__ method not producing an array", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\pyplot.py:197\u001b[39m, in \u001b[36m_draw_all_if_interactive\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 195\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_draw_all_if_interactive\u001b[39m() -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 196\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m matplotlib.is_interactive():\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\_pylab_helpers.py:132\u001b[39m, in \u001b[36mGcf.draw_all\u001b[39m\u001b[34m(cls, force)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.get_all_fig_managers():\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager.canvas.figure.stale:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43mmanager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:1893\u001b[39m, in \u001b[36mFigureCanvasBase.draw_idle\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1891\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_idle_drawing:\n\u001b[32m 1892\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._idle_draw_cntx():\n\u001b[32m-> \u001b[39m\u001b[32m1893\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[39m, in \u001b[36mFigureCanvasAgg.draw\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 385\u001b[39m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m.toolbar._wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.toolbar\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[32m 390\u001b[39m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[32m 391\u001b[39m \u001b[38;5;28msuper\u001b[39m().draw()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3153\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3150\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3153\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpatch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3154\u001b[39m mimage._draw_list_compositing_images(\n\u001b[32m 3155\u001b[39m renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m.suppressComposite)\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:588\u001b[39m, in \u001b[36mPatch.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 586\u001b[39m tpath = transform.transform_path_non_affine(path)\n\u001b[32m 587\u001b[39m affine = transform.get_affine()\n\u001b[32m--> \u001b[39m\u001b[32m588\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_paths_with_artist_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 589\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 590\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 591\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Work around a bug in the PDF and SVG renderers, which\u001b[39;49;00m\n\u001b[32m 592\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# do not draw the hatches if the facecolor is fully\u001b[39;49;00m\n\u001b[32m 593\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# transparent, but do if it is None.\u001b[39;49;00m\n\u001b[32m 594\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:573\u001b[39m, in \u001b[36mPatch._draw_paths_with_artist_properties\u001b[39m\u001b[34m(self, renderer, draw_path_args_list)\u001b[39m\n\u001b[32m 570\u001b[39m renderer = PathEffectRenderer(\u001b[38;5;28mself\u001b[39m.get_path_effects(), renderer)\n\u001b[32m 572\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m draw_path_args \u001b[38;5;129;01min\u001b[39;00m draw_path_args_list:\n\u001b[32m--> \u001b[39m\u001b[32m573\u001b[39m \u001b[43mrenderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mdraw_path_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 575\u001b[39m gc.restore()\n\u001b[32m 576\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33mpatch\u001b[39m\u001b[33m'\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:132\u001b[39m, in \u001b[36mRendererAgg.draw_path\u001b[39m\u001b[34m(self, gc, path, transform, rgbFace)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_renderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrgbFace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[32m 134\u001b[39m cant_chunk = \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m\n", - "\u001b[31mValueError\u001b[39m: object __array__ method not producing an array" - ] - }, - { - "ename": "ImportError", - "evalue": "cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\formatters.py:402\u001b[39m, in \u001b[36mBaseFormatter.__call__\u001b[39m\u001b[34m(self, obj)\u001b[39m\n\u001b[32m 400\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 401\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[32m 404\u001b[39m method = get_real_method(obj, \u001b[38;5;28mself\u001b[39m.print_method)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170\u001b[39m, in \u001b[36mprint_figure\u001b[39m\u001b[34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[39m\n\u001b[32m 167\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[32m 168\u001b[39m FigureCanvasBase(fig)\n\u001b[32m--> \u001b[39m\u001b[32m170\u001b[39m \u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 171\u001b[39m data = bytes_io.getvalue()\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m fmt == \u001b[33m'\u001b[39m\u001b[33msvg\u001b[39m\u001b[33m'\u001b[39m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:2164\u001b[39m, in \u001b[36mFigureCanvasBase.print_figure\u001b[39m\u001b[34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[39m\n\u001b[32m 2161\u001b[39m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[32m 2162\u001b[39m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[32m 2163\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[33m\"\u001b[39m\u001b[33m_draw_disabled\u001b[39m\u001b[33m\"\u001b[39m, nullcontext)():\n\u001b[32m-> \u001b[39m\u001b[32m2164\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[32m 2166\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches == \u001b[33m\"\u001b[39m\u001b[33mtight\u001b[39m\u001b[33m\"\u001b[39m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m 3153\u001b[39m \u001b[38;5;28mself\u001b[39m.patch.draw(renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3154\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3155\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n\u001b[32m 3158\u001b[39m sfig.draw(renderer)\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3070\u001b[39m, in \u001b[36m_AxesBase.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3067\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[32m 3068\u001b[39m _draw_rasterized(\u001b[38;5;28mself\u001b[39m.figure, artists_rasterized, renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3070\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3071\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3073\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33maxes\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3074\u001b[39m \u001b[38;5;28mself\u001b[39m.stale = \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:748\u001b[39m, in \u001b[36mText.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 745\u001b[39m renderer.open_group(\u001b[33m'\u001b[39m\u001b[33mtext\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28mself\u001b[39m.get_gid())\n\u001b[32m 747\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._cm_set(text=\u001b[38;5;28mself\u001b[39m._get_wrapped_text()):\n\u001b[32m--> \u001b[39m\u001b[32m748\u001b[39m bbox, info, descent = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 749\u001b[39m trans = \u001b[38;5;28mself\u001b[39m.get_transform()\n\u001b[32m 751\u001b[39m \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[32m 752\u001b[39m \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:442\u001b[39m, in \u001b[36mText._get_layout\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 440\u001b[39m corners_rotated = M.transform(corners_horiz)\n\u001b[32m 441\u001b[39m \u001b[38;5;66;03m# compute the bounds of the rotated box\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m442\u001b[39m xmin = \u001b[43mcorners_rotated\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 443\u001b[39m xmax = corners_rotated[:, \u001b[32m0\u001b[39m].max()\n\u001b[32m 444\u001b[39m ymin = corners_rotated[:, \u001b[32m1\u001b[39m].min()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:14\u001b[39m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m numerictypes \u001b[38;5;28;01mas\u001b[39;00m nt\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _exceptions\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_ufunc_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _no_nep50_warning\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_globals\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _NoValue\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcompat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pickle, os_fspath\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_ufunc_config.py:11\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcontextvars\u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_utils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m set_module\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mumath\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 12\u001b[39m UFUNC_BUFSIZE_DEFAULT,\n\u001b[32m 13\u001b[39m ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,\n\u001b[32m 14\u001b[39m SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,\n\u001b[32m 15\u001b[39m )\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m umath\n\u001b[32m 18\u001b[39m __all__ = [\n\u001b[32m 19\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mseterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33msetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mseterrcall\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterrcall\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 20\u001b[39m \u001b[33m\"\u001b[39m\u001b[33merrstate\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m_no_nep50_warning\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 21\u001b[39m ]\n", - "\u001b[31mImportError\u001b[39m: cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)" - ] - }, { "data": { + "image/png": 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", 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" ] }, + "execution_count": 4, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -343,91 +201,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "id": "d3a26fc9-9090-4527-a749-a412661260b6", "metadata": {}, "outputs": [ - { - "ename": "TypeError", - "evalue": "__array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m marked_states = [\u001b[33m\"\u001b[39m\u001b[33m011\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m100\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 3\u001b[39m oracle = grover_oracle(marked_states)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[43moracle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43miqp\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\circuit\\quantumcircuit.py:3440\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 3437\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m 3438\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3440\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3441\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3442\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3443\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3444\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3445\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3446\u001b[39m \u001b[43m \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3447\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3448\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3449\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3450\u001b[39m \u001b[43m \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3451\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3452\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3453\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3454\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3455\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3456\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3457\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3458\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3459\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:331\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m 317\u001b[39m circuit,\n\u001b[32m 318\u001b[39m filename=filename,\n\u001b[32m (...)\u001b[39m\u001b[32m 328\u001b[39m wire_order=complete_wire_order,\n\u001b[32m 329\u001b[39m )\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[43m \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 334\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 335\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[43m \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[43m \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 339\u001b[39m \u001b[43m \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 340\u001b[39m \u001b[43m \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 342\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 343\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 344\u001b[39m \u001b[43m \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 345\u001b[39m \u001b[43m \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 346\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 347\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 348\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 349\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m 350\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 351\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 352\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\circuit_visualization.py:727\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m 709\u001b[39m fold = \u001b[32m25\u001b[39m\n\u001b[32m 711\u001b[39m qcd = _matplotlib.MatplotlibDrawer(\n\u001b[32m 712\u001b[39m qubits,\n\u001b[32m 713\u001b[39m clbits,\n\u001b[32m (...)\u001b[39m\u001b[32m 725\u001b[39m expr_len=expr_len,\n\u001b[32m 726\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m727\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mqcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:375\u001b[39m, in \u001b[36mMatplotlibDrawer.draw\u001b[39m\u001b[34m(self, filename, verbose)\u001b[39m\n\u001b[32m 373\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._global_phase:\n\u001b[32m 374\u001b[39m plt_mod.text(xl, yt, \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mGlobal Phase: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpi_check(\u001b[38;5;28mself\u001b[39m._global_phase,\u001b[38;5;250m \u001b[39moutput=\u001b[33m'\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m'\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m375\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_regs_wires\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_folds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_x_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqubits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclbits_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 376\u001b[39m \u001b[38;5;28mself\u001b[39m._draw_ops(\n\u001b[32m 377\u001b[39m \u001b[38;5;28mself\u001b[39m._nodes,\n\u001b[32m 378\u001b[39m node_data,\n\u001b[32m (...)\u001b[39m\u001b[32m 385\u001b[39m verbose,\n\u001b[32m 386\u001b[39m )\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m filename:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:980\u001b[39m, in \u001b[36mMatplotlibDrawer._draw_regs_wires\u001b[39m\u001b[34m(self, num_folds, xmax, max_x_index, qubits_dict, clbits_dict, glob_data)\u001b[39m\n\u001b[32m 969\u001b[39m \u001b[38;5;66;03m# Mask off any lines or boxes in the bit label area to clean up\u001b[39;00m\n\u001b[32m 970\u001b[39m \u001b[38;5;66;03m# from folding for ControlFlow and other wrapping gates\u001b[39;00m\n\u001b[32m 971\u001b[39m box = glob_data[\u001b[33m\"\u001b[39m\u001b[33mpatches_mod\u001b[39m\u001b[33m\"\u001b[39m].Rectangle(\n\u001b[32m 972\u001b[39m xy=(glob_data[\u001b[33m\"\u001b[39m\u001b[33mx_offset\u001b[39m\u001b[33m\"\u001b[39m] - \u001b[32m0.1\u001b[39m, -fold_num * (glob_data[\u001b[33m\"\u001b[39m\u001b[33mn_lines\u001b[39m\u001b[33m\"\u001b[39m] + \u001b[32m1\u001b[39m) + \u001b[32m0.5\u001b[39m),\n\u001b[32m 973\u001b[39m width=-\u001b[32m25.0\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 978\u001b[39m zorder=PORDER_MASK,\n\u001b[32m 979\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m980\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_ax\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd_patch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbox\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 982\u001b[39m \u001b[38;5;66;03m# draw index number\u001b[39;00m\n\u001b[32m 983\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._style[\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m]:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2384\u001b[39m, in \u001b[36m_AxesBase.add_patch\u001b[39m\u001b[34m(self, p)\u001b[39m\n\u001b[32m 2382\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.get_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 2383\u001b[39m p.set_clip_path(\u001b[38;5;28mself\u001b[39m.patch)\n\u001b[32m-> \u001b[39m\u001b[32m2384\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_update_patch_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2385\u001b[39m \u001b[38;5;28mself\u001b[39m._children.append(p)\n\u001b[32m 2386\u001b[39m p._remove_method = \u001b[38;5;28mself\u001b[39m._children.remove\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2406\u001b[39m, in \u001b[36m_AxesBase._update_patch_limits\u001b[39m\u001b[34m(self, patch)\u001b[39m\n\u001b[32m 2403\u001b[39m \u001b[38;5;66;03m# Get all vertices on the path\u001b[39;00m\n\u001b[32m 2404\u001b[39m \u001b[38;5;66;03m# Loop through each segment to get extrema for Bezier curve sections\u001b[39;00m\n\u001b[32m 2405\u001b[39m vertices = []\n\u001b[32m-> \u001b[39m\u001b[32m2406\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m.\u001b[49m\u001b[43miter_bezier\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimplify\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2407\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Get distance along the curve of any extrema\u001b[39;49;00m\n\u001b[32m 2408\u001b[39m \u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdzeros\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxis_aligned_extrema\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2409\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calculate vertices of start, end and any extrema in between\u001b[39;49;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:437\u001b[39m, in \u001b[36mPath.iter_bezier\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 435\u001b[39m first_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 436\u001b[39m prev_vert = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43miter_segments\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfirst_vert\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m!=\u001b[49m\u001b[43m \u001b[49m\u001b[43mPath\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMOVETO\u001b[49m\u001b[43m:\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\path.py:404\u001b[39m, in \u001b[36mPath.iter_segments\u001b[39m\u001b[34m(self, transform, remove_nans, clip, snap, stroke_width, simplify, curves, sketch)\u001b[39m\n\u001b[32m 402\u001b[39m codes = \u001b[38;5;28miter\u001b[39m(cleaned.codes)\n\u001b[32m 403\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m curr_vertices, code \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(vertices, codes):\n\u001b[32m--> \u001b[39m\u001b[32m404\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mSTOP\u001b[49m:\n\u001b[32m 405\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 406\u001b[39m extra_vertices = NUM_VERTICES_FOR_CODE[code] - \u001b[32m1\u001b[39m\n", - "\u001b[31mTypeError\u001b[39m: __array_wrap__() argument 1 must be numpy.ndarray, not numpy.ndarray" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" - ] - }, - { - "ename": "ValueError", - "evalue": "object __array__ method not producing an array", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\pyplot.py:197\u001b[39m, in \u001b[36m_draw_all_if_interactive\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 195\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_draw_all_if_interactive\u001b[39m() -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 196\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m matplotlib.is_interactive():\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\_pylab_helpers.py:132\u001b[39m, in \u001b[36mGcf.draw_all\u001b[39m\u001b[34m(cls, force)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.get_all_fig_managers():\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager.canvas.figure.stale:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43mmanager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:1893\u001b[39m, in \u001b[36mFigureCanvasBase.draw_idle\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1891\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_idle_drawing:\n\u001b[32m 1892\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._idle_draw_cntx():\n\u001b[32m-> \u001b[39m\u001b[32m1893\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[39m, in \u001b[36mFigureCanvasAgg.draw\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 385\u001b[39m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m.toolbar._wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.toolbar\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[32m 390\u001b[39m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[32m 391\u001b[39m \u001b[38;5;28msuper\u001b[39m().draw()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3153\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3150\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3153\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpatch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3154\u001b[39m mimage._draw_list_compositing_images(\n\u001b[32m 3155\u001b[39m renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m.suppressComposite)\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:588\u001b[39m, in \u001b[36mPatch.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 586\u001b[39m tpath = transform.transform_path_non_affine(path)\n\u001b[32m 587\u001b[39m affine = transform.get_affine()\n\u001b[32m--> \u001b[39m\u001b[32m588\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_paths_with_artist_properties\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 589\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 590\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 591\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Work around a bug in the PDF and SVG renderers, which\u001b[39;49;00m\n\u001b[32m 592\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# do not draw the hatches if the facecolor is fully\u001b[39;49;00m\n\u001b[32m 593\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# transparent, but do if it is None.\u001b[39;49;00m\n\u001b[32m 594\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_facecolor\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\patches.py:573\u001b[39m, in \u001b[36mPatch._draw_paths_with_artist_properties\u001b[39m\u001b[34m(self, renderer, draw_path_args_list)\u001b[39m\n\u001b[32m 570\u001b[39m renderer = PathEffectRenderer(\u001b[38;5;28mself\u001b[39m.get_path_effects(), renderer)\n\u001b[32m 572\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m draw_path_args \u001b[38;5;129;01min\u001b[39;00m draw_path_args_list:\n\u001b[32m--> \u001b[39m\u001b[32m573\u001b[39m \u001b[43mrenderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mdraw_path_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 575\u001b[39m gc.restore()\n\u001b[32m 576\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33mpatch\u001b[39m\u001b[33m'\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:132\u001b[39m, in \u001b[36mRendererAgg.draw_path\u001b[39m\u001b[34m(self, gc, path, transform, rgbFace)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_renderer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrgbFace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[32m 134\u001b[39m cant_chunk = \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m\n", - "\u001b[31mValueError\u001b[39m: object __array__ method not producing an array" - ] - }, - { - "ename": "ImportError", - "evalue": "cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\formatters.py:402\u001b[39m, in \u001b[36mBaseFormatter.__call__\u001b[39m\u001b[34m(self, obj)\u001b[39m\n\u001b[32m 400\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 401\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[32m 404\u001b[39m method = get_real_method(obj, \u001b[38;5;28mself\u001b[39m.print_method)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170\u001b[39m, in \u001b[36mprint_figure\u001b[39m\u001b[34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[39m\n\u001b[32m 167\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[32m 168\u001b[39m FigureCanvasBase(fig)\n\u001b[32m--> \u001b[39m\u001b[32m170\u001b[39m \u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 171\u001b[39m data = bytes_io.getvalue()\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m fmt == \u001b[33m'\u001b[39m\u001b[33msvg\u001b[39m\u001b[33m'\u001b[39m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\backend_bases.py:2164\u001b[39m, in \u001b[36mFigureCanvasBase.print_figure\u001b[39m\u001b[34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[39m\n\u001b[32m 2161\u001b[39m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[32m 2162\u001b[39m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[32m 2163\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[33m\"\u001b[39m\u001b[33m_draw_disabled\u001b[39m\u001b[33m\"\u001b[39m, nullcontext)():\n\u001b[32m-> \u001b[39m\u001b[32m2164\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[32m 2166\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches == \u001b[33m\"\u001b[39m\u001b[33mtight\u001b[39m\u001b[33m\"\u001b[39m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[39m, in \u001b[36m_finalize_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m95\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 97\u001b[39m renderer.stop_rasterizing()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3151\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m 3153\u001b[39m \u001b[38;5;28mself\u001b[39m.patch.draw(renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3154\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3155\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3157\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.subfigs:\n\u001b[32m 3158\u001b[39m sfig.draw(renderer)\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3070\u001b[39m, in \u001b[36m_AxesBase.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3067\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[32m 3068\u001b[39m _draw_rasterized(\u001b[38;5;28mself\u001b[39m.figure, artists_rasterized, renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3070\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3071\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3073\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33maxes\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3074\u001b[39m \u001b[38;5;28mself\u001b[39m.stale = \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 135\u001b[39m image_group = []\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[39m, in \u001b[36mallow_rasterization..draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 70\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:748\u001b[39m, in \u001b[36mText.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 745\u001b[39m renderer.open_group(\u001b[33m'\u001b[39m\u001b[33mtext\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28mself\u001b[39m.get_gid())\n\u001b[32m 747\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._cm_set(text=\u001b[38;5;28mself\u001b[39m._get_wrapped_text()):\n\u001b[32m--> \u001b[39m\u001b[32m748\u001b[39m bbox, info, descent = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 749\u001b[39m trans = \u001b[38;5;28mself\u001b[39m.get_transform()\n\u001b[32m 751\u001b[39m \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[32m 752\u001b[39m \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\matplotlib\\text.py:442\u001b[39m, in \u001b[36mText._get_layout\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 440\u001b[39m corners_rotated = M.transform(corners_horiz)\n\u001b[32m 441\u001b[39m \u001b[38;5;66;03m# compute the bounds of the rotated box\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m442\u001b[39m xmin = \u001b[43mcorners_rotated\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 443\u001b[39m xmax = corners_rotated[:, \u001b[32m0\u001b[39m].max()\n\u001b[32m 444\u001b[39m ymin = corners_rotated[:, \u001b[32m1\u001b[39m].min()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:14\u001b[39m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m numerictypes \u001b[38;5;28;01mas\u001b[39;00m nt\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _exceptions\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_ufunc_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _no_nep50_warning\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_globals\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _NoValue\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcompat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pickle, os_fspath\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\numpy\\core\\_ufunc_config.py:11\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcontextvars\u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_utils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m set_module\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mumath\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 12\u001b[39m UFUNC_BUFSIZE_DEFAULT,\n\u001b[32m 13\u001b[39m ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,\n\u001b[32m 14\u001b[39m SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,\n\u001b[32m 15\u001b[39m )\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m umath\n\u001b[32m 18\u001b[39m __all__ = [\n\u001b[32m 19\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mseterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterr\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33msetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgetbufsize\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mseterrcall\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgeterrcall\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 20\u001b[39m \u001b[33m\"\u001b[39m\u001b[33merrstate\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m_no_nep50_warning\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 21\u001b[39m ]\n", - "\u001b[31mImportError\u001b[39m: cannot import name 'ERR_IGNORE' from 'numpy.core.umath' (C:\\Python312\\Lib\\site-packages\\numpy\\core\\umath.py)" - ] - }, { "data": { + "image/png": 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", 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" ] }, + "execution_count": 5, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -450,10 +237,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "283d5265", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "grover_op = grover_operator(oracle)\n", "grover_op.decompose().draw(output=\"mpl\", style=\"iqp\")" @@ -470,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "f4c3d4b5", "metadata": {}, "outputs": [], @@ -494,10 +293,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "4933ae44", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "qc = QuantumCircuit(grover_op.num_qubits)\n", "# Create even superposition of all basis states\n", @@ -519,10 +330,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c9a3020e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "target = backend.target\n", "pm = generate_preset_pass_manager(target=target, optimization_level=3)\n", @@ -546,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "0eb154d4", "metadata": {}, "outputs": [], @@ -569,10 +392,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "a5ef9913", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plot_distribution(dist)" ] From b63965282ba994168f605e9e8465ec6cd41d6fe6 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Thu, 7 Aug 2025 11:44:21 -0500 Subject: [PATCH 05/26] Use generic eagle processors for estimates --- ...ime-benchmarking-for-qubit-selection.ipynb | 776 ++++++++++++++++++ .../advanced-techniques-for-qaoa.ipynb | 263 +----- .../circuit-transpilation-settings.ipynb | 4 +- .../tutorials/operator-back-propagation.ipynb | 2 +- .../pauli-correlation-encoding-for-qaoa.ipynb | 2 +- ...m-approximate-optimization-algorithm.ipynb | 299 ++----- docs/tutorials/quantum-kernel-training.ipynb | 2 +- ...ime-benchmarking-for-qubit-selection.ipynb | 106 ++- ...sample-based-quantum-diagonalization.ipynb | 2 +- docs/tutorials/spin-chain-vqe.ipynb | 70 +- .../variational-quantum-eigensolver.ipynb | 2 +- 11 files changed, 981 insertions(+), 547 deletions(-) create mode 100644 Untitled Folder/real-time-benchmarking-for-qubit-selection.ipynb diff --git a/Untitled Folder/real-time-benchmarking-for-qubit-selection.ipynb b/Untitled Folder/real-time-benchmarking-for-qubit-selection.ipynb new file mode 100644 index 00000000000..3d7f317cd22 --- /dev/null +++ b/Untitled Folder/real-time-benchmarking-for-qubit-selection.ipynb @@ -0,0 +1,776 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Real-time benchmarking for qubit selection\n", + "\n", + "*Usage estimate: 4 minutes on ibm\\_cusco (NOTE: This is an estimate only. Your runtime might vary.)*\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", + "\n", + "The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "\n", + "Before starting this tutorial, be sure you have the following installed:\n", + "\n", + "* Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", + "* Qiskit Runtime 0.29 or later ( `pip install qiskit-ibm-runtime` )\n", + "* Qiskit Experiments 0.7 or later ( `pip install qiskit-experiments` )\n", + "* Rustworkx graph library (`pip install rustworkx`)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2\n", + "from qiskit.transpiler import generate_preset_pass_manager\n", + "from qiskit.quantum_info import hellinger_fidelity\n", + "from qiskit.transpiler import InstructionProperties\n", + "\n", + "\n", + "from qiskit_experiments.library import (\n", + " T1,\n", + " T2Hahn,\n", + " LocalReadoutError,\n", + " StandardRB,\n", + ")\n", + "from qiskit_experiments.framework import BatchExperiment, ParallelExperiment\n", + "\n", + "from qiskit_ibm_runtime import QiskitRuntimeService\n", + "from qiskit_ibm_runtime import Session\n", + "\n", + "from datetime import datetime\n", + "from collections import defaultdict\n", + "import numpy as np\n", + "import rustworkx\n", + "import matplotlib.pyplot as plt\n", + "import copy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Map classical inputs to a quantum problem\n", + "\n", + "To benchmark the difference in performance, we consider a circuit that prepares a Bell state across a linear chain of varying length. The fidelity of the Bell state at the ends of the chain is measured.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting up backend and coupling map\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To run on hardware, select the backend with the fewest number of jobs in the queue\n", + "service = QiskitRuntimeService()\n", + "backend = service.least_busy(\n", + " operational=True, simulator=False, min_num_qubits=127\n", + ")\n", + "\n", + "qubits = list(range(backend.num_qubits))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "coupling_graph = backend.coupling_map.graph.to_undirected(multigraph=False)\n", + "\n", + "# Get unidirectional coupling map\n", + "one_dir_coupling_map = coupling_graph.edge_list()\n", + "\n", + "# Get layered coupling map\n", + "edge_coloring = rustworkx.graph_bipartite_edge_color(coupling_graph)\n", + "layered_coupling_map = defaultdict(list)\n", + "for edge_idx, color in edge_coloring.items():\n", + " layered_coupling_map[color].append(\n", + " coupling_graph.get_edge_endpoints_by_index(edge_idx)\n", + " )\n", + "layered_coupling_map = [\n", + " sorted(layered_coupling_map[i])\n", + " for i in sorted(layered_coupling_map.keys())\n", + "]\n", + "\n", + "flattened_layered_coupling_map = []\n", + "for layer in layered_coupling_map:\n", + " flattened_layered_coupling_map += layer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Characterization experiments\n", + "\n", + "A series of experiments is used to characterize the main properties of the qubits in a QPU. These are $T_1$, $T_2$, readout error, and single-qubit and two-qubit gate error. We'll briefly summarize what these properties are and refer to experiments in the [`qiskit-experiments`](https://qiskit-community.github.io/qiskit-experiments/index.html) package that are used to characterize them.\n", + "\n", + "#### T1\n", + "\n", + "$T_1$ is the characteristic time it takes for an excited qubit to fall to the ground state due to amplitude-damping decoherence processes. In a [$T_1$ experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t1.html), we measure an excited qubit after a delay. The larger the delay time is, the more\n", + "likely is the qubit to fall to the ground state. The goal of the\n", + "experiment is to characterize the decay rate of the qubit towards the\n", + "ground state.\n", + "\n", + "#### T2\n", + "\n", + "$T_2$ represents the amount of time required for a single qubit's Bloch\n", + "vector projection on the XY plane to fall to approximately 37% ($\\frac{1}{e}$) of\n", + "its initial amplitude due to dephasing decoherence processes. In a [$T_2$ Hahn Echo experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t2hahn.html), we can estimate the rate of this decay.\n", + "\n", + "#### State preparation and measurement (SPAM) error characterization\n", + "\n", + "In a [SPAM-error characterization experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/measurement/readout_mitigation.html) qubit are prepared in a certain state ($\\vert 0 \\rangle$ or $\\vert 1 \\rangle$) and measured. The probability of measuring a state different than the one prepared then gives the probability of the error.\n", + "\n", + "#### Single-qubit and two-qubit randomized benchmarking\n", + "\n", + "[Randomized benchmarking](https://qiskit-community.github.io/qiskit-experiments/manuals/verification/randomized_benchmarking.html) is a popular protocol for characterizing the error rate of\n", + "quantum processors. An RB experiment consists of the generation of random Clifford\n", + "circuits on the given qubits such that the unitary computed by the circuits is the\n", + "identity. After running the circuits, the number of shots resulting in an error (that is, an output different from the ground state) are counted, and from this data one can infer error estimates for the quantum device, by calculating the Error Per Clifford.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Create T1 experiments on all qubit in parallel\n", + "t1_exp = ParallelExperiment(\n", + " [\n", + " T1(\n", + " physical_qubits=[qubit],\n", + " delays=np.linspace(\n", + " 1e-6, 2 * backend.properties().t1(qubit), 5, endpoint=True\n", + " ),\n", + " )\n", + " for qubit in qubits\n", + " ],\n", + " backend,\n", + " analysis=None,\n", + ")\n", + "\n", + "# Create T2-Hahn experiments on all qubit in parallel\n", + "t2_exp = ParallelExperiment(\n", + " [\n", + " T2Hahn(\n", + " physical_qubits=[qubit],\n", + " delays=np.linspace(\n", + " 1e-6, 2 * backend.properties().t2(qubit), 5, endpoint=True\n", + " ),\n", + " )\n", + " for qubit in qubits\n", + " ],\n", + " backend,\n", + " analysis=None,\n", + ")\n", + "\n", + "# Create readout experiments on all qubit in parallel\n", + "readout_exp = LocalReadoutError(qubits)\n", + "\n", + "# Create single-qubit RB experiments on all qubit in parallel\n", + "singleq_rb_exp = ParallelExperiment(\n", + " [\n", + " StandardRB(\n", + " physical_qubits=[qubit], lengths=[10, 100, 500], num_samples=10\n", + " )\n", + " for qubit in qubits\n", + " ],\n", + " backend,\n", + " analysis=None,\n", + ")\n", + "\n", + "# Create two-qubit RB experiments on the three layers of disjoint edges of the heavy-hex\n", + "twoq_rb_exp_batched = BatchExperiment(\n", + " [\n", + " ParallelExperiment(\n", + " [\n", + " StandardRB(\n", + " physical_qubits=pair,\n", + " lengths=[10, 50, 100],\n", + " num_samples=10,\n", + " )\n", + " for pair in layer\n", + " ],\n", + " backend,\n", + " analysis=None,\n", + " )\n", + " for layer in layered_coupling_map\n", + " ],\n", + " backend,\n", + " flatten_results=True,\n", + " analysis=None,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### QPU properties over time\n", + "\n", + "Looking at the reported QPU properties over time (we'll consider a single week below), we see how these can fluctuate on a scale of a single day. Small fluctuations can happen even within a day. In this scenario, the reported properties (updated once per day) will not accurately capture the current status of the QPU. Moreover, if a job is transpiled locally (using current reported properties) and submitted but executed only at a later time (minutes or days), it may run the risk of having used outdated properties for qubit selection in the transpilation step. This highlights the importance of having updated information about the QPU at execution time. First, let's retrieve the properties over a certain time range.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "errors_list = []\n", + "for day_idx in range(10, 17):\n", + " calibrations_time = datetime(\n", + " year=2024, month=10, day=day_idx, hour=0, minute=0, second=0\n", + " )\n", + " targer_hist = backend.target_history(datetime=calibrations_time)\n", + "\n", + " t1_dict, t2_dict = {}, {}\n", + " for qubit in range(targer_hist.num_qubits):\n", + " t1_dict[qubit] = targer_hist.qubit_properties[qubit].t1\n", + " t2_dict[qubit] = targer_hist.qubit_properties[qubit].t2\n", + "\n", + " errors_dict = {\n", + " \"1q\": targer_hist[\"sx\"],\n", + " \"2q\": targer_hist[\"ecr\"],\n", + " \"spam\": targer_hist[\"measure\"],\n", + " \"t1\": t1_dict,\n", + " \"t2\": t2_dict,\n", + " }\n", + "\n", + " errors_list.append(errors_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, let's plot the values\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(5, 1, figsize=(10, 20), sharex=False)\n", + "\n", + "\n", + "# Plot for T1 values\n", + "for qubit in range(targer_hist.num_qubits):\n", + " t1s = []\n", + " for errors_dict in errors_list:\n", + " t1_dict = errors_dict[\"t1\"]\n", + " t1s.append(t1_dict[qubit] / 1e-6)\n", + "\n", + " axs[0].plot(t1s)\n", + "\n", + "axs[0].set_title(\"T1\")\n", + "axs[0].set_ylabel(r\"Time ($\\mu s$)\")\n", + "axs[0].set_xlabel(\"Days\")\n", + "\n", + "# Plot for T2 values\n", + "for qubit in range(targer_hist.num_qubits):\n", + " t2s = []\n", + " for errors_dict in errors_list:\n", + " t2_dict = errors_dict[\"t2\"]\n", + " t2s.append(t2_dict[qubit] / 1e-6)\n", + "\n", + " axs[1].plot(t2s)\n", + "\n", + "axs[1].set_title(\"T2\")\n", + "axs[1].set_ylabel(r\"Time ($\\mu s$)\")\n", + "axs[1].set_xlabel(\"Days\")\n", + "\n", + "# Plot SPAM values\n", + "for qubit in range(targer_hist.num_qubits):\n", + " spams = []\n", + " for errors_dict in errors_list:\n", + " spam_dict = errors_dict[\"spam\"]\n", + " spams.append(spam_dict[tuple([qubit])].error)\n", + "\n", + " axs[2].plot(spams)\n", + "\n", + "axs[2].set_title(\"SPAM Errors\")\n", + "axs[2].set_ylabel(\"Error Rate\")\n", + "axs[2].set_xlabel(\"Days\")\n", + "\n", + "# Plot 1Q Gate Errors\n", + "for qubit in range(targer_hist.num_qubits):\n", + " oneq_gates = []\n", + " for errors_dict in errors_list:\n", + " oneq_gate_dict = errors_dict[\"1q\"]\n", + " oneq_gates.append(oneq_gate_dict[tuple([qubit])].error)\n", + "\n", + " axs[3].plot(oneq_gates)\n", + "\n", + "axs[3].set_title(\"1Q Gate Errors\")\n", + "axs[3].set_ylabel(\"Error Rate\")\n", + "axs[3].set_xlabel(\"Days\")\n", + "\n", + "# Plot 2Q Gate Errors\n", + "for pair in one_dir_coupling_map:\n", + " twoq_gates = []\n", + " for errors_dict in errors_list:\n", + " twoq_gate_dict = errors_dict[\"2q\"]\n", + " twoq_gates.append(twoq_gate_dict[pair].error)\n", + "\n", + " axs[4].plot(twoq_gates)\n", + "\n", + "axs[4].set_title(\"2Q Gate Errors\")\n", + "axs[4].set_ylabel(\"Error Rate\")\n", + "axs[4].set_xlabel(\"Days\")\n", + "\n", + "plt.subplots_adjust(hspace=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that over several days some of the qubit properties can change considerably. This highlights the importance of having fresh information of the QPU status, to be able to select the best performing qubits for an experiment.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit\n", + "\n", + "ideal_dist = {\"00\": 0.5, \"11\": 0.5}\n", + "\n", + "num_qubits_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 127]\n", + "circuits = []\n", + "for num_qubits in num_qubits_list:\n", + " circuit = QuantumCircuit(num_qubits, 2)\n", + " circuit.h(0)\n", + " for i in range(num_qubits - 1):\n", + " circuit.cx(i, i + 1)\n", + " circuit.barrier()\n", + " circuit.measure(0, 0)\n", + " circuit.measure(num_qubits - 1, 1)\n", + " circuits.append(circuit)\n", + "\n", + "circuits[-1].draw(output=\"mpl\", style=\"clifford\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Optimize problem for quantum hardware execution\n", + "\n", + "No optimization of the circuits or operators is done in this tutorial.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Execute using Qiskit primitives\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute a quantum circuit with default qubit selection\n", + "\n", + "As a reference result of performance, we'll execute a quantum circuit on a QPU by using the default qubits, which are the qubits selected with the requested backend properties. We will use `optimization_level = 3`. This setting includes the most advanced transpilation optimization, and uses target properties (like operation errors) to select the best performing qubits for execution.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pm = generate_preset_pass_manager(target=backend.target, optimization_level=3)\n", + "isa_circuits = pm.run(circuits)\n", + "initial_qubits = [\n", + " [\n", + " idx\n", + " for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", + " if qb._register.name != \"ancilla\"\n", + " ]\n", + " for circuit in isa_circuits\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute a quantum circuit with real-time qubit selection\n", + "\n", + "In this section, we'll investigate the importance of having updated information on qubit properties of the QPU for optimal results. First, we'll carry out a full suite of QPU characterization experiments ($T_1$, $T_2$, SPAM, single-qubit RB and two-qubit RB), which we can then use to update the backend properties. This allows the pass manager to select qubits for the execution based on fresh information about the QPU, possibly improving execution performances. Second, we execute the Bell pair circuit and we compare the fidelity obtained after selecting the qubits with update QPU properties to the fidelity we obtained before when we use the default reported properties for qubit selection.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare characterization experiments\n", + "batches = [t1_exp, t2_exp, readout_exp, singleq_rb_exp, twoq_rb_exp_batched]\n", + "batches_exp = BatchExperiment(batches, backend) # , analysis=None)\n", + "run_options = {\"shots\": 1e3, \"dynamic\": False}\n", + "\n", + "with Session(backend=backend) as session:\n", + " sampler = SamplerV2(mode=session)\n", + "\n", + " # Run characterization experiments\n", + " batches_exp_data = batches_exp.run(\n", + " sampler=sampler, **run_options\n", + " ).block_for_results()\n", + "\n", + " EPG_sx_result_list = batches_exp_data.analysis_results(\"EPG_sx\")\n", + " EPG_sx_result_q_indices = [\n", + " result.device_components.index for result in EPG_sx_result_list\n", + " ]\n", + " EPG_x_result_list = batches_exp_data.analysis_results(\"EPG_x\")\n", + " EPG_x_result_q_indices = [\n", + " result.device_components.index for result in EPG_x_result_list\n", + " ]\n", + " T1_result_list = batches_exp_data.analysis_results(\"T1\")\n", + " T1_result_q_indices = [\n", + " result.device_components.index for result in T1_result_list\n", + " ]\n", + "\n", + " T2_result_list = batches_exp_data.analysis_results(\"T2\")\n", + " T2_result_q_indices = [\n", + " result.device_components.index for result in T2_result_list\n", + " ]\n", + "\n", + " Readout_result_list = batches_exp_data.analysis_results(\n", + " \"Local Readout Mitigator\"\n", + " )\n", + "\n", + " EPG_ecr_result_list = batches_exp_data.analysis_results(\"EPG_ecr\")\n", + "\n", + " # Update target properties\n", + " target = copy.deepcopy(backend.target)\n", + " for i in range(target.num_qubits - 1):\n", + " qarg = (i,)\n", + "\n", + " if qarg in EPG_sx_result_q_indices:\n", + " target.update_instruction_properties(\n", + " instruction=\"sx\",\n", + " qargs=qarg,\n", + " properties=InstructionProperties(\n", + " error=EPG_sx_result_list[i].value.nominal_value\n", + " ),\n", + " )\n", + " if qarg in EPG_x_result_q_indices:\n", + " target.update_instruction_properties(\n", + " instruction=\"x\",\n", + " qargs=qarg,\n", + " properties=InstructionProperties(\n", + " error=EPG_x_result_list[i].value.nominal_value\n", + " ),\n", + " )\n", + "\n", + " err_mat = Readout_result_list.value.assignment_matrix(i)\n", + " readout_assignment_error = (\n", + " err_mat[0, 1] + err_mat[1, 0]\n", + " ) / 2 # average readout error\n", + " target.update_instruction_properties(\n", + " instruction=\"measure\",\n", + " qargs=qarg,\n", + " properties=InstructionProperties(error=readout_assignment_error),\n", + " )\n", + "\n", + " if qarg in T1_result_q_indices:\n", + " target.qubit_properties[i].t1 = T1_result_list[\n", + " i\n", + " ].value.nominal_value\n", + " if qarg in T2_result_q_indices:\n", + " target.qubit_properties[i].t2 = T2_result_list[\n", + " i\n", + " ].value.nominal_value\n", + "\n", + " for pair_idx, pair in enumerate(flattened_layered_coupling_map):\n", + " qarg = tuple(pair)\n", + " try:\n", + " target.update_instruction_properties(\n", + " instruction=\"ecr\",\n", + " qargs=qarg,\n", + " properties=InstructionProperties(\n", + " error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", + " ),\n", + " )\n", + " except:\n", + " target.update_instruction_properties(\n", + " instruction=\"ecr\",\n", + " qargs=qarg[::-1],\n", + " properties=InstructionProperties(\n", + " error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", + " ),\n", + " )\n", + "\n", + " # transpile circuits to updated target\n", + " pm = generate_preset_pass_manager(target=target, optimization_level=3)\n", + " isa_circuit_updated = pm.run(circuits)\n", + " updated_qubits = [\n", + " [\n", + " idx\n", + " for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", + " if qb._register.name != \"ancilla\"\n", + " ]\n", + " for circuit in isa_circuit_updated\n", + " ]\n", + "\n", + " n_trials = 3 # run multiple trials to see variations\n", + "\n", + " # interleave circuits\n", + " interleaved_circuits = []\n", + " for original_circuit, updated_circuit in zip(\n", + " isa_circuits, isa_circuit_updated\n", + " ):\n", + " interleaved_circuits.append(original_circuit)\n", + " interleaved_circuits.append(updated_circuit)\n", + "\n", + " # Run circuits\n", + " # Set simple error suppression/mitigation options\n", + " sampler.options.dynamical_decoupling.enable = True\n", + " sampler.options.dynamical_decoupling.sequence_type = \"XY4\"\n", + "\n", + " job_interleaved = sampler.run(interleaved_circuits * n_trials)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Post-process and return result in desired classical format\n", + "\n", + "Finally, let's compare the fidelity of the Bell state obtained in the two different settings:\n", + "\n", + "* `original`, that is with the default qubits chosen by the transpiler based on reported properties of the backend.\n", + "* `updated`, that is with the qubits chosen based on updated properties of the backend after characterization experiments have run.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "results = job_interleaved.result()\n", + "all_fidelity_list, all_fidelity_updated_list = [], []\n", + "for exp_idx in range(n_trials):\n", + " fidelity_list, fidelity_updated_list = [], []\n", + "\n", + " for idx, num_qubits in enumerate(num_qubits_list):\n", + " pub_result_original = results[\n", + " 2 * exp_idx * len(num_qubits_list) + 2 * idx\n", + " ]\n", + " pub_result_updated = results[\n", + " 2 * exp_idx * len(num_qubits_list) + 2 * idx + 1\n", + " ]\n", + "\n", + " fid = hellinger_fidelity(\n", + " ideal_dist, pub_result_original.data.c.get_counts()\n", + " )\n", + " fidelity_list.append(fid)\n", + "\n", + " fid_up = hellinger_fidelity(\n", + " ideal_dist, pub_result_updated.data.c.get_counts()\n", + " )\n", + " fidelity_updated_list.append(fid_up)\n", + " all_fidelity_list.append(fidelity_list)\n", + " all_fidelity_updated_list.append(fidelity_updated_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "plt.errorbar(\n", + " num_qubits_list,\n", + " np.mean(all_fidelity_list, axis=0),\n", + " yerr=np.std(all_fidelity_list, axis=0),\n", + " fmt=\"o-.\",\n", + " label=\"original\",\n", + " color=\"b\",\n", + ")\n", + "# plt.plot(num_qubits_list, fidelity_list, '-.')\n", + "plt.errorbar(\n", + " num_qubits_list,\n", + " np.mean(all_fidelity_updated_list, axis=0),\n", + " yerr=np.std(all_fidelity_updated_list, axis=0),\n", + " fmt=\"o-.\",\n", + " label=\"updated\",\n", + " color=\"r\",\n", + ")\n", + "# plt.plot(num_qubits_list, fidelity_updated_list, '-.')\n", + "plt.xlabel(\"Chain length\")\n", + "plt.xticks(num_qubits_list)\n", + "plt.ylabel(\"Fidelity\")\n", + "plt.title(\"Bell pair fidelity at the edge of N-qubits chain\")\n", + "plt.legend()\n", + "plt.grid(\n", + " alpha=0.2,\n", + " linestyle=\"-.\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With increasing chain length, and thus less freedom to choose physical qubits, the fact that we have updated information about qubit properties becomes less substantial, with there being no difference when all of the qubits are used.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Call to Action: Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tutorial survey\n", + "\n", + "Please take this short survey to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", + "\n", + "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_0w6FZ9QrWkKfTQq)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "© IBM Corp., 2017-2025" + ] + } + ], + "metadata": { + "description": "Run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU.", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + }, + "title": "Real-time benchmarking for qubit selection\n" + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index be9537d1ed1..446dfac5b1d 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "9f81f6b2-d7f3-4cc2-b09b-cf3a627c7b0f", "metadata": { "tags": [ "remove-cell" @@ -14,16 +13,14 @@ }, { "cell_type": "markdown", - "id": "1a869c2d-ae51-47d3-8a41-bfcf5e505f59", "metadata": {}, "source": [ "# Advanced techniques for QAOA\n", - "*Usage estimate: 25 minutes on ibm_sherbrooke (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 25 minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { "cell_type": "markdown", - "id": "ea97567d-810f-4cca-8edf-a47d70ea870a", "metadata": {}, "source": [ "## Background\n", @@ -40,7 +37,6 @@ }, { "cell_type": "markdown", - "id": "40fb546e-85e0-450b-a5ea-5d08950d129f", "metadata": {}, "source": [ "## Requirements\n", @@ -53,7 +49,6 @@ }, { "cell_type": "markdown", - "id": "50285e5f-1a7b-471c-a223-1ae0af19d9ed", "metadata": {}, "source": [ "## Setup" @@ -61,8 +56,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "d019ea68-61e0-4341-84c7-e612ca10dde7", + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +92,6 @@ }, { "cell_type": "markdown", - "id": "8b77b0c9-f5a6-476e-86b8-069ba14f9ab3", "metadata": {}, "source": [ "## Step 1: Map classical inputs to a quantum problem\n", @@ -116,15 +109,17 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "d19a5086-a858-45e1-b60b-f31db350e9d9", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] } - }, - "outputs": [], + ], "source": [ "service = QiskitRuntimeService()\n", "backend = service.least_busy(operational=True, simulator=False)\n", @@ -133,14 +128,14 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "c989c08a-1e79-4059-b9d7-12651909b3bb", + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, "metadata": {}, @@ -161,15 +156,14 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "e39c4e42-ce97-4a04-8879-da4d33a684bc", + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Cost Function Hamiltonian: SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIZIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIZIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIZIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIZIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 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'IIIIIIIIIIIIIIZIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIZIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIZIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIZIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIZIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIZIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIZIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII'],\n", " coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", @@ -212,7 +206,6 @@ }, { "cell_type": "markdown", - "id": "cf94748d-25b2-47bc-8e65-8504dee843b6", "metadata": {}, "source": [ "### Hamiltonian → quantum circuit" @@ -220,17 +213,17 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "8ff1859a-7487-4113-b8d7-1342cd6bf527", + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -244,7 +237,6 @@ }, { "cell_type": "markdown", - "id": "4e576068-53e7-4a06-a83b-87e95de141e9", "metadata": {}, "source": [ "## Step 2: Optimize problem for quantum hardware execution\n", @@ -256,8 +248,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "7c54402a-7696-4b0e-826a-6e0ac4c54395", + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -285,7 +276,6 @@ }, { "cell_type": "markdown", - "id": "ba53fd1d-0d68-43e3-9102-4c6e64f04de7", "metadata": {}, "source": [ "#### Remap the graph using a SAT mapper\n", @@ -299,8 +289,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "df9d861a-64e8-49ee-aed1-c41f437fa743", + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -522,29 +511,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "e689e09e-6ca7-4154-8602-d1d954ebe80b", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Layers: 0, Status: True, Time: 0.00903300000000229\n", - "Map from old to new nodes: {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, 30: 30, 31: 31, 32: 32, 33: 33, 34: 34, 35: 35, 36: 36, 37: 37, 38: 38, 39: 39, 40: 40, 41: 41, 42: 42, 43: 43, 44: 44, 45: 45, 46: 46, 47: 47, 48: 48, 49: 49, 50: 50, 51: 51, 52: 52, 53: 53, 54: 54, 55: 55, 56: 56, 57: 57, 58: 58, 59: 59, 60: 60, 61: 61, 62: 62, 63: 63, 64: 64, 65: 65, 66: 66, 67: 67, 68: 68, 69: 69, 70: 70, 71: 71, 72: 72, 73: 73, 74: 74, 75: 75, 76: 76, 77: 77, 78: 78, 79: 79, 80: 80, 81: 81, 82: 82, 83: 83, 84: 84, 85: 85, 86: 86, 87: 87, 88: 88, 89: 89, 90: 90, 91: 91, 92: 92, 93: 93, 94: 94, 95: 95, 96: 96, 97: 97, 98: 98, 99: 99}\n", - "Min SWAP layers: 0\n" - ] - }, - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sm = SATMapper(timeout=10)\n", "remapped_graph, edge_map, min_swap_layers = sm.remap_graph_with_sat(\n", @@ -557,31 +526,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "ecf6e8c3-65c2-4430-8dd3-d67b8842045d", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII', 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'IIIIIIIIZIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIZIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII'],\n", - " coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j])\n" - ] - } - ], + "outputs": [], "source": [ "remapped_max_cut_paulis = build_max_cut_paulis(remapped_graph)\n", "# define a qiskit SparsePauliOp from the list of paulis\n", @@ -591,7 +538,6 @@ }, { "cell_type": "markdown", - "id": "5ae531be-80eb-4acd-b84a-7d466fd872e7", "metadata": {}, "source": [ "#### Build a QAOA circuit with the SWAP strategy and the SAT mapping\n", @@ -605,7 +551,6 @@ { "cell_type": "code", "execution_count": null, - "id": "57d5eb53-9cda-4c38-a00b-26ed4b533bcd", "metadata": {}, "outputs": [], "source": [ @@ -781,20 +726,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "25db3ad8-0666-42f9-8569-544f89d99391", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "draw_graph(remapped_graph, node_size=200, with_labels=True, width=1)" ] @@ -802,7 +736,6 @@ { "cell_type": "code", "execution_count": null, - "id": "7793ef92-ce59-4fd7-b43f-48d4e3427e3a", "metadata": {}, "outputs": [], "source": [ @@ -819,21 +752,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "82ae28b3-85eb-4487-8100-1e622e93cccf", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "qaoa_circ = create_qaoa_swap_circuit(\n", " remapped_cost_operator,\n", @@ -846,7 +767,6 @@ }, { "cell_type": "markdown", - "id": "e2afd1a7-0980-433b-a3a8-303d7e7718b1", "metadata": {}, "source": [ "## Step 3: Execute using Qiskit primitives\n", @@ -861,7 +781,6 @@ { "cell_type": "code", "execution_count": null, - "id": "5e4745d4-df78-4ec6-b390-549ee91bba20", "metadata": {}, "outputs": [], "source": [ @@ -875,8 +794,7 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "e6794cf3-7fbe-46a5-bdc0-5faad1235365", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -912,7 +830,6 @@ { "cell_type": "code", "execution_count": null, - "id": "c5e5f7a4-01f6-4a02-9114-3bb0e24be1a2", "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +933,6 @@ }, { "cell_type": "markdown", - "id": "63fa2ab4-5354-4022-ab46-e9bbf73870de", "metadata": {}, "source": [ "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the circuit's error rate." @@ -1024,21 +940,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "032bf312-4bf4-40f4-81f0-2ae8a719b98b", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "layer fidelity 0.18178076404510374\n", - "\n", - "The corresponding CVaR aggregation value is: 0.06567448516285564\n", - "To mitigate the twirled noise, increase shots by 15.226613463664926\n" - ] - } - ], + "outputs": [], "source": [ "num_2q_ops = transpiled_qaoa_circ.count_ops()[\"ecr\"]\n", "\n", @@ -1062,43 +966,8 @@ { "cell_type": "code", "execution_count": null, - "id": "4e608d54-60ee-4a63-91f7-aace7c036694", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 1: -20.770192185845673\n", - "Iteration 2: -20.662187834467975\n", - "Iteration 3: -20.530182516117474\n", - "Iteration 4: -6.094164949444625\n", - "Iteration 5: -31.42021837469251\n", - "Iteration 6: -5.8402352967163145\n", - "Iteration 7: -27.110205884627188\n", - "Iteration 8: -24.666187995630317\n", - "Iteration 9: -34.10016519118837\n", - "Iteration 10: -32.97620048569601\n", - "Iteration 11: -34.346175102659636\n", - "Iteration 12: -32.58618477238797\n", - "Iteration 13: -34.51018171030716\n", - "Iteration 14: -34.72819049364342\n", - "Iteration 15: -34.62218622284688\n", - "Iteration 16: -35.13220677101896\n", - "Iteration 17: -34.990201049763236\n", - "Iteration 18: -34.732190654805564\n", - "Iteration 19: -34.37817639195668\n", - "Iteration 20: -34.85019540908861\n", - " message: Optimization terminated successfully.\n", - " success: True\n", - " status: 1\n", - " fun: -34.85019540908861\n", - " x: [ 2.739e+00 1.124e+00]\n", - " nfev: 20\n", - " maxcv: 0.0\n" - ] - } - ], + "outputs": [], "source": [ "iter_counts = 0\n", "result_dict = {}\n", @@ -1136,7 +1005,6 @@ }, { "cell_type": "markdown", - "id": "1d190fa4-3bbe-412a-b296-6dddd3ad2b12", "metadata": {}, "source": [ "## Step 4: Post-process and return result in desired classical format" @@ -1145,19 +1013,8 @@ { "cell_type": "code", "execution_count": null, - "id": "761821cb-9a0c-4efb-806b-75513302d34a", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12, 6))\n", "plt.plot(\n", @@ -1176,7 +1033,6 @@ }, { "cell_type": "markdown", - "id": "38aadfcb-aec9-4dbb-a9d3-319239eae196", "metadata": {}, "source": [ "The following cost is the result of the standard expectation value." @@ -1184,18 +1040,9 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "7e8af29e-c99b-41f2-b6dd-2be471e1af21", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bitstring (int): 426224160272806387142248724306, probability: 6.567713122290818e-05, objective value: -54.0\n" - ] - } - ], + "outputs": [], "source": [ "# sort the result_dict[iter_counts]['evaluated] by the CVaR value\n", "sorted_result_dict = [\n", @@ -1213,18 +1060,8 @@ { "cell_type": "code", "execution_count": null, - "id": "5ea9e6aa-4297-4687-b484-1695d415bad5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result bitstring (binary) : [0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0]\n", - "The value of the cut is: 83\n" - ] - } - ], + "outputs": [], "source": [ "def to_bitstring(integer, num_bits):\n", " result = np.binary_repr(integer, width=num_bits)\n", @@ -1253,7 +1090,6 @@ }, { "cell_type": "markdown", - "id": "c5879546-35ab-4876-bed9-262b85f130cc", "metadata": {}, "source": [ "Finally, let's draw a graph based on the CVaR result.\n", @@ -1264,20 +1100,9 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "852dfeed-2871-4ca1-9754-15c95293198e", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def plot_result(G, x):\n", " colors = [\"tab:grey\" if i == 0 else \"tab:purple\" for i in x]\n", @@ -1292,7 +1117,6 @@ }, { "cell_type": "markdown", - "id": "82f5c13b-a141-4657-adfd-bb18e88ad9f2", "metadata": {}, "source": [ "## References\n", @@ -1308,7 +1132,6 @@ }, { "cell_type": "markdown", - "id": "c048cd84-d6e8-4b8a-926e-a7f7724a86e2", "metadata": {}, "source": [ "## Tutorial survey\n", @@ -1322,7 +1145,7 @@ "metadata": { "description": "This notebook introduces advanced techniques to improve the performance of the Quantum Approximate Optimization Algorithm (QAOA) with a large number of qubits.", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1336,7 +1159,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.2" }, "title": "Advanced techniques for QAOA" }, diff --git a/docs/tutorials/circuit-transpilation-settings.ipynb b/docs/tutorials/circuit-transpilation-settings.ipynb index 9bc964af286..1132eedcfe1 100644 --- a/docs/tutorials/circuit-transpilation-settings.ipynb +++ b/docs/tutorials/circuit-transpilation-settings.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Compare transpiler settings\n", - "*Usage estimate: under one minute on ibm_nazca (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: under one minute on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -93,7 +93,7 @@ { "data": { "text/plain": [ - "'ibm_kyoto'" + "'ibm_brisbanse'" ] }, "execution_count": 28, diff --git a/docs/tutorials/operator-back-propagation.ipynb b/docs/tutorials/operator-back-propagation.ipynb index b96329d1402..9c010793a10 100644 --- a/docs/tutorials/operator-back-propagation.ipynb +++ b/docs/tutorials/operator-back-propagation.ipynb @@ -8,7 +8,7 @@ "{/* cspell:ignore simeq // This in an equation and isn't being ignored correctly */}\n", "\n", "# Operator backpropagation (OBP) for estimation of expectation values\n", - "*Usage estimate: 16 minutes on ibm_nazca (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 16 minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb index e900c015ad3..943408f9d3b 100644 --- a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb +++ b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb @@ -9,7 +9,7 @@ "\n", "# Pauli Correlation Encoding to reduce Maxcut requirements\n", "\n", - "*Usage estimate: 30 minutes on ibm_sherbrooke (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 30 minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb b/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb index 4661bfd64e3..4339eb416bb 100644 --- a/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb +++ b/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb @@ -18,7 +18,7 @@ "metadata": {}, "source": [ "# Quantum approximate optimization algorithm\n", - "*Usage estimate: Eight minutes on ibm_sherbrooke (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: Eight minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -44,8 +44,7 @@ "\n", "Before starting this tutorial, be sure you have the following installed:\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "- Qiskit Runtime 0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "- Rustworkx graph library (`pip install rustworkx`)" + "- Qiskit Runtime 0.22 or later (`pip install qiskit-ibm-runtime`)" ] }, { @@ -58,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "id": "37b3acfc", "metadata": {}, "outputs": [], @@ -123,14 +122,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "6ced6bea", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, "metadata": {}, @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "52d1ba92", "metadata": {}, "outputs": [ @@ -330,17 +330,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "7bd8c6d4-f40f-4a11-a440-0b26d9021b53", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -354,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "315c495a", "metadata": {}, "outputs": [ @@ -364,7 +365,7 @@ "ParameterView([ParameterVectorElement(β[0]), ParameterVectorElement(β[1]), ParameterVectorElement(γ[0]), ParameterVectorElement(γ[1])])" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -405,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "3f28a422-805c-4d3d-b5f6-62539e9133bd", "metadata": {}, "outputs": [ @@ -413,16 +414,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] }, { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -472,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "afa5747f-44dc-4e41-a875-7b6f896f13e2", "metadata": {}, "outputs": [], @@ -507,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "3e64a862", "metadata": {}, "outputs": [], @@ -570,17 +572,7 @@ "execution_count": null, "id": "e14ecc92", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12, 6))\n", "plt.plot(objective_func_vals)\n", @@ -601,21 +593,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "2989e76e-4296-4dd8-b065-2b8fced064cf", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "optimized_circuit = candidate_circuit.assign_parameters(result.x)\n", "optimized_circuit.draw(\"mpl\", fold=False, idle_wires=False)" @@ -626,15 +607,7 @@ "execution_count": null, "id": "d8f0e302", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{16: 0.0356, 18: 0.0753, 10: 0.0456, 21: 0.0456, 8: 0.0159, 19: 0.0288, 17: 0.0385, 1: 0.0234, 11: 0.0955, 31: 0.0038, 22: 0.0732, 28: 0.0137, 20: 0.08, 9: 0.0977, 6: 0.0138, 30: 0.0102, 2: 0.0127, 26: 0.0532, 13: 0.0354, 27: 0.0114, 0: 0.0087, 4: 0.0092, 5: 0.0432, 14: 0.0195, 25: 0.0196, 3: 0.0264, 24: 0.0128, 12: 0.0164, 15: 0.0094, 23: 0.009, 7: 0.0111, 29: 0.0054}\n" - ] - } - ], + "outputs": [], "source": [ "# If using qiskit-ibm-runtime<0.24.0, change `mode=` to `backend=`\n", "sampler = Sampler(mode=backend)\n", @@ -668,18 +641,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "d4f7fc70-883f-4b6b-8e92-2fc4afbbea46", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result bitstring: [1, 0, 0, 1, 0]\n" - ] - } - ], + "outputs": [], "source": [ "# auxiliary functions to sample most likely bitstring\n", "def to_bitstring(integer, num_bits):\n", @@ -701,25 +666,7 @@ "execution_count": null, "id": "650875e9-adbc-43bd-9505-556be2566278", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/dk/j4n5f_1d3xs7m8bq81g9gfk80000gn/T/ipykernel_76646/1639367914.py:19: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " ax.get_children()[int(p)].set_color(\"tab:purple\")\n" - ] - }, - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "matplotlib.rcParams.update({\"font.size\": 10})\n", "final_bits = final_distribution_bin\n", @@ -752,20 +699,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "33135970-8bc4-4fb2-ab87-08726a432ce4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# auxiliary function to plot graphs\n", "def plot_result(G, x):\n", @@ -792,15 +729,7 @@ "execution_count": null, "id": "2f6a73c4-f5ae-4647-a0dd-d77a13f66388", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The value of the cut is: 5\n" - ] - } - ], + "outputs": [], "source": [ "def evaluate_sample(x: Sequence[int], graph: rx.PyGraph) -> float:\n", " assert len(x) == len(\n", @@ -828,20 +757,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "590fe2ce", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n = 100 # Number of nodes in graph\n", "graph_100 = rx.PyGraph()\n", @@ -873,31 +792,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "a6bdceed", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cost Function Hamiltonian: SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIZIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 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'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIZIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIZIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIZIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIZIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIZIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIZIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIZIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIZIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIZIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII'],\n", - " coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j])\n" - ] - } - ], + "outputs": [], "source": [ "max_cut_paulis_100 = build_max_cut_paulis(graph_100)\n", "\n", @@ -918,18 +816,7 @@ "execution_count": null, "id": "9693adfc", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "circuit_100 = QAOAAnsatz(cost_operator=cost_hamiltonian_100, reps=1)\n", "circuit_100.measure_all()\n", @@ -951,18 +838,7 @@ "execution_count": null, "id": "3a14e7ad", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pm = generate_preset_pass_manager(optimization_level=3, backend=backend)\n", "\n", @@ -1001,21 +877,7 @@ "execution_count": null, "id": "9521a963", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " message: Optimization terminated successfully.\n", - " success: True\n", - " status: 1\n", - " fun: -43.594336255533044\n", - " x: [ 2.751e+00 3.347e-01]\n", - " nfev: 30\n", - " maxcv: 0.0\n" - ] - } - ], + "outputs": [], "source": [ "initial_gamma = np.pi\n", "initial_beta = np.pi / 2\n", @@ -1053,21 +915,10 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "1c432c2e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "optimized_circuit_100 = candidate_circuit_100.assign_parameters(result.x)\n", "optimized_circuit_100.draw(\"mpl\", fold=False, idle_wires=False)" @@ -1083,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "a5cc531b", "metadata": {}, "outputs": [], @@ -1123,17 +974,7 @@ "execution_count": null, "id": "0fda3611", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12, 6))\n", "plt.plot(objective_func_vals)\n", @@ -1160,18 +1001,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "080e93a9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result bitstring: [0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0]\n" - ] - } - ], + "outputs": [], "source": [ "_PARITY = np.array(\n", " [-1 if bin(i).count(\"1\") % 2 else 1 for i in range(256)],\n", @@ -1221,20 +1054,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "b4a25e28", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_result(graph_100, best_sol_bitstring_100)" ] @@ -1249,18 +1072,10 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "dd015e77-c1b9-4d06-9163-3ef56cc810f7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The value of the cut is: 84\n" - ] - } - ], + "outputs": [], "source": [ "cut_value_100 = evaluate_sample(best_sol_bitstring_100, graph_100)\n", "print(\"The value of the cut is:\", cut_value_100)" @@ -1317,7 +1132,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "33f0580d", "metadata": {}, "outputs": [], @@ -1337,20 +1152,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "4381a2b3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", "plot_cdf(result_dist, ax, \"Eagle device\")" @@ -1382,7 +1187,7 @@ "metadata": { "description": "Learn the basics of quantum computing, and how to use IBM Quantum services and systems to solve real-world problems.", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1396,7 +1201,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.2" }, "title": "Quantum approximate optimization algorithm" }, diff --git a/docs/tutorials/quantum-kernel-training.ipynb b/docs/tutorials/quantum-kernel-training.ipynb index b7a219d0cf5..4eafc8e77fa 100644 --- a/docs/tutorials/quantum-kernel-training.ipynb +++ b/docs/tutorials/quantum-kernel-training.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Quantum kernel training\n", - "*Usage estimate: under one minute on ibm_nazca (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: under one minute on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 66a6a8f0158..915dd621069 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Real-time benchmarking for qubit selection\n", - "*Usage estimate: 4 minutes on ibm_cusco (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 4 minutes on ibm_brisbane (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -41,8 +41,7 @@ "\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", "- Qiskit Runtime 0.29 or later ( `pip install qiskit-ibm-runtime` )\n", - "- Qiskit Experiments 0.7 or later ( `pip install qiskit-experiments` )\n", - "- Rustworkx graph library (`pip install rustworkx`)" + "- Qiskit Experiments 0.7 or later ( `pip install qiskit-experiments` )" ] }, { @@ -103,20 +102,40 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "service = QiskitRuntimeService(instance=\"crn:v1:bluemix:public:quantum-computing:us-east:a/26b15df5f5684154b2c791c32e07e69b:08dcfec2-410b-45a4-8185-10fd26ce26f6::\")\n", + "\n", + "# To run on hardware, select a backend that supports the ecr gate\n", + "service.backends(filters=lambda x: (\"ecr\" in x.basis_gates))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ - "# To run on hardware, select the backend with the fewest number of jobs in the queue\n", - "service = QiskitRuntimeService()\n", - "backend = service.least_busy(\n", - " operational=True, simulator=False, min_num_qubits=127\n", - ")\n", + "\n", + "backend = service.backend(\"ibm_brisbane\")\n", "\n", "qubits = list(range(backend.num_qubits))" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -173,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -291,12 +310,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -387,17 +406,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -447,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -474,29 +493,54 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "ename": "RequestsApiError", - "evalue": "'400 Client Error: Bad Request for url: https://us-east.quantum-computing.cloud.ibm.com/sessions. {\"errors\":[{\"code\":1352,\"message\":\"You are not authorized to run a session when using the open plan.\",\"solution\":\"Create an instance of a different plan type or use a different execution mode .\",\"more_info\":\"https://cloud.ibm.com/apidocs/quantum-computing#error-handling\"}],\"trace\":\"d957770b-0f25-4b3d-974d-cf62562ecc4e\"}\\n'", + "evalue": "\"('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\"", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mHTTPError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:328\u001b[39m, in \u001b[36mRetrySession.request\u001b[39m\u001b[34m(self, method, url, bare, **kwargs)\u001b[39m\n\u001b[32m 327\u001b[39m response = \u001b[38;5;28msuper\u001b[39m().request(method, final_url, headers=headers, **kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m328\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 329\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m RequestException \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[32m 330\u001b[39m \u001b[38;5;66;03m# Wrap the requests exceptions into a IBM Q custom one, for\u001b[39;00m\n\u001b[32m 331\u001b[39m \u001b[38;5;66;03m# compatibility.\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\requests\\models.py:1021\u001b[39m, in \u001b[36mResponse.raise_for_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1020\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[32m-> \u001b[39m\u001b[32m1021\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response=\u001b[38;5;28mself\u001b[39m)\n", - "\u001b[31mHTTPError\u001b[39m: 400 Client Error: Bad Request for url: https://us-east.quantum-computing.cloud.ibm.com/sessions", + "\u001b[31mRemoteDisconnected\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:787\u001b[39m, in \u001b[36mHTTPConnectionPool.urlopen\u001b[39m\u001b[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[39m\n\u001b[32m 786\u001b[39m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m787\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 788\u001b[39m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 789\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 790\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 791\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 792\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 793\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 794\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 795\u001b[39m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 796\u001b[39m \u001b[43m \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 797\u001b[39m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 798\u001b[39m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 799\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 800\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 802\u001b[39m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:534\u001b[39m, in \u001b[36mHTTPConnectionPool._make_request\u001b[39m\u001b[34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[39m\n\u001b[32m 533\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m534\u001b[39m response = \u001b[43mconn\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connection.py:565\u001b[39m, in \u001b[36mHTTPConnection.getresponse\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 564\u001b[39m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m565\u001b[39m httplib_response = \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 567\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:1423\u001b[39m, in \u001b[36mHTTPConnection.getresponse\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1422\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1423\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1424\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:331\u001b[39m, in \u001b[36mHTTPResponse.begin\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m version, status, reason = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m status != CONTINUE:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:300\u001b[39m, in \u001b[36mHTTPResponse._read_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 297\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m line:\n\u001b[32m 298\u001b[39m \u001b[38;5;66;03m# Presumably, the server closed the connection before\u001b[39;00m\n\u001b[32m 299\u001b[39m \u001b[38;5;66;03m# sending a valid response.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RemoteDisconnected(\u001b[33m\"\u001b[39m\u001b[33mRemote end closed connection without\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 301\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m response\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 302\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[31mRemoteDisconnected\u001b[39m: Remote end closed connection without response", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mProtocolError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\adapters.py:667\u001b[39m, in \u001b[36mHTTPAdapter.send\u001b[39m\u001b[34m(self, request, stream, timeout, verify, cert, proxies)\u001b[39m\n\u001b[32m 666\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m667\u001b[39m resp = \u001b[43mconn\u001b[49m\u001b[43m.\u001b[49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 668\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 669\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m=\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 670\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 671\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m.\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 672\u001b[39m \u001b[43m \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 673\u001b[39m \u001b[43m \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 674\u001b[39m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 675\u001b[39m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 676\u001b[39m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 677\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 678\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 679\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 681\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:841\u001b[39m, in \u001b[36mHTTPConnectionPool.urlopen\u001b[39m\u001b[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[39m\n\u001b[32m 839\u001b[39m new_e = ProtocolError(\u001b[33m\"\u001b[39m\u001b[33mConnection aborted.\u001b[39m\u001b[33m\"\u001b[39m, new_e)\n\u001b[32m--> \u001b[39m\u001b[32m841\u001b[39m retries = \u001b[43mretries\u001b[49m\u001b[43m.\u001b[49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 842\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnew_e\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m=\u001b[49m\u001b[43msys\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m 843\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 844\u001b[39m retries.sleep()\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:125\u001b[39m, in \u001b[36mPostForcelistRetry.increment\u001b[39m\u001b[34m(self, method, url, response, error, _pool, _stacktrace)\u001b[39m\n\u001b[32m 116\u001b[39m logger.debug(\n\u001b[32m 117\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mRetrying method=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, url=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, status=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, error=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, data=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, headers=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m,\n\u001b[32m 118\u001b[39m method,\n\u001b[32m (...)\u001b[39m\u001b[32m 123\u001b[39m headers,\n\u001b[32m 124\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m125\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 126\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 127\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m=\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 128\u001b[39m \u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 129\u001b[39m \u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m=\u001b[49m\u001b[43merror\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 130\u001b[39m \u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 131\u001b[39m \u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 132\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\util\\retry.py:474\u001b[39m, in \u001b[36mRetry.increment\u001b[39m\u001b[34m(self, method, url, response, error, _pool, _stacktrace)\u001b[39m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m read \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_method_retryable(method):\n\u001b[32m--> \u001b[39m\u001b[32m474\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43merror\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 475\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m read \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\util\\util.py:38\u001b[39m, in \u001b[36mreraise\u001b[39m\u001b[34m(tp, value, tb)\u001b[39m\n\u001b[32m 37\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m value.__traceback__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tb:\n\u001b[32m---> \u001b[39m\u001b[32m38\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m value.with_traceback(tb)\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m value\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:787\u001b[39m, in \u001b[36mHTTPConnectionPool.urlopen\u001b[39m\u001b[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[39m\n\u001b[32m 786\u001b[39m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m787\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 788\u001b[39m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 789\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 790\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 791\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 792\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 793\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 794\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 795\u001b[39m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 796\u001b[39m \u001b[43m \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 797\u001b[39m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 798\u001b[39m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 799\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 800\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 802\u001b[39m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:534\u001b[39m, in \u001b[36mHTTPConnectionPool._make_request\u001b[39m\u001b[34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[39m\n\u001b[32m 533\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m534\u001b[39m response = \u001b[43mconn\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connection.py:565\u001b[39m, in \u001b[36mHTTPConnection.getresponse\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 564\u001b[39m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m565\u001b[39m httplib_response = \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 567\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:1423\u001b[39m, in \u001b[36mHTTPConnection.getresponse\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1422\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1423\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1424\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:331\u001b[39m, in \u001b[36mHTTPResponse.begin\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m version, status, reason = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m status != CONTINUE:\n", + "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:300\u001b[39m, in \u001b[36mHTTPResponse._read_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 297\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m line:\n\u001b[32m 298\u001b[39m \u001b[38;5;66;03m# Presumably, the server closed the connection before\u001b[39;00m\n\u001b[32m 299\u001b[39m \u001b[38;5;66;03m# sending a valid response.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RemoteDisconnected(\u001b[33m\"\u001b[39m\u001b[33mRemote end closed connection without\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 301\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m response\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 302\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[31mProtocolError\u001b[39m: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mConnectionError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:327\u001b[39m, in \u001b[36mRetrySession.request\u001b[39m\u001b[34m(self, method, url, bare, **kwargs)\u001b[39m\n\u001b[32m 326\u001b[39m \u001b[38;5;28mself\u001b[39m._log_request_info(final_url, method, kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m327\u001b[39m response = \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfinal_url\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 328\u001b[39m response.raise_for_status()\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\sessions.py:589\u001b[39m, in \u001b[36mSession.request\u001b[39m\u001b[34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[39m\n\u001b[32m 588\u001b[39m send_kwargs.update(settings)\n\u001b[32m--> \u001b[39m\u001b[32m589\u001b[39m resp = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 591\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\sessions.py:703\u001b[39m, in \u001b[36mSession.send\u001b[39m\u001b[34m(self, request, **kwargs)\u001b[39m\n\u001b[32m 702\u001b[39m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m703\u001b[39m r = \u001b[43madapter\u001b[49m\u001b[43m.\u001b[49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 705\u001b[39m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\adapters.py:682\u001b[39m, in \u001b[36mHTTPAdapter.send\u001b[39m\u001b[34m(self, request, stream, timeout, verify, cert, proxies)\u001b[39m\n\u001b[32m 681\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m--> \u001b[39m\u001b[32m682\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request=request)\n\u001b[32m 684\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m MaxRetryError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[31mConnectionError\u001b[39m: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[31mRequestsApiError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m batches_exp = BatchExperiment(batches, backend) \u001b[38;5;66;03m# , analysis=None)\u001b[39;00m\n\u001b[32m 4\u001b[39m run_options = {\u001b[33m\"\u001b[39m\u001b[33mshots\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m1e3\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mdynamic\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m}\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mSession\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[32m 7\u001b[39m sampler = SamplerV2(mode=session)\n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m# Run characterization experiments\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:132\u001b[39m, in \u001b[36mSession.__init__\u001b[39m\u001b[34m(self, backend, max_time, create_new)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28mself\u001b[39m._instance = \u001b[38;5;28mself\u001b[39m._backend._instance\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.configuration().simulator:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28mself\u001b[39m._session_id = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_create_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcreate_new\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcreate_new\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:137\u001b[39m, in \u001b[36mSession._create_session\u001b[39m\u001b[34m(self, create_new)\u001b[39m\n\u001b[32m 135\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Create a session.\"\"\"\u001b[39;00m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m._service, QiskitRuntimeService) \u001b[38;5;129;01mand\u001b[39;00m create_new:\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m session = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_service\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_api_client\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_instance\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate_session\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 138\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_instance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_max_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_service\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchannel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdedicated\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 139\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 140\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m session.get(\u001b[33m\"\u001b[39m\u001b[33mid\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 141\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\clients\\runtime.py:237\u001b[39m, in \u001b[36mRuntimeClient.create_session\u001b[39m\u001b[34m(self, backend, instance, max_time, channel, mode)\u001b[39m\n\u001b[32m 224\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate_session\u001b[39m(\n\u001b[32m 225\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 226\u001b[39m backend: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m (...)\u001b[39m\u001b[32m 230\u001b[39m mode: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 231\u001b[39m ) -> Dict[\u001b[38;5;28mstr\u001b[39m, Any]:\n\u001b[32m 232\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Create a session.\u001b[39;00m\n\u001b[32m 233\u001b[39m \n\u001b[32m 234\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m 235\u001b[39m \u001b[33;03m mode: Execution mode.\u001b[39;00m\n\u001b[32m 236\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m237\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_api\u001b[49m\u001b[43m.\u001b[49m\u001b[43mruntime_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43msession_id\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minstance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\n\u001b[32m 239\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\rest\\runtime_session.py:65\u001b[39m, in \u001b[36mRuntimeSession.create\u001b[39m\u001b[34m(self, backend, instance, max_time, channel, mode)\u001b[39m\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 64\u001b[39m payload[\u001b[33m\"\u001b[39m\u001b[33mmax_ttl\u001b[39m\u001b[33m\"\u001b[39m] = max_time \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m65\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msession\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpayload\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_HEADER_JSON_CONTENT\u001b[49m\u001b[43m)\u001b[49m.json()\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\requests\\sessions.py:637\u001b[39m, in \u001b[36mSession.post\u001b[39m\u001b[34m(self, url, data, json, **kwargs)\u001b[39m\n\u001b[32m 626\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\u001b[38;5;28mself\u001b[39m, url, data=\u001b[38;5;28;01mNone\u001b[39;00m, json=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 627\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33mr\u001b[39m\u001b[33;03m\"\"\"Sends a POST request. Returns :class:`Response` object.\u001b[39;00m\n\u001b[32m 628\u001b[39m \n\u001b[32m 629\u001b[39m \u001b[33;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 634\u001b[39m \u001b[33;03m :rtype: requests.Response\u001b[39;00m\n\u001b[32m 635\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m637\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPOST\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:356\u001b[39m, in \u001b[36mRetrySession.request\u001b[39m\u001b[34m(self, method, url, bare, **kwargs)\u001b[39m\n\u001b[32m 350\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m status_code == \u001b[32m503\u001b[39m: \u001b[38;5;66;03m# Planned maintenance outage\u001b[39;00m\n\u001b[32m 351\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RequestsApiError(\n\u001b[32m 352\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mUnexpected response received from server. Please check if the service \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mis in maintenance mode \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 354\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mhttps://docs.quantum.ibm.com/announcements/service-alerts \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmessage\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 355\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m356\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RequestsApiError(message, status_code) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mex\u001b[39;00m\n\u001b[32m 358\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m response\n", - "\u001b[31mRequestsApiError\u001b[39m: '400 Client Error: Bad Request for url: https://us-east.quantum-computing.cloud.ibm.com/sessions. {\"errors\":[{\"code\":1352,\"message\":\"You are not authorized to run a session when using the open plan.\",\"solution\":\"Create an instance of a different plan type or use a different execution mode .\",\"more_info\":\"https://cloud.ibm.com/apidocs/quantum-computing#error-handling\"}],\"trace\":\"d957770b-0f25-4b3d-974d-cf62562ecc4e\"}\\n'" + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m batches_exp = BatchExperiment(batches, backend) \u001b[38;5;66;03m# , analysis=None)\u001b[39;00m\n\u001b[32m 4\u001b[39m run_options = {\u001b[33m\"\u001b[39m\u001b[33mshots\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m1e3\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mdynamic\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m}\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mSession\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[32m 7\u001b[39m sampler = SamplerV2(mode=session)\n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m# Run characterization experiments\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:132\u001b[39m, in \u001b[36mSession.__init__\u001b[39m\u001b[34m(self, backend, max_time, create_new)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28mself\u001b[39m._instance = \u001b[38;5;28mself\u001b[39m._backend._instance\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.configuration().simulator:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28mself\u001b[39m._session_id = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_create_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcreate_new\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcreate_new\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:137\u001b[39m, in \u001b[36mSession._create_session\u001b[39m\u001b[34m(self, create_new)\u001b[39m\n\u001b[32m 135\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Create a session.\"\"\"\u001b[39;00m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m._service, QiskitRuntimeService) \u001b[38;5;129;01mand\u001b[39;00m create_new:\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m session = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_service\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_api_client\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_instance\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate_session\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 138\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_instance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_max_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_service\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchannel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdedicated\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 139\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 140\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m session.get(\u001b[33m\"\u001b[39m\u001b[33mid\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 141\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\clients\\runtime.py:237\u001b[39m, in \u001b[36mRuntimeClient.create_session\u001b[39m\u001b[34m(self, backend, instance, max_time, channel, mode)\u001b[39m\n\u001b[32m 224\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate_session\u001b[39m(\n\u001b[32m 225\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 226\u001b[39m backend: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m (...)\u001b[39m\u001b[32m 230\u001b[39m mode: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 231\u001b[39m ) -> Dict[\u001b[38;5;28mstr\u001b[39m, Any]:\n\u001b[32m 232\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Create a session.\u001b[39;00m\n\u001b[32m 233\u001b[39m \n\u001b[32m 234\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m 235\u001b[39m \u001b[33;03m mode: Execution mode.\u001b[39;00m\n\u001b[32m 236\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m237\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_api\u001b[49m\u001b[43m.\u001b[49m\u001b[43mruntime_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43msession_id\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minstance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\n\u001b[32m 239\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\rest\\runtime_session.py:65\u001b[39m, in \u001b[36mRuntimeSession.create\u001b[39m\u001b[34m(self, backend, instance, max_time, channel, mode)\u001b[39m\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 64\u001b[39m payload[\u001b[33m\"\u001b[39m\u001b[33mmax_ttl\u001b[39m\u001b[33m\"\u001b[39m] = max_time \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m65\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msession\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpayload\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_HEADER_JSON_CONTENT\u001b[49m\u001b[43m)\u001b[49m.json()\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\sessions.py:637\u001b[39m, in \u001b[36mSession.post\u001b[39m\u001b[34m(self, url, data, json, **kwargs)\u001b[39m\n\u001b[32m 626\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\u001b[38;5;28mself\u001b[39m, url, data=\u001b[38;5;28;01mNone\u001b[39;00m, json=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 627\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33mr\u001b[39m\u001b[33;03m\"\"\"Sends a POST request. Returns :class:`Response` object.\u001b[39;00m\n\u001b[32m 628\u001b[39m \n\u001b[32m 629\u001b[39m \u001b[33;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 634\u001b[39m \u001b[33;03m :rtype: requests.Response\u001b[39;00m\n\u001b[32m 635\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m637\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPOST\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:356\u001b[39m, in \u001b[36mRetrySession.request\u001b[39m\u001b[34m(self, method, url, bare, **kwargs)\u001b[39m\n\u001b[32m 350\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m status_code == \u001b[32m503\u001b[39m: \u001b[38;5;66;03m# Planned maintenance outage\u001b[39;00m\n\u001b[32m 351\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RequestsApiError(\n\u001b[32m 352\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mUnexpected response received from server. Please check if the service \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mis in maintenance mode \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 354\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mhttps://docs.quantum.ibm.com/announcements/service-alerts \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmessage\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 355\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m356\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RequestsApiError(message, status_code) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mex\u001b[39;00m\n\u001b[32m 358\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m response\n", + "\u001b[31mRequestsApiError\u001b[39m: \"('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\"" ] } ], diff --git a/docs/tutorials/sample-based-quantum-diagonalization.ipynb b/docs/tutorials/sample-based-quantum-diagonalization.ipynb index 2c5868c991f..477aaaf700a 100644 --- a/docs/tutorials/sample-based-quantum-diagonalization.ipynb +++ b/docs/tutorials/sample-based-quantum-diagonalization.ipynb @@ -432,7 +432,7 @@ " will be mapped and run. This function takes the coupling graph as a undirected\n", " `rustworkx.PyGraph` where there is only one 'undirected' edge between two nodes,\n", " that is, qubits. Usually, the coupling graph of a IBM backend is directed (for example, Eagle devices\n", - " such as ibm_sherbrooke) or may have two edges between two nodes (for example, Heron `ibm_torino`).\n", + " such as ibm_brisbane) or may have two edges between two nodes (for example, Heron `ibm_torino`).\n", " A user needs to be make such graphs undirected and/or remove duplicate edges to make them\n", " compatible with this function.\n", "\n", diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb index fa7172c9d59..1dea52b3925 100644 --- a/docs/tutorials/spin-chain-vqe.ipynb +++ b/docs/tutorials/spin-chain-vqe.ipynb @@ -8,7 +8,7 @@ }, "source": [ "# Ground-state energy estimation of the Heisenberg chain with VQE\n", - "*Usage estimate: Two minutes on ibm_cusco (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: Two minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -31,7 +31,9 @@ "Before starting this tutorial, ensure that you have the following installed:\n", "\n", "* Qiskit SDK 1.2 or later, with visualization support (`pip install 'qiskit[visualization]'`)\n", - "* Qiskit Runtime 0.28 or later (`pip install qiskit-ibm-runtime`) 0.22 or later" + "* Qiskit Runtime 0.28 or later (`pip install qiskit-ibm-runtime`) 0.22 or later\n", + "* Qiskit Serverless (pip install qiskit_serverless)\n", + "* IBM Catalog (pip install qiskit-ibm-catalog)" ] }, { @@ -45,7 +47,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "e7754922", + "id": "c37bfdb1-bb7c-4177-a3af-37ec32558fd0", "metadata": {}, "outputs": [], "source": [ @@ -71,9 +73,16 @@ "from qiskit_ibm_runtime import QiskitRuntimeService\n", "from qiskit_ibm_runtime import Session, Estimator\n", "\n", - "from qiskit_ibm_catalog import QiskitServerless, QiskitFunction\n", - "\n", - "\n", + "from qiskit_ibm_catalog import QiskitServerless, QiskitFunction" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "faef6e0d-f09d-46fb-8b4f-e1b56f7c6b3a", + "metadata": {}, + "outputs": [], + "source": [ "def visualize_results(results):\n", " plt.plot(results[\"cost_history\"], lw=2)\n", " plt.xlabel(\"Iteration\")\n", @@ -126,17 +135,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "7e8d2f10-f1d6-4ec2-bac9-9db23499c9e1", "metadata": {}, "outputs": [ { "data": { + "image/png": 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9Uv3ej9UwmeI2ET0cG2sLvNr71iAvfYzoH1gh+0NEle+lnnVhZ6NfpbFrWz+TKW4T6UO1BW4/Pz+Ehoaie/fusLGxQUREhFKgXrBgATZu3IgLFy6UWuBu0KBBqb22xXPiNSIiIiIiU1fVzRYzx7Ys8/Kebrb44r3Km8SJiCreB682QWCde9tv3s97LzVEcH39WxsRkWlydrLGnHElW/Y+iGsVa3w9tlWF7hNRRVFtgVsQBekNGzYgLS1NuR04cABDhw5VJqAUBW8zMzM0bNiwxDo9evTA7t27ERUVVfycWC88PBw9e/Y0wm9BRERERIRyjcScMfrfi9bVvOyx7buuqOnrWCn7RUSVV9za8m1XNA749yuR3x4QxJNcRCo05JkAzBvfBmZmd+aeK42Xuy22LuiKgJpVKm3fiAxJ1QXu+zlz5gxE63HRssTOrmTDfVEA9/b2xtNPP60Ux1esWIEXXnhBaW0iniMiIiIiksXolxvh8K9PY8gz/rCxNi/xWi1fR0x/tzmO/94bDf3Zio9IjXw97bF3cQ98O6FtqYXuZzvXxPbvu+Hr91v9awGMiOT0Zr8GOLr8abz+bL17WpZU97bH52+F4OSK3ngk0N1o+0j0sDRZ4D516lSp7UkEJycn7NixQyly9+/fH6+99hratGmjFLvFiG8iIiIiIpmEBLpj0aQOiNn+AjxcrJXnqrra4NLGvhgzpLFySTIRqZe9nSWG9a2P478/g7NrnoW7s3XxiM0VXz2Ox1r6GHsXiaiCNannhu8+blciF/BwscHlTc9j3OvB8HC1NfYuEj0UTc5t+6ACt1CnTh2loE1EREREpBYuTtawsrw1iltMQMnRmkTaotPp0KC2M6ytbsUBc8YAIs2p4mhVnAtYWZrB3JwDOUkdWOBWEZfAGmgz4w1YOtgiI+oGdr01Gy4B1dD553FIDY/Blv6fIjsxFea2Vmj75XC4B9dBUWERjk75BZEb9yvbaDZhEGo+3QZJp65gx5DpMHWZV04gcu7rKMxKg5VHDdR8dwmyr57BxUndYONbD/6fbIGlc1UkbFuE+HUzkX0tDH5DZsCz1zvF2yjMyUTE7FeRefEQYGYG34GT4dL2OeW1qB/HIGn3ctjVeQR1x60x4m9KREREROXNfx/54EXUeKolCnLyUJhfgKNTf0HMzhO3NqLToeWnQ+D7+CNAURHOfr8R537crLwUOLQH6g9+EvkZ2VjXZQxkzoejl4xHyqF10JndKmx49fkvXDv0V+4zHyYtxIHbqvj7ouef03Fh6VYc/Ogn6eNAWWNAxDeDkXp8KyyqeCjrOTXpAr8hXyj3GQNIS3Gg3stPosGr3VCUX6DUxDZ2/0DJD2SulWWWMQ6ET+6NnOtXitfLijiJOh+sgXPLXigqLMS1H97GzSOboIMOVXu9g6rdRyrLxa+diRub5sLM1gGBXx+HKdJkgVu0IFGjdl+PxJ535yLpTATq9n8MzT96CZd+/Uv5MN/9RdzwjV4ozM3DqjZvwaFaVXTfNAVxe08jJzkdhz9dgpTz11C9mxwTjIgv6ZqjfoRd7WCliB3143/g/vgQ5QN894fOrk4Iao/5DXErptyzjbjVM2BmaY2GCy4hJ/4Kzo1pCcdGnWDh5KZ84dtUD0LKAX6RExEREcma/8YfCMOJmStQkJ2rHAR3Wz0JvwUPRX5WDuo81wFVAqphddtRsHSyQ6+tXyBuz2mkXIjC2e82KAezLSYNhuz5sGfvMfAd9LlyPzcxGmdGNIBTcGdYOLkzHyZNxAFBZ2GONl+8gcg/DpZ4XuY4UNYYcDsO3D3Y6zbGANJKHKj2ZHPU7tMeG7uPQ15aJqzdnFCYVyB9rSyijHGgzrjVxfczLh7GxYld4fRIV+Vx0s6lyL52Fg3nXUBB5k2EvdtUiQO21YPg+fS7sKvdFNcW3hs/TAWvRVAJ14a1kJ+ZrXyYhfDfdqLaE81gZnXvOYyaT7fF+cVblPvp164jbu8ZVO/WErLJvHwM5rYOygdYcOv0Mm4eXIei/Nx7lrWr1QS21RpAV0of9eTdy+He9Q3lvrVnLTg27IiU/Xc+9EREREQkd/4bveOYUtwWksOuKqM1bdyclMc1e7XBxZ+3KSOXclPScWXtHtTq3Q5qy4ctHJyL7xdmpSujVMXvLDAfJi3EASH4vb6IWL8PaZdjSzwvaxzQJwY8CGMAaSUONBzeCye++l0pbgs5ianF34Wy1srKGwcSti2EW8eBMLO0Uh6LKzXcu7wOnbk5LBxd4dKuH5J2LYMsNDmCW40cqleFS/3qypnm2yxsrWHnde9M2Q6+7kiPulH8WHxw7X3lmy1XnFnOijyFs+/c+hDfvrRKjEjRR+6Nq7D2qFH82KpqTeU5IiIiIlJH/ns3//6dkB4ZX5wP35sb34BHSADUmA9fXz8L1zfNRV5iFGqM/EG5XFlgPkxaiAPuTf2Vz/aWfpMQPPr5ktuRNA7oHwO+QeK2RbDyqA6fAZ8VF8QYA0grccDZ3w9uTeqgyXt9YW5tifDf/0bYwk1S18rKUxsrzMlCcugy1JscWvxcbsJVWFUtGQcyzt9q0SIDFrhV5Maxi9j6wmfFj/ufXgi1s/dvCf+JfxY/PjHoVj8xIiIiIlI/ffNf73aN0GR0X2zp9ym0mA9X7TlKuYlenVdmDoRT8BNKCwIitccB0Vu31dTXsPO1L6E2ZY0BPgM/h6WLt3JVc/K+1Upv3obzLyojP4m0kg+INkWO1atic++PYOVsj66rJiEtMh5R245AS7Wx5L0rYO0TANuajaAWbFGiEmIUyt1nlkRjfQs7G2TGJd27bHQCHPzu/GMXvYUyohMgG2vP2soZptsKMlNRkJ0BKzdfvbYjzl7n3Igsfpx7PUJ5joiIiIjUkf8Knq0D0fbr4dj+0lSlJ+f9c2MPZETfGcGlxnxYtO+zcvVF2umdymPmw6T2OOBYw0sZndl15Sd47uA8BL7eXenT2+6bkVLHAX1igHjudstOl9a9YW7rhOzo87deYwwgjeQDovZ1efVupS1JTlIaorcfhUeIv9S1svLkAgnbFsK986slnrNyr47c6/LGARa4VUL0GhKN8b07NFYei1meRd+wwtz8e5aNXL8P9V56ovgD69UmCFc3l5xkQwbiciqduaUyE7RwY9M8uLbrB53Frf5BZeXSti8SNn9bfGmHSPSdWz1TIftMRERERJWf/3q2aoD2s9/CjsHTkXz2zsHb7dzYf0BnpfBj5eyAWk+3xZW1e6G2fDjr6tni+zmx4UrPTptqgcpj5sOk9jiQcu4qfm34Kla0GK7czn6/EZd+3YHdb8+ROg7oEwNyE6KK76ef34/8tETYeNdVHjMGkFbyAVHc9u10q5WHuY2VUg9LOhMpda1M39pYduwlZF46DNcOL5R4XokDW79HUUEB8tOSlN78YjuyYIsSFdk14hu0+3oErKa+jrSIOOwaOQsu9e4923J63lq0nTkcffbNQVFBIQ6M+0E5cyWjWu/9jIhZQ3B1/puw9q6LWu8tRVbk6XuWS9j+E2J+/hAF6cnK7M/xa2ag7ofrlVlgxUzSkbNewalhdaAzM0e1oXOU2eSJiIiISB35b9svh8PcyhLtZg6/s+5bs5WiV/iKXXALros+e2ejqKgIZxasV55XWz4c/b+xSuFKZ2EJnZkFqg+bo0zCLjAfJi3EgQeROQ6UNQZEfDMYeTfjlc+4mZUt6oz9Heb2VZTXGANIK3Hg7IL1aD19GJ75e6aYaxmRG/cjcsM+6WtltcoYBwTRh9+59bMwt7s12fZtbh0HIfPiIZx+01+ZjLvq0+9J1cKEBW4VEV/AG7q+/6/L5Wfl4O83ZkINxIetwVeH/3U598cHK7fSmNvYo/bY5RWwd0RERERkCvnvqrZv3fc1cZmyOIg9AHXnw3UnbLjva8yHSQtx4G7Hv/xNNXGgrDEg4NNt932NMYC0EgcKcvKKr9xQU63MtoxxQPAdNLnU53Xm5qj+xlzIii1KVK4gLx/WLo7KbLI2biXPzpSm2YRBaPRWb+SkpENW4jKM/NREZQbZvJTrD7WtqB/HIG7FFFjYuxhs/4iIiIjIdPLfBwkc2kOZmC5bkhFctzEfJq3TehxgDCAybByQsVamM2AciF87E1e/HQ4LR9O9skNXJK6/IZOTl5mNn+sMhCwGhC+FpZ2NwbaXlQ+03wSphD4F2PKaCCIiIjJhfp2XIfp6Jnyr2iFqW8nei6ZGtnzY0Dkx82HSehzQegwQGAdIyzFA0HocYAwoO47gJiIiIiIiIiIiIiIpscBNRERERERERERERFLihSMmysLWWrmsQab9NSQb81uXNchE7DMRERERaTMfNnROzHyYtE7rMUBgHCCt03ocYAwoOxa4TZROpzNo7y7Z6HTs20VERESkZcyHmQ+Ttmk9BgiMA6R1Wo8DjAFlxxYlRERERERERERERCQlFriJiIiIiIiIiIiISEoscBMRERERERERERGRlFjgJiIiIiIiIiIiIiIpscBNRERERERERERERFJigZuIiIiIiIiIiIiIpMQCNxERERERERERERFJiQVuIiIiIiIiIiIiIpISC9xEREREREREREREJCUWuImIiIiIiIiIiIhISixwExEREREREREREZGUWOAmIiIiIiIiIiIiIimxwE1EREREREREREREUmKBm4iIiIiIiIiIiIikxAI3EREREREREREREUmJBW4iIiIiIiIiIiIikhIL3EREREREREREREQkJQtj7wCVrqioCPlZOZCFha01dDqdwbZXVARkF0AqNuaAAf8ERERERJomWz5s6JyY+TBpndZjgMA4QFqn9TjAGFB2LHCbKPEB/rnOQMhiQPhSWNrZGGx74gPcfhOkEvoUYMtPFBEREZEm82FD58TMh0nrtB4DBMYB0jqtxwHGgLJjixIiIiIiIiIiIiIikhIL3EREREREREREREQkJRa4iYiIiIiIiIiIiEhKLHATERERERERERERkZRY4CYiIiIiIiIiIiIiKXFuWyIiIiIilcrKzseJ80k4fDYBx84lIjElR3k+8WYOxnx5ECGBbmgW5IE61Ryh0+mMvbtEVAGuxaXj8JkEHDmbgAuRqUhIyVaeT0rNwVeLTyEk0B2PNHCDo72VsXeViCpATm6BkguIGHA0LKFELjB6xgGENHBX8oGAmlWYC5C0WOAmIiIiIlKZs+HJmP/bOfxv3UWkZeTd83p2TgFm/O9U8eOgOs4Y3q8BBvaoCycHFrmIZJedk48VWyMwb3kY9p24XuoyWdkFGD3joHLf2soc/Z6spcSBFo08WOQiUoELETcx/7cw/LT2IlLSckvNBb5afLr4cb2aVfDm8/Xxci9/ODtZV/LeEj0ctighIiIiIlKJuIRMPPvudgT1XoU5y86WWtwuzZnwFIyYvA9+XX7F7F/OoLCwqML3lYgMr6ioCMs3X0aNJ5dj0Li/71vcLm2E5+L1l9Bq4Ho8OmSjUhgjIjndSMrCC2P/Qr1eK/D10jOlFrdLcz7iJt6ZfgC+nX/FFz+eREFBYYXvK5GhcAS3ini1DkLXVRNLPJeXkYXUy7EIX7ELYQs3oUjFASrt1E5c+LBTiefMbOxh7RMAt46DULXHW9CZ8588ERERqdMvG8MxcspeJKeW7UC2NKIgPmrqfmXk56JJ7VGnmhNkovV8WGBOrO2i1puf7cXKbREPtZ3Qo/Fo0nc1Jo9qhrcHBMHMTK7R3FqPA4wB2rZqWwTe+HQPbiTfakVUHpnZ+Rg78xBWbY/Aj592QP1azpAN48BOzcUBdf02pLi8KhRRO44COh1sPZxRt++jaDFxMKr4+2LfmAVQO5cOL6BKyFNi+ALykuOQuHMxoha9h+yoMNQY8Z2xd4+IiIjI4CM2J8w5gs+/P2Gwbe46EoeWA9Zh8/wnlR7dstF6PiwwJ9aWy1Gp6DJ0My5HpRlke6J1wXtfHMCxsETlZJeFhXwXf2s9DjAGaM+UH05g3KzDBtve/pM3lFxg45wn0O4RL8iIceAFzcQB+b6l6F8lnrqCyytDcXnFLpyZvw4bu49DRnQCAl58HNZuco3CKQ+72o/AreNAuHUaBK8+Y1B/+n5YuvkhYesPyLt5w9i7R0RERGRQhi5u3yYmoeo8dDOOn0uEbLSeDwvMibXjamw6Or6yyWDF7bst2XAJgyfskrJtkdbjAGOAtkxdaNji9m2p6Xno+uaf2F/GdkemhnHgEc3EARa4NSA/Kwc3jl6EzswMTjU8oTXmNvawr9dKOWOVExdu7N0hIiIiMpilGy5VSHH7tptpuXhqxBYk3cyBzLSeDwvMidUpN68AvUZtxbW4jAp7j583huOz745DdlqPA4wB6rV6ewQ++Mbwxe3bMrLy0eOtLco8H7JjHLBXbRxggVsjHGve+uDmpKRDi25/cC0cXI29K0REREQGEXM9A29N2afXOoeW9cK1rf2Vn2UVeyMTb0/T731MkdbzYYE5sfqIwvOJ80kVHgc+/e4YTpyX72qOf9J6HGAMUJ+E5Gyl53ZFxwBxVZfo8S/aosmOcSBclXFAEwXuhIQEjB07FnXr1oWNjQ2qVauGt99+GxkZGXj11Veh0+kwZ84cqIWFrRWsXR2Vyy2c61dHy8mvwa1RbeUslWior3aFOZnIT01QLrfIijiFq9+OQNblY7DzbwEb3wBj7x4RERGRQQz/fC9S0vSbUNLL3Q5+nvbKT30s3RCO9TuvQhZaz4cF5sTqJ9oHTf5B/ys4yhMH8vOLlFYlBRJNyqb1OMAYoA1vT9uP60nZlZILrNkRieWbL0MmjAOZmokDqp9k8vjx4+jWrRvi4uJgb2+PwMBAxMTEYNasWQgPD0dS0q2z3cHBwVCLpmP7K7e7RWzcjwMf/AAtiF32sXK7m3PrPqg+bK7R9omIiIiMJyouA9+tPKcUaWNuZMLcTIeavg54tXcABj8dANcq1pDNyQtJWPvX1UofKdqzY3XIQOv5sMCc+A5RlN28JwrzfzuHPcfilcvtHe0t0bmVD4b3a4AOIV7KoCfZTFt0EgUFlTea8vi5JGzcdQ29OtWADLQeBxgD7r0a6YdV57F4/SVExWeIOQdRw9sBQ54OwCu9A+DuYgPZXLqail82hVd6LtCva21pYibjwMeaiQMWah+53bNnT6W4PXr0aHz88cdwdHRUXps+fTref/99WFhYKB/Mxo0bQy3OL9mCiPX7YGZpAZf61dFwxDOw93ZDQc6dET6Pzn8XMNPh72FfFT9n5eyAZ3bOxOFJi5WZZmXl/uRQuLTpi6KCPGRFnkLcqmnITYiCzvLOF1bamVBcmtTtnnWL8nNRVFiAkNUFlbzXREREZGjiMtpP5h3D5z8cv6cIdDY8BaNnHMT42Ucwb3wbDHlGrlEs85eHVfp7Hjx9A0fOJiAk0B2mTuv5sMCc+E4BSPSoDrucUuJ50Vf+tz+vKLdWjT2w5psu8HSzhSxEL9yV2yIq/X3nLQ+TpsCt9TjAGHAnFxAng8SEzPn/yAXOXbmJ978+hAlzj+CrMS0xon8gZLLg93OV/p5nwlMQeiQOHZp5QwaMA0M1EwdU3aJk1KhRiIqKwsiRIzFjxozi4rYgWpY0adIE+fn5qFmzJpyc1DN7aurlOMSGnkL0jmM4PW8ttr88Fe7BddB62rDiZfZ98D2qNq+HWs+0LX6u1eTXcP3gOak/vIK1tz+cgjujSkg3ePUZi7rj1yPz0iFcnf9G8TKOQe3RdHl6iVvQvAuwcHSHz4ufGnX/iYiIyDBGzziASQuOPXCEY3ZOAV75KBTf/lb5BePyysjMw5INl4zy3t+tqPyD6fLQej4sMCcGLkelot3LG+4pbv/T/pM30H7wBqWXrSx+WnsRefmV3y7kz73RuBKVBhloPQ4wBtzy0dyjygSM/yxu3y03rxAjJ+/DjJ9OQRZ5eYVYtOaCUd57wYrzkAXjgL9m4oBqC9xhYWFYvnw53N3dMWXKlFKXCQkJUX6KQvdttwviLVq0gLW1tTSXXTzIjcPnEb5il/Jh9WhWT3kuNyUde0fPR8vPX4OtpwtqdG8FrzZB2Pf+AqiNQ4M2cO04CMm7lyM9bG+pyxTm5eDy1D5wCGwH777jKn0fiYiIyLDW/hWJmUvOlHn5EZP34fRF/SZqM5ajYYlKiwVj+PtwHGSk9XxYizmxGLXZf+xfiE/MKtPyFyNT8frE3ZCFMT+LoUcZB2SktRggbN0XrbTUKKsxXx3EgZPXIYPTl5KUK1GM4e/D8vauZhxoo9o4oNoC97Jly1BYWIgBAwbAwcGh1GVsbW3vKXBfunQJK1euhJeXF5o3bw61ODFzBQrzC9B0TL/i56L/Oo6I9XvRYc4otJr6uvKBzklW5yyy3v0mAGbmiPnlo1JfvzrvDRTmZaPm2z9V+r4RERGR4X2ztOzFbaGwsAhzf5VjFLdoE2IsFyJvIjVdv4ktTYXW82Gt5cQHTt7AodMJep8Yi4hOk6J4b8w4YMz3flhajwNaigHCrJ/1ywUEeXKBRKO9d/T1zDKfPDRFjAMTVBkHVFvg3rFjh/KzU6dO911GjNb+Z4G7Q4cOiI2Nxbp169C5c2eoRVpEHK6s3QOfDo1RtWWD4ucPT1wMx1peyuUaUduPQq1svOvCtX1/pJ3crvQXutv19bNw8/AG1PlgDcys9ZtFmIiIiEzPuSsp+OuQ/qOLRNuPtAzTL94eO2e8g9qiIjHRnPHe/2FoPR/WWk48vxxth8S/7wUStOGJjs/EDSO2UzkSJmcMELQeB7QUAyJj0rAx9Jre6y3/87IU7YqOhhn3RNNRiU90MQ7UVWUcUO0kk5GRkcrPGjVKnwBD9N7es2fPPQVuMzPD1/ybNWumTHSpD8siM3yMFgbdj5PfrFQuwxBnqf587hPlufysHKRHXkdy2NWH2naAfwDydIbrAaezsoXn1xdhSF59xyMpdJlylqre538pz6Wd/AtRi9+H/0d/wNqz5kNtPyDAH0W58p7FJCIiUotMq4aAQ1+91xNtP+oEtoFVgWlffp/gMACwKn1SzEPLesHL/f4HJF7utsU/r23t/6+T2DV/Yd09z/fuOwi2eRVfBJQtHzZ0TlwR+XBF58SmlA/HO70JWHjpvd7M+Sux5IveMGV55p5AleHligH6xIH7xYADh0/Bz+9O/1aZYoDA42JtHBdnWQagyHGA3uuJftwNmj4G6/yH/06oSEn2fQHrhkbLBQa8PAx2uSdR0bQeB7SWC3h5eeHw4cPlWle1Be6MjAzlZ1ZW6X9U0Z87ISFBmXiyVq1aFbovorgdHR2t1zpWOnPAU8/32XcGP3k/d9/Xb16MxmK/O5dgGFJMbAxyiww3s6o4U6Tnrw/HRh0Rsvb+E0fYVmtQYvbXnPgIXP7iefgN/kJZ92HFxMSgMCfzobdDRERED8mlJlB6h7p/dSMhBcjUL2+rdDVzAavSXxIHtH6e9v+6CQtzszItV5qk5JvAzYr/G8mWDxs6Jy5PPmzsnNik8mF7s3Id7ebkFup97FbpbCyAKg8XAx4mDuTnF1XK36g8MUDgcTGPixVVvAHH8q2akHgTSDfxOFA9F7A2Xi6QnJKK5GTGgdIwF4gxSgxQbYFbVP2Tk5Nx9OhRtG7dusRrogXJmDFjlPuNGzeu8Ikkxb6U5ywVKn9S7HLz8fYx+JnqiiQ+bOFTnkGVFr1QtftIg2zTx8fHJM5UExERaV2WpQ2SytObQKdDVTdHWLr4wpQlWlsg+wEjrR5EjNYSB7T5BYWIS3hw3nK/bbm6OMHWoeL/RrLlw4bOiSs6H66InNiU8uHr5nnIK8d6NlaFcPM17RiQZ+6K6+WMAfrEgftty0LUmyrhb6T1GCDwuLj8si1sUd5mOh6u9rCqYtpxIMnGEllGzAVcnB1gZ8c4UBrmAj4PNYK7vFRb4Bb9s8PCwjBt2jR06dIFAQG3LuM8dOgQBg0apIzeFoKDgyt8X8ozvD4vMxs/1xkIWVy4eAGWdjYG215WPtB+EypM8t6VyLpyAtnRF5TZY/8paM5ZWHlU12ubFy5chK1qP1FERETyuJmWC5/HlyEzO7/sK+l0qFvdCeePH4WZWcUOfnhYb0/dh1m/nC31tdIuI76buBRZjNYSB7TVuvxarvff+efvaBTgioomWz5s6Jy4ovPhisiJTSkf/nTBMXw0V/8eqnOnDMUrvWfAlImJXqu0WVKuGGCIOND1sWZYP+fWfFYVSesxQOBxcfllZuXDt/MypKTpN7eGb1U7RBzdDwsL056ybtw3hzFl4Qmj5QKbVi9GqyZVUdG0HgeYC5SdCYSdijF27Fj88ssvuHbtGoKCglC/fn1kZ2fj0qVL6NatG2rWrIk///yzRP9trdr87MfQGrdOg5QbERERqU8VRysM7FEH3604r9d6bz5f3+SL20JIoLvR3tvWxhwNajtDbbSYD6s9J36tTz1M+vYY8gvuf4n2Pzk7WqF/19owdU4OVgioUQUXIm9qLgZVJC3GATXHADtbCwx5xh8zl5zRa71hfeubfHFbCAl0M9p7m5vr0KRexZ/oNgbGAXmZ/qe2nPz8/BAaGoru3bvDxsYGERERcHV1xYIFC7Bx40ZcuHBBWY4FbiIiIiL1eW9QQzjaW5Z5+ere9hjyTOkTN5oaYxaXmgS4SnHgT+TtYYc3nm+g1zrvv9JYKYrJoFmQ8eKAWgvcpD6jXgyCi9N9Jq24T+uOoc/VhwyMGQMCazvDVswFQGRCVJ2dNmjQABs2bEBaWppyO3DgAIYOHapMQCkK3mZmZmjYsPRZZ4mIiIhIXvVqOWPVzMdhV4YDME83W2ya+yRcnO4zW5OJESOoRTsVY3i6Uw2jvC9ReXz1n5Z4ulP1Mo/aFAVuWZT19zI0ceKwY/Py90glqkw1fR2xblaXMp3wdq1ijY1znlByAhlU93ZA40poF1aaXh2NE3+INFvgvp8zZ86gqKgI/v7+sLOzu+f1FStWKLezZ8+WeFyeXtpEREREZBydW/ni7x+fQqfm3qW+LtqRPPNYDexf2hNBdV0gC7Hfop1KZbOyNMMrkoxyJxIsLc2w8qvH8cmbTeHuUno/VNGH9pv3W2H+h22g05l+i6LbROwSo00r28u9/OFoX/YRsUTG1u4RL+z+Xw880ab0CRHFx75Hh2rYt6QnHpHo6gQRr4b30+8qFUPlIOKEIJGp0eQ1BadOnXpge5K+ffuW+vjll1/GTz/9VAl7SERERESG0CzIAzsWPoWz4cn4eWM4vl56GpnZBcporjOr+6CalwNkNPjpAHw45wiysgsq7T37PlELVSUZ2UZ0m7m5GT5+8xH899Um+H3LFbzx6R5kZOXD3tYCS6c8ih4dqkvZdsfK0hyvP1sPny44Xqnva4yTa0QPS4x0/vPbrrgQcRNLNlzCV4tPFecCJ1f0VkZ6y2hA9zoYO/MgUtPzKnX0tqy5E6mbfN/klVDgFqO7S7uxuE1EREQkp8A6Lvh8VLPiNiRO9pZSH6CJS6k/fqNppb2fg50lPhsZUmnvR2Ro1lbmGNijrjKRpCB+PvNYTSmL27eNfqmRMgK9sohRmyKWEskqoGYVfDoypEQuIGtx+/Z38+dvNau097OxNsfUtyvv/Yj0Ie+3eQUWuGXlElgD3TdNwTO7vkaXX8bD2s0JXq2DMPDyz+i19QvYuN3q1Vi3/2N4eseXeOnacgS+3r3ENppNGITnDs/HYz+OhQwyr5xA2H9a4MyIBrj4SVfk3byBtFM7cbSvLc6+E4y8lOvKcgnbFuHMqEY40tsC8eu+LrGNB70W9eMYnHy1Oi5NfqZSfy8iIiKishS3WjT0qJT3+uK95iZZBChr/vvIBy+id+g36LVtBnpsngafjneOA/wefwQ9/pyGQRHL0GLS4BLbDxzaA332zla2JXs+HL1kvJLziufELWnXr8XbYD4spyqOVvjhk3aV8l5iIl4RB0xRWePAbVX8fZXX7v68yxwHyhoDIr4ZjJNDfItjgPhs38YYIC/RpqTjfVqxGZo40S3mOJE9DtR7+UllOVEXE3mBubWl9LWyzDLGgfDJvYtjgLgdecYMKQfWKa/dPLwRYe+F4Oiz1rj2wzslth+/diZOD6urrGOqNNmiZMeOHVCjdl+PxJ535yLpTITywWz+0Uu49OtfSA2Pwboud768Ek+GY+ewr9DorT73bOPwp0uQcv4aqndrARmIL+mao36EXe1g5Us56sf/wP3xIbDxrYfAr+9crmdXJwS1x/yGuBVT7tnGg17zG/IFbKoHIeXAmgr/XYiIiIj0IUae/vRZB7QauK7MlyfHJWSW+FkWojepqfbbLGv+G38gDCdmrkBBdq5yENxt9ST8FjwU+Vk5SL0Siz3vzkPNnq1haV+yT/PZ7zYg6dSVewpeMubDnr3HwHfQ58r93MRo5SDYKbgzLJzcmQ9L7Mm2fhj5QiDmLLs1f1RFxAFLCzP877NHTbb3dlnjgKCzMEebL95A5B8HSzwvcxwoawy4HQc8e5UsXAmMAfISPbEXTWyP5i+uRWJKToXlAmI+k3cGBsFUlTUOVHuyOWr3aY+N3cchLy1TKYQX5hVIXyuLKGMcqDNudfH9jIuHcXFiVzg90lV5bO3tjxpvLULynt9RmJ1eYvueT78Lu9pNcW3hvfHDVGhyBLcauTashfzMbOXDLIT/thPVnmgGM6t7z2Ekn43EzYvRQGEhZJZ5+RjMbR2UD7Dg1ull3Dy4DkX5ufcsa1erCWyrNYDOzEyv14iIiIhMWYPaztg45wmln3BZNH9hHap1+VX5WRbtH/HE8i8eM8nJ9/TJf6N3HFOK20Jy2FVlVrHbo7lSL8cq+XFRfqGq82ELhzuj7gqz0kVfRhT9//EA82G5fT22Jfp3rV3m5fWJA+bmOqVXeWWNEK3IOCAEv9cXEev3Ie1ybInnZY0D+sSAB2EMkFstP0dsnv8knBxujUQ2dC7QvKE71nzTWZnTQPY40HB4L5z46neluC3kJKYWfxfKWisrbxxI2LYQbh0Hwszy1slLG98AJRbozOUcCy3nXtM9HKpXhUv96iUum7KwtYadlyvUKif+CrIiT5W4RKIwJ1MZkUJERESkFe0e8cK277qh56itSEjONth2n2rvh99nPA67MhbPZcl//ft3QnpkPNKjbkBr+fD19bNwfdNc5CVGocbIH2DpXLUS95Yqiig6iSK0KG59t+K8wbZra2OOZdM64elONWCq9IkD7k394RESgC39JiF49PNQA/1jwDdI3LYIVh7V4TPgs+KCGKljUu2dC5/CUyO2IC4hy2DbfayFN1Z/3RlODqZ5BYe+ccDZ3w9uTeqgyXt9ldYk4b//jbCFm6C12lhhThaSQ5eh3uRQqIVpZqtULjeOXcTWFz4rftz/9EKonb1/S/hP/LP48YlBldOHkoiIiMiUtGpSFadX9cHwz/Zi1fZbI5jKS4wGn/5uc7zxfAPl0mc15b/e7Rqhyei+2NLvU2gxH67ac5RyE706r8wcCKfgJ2Dh5FZJe0oVXeRe8FE7dGnli+Gf78WNhzzZ1SHEC4smtUedaiX7V8saB8xtrdBq6mvY+dqXUJuyxgCfgZ/D0sVbGaWdvG81Lk7qhobzLyojP0kdmjZwV3KBUVP345dN4Q89oeTkUc0w6sVAkx25XZ58QLQpcqxeFZt7fwQrZ3t0XTUJaZHxiNp2BFqqjSXvXQFrnwDY1mwEtTD9f6VUJmIUir2ve/FjSwdbWNjZIDMuCWpl7VkbuQlXix8XZKaiIDsDVm6+Rt0vIiIiImPwdLPFiq8ew28zHkPjAP2v4hN9dkWbg1Mr+2B4/0CTL27rm/96tg5E26+HY/tLU5WenFrOh8UlyFauvkg7vbMS9pIq03NP1MKZ1X3wRt/6sLPRfzxbbT9HzBvfBn8tfEqK4nZZ44BjDS84+Lqj68pP8NzBecoEcqJPb7tvRkIrMUA8d7sFiUvr3jC3dUJ2tOFG/JNpcHO2wc9TOyotRUIC73w2ykq0JXq2c02c+L033h3UUIritj75QEZ0Ai6v3q20JclJSkP09qPwCPGH1nKBhG0L4d75VaiJ6f9LpTIRvYZEY3zvDo2Vx/UHP4kra/egMDcfaiUup9KZWyL1+Fbl8Y1N8+Darh90FqZ76QwRERFRRRK9svs+UQvHf38Gexb3wEs966Kmz/1H51lZmqFZkDs+GxmCq1v6Ydn0TkovT7Xlv56tGqD97LewY/B0pcemFvPhrKt3JiHMiQ1XenbaVAus1P2lyuHhaov5E9oiZvsLmPXfVmjb1POBxW5vDzuloPXHvCdxcUNfvNnP9K/e0DcOpJy7il8bvooVLYYrt7Pfb8SlX3dg99tzoJUYkJsQVXw//fx+5Kclwsa7bqXuL1Ue0Vro0LJeOPBzLwx5xl85efWgE9xN67vho2FNEbm5H1Z89TgCalaBLPTJB0Rx27fTrVYe5jZW8GoThKQzkZqqjWXHXkLmpcNw7fAC1IQtSlRk14hv0O7rEbCa+jrSIuKwa+QsuNSrfs9ydZ/viKbvv6BcjlG9awsEvdEL21+eiqTTVyCbWu/9jIhZQ3B1/puw9q6LWu8tRVbk6XuWS9j+E2J+/hAF6cnK7M/xa2ag7ofrlVlgH/QaERERkayF7jbBnspNSEzJxvFzSUi8mY28/ELYWJkrB7tBdV1gZWkOtee/bb8cDnMrS7SbOfzOum/NVopeom2JGMVp6Wir/N1qdG+N/R98j2tbDkNN+XD0/8YqfTp1FpbQmVmg+rA5yqRyAvNhdariaIW3XgxSbgUFhTh35SYuRN5EVnaBMkqzioMVmtRzVQrcMitrHHgQmeNAWWNAxDeDkXczHjozc5hZ2aLO2N9hbn+riMkYoE7i33KLRh7KTUhOzcGxsEQk3sxBbl6BkgvU8HFAI39XWFvJmwvoEwfOLliP1tOH4Zm/Z4q5lhG5cT8iN+yTvlZWq4xxQBB9+J1bPwtzu5JX6aSe2I6Ib15WRoCLP45oY1J92Dw4t+wFGbDArSIiQd/Q9f1/Xe7SbzuVmxqIfkENvvr3pMP98cHKTd/XiIiIiNRyyfLjrXyg1fx3Vdu37vta7O5T+D1kGNSeD9edsOG+rzEfVj/RZkCc0BI3rcaBux3/8jfVxIGyxoCAT7fd9zXGAG1wcbLGYy3VlwvoEwcKcvLue+WGzLUy2zLGAcF30ORSn3dq8jgaL7pzpYds2KJE5Qry8mHt4qjMJmvj9u891JpNGIRGb/VGTko6ZCUuw8hPTVRmkM1Luf5Q24r6cQziVkyBhb36EkEiIiIiNdI3/32QwKE9lInpspPSIBPmw6R1Wo8DjAFEho0DMtbKdAaMA/FrZ+Lqt8Nh4ah/X/fKoisqEoPyydTkZWbj5zoDIYsB4UthaWdjsO1l5QPtN0EqoU8BtrwmgoiIyKT5dV6G6OuZ8K1qh6ht6uo9qDay5cOGzomZD1cMxgB5aD0GCIwDFYNxQB5ajwOMAWXHEdxEREREREREREREJCUWuImIiIiIiIiIiIhISiZ+4Yh2WdhaK5c1yLS/hmRjfuuyBpmIfSYiIiIibebDhs6JmQ+T1mk9BgiMA6R1Wo8DjAFlxwK3idLpdAbt3SUbnc70+3YRERERUcVhPsx8mLRN6zFAYBwgrdN6HGAMKDu2KCEiIiIiIiIiIiIiKbHATURERERERERERERSYoGbiIiIiIiIiIiIiKTEAjcRERERERERERERSYkFbiIiIiIiIiIiIiKSEgvcRERERERERERERCQlFriJiIiIiIiIiIiISEoscBMRERERERERERGRlFjgJiIiIiIiIiIiIiIpscBNRERERERERERERFJigZuIiIiIiIiIiIiIpMQCNxERERERERERERFJiQVuIiIiIiIiIiIiIpISC9xEREREREREREREJCUWuImIiIiIiIiIiIhISixwExEREREREREREZGUWOAmIiIiIiIiIiIiIimxwE1EREREREREREREUrIw9g5Q6YqKipCflQNZWNhaQ6fTGWx7RUVAdgGkYmMOGPBPQERERBomWy4oMB9kPkiGpfU4wBhAWqf1GCAwDlBZscBtokQQ+7nOQMhiQPhSWNrZGGx7IoC13wSphD4F2PITRURERBrMBQXmg8wHybC0HgcYA0jrtB4DBMYBKiu2KCEiIiIiIiIiIiIiKbHATURERERERERERERSYoGbiIiIiIiIiIiIiKTEAjcRERERERERERERSYkFbiIiIiJStaKiIlyNTceWvVHIyslXnsvOLcD1xCxj7xoRVZLsnHwcOn2jOAZk5RTg+LlE5OUVGnvXiKiScoHo+Axs3RddIheIS8g09q4RkQFwXk8iIiIiUp2c3AKs2HoFP28Mx+EzCbiRnF3i9cSUHHh2+gV+nvZo1dgDg5/2R9e2fjA35/gPIrW4GHkTC34/h20HYnAmPBn5+UXFryXdzEHT59fAytIMjQNc0a2dH4Y+Wx9+XvZG3WciMpzcvAKs2RGJJesv4dCZBMT/48S2yAW8H1sGn6p2aNHQAy/3qoseHarDwoK5AJFsWOAmIiIiItXIzMrHlIUnlKLWP4vapYmKz8CKreIWgVq+jnhnYBBG9G/AQjeRxPadiMcn849hy97of102N69QOQkmbpN/OIFeHavj0xEhCKrrUin7SkQVc8XGjP+dwtxfwxCX8O9Xa8Vcz1QK4eJWzcseo14MwtsDgmBpyVyASBYscKuIV+sgdF01scRzeRlZSL0ci/AVuxC2cBOKCtR7CV7aqZ248GGnEs+Z2djD2icAbh0HoWqPt6Az5z95IiIitQo9EochH+1C+LW0cq1/JToNb0/bj182heOnzzqgfi1nyIb5IPNBLcvKzseEOUfw1ZLTKLozWLvMCgqKsHp7JDbuuoZP3nwEYwY3knIkJ+MA44CWHTx1A4Mn7ELY5ZRyrX8tLgNjvjpYnAuIKzxkwxjAGKBF/D+qQpdXhSJqx1FAp4OthzPq9n0ULSYORhV/X+wbswBq59LhBVQJeUo02UJechwSdy5G1KL3kB0VhhojvjP27hEREVEF9NX8/Pvj+Gju0XIVtf7pwKkbCO67BgsntsOA7nUhI+aDzAe1RvTZ7/rmn+Uuav1zVPe4WYex/u+rWD+7C9ycbSAjxgHGAa35avEpjPnqEAoLHz4ZOHYuEc36r8W88W3w2rP1ICPGAMYALZHvdDT9q8RTV3B5ZSgur9iFM/PXYWP3cciITkDAi4/D2s0JamdX+xG4dRwIt06D4NVnDOpP3w9LNz8kbP0BeTdvGHv3iIiIyMDF7Q++OYwJcwxT3L67h/fAD/7GDyvPQ0bMB5kPasmVqDS0e3mDQYrbd9t34jo6vrIJCWVod2SKGAcYB7Tk0wXHMHrGQYMUt2/Lyy/E6xN3Y9bPZyAjxgDGAC1hgVsD8rNycOPoRejMzOBUwxNaY25jD/t6rZSzdjlx4cbeHSIiIjKgrxafxrRFJyts+0Mn7cbavyIhO+aDzAfVKjk1B12G/aG0FagIpy8lo9vwP5WevrJjHGAcUKtvfwtTruKqKKJ92bJN8v+bYQxgDFAzFrg1wrHmreCVk5IOLbodvCwc5OufRURERKU7eSFJGb2tj0PLeuHa1v7Kz7IQo8Jf+2Q3rif++yRVpo75IPNBNXp76n69+u7rGwMEMQHlpG+PQw0YBxgH1OZCxE28+8UBvdYpTxx447M9iKqgE2mViTGAMUCt2INbhSxsrWDt6ljcZ6neS0/ArVFt5UydmFRA7QpzMpGfmqBcspyfHIcbm79F1uVjsPNvARvfAGPvHhERERlAXl4hBn+4S7l8WB9e7nbw87TXax3RnmDE5L34/cvHIQvmg8wHtWD9zqtYsuFShccAYdqPJ9H78Rpo3tADsmAcYBxQu4KCQmVy6eycggqPA6npeUq7kk3znoBOp4MMGAMYA7REEwXuhIQETJ8+HatWrUJUVBQ8PDzQp08fTJ48GaNGjcKiRYswe/ZsjBw5EmrQdGx/5Xa3iI37ceCDH6AFscs+Vm53c27dB9WHzTXaPhERERmT6Ef5554obD8Qg7TMPNjbWqBdUy/06lgdFhZyXtD309oLygRQlWXF1gjsOhyLDs28IQPmg8wH/yk+MQtL1l9CREyaEhN8qtrhxafqoLafk7SFLdE2oLKIv5kYJbr7fz0gC8YBxoF//hvecTAGf+6Jxs30XCUXaNW4qnLixsrSHDJavvkK9h6/Xmnvt3lPFP7YHYWn2leDDBgDGAO0RPUF7uPHj6Nbt26Ii4uDvb09AgMDERMTg1mzZiE8PBxJSUnKcsHBwVCL80u2IGL9PphZWsClfnU0HPEM7L3dUJCTW7zMo/PfBcx0+HvYV8XPWTk74JmdM3F40mJltl1ZuT85FC5t+qKoIA9ZkacQt2oachOioLO8M/t52plQXJrU7Z51i/JzUVRYgJDV+p0BJiIiMkVixMp3K85j+o8ncTmq5CX8M5ecgW9VO7w9IAijX24EMzM5RiPd/r3mLg+r9PcV7ylLgZv5IPPB267FpWPsV4ewclvEPVc8iJ613dr5YerbzdEoQK7Ltf/cG40r0WVvTWIIe47F48T5RDSp5wYZMA4wDtz2v7UXMfmHE7gQefMfr5yBl7stRvYPxH9fbQxzc7lOes9dfrbS33Pe8jBpCtyMAYwBWiJX9CrHyO2ePXsqxe3Ro0cjNjYWR48eVR5PmzYNGzduxKFDh5TLSxo3bgy1SL0ch9jQU4jecQyn563F9penwj24DlpPG1a8zL4PvkfV5vVQ65m2xc+1mvwarh88J3UAE6y9/eEU3BlVQrrBq89Y1B2/HpmXDuHq/DeKl3EMao+my9NL3ILmXYCFozt8XvzUqPtPRERkqCKwGN34xqd77ilu3xZ9PRNjZx7CwA92KqMhZbHvxHWcOH9rkEJlWrU9ArE3MiED5oPMB4VzV1LQauB6/Lr5cqntfESP+U2hUWj78gaEHomDTESRSUvvWx6MA4wDwrhvDmPwhF2lFLdviUvIwodzjuC50TuU9l+yOH4usVJHb9+2KfQartwnrzI1jAGMAVqi6gK3aD8iWpKI1iMzZsyAo6Nj8Wtjx45FkyZNkJ+fj5o1a8LJSc5L88rixuHzCF+xSwlYHs3qKc/lpqRj7+j5aPn5a7D1dEGN7q3g1SYI+95fALVxaNAGrh0HIXn3cqSH7S11mcK8HFye2gcOge3g3Xdcpe8jERGRoX35v9OY/UvZRjYt++My/vu1fpM1GtPq7ZFGed/8/CJs3HUNMmI+qL18MCU1B93e/BMx1//9pExaRh56jdqKy1GpkEFWdr7SKsBY8UecQJQR44D24sC3v4VhysITZVp2zY5IvD1tH2Qh9tcYxMd/3U7jvPfDYgzQXgzQEtUWuMPCwrB8+XK4u7tjypQppS4TEhKi/BSF7ttWrFiBZ599FjVq1ICdnR3q16+P8ePHIz1d7hlmT8xcgcL8AjQd06/4uei/jiNi/V50mDMKraa+rgS1nGS5f8/78e43ATAzR8wvH5X6+tV5b6AwLxs13/6p0veNiIjI0DKz8jH5h+N6rTPrlzO4kZQFGRw5m6DJ935YzAe1lQ/+uPYiImLK/v8yJS1XaV0kA3EFR0GBcYrMN5KzERWfAVkxDmgnDojR2BO/PabXOgtWnEdUnBz/vo2bC1TeHCCGxhignRigNaotcC9btgyFhYUYMGAAHBwcSl3G1tb2ngK3GOltbm6uTED5xx9/4M0338T8+fPRtWtXZXuySouIw5W1e+DToTGqtmxQ/PzhiYvhWMtLuWQlavtRqJWNd124tu+PtJPblR5Ld7u+fhZuHt6AOh+sgZm1ndH2kYiIyFCW/3kZyal3+iuWRW5eIRatuQBTJ0ZOHq3EySX/6UiYvAVu5oPayQfFZHLzf9O/lcb/1l1EemYeTJ2xP4cyn+hiHNBOHFjzV6TSfkTf2PHdynOQwZEw5gLlwRignRigNaotcO/YsUP52alTp/suI9qX/LPAvX79evz2229KYfzRRx/F22+/jTlz5mDPnj3YvXs3ZHbym5UoLCh5pi4/KwfpkdeRHHYVaufVdzxgZlbiTF3ayb8Qtfh91B77O6w9axp1/4iIiAxl7V+RUl3uq4+kmzm4maZf8d6QrkTLPaKJ+aA28sGwyym4GKl/uxHRquSvg7EwdfebV6CyMA7ITStxoLzf6cZqA6aPnNwCo86JYewY9LAYA7QRA7RGVyRrA7F/Ua1aNaWAfezYMQQHB9/zuui97e3trUxEGR4ejtq1a993WxcuXEC9evXwyy+/4IUXXtB7X5o1a6ZMbKkPyyIzfFzYApWh68qJuLb1CM58u67c25hodhB5OsONcNdZ2cLz64uoSDnxETj3n+bw7v8xqnYf+dDbi3/HH0W5clzaTURE6nbD8RXkWtbQez2LggR43pwNU1agc0Scy3/u+/qhZb3g5X7/UTde7rawMDdDfkHhA0e2xSVkovkL9+ZGuqIc+CRPRkWrzFxQYD6ornwwx6I6EpxeLde6zumrYJ9btp69xpJi1x0ZNi0qNAY8KA44ZW6DY3bFT76m9ThQGTHA0HHAVGKAkOAwEDlW/nqvZ1aYCu+UL2HKCnXWiHUZZ7RcQPBJ+hg6VCytxwCBuYC2eHl54fDh8s0LZAGVysi41TcqK6v0f1SiP7cobouJJ2vVqvXAbf3111/KzwYN7ly+oQ9R3I6OjtZrHSudOeAJacTExiC3qMBg2xOXg1Tkr1+Yk4nwKc+gSoteBglgQkxMjLJdIiIio6uZDljqv1p+bqbeOUulM3cEXO7/sjig9fO0/9fNiAPbsiz3T0WF+ZXyN5ItFxSYD5pQPmhrCTiVb9WUpOtIuWniccA7FbAxTgwQUm8mIzWBcaCi40BFx4CKiAMmEwOE6mmAlf6rif7DJp8L6KyMmgugKB8xzAVKxVzAxOKAhlioueqfnJyMo0ePonXr1iVei42NxZgxY5T7jRs3hk53//NuIrBPmDBB6cFd2kjwsu5Lec7UQaKW3z7ePgY/S1eRkveuRNaVE8iOvqDMoPtPQXPOwsqjul7b9PHx4Vk6IiIyCTctUlCeC+htzZLg6usLU1YEC8QUFQI6s/uOtnoQfUZtlcYcOfCqhL+RbLmgwHzQdPLBQp054oryUKTT40yXuLBXp0NVpzxYOph2HEi1McP9GgQYKgY8aFvOjlawt2YcqOg4UNExoCLigKnEACHV6uZ9PycPYqNLgJvJ5wI6xBTlAfeJcRWdC5gVZcObuUCpmAuYVhyQTXnqp6pvUTJq1CjMnj1baVWybds2BAQEKM8fOnQIgwYNwuXLl5GXl4cRI0YoPbZLk56ejo4dOyojsMV6oqVJZcnLzMbPdQZCFgPCl8LS7j7DKMohKx9ovwlSCX0KsFXtKSMiIpLJxcibCOi5Qu/19i7pgdZNTH+oUKM+q3D6UnK51r22tb8yWisqPgPVuvyq9/rPdq6JFV89joomWy4oMB80rXzw1Y9DsWi1fhPHdmzujb8WPgVTt37nVfQatdUoMUA48uvTeCTQHRVN63GAMeDhRMVloGa35Sgo0K/ks/W7rujcyrQL3EKrAetw4NQNo8SBrm398Mf8J1HRtB4DBMYBgtYnmRw7dizc3Nxw7do1BAUFoVGjRvD390eLFi2UftuPPfbYPRNM3k20NunZsyeuXLmCLVu2VGpxm4iIiOhh+Neogm7t/PRaJyTQHa0aV4UMxL5q8b2J9DGin/7tFUf2L19LxsoWEuhmtPe2sjRDQ/8H9EYgMhF+Xvbo87h+k+XVr1UFj7XwgQyMmwsYLwYRkcYK3H5+fggNDUX37t1hY2ODiIgIuLq6YsGCBdi4caMyceT9CtxiZPdzzz2nNDb/448/EBgYaITfgIiIiKj8Fk5sjxo+DmVa1t3FBr9O7/TAtm2mpENI+S9ffFjtHzH9Ee5Eghhh/OV/yj452Yj+DdCns37FMGPxqWqPOtUcjfLeLRt5wMrS3CjvTaSveePbIKBGlTItK1rvrPjycZiZMRf4N+0fMd57E5HGCty3J4XcsGED0tLSlNuBAwcwdOhQZQJKUfA2MzNDw4YNS6xTWFiIAQMGYPv27Vi7dq0y4puIiIhINt4edtj9Uw8E13d94HJ1qzth90/dlZ+yeP7JWnByKMcsmg8psI4z2jZlgZvk8d5LjTBnXGtYWtz/sE+c13r/lcaY9d/W0pzkEoY+W98o7zusr3Hel6g8xAnsv398Ci0aejxwOXFCfNeP3RFUV56rE555rIby+1W2Wr6O6NxKjlHuRFqi6gL3/Zw5cwai9bhoWWJnZ1fiNdGT+/fff8e7776rvLZ///7i240b5evvRERERGSsy5OP/PoM/pj3JHo+Wh12NncaAlpbmmHVzMcRtuZZ1KvlDJk42Fni5V7+lf6+w/s1kKoASCSM6B+Iq1v64bORIUph5jYxSnPskEa4tLEvpr7TXJpRm7e90jsA1laVO5JaFNOe61KrUt+T6GF5udth39KeSm9t0bLk7lxAtNxZ/kUnXFj/HBoFPPiEuKkRn//X+tyaa60yvdG3PszNNVlKIzJpmvxUnjp16r7tSURLEmHq1Klo3bp1iZtobUJEREQkE1G06trOD+tmd0HGwZfh42FbXKjp/XhNWDxgZKcpe2dgEGxtKq+45VvVDi/1rFtp70dk6ALX+KHBuPzH88UxwNvdFtPebYHafvJcvXE3EcOGPVevUt9zzMuNKr2oTmSoXEBMHLly5uMlcgEPFxs8/2RtadvujOwfCEf7yruiS/y9Xnu2cuMOEZWNnEc0FVjgFq1LxOju0m6DBw82wt4SERERGY5aRiCLotyUUc0q7f2+/7gdHO2tKu39iCqKWmKA8PmoZiVGpVek5g3d8d5LJdtbEslKLXHA19MeX/2nZaW93/wP28C1inWlvR8Rld2da1M05EEFbpm5BNZAmxlvwNLBFhlRN7DrrdlwCaiGzj+PQ2p4DLb0/xTZial45IMXUeOplijIyUNhfgGOTv0FMTtPKNsIHNoD9Qc/ifyMbKzrMgamLvPKCUTOfR2FWWmw8qiBmu8uQfbVM7g4qRtsfOvB/5MtsHSuioRtixC/biayr4XBb8gMePZ6p3gb0UvGIXnfKphZWkNnbgmfgZ+jyiNPKq/Fr52JG5vmwszWAYFfHzfib0pERET/9NaLQVi5LQKhR+PLvE5cQmaJn2VthdCtfTWoNf9r8OpTCBjYGVAGdQCn563B5ZWhyms1e7VB8Oi+sPN0xS/1X4asuWD0kvFIObQOOrNboxS9+vwXrh36K/eZC8pLtCtaNKk9Hnttk/Jvt6JigBi1/eOkDiZ7xUtZ48BtVfx90fPP6biwdCsOfvSTZuJAxDeDkXp8Kyyq3OpH7dSkC/yGfKHcZxyQ16t9ArBi6xX8uTe6zOuUJw7061oLz5poiyJ9YkC9l59Eg1e7oSi/AEWFRdjY/QMlN5C5TlTWGBA+uTdyrl8pXi8r4iTqfLAGzi174fr6Wbix5TtlYgrxn2efsXDrOFBZLil0OWJ/nYi85BgE/5JixN+UHkSTBe4dO3ZAjdp9PRJ73p2LpDMRqNv/MTT/6CVc+vUvJaDdHYTiD4ThxMwVKMjOVQJht9WT8FvwUORn5eDsdxuQdOoKWkySY7S6SFJqjvoRdrWDlSJ21I//gfvjQ5QgdnfyYVcnBLXH/Ia4FVPu2YZDYHt4Pz8BZta2SmA8P64DGv8YA3Mbe3g+/S7sajfFtYV3CuJERERkOpdcr/jycbQfvBEXIm+WaZ3mL6zT6z06NvfGnA9aQ835X8r5a9jU60PkpWXCzscNvbZ+gRuHLyAtMh4R6/Yi4ehF9No2AzLngp69x8B30OfK/dzEaJwZ0QBOwZ1h4eTOXFBy4jM6d1wbDP98b4XEAHNzHZZN62jSk++VNQ4IOgtztPniDUT+cbDE81qIA7djwd2DnW5jHJB7NPov0zqh4ysbcepicoXEgVaNPfDDJ+0hewyo9mRz1O7THhu7j1M+69ZuTijMK5C+TlTWGFBn3Ori+xkXD+PixK5weqSr8timehDqT90Dc/sqyL1xDWffbQqHeq1h7V0Hru37wT6gJcLeDTbK70dlY5qnoElvrg1rIT8zWwloQvhvO1HtiWYws7r3HEb0jmNK0BKSw64qZ6hs3OTrvZd5+RjMbR2UICa4dXoZNw+uQ1H+rd/tbna1msC2WgPozO79J18lpJuSyAi2NRopoxbyb3JCUSIiIhlUdbPFXwu7IbCO4SfKfLylD9bP7gLbuybkUmP+F7v7lHKgK2TGJCLregrsfdyhplzQwuHOv4/CrPRbo1QLC5XHzAXl92a/Bpg7zvAnoiwtzPDrtE7KfAWmSp84IAS/1xcR6/ch7XJsiee1EAcehHFAbqJtyLbvuqFpfTeDb7ttU09lsm5xxYjsMaDh8F448dXvxZ/1nMTU4u9CWetE5Y0BCdsWKiO0zSxvtZ9zavK4UtwWrDyqwdLFC7kJ1yrhNyBDMc1snfTmUL0qXOpXV86032Zhaw07rwfPhOzfvxPSI+ORHiXfl3dO/BVkRZ7C2XfunEUrzMlURuWUV+L2H2HtVRtWVWsYaC+JiIioovlUtcfu//XAW1P24eeN4QYZGf6flxti0ogQk55QriLyP+/2jWBVxR4Jxy9BbbmguPz4+qa5yEuMQo2RPyiXK/8Tc0F5De8fiJq+jnh94m7EXC9724H78a/hhJ8+7YA2wZ4wZfrEAfem/vAICcCWfpMQPPr5+25T3XHgGyRuWwQrj+rwGfBZcVHsbowD8p7w3rnoKbz3xQEsXH3BINsc9WIgprzdHHa2FqqIAc7+fnBrUgdN3usLc2tLhP/+N8IWbpK6TlSeulBhThaSQ5eh3uRbbZj+KfX4NhSkJ8POv3mF7DNVDNP9lJLebhy7iK0vfFb8uP/phQ9c3rtdIzQZ3Rdb+n0KWdn7t4T/xD+LH58YdKufWnmkntiOmF8nImDiVtVMukFERKQVLk7WWDqlI/o+UQtvfrYXsTfKV+BqUNsZiya2R6sm9xY/1Z7/OdevjnYzR+DvN2YqlySrLRes2nOUchPtB67MHAin4Cdg4XRntB9zQfk91b4aTq/qg3enH8D/1l0s1zZESxJR1PpsZDOTLmrpGwfMba3Qaupr2Pnalw/clprjgOirbenirVzVm7xvtdKft+H8i8roz9sYB+Tm5GCFHya2V3KBoZN242psRrm2U7e6ExZ+0g4dmnlDTbmAaFHkWL0qNvf+CFbO9ui6apLShihq2xGp60T61oWS966AtU8AbGs2uue1rIhTiJg1BLXHLFdaFJE85PjGpn8lzq7Z+965hExMLmBhZ4PMuKRSl/dsHYi2Xw/H9pemKn2ZZGTtWRu5CVeLHxdkpqIgOwNWbr56byvt9N9KEKv74XrY+NUz8J4SERFRZXm6Uw10beuHlVsjMO+3MOw59u8TUIoahiiMjejXAE+29VNGcGst/6sS4IfOSz7A7vfm4frBc1BzLiha11m5+iLt9E64tHlWeY65oLpOdv30WQeMf70Jvv39HH5ccwHJqf/ersLL3RZDn62P15+tBz8veYoaZY0DjjW84ODrjq4rP1EeWznZA2Y6WFVxwO6352giDtz9nEvr3ohe/F9kR5+Hfd0Q5TnGAfUQ3+WXNjyPNX9FYt7yMOw8VLIlz/080cZXyQVETmCqk8o+TC6QEZ2Ay6t3K21JcpLSEL39KDxC/IsL3DLWicqTC4j2JO6dX73n+ayrZ3Hpsx6oOWoRHALbVdg+U8VggVslRL8lMTmAd4fGiN11Upnh9sraPSjMzb9nWc9WDdB+9lvYMXg6ks9GQlbicjIxw7WYCdspuAtubJoH13b9oLO41UOprNLO7MKVmYNQd/xa5YCHiIiI5CbairzYvY5yi4hOw+EzCTgSloCz4SnIzM5XCtgOtpZo5O+CZkHuaN7QA55ut3qvajH/q+Lviy5Lx2HvmG+V7agxFxQHrbbVA5X7ObHhSs9Om2q3HjMXVCf/GlXw5X9a4vO3QnDsXCKOnE1UYsH1pCzk5hXC2soMvlXtERLojpBANzQJcIOlpRwFrfLEgZRzV/FrwzsFHdGixKqKHQ5+9JNm4kBuQhSs3P2U++nn9yM/LRE23nWVx4wD6iM+z2Ikt7hdi0vHodMJOHI2AWfCk5GRdSsXsLe1QMO6LkocaB7krrQ8U3MuIIrbvp2CEbfnNMxtrODVJgin5q6Vuk6kb10oO/YSMi8dRt3xJScazboWhkufPoXqw79TtkPyYYFbRXaN+Abtvh4Bq6mvIy0iDrtGzoJLver3LNf2y+Ewt7JEu5nD76z71mwl6ZFNrfd+Vs6yX53/Jqy966LWe0uRFXn6nuUStv+EmJ8/VPoopRxYg/g1M5Qz82I27MjZr6IoL0fZTvF231lS6uUqREREJBfRk1fcnnuiFtTIEPlfy09fgaWjHZqNHwiIG4DDny9FzM4TUEsuGP2/sUqfTp2FJXRmFqg+bI4yAbnAXFDdbKwt0LqJp3JTq7LGgQfRQhyI+GYw8m7GQ2dmDjMrW9QZ+3vxpHKMA+pWzctBufXpbLoTxlZGDDi7YD1aTx+GZ/6eKeZRReTG/YjcsE/6OlFZY4AgevA7t34W5nYlJ9C89v0oFGTcRPTi95Wb4PvSNFR55MlK+R3o4bHArSIi8GzoeuuD+CCr2r4FtRAJR4OvDv/rcu6PD1ZupWn4bfn68xERERGpIf/b0l+ePpvlzQXrTthw39eYC5JW4sDdjn/5m+biQMCn2+77GuMAaSEGFOTkFbckUlOdqKwxQPAdNLnU5wMmbTXwXlFlk+8aLNJLQV4+rF0clRl1bdxKnqEqTeDQHsrkI9lJaZCVuBQlPzVRmUU3L+X6Q20rfu1MXP12OCwc7/S0IiIiIlJT/vcgNXu1weOL/4usGymQBXNBIsYBxgHSOkPGABnrRIaMAUmhy3Hp856wqKLeK4HUQFdUJC5MIFOTl5mNn+vcujRMBgPCl8LSzsZg28vKB9pvglRCnwIkmWidiIg0zK/zMkRfz4RvVTtEbXvB2LtDKskFBeaDcuSDjAHy0HocYAyoOIwDctB6DBAYB6isOIKbiIiIiIiIiIiIiKTEAjcRERERERERERERSYmD5k2Uha21cmmHTPtrSDbmty7rkInYZyIiIiIt5oIC80Hmg2RYWo8DjAGkdVqPAQLjAJUVC9wmSqfTGbRvkWx0OvYsIiIiIu3Sei4oMB8krdN6HGAMIK3TegwQGAeorNiihIiIiIiIiIiIiIikxAI3EREREREREREREUmJBW4iIiIiIiIiIiIikhIL3EREREREREREREQkJRa4iYiIiIiIiIiIiEhKLHATERERERERERERkZRY4CYiIiIiIiIiIiIiKbHATURERERERERERERSYoGbiIiIiIiIiIiIiKTEAjcRERERERERERERSYkFbiIiIiIiIiIiIiKSEgvcRERERERERERERCQlFriJiIiIiIiIiIiISEoscBMRERERERERERGRlFjgJiIiIiIiIiIiIiIpscBNRERERERERERERFJigZuIiIiIiIiIiIiIpGRh7B2g0hUVFSE/KweysLC1hk6nM9j2ioqA7AJIxcYcMOCfgIiISNNky4UMnQ8xFyKSLw7wmIhxgLQdAwTmAowBZBwscJsoEcR/rjMQshgQvhSWdjYG254I4u03QSqhTwG2/EQRERFpMhcydD7EXIhIvjjAYyLGAdJ2DBCYCzAGkHGwRQkRERERERERERERSYkFbiIiIiIiIiIiIiKSEgvcRERERERERERERCQlFriJiIiIiIiIiIiISEoscBMRERERERERERGRlDi3KREREZFKhV9LxZa90ThyNgGHzyYg+nomElOyldfiE7Pw8vi/ERLojs6tfBBYx8XYu0tEBpaXV4it+6Ox/+R1JQ6cupiMmBuZymuxCVno/PofaBbkjuZB7ujWrhrsbHl4SKQ2kTFp2LznVi4gbtfiMpBwVy4waNxOhDRwx2MtfdA4wNXYu0tEVC7MYIiIiIhUpLCwCBv+vop5y8Pw597o+y6XX1CExesvKTehQ4gXhvdrgD6P14SlJS/yI5JZzPUMfLfiPL5beR6x/1/QLi1WbD8Qo9wEZ0crDHnGH28+3wD+NapU8h4TkSEVFRVh8+4oJRfYGHoNRUX3zwWWbghXbkKb4KpKDOj7RC1YW5lX7k4TET0EFrhVxKt1ELqumljiubyMLKRejkX4il0IW7gJRQWFUKu0Uztx4cNOJZ4zs7GHtU8A3DoOQtUeb0Fnzn/yRESkXpeupmLIhF3YfSxe73V3HYlTbsH1XfHTpx3QpJ4bZMR8iPmQlomi9fzfwvD+zEPIyMrXa92UtFzMXHIGs345i3GvNcGHQ4NhZSlfgYsxgDFA6yKi0/Dqx6HYcTBW73X3Hr+u3KYuPIGfPuuAZkEekBHjAOMAaQ//RavQ5VWhiNpxFNDpYOvhjLp9H0WLiYNRxd8X+8YsgNq5dHgBVUKeEqetkZcch8SdixG16D1kR4WhxojvjL17REREFWLer2fxn68OIiu74KG2c/xcEpq9sBafvPEIxr3eBDqdDjJiPsR8SGui4zMwcNzf2HlI/6LW3QoKivDpguNYsyMSy6Z1QlBdOdsXMQYwBmjRotUX8Pa0/UjPzHuo7ZwJT0GrgevxwatNMHH4IzAzYy4gI8YB0hJef6pCiaeu4PLKUFxesQtn5q/Dxu7jkBGdgIAXH4e1mxPUzq72I3DrOBBunQbBq88Y1J++H5ZufkjY+gPybt4w9u4REREZ/DLkD745hBGT9z10cfu2/PwifDjnCIZO3K2MCJUR8yHmQ1rrt9/u5Q0PXdy+m+jX3X7wBhw8Jee/F8YAxgCt+ey7Y8rI7Yctbt99suuz745j0Li/kZ8v52hnxgHGAdIOFrg1ID8rBzeOXoTOzAxONTyhNeY29rCv10o5a5kTd6u3GBERkVpM+vYYpi48WSHb/mHVBYycvFcposuO+RDzITWP3H789T8QEZNu8G0np+biyTc24+SFJMiOMYAxQM1m/HQKE+YcrZBt/7IpXCmcy3rC+26MA4wDpF4scGuEY81bwTsnxfCJrwxuB28LB84KTURE6vFH6DV8Mv9Yhb7H/N/OYcn/T0QpO+ZDzIfURhScXvzvTkRWQHH77t7cz763HZl69vQ2RYwBjAFq9PfhWIz56mCFvoeYkPrb38KgBowDjAOkTuzBrUIWtlawdnUs7jNV76Un4NaotnKmUkyqoHaFOZnIT01QRpvlJ8fhxuZvkXX5GOz8W8DGN8DYu0dERGQQKak5eH3ibr3XO7SsF7zc7RCXkInmL6wr0zqin2fnVj7wqWoPWTAfYj6kBXN/PatMDlvRMUBMYPvhnMP4akwryIIxgDFACzIy8/DKR6F6r1eeODB25iF0a1cNtfwcIQvGAcYB0g5NFLgTEhIwffp0rFq1ClFRUfDw8ECfPn0wefJkjBo1CosWLcLs2bMxcuRIqEHTsf2V290iNu7HgQ9+gBbELvtYud3NuXUfVB8212j7RERExpd0MweRMWnIyy9CVVcb1PSV5wCtNONmHUb09Uy91xMHtH6e9nqP4Hxryn6snPk4ZMF8iPnQP4kD/IuRqUossLO1QG0/RzjYWUJWUXEZ+O/XhyslBghfLz2DF5+qg2ZBHpABYwBjwP1ODot2Pjm5BfBwtUEtX0dpJ1MWJn57DJej0iolDmRk5ePNz/Zg87ddIQvGAcYB0g7VF7iPHz+Obt26IS4uDvb29ggMDERMTAxmzZqF8PBwJCXd6icXHBwMtTi/ZAsi1u+DmaUFXOpXR8MRz8De2w0FObnFyzw6/13ATIe/h31V/JyVswOe2TkThyctVmYblpX7k0Ph0qYvigrykBV5CnGrpiE3IQo6S5viZdLOhOLSpG73rFuUn4uiwgKErDbMJF1ERGR8e47FY97yMPy+5Qry7pokqWUjDwzv1wDPP1kLNtZypUSJKdn4ce3FSn3P1TsicDkqFbX95JiUifkQ86Hbbqbl4n/rLmL+b2E4d+Vm8fMOdhZ4qae/EgeC6rpANgtWnENmduW1DRGt+EWRe+mUjpABYwBjwN3EZKniioflf15Ritu3PdLATYkBL3Sro5z4komYTPLb389V6nv+uTcaZy4lSxMzGQcYB0g7zNQ+crtnz55KcXv06NGIjY3F0aNHlcfTpk3Dxo0bcejQIeWMbePGjaEWqZfjEBt6CtE7juH0vLXY/vJUuAfXQetpw4qX2ffB96javB5qPdO2+LlWk1/D9YPnpA7ggrW3P5yCO6NKSDd49RmLuuPXI/PSIVyd/0bxMo5B7dF0eXqJW9C8C7BwdIfPi58adf+JiMhwvWnHfnUQ7V7eoEyQdHdxWzhw6gZe/nAXOgzZiOuJWZDJT2svIjuncg84RHFrQSUfSD8M5kPMh4RzV1IQ3He10mbn7uK2kJ6Zr5z8avzcasxfLldv2dy8Any/8nylv684UShLvGQMYAy4feXGx3OPouWAdUof6buL28LRsES89slutB60XpmwVSZLN1xCWkZepb+viJuyYBxgHCDtUHWBW7QfES1JROuRGTNmwNHxzqXIY8eORZMmTZCfn4+aNWvCyUmO0UjlcePweYSv2KUEbI9m9ZTnclPSsXf0fLT8/DXYerqgRvdW8GoThH3vL4DaODRoA9eOg5C8eznSw/aWukxhXg4uT+0Dh8B28O47rtL3kYiIKqaFxxc/nfrX5Q6dTsCTb25GWsad0Tym7sc1F4z0vheVYoGMmA9pLx+6GpuOx177Q2lH8G8nw4Z/vheLVhvnc1Uem3dHId4IhebcvELlhKGMGAO0FwOEz747jkkL/n0y5pMXktBl2GYkp+ZAFuI72RiWbLiE/H8MGpAF44A24wBpg2oL3GFhYVi+fDnc3d0xZcqUUpcJCQlRfopC922hoaHo3LkzvL29YW1tDT8/P/Tr10/ZnsxOzFyBwvwCNB3Tr/i56L+OI2L9XnSYMwqtpr6uBPWcZHXOJOzdbwJgZo6YXz4q9fWr895AYV42ar79U6XvGxERGd7xc4mYtuikHssnYfqP/14MNwWp6bk4E55ilPe+kZyNK9H69/o0FcyHtJUPjZ5xALE3yt6nfsTkvUr7HxnsP3nDeO996jpkxRigrRhwIeImPpp7tMzLh11OwaRv/70YbgrESPRj5xKN8t5i1Lj4W8mKcUBbcYC0Q7UF7mXLlqGwsBADBgyAg4NDqcvY2treU+BOTk5Go0aNlB7dW7ZsUVqZnDlzBq1bt1ZGg8sqLSIOV9bugU+HxqjaskHx84cnLoZjLS/lkp2o7WX/8peNjXdduLbvj7ST25UeU3e7vn4Wbh7egDofrIGZtZ3R9pGIiIx7+ay43F9c9m/qjHVAe9uRs8Z9/4fBfEg7+VDM9Qys2RGp1zqi7Y9o/yODI2EJxntvxgBpaSkGCN/+rn8uIGJAZlbl9bYvr1MXk+5pvaaVGPSwGAe0FQdIO1Rb4N6xY4fys1OnTvdd5nbB+u4Cd69evTBz5kz07dsXjz76qFIgX7VqFW7evImVK1dCZie/WYnCgpJnKvOzcpAeeR3JYVehdl59xwNmZiXOVKad/AtRi99H7bG/w9qzplH3j4iIDCMrOx8/b9T/Enpxuf/GXddg6k5dTDbq+4vLuGXGfEgb+ZByCX2B/u10flhV+X2tZYsDl66mSlEAvB/GAG3EgIKCwnK18EhJy8Wq7REwdcwFHg7jgDbiAGmLrkjWRor/olq1akoB+9ixYwgODr7nddF7W7QhERNRhoeHo3bt2vfdVmJiotLqZM6cORgxYoTe+9KsWTNlYkt9WBaZ4ePCFqgMXVdOxLWtR3Dm23Xl3sZEs4PI0xnuDLLOyhaeX1fsCJqc+Aic+09zePf/GFW7j3zo7cW/44+iXDkm3SEiUrN8syqId36vXOs6ZW6GY/Y+mLI0m/ZItetc6muHlvWCl/uDR9x4udvCwtwM+QWFiEu4//dWXEImmr9wb25gn70PzpmbUdEqMxcyxXyoMnIhQ+dDppQLJdv1RKZNM/1XLMqDb/JnMHUxLuNQpLMuVxx42BigbCN5GsyLyt7+pbx4TMRjovIq0NkizuW/5VrXMXMHnLL/hilLt26Jm/ZPGS0XsMs+ApfM8n9Wyoq5AHMB0hYvLy8cPny4XOtaQKUyMm7NgJyVVfoHS/TnFsVtMfFkrVq17nm9oKBAaXESGRmJDz74QPkjP//88+XaF1Hcjo6O1msdK5054AlpxMTGILfIcJd1i8thKvLXL8zJRPiUZ1ClRS+DJHJCTEyMsl0iIjIyyxzAuXyrpt5MQ2qCft/Zlc4jDbjPcas4oPXztC/TZsSBbVmXvVtGeiYyYiv+byRbLmTofKiic6GKyIdMKhfyzQJsyrOiTu+83ShEjNM9XBwobwwQ4uLigfxUVDTZ4gCPiUwoDlg4AS7lWzUtLR1p1008DrjdBOyNlwtkZmYisxJipWwxQGAuYCIxgDRHtQVuUZAW/bSPHj2q9M++W2xsLMaMGaPcb9y4MXS6e7ND0Z5kz549yv26desqLU88PDzKvS/lOVMJiSYm9vH2MfhohYqUvHclsq6cQHb0BWUG4X8KmnMWVh7V9dqmj48Pz1QSEZmAQlghtqgQ0Onfic3F0Qx21r4wZenWtrj5gJFW/0afUVulcbC3QhXfiv8byZYLGTofquhcqCLyIVPKhVJtC1Ge6VDNC9PhVQn/vh9WLPJRCOtyxYGHjQGCt6cbzOCIiiZbHOAxkenEgSKYIaYoD9BZ6r2us4MO9pamHQcyrO2RYsRcwN7OEs7MBUrFXMA0YgDJqTz1U9W3KBk1ahRmz56ttCrZtm0bAgIClOcPHTqEQYMG4fLly8jLy1NajojWI/90/vx5pKSk4MqVK/jiiy9w/fp1peBdvbp+X/DllZeZjZ/rDIQsBoQvhaVduYbJlEq09Wu/CVIJfQqwVe0pIyIiuTz77na9e2g62FkgetsLcHKwginbvDsK3Yb/We71r23tr4zWiorPQLUuv+q9/v8+64CXevmjosmWCxk6H2Iu9HDOXUlBg6f1nz/ng1ebYPLb5WhtUsk6DN6A0KPxRokBvlXtELXtBVQG2eIAj4lMKw68PP5vLF5/Sa91rK3MEbW1P9xdDPf/sSKEHolDhyEby73+w8aBeePb4M1+dyZorCiyxQCBuYDpxADSFtVOMjl27Fi4ubnh2rVrCAoKQqNGjeDv748WLVoo/bYfe+yxeyaYvFu9evXQsmVL9O/fH9u3b0daWhqmT59eyb8FERERlcfwchx0DepR1+SL20JIoJuR39/dqO9PVBb1aznjsRbeeq1jZqbDsL71IANjfg4ZA0jNuUC/J2uZfHFbaNrADaVciF5pGAeIyNSotsDt5+eH0NBQdO/eHTY2NoiIiICrqysWLFiAjRs34sKFCw8scN/N2dlZaVNy6ZJ+Z3+JiIjIOB5r6Y0+j5d9BnifqnYY99q9k1KbIg9XW9TwcTDKezvYWaJ+rSpGeW8ifU1/rwXsbMo+jGzskEao4VPxbTcMoXmQh/HeuyELWySHFo08MLBHnTIvLwrbH73RFDIQ38cNapdzwpGHZGlhhsYB5WxwTkRUQVRb4BYaNGiADRs2KKOvxe3AgQMYOnSoMgGlKHibmZmhYcOG/7od0Z5EtCypU6fsX45ERERkPGJ+jaVTHkWPDtXKdLn9n/OfhJ9X+SZbM4YBTxknJ+nftRbMzVWdPpKKiBGGa2d1VtoPlWWk5+dvmX5rktt6PFpNKXAZwwvdeExE8uQCCye2x7Od//2Ed1VXG2ye/yTqVHOCLIyVC4i/p401e1AQkWnR5BHKmTNnIFqPi5YldnZ2JV4bOHAgPvnkE6xZswY7d+7E999/j44dO8LCwgLvvvuu0faZiIiI9GNrY4HVX3fG/A/boJH/vSONXKtYY8zgRjj4Sy809HeFTIY+V88olya/+XzF99skMqTOrXxxaNnTeP3ZeqWO5m7/iCd+m/EY5oxrrbQokYVopzRIj5GphtK1rZ9UBUAiK0tz5TMuCt1N69/b4quKoxXeGRiEw78+LV3bjVf71FNGU8vQ+oWIqKJpssB96tSp+7YnadWqFTZt2oQhQ4agW7duygST7du3x/Hjx5U2JURERCQPCwszvPF8A5xY0Rv7lvSEi+OtHtuuVayUSaRECwOfqvKM3L5NtFF45rEalfqebYKr4hHJDv6Jbvfj/u7jdoje1h8rvnwMzv8fB8SIzV0/9UDfJ2opIz1lM6J/YKUX5d96MbBS34/IEMTn5JXeATiy/GnlpPbduUD01v6YObYVqnkZp/XXw/B0s0X/rrUr9T2D67ui3SOelfqeRERlwQL3P4wcORIHDx5EcnIysrKylF7dom93jRqVexBJREREhiOKV62aVIXd/0/rbmttoYzwltnMMS3haF85LQrECLFvP2xbKe9FVFGcnazxbJdasP//OGCMkY+GFFTXBe8N+vd2i4ZsS9CtnV+lvR9RReQCzRt6lMgF7I3U6sdQpr/XHC5OVpV2omDBhHZSnhAkIvWT+8iuAgrcMnMJrIE2M96ApYMtMqJuYNdbs+ESUA2dfx6H1PAYbOn/KbITU9H0vy+g+hPNUFRQqKx3as4aXFm7R7nfbMIg1Hy6DZJOXcGOIdNh6jKvnEDk3NdRmJUGK48aqPnuEmRfPYOLk7rBxrce/D/ZAkvnqkjYtgjx62Yi+1oY/IbMgGevd4q3Eb1kPFIOrYPOzFx57NXnv3Dt0F+5H/XjGCTtXg67Oo+g7rg1Rvs9iYiIShvFPWN0CwybdOs7vKziEjJL/CyLj94IRqMAV6nzn0c+eBE1nmqJgpw8FOYX4OjUXxCz84SyjfqDu6LeS12U3EhnYY4LS7chbOEm5bXAoT1Qf/CTyM/IxrouYyBrLvSgfIe5kLwmjXgE6/++ivMRNys0Brg5W2Pu+DYmWdgqawy4rYq/L3r+OR0Xlm7FwY9+kj4G6BMHIr4ZjNTjW2FR5dYkpU5NusBvyBfKfcYBOXm522H2B60x8IO/9VqvPHFAtHUTE3eaIn3iQL2Xn0SDV7uhKL8ARYVF2Nj9AyU3kLlOUtYYED65N3KuXyleLyviJOp8sAbOLXvh+sa5uPHnt0oMKCrIh8cTQ1G15yhlufi1M3Fj01yY2Tog8OvjRvxNie5PkwXuHTt2QI3afT0Se96di6QzEajb/zE0/+glXPr1LyWg352MnZm3FsemLlPu23m54pldXyMm9CRyktJw+NMlSDl/DdW7tYAMRJJWc9SPsKsdrBSxo378D9wfH6IE8bsDr12dENQe8xviVky5ZxuevcfAd9Dnyv3cxGicGdEATsGdYeHkriR8NtWDkHKAiRwREZke0Vf4r4Ox+HXz5TKv0/yFdXq9R5fWPnh/SBPp85/4A2E4MXMFCrJzlQPhbqsn4bfgocjPykH4yl0499NmZTlxcPz0zq+U5ZNOX8HZ7zYoB7QtJg2GzLnQg/Id5kLyEleiLJvWEY++sglpGXkVEgMsLHRY8vmjSjsEmWOAIIrXbb54A5F/HCzxvMwxQJ84IIjP+92Dfe5+nnFATi8+VQc7DsZi0eoLZV5H3zjQrqknPnmzKUxVWeNAtSebo3af9tjYfRzy0jJh7eaEwrwC6eskZY0BdcatLr6fcfEwLk7sCqdHuiqP3ToORNXuI5T7BZmpOPNWQzgEtYdd7abwfPpd5ee1hffGDiJTIfd1eVTMtWEt5GdmKwFdCP9tJ6o90QxmVveew8hNvXOW1sLeRhmJYYqjMf5N5uVjMLd1UIK44NbpZdw8uA5F+bn3LGtXqwlsqzWAzuzef/IWDs7F9wuz0oGiIhQV3jprS0REZMrE9/f/Pu9QYf24H23mhdUzO8PS0kz6/Cd6xzGluC0kh10VfzzYuN2aLE8c5N5mYWcNMwsL1eVCD8p3mAvJrWkDd2yc8wQcKqDVgihu/zylI7q1rwbZY4AQ/F5fRKzfh7TLsSWelzUG6BsHHoRxQO5cYMGEtnihW8X0427ZyAMb5jwBG2sL6eNAw+G9cOKr34s/8zmJqcX/zmWtk5Q3BiRsW6gUtc0sb7W4MbevUvxaYXYGigrKdtKUyFSYZoQivTlUrwqX+tXRa+utS8wEC1tr5cxjaRq8+pRyqZ2djxv2jp5f4rI9WeTEX0FW5CmcfedWIBcKczKVEQf6ur5+Fq5vmou8xCjUGPmDcvkOERGRDKwszfHbF49h1NR9+Pb3cwbb7vNP1sJPn3Yw6V7l+uY/t/n374T0yHikR90ofq5G91YIHtMPTjW9cHTKL8rITbXlQg/Kd5gLya19iBd2/NANz43ejquxGQbZpmsVayz+vAO6d6gONcQA96b+8AgJwJZ+kxA8+vl7XpcxBpQvDnyDxG2LYOVRHT4DPisuit16jXFA5km1l0y+daXF10vPGGy7T3eqjqVTOlbICTRjxAFnfz+4NamDJu/1hbm1JcJ//7u4HZGsdZLy1EUKc7KQHLoM9SaHlng+ec8KxCz7GDmxl+A7aLIyaptIFqZ7xEJ6u3HsIra+8Fnx4/6nF953WRHExU1cotthzijE/H0COcnpkI29f0v4T/yz+PGJQeXrCSZ6S4mb6F11ZeZAOAU/AQsnNwPuKRERUcURI6znT2iLpzvVwOsTdyMqvvwFLncXG8wb3wZ9n6gFteU/gne7Rmgyui+29Pu0xPORG/crNwc/D3RaNBbXth1RLm1WUy70oHyHuZD8xOR5p1f1wZivDmHBQ57sEleFzP+wjdLfVw0xwNzWCq2mvoadr3153+3IGgP0iQM+Az+HpYu3clVr8r7VSn/ehvMvKqM/BcYBuZmbm2Hm2Fbo1bE6XvkoFBEx5T++d3a0wqz/tsLAHnWlGMVc1lxAtClyrF4Vm3t/BCtne3RdNQlpkfGI2nZE6jqJvnWR5L0rYO0TANuajUo879L2OeWWEx+B8Cm9UaVZD9j41auw/SYyJNO83pT0JkYh2fu6Fz8WveMs7GyQGZf0wPWSz0Yqy3i1CYJsrD1rIzfhavFj0SeqIDsDVm6+5d6maGVi5eqLtNM7DbSXREREladrOz+lwPX5WyGo5mWv17pi1NeHQ4NxZlUfaYrb+uY/nq0D0fbr4dj+0tT7Fq7EqO6EYxdRrXNIhe23sXOhB+U7zIXk5mhvhW8ntEXoT93R5/GaMDfX6d1zf92sLlg183EpittljQGONbzg4OuOris/wXMH5yHw9e5Kn95234yUOgboGwfEc7dbNrq07g1zWydkR5+/ZznGAbl1auGDU6v6YNo7zVHL11Hvk9zvv9IYZ9c8i0E9/aUobuuTC2REJ+Dy6t1KWxLRWzt6+1F4hPhLXScpTy4g2pO4d371AdusCfuAlrh5eIPB95eoonAEt0qIflNicgTvDo0Ru+ukclmNmPG3MDf/nmWrBPjh5oUo5b5jDU+lZ1XK/z+WibicTmduqcwE7hTcBTc2zYNru37QWdzqIVVWWVfPwrZ6oHI/JzZc6WFlU+3WYyIiItlUcbTCuNeDMXZIY2wKvYbNe6Jw5GwiTlxIQk7urYmUBEsLMzTyd8EjDdzQuZUvej9eQ2l3otb8x7NVA7Sf/RZ2DJ6uHLjeLzcSE055tW2IiI37oaZc6EH5DnMh9Wn3iJdyi4rLwM+bLuHAqRs4cjbhnvYl4sRWSKAbmgd5KP1769W604dZTTEg5dxV/NrwTjFHtCixqmKHgx/9JHUM0DcO5CZEwcrdT7mffn4/8tMSYeNdV3nMOKAuoqXI2Fca4z+DGyl5gMgHRAw4fj4J2TkFJfrsB9VxQUigOx5r4Y1nO9c02V7bhsgFRHHbt1Mw4vachrmNlVLAPjV3rdR1En3rItmxl5B56TDqji850ejdMSDv5g2kndoBlzbPVsrvQGQIckUueqBdI75Bu69HwGrq60iLiMOukbPgUu/ennnNPhyk9KkqystHYUEh9o9biJsX9e9bbQpqvfczImYNwdX5b8Lauy5qvbcUWZGn71kuYftPiPn5QxSkJyuzf8evmYG6H65XekpF/2+s0rdKZ2EJnZkFqg+bo0xISUREJHs/zl6daig3IS+vEEmpOUqR28rSDC5O1rC2kqug/TD5T9svh8PcyhLtZg6/s+5bs5XCV+BrT8GzZYNbB8M6Hc5+v1E5SFZTLvSgfIe5kHr5ednj/VeaFD++mZaL9Mw8MX8g7O0slDYEMozQNEQMeBCZY4A+cSDim8HIuxkPnZk5zKxsUWfs78UTyzEOqJOZmQ5Pta+m3IT8/EIk3cxBtkZzgbML1qP19GF45u+ZShxUWhNt2Cd9naSsMUAQPfidWz8Lc7tbE23fdn3DN0g/EwqdmHSyqAhVe76jFMyJZMECt4qIA7QNXd//1+W2vzQFaiF6RjX46vC/Luf++GDlVpq6E3jZDRERaaNPtxitqdX8Z1Xbt+772r6x30HtudCD8h3mQtq6wkPctBgD7nb8y99UEwP0iQMBn26772uMA9o5+V1Vw7lAQU4edr89R3V1krLGAEFMHlmaGsMXGHiviCoXe3CrXEFePqxdHJUZhW3cSp6hK02zCYPQ6K3eyEkx/YkU7kdcipOfmqjMIpyXcv2hthX14xjErZgCC3sXg+0fERERmVb+8yCBQ3sok9NlJ6VBFsyFSOu0HgMExgHSOkPGARnrJIaMAfFrZ+Lqt8Nh4Xin1zmRqdEVFYkLM8jU5GVm4+c6AyGLAeFLYWlnY7DtZeUD7TdBKqFPAba8JoKIyKT5dV6G6OuZ8K1qh6htLxh7d0hFuZCh8yHmQhWHcUAessUBHhPJEQcYA+QhWwwQmAuYfgwgdeIIbiIiIiIiIiIiIiKSEgvcRERERERERERERCQlXjhgoixsrZVLW2TaX0OyMb91aYtMxD4TERGRNnMhQ+dDzIWI5IsDPCZiHCBtxwCBuYCx94C0igVuE6XT6Qzav002Oh37NhEREWkZcyHmQkSMA4wDpG2MAYwBRGXFFiVEREREREREREREJCUWuImIiIiIiIiIiIhISixwExEREREREREREZGUWOAmIiIiIiIiIiIiIimxwE1EREREREREREREUmKBm4iIiIiIiIiIiIikxAI3EREREREREREREUmJBW4iIiIiIiIiIiIikhIL3EREREREREREREQkJRa4iYiIiIiIiIiIiEhKLHATERERERERERERkZRY4CYiIiIiIiIiIiIiKbHATURERERERERERERSYoGbiIiIiIiIiIiIiKTEAjcRERERERERERERSYkFbiIiIiIiIiIiIiKSEgvcRERERERERERERCQlFriJiIiIiIiIiIiISEoWxt4BKl1RURHys3IgCwtba+h0OoNtr6gIyC6AVGzMAQP+CYiISOOYCzAXIG2TLQYIWo8DjAFkaFqPA7LFAIFxgMg4WOA2UeJL7Oc6AyGLAeFLYWlnY7DtiS+x9psgldCnAFt+ooiIyECYCzAXIG2TLQYIWo8DjAFkaFqPA7LFAIFxgMg42KKEiIiIiIiIiIiIiKTEAjcRERERERERERERSYkFbiIiIiIiIiIiIiKSEgvcRERERERERERERCQltr4nIiIi1SoqKkJ0fCaOhCXgxPkk3EzLVZ6/mZ6Ln9ZeQLNAd9Sv5QwLC57zJ1KrnNwCnLqYhCNnE3ElOg0p/x8H0jLy8OeeKIQEusPdxXATIxKR6Ym5nqHEgBMXEkvkAotWX0BIoBsCa7vA0pK5ABGRrFjgJiIiItWJvZGJ71eex8LV53E1NuOe19Mz8zFkQqhy387GAs91qYnh/RqgRSMP6HQ6I+wxERlSYWERtu6LxrzlYfhjdxTy8gvvWSY1Iw9d3/xTuR9YxxnDnquPl3rWhbOTtRH2mIgMLT4xCwtXnccPqy4oJ7dKywVe/fhWLmBjbY7ej9VQcoG2TT2ZCxARSYYFbiIiIlKNpJs5+M+XB7BkwyXk5xeVaZ3M7HwsXn9JuTULcse88W3QvKFHhe8rEVWMjbuu4p3pB3DpamqZ1zkbnoK3p+3HB98cxlsvBuKTN5vCxpqHSkQyEiO03//6kDI6u7STW6XJzinAsj8uK7cm9Vwxd1wbpdBNRERyYNamIl6tg9B11cQSz+VlZCH1cizCV+xC2MJNKCoo2xe8jNJO7cSFDzuVeM7Mxh7WPgFw6zgIVXu8BZ05/8kTEanV2r8iMWzSHmXEVnkdPpOA1oPWY+zgxvj4zaawtjKHTLSeCwjMB7QrOTUH70zbr5ysKi9xwmvaopNKPPlxUge0alIVstF6HGAM0LbNu6Pw+sTdiIq/9+qtshItzdoP3oB3Bgbhs5HNYGcr178XrccAgXGASHv4iVahy6tCEbXjKKDTwdbDGXX7PooWEwejir8v9o1ZALVz6fACqoQ8JRqvIi85Dok7FyNq0XvIjgpDjRHfGXv3iIioAvpsfzzvKD5dcNwg2ysoKMKUhSfw95FYbJzzhJTtCrSeCwjMB7QlIjoNnYf+gfBr97YhKI9zV26i7csbsGhie7z8tD9kpPU4wBigPdMWncB/vz5skG0VFQEzl5zBriPx2Dz/SSn79Gs9BgiMA0TawVkUVCjx1BVcXhmKyyt24cz8ddjYfRwyohMQ8OLjsHZzgtrZ1X4Ebh0Hwq3TIHj1GYP60/fD0s0PCVt/QN7NG8bePSIiMrDxs44YrLh9t73Hr6Pz0M1ITb81GZVMtJ4LCMwHtCMyJg0dhmw0WHH77j7egyfswo9rLkBGWo8DjAHaMvn74wYrbt/tyNkEdHp1k9ICTTZajwEC4wCRdrDArQH5WTm4cfQidGZmcKqhvT5i5jb2sK/XSjlrmxMXbuzdISIiA/p+xTlltHVFEQe2z/9nhzJKXGZazwUE5gPqlJmVj27Dt+BaXPnbEfyb1z7ZjR0HYiA7rccBxgD1+nnjJYyffaTCtn/6UjKeeXsrCiRv66H1GCAwDhCpFwvcGuFY89YXWE5KOrTo9peXhYOrsXeFiIgM2JLg3S8O6LXOoWW9cG1rf+VnWf25NxrfrzwP2Wk9FxCYD6jPhLlHEHY5pULjgBjJ/crHoUjLkO9qjn/SehxgDFCfmOsZGDl5X4XnAqFH4/HNz2cgO63HAIFxgEid2INbhSxsrWDt6ljca6veS0/ArVFt5WytmFhC7QpzMpGfmqCMtstPjsONzd8i6/Ix2Pm3gI1vgLF3j4iIDEDE+Fc/DkVGVr5e63m528HP017v9xs94yCebOOLGj6OkIHWcwGB+YD67T0ej5lLTuu9XnniQGRMOsZ+dQjzJ7SFLLQeBxgD1E/8vxWTS6ek5VZKLiBGiXdvXw31ajlDBlqPAQLjAJF2qL7AnZCQgOnTp2PVqlWIioqCh4cH+vTpg8mTJ2PUqFFYtGgRZs+ejZEjR0Itmo7tr9zuFrFxPw588AO0IHbZx8rtbs6t+6D6sLlG2yciImO6mZaLJRsu4fctV5CQkg0rSzM0qOWMN/rWR/sQL+h0Osjmr4Ox2HGw8g7O0jPzMP3HU5g7vg1koPVcQGA+UNKh0zcw/7cwnDifhKycArg4WeHpjjXwSu8AKSdPEz6ae1SZCK6yfLfyPP77amNpTnRpPQ4wBtz7PbZ0wyUs//MKridlwdLCDPVqVsHQZ+vjsZbeUuYCB0/dwIZd1yrt/bJzCjD5hxP43+ePQgZajwEC4wCRdqi6wH38+HF069YNcXFxsLe3R2BgIGJiYjBr1iyEh4cjKSlJWS44OBhqcn7JFkSs3wczSwu41K+OhiOegb23Gwpy7pzZfnT+u4CZDn8P+6r4OStnBzyzcyYOT1qszLgsK/cnh8KlTV8UFeQhK/IU4lZNQ25CFHSWdw7e0s6E4tKkbvesW5Sfi6LCAoSsLqjkvSYiMjwxWmXqwpP4/Pvj94x0Pn4uCcv+uIxG/i74ZWpHNPSX6zLNecvDKv09F6+/hKnvNIOjvRVMndZzAYH5wC1XotLw4n//wv6TN0qdSPWjeUcx6sVATHm7GczN5eleeO5KCrZXcl9s0arkuxXn8fmoZpCB1uMAY8CdXODrpWfw8byjSMvIK/GaOOH1259XUL9WFSyd0hEhge6QiTFyAXGC4KsxLeHmbPonBrUeAwTGASLtkCeLLcfI7Z49eyrF7dGjRyM2NhZHjx5VHk+bNg0bN27EoUOHlDPVjRs3hpqkXo5DbOgpRO84htPz1mL7y1PhHlwHracNK15m3wffo2rzeqj1zJ3LLFtNfg3XD56T/kvM2tsfTsGdUSWkG7z6jEXd8euReekQrs5/o3gZx6D2aLo8vcQtaN4FWDi6w+fFT426/0REhjqgfWfafoybdfiBbTxOXUxG25c34OjZBMjUb3PNX5FGGv0mx4REWs8FBOYDwKWrqWg9aH2pxe3bcnIL8MVPpzBo3N9KAVcWC34/Z5T3Ff34c/PkKHZoPQ4wBtzy4ewjeO+LA/cUt+927spNPDpkI/adiIcskm7mKMXmyiZi5o9rLkIGWo8BAuMAkXaotsAt2o+IliSi9ciMGTPg6HjnUsKxY8eiSZMmyM/PR82aNeHk5AQ1u3H4PMJX7FK+tDya1VOey01Jx97R89Hy89dg6+mCGt1bwatNEPa9vwBq49CgDVw7DkLy7uVID9tb6jKFeTm4PLUPHALbwbvvuErfRyIiQ1u87hJm/XK2TMumpuehx1tbkZF5/4NfU7LzUBwKCoxTiNu2v3JHjBqK1nMBLeYDBQWF6PnWFsQnZpVpeXFFx4yfTkEWW/dFG+V9byRnKycGZaT1OKC1GCCs2HJFaalRFuJkeK9R25CSmgMZ7D4apxSbjWHbfuPEn4el9Rig1ThApBWqLHCHhYVh+fLlcHd3x5QpU0pdJiQkRPkpCt33I9qbiBHen3zyCWR3YuYKFOYXoOmYfsXPRf91HBHr96LDnFFoNfV15YstJ1mdsyl795sAmJkj5pePSn396rw3UJiXjZpv/1Tp+0ZEVBGjt2f8T79CVeyNTPy6+TJkcCQsQZPv/bC0ngtoLR/YFBqljMrUxze/nEFeXiFMnTgZF6bn72ZIRyS64uWftB4HtBQDhC8X65cLJCRnK+24ZHDkbKLx3jssUcm1ZKT1GKDFOECkFaoscC9btgyFhYUYMGAAHBwcSl3G1tb2gQXu3377TenhrRZpEXG4snYPfDo0RtWWDYqfPzxxMRxreSmXLUVtPwq1svGuC9f2/ZF2crvSY+tu19fPws3DG1DngzUws7Yz2j4SERnK7qPxOH1J/xGGc38Nk+KA7WiY8Q5qI2PSlQKAjLSeC2gtH5j7a9mu4LhbzPVMrDVC+x99nbiQZNR2KjIXuLUeB7QUA0TrsQe1J3pQX2sZcgFjnnAWecDVWDkLwFqPAVqLA0RaospJJnfs2KH87NSp032XEe1L7lfgTk1NxTvvvKO0Nhk4cOBD70+zZs2U3t/6sCwyw8doAUM6+c1K5XIkcbb2z+dujUrPz8pBeuR1JIddfahtB/gHIE9nuBE/OitbeH5t2N5mXn3HIyl0mXKmtt7nfynPpZ38C1GL34f/R3/A2rPmQ20/IMAfRblluwyYiKgipdm0B+w6673esXOJ8K1WG2Yw7VYl8VVGAuYepb52aFkveLnf/4DEy922+Oe1rf0f+D5xCZlo/sK6e54PCm4NywL9iwb6Yi5g+FygovMBU8oFYlzGiz+i3usNHvk53sncDFOWZdkAcOxfrhigTxy4XwxY/MsabPzuwfHDVGOAoPU4oJUYkG7dCrC/d+K8f3M+4iZ8qteHeVEGTNl1p6GAha/RcoHmrTvDqqDiW5XIlgsYOg4wFyDSFi8vLxw+fLhc66qywB0ZeWvkSY0aNUp9XfTe3rNnz30L3OPHj0dAQIAyAtwQBW5R3I6O1u/Lz0pnDnjq+T77zuAn7+fu+/rNi9FY7HfnUiRDiomNQW6R4XqgibOlev76cGzUESFr7z/awLZagxIzIOfER+DyF8/Db/AXyroPKyYmBoU5mQ+9HSKih+aZA5Rz0ElsfBKQnwqTZl8EmJf+kjig9fO0/9dNWJiblWm50ly/nghkV/xBLXMB/XMBY+cDppML6ABX/YvbQkZmPjL0zFsrXRUf4M70OuWKAQ8TB7Jz8vXO7SsrBghajwOMAf/PIxso39cc4kQukGfiVyrYFt23mlEZucCNhGQg0zTjgDFjgKHjAHMBItJ0gTsj49bZ5qys0s+aif7cCQkJysSTtWrVKvGaOFPw/fff48iRIwY9A1GeM7Uw/RaIxXy8fQw+WqMiiS+c8CnPoEqLXqjafaRBtunj48MztURkEtJsrFDeErW3pyvM7lc5MhHxFjrkP2Ck1YOI0VrigDa/oBBxCQ+O2ffbVlUPN1gWVvyXNHOBis0FKiIfMKVcIKYoB0U6a73Xs7ezgLNv6aMiTUWWZRUklTMG6BMH7rctG2sLuFXC30i2GCBjHFBzDEi3tkZ5O9V7ebrAvEj/+FGZrlvq7nu9WWXkAh7uLrAqYByo6DjAXIBIW7zKUT9VdYFb/EGSk5Nx9OhRtG7dusRrsbGxGDNmjHK/cePGyiSStxUUFGDYsGEYOXIkgoKCDLY/5Rlen5eZjZ/rPPzo8cpy4eIFWNrZGGx7WflA+02oMMl7VyLryglkR19QZlD+p6A5Z2HlUV2vbV64cBG2qvxEEZFsDp66gZYD7r2c9t+0auyBfUuvwNR1fv0PbD8QU+prpV1GfDdxKbIYrSUOaKt1+bVc73/u9H64OFX8gT9zgYrNBSoiHzClXODpUVuxbqf+l5r/+v1H6PHoDzBlB05eR6uB68sVAwwRB4YM7I15H85ARZMtBsgYB9QcA05fTEKjZ1frvV4jfxecWHG+xHGyKXpQjKuMXODYwR3wLefob31oPQ4wFyCislLlx65z584ICwvDtGnT0KVLF6XdiHDo0CEMGjRIGb0tBAcHl1hvzpw5iI+Pxyef3OpDpRWbn/0YWuPWaZByIyJSo+YN3RES6K73RGjD+92ZbMiUPdLA7b4F7opW28+xUorblU2LuYDa8wHxeda3wF3DxwHd2vnB1DUOcIW5uQ4FBcaZCE/EVzXSYhxQcwxo6O+KDiFe2HUkTu/YYerF7dufw/KcxDMETzdb+FRV3wSEWowBao8DRFpiBhUaO3Ys3NzccO3aNWUkdqNGjeDv748WLVqgdu3aeOyxx+7pvy2K3hMmTMBHH32k9OhOSUlRbkJ2drZyv7ASLkcmIiJ6WOLAdOyQRnqtU9PHAX2fKNm2y1QZs7ik1sIWqU+X1r5oWt9Nr3X+83IjmJub/uGBrY0Fguq4GO39GQdIFvrmAqJoO6B7HcggJNDNqO8tw0kAIiItMf0Mthz8/PwQGhqK7t27w8bGBhEREXB1dcWCBQuwceNGXLhw4Z4Cd1RUFNLS0pQWJS4uLsU3QYwEF/evXjXOGWIiIiJ9Pf9kbUwYVvJKpfvxcLHBpnlPKn1lZdCpuTcsLYyTwnRta/qjW4kEMzMd1s3qoozKLothfetjRH85ruIQurY1Tp9wUQBsWNd4xXUifXTvUB3T3mlepmVdnKywcc4TcLQv3wS1la1dUy/Y2Rgnb3myDXMBIiJTo8oCt9CgQQNs2LBBKVqL24EDBzB06FBlAkpR8DYzM0PDhg2Ll69bty7++uuve27Cyy+/rNx/mGbnRERElW3SiBDMG98G7i7374PY/hFP7P+5JxrUdoYsqrrZGmW0ubOjFfp3rV3p70tUXn5e9ti/tOcDT8xUcbTC5FHNMP/DNlKNSBQFeWPs7rDn6sPCSCfYiMpj7CuN8eOn7ZW2GvfTspEH9i7pieD/Y+8+oKOo3jaAP5tsei+QRgkloUPoHekdBBQBAYW/UgTEgqCgooAiYkG6qGChFwFpIk0k0nsNLSSBNCAkIb3nO3f4CEQCZMMmu3fn+Z2zh2TL7IDuM3fevfNeHa/6MCSRXYaYbS6K6q/0qFzi70tERE8mx1QtPTp//jxyc3OVvty2tg/6Ztnb26N169YFvsbX1/exjxERERmzN/pVw/96+2PdzhCs3RGCP/8NR0ZmDuxstMrJrOhlKyPRI3TFtuASfc+hvfxgy1WDSDKe7rb4c2EnXA69i0XrLmL+ygtIz8yBlaUZ5n7QFC93rQQ7WwvIpmIZR6Vf+LbA8BJ7T61Wg9f73Fvbh0gmQ573Vz7rG3aHYdX2a9gWeEMZC4hi7b5fuknbdkeMBX78/VKJvqcoqjub4FocRESyU930g7Nnzz7SnoSIiMiUWVmaY2C3ytg4u4PSjuT+bGRZi9tCs4DS6N6qbIm9n7h0e/yQ2iX2fkT65u/rhG/ea5x3RYe7szWGvVhVyuL2fdNG11cWmywpY1+uAe/SdiX2fkT6ZGlhjn6dK2LDd+3zxgLi2CZrcVsQM877dS65K7rsbS0w6XXWEYiIjBEL3E8hZnt/+umnkIFL9fLotu0L9Nr3HTqs+BBWbo7wbFoDg64tR8+dX8HazTHf8538fJTHGk0dkndf9eHd0efAXOX5MkgJOY2g9xrh/OhquPJpZ2TevY3Es3txoq8NLrwdgMz4W8rzYnYtwfmxtXC8txY3N31X4LZSbwThRF9b3Pjp7bz7bv4xC+dGVFa2RURExkO0Ulg0ublSqC8Jcz5oCq9SD678ku3YX2/iy+gdOBs9d32N7tu/hHfrR8dB4rn9zvyEtj9PyLvPt2cz9PpnFl6++CtkHwtELP1QGQuI+8Qtdt+qR7bFsYBc6lV3x8TXSqbY5F/eCZ+NqQ9TPQeQOQcKmwGhs4fgzFCfvAwI/3n8I9tiBshHXIlyv2Bf3L4e1wi+Pg6QOQOqvNpJed7ze75RxgTmVhYmUSMobA4ET++dlwHidryXGeIPb8q3LfHc06944Or0Xnn3xQauxvnR1XHqZXla+hGpjequszXlGdwtvhuD/e/MR+z5UFTu3xYNJ7+Cq6v+RkJwJDZ1yD+A02jN0eyrkQj780i++y/8sAWxZ0MKHPAaIzFQ9R37M2wrBihF7PCf34N7u6Gw9qmC6t+dynuebaX6qDh+DaLXfVHgdnKzMnF9wXC4NOmd736P59+BbcW6uLH4wSCXiIiMg5hJOX9SMwycuLfQr4mOScn3Z2H0aedrkD6f+jz23zwchNOz1iE7LUM5Ee6yYSrWBAxHVmp63nOafjUCN3Yeh7Xrg5P30E0HEHPiinISLPtYwKP3ePgM/lz5OeNOhHIS7BjQHlrHe7MXORaQk1hMV7RbOBF0p9hyQCxq+8tnLWFjoAXtSuIcQOYcKGwG3M8Bj54Ff5aZAXIq5WqDHz5pjt5v7y7WsYBYy2D4i1UgcwaU7dQQFfu0xNZuk5CZmKIUwnMys02iRlDYHKg0aUPez8lXjuHKlM5wrNc537auLxgBp4bdkZX44Lji2rIf7PwbI+gdftFFZKxUN4N7z549yqzsbt26wZS41qyArJQ05aAmBK/Zi7IdG8DMsuCBeMC7fRG6+SASr0VBVinXTsLcxl45iAlubV7F3SObkJuV8chzbSvUgU3ZatCYFfy/fOTqqXBp1hdW3n7Fvt9ERKQ/L3erhBlvNyj08xsO2ISyHVYpfxZGq/qeWDr9OaNcfE+XY3/EnpNKcVuIC7oupsDnm9HlN6Atkq7fwq3DQTDVsYDW/sGsq5zUJHGZHnJzcvLu41hA3rYL2xZ0gl/5/LOU9ZUDZmYarJjRGk3reMDUzwFkzAFdMuBpmAHy6tXWF3MnNi22sUCjmqWw5us20o8Fao7qidPfrlWK20L6nYR8x0FZawRFzYGYXYvh1noQzCweXA0Ys3MxLD0qwL56y2LfbyLSL+OchkA6sy9XGi5Vy+W7bEhrYwVbz0f7q7rX9UOp+v7Y0W8qAsa9BFml3wxBatjZfJcL5qSnKLOydJF86TCSLx6E39SdiFo1pRj2lIiIitP7/6ujFLne/eqwXrfbsZkP1n/bzmgXltTl2P8wv/5tkBR2E0nht+9tp2xpVHmlI/7sPRkVejaDKY8Fbm2eg1vb5iPzTjjKj/kJFs6llfs5FpCbh5sN/lnSDZ1GbsfZK3F6266lhRlWzWyD3u18YernALLmgO4ZMBt3di2BZaly8B74WV5BjBkgvzEDqitXW7zx2X7x/aXeiC+6N8/tAAe7kmmJVpwZ4OxXBm51KqHOu32V1iTBa/9B0OJt0tcIilIXyElPRVzgSlSZHphvO7e3f48q0/ch9t/Vxb7fRKRfxnnGRkVy++QV7BzwWd7v/c8tfuQ55jaWaDLjdex9/RuYAju/xvCb8lfe76cHl9Lp9eLAd33RKFR8f51RfiNPRESF887gmqhb1Q3/mxyIkIjEZ16Uc9roenj3lZowNzeT/tj/MK8WtVBnXF/s6Dct777ms0bh0KTFeTO8TXksULrHWOUmenWGzBoEx4COMLOy4VjABIge+YeW9cSkOccwZ8X5Zy5wNajhjp+ntkRNP1dVnAPInAOFzQDvQZ/DwsVLuaIz7uAGXJnaBTUXXlF+ZwaYhhF9q6KWnwuGfhyIy2F3n2lbolguWiB98L86sLAwjbGAaEHiUK40tveeDEtnO3RePxWJYTcRtf+s9DUCXesCcQfWwcrbHza+tZTfxVX+oXP+h3LD5ynjAiKSDwvcJkLMxLLzebACtoW9DbS21kiJjs33PIfynrD3cUfn3+8tnGnpaAeYaWDpZI9/35oHmVh5VERGzPW837NTEpCdlgxLN59CbyM9KhgZt6/j8kdt7m0jOV65TCsrKQ4V3jbOhXSIiKhgrRt64czvvTF5/nEsWnsJKWlZOm+jQ1NvzH6/KapVdDaZY/99Hk2ro/l3o7D7lRlKX07lNQ62cKlWHq0XvaP8rrWzVmZ+dVzzCXa8NMVkxwKidZmlqw8Sz+2Ftbc/xwImQlxt8d37TfBCe1+889VhHL8Qo/M23Jyt8N6rtZSbVmvcRS19nQMc/miJtDmgSwY8fJ9L096I+O0DpEVcgpnWkhlgQpoFeODU2l6Y8v1JzF8VhKSUTJ238VwDT2Vx6dr+riY1FkiOiMG1Df8q/3+nxyYiYvcJlKrvp1zRJXONoChjAdGexL39a3m/56QkIDXsDK593e/eNlKTlMlwlz9uB/9phe/vTkSGwwK3iRA9t8QCEV6taiNq3xlUHdIJIX/sR05G/pP7+IvXsarmgyAXlx9ZOtniyORfIBtxSaHG3AIJp3bCMaADbm9bANcW/aDRFv7yMfGNbZ2l9y7RFiJXfqoMasu+/l0x7TURERUne1sLfDu+CT4ZWQ+/bb6Cn9ZfxtkrsU+czSnaG/TvXBEjX6qKqhWMv7Ct67Ff8GhSDS3nvok9Q2Yi7kJY3v2iD+eqGkPzfq/8UmuU69IIe4bOhKmNBVKvX4BNuep5X3CLnp3WZasra3RwLGBaWtb3xNGVPXH0XAwWrA7Cxr/DcDfx8TOTtVqN0mN3ZN+q6NuxAqyttKo7B5A1B3TJgIyYcFi6l1F+Trp0SFlAztqrMsztnJgBJkYsCDvj7Yb4cFgdLNsSjB9/v4RTl+48cSzg7mKNlzpWwBsvVZXiyo2ijAVEcdunTQCi95+DubUlPJvVwNn5f0hfI9C1LpAWdRUpV4+h8ocPerCLHAhY9mBRyZjdvyD+8EZUnrSxRP4ORPTs5Bm90VPtGz0bLb4bDcsZw5AYGo19Y+bApUo5mLIK7y5H6JyhuL7wDVh5VUaFd5chNezcI88TB6jI5R8hOylOOVDd3Pg1Kn+0WVkRnYiITI+TgyXefLmGcktMzsDJoDs4fTkWCUmZyM7JhY2VOfx9ndCguju8S9tKe1l6YY/9zb8ZBXNLC7SYNerBa9+cq5zUqmUsEPHrBKW/pkZrAY2ZFuVGzFOK22SaxGe6Ua1Syk1cen4tPFGZ0S3+TM/IVtoPuDpZoV41d9T2d5GqqP1fajwHKEoGhM4egsy7N6ExM4eZpQ0qTVirFLXIdIm+2W/0q6bcxEzuUxfv4NSlWOULr6zsHNhYaZUFautXd0dZTzuTHwtcWLQZTWeOQK9/ZinF/rCthxC25SDUlAOC6MPv3PQFmNsWfnFiIjJ+8o7k6BHiJHVL5/d1es2pb9ZAZmIGdrVvjz31ee7thii3p/EecO+yLCIiMq0T3FYNvJSbWo/965u/WajtXV2zV7mZ4lig8sdbCrU9jgVMjyhaVSrrqNxMkb7PAWTLgcJmgP+0XYXaHjPAdK/walHPU7mpNQOy0zML1XJExhpBYXNA8Bk8XW/1AyIyHsbfWI6eSXZmFqxcHJRVla3dnj6orz68u7LARFrssy3QZUjiUqSshDvKKsqZ8beeaVs3/5iF69+PgtbhQV8zIiIiUzr2P4lvz2Zo99sHSL0dD5lwLEBqp/YcYAaQ2ukzA2StEegzB2IDV+Pq5z2gdfLQ2/4RkX5pcsU1e2R0MlPSsLzSIMhiYPAyWNha6217qVlAy22QSmBXwIbXRBCRkSvTfiUibqXAp7QtwncNMPTu0BNwLMCxQHFgBshDtgwQ1J4DMmSAwByQh9pzQLYMkCkHiEwNZ3ATERERERERERERkZRY4CYiIiIiIiIiIiIiKfHCCSOltbFSLu2RaX/1ydr83qU9MhH7TEREpC8cC3AsQOomWwYIas8BZgDpm9pzQLYMEJgDRIbBArcRr/auz/51stFo2LeKiIjUjWMBjgVI3dSeAQJzgNRO7TnADCCiwmKLEiIiIiIiIiIiIiKSEgvcRERERERERERERCQlFriJiIiIiIiIiIiISEoscBMRERERERERERGRlFjgJiIiIiIiIiIiIiIpscBNRERERERERERERFJigZuIiIiIiIiIiIiIpMQCNxERERERERERERFJiQVuIiIiIiIiIiIiIpISC9xEREREREREREREJCUWuImIiIiIiIiIiIhISixwExEREREREREREZGUWOAmIiIiIiIiIiIiIimxwE1EREREREREREREUmKBm4iIiIiIiIiIiIikxAI3EREREREREREREUmJBW4iIiIiIiIiIiIikhIL3EREREREREREREQkJa2hd4AKlpubi6zUdMhCa2MFjUajt+3l5gJp2ZCKtTmgx38CIiJVk+04KKj9WMjjIOmb2nNAtgwQmAOkT2rPAIE5QERUOCxwGylxIF9eaRBkMTB4GSxsrfW2PXEQb7kNUgnsCtjwE0VEpMrjoKD2YyGPg6Rvas8B2TJAYA6QPqk9AwTmABFR4bBFCRERERERERERERFJiQVuIiIiIiIiIiIiIpISC9xEREREREREREREJCUWuImIiIiIiIiIiIhISmz9T0REpALpGdm4HpWEzKwc5ffs7Bzk5uZCw2XuiVTj5p3UvAwQf6amZcHGmqcDRGqRkSnGAskcCxARkcnhiJaIiMgE5eTkYvfhSKzbGYJj52Nw9kpc3gmtEH0nDR6tV6B+dXc0r1saQ3r6o4ynnUH3mYj063ZsKn7ddBX7jkfj2IUYRN1OyXvsVmwaHJr+huoVndGghjt6tSmPbq3KwtycF3gSmQpRvN57NApr/gpRMuDM5VhkZOYfC5R6brkyFmhWpzSGPO+H8t4OBt1nIiKiomCBm4iIyMRmai9cHYQFa4JwJSzhic+9HZeG7fvDldunC0/i+dblMX5ILTSpU7rE9peI9E8UsWb+fAZrd4TkK2b9V3Z2rvLll7j9vPEKynraYcSLVfH2oBqws7Uo0X0mIv3JzMzBD79fxPxVQQi6Fv/E596JT8eOAxHKbeqiU+jWsizee7UmWjXwKrH9JSIielYscJsQz6Y10Hn9lHz3ZSanIuFaFILX7UPQ4m3IzX78SY7sEs/uxeWP2uS7z8zaDlbe/nBrPRilu78JjTn/lyci03X8QgyGfLQP567G6fxaUehavzsUG/aE4q2BNfD5mw1gayNfZvJYyGOh2tsPTP/xND7/6RSysnJ1fv2N6GR8NO84lmy8jCVTW+I5CQtcas8AgTmgbuILrqEf78OJoDtFuvpr8z/XldvIvlUx892GcLCzhGzUngPMACJSI6aaCbq2PhDhe04AGg1sSjmjct/n0GjKEDj5+eDg+EUwdS6tBsCpfldxTR4y46JxZ+9vCF/yLtLCg1B+9A+G3j0iomK5BFnM1vxw7nGlUP1s2wK+W3YeW/bdwKY5HVCtojNkxGMhj4VqI3rsP//WTpy6GPvM27oWnojW/9uGCUNr4Yu3GsLMTL7+vGrPAIE5oD5zV5zHuK+P5GtJVlTfr72IP/eH44/Z7VGnihtkpPYcYAYQkZqwyZ4JunM2BNd+D8S1dftwfuEmbO02CckRMfB/uR2s3Bxh6mwr1oNb60FwazMYnn3Go+rMQ7BwK4OYnT8h8+5tQ+8eEZHei9sTZx/DB98de+bi9sOuXk9Aq6FbcfqS7jPAjAGPhTwWqsm18AS0eHWLXorbD5v581kMm/KvMqtTNmrPAIE5oC7TFp3E2BmH9FLcvi8sMkn5suvIWTn/f1F7DjADiEhNWOBWgazUdNw+cQUaMzM4lveA2phb28GuShPlm+v06GBD7w4RkV7NWHwGXy45UyzbjolLQ8cR25Ximex4LOSx0FTdupOK9sO2K+1FisOSDZcx7uvDkJ3aM0BgDpiuOcvPY/L8E8Wy7fjEDHQZ9Rcuhjy5l7cM1J4DzAAiMmUscKuEg++9A3h6fBLU6P4BXGvvauhdISLSm0Onbyn9cnVxdGVP3NjZX/mzMG7FpuHVD/dJOYPzv3gs5LHQFK/gGDFtP0IiEostAwTRtmjrvuuQndozQGAOmB5xpdW4b3T7EkrXHIi9m45BE/9Blh5nhxuK2nOAGUBEpoo9uE2Q1sYSVq4Oeb3GqrzSEW61KirfVouFNUxdTnoKshJilJO+rLho3N7+PVKvnYStXyNY+/gbeveIiPQiLT0LQyfrXnj2dLdFGQ87nV7z78mbSl/PtwbVhCx4LOSxUA1W/XkNG/eEFXsGCMOn7se59R5wcbSCDNSeAQJzwPRlZuZg6MeBOi8qW5QcEAtZi/U+Jg0LgCzUngPMACJSE1UUuGNiYjBz5kysX78e4eHhKFWqFPr06YPp06dj7NixWLJkCebOnYsxY8bAFNSd0F+5PSx06yEcnvgT1CBq5SfK7WHOTfug3Ij5BtsnIjI8cWJ2+MwtJKdmwcnBEh2a+KBCGQfIatbS87gYcrfE3m/inGMY2K0y3F2sIQMeC3ks/K/klExs/uc6Im+nwEyjQcUyDujSoiwsLOS8oDE1LQtjvzxUYu8XeSsFU78/iVkTmkAGas8AgTnwqDOXY7H/5E0kpWTC0d4SbRt5wa+8E2T1/dognLxYcmtlfLrwJF7t6QefInxJZghqzwFmABGpickXuE+dOoUuXbogOjoadnZ2qF69OiIjIzFnzhwEBwcjNvbeYjwBAfJ8E/00l5buQOjmgzCz0MKlajnUHN0Ldl5uyE7PyHvOcwvfAcw0+GfEt3n3WTrbo9feWTg29TdlxWlZuXcaDpdmfZGbnYnUsLOIXv8lMmLCobF4UJRJPB+Iq1O7PPLa3KwM5OZko/6G7BLeayIqDmLGyurt15Ri8JFz+RfT0WiALi3K4P2htdGqgRdkIi4RXrgmqETfMzUtW+nFO+F/tSEDHgt5LLwv8lYyZiw5g183XUFCUma+xzzdbTDshSoYP6QWHOwsIZPVf11T+uSXpCUbL+OzMfVhZ2sBY6f2DBCYAw+s3xWKb5eeU4rb/9WxmQ8mDKmNdk28IRNxBde8lSU7FhALWP6w7hKmjK4HGag9B5gBRKQmck5Z0WHmdo8ePZTi9rhx4xAVFYUTJ04ov3/55ZfYunUrjh49Co1Gg9q15ThhL4yEa9GICjyLiD0ncW7BH9j96gy4B1RC0y9H5D3n4MQfUbphFVTo1TzvvibTX8etIxelPogLVl5+cAxoD6f6XeDZZwIqf7gZKVeP4vrCkXnPcajREnVXJ+W71VhwGVoHd3i/PM2g+09E+ituvzPzMAa8v/eR4va9x4FtgeFoO+xPLF5/CTLZGnij2BaUe5Lv115EdrYc/Td5LOSxUAi6Fo/GAzdj7ooLjxS3heiYVExbdAqthm5VFmuUyYLVJVvYEsS/4YptcixMpvYMEJgD98YCk2Yfwwvv7i6wuC3sOBCBDiP+VFpxyWTPkUhcDiu5K7nu++H3i0prFBmoPQeYAUSkJiZd4BbtR0RLEtF65Ouvv4aDw4NL0SdMmIA6deogKysLvr6+cHR0hKm6fewSgtftUw7apRpUUe7LiE/CgXEL0fjz12Hj4YLy3ZrAs1kNHHx/EUyNfbVmcG09GHH/rkZS0IECn5OTmY5rM/rAvnoLePWdVOL7SET699kPpzB7+dNPVrOzczFsyr/442/d+tgauu+uIYiF7I6ei4GMeCxU37Hw5p1UdH5jO8JvPv3LoFMXY9H9zR1Kb3sZhIQb7rO4arth8udZqT0D1JoDs5aewxeLTz/1eeJL77EzDmHVn3J8gWPIsYD4YnDf8WjISO05oMYMICL1MNkCd1BQEFavXg13d3d88cUXBT6nfv36yp+i0H3f3r17lRnd/73J3sLk9Kx1yMnKRt3x/fLui/j7FEI3H0CreWPRZMYw5cCeHmeaq0l79fsYMDNH5IrJBT5+fcFI5GSmwfetX0p834hI/8Rl+6LAXVjixHb8t0d0XrDRUI5dMFyR+XiQnAVugcdCdR0LZy87j+tRhb/SQRSMV28PgQwMmwF3lFmxMlJ7BqgtBxKSMjB5/gmdXjNh1lGlDZgMDJkDxy48emWcLNSeA2rKACJSF5MtcK9cuRI5OTkYOHAg7O3tC3yOjY3NIwXu++bPn4+DBw/m3ZYuXQqZJYZGI+SP/fBuVRulG1fLu//YlN/gUMFTuWwrfLduA0CZWHtVhmvL/kg8s1vpM/awW5vn4O6xLag0cSPMrGwNto9EpD+iV3SGjpfPXglLUC73NXZ3EzNw9XqCQRfrlBWPheo5FqZnZOOnIrQeMkTbD9k+hyKDgm8kQkZqzwC15cDSLVeVhaV1Idp/iTZgxk5cbXI+OM5g73/8QsktbKlvas8BNWUAEamLyRa49+zZo/zZpk2bxz5HtC95XIFbLEbZpEmTvFutWrUguzOzf0dOdv5vq7NS05EUdgtxQddh6jz7fgiYmeX7tjrxzN8I/+19VJywFlYevgbdPyLSn5Xbi3aJ8UoDXe6rC0P023zYpVDDvv+z4rFQHcfCPYcjcbsICzCKfv3Xwg33BZIsn8NLofGQldozQE05UNQWHislaFNyLTwRWVm5qs2gZ6X2HFBLBhCRumhyZb3G8CnKli2rFLBPnjxZYHsR0Xvby8tLWYgyODgYFStWzGtRIorif//9N1q3bq2XfWnQoIGysKUuLHLN8ElOI5SEzr9PwY2dx3H++01F3sYUsyPI1Ojvcj6NpQ08vruC4pR+MxQX32sIr/6foHS3Mc+8vZtv+yE3Q64FqohMVZTzOOSY6b62gnXGJbglrYAxS9eWR4zj/wp87OjKnvB0f/KMG093G2jNzZCVnaP00Xyc6JgUNBzw6HHBIisKpRO+R3EryeOgwGPhsx8Ljek4mGxZB/H2fYr0WveEH2GVdW8ShLGKsR+EdEu/IuVAYTPgSTngmrgaNpkXUNxkGw/rOwdKIgNMOQeind5Etrm7zq+zzAxBqUTjbs+QYe6F204PFgos6bGAeXYMPO/ORXHjWEC+sYCx5QARycXT0xPHjh0r0mu1MFHJyfd6LqamFhysoj+3KG6LhScrVKjwyOP9+vVTHndzc0PPnj0xY8YMpZ93UYjidkREhE6vsdSYAx6QRmRUJDJys/W2PXFJVHH+9XPSUxD8RS84Neqpl4O4EBkZqWyXiIyAfSZgqfvL0lKTdc7rEmdrAzymdi9OaMt42BVqM+LEtrDPfVhmRnqJ/BvJdhwU1H4sNKrjoHMZoOAOdU8VcysaSDXyHPBNe2zGFTYHipoBQmzsHSCBOVDcOVDcGWDyOWCbDpjr/rKMtBTjHwtYmwFOhhsLZGdmcizwGGofCxhdDhCRamhNueofFxeHEydOoGnTpvkei4qKwvjx45Wfa9eurSwieZ+Tk5PyWKtWrZTe3aL/tlik8tChQ8q3CNbW1kXal6J8Ww051jdReHt56/2b6uIUd+B3pIacRlrEZWUV6f+qMe8CLEuV02mb3t7e/KaayEjEaOKRDjedX2dvmQInHx8Ys0xzB9x6wkyrp9Fl1lZBLC1yUKoE/o1kOw4Kaj8WGtNxMMM8F8oSaOJCxYfGeU+Vmw1PN0uY5xp3DsRaAqlFzAFdZ3AXxM3FDtYOzIHizoHizgBTz4E7ZglIg+7/n9pZJMPZyMcCWWZOuGnAsYCFNhulORYokNrHAsaWA0Qkl6LUT02+RcnYsWMxd+5cpVXJrl274O/vr9x/9OhRDB48GNeuXUNmZiZGjx6NefPmPXFbmzdvVmZxL1myBEOHDi2R/c9MScPySoMgi4HBy2Bhq3vx/3HEejAtt0EqgV0BG5P9yohILmv+uoZ+4//W+XWXN78Iv/KPmRJlJDIys+HQ5DedF9G878bO/spsrfCbySjbYZXOr397UA3MmtAExU2246Cg9mOhMR0HxfC27ksbcfpSrE6ve6lTBaz+qi2M3Rc/ncakOccMkgHCrb0vo5Rr8Rdf1Z4DsmWAseXA1n3X0X3MTp1fd3JNLwRU1f1L8pKUk5ML5+ZLkZicaZAceL2PP378tCWKm9ozQGAOEBGpfJHJCRMmKO1Fbty4gRo1aiiLRPr5+aFRo0ZKv+22bds+doHJ/+revTvs7OyK3AeGiIhKVq+25ZXZSbro0NTb6IvbgqWFOWr7uxrs/etXL1q7LqKSJK7OG9Wvms6vK8prDKF+dcMV38p52ZVIcZvoWXVuXgYVfBx0ek2zgNJGX9wWzMw0qFfNcPvZoEYpg703ERGRqgrcZcqUQWBgILp166a0FQkNDYWrqysWLVqErVu34vLly4UucN/3cCsTIiIy7iLwsumtoTUvXG6XdrXG9x83hyya1iltsPduUttw702ki//18ke3VmUL/fy3BtbAcw28IANRXNJqNarLHyJdmJubYdkXz8HKsnCnvC6Ollg8pfhnJZvCZ5E5QERExsZkC9xCtWrVsGXLFiQmJiq3w4cPY/jw4coClKLgbWZmhpo1az51O5s2bVJeI2Z/ExGRHNo18camOR1g95RrJMVsxL8Xd0XFMo9ZudFIC3eG8FwDT1QuJ8+/E6mbVmuGtV+3Re925Z/63HdfqYlvxzeGLFydrNC7ra9B3vu13lUM8r5ERdEswAN/LugEJ/snrzztVcoWe37qiqoVnCGLoc8bZizQoIa7Qa8kIyIiKogqOyOdP39e6c0o+nLb2trme2zQoEFKC5N69erlLTI5c+ZMBAQEoH///gbbZyIi0l2XlmVxdWtfLF5/GYvWXcSN6OS8x8TJmWhHMLBbJdjbWkAm4vJpcRn1gVOPW26yeMjSvoHoPhtrLdZ90w57jkRiweog/PH3daV37X1De/nhjZeqoWFN+S63F5/HtTtCSvQ9/co7ol1j7xJ9T6Jn1aaRN65seRE/b7yChWuCEBqZlPdY9UrOymdpcPfKcHxKEdzY+Ps6Ke3Vdh6MLNH35ViAiIiMkUnP4H6cs2fPPrY9iejXvWHDBrzyyivo0qWLsrDksGHDsHfvXlhayjXoISIiwNPdFh8OD0DIny/B0+3eoj+ebjY4tbYXRvStKl1x+77xQ2qV6PtVKutgsBmjRM/aq7Z9Ex+sn9UesYGD4OF6Lwe8StlgydRWUha3719R0bBmyfbEH/dKLeXfk0g2om/8hP/VVr70fjAWsMa59X0wun916Yrb940fUrtE308sTNm/c8USfU8iIqLCYIH7PyZOnKg8npCQgMzMTISEhODbb7+Fk5PxLzxGRERP7sUpbvd+1ki/rkKvtr54sUPJFZyXTGkJCwtVDhvIhDg5WCqtSwQzyTNAZNjiT1vC4v//PsWtdUMvDHuB7UnIlMYCZtKPBTo09cGrPf1K7P1++rSFcmUMERGRsVHl0elJBW6ZuVQvj2Zfj4SFvQ2Sw29j35tz4eJfFu2XT0JCcCR29J+GtDsJaPHdaHi1qo30OwnK6yL3ncGxaUuVnxt8PBi+zzdD7NkQ7Bk6E8YuJeQ0wuYPQ05qIixLlYfvO0uRdv08rkztAmufKvD7dAcsnEsjZtcS3Nw0C2k3glBm6Nfw6Pl23jZCZw9Bwqmd0Drdm8HlWKcDygz9Svk5/OfxiP13NWwr1UPlSRsN9vckIirI/EnNsPdYNGLi0gr9muiYlHx/FsbYl6ujlZEuvlfYY1+9iS+jfNfGyE7PRE5WNk7MWIHIvaeVbQSMewlVh3ZCSlSs8nvc5XAEjp6t/Fx9eHdUHdIJWclp2NRhPGQ9DkYs/RDxRzdBY2auvM6zzwdwbXWv9RqPg/Kq5e+KT0bWxUfzjhdrBthaa7F4SgujnL1d2Ay4z8nPBz3+monLy3biyORfpM8AXXLgSZ915oC8vpvQGDsPRSDyVkqx5sBrvf3RqXkZGCNdcqDKq51Q7bUuyM3KRm5OLrZ2m6iMDWQ+Ry5sBgRP7430Ww9aW6WGnkGliRvh3LgnIld+itvb5sPCzUd5zKZsDVQYt1z5+eYfs5THzGzsUf27Uwb7exIRPYkqC9x79uyBKWrx3Rjsf2c+Ys+HonL/tmg4+RVcXfW3clD/74D8/MJNuPDj1ke2IQ7i8ZduoFwXORbUFINx37E/w7ZigFLEDv/5Pbi3G6ocyB8++NpWqo+K49cget0XBW7Ho/f4fEXv+8TA3rpcDcQf5mCeiIxPaTcbrP+2HTqO3I609OxCvabhgE06vUfbRl748p2GkP3Yd/NwEE7PWofstAzlRLjLhqlYEzAcWanpyuPX1gfmFbseduGHLcoJbaOpQyDzcVAc53wGf678nHEnAudHV4NjQHtoHe+1uOBxUF4fvFYbpy7dwbqdocWSAeKKl5VftjbahXh1Gf9qtOZo9tVIhP155JHtyJoBuuTAkz7rT3qMOWDcnB2tsGFWe7Qb9ieSUjKLJQfEuh+z328CY1XYHCjbqSEq9mmJrd0mITMxBVZujsjJzJb+HLmwGVBp0oa8n5OvHMOVKZ3hWK9z3n2uzw1E2de/e2T7Hs+/A9uKdXFjccHZQURkDHitsYlwrVkBWSlpykFdCF6zF2U7NoCZpel+h5Fy7STMbeyVA7ng1uZV3D2yCblZGY8817ZCHdiUrQaNGf+XJyLT0rK+JzbP7aDMsNS3Ng298MecDrC20kp/7IvYc1IpbgtxQddFfwdYuxlnwa44joNae+e8n3NSk4DcXOTm5JTo/lLxEG0Wln3RGn3a6b9lkWh/svLLNujZpjxMYfwb8G5fhG4+iMRrUTAVuuQAma5GtUrhzwUd4Wiv/3VFRHF767yOsDPSNUt0yYGao3ri9LdrleK2IGZry34sLGoGxOxaDLfWg2BmIWf/eSKi/zLOM1bSmX250nCpWg49d967lFDQ2ljB1tO1wOdXe72r8u12ckQMTn65Mm9AIJP0myFIDTuLC2/fO5gLOekpysw0Xd3aPBt3di2BZaly8B74Wd4AgYhIBmIBvX9+7opBE//BpdC7etnmGy9VxbfjGxttcbsox777/Pq3QVLYTSSF3867r3z3pvBsVhPpcYnKTO/oA+dhasfBW5vn4Na2+ci8E47yY35SLld+8BiPgzKzsjTH6q/aYPL8E/jy5zPIycl95m2W9bTDr5+1QptG3jCFDHCv64dS9f2xo99UpSXJf8mYAUXLgcd/1pkDcmtRzxOBv3THoIl7cfZKnF62+b/e/pjzfhOjLW7rmgPOfmXgVqcS6rzbF+ZWFghe+w+CFm+T+hy5KOfEOempiAtciSrTA/PdH7d/LRLP/g2tgxu8XvoYDrXbFOu+ExHpk/GetZLObp+8gp0DPsv7vf+5xQU+78SMlUi5GafM3hKXWbVf/iHWN3tT+eZbNnZ+jeE35a+8308Pvtc3UBfegz6HhYuXMrs77uAGpVdZzYVXlG/CiYhk0aBGKZxc00spcH3z21kR8UVS3tteWVCybWPjLWoV5dh3n1eLWqgzri929JuWd9+l33bg9OzflX6cpRtWQZslE7Cly/tIDo+BKR0HS/cYq9xEr86QWYPgGNARWkc3HgdNhFg8c/pbDdCrbXkM+Xgfgq7FF3lbYjHJr8c1gqO9pUlkgLmNJZrMeB17X/+mwG3InAG65MCTPuvMAdNQ298VR1c+j2mLTmLGkjPIzi7aYMCntC1+/KQFurQsC1MaC4g2RQ7lSmN778mwdLZD5/VTkRh2E+G7jkt9jqzrOXHcgXWw8vaHjW+tvPtKdR4Jr74fQqO1QFLQfgR/0RtVvz4Kq9LGeQUPEdF/sV+DiRAz0ex87vXRFMQCG1pba6RE31ss52HKff9f+bj+5xHlEi2nynIUMh5m5VERGTHX837PTklAdloyLP9/YYzCEs+/37rEpWlvmNs4Ii3ikt73l4iouNlYa/HVuEa4tOlFvPtKTTg7FL441ahmKfwyrRWCNr4gTXFbl2Of4NG0Opp/Nwq7X5mh9OW8L/V2vFLYEm4dvYTYcyFwr1MJpnocFG27LF19kHhur/I7j4Om16rg1Npeyozu5xp4Fvp19rYWGNm3Kk6v640fPmkhRXG7sBngUN4T9j7u6Pz7p3jxyAJUH9ZNmaXZYvYYqTNA1xx40medOWBaV3R89mYDXN78IiYMrQU3Z6tCv7ZuVTelsC3GEbIUt3UZC4iZ2dc2/Ku0JUmPTUTE7hMoVd9P6nPkoowFRHsS9/av5bvPwsVTKW4L9tWaw7ZCXaRcPVaMe05EpF+cwW0ixOVTYoEMsfJz1L4zymrvIX/sR05G1iPPtfVyzVslvlQ9P1i5OiAhJBqyEZdNaswtlBXfHQM64Pa2BXBt0Q8arW4nZBkx4bB0v7cieNKlQ8hKvANrr8rFtNdERMXPr7wTvnmvMaaNro89RyJx/EIMjl+4g8thd5GangWtuRlcHK0QUMUV9au7o3ldD2XWlykf+zyaVEPLuW9iz5CZiLsQ9tjjokMFT7jW8L3Xp9uEjoOp1y/Aplx15ef0qGClZ6d12Xu/8zhoeiwtzPFSp4rK7VJIPAJP3MSxCzE4GXQHd+6mITMrB9aWWlQs44D61d2UHGjX2FuKonZRMiD+4nWsqvmgmCNalFg62eYtKilrBuiaA0/6rDMHTI9YGPbLdxphyqh6+PtIlJIBYjwgWpmlpmfD3EwDJ3tLBFS9NxZoVqc0Aqq6QaPRwFTHAqK47dMmANH7z8Hc2hKezWrg7Pw/pD5H1vWcOC3qqlK4rvxh/oVGH86AtMgrSAk5BZvyD2Z4ExEZOxa4Tci+0bPR4rvRsJwxDImh0dg3Zg5cqpQrcJVpm1JOyM3OQVZaBvYO+yZvoQ3ZVHh3OULnDMX1hW/AyqsyKry7DKlh5x55XszuXxC5/CNkJ8UpK8Df3Pg1Kn+0WVkNWqw6nXn3JjRm5jCztEGlCWthbudkkL8PEZE+2dpo0f25cspN7ce+5t+MgrmlBVrMGvXgtW/OVQpf9T54GW61KyozOHNycnBo4k9IkGQRusIeByN+naD06RSzszRmWpQbMU9ZfFngcdC0VangrNxef6EK1JwBTyJzBuiSA0/6rDMHTJdYS0PMxpZlRnZx5sCFRZvRdOYI9PpnljJZO2zrIYRtOSj9OXJhM0AQffadm74Ac9v8C21HLPsQKcHHoTHXKjlQbsR8WPv4l9DfgIjo2bHAbULESfqWzu8/9XlicR1TIfqGVfv26ZdOubcbotwK4j9tVzHsGRERGdOxb33zNx/72L9vzYOpHwcrf7zlsY/xOEhqyICHnfpmjclkgC458KTPOnOA1JAD2emZj/28y3yOXNgMEHwGTy/w/gpv/6rnvSIiKlnswW3isjOzYOXioKwqbe2W/1vagjT4eDBqvdkb6fFJkJW4HCsr4Y6yknRm/K1n2lb4z+MRve4LaO1c9LZ/RERkXMe+J6k+vLuyOF1abCJkweMgqZ3aM0BgDpDa6TMHZDxH1mcG3PxjFq5/Pwpahwe9zomIjI0mN/f/V1Igo5KZkobllQZBFgODl8HC1lpv20vNAlpug1QCuwI2vCaCyKiVab8SEbdS4FPaFuG7Bhh6d8iEjoOC2o+FshwHmQPyUHsOyJYBsuQAM0Aeas8AgTlARFQ4nMFNRERERERERERERFJigZuIiIiIiIiIiIiIpMQLR4yU1sZKubxJpv3VJ2vze5c2yUTsMxERqfM4KKj9WMjjIOmb2nNAtgwQmAOkT2rPAIE5QERUOCxwGymNRqPX3l2y0WjYt4uISM3UfhwUeCwktVN7DjADSO3UngECc4CIqHDYooSIiIiIiIiIiIiIpMQCNxERERERERERERFJiQVuIiIiIiIiIiIiIpISC9xEREREREREREREJCUWuImIiIiIiIiIiIhISixwExEREREREREREZGUWOAmIiIiIiIiIiIiIimxwE1EREREREREREREUmKBm4iIiIiIiIiIiIikxAI3EREREREREREREUmJBW4iIiIiIiIiIiIikhIL3EREREREREREREQkJRa4iYiIiIiIiIiIiEhKLHATERERERERERERkZRY4CYiIiIiIiIiIiIiKbHATURERERERERERERSYoGbiIiIiIiIiIiIiKSkNfQOUMFyc3ORlZoOWWhtrKDRaPS2vdxcIC0bUrE2B/T4T0BEKifbcUDfxwIeB0jt1J4BAnOA1E7tOcAMICKiwmKB20iJgczySoMgi4HBy2Bha6237YmBTMttkEpgV8CGnygiUulxQN/HAh4HSO3UngECc4DUTu05wAwgIqLCYosSIiIiIiIiIiIiIpISC9xEREREREREREREJCUWuImIiIiIiIiIiIhISixwExEREREREREREZGUWOAmIiIiIiIiIiIiIilxfV8iIjJZsXfTceDUTRw7H4MTQXdwKzZNuf92bBr6jd+D+tXc0aCGO5rWKQ0bax4SiUxNbm4uTl+KxdFzt3E86A6CrsU/yIG4NLw146CSAU1ql4ZfeSdD7y4RFYP4hHQcOH0Lxy/EKLeHxwJ9x+1G/eruyq1ZndKws7Uw9O4SERFREfBsnoiITK6gdfjMbSxYHYQ1O0KQnpH9yHMysnKw5q8Q5Sa4OFpiaC9/jOxblUUuIhOQkJSBpVuuKjlwITi+wOdkZOZgzooLeb83r+uBUS9VwwsdfGFlaV6Ce0tExUEUs0UGrNgWjLT0gscC63aGKjfB0d4Cr/b0wxsvVUO1is4G2GMiIiIqKha4iYjIZNyITsLwKfuxfX+4Tq+LS8jAt7+dU26iyD3z3YZwsLMstv0kouL7guu3TVfx9sxDiE/M0Om1+0/eVG4T59jhx09aoGOzMsW2n0RUfKJup+CNz/bjj7+v6/S6hKRMzF1xQbkN7eWHb99rDGdHq2LbTyIiItIfFrhNiGfTGui8fkq++zKTU5FwLQrB6/YhaPE25GbnwFQlnt2Lyx+1yXefmbUdrLz94dZ6MEp3fxMac/4vT2Sqft54WSlqiRPUZ/H92ov4c384fp7aEm0aeUM2PBbwWKBW0TEpeP3Tf7F1341n2s71qGR0GvkXhr1QBd+Obwx7yVoWMAOYAWq2clswRk8/oHxx/Sx+3ngFOw5EYPGUlujUXL4vu5gDzAEiIrVhqpuga+sDEb7nBKDRwKaUMyr3fQ6NpgyBk58PDo5fBFPn0moAnOp3FdO4kBkXjTt7f0P4kneRFh6E8qN/MPTuEVExzNj8aO5xTP/ptN62GRaZhI4jt+O3z57DgK6VICMeC3gsUJNr4QloP2w7QiIS9bbNH3+/hDOXY7FtQSe4Osk3i5MZwAxQm+k/nsKHc4/rbXsRt1LQdfQO5YqO//X2h4yYA8wBIiK1MDP0DpD+3Tkbgmu/B+Laun04v3ATtnabhOSIGPi/3A5Wbo4wdbYV68Gt9SC4tRkMzz7jUXXmIVi4lUHMzp+Qefe2oXePiPRs8vwTei1u35eVlYtBk/7Buh33+nTLhscCHgvU1JqozWvb9Frcvu/w2dvo/MZ2JCY/22xQQ2AGMAPUZMbi03otbt+Xk5OL1z4JxG+brkBGzAHmABGRWrDArQJZqem4feIKNGZmcCzvAbUxt7aDXZUmyjf36dHBht4dItKjtTtC8NkPp4pt++LEduDEvbgQHAfZ8VjAY4EpysrKQZ93dittRYrL0XMxGDblX8iOGcAMMFXbAm9g4uxjxfoe//skUFm0UnbMAeYAEZGpYoFbJRx87w1g0uOToEb3BzBae1dD7woR6cnt2FSM+vyATq85urInbuzsr/xZWBmZORjy8T6lkCY7Hgt4LDA1X/1yFsfOxxR7DqzeHoLfd8p5NcfDmAHMAFMTn5Cu8xdQRcmA7OxcDPloHzIysyE75gBzgIjIFJl8gTsmJgYTJkxA5cqVYW1tjbJly+Ktt95CcnIyXnvtNWg0GsybNw+mRGtjCStXB+WyM+eq5dB4+utwq1VR+bZeLCxi6nLSU5CVEKNcdpYaehbXvx+N1GsnYevXCNY+cvbPI9LXbOS7iRnKyaD4WXZvfXkIMXFpOr3G090WZTzslD91ncE5a+k5yITHAh4LCiKKM3fi05CWngXZXQqJx6cLT+j8uqLmwBufiYXr0iELZgAz4HHrViQkmc5YYPy3RxB5K6VEMuDc1ThM/1H/LdGKE3OAOUBEpBYmvcjkqVOn0KVLF0RHR8POzg7Vq1dHZGQk5syZg+DgYMTGxirPCwgIgCmpO6G/cntY6NZDODzxJ6hB1MpPlNvDnJv2QbkR8w22T0SGLgItXHMRv2y6ohS4BQc7CwzuXhmj+lVDjcoukE1IeCJWbb9Wou/57dJzeGtQDVhamEMGPBbwWPBwUXv9rlAsWB2EwBM38+6vX91dyYD+nSvC1ka+IeGsZeeVKyxKyu24NPzyxxW8M7gmZMAMYAY8LPhGAr5fcxFLNl5G7N17X9TYWmsxqHslJQfqVHGDbKJupyhjm5I0Z8V5TBhaW5rMZA4wB4iI1EKOI3MRZ2736NFDKW6PGzcOn3zyCRwcHJTHZs6ciffffx9arVaZwV27dm2YkktLdyB080GYWWjhUrUcao7uBTsvN2SnP1gg6bmF7wBmGvwz4tu8+yyd7dFr7ywcm/qbsuK2rNw7DYdLs77Izc5EathZRK//Ehkx4dBYWOc9J/F8IK5O7fLIa3OzMpCbk436G+S//JBIzNL6aO7xAhdgTEzOVIpd4vbO4Br4elxjmJlpIItF6y6K9oklKjomFRv3hOGlThUhAx4LeCwQroTdRdfRO3D1esIjj4l+smLxtI/nH8eWuR1Qt5o7ZCG+rFu25WqJv+/CNUF4a2ANKfKSGcAMuD8WELOOxef8v8fNlLQs/LDuknIb0bcq5k1sCq1Wngt8f1p/SVkQuiTFJWRg9V/XMLSXHLN/mQPMASIitZBnBKOjsWPHIjw8HGPGjMHXX3+dV9wWRMuSOnXqICsrC76+vnB0NK0VpBOuRSMq8Cwi9pzEuQV/YPerM+AeUAlNvxyR95yDE39E6YZVUKFX87z7mkx/HbeOXJR6ECNYefnBMaA9nOp3gWefCaj84WakXD2K6wtH5j3HoUZL1F2dlO9WY8FlaB3c4f3yNIPuP5G+jP/mSIHF7f+atfQ8Rn9+QDkJlkF2dg4Wb7hskPf+8fdLkAWPBTwWhEYkotXQrQUWtx8mLu9v/do2nL1878o2GYgrOJJTS77NypWwBPxzTI7L+pkBzADh0wUn8dG8R4vb/7Vo7UX8b3KgNGMBsZ+iwG0IP6y7CFkwB5gDRERqYZIF7qCgIKxevRru7u744osvCnxO/fr1lT9Fofu/NmzYgGbNmiltTZycnNC8eXOcP38esrp97BKC1+1TBi2lGlRR7suIT8KBcQvR+PPXYePhgvLdmsCzWQ0cfH8RTI19tWZwbT0Ycf+uRlJQwQvS5WSm49qMPrCv3gJefSeV+D4S6duuQxH45rfC94z+fu1FbNp7HTK4HJagc+9tfTl4+pZSYJcRjwXqOxaIYpW48qAwEpIyMeD9v6Upbv17Mtpg773/1IM2LzJhBqgvAw6evompi04W+vlLt1zFim33FuAzduE3k3E9Ktkg733sQoy06xgwB9SXA0REamGSBe6VK1ciJycHAwcOhL29fYHPsbGxKbDALfpzv/TSS2jRogU2bdqkbKt9+/ZITS3cCaKxOj1rHXKyslF3fL+8+yL+PoXQzQfQat5YNJkxTBnYpMeZ5mraXv0+BszMEblicoGPX18wEjmZafB965cS3zei4jB/VVARXnMBMhBtFQxFzBgVBXZZ8VignmPBheA4/H1Ut5nG54Pjse+44QrHujh+4Y4q3/tZMQPUkwFFHQuI1mUyMORYQLRFOXslDrJiDqgrB4iI1MIkC9x79uxR/mzTps1jnyPal/y3wC0Wnhw/fjxmzZql9Olu164dunbtiilTpqBBgwaQWWJoNEL+2A/vVrVRunG1vPuPTfkNDhU8lcvWwnefgKmy9qoM15b9kXhmt9Jn7WG3Ns/B3WNbUGniRphZ6baaOpExiryVXKTZ2DsPRiqLUBm7UxcNW1w6GSRvcYvHAvUcC0RP3aIQi9AZu9S0LFwMiVdtBj0LZoB6MuBOfBrW7gjR+XUHTt2Sol3RySDD7iNzQF5qygEiIjUxyUUmw8LClD/Lly9f4OOi9/b+/fsfKXAvWbIEFhYWGDZsmF73RxTHxWKXurDINcMnaKTX/Tgz+3flcjTxbf1fL36q3JeVmo6ksFuIC3q21gT+fv7I1Ojvsn2NpQ08vtPvquiefT9EbOBK5dv6Kp//rdyXeOZvhP/2Pvwm/wkrD99n2r6/vx9yM+Se6U+mIV3rixzHoUV6baNWvWCTaZj+1oUVZ9sTsL7XZuq/jq7sCU/3x5+QeLrb5P15Y2f/J75PdEwKGg7Y9Mj9o8aOx4QRR1DciuM4INOxoDiOA8V9LDCm40CMwyuARSWdX7duywEErhgMY5atsUOuy4THPq6vHHhcBoSF30aZMmVQ3NSeAQLHg0WXYe6DDKfhRXrtc50HwDbDuNszxtt2AaybGGws8N4Hn2DK2/fOJ4uT2nOAYwEiInXx9PTEsWPHivRakyxwJyff68f2uLYioj93TEyMsvBkhQoV8u4/cOAAqlSpgmXLluGzzz7DjRs34Ofnh8mTJ2PAgAFF3h9R3I6IiNDpNZYac8BDx/c5eB6/eL342MfvXonAb2UeXIqmT5FRkcjI1d8K0+Ibcx3/+nCo1Rr1/3h871CbstXyrYKdfjMU1756CWWGfKW89llFRkYiJz3lmbdD9MzsnYEirp0bG3sXSNAtr0pcmTTAuuCHxAltGQ+7p25Ca25WqOcV5O7dBNy9U/z/RkU5DpjSsaAoxwFDHwuM6jhQMRuw0P1lWdnQecxS4rROgMvjHy7uHMjN1ZTIv5HaM0DgePAZ2NoCTkV7aVxcIuLijTwHvFMMOhZISEhCwm3mQEE4FjCSDCAiUhmtqVb84+LicOLECTRt2jTfY1FRUUobEqF27drQaDT5HhMnLBMnTsSXX36JsmXLYvHixXj55ZdRqlQppRd3UfenKN/WQ6J1zLy9vPU+Y6c4iUFH8Be94NSoJ0p3G6OXbXp7e/PbejIKGea2uF3E17q7WMPKwQfGLN7GEslPmGn1JGK2ljihzcrOeerie4/blrOTHeysi//fSLbjgL6PBcV9HCiOY4ExHQfuWGShKEuxWpqlo5SPcWdAjsYGT+ourq8ceNx2zDTZ8CqBfyO1Z4DA8WDRZZrZ4pb4QSwc+9D5TmG4OVvB2s64c+CujRWSDDgWcHKwhb0lc6AgHAsYRwYQEcmoKPVTky5wi0J0UFCQUqTu0KED/P39lfuPHj2KwYMHK7O3hYCAgHyvEwtTJiUlYenSpejVq5dyn+jDfeHCBUybNq3IBe6iTK/PTEnD8kqDIIvLVy7DwvYx0yiKIDULaLkNxSbuwO9IDTmNtIjLyira/1Vj3gVYliqn0zYvX74CG5P8RJFssrNz4Nd9HUIiEnV6nTjhu348EBYWxr08w9e/nMX4bwtuEVLQZcQPE5cii9la4oS2bIdVRXr/9SsXok0jbxQ32Y4D+j4WFPdxoDiOBcZ0HFixNRgDJ+7V+XVfTnoJbw+eCmOWm5sLt5bLEJeQYZAcaBTgi4PL7q3lUpzUngECx4PP9jmp1We9snisLlwcLXHjyE7YWBvBX+IJvl8ThDc+O2CwscDSxd+gR2vd/t8oCrXnAMcCRERUWCYZvRMmTMCKFSuUFiM1atRA1apVkZaWhqtXr6JLly7w9fXFX3/9la//tuDq6qr8+XAhW8zwFr//8ovprqK8/YVPoDZubQYrNyJTZG5uhpF9q+L9747q9LrhL1Q1+uK2UL+6m0Hfv141d5giHgtMywsdfPH2TGvcjiv8PG4ba3O8+rwfjJ0Ym9Wv7o5dhyIN8v7ivU0RM8C0iM/JqH7VMHr6QZ1e979e/kZf3DaGz6GhxyLFhTlARESyMv5KRhGIhX8CAwPRrVs3WFtbIzQ0VCleL1q0CFu3bsXly/cWUPtvgVsUwx9HFMiJiGQx7MUqqFjGodDPFzOZRvevBhkYssBcuZwjnBwsDfb+RIVlZWmOqaPr6fSa94fWhoujFWTQoIa7agtrRIX1Sk8/VKvoXOjnl3a1xtuDakIGtf1dYaE1zKmsuOLNu3TRencTERFR8TDJArdQrVo1bNmyBYmJicrt8OHDGD58uLIApSh4m5mZoWbN/AO4559/Xvlzx44d+dqW7Ny5Ew0bNizxvwMRUVGJItWfCzqhrKddoU7U/lzQEaXdir/PoT6IAnPHZobpDdq3w4OFiYmM3ciXquHjEQGFe27fqpg8si5k8aKBPovii4MezxV/WwIifbC3tcC2+R1RqaxDodbg2LagE8oUYtxgDAz5WezbkWMBIiIiY2OyBe7HOX/+vNKTzs/PD7ZidfGH9OjRAy1btlQK4T/++KPSxqRfv37KayZPnmywfSYiKgp/XyccWtYDr/cRlxubF3hyOOR5Pxxe3hM1/e61aJKFuOy6pIk1ukb0rVLi70v0LKaOro9VM9ugbtWCL6evWsEJP37SAgs+apZv4W1jJ2ZRN6pZqsTf96WOFZRCIJEsfH0ccHBpD+VLLHvbR1uPWFqYYWC3Ssp4QbarEwwxFhDeeEmOK96IiIjURHUF7rNnzxbYnkQQJ3abNm3CCy+8gEmTJqFnz54ICwvDtm3b0LZtWwPsLRHRsxGX0P74aUtE7hqARZObw+H/T26d7C0Qsas/fp7WCuW87CGbbi3Lorx3ye5391blUN678G1fiIxFv84VcXz180oB67Mx9fOKXO7OVriw8QW8/kIVqYrb9xmirZIsrZyIHlbK1QYLP26OiF0D8NOnLfKNBW7s6I9lX7RGpbKOkE3bxl7Kl3Ql+p6NvHRq+0JEREQlgwXu/3B2dlZ6dd++fRvp6ek4cuQIOnXqVMJ7SUSkX86OVhj+YlU42lvmXbbs5izvLESt1gxzP2haYu8nZsB/816jEns/In0TBezGtUvjw+EBcPr/HBBXcchY2L5PzDptFlC6xN7v1Z5+yr8hkazEGOC1PlXyjQVkaU9WEJFf8yY2K7H3Ez2/v5vQpMTej4iIiArP+JfILuECt8xcqpdHs69HwsLeBsnht7Hvzblw8S+L9ssnISE4Ejv6T0PanQTluVVe7YRqr3VBblY2cnNysbXbRGSnZ6LBx4Ph+3wzxJ4NwZ6hM2HsUkJOI2z+MOSkJsKyVHn4vrMUadfP48rULrD2qQK/T3fAwrk0YnYtwc1Ns5B2Iwhlhn4Nj55v59vOrW0LcHvrXMBcC43GDFW/OgwzS2uE/zwesf+uhm2leqg8aaPB/p5E9KgercthUPdKWLYluNjfa/qbDeBXvmRniek7++tNfBnluzZWsj4nKxsnZqxA5N7Tedsp360xAsa9dK8XC4Ddg79AUvhtVB/eHVWHdEJWcho2dRgPWY8DEUs/RPzRTdCY3WvX49nnA7i26p+3HR4H5GNuboafp7ZCnb4bkJaeXazv5V3aFrMmNIbs4z/Byc8HPf6aicvLduLI5F9UkwGhs4cg4dROaJ3utbZxrNMBZYZ+lbcdZoCc2jXxVtqvfL/2YrG/1+SRAajl72qy54EynyMWNgeCp/dG+q2QvNelhp5BpYkb4dy4p/J73IHfEbnqUyA3V/m98kdbYOXhi5t/zMLtbfNhZmOP6t+dMtjfk4iIHk91Be49e/bAVLX4bgz2vzMfsedDUbl/WzSc/AqurvpbGdQ8fEJStlNDVOzTElu7TUJmYgqs3ByRk3nvxPDYtKWIv3QD5brIMVNRnKz4jv0ZthUDlCJ2+M/vwb3dUGUg8/Dgw7ZSfVQcvwbR6754ZBvxh/9A7D/LUXXmIZjbOSHz7m1ozC2Ux8SJj3W5Gog/zBMaImM0+/2mOHz2Nq6EPSjePEl0TEq+Pwujc/MyGDuwBmTP/puHg3B61jpkp2UoJ8JdNkzFmoDhyEpNh2vNCqj3wcvY/uKnSL0ZB62dNXJzcpTXXfhhi3JC22jqEMh8HPDoPR4+gz9Xfs64E4Hzo6vBMaA9tI7uPA5IvtbA7PebYMTU/YV+ja45oNVq8OtnrZTFe2XOAEGjNUezr0Yi7M8j+e5XQwbcz4H/TnIQmAFym/luQ/x78ibOXY0rtrHAcw088f7QOiZ9HijzOWJhc6DSpA15PydfOYYrUzrDsV5n5feUaycRsexD+E/bA0s3b2SnJEJjfu9LcY/n34Ftxbq4sfjR/CAiIuOguhYlpkqcmGSlpCmDGiF4zV6U7dgAZpaPfodRc1RPnP52rTJwEdLvJOSdxMhEDELMbeyVgYzg1uZV3D2yCblZGY8817ZCHdiUrQaN2aP/y0dv+Are/T9RTmgEC6dSeYMZIjJurk5W2Lmoc6H7cTccsAllO6xS/iyMlvU8sO6btjAz00if/RF7TirFbSEu6LoyS9Pa7V7P1Roje+D8os1KYUsQMzWzUx/NUpmPA1r7Bz1Tc1KTlNlZ9499PA7ITbRfmj62QaGfr0sOmJtrsPyL1mjfxAeyZ4AQ8G5fhG4+iMRrUfnuV0MGPAkzQG4OdpbYsagz/Mo7FstYQCxou2lOB1hYmJn0eaCs54hFzYGYXYvh1noQzCzutey5ufEbeDz/rlLcFsxtHWBmZVsCfwMiItIH1c3gNlX25UrDpWo59Nz54FJLrY0VbD0fvYzO2a8M3OpUQp13+8LcygLBa/9B0OJtkE36zRCkhp3FhbfvDWaEnPQUZWaeLtJuXEDy1WOIXDUFuZnpcGvzCkr3GFsMe0xExUEs/PjvL93R/c0dOH0pVm/bfb5NOayY0Qa2NlqTyP6H+fVvg6Swm0r7AcHZvwySbtxC5/VTYGFvi/Bdx3Hq6zVGf2Kr63Hg1uY5uLVtPjLvhKP8mJ+Uy5UFHgfkN/H1OnCws8A7Xx1CVta9S8ufldjesunPoWeb8jCFDHCv64dS9f2xo9/Ue61IHqKeDJiNO7uWwLJUOXgP/CyvIMYMkJ9XKVsE/tIdPd7cgaPnYvS23U7NfLD2m7ZKEd3UzwNlPUcsyjlhTnoq4gJXosr0wLz7Um9cgGVpX1ya9ByyUxLg1LA7vPt/yi+7iIgkYbxn7aSz2yevYOeAz/J+739ucYHPE5enOpQrje29J8PS2Q6d109FYthN5WRGNnZ+jeE35a+8308PvtdXURe52VnIuBmCKtP3ITs5ThnUWHpWhHPD7nreWyIqLmU87XBkRU9MW3QKXyw+jezsohe4HO0tlEWkhjzvJ8UCfIXN/vu8WtRCnXF9saPftLz7xMmbmAG28+XPlb9z218/QJVXO+Liz9thSscBUbASN9GrM2TWIDgGdITW0Y3HARMxZkB1tKjrgSEf73vmL7s6NPXGT5+2RDmvwl0dYuwZYG5jiSYzXsfe178pcBtqyADvQZ/DwsVLuZov7uAGpTdvzYVXlJmfzADT4OFmg/2/9sDMn89gyvcnkZlV9C9o7Gy0+OrdRhjRt6rRXsWl7/NAmc8RdT0njDuwDlbe/rDxrfXgzuwsZTa43yfbkZubg+DPe+L29oUo3W1Mce46ERHpiXFeZ0U6EzPx7Hzc834XC4xoba2REv3oCV5yRAyubfhXmZWTHpuIiN0nUKq+H2Rj5VERGTHX834X37RnpyXD0k23y4jFLB6XVgOUkzvRi9WpflckXzpUDHtMRMXJ0sIc08bUx5HlPdGnna/SWkDXk9nhL1bB+fUvYGgvfymK27pkv+DRtDqafzcKu1+ZofTlfPi4ELb1sNLCRPTkvr7tsDLT01SPA6JtlaWrDxLP7VV+53HAdARUdVO+7PrmvUao4OOg8+vrVHHFb5+3wl/fd5aiuF3YDHAo7wl7H3d0/v1TvHhkAaoP66b06W0xe4xqMkDcd79VnUvT3jC3cURaxKV7jzEDTIZoI/Lh8ACcWP08+nWuoPTQ14W1lTn+19sfZ3/vgzf6VZOiuK2v80BZzxGLMhYQ7Unc27+W7z4lB5q9ADMrG5hb28G5SR/mABGRRFjgNhGi55pYBMSrVW3ld7Hafcgf+5GTkfXIc8XAxafNvUu4zK0t4dmsBmLPh0E24rJSsQBQwqmdyu+3ty2Aa4t+0Gh1u4TQtdXLSDixPe9yNVHwEMUPIpJTveru+H1WO4T+2Q+fjKyLVvU9YW97b7Gw/yrlYq0sIjnngyaI2DUAiya3UGaDm2L2ezSphpZz38SeITMRdyF/5l/bEAif5+oofbk15mbwfq5OXi9PUzkOpF6/kPdzelSwMkvLumx15XceB0zvy653X6mFK1texNb5HfFy10qP7c0rvgir5eeC1/v4Y/9v3XFyTS8M7iHH1Ru6ZED8xetYVfM1rGs0Srld+HErrq7ag3/fmqeaDMiICc/7OenSIWQl3oG1V2Xld2aA6anp54pVM9vi+l/9MW1MPbRu6KVcofW49TzEVRviizExFlg8pSUqlNH9CzLZzwNlPUfU9ZwwLeoqUq4eg2urAfnuV3Lg5A6lwC+u6kg4tQM2vswBIiJZsEWJCdk3ejZafDcaljOGITE0GvvGzIFLlXKPPO/Cos1oOnMEev0zS6yxhbCthxC25SBkVOHd5QidMxTXF74BK6/KqPDuMqSGnXvkeTG7f0Hk8o+QnRSH+MMbcXPj16j80WZlNWyxmEjYghE4P7q6cmLn0vQFuDTva5C/DxHpjyhUfzqqHj4VBYucXFy9noBbsanIyMxRZmiV9bRDGQ87aQpZz5r9zb8ZBXNLC7SYNerBa9+cqxS+Qjbuh1utiveOC9k5uHk4CEE/GX/fTV2OAxG/TlD6dGq0FtCYaVFuxDxl8WGBxwHTZG5uhq4tyyo34W5iBi6F3kVKWpb4zwwHWwtUq+gMG2utKjLgSdSQAaGzhyDz7k1ozMxhZmmDShPW5i0qyQww7d7cHw2vq9zEWCD4RgJu3rk3FrCyNEcZD1vlag21jAWedB4o8zliYXNAEH34nZu+AHPb/F98urTsj5RrJ3DhzRqAmTnsq7dE6R5vldDfgIiInpXcI3rKRxQptnR+/6nPy07PzJuxIzvRN63at8ee+jz3dkOUW0HMLK1R4e1fi2HviMhYiEuM/X2dlJupKWz2r2/+5uMfzM3Fsam/KTdTPQ5U/njLYx/jcUAdnBws0aiW7mt1mEoGPOzUN2tUlwH+03Y99jFmgHrGAn7lnZSbqdHHeaDM54iFzQHBZ/D0Au8XLYzKDP1auRERkXzYosTEZWdmwcrFQVlV29qt4MtzH9bg48Go9WZvpMcnQVbicrSshDvKStqZ8beeaVvhP49H9LovoLVz0dv+EREZW/Y/SfXh3ZXF6dJiEyELHgdI7ZgBzAAifeaAjOeI+syBm3/MwvXvR0Hr8KDXORERGRdNbq64AImMTWZKGpZXGgRZDAxeBgtba71tLzULaCnHlbF5ArsCNrwmgoxcmfYrEXErBT6lbRG+K3/vQTIush0H9H0s4HGg+DAH5KD2DBCYA8WDGSAPtecAM4CIiAqLM7iJiIiIiIiIiIiISEoscBMRERERERERERGRlHjxjJHS2lgpl3fJtL/6ZG1+7/IumYh9JiJS63FA38cCHgdI7dSeAQJzgNRO7TnADCAiosJigdtIaTQavfYwlI1Gw95lRKRuPA7wOEDqpvYMEJgDpHZqzwFmABERFRZblBARERERERERERGRlFjgJiIiIiIiIiIiIiIpscBNRERERERERERERFJigZuIiIiIiIiIiIiIpMQCNxERERERERERERFJiQVuIiIiIiIiIiIiIpISC9xEREREREREREREJCUWuImIiIiIiIiIiIhISixwExEREREREREREZGUWOAmIiIiIiIiIiIiIimxwE1EREREREREREREUmKBm4iIiIiIiIiIiIikxAI3EREREREREREREUmJBW4iIiIiIiIiIiIikhIL3EREREREREREREQkJRa4iYiIiIiIiIiIiEhKLHATERERERERERERkZRY4CYiIiIiIiIiIiIiKWkNvQNUsNzcXGSlpkMWWhsraDQavW0vNxdIy4ZUrM0BPf4TEKmabBkoMAeZg6Rfas8BZgCRfDnAsQBzgIiIDIMFbiMlBnLLKw2CLAYGL4OFrbXeticGci23QSqBXQEbfqKIVJmBAnOQOUj6pfYcYAYQyZcDHAswB4iIyDDYooSIiIiIiIiIiIiIpMQCNxERERERERERERFJiQVuIiIiIiIiIiIiIpISC9xEREREREREREREJCUu/0BEZOIyMrNx7koczgfHISklU7kvJTULx87fRi0/V1hZmht6F4momN28k4rjF2IQcTM5LwdS07OU371L20Kj0Rh6F4moGGVm5uDCtTicvfJgLJCcmoUjZ2+jtr8LrK14WkhERETy4kiGiMgEJSZnYNmWYPy66QpOXryDjMycfI/HJWag4YBNsNCaoU4VVwzuXhmv9KgMZ0crg+0zEenXiQsxWLA6CNv3hyPiVsojj8fezUCZDqvg4WaDdo298cZLVdG8rgeL3UQmIjklEyu2BeOXTVdw/MIdpGdk53s8PjEDjQduglarQa3KrhjYrRKG9vKHqxPHAkRERCQXFriJiExIQlIGPllwAj+tv5w3Q+tJMrNycOx8jHKbOPsYhvbyw2dj6rPQTSSxvUej8MF3R3H47O1Cz+4WRTBxq+3vimmj66Fnm/LFvp9EVHyF7Wk/nML3ay/ibmLGU5+flZWrfBkubh/NO6586T19bAO4u1iXyP4SERERPSsWuE2IZ9Ma6Lx+Sr77MpNTkXAtCsHr9iFo8TbkZuefxWlKEs/uxeWP2uS7z8zaDlbe/nBrPRilu78JjTn/lyfTtetQBF77JBDXo5KL9PqUtCzMXxWEDXvC8OMnLdC1ZVnIhjnIHFQz8aXW+7OOKrO2i+rM5Vg8/9YuDOpeCbPfbyrlTE7mAHNAzQKPR2Po5H0IvpFYpNenpWfjx98v4Y+/w7Dwo+bo094XsmEGMAOIiEh9eGQzQdfWByJ8zwlAo4FNKWdU7vscGk0ZAic/HxwcvwimzqXVADjV7wrk5iIzLhp39v6G8CXvIi08COVH/2Do3SPSu9zcXEz9/iQ+XXhSL9uLvJWCbqN3YMLQWpjxdkMp2xUwB5mDahMWmYiOI/7C5bC7etmeaHH095Eo/PV9Z9So7AIZMQeYA2rz1c9n8P53R8V/8md2KzYNL7y7G2MGVMfs95vAzIxjAdkwA4iISE3MDL0DpH93zobg2u+BuLZuH84v3ISt3SYhOSIG/i+3g5WbI0ydbcV6cGs9CG5tBsOzz3hUnXkIFm5lELPzJ2TeLdzl2kQyEa1F9FXcftjMn89i7IxDSgFdNsxB5qCahEYkosWrW/VW3L5P9O1uNXQrzl2JhYyYA8wBNZm26CQmzNJPcfth81ZewLAp/yInh2MB2TADiIhITVjgVoGs1HTcPnEFGjMzOJb3gNqYW9vBrkoTZfZCenSwoXeHSK/mrjiPL5ecKbbtixPbGYuLb/slhTnIHDTlvvsdR25H+M2itSZ6mti76eg48i9Exzy6SKVsmAPMAVO1eP0lTJ5/oti2v2TDZUyefxyyYwYwA4iIyHSxwK0SDr73BnHp8UlQo/uDOK29q6F3hUhvLobEY/y3R3V6zdGVPXFjZ3/lz8KavOA4Tl28A9kxB5mDpui9b47gSlhCseZA1O0UjJy2X8qrOf6LOcAcMDUh4Yl468tDxT4W+GLxGRw6fQuyYwYwA4iIyDSxB7cJ0tpYwsrVIa/fXJVXOsKtVkVlxoJYXMXU5aSnICshRjkRz4qLxu3t3yP12knY+jWCtY+/oXePSC+ys3Mw9ON9SM/I1ul1nu62KONhp9NrsrJyMeTjfTiyoicsLcwhA+Ygc1ANdhwIVxaD01VRcuCPv69j5bZreLlbJciCOcAcMHWibYhYXDo5NavYM0C8lxgLnFzTCzbWcpxCMgOYAUREpB5yjE6eUUxMDGbOnIn169cjPDwcpUqVQp8+fTB9+nSMHTsWS5Yswdy5czFmzBiYgroT+iu3h4VuPYTDE3+CGkSt/ES5Pcy5aR+UGzHfYPtEhpWRmY31u0KxNfAG7iZmwtbGHPWruWNoL3+4u1hDRut2huLQmZLrn3j6Uqyy6Nz/estxQsQcZA7+17XwBPz0+2WlT3VWdg5Ku9qgX6eKaNvYS8qFVEXBQtcrOJ7VhFlH8FKnCtBq5bgAkDnAHHhYVlYO/vg7DJv2XkdcQgZsrM0RUMVNOa55uNlARtsCb+DvoyVXqL0Uehc/rb+EN1+uARkwA5gBRESkHiZf4D516hS6dOmC6Oho2NnZoXr16oiMjMScOXMQHByM2Nh7CycFBATAVFxaugOhmw/CzEILl6rlUHN0L9h5uSE7PSPvOc8tfAcw0+CfEd/m3WfpbI9ee2fh2NTflFXHZeXeaThcmvVFbnYmUsPOInr9l8iICYfG4kEhM/F8IK5O7fLIa3OzMpCbk436G3SbFUvGWwCas/y8clntzTup+R5bvT0EH88/gUHdKuG795vA3tYCMlmwOqjE33P+qgsY2stPimIgc5A5eF9YZCJGfX4Af/4b/sjia2L2cxVfJ8x8pyF6tikPmRw4dQtnLpfs4o9i0cnN/1xH73a+kAFzgDlw36K1FzF10UlE3srfS37NXyH4ZMEJ9O9cEXM+aAJnRyvIxBBjAfGeYwZU51hAAswAIiJSEzmm4DzDzO0ePXooxe1x48YhKioKJ06cUH7/8ssvsXXrVhw9elQZoNWuXRumIuFaNKICzyJiz0mcW/AHdr86A+4BldD0yxF5zzk48UeUblgFFXo1z7uvyfTXcevIRakHcoKVlx8cA9rDqX4XePaZgMofbkbK1aO4vnBk3nMcarRE3dVJ+W41FlyG1sEd3i9PM+j+k/6K26In5dszDz9S3L5PtPdYvOEyWv9vG+4mPjjZMXbnrsRi3/HoEn/fE0F3cORsyc0afxbMQeagcDn0LpoM2oxtgY8Wtx+ekdjr7V3KIm0yMURhS5i/yjDvWxTMAeaAMHH2UaWH/H+L2/dlZuVg6ZaraDlkK+7Ep0Gmq1K27w8v8fe9GHIXfx+Ro70HM4AZQERE6mHSBW7RfkS0JBGtR77++ms4ODjkPTZhwgTUqVMHWVlZ8PX1haOjI0zV7WOXELxunzJwK9WginJfRnwSDoxbiMafvw4bDxeU79YEns1q4OD7i2Bq7Ks1g2vrwYj7dzWSgg4U+JyczHRcm9EH9tVbwKvvpBLfRyqeIszcFRcK9dzjF2Lw8gd/Qxai1Yoa3/tZMAfVl4MpqVnoMuovRMcU/AXXw0Txe8S0/dh3LEqaL/AM9VkU7RCSUzIhI+aA+nJgyYbLmLH4TKGee+5qHF54d7c0i6k+6Yu74saxgJzUmAFERKQeJlvgDgoKwurVq+Hu7o4vvviiwOfUr19f+VMUuu9r3bq1MqO7oNvIkQ++7ZbN6VnrkJOVjbrj++XdF/H3KYRuPoBW88aiyYxhyuAuPc40VxT36vcxYGaOyBWTC3z8+oKRyMlMg+9bv5T4vpH+ZWbm4IvFp3U+UTwZFAMZHL9wx4DvLce/UUGYg+rKwZV/BuNaeGKhn5+dnYsZSwpXCDO04BuJBrvqRCw0d7qEW6PoE3NAPTkg/l+d/tMpnV7zz7Fo7D95EzIw5PGYYwF5qSkDiIhIXUy2wL1y5Urk5ORg4MCBsLe3L/A5NjY2jxS4FyxYgIMHD+a7ffTRR8pj3bt3h6wSQ6MR8sd+eLeqjdKNq+Xdf2zKb3Co4Klcuhe++wRMlbVXZbi27I/EM7uVXnMPu7V5Du4e24JKEzfCzMrWYPtI+rNpb9hjL0V+koVrLkIGhj2pvSPN7Lb/Yg6qJwfF/6NFaaUhLvcPvpEAY2fo4pKh3/9ZMAfUkwM7D0YoXwbJ0v6nKG3DDPne4gsEGTED1JMBRESkLiZb4N6zZ4/yZ5s2bR77HNG+5L8FbrEIZZMmTfLdxEKVpUqVQufOnSGzM7N/R052/hkLWanpSAq7hbig6zB1nn0/BMzM8s1YSDzzN8J/ex8VJ6yFlYcci2bR0xW1J6VYhE4G16MNN6tI9DPPypLzpFZgDqojB2Pi0nDyou7FH/HdzY4DETB216MMO7PwelQyZMYcUEcOmPxYwIA5kJicibtJ8qxd8l/MAHVkABERqYsmV9apeE9RtmxZpYB98uRJBAQEPPK46L3t5eWlLEQZHByMihUrFrid27dvw9vbG6NGjcLs2bOLtC8NGjRQFrbUhUWuGT7JaYSS0Pn3Kbix8zjOf7+pyNuYYnYEmZocve2TxtIGHt9dQXFKvxmKi+81hFf/T1C625hn3t7Nt/2Qm/H0Xq9U/GLt+iLVqqbOr9PkpsM7bjqMWS40iHT99LGPH13ZE57uj5914+luA625GbKyc57Ymzg6JgUNBxScCV6xn8MMxXtiW5IZKDAHTSsHs8xccdP5rSK91jFlJxzS/oUxS7BuhUTbdkXKAH3kgF3aYTinbENxU3sOlEQG6DsHjCUDhDi755FiVU/3F+bmwDtuCjQwbpEuHyJXY2mwsYBn3Fcwzy3+IjvPiTgWICIi9fD09MSxY8eK9FotTFRy8r3ZRampBR9cRX9uUdwWC09WqFDhia1ORDF88ODBRd4XUdyOiNBtRpilxhzwgDQioyKRkZutt+2Jy+KK86+fk56C4C96walRT70M5ITIyEhlu2QEytwFrHR/WW5Wqs6fVYNwyQZERhRAnNCW8bB76ibEiW1hnleQqMgb4h8LxUm2DBSYg0aUg9okwLloL02Iv42EO0aeA+5xwGNqV4XNgGfJgeTEu0iOLv5/I7XnQHFnQHHkgNFkgOAdX6SxAHIyECnDWMApCzC3NNhYIDoqHMgu/qs5ZMsBjgWMLAeIiEg1tKZc9Y+Li8OJEyfQtGnTfI9FRUVh/Pjxys+1a9dWFpB8nKVLl6JatWrKLOxn2ZeizFaA/r78L3beXt56n61QnOIO/I7UkNNIi7isrCT+XzXmXYBlqXI6bVPM9OdsBeOQZBmHu0V4nXVuNNx8fGDsonJTkKNxeOxsqyfRZdZWQTS5afDy9ij2mW2yZaDAHDSeHBRXOtzMjke2uQ5VbnFBm0aDUnZJsLQ27hxIttQi/jGPPS0D9JEDjnYaOJRAVqo9B4o7A4ojB4wlA4QUy3jEFeF1VjmRcJdgLBCNVGQ/5puu4h4LiC+5vT1doSnqN4kmnAMcCxhXDhARkVyKUj81+RYlY8eOxdy5c5VWJbt27YK/v79y/9GjR5XZ2NeuXUNmZiZGjx6NefPmFbiNixcvKsXt6dOnY+LEiSW6/5kpaVheaRBkMTB4GSxsrfW2vdQsoGXxX/2sV4FdARuT/cpILglJGfBpvxJJKbrNMv5zQSd0blEGxq7b6L+wLbBoPUJv7OyvzNYKv5mMsh1W6fz65xp4Yu+SbihusmWgwBw0rhyc/uMpfDj3uE6vqVfNDcdWPf/EL76NwelLdxDQd2ORX/+sObBjUWd0aFr8BUC15wAz4NmkpWfBp/0qxN5N1+l1675pixc6PP7qTmPx0nt7sHZHiEEyoEENdxxd+TxKgmw5wLGAceUAERGph8kuMjlhwgS4ubnhxo0bqFGjBmrVqgU/Pz80atRI6bfdtm3bRxaYLGj2tjjJHThwYAnuORE9K0d7S7zWu4pOr6lW0Rkdmxn/jC2hfnV3Vb43kS5e61MF9ra6nWG/PaiG0Re3heoVXWBlWXCbopIgvgggMnbWVlqM7FtVp9f4etujZ+vykEH96ob7HHIsQERERMbGZAvcZcqUQWBgILp16wZra2uEhobC1dUVixYtwtatW3H58uUnFrjFxPbly5ejdevWKFdOt8uyiMjwZrzdAK3qF+7yFncXa2z8rj3MzIy/sCW0aeilyvcm0oWHmw3WfNUWWvPCfa7feKkqBnWvDBlYWJihZT3DNKWt7e8KN2f9zU4kKk6fvlEPnQr55bWTvSU2zemgfL5k0KahtwHfm2MBIiIiMi5yjOCKSLQX2bJlCxITE5Xb4cOHMXz4cGUBSlHwNjMzQ82aNQt87b59+xAWFvZMi0sSkWFnbomWI/06P/ky4xqVnLH/1+7w93WCLFo39EIVA+xvOS87dJGghQvRfV1alsX2hZ2VYvfjWGjN8NHwAMyb1EyK2dv36TozVfb3JSoKUaz+Y04HvNrTT7TYfyz/8k7499duqOXvClk0rOmOulVLfhZ3aVdr9Gorxyx3IiIiUg+TLnA/zvnz55UZ2qJlia2t7WPbk9jY2ODFF18s8f0jIv2wtdFi1cy2uPjHC0rrAb/yjrg/Sdvayhy7fuiCs+v7SFXcFkQRblS/aiX+viP7VoO5uSoPGySxdk28cX1HP6ya2QZtG3nB/P9DQMzs/vzN+kov2mlj6ktzBcd9oo2Cd+mCxzDFxd7WAoO6VyrR9yR6VqKdzy+ftcKVLX0xfkgt5QvivLGApTm2L+yEoD9eQE0/eYrb98cCo/uX/Fhg2AtVDNoiiYiIiKggqqxUnD179ontSdLS0rBu3Tr06tULDg4OJbx3RKRvVSo4Y9aEJri8uS+8St0rCLk5WSmFL5lmbD5saC8/lPW0K7H3EzNgR3DmJknK0sIc/TpXxO6fusLT3Sbv/+lJwwKeOLvb2GemfjQsoETfc9wrNeFgZ1mi70mkL5XKOmLmu41wcdOLD8YCzlbo1LyMdF9w3fdy10qoXM6xxN7PxdESYwZUL7H3IyIiIiosFrgLIHp2x8fHY8WKFSW8Z0REhSOKTD992qLE3u/7j5vB1cmqxN6PiJ5OfOkkWhaVhDpVXDFp2OMX5iaikmdjrcXPU1s+sf2KPs2d2BSe7iV75QgRERFRYWihQk8rcMvKpXp5NPt6JCzsbZAcfhv73pwLF/+yaL98EhKCI7Gj/zSk3UlAmyXj4VC2dL7X7Rk6Ezd2HEP14d1RdUgnZCWnYVOH8TB2KSGnETZ/GHJSE2FZqjx831mKtOvncWVqF1j7VIHfpztg4VwaMbuW4OamWUi7EYQyQ7+GR8+387YRPL030m+F5P2eGnoGlSZuhHPjnrj5xyzc3jYfZjb2qP7dKQP9LYkK1rFZGaXAtWjtxUK/JjomJd+fhTGwWyX0ausLmXOv3sSXUb5rY2SnZyInKxsnZqxA5N7TyjYaT38dHg2r5G3TqbIPjn22DEGLt8G3ZzMEjOsLWw9XrKj6KmTOwYilHyL+6CZozO5dWu7Z5wO4tuqv/MwclJOYdbpkSkvU6bsBicmZxZYDlhZm+GVaK2UmvMw5cJ+Tnw96/DUTl5ftxJHJv0ifA4XNgNDZQ5Bwaie0TqWU1znW6YAyQ79SfmYGyKtFPU+8M7gmvv3tXLGOBUTfbTFjXPYMqPJqJ1R7rQtys7KRm5OLrd0mKmMD2c+PCpsDT/qsX/9+NJIu7s97LC38Isq8OhOle4xFbOBqRK2agsy4SASsiDfQ35KIiOjxVFng3rNnD0xRi+/GYP878xF7PhSV+7dFw8mv4Oqqv5WB3cODsb//d+9kRnCrUwkdVnyIiL/vnbBc+GELYs+GoNHUIZCBOFnzHfszbCsGKEXs8J/fg3u7ocpA7uGTMNtK9VFx/BpEr/vikW1UmrQh7+fkK8dwZUpnONbrrPzu8fw7sK1YFzcWPyiIExmTOR80QVhkErbvDy/U8xsO2KTT9p9r4IkfPym5meLFlXs3Dwfh9Kx1yE7LUE5au2yYijUBw5GVmo7Dk37Ke55NKWe8cHg+QjcdUH4Xf8acuIKeu76G7Dno0Xs8fAZ/rvyccScC50dXg2NAe2gd3ZmDEqtQxgGb5nRA5zf+QnpGtt5zQBTRRf/yAAMsZqfvHBA0WnM0+2okwv48ku9+mXOgsBlwPwce/pL/PmaA3L58uyFCIhKxYXdYsYwFGtcqhaXTnzPatm6FzYCynRqiYp+W2NptEjITU2Dl5oiczGyTOD8qbA486bNebuT8vMcy46JxdngFuLR4SfndtWU/2Pk3RkRi270AAE44SURBVNA7Jdsai4iIqLBU2aLEFLnWrICslDRlYCcEr9mLsh0bwMzyyd9h+A1oi2u/70NOZhZkk3LtJMxt7JWBnODW5lXcPbIJuVkZjzzXtkId2JStBo3Zk/+Xj9m1GG6tB8HMgj1GSQ5iRuX6We3QvVVZvW+7Q1NvbJnXUbkEWvbci9hzUiluC3FB18XqXLB2e7RvaaWXWiPyn9NIvR1vcjmotXfO+zknNQnIzUVuTs4jz2MOyke0Kdk2vyPsbPT7WRUzt9d+3Ra92xnnFRxFGf8EvNsXoZsPIvFa1GO3KVMO6JIBhcUMkI9Wa6Z8EdW3YwW9b7tFXQ/89X1nZZFZ2TOg5qieOP3tWqW4LaTfSSjwOCjb+VFRc+BJn/U7e36FY91OsHDxLLb9JiIi0ifjrFqQzuzLlYZL1XLoufPB7AOtjRVsPR+/Iry5tSUq9mqBbb0+hozSb4YgNewsLrz9YCZBTnqKMjOxKHLSUxEXuBJVpgfqcS+Jip8oQG/4rj2+XHIGU74/icysR0/WdGFursGk1+vgo+EBRtuSoKi5J/j1b4OksJtICr9d4GNHp/4GU83BW5vn4Na2+ci8E47yY35SLld+GHNQXm0be+Poyucx5KN9OHLu0f+3dVW9kjN+/awVGtS4187CFHLAva4fStX3x45+UxEw7t6sxILIlAO6Z8Bs3Nm1BJalysF74Gd5BbEHr2UGyEocr1d+2VqZbf3RvONISy/cFR1PunpDLCw7dXQ9WFtpTSIDnP3KKLOz67zbF+ZWFghe+4/Shkj286OinBM97bMuZoGLto5ERESyMN7RCuns9skr2Dngs7zf+59b/MTnl+/eBHevRSL+4nXIys6vMfym/JX3++nBRT8RjzuwDlbe/rDxraWnvSMq2dlbHw4PQM/W5TB86r84dKZoBa561dyUliT1qrvDFHPPq0Ut1BnXFzv6TXvksdKNqyn9OyN2n4Sp5qDooyluoldnyKxBcAzoCK3jg9YTzEG5VavojP2/dcc3v53FZz+cRlJK4fty32dtZY53BtXEJ2/UhZWl8X7BpWsOmNtYosmM17H39W+euC0Zc6CwGeA96HNYuHgpV7PFHdyg9OatufCKMvPzPmaA3MzNzTDu1VrKVV3Dp+7HvuPRRdpOLT8X/DC5BZrUyf8lqOxjAdGiyKFcaWzvPRmWznbovH4qEsNuInzXcenPj3Q9J3rSZz3xfKDSy9upftdi2VciIqLiwAK3iRCzEe18HhSkxMmZ1tYaKdGxj32N/4B2uLJS3n7kVh4VkRHzYPCZnZKA7LRkWLr5FGl74jI99/av6XEPiUpeLX9XHFjaA0fO3saC1UFY/VfIU/vyWmjNlMuaR/WrhmYBpY22x+az5p5H0+po/t0o7H5lhtKX87/8B7TF1bV7C7xc2dRyULRtsnT1QeK5vXBp9kLe/cxB0/iy6/3/1VE+z8u2BCs5cO5q3FNfV6msA954qRqGPO8HN2dryKKwOeBQ3hP2Pu7o/Punyu+WjnaAmQaWTvb496150uaALhnw8H0uTXsj4rcPkBZxCXaV6+fdzwwwDVUqOOOfn7vh+IUYLFwdhBV/BiM17cljAa1Wg95tfZXsEOtvmOJYIDkiBtc2/Kt8vtNjExGx+wRK1ffLV+CW8fyoKGOBJ33W74jWJW1fhcZcji85iYiIBBa4TYToOycWSfFqVRtR+84oK32H/LEfORkF945z8PWEW52K2P3qDMhKXFarMbdAwqmdcAzogNvbFsC1RT9otLr3jEyLuoqUq8dQ+UPdFt0hMkbipLRx7dLKbdHk5jhzOU45yRVFLjGjMzcXSq/emn4uqF/NHXWquBptn2195Z5Hk2poOfdN7BkyE3EXHl2ES5wQi1lb/12QzpRyMPX6BdiUq678nB4VrPTstC5773eBOWhaHOws8Ua/ahj5UlVE3krBsQsxSg5E3EpRvvQS/bU93WxRv7ob6ld3R3lve2kKWkXJATEbc1XNB8Uc0aLE0skWRyb/InUO6JIBGTHhsHQvo/ycdOkQshLvwNqrct7jzADTIz7bP01pifkfNsPZK7E4fuGO8mdi8oOxgLjyo0ENd9Txd4WdkfbZ1tdYQBS3fdoEIHr/OaUViWezGjg7/w/pz490PSd60mddFMfF7O5qs+S5ioWIiEiQr6JBj7Vv9Gy0+G40LGcMQ2JoNPaNmQOXKuUKfK5YPCVs62FkJqVCZhXeXY7QOUNxfeEbsPKqjArvLkNq2LlHnhez+xdELv8I2UlxiD+8ETc3fo3KH22GbcW6yuOiH6Vz0xdgbvvoonNEMhN9MxvVKqXc1Jx7zb8ZBXNLC7SYNerBa9+cm3cJcoVezXHnzDUkhhTtcm4ZcjDi1wlKn06N1gIaMy3KjZinLL57H3PQNImitY+HnXJ7vk15qH388ySy5kBhMyB09hBk3r0JjZk5zCxtUGnCWpjbOeU9zgwwXaLdkOinb+w99Ys7Ay4s2oymM0eg1z+zlAJ/2NZDCNty0CTOjwqbA0/7rMcGroJtpfqw9vYrgb0mIiLSHxa4TYgo1Gzp/H6hnnviixUwBaJvXLVvjz31ee7thii3x/EZPF3Pe0ZExpR765u/+cTHLy/bpdxMOQcrf7zliY8zB0kN45/7Tn2zxmRyoLAZ4D/tyX83ZgCZegZkp2fma0lkSudHhc2Bp33WS3UartyIiIhkY2boHaDilZ2ZBSsXB2VlcWu3p8/IqT68u7IIU1psImQlLsfLSrijrCSeGX/rmbZ1849ZuP79KGgd5Fhwj4h0z70n8e3ZDO1++wCpt+MhE+YgqZ3ac4AZQGqnzwyQ9fxInzkQG7gaVz/vAa2Th972j4iISJ80ubniAi0yNpkpaVheaRBkMTB4GSxs9bcoVWoW0HIbpBLYFbDhNRFGr0z7lUoPWp/StgjfNcDQu0MmkoECc1COHGQGyEPtOcAMKD7MAXnIlgMcC8iTA0REZFo4g5uIiIiIiIiIiIiIpMQCNxERERERERERERFJiRcPGSmtjZVyiZtM+6tP1ub3Lm+TidhnIlJnBgrMQeYg6Zfac4AZQCRfDnAswBwgIiLDYIHbSGk0Gr32b5ONRsPebURqpvYMFJiDpHZqzwFmABFzgDlARERUOGxRQkRERERERERERERSYoGbiIiIiIiIiIiIiKTEAjcRERERERERERERSYkFbiIiIiIiIiIiIiKSEgvcRERERERERERERCQlFriJiIiIiIiIiIiISEoscBMRERERERERERGRlFjgJiIiIiIiIiIiIiIpscBNRERERERERERERFJigZuIiIiIiIiIiIiIpMQCNxERERERERERERFJiQVuIiIiIiIiIiIiIpISC9xEREREREREREREJCUWuImIiIiIiIiIiIhISixwExEREREREREREZGUWOAmIiIiIiIiIiIiIimxwE1EREREREREREREUtIaegeoYLm5uchKTYcstDZW0Gg0ettebi6Qlg2pWJsDevwnIJWTLQP0nQPMACL5coBjAeYAqTsDBLXnADOAiIjIMFjgNlJiMLu80iDIYmDwMljYWutte2Ig23IbpBLYFbDhJ4pUmgH6zgFmAJF8OcCxAHOA1J0BgtpzgBlARERkGGxRQkRERERERERERERSYoGbiIiIiIiIiIiIiKTEAjcRERERERERERERSYkFbiIiIiIiIiIiIiKSEgvcRKQa2dk5yMnNVX7O/f8/iUg9xOf+/mefGUCkThwLEBEREZkervFMRCbr1MU72PzPdRy/EINj52MQcSsl77GomFS0eW0b6ldzQ+uGXujSogzMzfmdH5EpSUnNwvrdoThw6iaOX7iDM1dikZaerTwWeTsV/j3WokF1dzSo4Y4XO1RAOS97Q+8yEenZuSux+OPv6zgeFKOMB65HJecbCzw3dCvqV3fDcw280LVFWVhYcCxAREREJBsWuInIpGRl5WDV9mtYsDoIB0/feuzzxKStvUejlNs3v51DOS87jHixKkb0rQo3Z+sS3Wci0q9r4QmYu+ICfvnjCuITMx77vCthCcpt5Z/XMP7bo+jWsizGDKiGDk19oNFoSnKXiUjPs7TX7QxVxgL7jkc/cSwgHhe3WUvPw7u0LYa/UAVvvFQNpd1sSnSfiYiIiKjoWOA2IZ5Na6Dz+in57stMTkXCtSgEr9uHoMXbkJudA1OVeHYvLn/UJt99ZtZ2sPL2h1vrwSjd/U1ozPm/vCm7EByHIR/vw9FzMTq/Vszo+nDucXy37DwWftQML3SoABkxB5gDai9qiSLVR/OOIz3j3kztwsrJyVWu+BC3F9r7YsGHzaQscKk9AwTmgLpdCbuLoZMDsf/kTZ1fG3krBZ8uPIk5Ky5g7gdNMaBrRSm/7FJ7DjADiIiI1IdHdhN0bX0gwvecADQa2JRyRuW+z6HRlCFw8vPBwfGLYOpcWg2AU/2uyrSczLho3Nn7G8KXvIu08CCUH/2DoXePisnsZecwYdZRZGQ+2wnb7bg0vDhuDwZ0qYifPm0JWxs5Y5I5wBxQm/DoZLw0fs8Tr9worN93heKf49H49bNW6NqyLGSk9gwQmAPq88O6i3h75iGkpun2Bdd/xd5Nx8CJe7FuV4iSAw52lpCR2nOAGUBERKQebDJngu6cDcG13wNxbd0+nF+4CVu7TUJyRAz8X24HKzdHmDrbivXg1noQ3NoMhmef8ag68xAs3MogZudPyLx729C7R3omFoiaOPso3p55+JmL2w8TLQs6jdyOxOTHtzcwZswB5oCaBN9IQIshW/RS3L4vJi4NPcfuxIqtwZCR2jNAYA6oy2c/nMSIqfufubj9sA27w9D29T8Rl5AOGak9B5gBRERE6sECtwpkpabj9okr0JiZwbG8B9TG3NoOdlWaKLM30qPlLFTQ401bdAozFp8plm3/e/Imeo7dhbT0LMiOOcAcMFWRt5LRftifCItM0vu2s7NzMfjDf7BxTyhkp/YMEJgDpuvb387i43knimXbYpHqrqP+QnJKJmSn9hxgBhAREZkuFrhVwsH33iA2PV7/BQAZ3B/Eau1dDb0rpEd7DkfikwXFc0J7n1iEUvTjNAXMAeaAKV7BIfruhxZDcfvh3tyvfrQPN6Ll/9yoPQME5oDpOXT6lrJIbLG+x5nb+GD2MZgCtecAM4CIiMg0ydlclp5Ia2MJK1eHvH57VV7pCLdaFZUZG2JxGVOXk56CrIQYpfCRFReN29u/R+q1k7D1awRrH39D7x7pSVJKJl77JFDn1x1d2ROe7raIjklBwwGbCvWar345i95ty6Nx7dKQBXOAOaAGP/1+CTsPRhZ7BiQkZWLYp//iz4WdpFlwTu0ZIDAHTJ+4wmro5H3KF1HFnQPzVl5QFqBt3dALslB7DjADiIiI1MPkC9wxMTGYOXMm1q9fj/DwcJQqVQp9+vTB9OnTMXbsWCxZsgRz587FmDFjYCrqTuiv3B4WuvUQDk/8CWoQtfIT5fYw56Z9UG7EfKjZ9agkXAiOR1pGFtycrNGkdmlYWMh7EceUhSeLNGtTnNCW8bDT6TXixHnYlH9xel1vaYpbzAHmwH9lZeXg8NnbiIlPg6XWDFUrOKNCGQfI6k58GsZ9c6REMkD460AEVm+/hv5dKkEGas8AgTlQ8GKs54PjkJqeBRdHK2UsYGVpDll9ueQMLobcLbEcEGOBi3+8AHNzOcZPas8BZgAREZF6mHSB+9SpU+jSpQuio6NhZ2eH6tWrIzIyEnPmzEFwcDBiY2OV5wUEBMCUXFq6A6GbD8LMQguXquVQc3Qv2Hm5ITv9wWJ5zy18BzDT4J8R3+bdZ+lsj157Z+HY1N+UVddl5d5pOFya9UVudiZSw84iev2XyIgJh8bCOu85iecDcXVql0dem5uVgdycbNTfoL8Figxt+7/hmLfqArYF3hAtB/N4lbLFsD5VMKp/NXi42UAmog/mj+svleh7nr0Sh7+PRKFtY2/IgDnAHHh4scSFa4Lww7pLCL+ZnO+xjs18MLpfNfRoXU6aL2/u+3njFSQml2xP3NnLz0tT4FZ7BgjMgfwtveauvIBNe6/nm+1cysUar/epgtH9q8GnCAVfQ8rIzMaC1UEl+p5XrydgW2C4kpkyUHsOMAOIiIjUQ47pB0Wcud2jRw+luD1u3DhERUXhxIkTyu9ffvkltm7diqNHjyon9LVr14YpSbgWjajAs4jYcxLnFvyB3a/OgHtAJTT9ckTecw5O/BGlG1ZBhV7N8+5rMv113DpyUeqBrGDl5QfHgPZwqt8Fnn0moPKHm5Fy9SiuLxyZ9xyHGi1Rd3VSvluNBZehdXCH98vTYArE5ZjvfnUIXUb9ha378he3hajbKZi66CTq9duI05fuQCYrtgXjbuKDk7OSUtIn0s+COcAcEIKuxaN+/42YPP/EI8VtYceBCDz/1i6M+uwAsrNzIAtRoBNF+5Im+vCeuBADGag9AwTmwL2xwMfzjqPdsD+xcU/YI608bsel4YvFp1H3pY04cvY2ZPL7zlDcik0r8fedv+oCZKH2HGAGEBERqYfJFrhF+xHRkkS0Hvn666/h4PDgMuwJEyagTp06yMrKgq+vLxwdHWHKbh+7hOB1+5SBa6kGVZT7MuKTcGDcQjT+/HXYeLigfLcm8GxWAwffXwRTY1+tGVxbD0bcv6uRFHSgwOfkZKbj2ow+sK/eAl59J8EUiBPaWUvPP/V5kbdS0HHEdoSEJ0IWS7dcNcj7bvw7DAlJJV9Y1wfmgPpyQLQi6DD8T1yPerSw/V/fr72IcV/r3u7DUA6cuolrBsqs3zYbJn+eldozQK05MGPxGXz2w6mnPk8Uuju/sR0XQ+IhC0ONBUS7opt3UiEjteeAGjOAiIhILUyywB0UFITVq1fD3d0dX3zxRYHPqV+/vvKnKHQ/LDAwEO3atVNe6+zsjCZNmij9u2V3etY65GRlo+74fnn3Rfx9CqGbD6DVvLFoMmOYMrhNjzPNFdW9+n0MmJkjcsXkAh+/vmAkcjLT4PvWLzAFwTcS8PmPpwv9fDED6sO5xyADMcv0+AXDzDjPzs7FyYtyzXZ/GHNAXTnw6cITiLiVolP7jbOX77XuMnZHzhlupunR83LNcn2Y2jNAbTkQcTMZH88/XujnxyVkYMK3RyHLzHRD5sAx5oC01JQBREREamKSBe6VK1ciJycHAwcOhL29fYHPsbGxeaTAffr0aXTo0AHm5ub45ZdflCJ52bJl8eKLL2LLli2QWWJoNEL+2A/vVrVRunG1vPuPTfkNDhU8lUsXw3efgKmy9qoM15b9kXhmt9Jr72G3Ns/B3WNbUGniRphZ2cIUfL/mos6vWbczVIoZSWIxqZS0LIO9/7HzcrQnKAhzQD05EJeQjhV/Buv8OkO0/SgKQ33JJYgvucSCnTJSewaoLQd+/P2S8sWsLrbsu46wSOO/oissMgl34tNVmUHPSu05oKYMICIiUhOTLHDv2bNH+bNNmzaPfY5oX/LfArcoaIue3Bs3bkT37t3RqVMnrFq1SilyL1++HLI7M/t35GTnn7GRlZqOpLBbiAu6DlPn2fdDwMws34yNxDN/I/y391FxwlpYefjCVPyy6YrOr8nMysHKbboXxEra+eA4Vb//s2IOqCMH1u0IQWqa7gtjLd0SLEUvbkN+DsW/a2ikvDMb1Z4BasqBoowFxHody7ZwLGDs7/+s1J4DaskAIiIiNdHkimv8TIwoSIsC9smTJxEQEPDI46L3tpeXl7IQZXBwMCpWrKjc/9577+HHH39EfHy8Uui+r2bNmqhRo4ZSAC+KBg0aKItb6sIi1wyf5DRCSej8+xTc2Hkc57/fVORtTDE7gkyN/ooiGksbeHyn+4mZLtJvhuLiew3h1f8TlO425pm3d/NtP+RmGH4GdC60iHT9uEivtU89AKfUv2DMki3rIt6+V4GPHV3ZE57uT55x4+luA625GbKycxAd8/j/XtExKWg44NHPhE36Obgmr0VxK8kMMMYcKIkM0HcOGEsGCAk2bZBo07pIr/WMmwHzXOP4ezxOtNNYZJu7FSkHCpsBT8qBUncXwjJbt+N6UXAswBwoKjG4j3SZDGjMdX6tbdoxuKRshjFLsayBOPuXDDYWsMq4DPek4p/8ovaxgIznBMaSAURERDLy9PTEsWNFa5+rhQlKTr63oFZqasGDC1GoFsVtsfBkhQoV8u4fPHgw5s+fj3HjxuH999+HVqvFokWLcOXKFSxYsKDI+yOK2xERETq9xlKckHhAGpFRkcjI1X224OOIywKL86+fk56C4C96walRT70Ut4XIyEhluwan0QKuRXtpUlIikqJ1+3+1xLn4AgV3HlJOaMt42BVqM+LEtrDPfZjIFV0/z0UhWwboOweKOwOKIweMJgOE0onAvU5cOouOigKyn74wpUHZZQHmz5YDRc0A4fatm0Aac0D2sYDJ54BL0V6WkpyElEgjHws4eRl0LJCexrGAqeSASWcAERGRimhNteIfFxeHEydOoGnTpvkei4qKwvjx45Wfa9eunW+mtmhXsnv3bvTp0wezZs1S7rOzs8PatWvRqlWrZ9qfoszYgPFfJZ7H28tb77M1ilPcgd+RGnIaaRGXlZXU/6vGvAuwLFVOp216e3sbzYyNqJwk5Jg95szvCZxsc2Dv4wNjlmphi9gnzLR6Gl1mbRXE1sYcLiXwbyRbBug7B4o7A4ojB4wpA5KtchBfhNdpctPg5ekCDZxhzG6Z5yCziDmg6wzugniUcoE2p/gvgJMtB2QbC5h6DkTnJCDbXPcqt6NNNhyMfCyQZmGPOwYcC9hYmcOVYwGTyAFTzgAiIiLZFKV+atItSsaOHYu5c+cqrUp27doFf39/5f6jR48qs7SvXbuGzMxMjB49GvPmzct7nZip3bZtW6WtyahRo5TFJkXvbVHgFotMisdKSmZKGpZXGgRZDAxeBgtba71tLzULaLkNUgnsCtgYyVdG7886gpk/n9XpNZYWZrixoz9KuxV/QeFZXAiOQ43e64v8+hs7+yuztcJvJqNsh1U6v/6b9xrh3VdqobjJlgH6zgFmwLOJT0iHT/tVOi/IOrp/Ncyb1AzGbtDEvVi+NdggGWBrrUXCwcEwNy/+ZUxkywGOBYwrB6Z+fxKfLNBtsUAzMw1C/nwJ5bx0/5K8JImFMH07ryny6581B6aOroePR9RFcZMtAwS154AxZQAREZGamOQikxMmTICbmxtu3Lih9M6uVasW/Pz80KhRI6Xf9v1C9cMLTAqTJk2Cra0tNmzYgC5duqBjx4749ddf0bhxY6VtCZEsRvStiocuTiiUvh0rGH1xW6ji6wQ7A5451K/ubrD3JiosZ0crDOxWSefXvfFSNcjAkJ/DgKquJVLcJnpWr/fxh9Zct8FAj+fKGn1xWxD76OZsZbD351iAiIiIyLiY5BlamTJlEBgYiG7dusHa2hqhoaFwdXVV+mlv3boVly9fLrDAffbsWeU+0Xv7v4tEBgUFlejfgehZVCzjiMk6zCwSl+p+/mZ9yEAUlgx1YqnValC3asEL2xEZm0/fqIuynoXvLTvulZqoUbmITXtLWKOapVT53kS68C5th+ljGxT6+aJgPPOdklvQ8FmIFoONaxnmsygmEDRggZuIiIjIqJhkgVuoVq2a0lYkMTFRuR0+fBjDhw9XFqAUBW8zMzPUrFnzkV4vp06dQlZW/ku6RWsTHyPvRUj0X5+8URfv/6/2U58nLtHduagzyns7QBav9KhskPft3dYXjvaWBnlvoqIUt3b90AUVfJ7+2X7z5eqY+a4chS2haZ3SqFTWMJn1ak8/g7wvUVG8N6QWpoyq99TnebjZ4K/vO8Pf1wmyeKWHYT6LnZuXkeKKNyIiIiI1MdkC9+OcP38eou24aFki2pE8TPTkFn24e/furRTH//zzT6Vn9z///IO33nrLYPtMVNTZTTPebqgUuHq1La/01fxvYXvamHo4sfp51PRzhUwGdKkEZ4eSLzSP6idH+wai+0Sx6tiq5zHj7QYo7/1o24HurcrizwWdMPv9Jo9khDET+2qIdiqisB7AqzhIsrHA5JF18c+Srkorsv+2LPEqZYtPRtbFqbW9pGu70btdeaUwX9I4FiAiIiIyPqorcIs2JAW1JxH69u2LzZs3Iz4+Hq+++ioGDBiAS5cuKQtNioUriWTUrok3NnzXHtf/6pfXr7KUi5WyiNRHw+uilKt8s5BsbbRKn/GSVKeKK55rUPQVfYkMxdXJCu//rw6Ct/bF4eU94eZ0Lwc83KyxeV5HdG5RRimCyWZoL3842luU6Hu+PahGib4fkb60auCFNV+3xfUd/eH+/2MBdxcrhG3vh09H1YOne/5JHzKwtDDHmAElW2z2L++ELi3KlOh7EhEREdHTscD9H927d1f6d9+5c0cpdB85cgQvv/xyCe8lkf75eNjB2tI876RQq5X74//x8ABULFMyLQrMzTVYPKWllEVAoof71zeqVQrWVvdyQCv5QomicD9rfJMSe79urcoqM2CJZCZmbFv9/1jAysIcFhZy58D4IbVRvZJzibyXGAL89GkLLjJLREREZITyr6aoAk8rcMvMpXp5NPt6JCzsbZAcfhv73pwLF/+yaL98EhKCI7Gj/zSk3UmAQwVPNPtqJKyc7GBubYkbu47j2NSlQG4uqg/vjqpDOiErOQ2bOoyHsUsJOY2w+cOQk5oIy1Ll4fvOUqRdP48rU7vA2qcK/D7dAQvn0ojZtQQ3N81C2o0glBn6NTx6vp23jbTIKwibPxzZyXHIyUiDU4NuKDPkK2jMzHDzj1m4vW0+zGzsUf27Uwb9u1J+drYWWDK1JVr/b5tOr4uOScn3Z2F88L/aRnvpdmE/9/UmvozyXRsjOz0TOVnZODFjBSL3nla2YeXmiBbfjoJdmVIwszBHzMmrOPj+D8hOy4Bvz2YIGNcXth6uWFH1VciaARFLP0T80U3QmN0r7Hj2+QCurforPzMD5DW0lx/W7QzBn/+GF2sGODlYYtHHzY3yS67CZsB9Tn4+6PHXTFxethNHJv8ifQbokgOhs4cg4dROaJ3uLU7oWKcDygz9SvmZOSAnUaz/ZVorNB28GdnZucWaA2NfroGW9Y3zSi5dcqDKq51Q7bUuyM3KRm5OLrZ2m6iMDWQ+PyhsBgRP7430WyF5r0sNPYNKEzfCuXFPZN69jbC5/0PGrTDkZmfC1q8Ryr/xPcysbBAbuBpRq6YgMy4SASviDfp3JSIiooKprsC9Z88emKoW343B/nfmI/Z8KCr3b4uGk1/B1VV/KwPbhwej4v7rfx5B0OJtMLeyQPc/ZyCqzVlE7DmJCz9sQezZEDSaOgQyECervmN/hm3FAKWIHf7ze3BvN1QZzD58EmpbqT4qjl+D6HVfPLKN8F/Gw6VJb5TuMVY5qQ16ryESTrSDU4Ou8Hj+HdhWrIsbix8UxMl4PNfAC9PHNsCkOccK/ZqGAzbp9B7tm3jj4xF1Ifvn/ubhIJyetU4pWIkT4S4bpmJNwHBkpaajzlt9kBAShd2vzlCKOe2XTUTlfm1w6de/ELrpAGJOXEHPXV9D5gzw6D0ePoM/V37OuBOB86OrwTGgPbSO7swAiYmCs/iiq8WrWxB8I7FYMkBcwbFs+nPKVTAyZ4Cg0ZorBaywP4/ku1/mDNAlB+5nwcNfct/HHJBXw5ql8O17jfHWl4cK/xodc6B5XQ9lvGGsCpsDZTs1RMU+LbG12yRkJqYoX27lZGZLf35Q2AyoNGlD3s/JV47hypTOcKzXWfk9eu3nsPLyQ+WPNiM3OxtXp3VDzO6fUbrrKLi27Ac7/8YIeifAIH8/IiIiejpeY2ciXGtWQFZKmjKwFYLX7EXZjg1gZvnodxhikU0Lx3u9FsUMDTMLLVJvyTcbIeXaSZjb2CuDWcGtzau4e2QTcrMyHnmubYU6sClbTTlx/y8NNMhOuav8nJORitysTFi4epXA34D04YPXauOj4cVzwiF6bov+5fcv55b5cy9OUEVxW4gLuq5ca23t5qj8npsLZdaXuE+81tzGCilRd2BKGaC1f3AJe05qkvKXzs3JUX5nBshN9A4Wi+lW8NF/yyKtVoMVM1qj+3PlIHsGCAHv9kXo5oNIvBaV735ZM0DXHHgS5oDcxg6sgS/eKp4CdJPapbBlbgdl/Q/Zc6DmqJ44/e1apbgtpN9JyDsWynp+UNQMiNm1GG6tB8HM4v6i5RplBrj49xCvzUlPgaUb+60TERHJwjhHaqQz+3Kl4VK1HHruvHepraC1sYKtp+sjzz0y+We0+3Uiqr7SEZZOdjj93e+IPffgcj1ZpN8MQWrYWVx4+0FxUwxGxexMXZR5/TsEf9YDt/9ciKzkOHi99LEyU4vkmcE5bUx9eLrb4L1vjiAt/d5MpGc1uHtlLJrcHDbWWpP43D/Mr38bJIXdRFL4beV3MbO7zU/vod+Zn6C1tsS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", "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -191,8 +201,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
" ] }, "execution_count": 4, @@ -235,27 +246,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "4c4b1b0b-5c61-4587-986c-7a9108bc2505", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iters. done: 101 [Current cost: -2.5127326712407005]\r" - ] - }, - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# SciPy minimizer routine\n", "def cost_func(\n", @@ -281,7 +275,7 @@ " \"cost_history\": [],\n", "}\n", "\n", - "# Evaluate the problem using a QPU via Qiskit IBM Runtime\n", + "# Evaluate the problem on a QPU by using Qiskit IBM Runtime\n", "with Session(backend=backend) as session:\n", " estimator = Estimator()\n", " callback = build_callback(\n", @@ -312,18 +306,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e5b58771-d543-4e75-9746-fbc7b28e4360", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated ground state energy: -2.594437119769288\n" - ] - } - ], + "outputs": [], "source": [ "print(f'Estimated ground state energy: {res[\"fun\"]}')" ] @@ -400,7 +386,7 @@ "metadata": { "description": "Build, deploy, and run a Qiskit pattern for simulating a Heisenberg chain and estimating its ground state energy.", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -414,7 +400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.2" }, "title": "Ground state energy estimation of the Heisenberg chain with VQE" }, diff --git a/docs/tutorials/variational-quantum-eigensolver.ipynb b/docs/tutorials/variational-quantum-eigensolver.ipynb index 14a2c22dd5c..e8026f0e26e 100644 --- a/docs/tutorials/variational-quantum-eigensolver.ipynb +++ b/docs/tutorials/variational-quantum-eigensolver.ipynb @@ -9,7 +9,7 @@ "{/* cspell:ignore nfev */}\n", "\n", "# Variational quantum eigensolver\n", - "*Usage estimate: 73 minutes on ibm_kyiv (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 73 minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { From 3150b1cbdf2a42e20367367e9eb0acedf1b49164 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Thu, 7 Aug 2025 11:51:01 -0500 Subject: [PATCH 06/26] courses - cusco --- .../courses/quantum-diagonalization-algorithms/vqe.ipynb | 4 ++-- .../variational-algorithm-design/cost-functions.ipynb | 8 ++++---- .../examples-and-applications.ipynb | 4 ++-- .../instances-and-extensions.ipynb | 2 +- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/learning/courses/quantum-diagonalization-algorithms/vqe.ipynb b/learning/courses/quantum-diagonalization-algorithms/vqe.ipynb index a3008a588ce..d38b753c596 100644 --- a/learning/courses/quantum-diagonalization-algorithms/vqe.ipynb +++ b/learning/courses/quantum-diagonalization-algorithms/vqe.ipynb @@ -694,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "8e0d33b8-7f9c-428d-a76d-d832747b8430", "metadata": {}, "outputs": [ @@ -745,7 +745,7 @@ } ], "source": [ - "# This required 13 min, 20 s QPU time on ibm_cusco, 28 min total time.\n", + "# This required 13 min, 20 s QPU time on an Eagle processor, 28 min total time.\n", "with Session(backend=backend) as session:\n", " estimator = Estimator(mode=session)\n", " estimator.options.default_shots = 10000\n", diff --git a/learning/courses/variational-algorithm-design/cost-functions.ipynb b/learning/courses/variational-algorithm-design/cost-functions.ipynb index 9ecb0843fac..9aa4c815dad 100644 --- a/learning/courses/variational-algorithm-design/cost-functions.ipynb +++ b/learning/courses/variational-algorithm-design/cost-functions.ipynb @@ -679,7 +679,7 @@ " operational=True, min_num_qubits=ansatz.num_qubits, simulator=False\n", ")\n", "# Or get a specific backend:\n", - "# backend = service.backend(\"ibm_cusco\")\n", + "# backend = service.backend(\"ibm_brisbane\")\n", "\n", "# Use a pass manager to transpile the circuit and observable for the specific backend being used:\n", "\n", @@ -951,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "389b7e86-cd61-4184-96c3-ae91b8ad59f5", "metadata": {}, "outputs": [ @@ -977,7 +977,7 @@ ")\n", "\n", "# Or get a specific backend:\n", - "# backend = service.backend(\"ibm_cusco\")\n", + "# backend = service.backend(\"ibm_brisbane\")\n", "\n", "# Use a pass manager to transpile the circuit and observable for the specific backend being used:\n", "\n", @@ -1346,7 +1346,7 @@ "# )\n", "\n", "# Or use a specific backend\n", - "backend = service.backend(\"ibm_cusco\")\n", + "backend = service.backend(\"ibm_brisbane\")\n", "\n", "# Initialize some variables to save the results from different runs:\n", "\n", diff --git a/learning/courses/variational-algorithm-design/examples-and-applications.ipynb b/learning/courses/variational-algorithm-design/examples-and-applications.ipynb index 4b3f70d5d19..856bb749189 100644 --- a/learning/courses/variational-algorithm-design/examples-and-applications.ipynb +++ b/learning/courses/variational-algorithm-design/examples-and-applications.ipynb @@ -1290,7 +1290,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], @@ -1304,7 +1304,7 @@ "service = QiskitRuntimeService()\n", "backend = service.least_busy(operational=True, simulator=False)\n", "# Or use a specific backend\n", - "# backend = service.backend(\"ibm_cusco\")\n", + "# backend = service.backend(\"ibm_brisbane\")\n", "print(backend)" ] }, diff --git a/learning/courses/variational-algorithm-design/instances-and-extensions.ipynb b/learning/courses/variational-algorithm-design/instances-and-extensions.ipynb index dcadc129105..9aa09879796 100644 --- a/learning/courses/variational-algorithm-design/instances-and-extensions.ipynb +++ b/learning/courses/variational-algorithm-design/instances-and-extensions.ipynb @@ -220,7 +220,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], From 9b90336378cf67a4f3a1e621abda689fb0c06e57 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Thu, 7 Aug 2025 12:05:18 -0500 Subject: [PATCH 07/26] Courses Kyiv --- .../courses/quantum-diagonalization-algorithms/skqd.ipynb | 2 +- .../sqd-implementation.ipynb | 2 +- .../quantum-machine-learning/quantum-kernel-methods.ipynb | 4 ++-- learning/courses/quantum-machine-learning/qvc-qnn.ipynb | 8 ++++---- .../quantum-circuit-optimization.ipynb | 8 ++++---- .../utility-scale-quantum-computing/utility-i.ipynb | 8 ++++---- .../utility-scale-quantum-computing/utility-iii.ipynb | 2 +- .../examples-and-applications.ipynb | 4 ++-- .../computer-science/quantum-key-distribution.ipynb | 2 +- 9 files changed, 20 insertions(+), 20 deletions(-) diff --git a/learning/courses/quantum-diagonalization-algorithms/skqd.ipynb b/learning/courses/quantum-diagonalization-algorithms/skqd.ipynb index 3093042021e..56b7246addc 100644 --- a/learning/courses/quantum-diagonalization-algorithms/skqd.ipynb +++ b/learning/courses/quantum-diagonalization-algorithms/skqd.ipynb @@ -244,7 +244,7 @@ "service = QiskitRuntimeService()\n", "# Use the least-busy backend or specify a quantum computer using the syntax commented out below.\n", "backend = service.least_busy(operational=True, simulator=False)\n", - "# backend = service.backend(\"ibm_kyiv\")" + "# backend = service.backend(\"ibm_brisbane\")" ] }, { diff --git a/learning/courses/quantum-diagonalization-algorithms/sqd-implementation.ipynb b/learning/courses/quantum-diagonalization-algorithms/sqd-implementation.ipynb index bb3a968c60f..16a5bb0b323 100644 --- a/learning/courses/quantum-diagonalization-algorithms/sqd-implementation.ipynb +++ b/learning/courses/quantum-diagonalization-algorithms/sqd-implementation.ipynb @@ -436,7 +436,7 @@ "service = QiskitRuntimeService()\n", "# Use the least-busy backend or specify a quantum computer using the syntax commented out below.\n", "backend = service.least_busy(operational=True, simulator=False)\n", - "# backend = service.backend(\"ibm_kyiv\")" + "# backend = service.backend(\"ibm_brisbane\")" ] }, { diff --git a/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb b/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb index bf1436c567e..f68c44e49d3 100644 --- a/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb +++ b/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb @@ -320,7 +320,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Benchmarked on ibm_kyoto, 7-11-24, took 4 sec.\n", + "# Benchmarked on an Eagle processor, 7-11-24, took 4 sec.\n", "\n", "# Import our runtime primitive\n", "from qiskit_ibm_runtime import Session, SamplerV2 as Sampler\n", @@ -1069,7 +1069,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], diff --git a/learning/courses/quantum-machine-learning/qvc-qnn.ipynb b/learning/courses/quantum-machine-learning/qvc-qnn.ipynb index d9ec74b3bc0..4fddba7bf02 100644 --- a/learning/courses/quantum-machine-learning/qvc-qnn.ipynb +++ b/learning/courses/quantum-machine-learning/qvc-qnn.ipynb @@ -1575,7 +1575,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "ibm_nazca\n" + "ibm_brisbane\n" ] } ], @@ -1585,7 +1585,7 @@ "# To run on hardware, select the least busy quantum computer or specify a particular one.\n", "service = QiskitRuntimeService()\n", "backend = service.least_busy(operational=True, simulator=False)\n", - "# backend = service.backend(\"ibm_kyoto\")\n", + "# backend = service.backend(\"ibm_brisbaneane\")\n", "\n", "print(backend.name)" ] @@ -1724,7 +1724,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "a8fe6724-f67d-41b9-a548-08428cf8faf0", "metadata": {}, "outputs": [ @@ -1746,7 +1746,7 @@ } ], "source": [ - "# This was run on ibm_nazca on 10-4-24, and took 7 min.\n", + "# This was run on an Eagle processor on 10-4-24, and took 7 min.\n", "\n", "from qiskit_ibm_runtime import (\n", " EstimatorV2 as Estimator,\n", diff --git a/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb b/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb index 3a8c7d30fca..ffb52b130ee 100644 --- a/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb +++ b/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb @@ -142,14 +142,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "f6be6161", "metadata": {}, "outputs": [], "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeKyiv\n", + "from qiskit_ibm_runtime.fake_provider import FakeBrisbane\n", "\n", - "backend = FakeKyiv()" + "backend = FakeBrisbane()" ] }, { @@ -500,7 +500,7 @@ "source": [ "The new synthesis produces a shallower circuit. Why?\n", "\n", - "This is because the new circuit can be laid out on linearly connected qubits, so on IBM® Kyiv's heavy-hexagon coupling graph as well, while the original circuit requires star-shaped connectivity (a degree-4 node) and hence cannot be laid out on the heavy-hex coupling graph, which has nodes at most degree 3. As a result, the original circuit requires qubit routing that adds SWAP gates, increasing the gate count.\n", + "This is because the new circuit can be laid out on linearly connected qubits, so on IBM® Brisbane's heavy-hexagon coupling graph as well, while the original circuit requires star-shaped connectivity (a degree-4 node) and hence cannot be laid out on the heavy-hex coupling graph, which has nodes at most degree 3. As a result, the original circuit requires qubit routing that adds SWAP gates, increasing the gate count.\n", "\n", "What we have done in the new circuit can be seen as a manual \"coupling constraint-aware\" circuit synthesis. In other words: manually solving circuit synthesis and circuit mapping at the same time." ] diff --git a/learning/courses/utility-scale-quantum-computing/utility-i.ipynb b/learning/courses/utility-scale-quantum-computing/utility-i.ipynb index 5f9e8507052..5fb58e0ae14 100644 --- a/learning/courses/utility-scale-quantum-computing/utility-i.ipynb +++ b/learning/courses/utility-scale-quantum-computing/utility-i.ipynb @@ -803,12 +803,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "8b900d9a-0351-426a-b25b-19e631fd257e", "metadata": {}, "outputs": [], "source": [ - "# backend_map = service.backend(\"ibm_kyiv\")\n", + "# backend_map = service.backend(\"ibm_brisbane\")\n", "backend_map = service.least_busy(operational=True, simulator=False)\n", "\n", "num_steps = 20\n", @@ -831,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "f9fadf76-48e3-43a3-b773-8fa3b5fd5f3d", "metadata": {}, "outputs": [ @@ -845,7 +845,7 @@ } ], "source": [ - "# backend = service.backend(\"ibm_kyiv\")\n", + "# backend = service.backend(\"ibm_brisbane\")\n", "backend = backend_map\n", "\n", "transpiled_qc = transpile(qc, backend, optimization_level=1, layout_method=\"trivial\")\n", diff --git a/learning/courses/utility-scale-quantum-computing/utility-iii.ipynb b/learning/courses/utility-scale-quantum-computing/utility-iii.ipynb index f09e3c6839e..ba8bb9bbe6e 100644 --- a/learning/courses/utility-scale-quantum-computing/utility-iii.ipynb +++ b/learning/courses/utility-scale-quantum-computing/utility-iii.ipynb @@ -2046,7 +2046,7 @@ "\n", "Build a GHZ circuit for 20 qubits or more so that the measurement result meets the criteria: The fidelity of your GHZ state > 0.5.\n", "\n", - "- You need to use an Eagle device (such as `ibm_kyiv`) and set the shots number as 40,000.\n", + "- You need to use an Eagle device (such as `ibm_brisbane`) and set the shots number as 40,000.\n", "- You should execute the GHZ circuit using the `execute_ghz_fidelity` function, and calculate the fidelity using the `check_ghz_fidelity_from_jobs` function.\n", "You need to find the biggest qubits - GHZ circuit which meet the criteria. Write your code below, show the result with the function `check_ghz_fidelity_from_jobs` .\n", "\n", diff --git a/learning/courses/variational-algorithm-design/examples-and-applications.ipynb b/learning/courses/variational-algorithm-design/examples-and-applications.ipynb index 856bb749189..9fce59fca66 100644 --- a/learning/courses/variational-algorithm-design/examples-and-applications.ipynb +++ b/learning/courses/variational-algorithm-design/examples-and-applications.ipynb @@ -1345,7 +1345,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Estimated compute resource usage: 25 minutes. Benchmarked at 24 min, 30 s on ibm_nazca on 5-30-24\n", + "# Estimated compute resource usage: 25 minutes. Benchmarked at 24 min, 30 sec on an Eagle processor on 5-30-24\n", "\n", "k = 2\n", "betas = [30, 50, 80]\n", @@ -1788,7 +1788,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Estimated hardware usage: 20 min benchmarked on ibm_nazca on 5-30-24\n", + "# Estimated hardware usage: 20 min benchmarked on an Eagle processor on 5-30-24\n", "\n", "real_prev_states = []\n", "real_prev_opt_parameters = []\n", diff --git a/learning/modules/computer-science/quantum-key-distribution.ipynb b/learning/modules/computer-science/quantum-key-distribution.ipynb index ef6842acd8a..49966a5776a 100644 --- a/learning/modules/computer-science/quantum-key-distribution.ipynb +++ b/learning/modules/computer-science/quantum-key-distribution.ipynb @@ -1093,7 +1093,7 @@ "source": [ "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", "\n", - "# This calculation was run on ibm_nazca on 11-7-24 and required 2 s to run, with 127 qubits.\n", + "# This calculation was run on an Eagle processor on 11-7-24 and required 2 s to run, with 127 qubits.\n", "# Qiskit patterns step 1: Mapping your problem to a quantum circuit\n", "\n", "bit_num = 127\n", From 70fa0ec813af29231128ad6b93f7d28774d76a40 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Thu, 7 Aug 2025 12:12:48 -0500 Subject: [PATCH 08/26] sherbrooke - courses --- .../quantum-machine-learning/quantum-kernel-methods.ipynb | 2 +- learning/courses/quantum-machine-learning/qvc-qnn.ipynb | 2 +- .../quantum-circuit-optimization.ipynb | 6 +++--- .../utility-scale-quantum-computing/teleportation.ipynb | 7 +++---- .../variational-algorithm-design/cost-functions.ipynb | 8 ++++---- .../computer-science/quantum-key-distribution.ipynb | 2 +- 6 files changed, 13 insertions(+), 14 deletions(-) diff --git a/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb b/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb index f68c44e49d3..d78580f179a 100644 --- a/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb +++ b/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb @@ -214,7 +214,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], diff --git a/learning/courses/quantum-machine-learning/qvc-qnn.ipynb b/learning/courses/quantum-machine-learning/qvc-qnn.ipynb index 4fddba7bf02..87640cac0ce 100644 --- a/learning/courses/quantum-machine-learning/qvc-qnn.ipynb +++ b/learning/courses/quantum-machine-learning/qvc-qnn.ipynb @@ -566,7 +566,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "ibm_sherbrooke\n" + "ibm_brisbane\n" ] } ], diff --git a/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb b/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb index ffb52b130ee..11945a41cad 100644 --- a/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb +++ b/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb @@ -1178,7 +1178,7 @@ "outputs": [], "source": [ "service = QiskitRuntimeService()\n", - "backend = service.backend(\"ibm_sherbrooke\")\n", + "backend = service.backend(\"ibm_brisbane\n", "sampler = Sampler(backend)" ] }, @@ -1265,12 +1265,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "270db9aa", "metadata": {}, "outputs": [], "source": [ - "# backend = service.backend('ibm_sherbrooke')\n", + "# backend = service.backend('ibm_brisbane\n", "backend = service.least_busy(\n", " operational=True, simulator=False, min_num_qubits=127\n", ") # Eagle\n", diff --git a/learning/courses/utility-scale-quantum-computing/teleportation.ipynb b/learning/courses/utility-scale-quantum-computing/teleportation.ipynb index 8bdc5ba7f42..158f4a93670 100644 --- a/learning/courses/utility-scale-quantum-computing/teleportation.ipynb +++ b/learning/courses/utility-scale-quantum-computing/teleportation.ipynb @@ -1100,7 +1100,6 @@ "data": { "text/plain": [ "[,\n", - " ,\n", " ]" ] }, @@ -1126,7 +1125,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The least busy device is \n" + "The least busy device is \n" ] } ], @@ -1144,7 +1143,7 @@ "outputs": [], "source": [ "# You can specify the device\n", - "# backend = service.backend('ibm_sherbrooke')" + "# backend = service.backend('ibm_brisbane')" ] }, { @@ -1626,7 +1625,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The least busy device is \n" + "The least busy device is \n" ] } ], diff --git a/learning/courses/variational-algorithm-design/cost-functions.ipynb b/learning/courses/variational-algorithm-design/cost-functions.ipynb index 9aa4c815dad..a598c4dee59 100644 --- a/learning/courses/variational-algorithm-design/cost-functions.ipynb +++ b/learning/courses/variational-algorithm-design/cost-functions.ipynb @@ -1120,12 +1120,12 @@ "id": "ca040383-9c8c-4a38-8e40-bad1b26c6085", "metadata": {}, "source": [ - "We can use a simulator to show the usefulness of an optimized transpilation. We will return below to using real hardware to demonstrate the usefulness of error mitigation. We will use QiskitRuntimeService to get a real backend (in this case, ibm_sherbrooke), and use AerSimulator to simulate that backend, including its noise behavior." + "We can use a simulator to show the usefulness of an optimized transpilation. We will return below to using real hardware to demonstrate the usefulness of error mitigation. We will use QiskitRuntimeService to get a real backend (in this case, ibm_brisbane), and use AerSimulator to simulate that backend, including its noise behavior." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "6f0e6f32-02f7-471f-a9f3-f30d22f8cca4", "metadata": {}, "outputs": [], @@ -1135,7 +1135,7 @@ "\n", "# get a real backend from the runtime service\n", "service = QiskitRuntimeService()\n", - "backend = service.backend(\"ibm_sherbrooke\")\n", + "backend = service.backend(\"ibm_brisbane\")\n", "\n", "# generate a simulator that mimics the real quantum system with the latest calibration results\n", "backend_sim = AerSimulator.from_backend(backend)" @@ -1329,7 +1329,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Estimated usage: 8 minutes, benchmarked on ibm_sherbrooke, 5-23-24\n", + "# Estimated usage: 8 minutes, benchmarked on an Eagle processor, 5-23-24\n", "\n", "from qiskit_ibm_runtime import QiskitRuntimeService\n", "from qiskit_ibm_runtime import (\n", diff --git a/learning/modules/computer-science/quantum-key-distribution.ipynb b/learning/modules/computer-science/quantum-key-distribution.ipynb index 49966a5776a..919b7c4206a 100644 --- a/learning/modules/computer-science/quantum-key-distribution.ipynb +++ b/learning/modules/computer-science/quantum-key-distribution.ipynb @@ -984,7 +984,7 @@ "source": [ "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", "\n", - "# This calculation was run on ibm_sherbrooke on 11-7-24 and required 3 s to run, with 127 qubits.\n", + "# This calculation was run on an Eagle processor on 11-7-24 and required 3 sec to run, with 127 qubits.\n", "# Qiskit patterns step 1: Mapping your problem to a quantum circuit\n", "\n", "bit_num = 127\n", From e35be3d66b19bb9ab5bbf16c89849dd6e1f0218d Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Thu, 7 Aug 2025 12:49:47 -0500 Subject: [PATCH 09/26] run fix --- docs/guides/visualize-results.ipynb | 16 ++++++- .../advanced-techniques-for-qaoa.ipynb | 43 ++++++++++++++++++- docs/tutorials/grovers-algorithm.ipynb | 4 +- ...m-approximate-optimization-algorithm.ipynb | 4 +- ...ime-benchmarking-for-qubit-selection.ipynb | 40 +++++++++++++++-- docs/tutorials/spin-chain-vqe.ipynb | 4 +- 6 files changed, 97 insertions(+), 14 deletions(-) diff --git a/docs/guides/visualize-results.ipynb b/docs/guides/visualize-results.ipynb index c336f6d355e..9121d7bf4dc 100644 --- a/docs/guides/visualize-results.ipynb +++ b/docs/guides/visualize-results.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "7e6b2936-3a18-4917-9c47-30e2d4ce5775", "metadata": {}, "source": [ "# Visualize results" @@ -9,6 +10,7 @@ }, { "cell_type": "markdown", + "id": "06a4399f-bca6-4c2e-9128-1494017d0249", "metadata": { "tags": [ "version-info" @@ -30,6 +32,7 @@ }, { "cell_type": "markdown", + "id": "e8d0138c-fd97-49c2-88a5-1929a5a09258", "metadata": {}, "source": [ "## Plot histogram \n", @@ -50,6 +53,7 @@ { "cell_type": "code", "execution_count": 1, + "id": "5cf67f92-a86d-496d-9e13-0d8a841c8dfa", "metadata": {}, "outputs": [], "source": [ @@ -67,6 +71,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "938e8206-d7e8-447d-b798-b7c2507f8901", "metadata": {}, "outputs": [ { @@ -98,6 +103,7 @@ { "cell_type": "code", "execution_count": 3, + "id": "57d8053e-d030-460d-9c1f-772e53b1a49b", "metadata": {}, "outputs": [ { @@ -117,6 +123,7 @@ }, { "cell_type": "markdown", + "id": "e3a68e3d-21e6-45a0-bc40-f9a2214dd5b3", "metadata": {}, "source": [ "### Options when plotting a histogram\n", @@ -134,6 +141,7 @@ { "cell_type": "code", "execution_count": 4, + "id": "bd70e13f-5c52-42fb-8dde-980b15e3604a", "metadata": {}, "outputs": [ { @@ -170,6 +178,7 @@ }, { "cell_type": "markdown", + "id": "019ee04e-0730-4536-94cc-7e2b50d921e1", "metadata": {}, "source": [ "## Plotting estimator results\n", @@ -182,6 +191,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "17c9893a-d1bf-4726-b444-6dce1d56805f", "metadata": {}, "outputs": [ { @@ -254,6 +264,7 @@ }, { "cell_type": "markdown", + "id": "a520f049-c2ee-4f14-8039-b5be671f25ae", "metadata": {}, "source": [ "The following cell uses the estimated [standard error](https://en.wikipedia.org/wiki/Standard_error) of each result and adds them as error bars. See the [`bar` plot documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html) for a full description of the plot." @@ -262,6 +273,7 @@ { "cell_type": "code", "execution_count": 6, + "id": "4eb79f4b-36b5-4797-a1a0-67d881d46ca4", "metadata": {}, "outputs": [ { @@ -301,7 +313,7 @@ "metadata": { "description": "Plot quantum circuit execution results using Qiskit", "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -315,7 +327,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3" }, "title": "Visualize results" }, diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 446dfac5b1d..a12a83c2097 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "6a1ed6d5-8c98-436d-8bfe-57a02c79b408", "metadata": { "tags": [ "remove-cell" @@ -13,6 +14,7 @@ }, { "cell_type": "markdown", + "id": "25e0965d-aa94-45fb-b85d-3ecffc643421", "metadata": {}, "source": [ "# Advanced techniques for QAOA\n", @@ -21,6 +23,7 @@ }, { "cell_type": "markdown", + "id": "ce199646-6b42-4529-8755-490c04d1054d", "metadata": {}, "source": [ "## Background\n", @@ -37,6 +40,7 @@ }, { "cell_type": "markdown", + "id": "608a70a0-9083-43d1-bcda-e1c92bd21a5d", "metadata": {}, "source": [ "## Requirements\n", @@ -49,6 +53,7 @@ }, { "cell_type": "markdown", + "id": "e7946fb0-6647-4c3f-bf7e-7e91dbaa833b", "metadata": {}, "source": [ "## Setup" @@ -57,6 +62,7 @@ { "cell_type": "code", "execution_count": 1, + "id": "d2123206-b7b8-4ffb-83ca-d28788e138d7", "metadata": {}, "outputs": [], "source": [ @@ -92,6 +98,7 @@ }, { "cell_type": "markdown", + "id": "c15d1762-ede3-4eec-924b-74cb6efff9df", "metadata": {}, "source": [ "## Step 1: Map classical inputs to a quantum problem\n", @@ -110,6 +117,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "3dfd9af8-23cf-4fa5-8bf8-1270bb7ab9ea", "metadata": {}, "outputs": [ { @@ -129,6 +137,7 @@ { "cell_type": "code", "execution_count": 4, + "id": "cf99fe0e-3b93-4bd1-ac27-8d75fc68fba7", "metadata": {}, "outputs": [ { @@ -157,6 +166,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "0668c4a4-cf6a-48dd-96b8-07992caaf869", "metadata": {}, "outputs": [ { @@ -206,6 +216,7 @@ }, { "cell_type": "markdown", + "id": "fffb3623-7d79-40df-ba21-448ad76821e2", "metadata": {}, "source": [ "### Hamiltonian → quantum circuit" @@ -214,6 +225,7 @@ { "cell_type": "code", "execution_count": 6, + "id": "6709745f-9648-4e2e-b63f-dc4fa260db7d", "metadata": {}, "outputs": [ { @@ -237,6 +249,7 @@ }, { "cell_type": "markdown", + "id": "7b0ee4e1-1418-4782-8ae2-c0d400515cf6", "metadata": {}, "source": [ "## Step 2: Optimize problem for quantum hardware execution\n", @@ -249,6 +262,7 @@ { "cell_type": "code", "execution_count": 7, + "id": "644d89ba-a8e2-40ca-979e-a6374d383725", "metadata": {}, "outputs": [], "source": [ @@ -276,6 +290,7 @@ }, { "cell_type": "markdown", + "id": "8480716b-00e2-482f-8352-1f46da3340ec", "metadata": {}, "source": [ "#### Remap the graph using a SAT mapper\n", @@ -290,6 +305,7 @@ { "cell_type": "code", "execution_count": 8, + "id": "ffb750c4-b7e0-4eba-bcbf-d815f4bd9e0b", "metadata": {}, "outputs": [], "source": [ @@ -512,6 +528,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1632f42d-3bf8-4459-9611-9b0f04a0c683", "metadata": {}, "outputs": [], "source": [ @@ -527,6 +544,7 @@ { "cell_type": "code", "execution_count": null, + "id": "bccc7411-bc85-4fca-9ae6-bcb7cb4fedc9", "metadata": {}, "outputs": [], "source": [ @@ -538,6 +556,7 @@ }, { "cell_type": "markdown", + "id": "2c2a483f-e433-49b8-b304-7814b33ddfbf", "metadata": {}, "source": [ "#### Build a QAOA circuit with the SWAP strategy and the SAT mapping\n", @@ -551,6 +570,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f6b501db-81c3-4a55-922a-abfc29bda728", "metadata": {}, "outputs": [], "source": [ @@ -727,6 +747,7 @@ { "cell_type": "code", "execution_count": null, + "id": "9f9ead0a-6a2c-4fea-8f26-cedb2412ecc2", "metadata": {}, "outputs": [], "source": [ @@ -736,6 +757,7 @@ { "cell_type": "code", "execution_count": null, + "id": "b611f04a-a0d3-437f-93bd-f61ba05c9e7a", "metadata": {}, "outputs": [], "source": [ @@ -753,6 +775,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6fd27d54-6c1b-4da8-bc36-fb46e266776e", "metadata": {}, "outputs": [], "source": [ @@ -767,6 +790,7 @@ }, { "cell_type": "markdown", + "id": "b68a9bdd-dad0-4e5b-a51d-a1284379dfa8", "metadata": {}, "source": [ "## Step 3: Execute using Qiskit primitives\n", @@ -781,6 +805,7 @@ { "cell_type": "code", "execution_count": null, + "id": "a895b56c-7931-435d-bd7b-5a9f63923e5a", "metadata": {}, "outputs": [], "source": [ @@ -795,6 +820,7 @@ { "cell_type": "code", "execution_count": null, + "id": "de425953-6a29-4a39-999c-8920ac77d93f", "metadata": {}, "outputs": [], "source": [ @@ -830,6 +856,7 @@ { "cell_type": "code", "execution_count": null, + "id": "84d57a1f-1bbf-404d-af09-f34cb6b07c91", "metadata": {}, "outputs": [], "source": [ @@ -933,6 +960,7 @@ }, { "cell_type": "markdown", + "id": "761357a4-b49e-49b2-a633-2aee82db7847", "metadata": {}, "source": [ "The CVaR can be used as an error mitigation technique as previously discussed [\\[4\\]](#references). In this example, we determine $\\alpha$ and the number of shots according to the circuit's error rate." @@ -941,6 +969,7 @@ { "cell_type": "code", "execution_count": null, + "id": "54b9472d-8679-48cc-bed1-769b84fa2970", "metadata": {}, "outputs": [], "source": [ @@ -966,6 +995,7 @@ { "cell_type": "code", "execution_count": null, + "id": "400b2935-5b5a-470e-b5ce-28e5a93112d2", "metadata": {}, "outputs": [], "source": [ @@ -1005,6 +1035,7 @@ }, { "cell_type": "markdown", + "id": "af01baf1-4d9d-4076-a96a-68eaf7f6c2b2", "metadata": {}, "source": [ "## Step 4: Post-process and return result in desired classical format" @@ -1013,6 +1044,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1eb0d340-8c7b-472a-947c-590a3a67a95d", "metadata": {}, "outputs": [], "source": [ @@ -1033,6 +1065,7 @@ }, { "cell_type": "markdown", + "id": "18f0e8e1-a23e-44b3-a2dd-db8de14569bc", "metadata": {}, "source": [ "The following cost is the result of the standard expectation value." @@ -1041,6 +1074,7 @@ { "cell_type": "code", "execution_count": null, + "id": "03127a04-021b-4114-969e-9193104cb800", "metadata": {}, "outputs": [], "source": [ @@ -1060,6 +1094,7 @@ { "cell_type": "code", "execution_count": null, + "id": "124b901d-9f3d-46ab-951e-8fcfb3cbdadd", "metadata": {}, "outputs": [], "source": [ @@ -1090,6 +1125,7 @@ }, { "cell_type": "markdown", + "id": "5c3fd1be-8ac6-435d-a752-8f115232b3cb", "metadata": {}, "source": [ "Finally, let's draw a graph based on the CVaR result.\n", @@ -1101,6 +1137,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1e876ddb-25f2-419a-a675-a268d020ad7e", "metadata": {}, "outputs": [], "source": [ @@ -1117,6 +1154,7 @@ }, { "cell_type": "markdown", + "id": "56a7c502-1c22-4bb9-b424-31aaa6b69f4c", "metadata": {}, "source": [ "## References\n", @@ -1132,6 +1170,7 @@ }, { "cell_type": "markdown", + "id": "ed86f4d5-d935-43be-9f6a-dbd510bcf036", "metadata": {}, "source": [ "## Tutorial survey\n", @@ -1145,7 +1184,7 @@ "metadata": { "description": "This notebook introduces advanced techniques to improve the performance of the Quantum Approximate Optimization Algorithm (QAOA) with a large number of qubits.", "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1159,7 +1198,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3" }, "title": "Advanced techniques for QAOA" }, diff --git a/docs/tutorials/grovers-algorithm.ipynb b/docs/tutorials/grovers-algorithm.ipynb index 8bd9c64b613..267faa50911 100644 --- a/docs/tutorials/grovers-algorithm.ipynb +++ b/docs/tutorials/grovers-algorithm.ipynb @@ -428,7 +428,7 @@ "metadata": { "description": "Learn the basics of quantum computing, and how to use IBM Quantum services and systems to solve real-world problems.", "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -442,7 +442,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3" }, "title": "Grover's algorithm" }, diff --git a/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb b/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb index 4339eb416bb..11410e79fbd 100644 --- a/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb +++ b/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb @@ -1187,7 +1187,7 @@ "metadata": { "description": "Learn the basics of quantum computing, and how to use IBM Quantum services and systems to solve real-world problems.", "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1201,7 +1201,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3" }, "title": "Quantum approximate optimization algorithm" }, diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 915dd621069..50e51aa4295 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "c60285b4-72df-4a5d-9ee1-41efc0123fce", "metadata": {}, "source": [ "# Real-time benchmarking for qubit selection\n", @@ -11,6 +12,7 @@ { "cell_type": "code", "execution_count": 1, + "id": "643fe0b3-d024-4d94-9603-6cdf680d297c", "metadata": { "tags": [ "remove-cell" @@ -24,6 +26,7 @@ }, { "cell_type": "markdown", + "id": "cda4fc0d-a40e-4fcd-bad1-9f373aef8395", "metadata": {}, "source": [ "## Background\n", @@ -34,6 +37,7 @@ }, { "cell_type": "markdown", + "id": "0d2ab297-142e-4919-9a17-519b407c8019", "metadata": {}, "source": [ "## Requirements\n", @@ -46,6 +50,7 @@ }, { "cell_type": "markdown", + "id": "af357ab3-1992-402f-869c-47302bd228ef", "metadata": {}, "source": [ "## Setup" @@ -54,6 +59,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "0d86f001-2679-4ce9-98e8-1d9a687f5c79", "metadata": {}, "outputs": [], "source": [ @@ -84,6 +90,7 @@ }, { "cell_type": "markdown", + "id": "9bb8631d-d401-4ee6-aae6-02e6bd0585f6", "metadata": {}, "source": [ "## Step 1: Map classical inputs to a quantum problem\n", @@ -93,6 +100,7 @@ }, { "cell_type": "markdown", + "id": "45f6acb2-11b6-44de-b578-911312869b96", "metadata": {}, "source": [ "### Setting up backend and coupling map" @@ -101,6 +109,7 @@ { "cell_type": "code", "execution_count": 3, + "id": "c24b5540-f94e-4fa8-96cb-a626573510ef", "metadata": {}, "outputs": [ { @@ -115,7 +124,9 @@ } ], "source": [ - "service = QiskitRuntimeService(instance=\"crn:v1:bluemix:public:quantum-computing:us-east:a/26b15df5f5684154b2c791c32e07e69b:08dcfec2-410b-45a4-8185-10fd26ce26f6::\")\n", + "service = QiskitRuntimeService(\n", + " instance=\"crn:v1:bluemix:public:quantum-computing:us-east:a/26b15df5f5684154b2c791c32e07e69b:08dcfec2-410b-45a4-8185-10fd26ce26f6::\"\n", + ")\n", "\n", "# To run on hardware, select a backend that supports the ecr gate\n", "service.backends(filters=lambda x: (\"ecr\" in x.basis_gates))" @@ -124,10 +135,10 @@ { "cell_type": "code", "execution_count": 4, + "id": "d14e123a-df7f-4fa9-9c8c-cf5a57e9f0b7", "metadata": {}, "outputs": [], "source": [ - "\n", "backend = service.backend(\"ibm_brisbane\")\n", "\n", "qubits = list(range(backend.num_qubits))" @@ -136,6 +147,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "406fd2a9-ef54-4f81-861c-b90e4d7434f0", "metadata": {}, "outputs": [], "source": [ @@ -163,6 +175,7 @@ }, { "cell_type": "markdown", + "id": "8ff27f9d-dbf4-43b8-ad73-3ee464a9d4d5", "metadata": {}, "source": [ "### Characterization experiments\n", @@ -193,6 +206,7 @@ { "cell_type": "code", "execution_count": 6, + "id": "d7cb8ad7-ea83-49ed-bde2-ef6953fdc128", "metadata": {}, "outputs": [], "source": [ @@ -266,6 +280,7 @@ }, { "cell_type": "markdown", + "id": "39d1b87e-54c2-44fe-bd2d-0159467475a9", "metadata": {}, "source": [ "### QPU properties over time\n", @@ -275,6 +290,7 @@ { "cell_type": "code", "execution_count": 7, + "id": "8bbb0de5-f450-4d8d-9508-3a979f25c994", "metadata": {}, "outputs": [], "source": [ @@ -303,6 +319,7 @@ }, { "cell_type": "markdown", + "id": "0e1bdb14-dca9-4256-9ae5-e1f6acc93c42", "metadata": {}, "source": [ "Then, let's plot the values" @@ -311,6 +328,7 @@ { "cell_type": "code", "execution_count": 8, + "id": "b1914005-8f5c-4e72-91f8-304eefe77018", "metadata": {}, "outputs": [ { @@ -399,6 +417,7 @@ }, { "cell_type": "markdown", + "id": "fb1d269a-349a-4b73-9f5f-abfa5ed6b71e", "metadata": {}, "source": [ "You can see that over several days some of the qubit properties can change considerably. This highlights the importance of having fresh information of the QPU status, to be able to select the best performing qubits for an experiment." @@ -407,6 +426,7 @@ { "cell_type": "code", "execution_count": 9, + "id": "c6e9b05f-88ef-4f10-9c4d-728a957812c0", "metadata": {}, "outputs": [ { @@ -443,6 +463,7 @@ }, { "cell_type": "markdown", + "id": "779527fd-02bc-41bd-88e3-70a17d99acb4", "metadata": {}, "source": [ "## Step 2: Optimize problem for quantum hardware execution\n", @@ -451,6 +472,7 @@ }, { "cell_type": "markdown", + "id": "8cc1a29c-b5b4-4b6c-8cc9-5b7e90e51871", "metadata": {}, "source": [ "## Step 3: Execute using Qiskit primitives" @@ -458,6 +480,7 @@ }, { "cell_type": "markdown", + "id": "e7bb078c-9dca-4215-aa93-55ec5761e81a", "metadata": {}, "source": [ "### Execute a quantum circuit with default qubit selection\n", @@ -467,6 +490,7 @@ { "cell_type": "code", "execution_count": 10, + "id": "4250450d-6596-4473-973a-211fd4578289", "metadata": {}, "outputs": [], "source": [ @@ -484,6 +508,7 @@ }, { "cell_type": "markdown", + "id": "0eb66644-5588-4724-8de2-e1d8af0f0b6c", "metadata": {}, "source": [ "### Execute a quantum circuit with real-time qubit selection\n", @@ -494,6 +519,7 @@ { "cell_type": "code", "execution_count": 12, + "id": "a260f4ee-8307-431c-bc9b-8cf333a42b91", "metadata": {}, "outputs": [ { @@ -674,6 +700,7 @@ }, { "cell_type": "markdown", + "id": "f1691250-bf7c-4b88-ad98-3fc2c891923b", "metadata": {}, "source": [ "## Step 4: Post-process and return result in desired classical format\n", @@ -686,6 +713,7 @@ { "cell_type": "code", "execution_count": null, + "id": "a670d0f2-3908-4e69-9945-8128db30c36b", "metadata": {}, "outputs": [], "source": [ @@ -718,6 +746,7 @@ { "cell_type": "code", "execution_count": null, + "id": "2537f016-484d-4273-8f32-e5ee63598b77", "metadata": {}, "outputs": [], "source": [ @@ -754,6 +783,7 @@ }, { "cell_type": "markdown", + "id": "9a7e3b2b-026d-46ae-9f5c-62e97769c1c0", "metadata": {}, "source": [ "With increasing chain length, and thus less freedom to choose physical qubits, the fact that we have updated information about qubit properties becomes less substantial, with there being no difference when all of the qubits are used." @@ -761,6 +791,7 @@ }, { "cell_type": "markdown", + "id": "b8c3bc0f-9cbe-4842-ac9a-eded8edfdf74", "metadata": {}, "source": [ "
\n", @@ -770,6 +801,7 @@ }, { "cell_type": "markdown", + "id": "bf568284-ae97-4c19-8408-714e7d97b0c3", "metadata": {}, "source": [ "## Tutorial survey\n", @@ -783,7 +815,7 @@ "metadata": { "description": "Run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU.", "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -797,7 +829,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3" }, "title": "Real-time benchmarking for qubit selection\n" }, diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb index 1dea52b3925..8e3c24a5935 100644 --- a/docs/tutorials/spin-chain-vqe.ipynb +++ b/docs/tutorials/spin-chain-vqe.ipynb @@ -386,7 +386,7 @@ "metadata": { "description": "Build, deploy, and run a Qiskit pattern for simulating a Heisenberg chain and estimating its ground state energy.", "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -400,7 +400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3" }, "title": "Ground state energy estimation of the Heisenberg chain with VQE" }, From d13f49f1ed5c218d9706b9601e37e2a91c345137 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Thu, 7 Aug 2025 13:01:25 -0500 Subject: [PATCH 10/26] missing quotes --- .../quantum-circuit-optimization.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb b/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb index 11945a41cad..5bde150fd40 100644 --- a/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb +++ b/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb @@ -1178,7 +1178,7 @@ "outputs": [], "source": [ "service = QiskitRuntimeService()\n", - "backend = service.backend(\"ibm_brisbane\n", + "backend = service.backend(\"ibm_brisbane\")\n", "sampler = Sampler(backend)" ] }, @@ -1270,7 +1270,7 @@ "metadata": {}, "outputs": [], "source": [ - "# backend = service.backend('ibm_brisbane\n", + "# backend = service.backend('ibm_brisbane')\n", "backend = service.least_busy(\n", " operational=True, simulator=False, min_num_qubits=127\n", ") # Eagle\n", From b72cda5eacf039cf3ea0ab7b15b65bb6c158dd90 Mon Sep 17 00:00:00 2001 From: Frank Harkins Date: Thu, 7 Aug 2025 21:09:20 +0100 Subject: [PATCH 11/26] ./fix --- .../advanced-techniques-for-qaoa.ipynb | 6 ++--- docs/tutorials/grovers-algorithm.ipynb | 21 ++++++------------ ...m-approximate-optimization-algorithm.ipynb | 9 +++----- ...ime-benchmarking-for-qubit-selection.ipynb | 6 ++--- 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public/docs/images/tutorials/real-time-benchmarking-for-qubit-selection/extracted-outputs/e0ba509d-e0e0-438b-aedf-5e01919c7d4f-0.avif delete mode 100644 public/docs/images/tutorials/spin-chain-vqe/extracted-outputs/4c4b1b0b-5c61-4587-986c-7a9108bc2505-1.avif diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index a12a83c2097..c6e094f435e 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -142,9 +142,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 6, diff --git a/docs/tutorials/grovers-algorithm.ipynb b/docs/tutorials/grovers-algorithm.ipynb index 267faa50911..3fa0f5ecfc5 100644 --- a/docs/tutorials/grovers-algorithm.ipynb +++ b/docs/tutorials/grovers-algorithm.ipynb @@ -157,9 +157,8 @@ "outputs": [ { "data": { - "image/png": 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vvuj44hgYKq43eP/EpZtwo6QMKjqheP0rG2RbtmyB0WjElClT4OHhUe8+rq6udYIsIyMDX3/9NTp06ICBAwc2WXnJcm5W1yD9yx/MPi5t43dQ2bLPTiA5rRirIkMR/fZgDAp+FAuif0J61lWopuRcDn4+etasY27eqEHG1n9CVcsUrn9lg+zAgQPa68iRI++7jzj7+mWQhYeHa3Npu3fvxujRo5ugpGRp+T+exPVLpWYfl7M/Qdlv5ILBYNK+fbu0cMAbL/RCXFIhPvnbaagoc1vjAilju7pBZlC4/h2hqOzsbO21S5cu9W4Xc2NHjhypE2R6veWzPSQkRFtgQk0j1NgeT6Ob2ceZjCYM7vM4inRVaG5GOAFeCy3+uVfLq3Gj+iacnRwQG5cDS65ADwgMhB41sAXPG/3RF+3MPi7/bCZ8fX1hC6zRBq5asf4ftg2IUbDExMRGHatskIlrxISqqvo7JTF/JlY1igUg3bqZ3+mZQ4RYXl6eVf8bdFepawugVeOO/bnoZ+QbbOCsTOcMeFn+Y2OWDtM6sbOZJVg4sz+2fXcRF3It87+3ID8fMNnG6s/KVh2BWzMHZrlpNCKv0Eb+Vq3QBmKsWP/N2QaUDTKR7iUlJdpy+8GDB9faJoYO582bp/1bLLu39oIOURZqQiZX8XXWbDdhgtujbeCj87SJb+MFFv7MOX/sjZGhHfHO6kTsOpiNpK0TsWHpMIx4OdYin+/dsaPNnJFVGx3vXi9mhjJ9DXx8fGALLN0G5li5/h+2DTxMP6lskIn5LbFyUVwvNmbMGAQGBmrvi+vGXnrpJe1srKkuhG7s6TI1jrHGgO0hf0ZVkXnzZH5PheH8+q9hCyoqa+ARtslin+ff2RNRc0NwPOUSVmw4pa1SW/JpEqLmDtQ6uOjN5i2MqM/59HS4uznBFpSm52Ln8DfNPu7p92bh7ZnjoVob8G+C+m/ONqDsYg9xnVjbtm2Rk5ODoKAg7e4eAQEB2rJ6Pz8/jBo1qs78GKlB7+SIwBfHmH1cj+ljoSIx4LBxWTgc9DpMW/jjnaXWK2NSkHD6ktbB+fnevcZSBa0DfdFhSB+zjnFwcYb/8yOgGp0d1L+yQSYmbOPi4jBu3Djt/opZWVnw8vLCunXrtDt6pKena/sxyNQU9Np4tO7ZucH7+00aBu+hwVBR5LRgDHmsPRavTULaxbtLrUWHNn3RITg66LUhJtWELp0OJ4+GT5SFvjcdLVrXf6mOzCLtoP6VDTKhV69e+Pbbb1FWVqb9HDt2DDNnztQWgohgEysU+/Qx71sbycHZ0x2/3bIQbXrXv2r1Xt0mDsGQj99Q8uL3nt1aYdmsx3H0ZBE++qLuUuuzmaXaENPwEG9tiEklXr27YvSXCxoUTgPfnYYeU38L1fS0k/rXmezwFtAi0MLCwtCjRw+kpaXV2f7VV19pr9u2bcP27du1H0HcykospSd51FRUIeO/D2oXO1/NqL0aTQw99Zw+Fl2eGgSdFS67sKU5sqZQHj/VZubI7lWRX4y0jX/H+c0/4HrxtTpn4j1ffhKPDrg1h25L2AYazi6D7PPPP8err76K559/XluG/0v3+2Y+bdo0bNy4sQlKSJYmmvmVlIvY87v5t1az6YDp+be+sNgidmKWJ+7ccTk5A/v+zyK2AcXagLKrFh8kJSXlgfNjdpjtyhNfTtr29dNeRf2qOIxID+bQwgntB/ViG1CQbY2n2EiQERGRPOzyjOz2fRiJiEh+dnlGRkRE6mCQERGR1BhkREQkNQYZERFJjUFGRERSY5AREZHUGGRERCQ1BhkREUmNQUZERFJjkBERkdQYZEREJDW7vNcika1zc3XUHokhW5nJctgGGo4tj8gGiUeM2PKzvcj62AYajkOLREQkNQYZERFJjUFGRERSY5AREZHUGGRERCQ1BhkREUmNQUZERFJjkBERkdQYZEREJDUGGRERSY1BRkREUmOQERGR1BhkREQkNQYZERFJjUFGRERSY5AREZHUGGRERCQ1PiHaRplMJlRWGSDTI87FE23JPutfYBug5sIgs1GiE/MI2wRZlMdP5WPZ7bj+BbYBai4cWiQiIqkxyIiISGoMMiIikhqDjIiIpMYgIyIiqTHIiIhIagwyIiKSGoOMiIikxiAjIiKpMciIiEhqDDIiIpIag4yIiKTGICMiIqkxyIiISGp2EWSXL1/G/Pnz4e/vDxcXF3Tq1Alz585FRUUFZsyYoT1Dac2aNVDR8ogBMJ2agT9NDKh3+8H1T+F64nQE+beBqqouleLkJ19j5/A3YTKatPfE6/ktP8BQeQOqYxsASs7lIP7t/8T2ga/XagO5B07AZDQ2d/HoISn/PLLk5GQ8+eSTKCwshLu7O3r37o38/HysXr0amZmZuHLlirZf//79oaIla09gwvDO+Pgvg7D/aB7yfq68s+3NF4MwYqA33vokAWcySqDiwylPfrwdp/79axhrbtbZfuT/foqEpf+FoZ/MQuexA6Eqe24DNZXXcThiDbL3xte7/fspy9HK3wcjN8xH6wCfJi8fWYZe9TOxCRMmaCEWGRmJgoICJCUlab+vWLECe/fuRUJCgnZG1rdvX6ioxmDEtIWH4O7qhPVLht15P7BrKyyfE4L4U0X4cGMKVAyx44s3InnVtnpD7Lbq0nIcfHklsvb8C6qy1zZgqLqBf0z+t/uG2G1XM/Kw7+kFKE3PbbKykWUpHWQRERHIzc3F7NmzsWrVKrRs2fLONjHU2K9fPxgMBnTt2hWenp5Q1YnUYkStP4mxQ3zx6rM9oNfrsGl5OMRT6UUHZ/zfoRaVZH97FKmf723QvmKI6dDsaJTlFEFV9tgGflr+NxQlpDVo3xul5Tjw8koOM0pK2SBLTU3F1q1b0a5dO0RFRdW7z4ABA7RXEWi3ffXVV3j22WfRpUsXuLm5oWfPnliwYAHKy8shs2WfnUByWjFWRYYi+u3BGBT8KBZE/4T0rKtQ0dn/jDVrf2N1DdL/6x9QmT21geprFTi/5YBZx1zLzEfewWSrlYmsR9kg27JlC4xGI6ZMmQIPD49693F1da0TZOLMzcHBAe+//z727duH119/HZ9++il+97vfaZ8nK4PBpH3zdmnhgDde6IW4pEJ88rfTUNGVs1kN/iZ+r/Obf8DNGzVQlT21gcyvDjVqIU/aF99ZpTxkXcou9jhw4Na3sZEjR953HzHs+Msg27NnDx555JE7vw8fPlz7XQTi4cOHER4ebnZZQkJCtHk5cxjhBHgthCVdLa/GjeqbcHZyQGxcDkwWHE0KCAyEHrYRAo8bH8EkdDf7uOvF1/CYX09c0TX/SkZr1L89tYFJxu54HHf/jhsq9R9HMd3X1yplogfr0KEDEhMT0RjKBll2drb2KoYI6yPmxo4cOVInyO4NsXuDSMjLy2tUWUSImX2szhnwgkXFLB2mdWBnM0uwcGZ/bPvuIi7kllnkswvy8wFTNWxBTzcXoJFTnqVFxcgz2MBQmxXq357agKGVL3BrwMUsjkYd8gob93dOzUfZIBPXiAlVVVX1bhfzZ2JVo1gA0q1btwd+1sGDB7XXXr16NfqbRmO+kRfAcub8sTdGhnbEO6sTsetgNpK2TsSGpcMw4mXz5pLux7tjR5v5Nu5i9ADMPNMwwQQddPB81As+uvqHopuSpevf3tqA3uhkdhsQbuiN8PHhMvzm0Jh+UvkgE/+nlJSUaMvtBw8eXGubWIY/b9487d9i2b1Yfn8/4kxq0aJF2hxZY681a8zpckVlDTzCNsES/Dt7ImpuCI6nXMKKDae0FWpLPk1C1NyBWucWvfnsQ/83zqenw93NCbag8ucSbA/5M0yG+y+7/yURYq0DfZHyz+0PbA9NxZL1b49tIDv2GA7O+NDs40JfGo/clXusUiayHmUXe4wePVp7FdeLpaen33lfXDcm5s3E2ZjwoHASKxWfeeYZODs7Y8OGDZCR6JM3LguHg16HaQt/vLPMemVMChJOX9I6Nz/fu5clqMCtfRt0eWqQ2cf1mD7WJkLM0uyxDXT6bQjcvM0fm+05baxVykPWpWyQievE2rZti5ycHAQFBSE4OBgBAQEIDQ2Fn58fRo0aVWd+7F5iSFJcTH3x4kXs378f3t7ekFHktGAMeaw9Fq9NQtrFu3M/ojObvugQHB302vCSavq8/jT0zg0fcHD3aYfuzw2HiuyxDegdHdA3YpLZ4ecV1NVqZSLrUTbIfH19ERcXh3Hjxmn3V8zKyoKXlxfWrVun3dHj9llafUFWU1OD5557ThsSFEvwxW2tZNSzWyssm/U4jp4swkdf1F1mfTazVBteGh7irQ0vqaRdf3+Er4mAztHhV/d1adcKY75cAOeWblCNPbeBHtPGIui1CQ3at91j/gj/61yrl4msQ2cS9/KxM2LIUNzJQwwjlZWVaRc+3yauFZs8eTJ2796N2NjYO2duss+RWFt5/FSbmR+5V37cKSQu3YQrp7PqbtTp4DOyP8KiXkHLzu1hS2Srf1ttA6J7S9v4d6Ss3oHKwlv3Vb2Xg4sz/J8fgZB3p8JJrHYlKSm72ONBzpw5ozXwwMDAWiEmzJo1C9u3b8dbb72lbYuPv3uftu7du9e7PJ9sV8dhfTFh/4e49FM6Luw4jKqiEm3YqWWXDgj4wyi07GJbAUaWJb6s9vrTk+jx4hjk7E9E7g9JqL5aAUe3FvAK9tNCrEXr5l+lSg/HLoMsJSXlvsOKYihR+OCDD7Sfe8XExGD69OlNVEqyZGf2aEgP7Yfsk97JEV3GhWk/pB4G2S+IuTQiIpKHsos9GhtkREQkF7s8I7t9H0YiIpKfXZ6RERGROhhkREQkNQYZERFJjUFGRERSY5AREZHUGGRERCQ1BhkREUmNQUZERFJjkBERkdQYZEREJDW7fB6ZDES1VFYZIAs3V0ftLvNkn/UvsA1Qc2GQERGR1Di0SEREUmOQERGR1BhkREQkNQYZERFJjUFGRERSY5AREZHUGGRERCQ1BhkREUmNQUZERFJjkBERkdQYZEREJDUGGRERSY1BRkREUmOQERGR1BhkREQkNQYZERFJjUFGRERSY5AREZHUGGRERCQ1BhkREUmNQUZERFJjkBERkdQYZEREJDUGGRERSY1BRkREkNn/B6gin6pDm89DAAAAAElFTkSuQmCC", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 3, @@ -182,9 +181,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 4, @@ -207,9 +205,8 @@ "outputs": [ { "data": { - "image/png": "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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 5, @@ -243,9 +240,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 6, @@ -299,9 +295,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 8, @@ -336,9 +331,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 9, @@ -398,9 +392,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 11, diff --git a/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb b/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb index 11410e79fbd..2392e0aa750 100644 --- a/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb +++ b/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb @@ -128,9 +128,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -336,9 +335,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 10, @@ -419,9 +417,8 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 13, diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 50e51aa4295..66b474f81e2 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -333,9 +333,8 @@ "outputs": [ { "data": { - "image/png": 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3n8mEop9fGh8JoflE0nzXkbhySS4Il2NFMpeEduoZhF/9LhxDL8GOaZdM3eIE1/4WY5WWRts5fnfCfdBf+hr0Aw8xy3IiWbEeznv+Dmi5sbjqSz/rsdQYi4oVxBiJs6ScnPWQ9J5RdKwgyGisdFSe77vw5iI2Auz8D+h7v4XHPOvxN0v/AEM245hyg9eOLy5vxnLnRUjxDPUAr/4bcOD7gJr/HgXbgev+CFj7HkCcf0GFvgM96Ww+TTHFasdOJjOz7ucWeGbkUagd2+hxwH0hBKSJybk6FPa+bAiuE4+VOxRSNgHVbZHgWnb3wgtVl4rOXwA///+MxZEtnwDu/oeLH3UzMTG5orlqxNfl4koRXzlNY25oX+sdxVhOKVpU/1lrLbYLGjo7O3Ho0CFMTU1HQexOO+rb6hFcGoTm0hZM6SuMGXX2pO1ckHhpYeE0s0aqcNnihSRIb4zCb3LYOvEo9O5nwBUmygDSvBP68nvg2PBuQyBdAJcsPdKP3u99Co2TL0Iki0SicRtw858CrbfMOQkgM5DeWG9ZdOxE6ARkbXbtULWjumjmQWKMxJnbchX2t5noAl79F+DQj9Flq8Nftf0RdvivYTdRo+S/bavHfZXeix85TIwDu74O7Pm/RvSNcNcC234X2PTRRYv4iKxgfyxVjIzti6VYxKwUeiUrnbaiGKOt2WYxo6PzoCUykEcTECrcEDwWcGaz7MWTSxpGGSS4up6Y/m4XHArZQtW9r2+h6lJw8IfAr36XjsjAtX8E3PF3pgAzMbmKiZni6+oQXxSRenHgRUxkYngqasOLqWpkYIgVrzaGpuyz4JKHwMc4VEQr0JhohFWbXi2PSlH0u/rZlhEXFlg8x7MJOEvbW4xwKhFYlO72ppu8RQbyhd+/AfpeLSv8DsODk/wyuK55L1a95SPgxQsvIDVNw2M/+gYqu36ATTgyLcKoGehNfwa03XbWiQAZk3SFu5gQoygZRcdOR07Pme65xLOkKMhoXB5YDqtw6VNZLwkDe4BX/pl9vnHBjn9q/jD+b8ODUDgBVo7D7zVV4Q+bq+EQLnGqUTYO7Ps28Nq/A/ER4zpyudz8MWDr755zREDRdJxIprEnNp2u2J/JzbpfhSQWHRU3exxY63bAdqlf++WCDFHovY70G1u4j43q5DgSI8uRSN4IHXZ2V07iIFY4IFbYja3SGKUKO3jHG2AR6RIvVDFreDKvKHUoXHGPYZpBvbgWiOxeMez9FvCbTxv7N/0pcMtfXO5nZGJicpkwxddVIr6oLmr7D7cXL2ucHWnPPUi57wJ4I8IiZY7BGfkJpNxpcDqH5lwzliSWwB/3g9enJ1BChQDPEg+qW6rhd/pnCSin5GQC7KqFfioTJ6brEArOdHkmxVp0Ks04jjY4W7bgrfffD7/ff1GfEtX3/fjHP8ZI137cwO/HZq5zOupWv8mYDNCq8TkIX0pNPDZ1DEcnjxYF2VBiaE5TEjL0KJh50Nbqbb08qZ4X6vM99bQhuvpeYf23flb9Fnyx/Y8wLrjYXe6s8ODv2urRbL/Mk0IlCxz+CfDqvwKTXcZ1JITX/xZw7R8CwaWv+6HHsnKZq+LheBq5GacJieOw1m0vqx2rtkpv3PqixOgMcWUILLZFB4GS6LCqe5BQ3o6Eel9RdPEIQwNFhudPXeedUpkokwriLGAHJ73Jj6u0UHXyMcM0Y8ZCFXzNhh08pWI3bnljmldQz74n/szYv+1/Gq0kTExMrjpipvi6OsQXfXwfffKjLCJVKpY4wY+XMvV4IeGEohsT71v8dvx5ax3Weoz0sXQ6jaNHj7K0ROqhVkCSJFYfRrb1ZNhBNv5XLTQxG9pn2BqT4AqdLrmRg9a4FaeE5XiiV0AYXtZ/7q677mLv3aWK9MmyjO9///vo7e1F0KrgY8sTcB770XTPm9r1hgijnjev8zmFMiGjbqxEkNF1c9XnrQysLBNk9a76KzvqSbUmVL9BBhfjR9lVh90r8RdrP4+9ohFJarVb8YX2etwW9Fx530+a1JJgHMw3nqcFErLBJnOOusW1zliIjKrhSCJdFGO7o0lMyrPrOKnmlEXHPIbV/UqnHeKVkIJH71FyvERc9U7vM3E1YDjXLQQvQXWtQFx9G5LhjdA1Q2hKFYDn5hrYAqPAc1+G0t8LRa+HwrdAqbwditgOOSRDiy3w+Bwg+G3FCFkhWkab4LW+MdMY2ULVyenj5oyFKlSvmTbMqO54c6TqvfQ14NnPG/t3fcVICTYxMbmqiJni6+oQX4zJU4C/Zc5+I4OZHP6pdxQ/HgmBKjvoFPeOaj/+pKUGS0pW70OhEA4fPsyEGPVJK+B2u7FmzRomJqqrr4Ai50uBkgN6X5ou/KZV8dLCb+ZQeB/6HR341dOvsPeOIEfJe+65By6XESW5lJC5zEMPPYTBwUFmIvOx97wVwRPfN2qE5HwbArJhZiJsbre8c4EOG8PJ4bL6MYqWkbPlTPxWf7F2jKUtBlcjaA/iiqg32f9dI4WPJuD0O3DU4isbv4SHhKUs8ZLSCj/TXI1PNFbCeiUvQtBhnCIKJMJOPTV9PZkUXP8Z4zt7gSa49NlTaiKJsULt2LFEZlaiKr13G92GEKMI2SaPAz5JvDivPTmRF1N9ZamBRXFVmto2F9RXzdtg9ImiSIy/Ob/fBFWqR/yAguTOUeiyUR8n1bvgua0JtpWB6YUFeh7dzwDPfQEYOWRcZ/MB130K2vqPQ4nxUCZTUCbSUCbTkCfTbF/PznakLCLykCoMYcbSGUuFmVO6cheqKKVwqrvkRg5o2m6ILeZQ2II3Jc9/GXjxq8b+ff8MXPPRy/2MTExMLiGm+LpaxJeqAF9bYeyvfjuw5kGj5mfGROtUMoN/ODOKRyYi7LLIAb9VG8Rnl9SgpiRdiL4ONIEnEUZmHeQeWYAcI8ktkcQYibI3FdmEMXGiSUPXk0C2tPDbDSzLOxS23Y4MLHj66aexb98+djO9F/feey/rJ3e+aJqO0HACoz0xRMZTcPtt8Nc44KtxsP2FVsEpkkktFsbGxth38mMf+xh8kgK89n+A3d8Acnmbe1ppvvFPjOjIBRQU1JftTPQMOqemBdnJ8Mk5m1bXOmuLkTESZKuCq1ha6yUhOQXs/i9g938DaWOhQXVW46HNn8dX+NWI5A0oaJHir5fWotZa3rftimfsqBHFO/Kz6fSu2nVGw+ZVD1yUtK64ouJAiZEHRcjiM4w8iGUOG7YwMWaIMooonjUqSqeo1FR5KmCpuKLtbH39KBroKYir/FYisOCum7V4pcZziO8YRHLnSLnour0JthUlomuu50tNs5/7EjBpONHCWQnc8DnDHKXEcIeOt1pCZmKMhBgTZGw/BSWUoRzHeV8S7xBL0hiNOjOJxFnQBk4SLvFC1aNGBLZQhzhjoYq10nBdBQ6q9Nk//TdGOjAJzrf9J7D+fZf7WZnQR6Pp0OI5cFYBvM3sdXkloGkq5EwWcjYDOZOGnM0il0lDyWSQY9cZWy5/m3Gf/PVZuj5TvLx8+w3Y9o73XO6XZIqvq0Z8UdTrm3cak5MCNJnoeJchxKpXld2d+gB9pWcEz4fi7LKd5/Cxhkr8flMVAjNWpRVFwalTp5gQ6+rqYgYPBE06li5dyoQYCQ6L5Q02OS2dhHc9bqTFnH5u2sqbcFblC7/vMxwK84XfJ06cwKOPPsoafhObNm3CHXfcwdINXw/pRA5jPTGM9kQxeiaKsd44lHlWwkWJh7faAX+1IcZo9Nc44a2yw5I/mSQSCdZ0m9wtA4EAa0LOhDIVuVOEZ9d/ATnjuaNyJXDTnwCr3nbR6ixyag4nQyfLBBkJtJmGHtSTjerFSiNky/zLZjWsPi9owk5CdP9D05N1fwt2bf0z/CW/Dp1J4/Nf5bThS8sasN136SOYFxT2ev/diO6VvF5WE0a1YZJRr3QxUHUdXckME2KUpkhi7Ex6dupdQBJwDdWMOThcgwjWZQfgiPbNFldztEgohwM8dUbUai6B5akHFumaykTXi4NI7ioRXQ0kupphW+5ffAotGXUc+Snwwt8bqY4ECUD6za1//1mfj67qUCMZyPlIWXGbSEGNniWN0WstM/soiDPBdwHSGJlD4TPGcfMsC1VXpEPhxYamVI//qbHAQ6L/nf8X6Hjn5X5Wb2q0rAo1lmW/CzbGclCj5SMJLzrt6ALgvqkB3lua3/y1lhcIVVHKRE9RFGXTJQKp5LaZYqoglGb8e0U+S7r3ObDmtjvxlk8YfX0vJ6b4ulrEV6FmpecFY6WbUuUKEQ6iahWw5l3Gwd+/pHj1a5EEvnx6hPX+KfT6IQe3TzRUwjlHj59UKlWsD6PIWAESXpRuR0JsyZIlV359GE3kaJWWJg79VPhdsjpP7w9NGqj4m6KHJYKERA017z527Bi7TMLm/vvvZ695sWiqhqnhJMZIaJHgOhNFdHz2ir1kE1C9xINArROJcBbhsRSi4yloC6yCu/xW+PLCzOLT8fKRR5FIxVFVVYWPfOQjcDgcxh1JhJFl+c6vT0+aKpYbkbCOd1ySYncyiaEUxULtGAmykWTJinlJe4Ll/uVlgowcF8/Z0GP0iBEJorquYiRoPca2fw5fENbiZ+NGNNgrCvjTlhp8qK7iyqhVuqCRvv82JoP5SB+LxGz9JLD544Ddd/GfQzqCicle7JucwJ54FntlCw5yAWS58gUfUVPQkTiFzbFOXBM7is3RTtTlJqat9QtpgbPEVQMgnp9Qpwla/MUBJHaNkg0ku05qdBuRrmXziy46hcqqDovIz398PvA94MV/mO5dRSKYXPHouPw6fnNaTi0RY6VpjCnomYXSGDmIwVJRNp3GSIYg8wrL0oWqnufL0zjnWai6qqGFyt98ylj4oJTW9zxkpFuanHu0KilPC6miwCrZj2YXTt0tQYUGAcbvNOJIYuomYO3WrfDbLq4x1qWAjkNMJJVGh8pEUZrtF4XQjAiScT2JqUKEaTrSRI97MeE4HpLNCslmh2QtjDZYbDY2SjTmb7OU3oddb9zHU1GFQF09Ljem+LqaxFcpuRRw6klDiFHtR2khecMWIxq2+m2Aq4r9WJ+ZiuErZ0ZwNGGcTIOSiE83V+ODdcF5raQpqlKoD4tEjIkrQe8DiTDaaNJ/RUBf7/HjeYfCR4DRw+W3Ux0U2RpT8TcJ1RmTD3qPDh48iCeffJKlYNLk5LrrrsNNN93EjEnOFtUikTV2lqgWiaaaVg9qWr1s89c6wc+Y/JNwi01lEBlNITyaQmQsyURZZCyFdHx2ny5VSCMSOARNyMHGebG27hZU1HqNFEaKnHllSPu/Aez89+neOsG2vAh715z1gxeTyfRk0cyDomS0H8lOf7cKOESHIcbyzaBpoxTGWZNG+typYSvVQNEqfYHWW5C77tP4hrgSX+sbQ1IlT0Pg/bVB1huvwvImTkdhNW4PGdG/fI0b66W06SPA9t83Ikevl0ysPC1wZmpgaXSk8HQ4EZ2uduzxdGCPtwN7vWsxagnMul+9xOEarwub/R4WJVvtskO6gOKYJnHxFwaR2D0tuixNblbTZZ0huigtuC+UQudQFJ3DURwbjrH9cEqG1y6hxmNDlcfKxhov7duMfY8N1U4dFce/D/7lrwGpyeno861/aQiXC1CTx9IYk9NpjKW1ZcpUesE0Rs4uGoKs4MboCEGKvgBh6ClwA68teqHKpCTy+ctPAkd+YqRgvveHQPvtl/tZXTFQVLksWjVX1IqiVQt8Z0tJ8RlMihFM0SZFSvajbKTLkseGNZOt+PDwW1GhGItOL7n3Y8fKo1i7dANuqL+BpcBfTFdn+o0quey0uJkZNSqJGM0dbZrenxlh0vPZSRcLXhCKYmdeMVQmmui6ecSUtSCqbBClN08fSVN8Xa3iq5R0xBAclPpCefmFkycdWCgXnybZK++DZvXg4fEIvnpmpJgaRA1kP9dSg3dXB+aNArDC+/5+JsSoPoxMHwrU1tYyk46Ojo5Lb0BBByByfisUfod6pm+j184Kv/NOW7RyPg9kPPLII4+gp6enWPP2wAMPsNd21qhWTxTRifmjWgWhVd3ige08C+czSZmJsPBoMi/MDIEWCk0i5D8InVcg5bzwhjrAYXqS5ApYUVmlY6X0GzSGfwBRMSbIeqAV3A1/DKx996JTtS40rPYwMTgtyCY7cTx0HOk5ansCtoAhxEiQBVehIzQE/65vAMP7pz9zSq287lN43tqKv+4eQnfK+K5u9Djw5fYGrPfkI4NXA0V3x38Gxo8V3fyw7j3AtZ8CKpfN3V+M7MLL6q5KXAMzs4XyLCjaVkwJbC43t/A2QBdtGCSb+4KRRzSJo8n0rLkXpUrT50UW92TkQdvMlOlFvQ3RLGIvDCC5h0SX8UcszR5DdLX7oGo6uicSODoUY0KLxmMjMSSyr38VWOA5NLs0fEx8Cm/P/BxOzchSCHtXY3jT52Bd/hbU+OxwWcWLEkVQI1kWHZuuLTNGup5yskSuD3Z+J+zCa7Dwpc6ugCy1QwneCm3J3eCXrIVU6WAujW9IN8ZLWZP9848Bx34NiDbg/T81ooNvYtgCQEqZFa0ix08lmoWWF1h0n8WgQUNEiGOyIKikvKgiQcX2DXElSypbiKt11bKxzlnH9gtjjaMGkiBB1mQcHjyI6NN9WHqqkkXCSLg9VPEbPBx4AT67D9fVXce2zRUb4dRt89YdlUaV5hVTc9QoscXBi4ggSWUiZ3YEKX/dDBFkXF8efSrcRvcXLkK/0jcbpvi6isTX6dOnmcMdPQ+73T73CkJ8FDj6SyMiNrR3+nrqDUQ5+msehLz0Dvx4Ks3cEUeyRjSlzWHF/2ipxX2VXvALrEyQ3TnVhZEQozqx0vqwtrY2JsSWL19+1mjReRV+n9lhRLio8DsxVv4al96SL/y+G3BWLPhQ9Nx37dqF5557jr0uURRx8803Y/v27RAEQ7yk4zmMnjFEFgmusb65o1oUaaomodXimTeqdbFQVQ2njp7Bzx/+EWRFht9eizpsZKmO2WT5iU/i0ljjeAzrnb+GnTdqwlJiHcYafhvq6vfAV+uFr8oO0XL5VrjJuKMn2lMmyE6FT0HRZ5/E62UFHbJiuCuu/yjcNTfiK70hPD4ZLTYN/qultXh3TWDB7/WFgL5PlLZKdYJ03KDvE9Xh0Ua/18uWqlvoa/by/zZScAuQIUvVSiNqXohepWe3FZiFIzi3uGJjI2A5d0OVJBl5xAsNoFPYF0siosz+ndFxitWO5cVYu8M67+dKE8A4iS6KdOWVndjkRmhDBQ5yKjpHYjg6FMWJ0Tiy+UhYKSSLqjQelTKHapVHlcrDq3FIcTqSEqD5LVBcIjJWDglOR0RVMZ7IYiKRLZtzeZDEb4uP4mPC43ByxmLALm0F/kl+EEelDlR78xGz/FZDEbWSaFql2wrpfBtdM4fCvdCPPgwc+w242JniTTp45LAaaXkb0tpWqHrN7H8vUBojuTGWGH4U0hhdC6QxXsGoySQSzzyD5M5d8DxwP1zbtp3/ueknHzLSNslU6IO/AJrO8zEvE7pC0arSCNUc0apYtriYcTZyvMyE04QYzkenopiUwiXRqyjCYhQqpzFDJiaqXHXFsVRgkYPuQhErEkCndr+K8d6eopgSkyLqEi3wq4b77hnLIP6t9kc47jAWXDkdqIhY0TBuQ/2EHcGYhdUnXyhEi3UBgZSPKpVEjua8b0mEyRBONhahMrk8mOLrKhFf1GT3C1/4QvFyYWJHz4m2ufZd8gQEJsR+Ou3GVSiWXvlWZDreiW9bOvCvAxMIycZEZ63LztKybgm4z3pCTSaTLBJGQmxoaLo5r9VqZfVhJMSamprOf9JJq/GFwm9KsczGpm+zeoBldxrRLSr8ti7OnZGcAh9++OHi825ubsZ9994HZGxFUwyKbMXmiGpZKKrV4smLrQsT1boQ9PX1MRt6MlCh/m3vete7kEurZVGyQuQsNRVGh/VxrHf+Cg7BeD9jaiX2Jd6FE5lb4Aq687VlzqLpB40Oz+VJG8gkxnFy5/9G58lfoRNZdFos6LWUuHdyFqQ89yHlJnt9OnFquMkZxWeWVGJjcDlbCT0f6D0tiKrSrfQ62p/vMEu/gYIQm2+j3yz9ds7r/ZXTJZGrOVwDC2lwC2H3zxBXpeYWjYv+jZ0Pmq6zqCUTY3lXxVP5KGYpVL9H1vYkxmjb4HbAmlAM0bVnWnT12zh8n8/hiVQac1WNSDqYwKpWOVQpNPIIaBQ/Nj4Lq0OEK0BpMzxCw0nI89Se2N0SfHVOCNU2wGdB1iEgIeiYTOaQCI1g2/B3cXviEVhgLHrtUNfgfynvxmF9/mbZ9HUIOkmQWWeINBsTbtX59EdKhyz77jCHwh3GcXOuhSrKiliZdyh0VkCdkcZIdvlyIY1xgUk2ZxOma8tKTD+YMLNeGZND+l1GT/dg8rHHkdi9G1pPD6RIBCKlDLLjB4fABz6Aqs98Gnyhbvb1IGeAH77XqJejc9OHfg3Ub8SVAr0PVCtYWkdVXmOVj1YlZqe4z0dSSrOUvzF+ChNiKB+pimJSnBZXCT5t9L/JZzDMFFelI+tfeo7HQHpdQyeP4egLz2DXkSM4VdmIjM2OqskRVE8Mw5kxat5bXWuxNnAzrIJhQrSL343/W/1LDPrK06UdsoSWRADt2RosU+vZcypGkBaqVyqLIBn3E61W8Ga67psOU3xdJeKLhA5NrOn5kCnGYqADWCFS5rEA7swQPOFOeLJDcCMBD20OK7Kr34H/qn8nvh6zIJG3jd7mdeIvWmuxZZFOcBMTE0yE0UbvVQF676g2jIRYRUXFObzgSWPCwAq/Xyh3KHRVGxMGmjgsufGcCvBpEr1jxw68/PLLLFIhiRa012yAFK3CeH9i3qhWIXXwUke1zpXu7m788Ic/ZGKd3nNKn5xL/KqKxtIlo4OTEDu/g6qBb8GqGVGPuFqBfYl34nj6NmiQZglPX42zzImRRh+lJl0MRymK5O78D2Dvt6ZFNznabf99xDregaPxPvxkeBC/jtcgBeO7KmWOwhV+CKJsCGsLb8GKwApWQ8YiZBWrmaFHYfWU0mgXElXn+puj9FsSU/T9Opd/y567JC0s0uxWuLUoLMmhucUVNRk+G1Yv4K42hFqU3qN81Cew1HBI3PCBy5aGuhAhWcE+ZnFvWN2T5X26pPahOq3hoz1ZPDCkMDFFHOBUfFPP4ECJ5LJrYFGsgtiq1njUu23wVtjh8tvgDtjgDtqYuQ2N1PpBFrN4rv85DCWGsLl6M1qF5QgPpTE1lMhvSdYyYlYTNJYNy7GFjIp6JwL1LlT746ge/G9Ix34ALt+eYbjmNrzU8Ds4rjVgLJbBaCyD8ViW7Sva4k7dVpFHi0fHWyxHcIO6G2tSO2FTp02ZNIsbevtbIKx66zktVLE0xiilMU47McpkkV9IY1zg6QkeS7G2rKx/md8KboFoHr3mhKqyWk3aaD9Fo0KXVXaeKlzP7jPz+pwMy/AQGk4eR0vPKSzv7UbbYB+EGbUyWUnCmL8CTeOGEVC8ohLx3/8jrH3HA6gsac1yzvXY338X0PeK0f/tI78xao4vMsw1M2EIKkr9Y58ZCamiwMqbVuSdPc+GymtIWDMIkbASpjDMjRmCqqS2KiRGIfPTGQl0TK1yVM1KBSyMJK7s4oVzX41PTWLPjhfw2MlT6HQF0NvQhoh3dm/JCl3Fcl7FSonDWgjYdFyBtduYU3B2AdpNHuysOY6Xh1/GzpGdZX0sBU7Ausp1uL7+erbRueSNGOk1ubCY4usqEV8LrcLPtU9jISXwbFiRYakxdklHf6ANB21ViFrsSFrt6KgM4vdXtGBjVXBRBxz6m1QfRiYd5BhYWh9WX1/PhBjVh5EonAVNIFnD40eB/hmF34HW6cLv+mvOuW8V1Wp17u/C088/gXjKqFuxZIJwxdogaNa5o1okuJZcGVGtc+H48eP4yU9+wlYDt2zZgrvvvvvsnx1NGPZ/B/rL/wwu32xatlajP/AhnMy9BVPjKuKT6XlT2Onh3RX2otGHYY9P+04WDTjnkxW1ViDnwsM/njaTqVzB6rlYDaNoYT3t/urUEF4Mx4v1i59tdKNWO43OqSMsbfHk2EkoaQV21Q67Yi+OLs0Fr+6FJEvAIkt7KBW1EF2eGW0ubPS9LqSslv5eC+mIC22lvfYW+5ulRRT3zFHU4Pb64QpWQ/TPTA9sKnc9JPFF4nbft6fdU71NwLV/YIiw15FCeKGgBQJyAU2EMojnN9ofnEiiK5RCTzqDPg8H3WfBe7IC3hrWi6Jrr1/AN9os2BcQIWZUVMRUtGU4bJOsuNbnQjDoKIosp9cCfg4hkFWzeGnwJTx25jHsGNzBLheg1fubG2/GrY23YmvtVthEG+ScyqJiTIwNGqJscigxK/W3QNAxie2Bn6JJeYZFanX6/6p3QrjtL4Dg0qLxx1Qyx0RYQZSNRWk/a+zHMshFx7Aptxt38ntwA98JKzcdtRjXfXha3YQntWvwmrYaMkT4HdK80TN2ndeGgMOyqAUmmsjLUykkx1PIjBs1ZtpkGtxUFkJ6/h+WygEht4hxl4Bhl4B+J48zDh7ddmBA1JE9x9mKqChoG+zF6tNd6OjpwuqeU6iMzE6hnfAFcLy1Hadb2zGwfBVCbe3gNB3+V1/CJ375Q9SEjMjwwfaVePIjv4O29Wtwc8DNoqrn1HydsjUeertRk+yoAD76GFC5HK8XLUu1VfO4ABZqrRKGxfpikC0qkrYMwlIc48IUBrlRDHGjLP2vUGsVFRLFaFUBWsiaGakq1l656pjwIvfai0kmm8Wje/bi0Z4+HBKdGK5ugF4SWaI9SkmutUroTKRxOpWd8225NQ589mgaNVHje8o1uVH59nZwVRbsH9/PfvsvD72M09HymshKeyWuq7+OmXZsr9sON2USmVx1xEzxdfWJr8VAIohW3BcSaLTlcovrv6Dn06YCXu+86Y604k/pkAWojurkyZNMiFFEpvAVpEhMe3s71q5Zg2U+BVL3E4boIqvwUqhhbMGhkCbe5zCBT8VyGMunDg6dnkLP5CGkrEPsZMKpEtyxNliyFQjUOKdNMVo97PIlKyyn9yMxDsTylv4UiVlw4xZ9nyOdx/Dwb37DJnTXXnc9br31tvL7zAdFQ8gpj+qDCnbZrhrg+k9DXfshRCJ6iROjkcJIY24B22tK2SoIMhYtq3Gyy95KO4SZtt2De42/TQK8cMps3Mb+PtrvZKKbGv3+U88wftgzAGs2A18ugzsdItaLOtIzvucUAVwMOT4HWZIh2SV4PV7UBGvQWt2K6kB18XtONv7nveJJJhjRwXzEqtw1MBcaRDwRRxzO/OaaNcbggjIjGrkQ9JxL0xrniqaRYOTJpXDP/zPaEyTzlu/2ALD1d4AtnwAcs90Jzwc6FuTSSl5UZRGfMoRVPJwp7idiWUQ5HWOCjnFBw1h+S+W/MjXg8CFYcTckSPlZ4klJx/PNNpxqdmDczuOUkpuVMWflOaxzO/K1Yw42UavMp7BSkf6ukV14/MzjeLb/WSRL+o5RtLTd385WxuOFHnr0Nol2VrR/a9OtuLHhRngpsljyOpORXEmEzNjCIykmrAi/MIAt7h+izfYau6zpPIYc92Kq7Xfhbm1HsN7FonJlxyW2UPWocdycsVCVcDahJ3gL9juuxX6tHaOxHBNqtOUUDTo9jMABIg+djRx0Ep/FfQ6ChYfDboHNLsJKaYUWAbxk3F8TOJY0SVPaQmRqrsmFJ6ejKaWhOalNj/l92wLrggkBTIz1O3gMOnmMu0VMekREvRIEmwgXz8M/PoqWQ/vReOwIanu6UTk5DnHGYiM9J9nnB9faCsfWrQjcczd8bUvLagRpUW5yMAGl9zie/Zevsc9rXfcJWGQZKsfh1zfdgW/f9yAUtxvbfC7c5HfjpoAbK5y2sx8LyAjru/cDI4eMYygJsLyontNifZ66Krp+sRbrOg/Idg0pe5YJK0oDHOLG0KcNYpgfZ8YVITGCLD93WqFbcs8ZsSqMQdviFmEvNH2pDB7p6sGTgyM4LDmRtZT322yAgttrKnBLpZ8trrhL2ugkFBVHEmkcjqdwOG6MlNJM3w9B0/Hefhm/052FQzUWBV5d7sLo9iqsDrqw1u2AJk8wEUZibNforjIzKIqKra9azyJiJMaoZ6UZFbs6iJni6+LyRhVfi4VW25kYC08ifupVxM4cQGxqBHHdgQnOjwk+CF6lKfziKKQ5zhRolE41OjqK7q6TkMYPYiVOYwW6EcB0iqLO8eCarzPqt2ijVfpFwBwIh5JGrVZ+i00aUYSsJYSE9xQ0wVi1DliasHHldjQtq2IRLqtDujQCK3TacGOcOj29HzpT3qvtUlIm2ITZAo4+cYo40Ymm1D2T6hgoXYkXi/enz03XOWb4pVDqC40ybTqobQh9e4xvEG/s63TZ+DuCJEK0ihCZU+MkJGUK4GQkORti9iWIu1oRE/yIyQJiiohexYmwLMKay+a7uJwdClx6rLRxcFt5uKwc0mIaY3wMvfokjuvjOKlMQJ1jCtlo8aLDXosOB211WOmog53qZc4qhjkjdXZmaiAJ2tKI7lxQWs7M/lb56JXubUJWcCJeEkkrCM2Z22Ij36Wpkm6XE+7MMNzje+DJDBjRNFGFe+19sN/we+AWcA2d+ZtMRnPFaFVBZJVGseQSwU7T9zCv5wXWtNjKzvEh14PDJy1O3Jjji56eQosHgbcsgbVlWvgQaVVjDeeZq2LMcFcs1LcW0TU0ogf+zG5EIq8iLU8fk6psVdji3YJl2jKIUyI7D6hQMSKMoFfsZVuSnxZonM6hVq1Fi9KCZqUZbt0972FBI1ODwiZr0JUsbIhCovoYdh8OGd2JBPzIihKqpRBWcSewWjmKBmW6xpbosTZjl+cavOzdii5HM2RBRJYXIPMCG3Mlm3oRjV8kVYFFVSFpKqyaCoumwaIX9o3NqqqoyOioy3CoznCozAioyAkI5CS4ZRH8AmcbWY5Dj48BsWFoibHipicnkRN5RCoqEK2pQbShHvG6OmhzGT/RzzzpBKIeIOYGp9KCoQ6nK42Ko0+jfngP7CuboR88yO4ec7nxjfvfg8euuwVa/r2rtoi4MeDGzX43GwvivezPyCrU0VHwP387+MhJaNZaxNu+ASUbnDaxIIv1RaaV6hYOiktHypZD1JJg5hUkqEhYndZ6Wc1VVIxDI/eIeSDxNF+tFY1XShQnpqh4JRzHM6NTeG48hJEZ0TR7LoP1WhZ3LWnA3Usa0GQ/t55zJMg6SwTZwFgc7zgYx21jRhRszMrhn1ZY8Vy1iCqrxETYWrcdqx0ikDmBo2OvsRTFM9Fp4xqCIn8kwkiMbavdBhe1+DB5U2KKr4vMm118zbtiRyuqZNRxZgeO2FvxTw0fwQHHKjhzGXizKWwRdSwTgGwicdYogwAFLRjASnRjOXrgwnQ+tQIBp9GM42jDoKMDTSs2YP369WhoaJjXqIOiWsx9MB/ZGu+LQcmVTzQ1Tkauug9xGNEbj9uL++9/K9ra23AlCSwNPOJSBThegMiTBTStppE00cHRJL246SX7c2yLzTe5jNAzzMDKojeFKE4M7vw4fV0ai6sJoFQtVrfIUu5ojBcvF66jTSzUNC1AmuNw0iLhiNWKTquFbf1zTNwEXcfSnIw1uRxWZ7PoyObQlpPPIRaVNzsoCqpSgZU3tyCXzvNcPSXhlU6n501xLPxmqZZ0sacGASrckg53sAZOXyWsoh0SZwOnWqBnRagpAbkYj1RYYdEeWtWfCxK5k3mBNWUDxiVgRFOQm+N5SAKHZdVudNR5scnnwIbBNOxd0eKE1drmY82RrUvKRdd80GulNhu7Iwk8N3IQB4afQTL6CgQ1PP3e8W4o9i1w6atQnXCjNhZCVSwMK60qzHw86IhYIhh2DGPYOYyYJVYu/JUgfHIL3GobRK4KCllgCyJkUWRjThCh5Ee5uAlsVHge65IncffUK7h78iW0ZKYFlwoeu7xr8VjFDXii4noM2uZwKDwLPIkhVYGoqrAoMhNMkqIwATV7U2ddZ/xbBZb8vyHzivNd8+d1Dn5ZQkNcQ2VagF+zwyF6ITiqwNvmP/9quoY4n0aUoy01vfEppGAsvEk5H6yZSlgzQfD6dJ2wxing9XLLf0GdRGOdhvoDj8LaZbgGx9va8bMP/jZ+Wt0Ia1ZDVUZHZVZHVUbDal3ASlVAowy4UyqrudLzaZc8wqi0/DkkfhCKVoPx3FegoaT+mdZwXBJUp460XUbUmmT1VCMkrPQhnFZ70a2cQUpYOC2ZojA1zhojFTCfDjgzcmWlY88VCNX4HYyn8EIojhdDMeyPJVH6beJVFXUTg7gGMt66bCnesn4NpAvco5IcV08dGoXjyQE4YkZk8NUKAV9dacOQo3wuUmkRsdblQKsUAZc6hKHQbhwe34uMOv0ZiZyIDdUbilGxNl+bGRV7E2GKr4vMVSm+SqFVxrxj4u54Bl9u+W3s9K1nNzm1HD7pSuGTa6+By+YspjnSpC4xNQJL34vwjb6MqughWPTpgxJNwLvQwgQXCa8c5jbMIHtuv98Pu8UJTrZCTvBIh3RkIzwEzQJetYLLxz8sdpHZvFe1eJASR7H78MtIp1PsYLd161bceuutsFgWb8yxoMCaKgirswssXRehohKycyUSQhvCSh1C2SASGTckzQkPrHCAw24oeAoydkJhKT006az12lHvs6PBb0c9bT5jbPQ7WF1Gmf00E2dzC7QXX3wBr736MjuV3XP3XVizetUCQm4ekadkga4ngAPfA+JGcTpsXmD1O4AV90ATrEimM4gnM4ixLYtYKoNYKot4KodYOodYSoacN3Q5G7yuw6bpLLIx7PGjO1CFhNWOnCjhhvAg3p44gyoXD4dLgN0pwuYUwDJez0WwnuU+US2Ho1oKnVoSnVoanXoaE3MUiFl1YAUkrNEkrNZFrNEENGlUyB0oEVclESxn1TnXLF4saMGEBNhMYRYJRREJG7/lVCqGHKVLLhJOE8DT71OzQBLtUC02pEQbwpyEYYXHmaSOmCYxAVGKTeKxqtaD1XVedNQbIwkvnvp0PTeA1IGxojcI9efy3N4Ma/O5HZO7w914+NTDeOLMExjJjJSIagtsWIGsYxsmfZshS+VpTZyuo1kA2m0ScuCQ1nSkdJ2NSTYCKTKmUMZhTe2DJb0PUraLxXyL77VQiaxjE3L2jZCty4yI8wwsWg7XRQ4wsXXX5CuokqfrlrKchFd8G/G8/0a84tiCuOaGRdFgUXRIbDT2rZoOj1WAzymxLei1otJvhdcuwsFzsHNgo3SeE8G0rCKUzCGczLExlCrZT8oI0+VUjrncz4ek5LBm8jQ2jHZhaXgIFYkIJEWeJeLIjVCtrIO4fAOk9vWQatvAZQRwEQWIKuDmcGMkx8xJRcewqmMkp6F0fU6y8qhpd6B2tQu+egvOdA3gzKFJpEdFiLKnzGbczuVQl4ugllPgsznAOwPg5vjs5kKXeIjkEOuYQjD8aUjKCJKWGjzR9gl0iXGc1vpwMteNiHz2/nk2wTZvSiBFragWSXgDuer1pbN4kcRWOI6XwnHEZrR78EcmsGSgG+vVNN7asRIbt98A2yXoJUoRy9jzA4i/OMjcUnWRQ/emIB5ut+NAKoOuZGbO5bwKQUML1wNb5jAmI3swmcqXE+QhYVww7aCoGFnqm7xxMcXXReaqF1+lhHqgH/k5XjhzDH8fuAuH3UYBcUCO4g+Vo/hIfRXsuZjR54Q5FJbUk1HO+4p7IbffhaivA/HUtMMcTfAikQhzTKTLi63TIaySDW6PG36/Dza7jVnHh0LGhCUQCODee+9Fa2vr4laczlFgaboVqh40NlRCtS6FKjVD5aqRU7yQ01YI8rmdDKlf0LO6jCch48iciXAGVP5BxfHl4szBRnbZZ4dNMv42/fSfeOIJ1tOM3ocHH3yQtQJYLAXDCPZ5RUKwnvw16rq/D2fWsK5Oc3a8ho3Ypa9DFmdfWbUjA3chSiUo8DStgmf5jSyiwnrYWZ1IRlU8NDSFb2TiiOfTaNYM5HDrgSQ8aX1+m+9ibdm0I6MnSP1QLozYGUuOsb5jnVOdbCRTj7gcn+M1WlHJ+1Hhr4HfGYRLcrGUHrIsplQU2mebZIx0HbtNcl30CZSSU5mRBdVWsRorSg2cKqm3Cmehzeh4rFMLVCEHjc9C43NQhRx4IQFOSkO3cVDJJEFNQz0HkaZyVMPjgNvlQmXAh9pKP7wldWkORYK6K4Tsoclp0bXMbzRHXoToou89NVA/1HsIT/U/hV3RXZhAvp6N1XsIqE3VojHZiOpUNdwON2uwXl1bi3RFNQbtbhxXOeau2JdZXG1saV0ZJQxa0wfBJ/dCTh6Grk8/hiR6UB/YirbK67HKuworx3ZjSe9TqOl/AWJJPZlm9UCrWQs+0g8+2m9cSaL++s8gu+ajCI2reWOPZNHkYyEbfKofCza4UEFjvQv+WgfE/HHiYkANrKeSWYxFsxiNphHu7oHlhWfgPbofvjGq2UzNGS3L8SJGHQF0Bluwo34djlQshZb/XQSdlnwPNKMfWrXbikaLhHqNgz+jITOUxHB/AoOTWeRKoq8WDqiVONRJPCpEjtV+UY8yMWiHniP79Ryrv8poOkZlHSOyhgmFYpsl7yF7DB61Fg4BSYW1xgvFLaFflHGck3GYB3otIsZtHMatPBIiRRhjkDKdaIm9hh/2PIFaRUaXJOFjtVWIlpjz0O9/rlTAgsjyW/1v6MhJIZWQRbfCcfSmy39TtkwKTUM9WDLYjZXRcWzfuAkdN9+OisbFpTpfaMjRM/Lr08h2G8KYXDp9DyyF1urFsUSapTMXasi6UplZTeJ5eQx++Qg82SNIJzuhatOvV+RFbKrahBsajBTFVu8i5ygmVwym+LrImOJrDqiz/WgnHj36Gh6KS1gZ72IrtVtinSxdrkiwzXAopK1+06zVfmoOTBMGSh0spBFSrZbCZ5CxjyJrH4cmTkfMOI6HzWpnvXYopYpEwbnYdxdr0Kw8PHwGHi0Md24MnlQfXLFT4MPTAkvT7VD1iukNJLIqoQoNUCl9SPVCVxYXSaPS3gnoiNJ51m2Bs8KOqjo36ho9sPhtLB8vdWQCqYMTLF2lgOKSMN7owHG/hOOyjKFIGkPhNAYjaVY4fzYqXJa8OHOgzmdDaKAbkaHTcAsyPvLg/Vi/egVzo5xpqz7zMkVGZsJDQwdO4EbsRgWMlK00rNhv2YZTgdtg81VPv99CDp6+p+Dp/iXcyiQsFD3yLzFszde/H5DK0wwpJewvTg2xnHxipdOGL7U3YLvXiUQkaxh+jCXzo2H8QYJhPniBg7eq1PBj2pHxfGv+KN2pP9bPmkHv6XkVe/t3Ypibgiq8/sOtQ3RMi7OSjYQZTdAKYo2JOalczNF99DRf4g6YLXMKpDEdP7tAonmA02dYrc+yXxcmIB/5Dk4e242jah2Oai3o5JahTw2A4rx2ToYDOTho5HIIWDRU23UmtCUtCyWTgjpHCt+c6CRkLXBZHfBU++GrCsxpGkL90SYnJ1ldKastHe3G3vhe9Fp7EbKFymqyalI1WMGtwJbAFjTVNjHBRZvL5YAsh5DNjhlbbhzZ7Ciy2XGMpGM4knZgDBVorXsb/PYquAQBToGHU+ThFAS4aJ9tAqQZpj0pOYXXRl5jdvUvDr4IIRXCzak0bkumsC2dhaX0uMkWqu4xjptLbjBaaVD46Ngvgee/DEx1T9/vxj8GNn642G6DUj3pMyYTiUXb4FfZmSAjMcZEWYOLfc7nOxnUczkkXnsN8SeeROrAAcjUU1Ge+7vHe71Ay1Ik123GyKbrMWTzTzs6krtjnMYscnNEzmltpknhsVwW0C4LcDBHEQNa+5IDEvxBG5o8EioVwJNWYYnlwM3jQknGI7yDhzxwCslMDEPBGozwbsRJSZGjRR4pF4c3egyn60bw0vJujDpHmO26KtYhZ++AbFuDnHUlwE8vSLWkBvHrQ3+AqlwYQ74WdN37ZTRUtDOR9WaLhJSmEu4Ix1nj9FKBIugaascGsKT/FJYMnkJtaAxtG67B6pvvQMv6TRBKzLsuFzRtTh+eQOQ3PdDyx037ukr47m2BQIXEJbWlMwXZyVJBpuVgyR6HlD4Ee+YwOKWk5x7ABDZLT2y4AVtqtsAhnUevOZNLgim+LjKm+JoBfY3IlbDgtDXWWXbzIddyvOZdi7ZUP25VR8CveQew5kGgejVScbnMFGOiLw5lZs8RDgjUOlkKIdm9i94ceoe6cPjIYRZ9KUDpiBS98fl8LKJDE6+C4UcwGEQ2nWSfXTq7uJV4SitygIdTt8ChO+HQHXDqVjh1Gxyg0djEYom/QZYDxqBhTNcwDo2JrAlomIQOV6UDzc1edLQFcc2SAOp8C9cy0cQp2xNB6sAE0p2TZQ5XUp0Tjg1VcKyrBOeyYDKZZUKMBNkgjfl9Js7CKSRzCqxQ2eSXNicbjUlxYZ9GC7e4KCPZp89lre52OVAz+Sp8h78BIZSfFJLb27ZPGk1cqT9X58+pCc20g+V1nwZWPUCqqOxvjGVlfOH0MH42Fi420P0fLTX4cF0FxLM4UOYyCqLjaea+WNZUejzFzAzmw+6xlPUsKzgxktBYlNW2rmPg6BHs/vVP0Xf4ALuOCt7tm9oQ0xMYGu5BTtKQEzWoVg62ukpYq/3QHCLicoK55iVyCRY9K3XROh8ETYRFscOi2mFlo41dtqrGdbRvhwNeuwd+pw8BtxcBrx9V/gCqKypQWeFjva0oWkivjybAnUMxdA5FcXQ4hqPDUYxE564/qefDWFVlQcfKVehoqmSpg2RjXjqRp8csGP2UbtHxMMK944hFY0hxWaSQg76AecBMsnwWQ84hDDoHMWGbKNpkk+Bq5puwzbcetzSuQR0ZN+pTyGTHkCuKrDHkchPQC9/TBXA6l2HzNb+AkG/UumjCvey4qR9/BBjYZdR05ukVRTzntOM5pxOWxu24uelW5p7Y4G4ofwwSrYd/BLzwVaAQCaPWADf/KbD2vcA8dTDnaoNPadzBemdRjJEwC9Q5YbHNPyGWx8YQe/xxJHbsQPbESaj5DIRZkDitrYV9/Tq43/IWuG68EbytPM1z3khmSmb2+iN03DsRRoTq/wZTEOTp70mK03FKUnFSUjEgatDm+BlvgID3w4JtJdWa9LuNVmfRXTeCfb7jQOcRvO8bp8HrwHdu4/HkJhuWT2xE+9QGVEeXgOOmnzOvZpCzTCFaOQmlOYpgsx+N9Y2odNVhEpU4mrbipUiSTczbk7345aE/QlCOYq9nNf75+v/E1uoa5qLY4bKXuTG+0ThbKmGdkkXDmeOo7+5E4/AZWOUsKpqWsAjXyutvhsNb0gbjCkLLKIg91YfEa2SYBHBWAZ63NMO1rQ4cuYTOAQmy4yTIisYeKZxMZpj7qiCPwpI+BEvmEKTMCRj+oQYCJ2Ft1Ubc3ngjrm+4Hi2eFjMqdgViiq+LjCm+mHUZmyywhsckuMjBrQDlvjdfC3nFffipZyu+NKFjSjNWB5fH+/GBE7vQNCxhTO1AXA7MaUNeaF5c0+JlNVtWuzingcCZM2eYbT31sSIb+wJOJNHEjeK6ihhqkIUWSkDNGZGrrF6BqB5EjPMiDgfisCDJqUjS5I7LGuM5TfJEZDQJKd2CiC4hRo+nW1jN05LaCqxeUoPNS2uwodkPl1U8r7zz9PEQUgfGkTkZnnbE4gCp1QusciFXJyCeNup1ZjYJjpEByiIjgzmdZ6+HNnotKRivT+aNlM6g34vagBf1AUcxkkapjZUu67RAoe/IsV8BL/4jMHF89h8hIUaii8YZJ5KcpuEbg5P4Wu8os62mW3+rNoA/b61DheX8Vj8LkYCiGKNxzBBoqej8qWRkge+tKulbRmmM+X2agOqahu69O7H71z/DaHdXMTK7/NobsPn+d6JqSSu7LjYxjuMvv4CjO55DeHi6BsDpD7DJxqobb0Vl0xLjfVBzCEUjGJucwlgohMlwCKFYBKFEBNFUDLFsDAklgZyQRk5MIyumkRMyyOYv0z4LA5wnVEcpcXZAsyMnW6HIVuiaDVBt0DU7dDbaUGH3ockfQLvfi5WZU1jb+3M0Jnvh0jSIVA+4+beBrZ8EXJUL/j15PIX4c/1IHZooRmdsKwJw3dKAkJREX18fhoeHWVoypSeX9kOTORkjjhEMuAYwZh8r+x0HMgGWUtiQbIBNNSbKPK/AYknDYknBYk3DSiNdthqj1Zphjo92RxBWa7WxWapgtdYg3J/CoZe+BUftJJaufws6Vv+vhSdFdModO2ocM+nYOTazlcZ66CvuQ2/dajyR6MVzg8/jROhE2V3IuppE2C2Nt2BlYOX036MazP3fBXb8I5DIr6IH24Fb/hxY9fZF1RTSlIB+A5MlgmymDf5MPJV2Q5DV2uBL9sLa+TKUzoOQ+/qgl/R0LIUnQbl0KZzbt8Fzzz2wLlvYjlvRFGSUDDMvYKOSYT3WUtkUpnoymDqaQ+wkoKVLHsOmQGmOINM8jlhwDBFa0MimEc+lkJTTyChZqEoOWyPL8UBoO5Zm69k/06Bhv/M4gooPLfnrCBUajji6EA7tx+bn90PNxfCdj9QjvqmdRSlq7LXw9VdAf3Ic0VQlcqXtBaAiZw1Dc0VRt8KD9hWtWLp0KUuDp0bhJEpOnd6L3372o/AqcbzqXY/3r/kq0oINAUnAjXk7e7K1r7OdR53yFZBK6BV4rFVTqDt5CN69L8EbN1L4bE4XVlx/EzpuvgNVLUvfMOIiR7+PX3VDHogXF0V9b2uDtWlxc8MMRciSaRzJR8dIjB+PR8BnjhtiLH0IgjqdGk04rTXoqN6Ou5puwj1N282o2BWCKb4uMlet+JIzwJkXAVqlPfk4kDIiSwzRBiy9zei/tewu1gMoGc1irCeGMz0R/Cgdx1NVHLKScUBtnJBxy5E0midyCIgDqPGFULO8BjXX3gBfS+P8fbVYDdaYUWs2cRra6CCUiUkkx2IYS/gR0pdA0StZdMpJxhW6FcIiDch5pwTBa2GpA5xHRNauI2WRkUQW/YkoekJhjIYjLNomKhkWPZK4xZlFUK8zI0pEKVE2eDwiXC4OdrsOm02DxapAEKiuJg5ZiULTcvC4O+DzbYEkNRbrqwqiKhqKIDw4iVgogkQudU5ikfo8zWwMTNHBV3ftwZmxMLKiG6u33YyYKuajZkYEbSI+fxpfAYvAo9ZnM2rMyBxE6Uf90BNYG3sOS7ix6agapRXSJHz7HwLOYNljvBCKsUbJ1HeF2OhxsBTDDZ6Lf4KhPlOFtMVCvzISZRRBI/vvuaDIiCicQi65G3LG+E3wgoTl196C7e98EP7a2nn+nY6RU104/OwzOLX7JeRS01Fcq7MWVlcHVL0Nqnz2aIog8SwNkKUABo2UQEoNdAbo+6xCd8hIavmoWj66FsvFjAhbLs6ibLFsHOPJCCZTEcSyCaTVBFRyIV1kJPRs2DUNbk2DRwdc9iDcgXa4nVVlaZSVSR9ajvjh7RaLdT+pWmCwNoH+lJE+mMnM/h6qnIqQtx+Drj70iyEoJb+Fak7ACjjRKgdhz3mQzTmQy9qRyzmgKIt3eqM0xtK0Ri4eQe+TD0PLp0sKNgXN65Zj3S0fQFPHeogFZ0y2ULU73yz+N0a0qwC1IGCtNO7Lt9JonPV3hxJDeGHgBZaeuG9sH9SSSBzVAJEIIzG2sXqj0cyWmqPv+YbRGy+dd2ysXgPc+pfGsfl1TGrpu0+/g9IoWahnGI7e3aiYPAJvfAC2bKLMTKTUGCPrdSK5tBoTm1swtLEBCZteLqbyY1bJsn2K+JK4KtxG4qv4luk86qJtWDq1AS2htbAr04YLaTGBM4HD6AkexJD3FPR5js8u1YF7wjfg/vBNTGQRGS6Hp32v4df+5zFkHYcEF6rirdgeW4cbUsuwTAmW/XbVqVNQp46i9m8/Cce6ZWWPnzl9Gif+/hsYGOYxXrkOGfv0YgNVjMmWCLK2SdirFLQub2JCrKWlBbapY9C/ez/4XAJHqrbj3Su/gPAM39R2h7UoxKh/lbOkf9XldyWMY3+8PJWQ3Ho3eZzYoKRQ1bkHuRefhJpJFxenmtdtYFGupZu2QjwfA6zLCC3oJfeMIvp4L/SMwhZEnVtq4L1zCfjXkcae1ShClmFi7FAshQNT3egP7YaQLkTFpn8POifB5epAe8VW3Nx4A26rWY5mm+UNI17fTJji6yJzVYmvTBQ49bQxaaCx1GCCVrKX3c0mDWrLLZgc04tW75RCSIX6paQtHHatcWBnqxVyXg/dlDqNvzzxVayNn5yejCy5AfrSO6E6lkGdDEMdn4IaTkCNKVDTIlTVlze0oKjZ2U88dLIjcZIRchB9Nnjrg/DUBSD6rExoFQWXZDypZFbBwYEI9vaGsbcvhIP9EcSzpREjHTZBxro6HhvrgTZ/BrWOFHg1gnR6EtlsGLIchaLGoGsJcFwaopSFKObYxvOLE2wFcjkrYtFqRKNViEarkUzSZGG2oCQ3LoduKaZGOkUbfLUVCLbXwN9Sxb6zNGksbXpdCtV6ffe732UGJSTGPvaxj7F0zQIZWWWpZZTCWJ7SaOyPRNMLtqchG/g7hX34rOVXWKYbvVByvB2nl7wX0Q2fhBKoxn9PhvFUyLDmrpBE/OXSWrynJnDZ025o1Z++z0VBNpZCaCiMsZ7XkI7sBvTC78ICwbYeonUDON7JahFZbVk+QkaPU9rXqmC/TgJOk89AzR2DJvew9XcDDrzYDLt3DXx1a+CtcBvCKmAtqbmyMeOEcznZZhUVXaMJdA5T2mCUpRAeH4khO0tg6rBIGtpqBLRWCWiq5FDjA3wuFRl1ATGXF3Q0LiZ1siXdgA9O3IetyTXFnk5n+HEcFHsxxZebl1BkImONIm0NY9I5gnFrCFNcDqUS0SPoWO2w4Rp/FZZ5G+G318HrqIfNRpGrmnzkqgqaJrKFjYV6o9E2s/G8kIrD3n+KpQmqVjt4JQeuxBSIpx5c9QGsDCaxUt0DW27mQtWthuAiMTRj8WEhIpkIdgztwPP9z+OV4VfK3luq/aOGzjc13MSavAq5FDJ7/x/SBx5CVkkjw3PIVC5HZs07kQkuLYsiFcWPmmWPWdinMa2mkZUz8A9G0XEgiraeNGonVdjnCRJnRWDEDxxr4vDqSg6n6jno59mkvlRwtYbWwlYiuGQpjVBdP+INw1BqorBZrLCJNmahTs2uaaTLtO9NOFB/3I1glwW8YjwnzclB3eSCtMkPm8fJHAQtgoVt5MT6SvckHj08gkNHxrApx+FmiFiN8mOoVGeHY30N7B0VEANGRJWmV4nnnsPo338FkbCG8cr1mGzYhoRY/nnLUhRZ2xRytinUNgexsSKL9Z1fAK+koS27C3vu/DpeiGZYFOlgLFXmqkfulNd4HbjZ72G9xajvlHAJjpVnSyVcajcE4mZRg+fAazjz4tOIjo0Wb/fX1mH1Tbdj1U23wh0osdh/g6Mmcog+dgap/ePFBV3vPS1wbKw6bzFEguxEMoO94SnsGN6FE+OvIRHbD16dLH8OYhXg2ICmwGZsqdmMjT4/60m2xG657OfRC82ZVBY/GQ3htqAH13gvf32kKb4uMm968UVW8icfMwRXz4uAVlIj5a5lYivdeCdG5A6M9qYweiaK8b747DoaDgjWOVmdVk0+jdBX5cBoKodvHR/Czv4QghmjJ8rWxBQ2hfphy0ksNVDD3AJjJuS4JiOBNJdmXXOrli+FrdIDwWsF77EglI3iSM9xHO48XGYSEQwGsHbtSqxc2YyklsGxwSGcHhvB4NQYIskQ7GIKTikFR350W9Lw27NwSmlIHE20F+/gNufz1ql2xgZVtUKWJWSzImTZAkUxNsLjmYDbPQlBKI88aJodHNcGq6UDHs81CAY3wOPxM8GkjqRYWiKla2mJkpzxgM2oD1tfCaly/ggStQb4zne+g7GxMfYd/+hHP8pq6BaDkoxg9JXvYXDfYxhK8RjSKzDE12HQtQZDeiWGY3K+OF7H7fx+fEr8OdbwRhQgzVnwndq34d+b34sJKYCakILNsoBW72znxvNJ3bwQpGJRHHjiNzj4xCPIJA3RZXP7sGTdbfBUb0YipDNxFh1PzXIInAtK0yyIKVfABptTRXzyCEa7dyE0eLp4P4vdgWXbrsfqG29F/YpV4BZpTZ/KKUxYFWq0OodjODUWZyvWM3FYBKyuMyzdaeyo96KtylXewuAcUNUMkukhhJJ9CKX6EUoMYmr4JCaHJxBLWeFItmJ9bAtaFWqTbEwMevlx7BfPIMQnIPMy65kVtUTZGLaEEbfEz6nuqwA9fqmD5EIGJjPdJ626FXpWRzqZxpnjh3HwB9+GLsuQfS6EWxthyVrhjEfhjw9CjlMEZ3pRSOA0NDqjcHhFRGsaMFDZDtVtg+ASwDk5cDaOvR46HZNZC20U3aKNIj6FLaflIKsyZE0uiqRwJlwUuOUefOeHNadjw2kNW04C7cM6KmJkhjD7fnRVzAH0VfPoXGbFkTUu5PwuiLBAVCXwMjWgFaCnBTYKmsSuF2nULGyTIMHrcSPg9yAQ9KKy0oeqah88LheS/TrGjqQxcqS8Fs3mlNC6oRJtG6tQv9y3oHMpva+5MzHEXx5C5vhUMY1VrHGw6IRU5YAazkKeSEOZSEGZTEMJZcBJAgSPpbg4B5eEM9kcdo7HsL8/gvZMDu+MT8Hma2YRnAJSvQv2NRVwkBCrsEPLZhH65jcx+V//DT2TQcpRhcRtH8SYdzUmBssXJhQxgax1CrX2PXiv+GNIUDBVcyPwzv+LQEUVooqKl8MJJnoo0jQww3HTLwq43u/GzQGj0XPjBUpRPFsqoY/9XRduDnhwrcuC7OF96HzhGfR3HjKyVeh9sdmxfPsNLMpVt7wkXfZNSLYnylIRFTK1oWP3Eg/8b2+DVH1hBUJWVfHs6HE80bcDh8dexVT8KKCXR8XI4CVnXweLawM6/C1MoK/LN4husVvfcIIsqah4eCKCH4+EsDNqzOneVe3H/1l1eRwwSzHF11UkvhI7h9kBnhqKcuJ5WGeTdXqhfotSZEpO5HqwHam6t2BIuB69Ew0YPZNgq/YzsTpF1DV7UFPrQGXABo9dABeNQp0IQQ0nocZVqGkJWr7W4uzIELhJCJiEwIeZoBKblkJpXYc9Xcdw4NQhZPk0vAELbr11G+rr/VAo4qREWeSJ0vdoX5FjyMmULjiOdHoKqhqHKGbPOQI1E+rrIooetkmiF6LkLRk905fZdfn7iF5IkgeC4Co7+VD9WmlPtFgyCbvTCZfTBkEYQDZzCLHoXsSi+6Cq5U6DPG+H17sRPt9m+Lyb4fGsg8DZkD1NRh3jSB+dhF7S0EZqcBlCbG0lBPfskzNFAr71rW9hamqK1SRQBMy1UC+V+Ciw8z+Bvd8EsvmGsu46YPvvA5s+DFjd+deoYyKRLUbKBqeSSA4/iztGv4X1iRNFEfYD3I7/St+Lcfjn/HM+h2T0N8v3OKN6s6K9vs/Obr8YJ/bY5Dj2/eZXOPzck1DytSy+mlpWz7XqhltnpcxoqobYVKbMgVEQOLjyaYGFjQw+5jPyCA0P4fjLz+PYjudYrVgBT2U1Vt14C1bdcAv8tdN1KdGUbESyWETLEFs9k9Q0efZj0/vEBBYJrXovOuo8WBJ0LspURCMxIE8il512/yuOeUfATGYc8biORCKAZCKARMKPZDIAWbbBr1EaUgtateriY/aJw+jx7oNQFUZFbQWqqgIIBmshWSpwMhHBM8MHsGN4J0KZUJkL5FLfUjS4GlhkoxCFK0TkKBpH+yRYLgSBmAV37qqCVRYw6s/gyIZhbMulcVsqha3pDOtOSO/1SMaNzng1uuJVyOamhRh9DKrTDcXth+LyQZcsLGUyISaQkBKIS3E2FjYyDDnvLsV54WnhRAQ0FTXZNGy6xnrmqVYXYt4G+JMOdByKofVkDMGRBKwpZc4/q4sC1JoK6GuWQ7rtRri3XwuPK8jee7LKXuh3l00rCOVryBZjg8+egF5u+EGCa9nmatQvW1hwseeqakgfmURsxyCU4eljJi3K8VYBajwHPXN+KbUaSd5MHJwmI2dxwiJai5Fbgs7NFA2zb6gEp0Yx9o//iPjjT7DbBK8Xzt/9DELN29FzaApDXWHWTrBAg20n7vP+LwicikP6Sjzvezdal7YVUxSp5yWJIBJDFIV6ORxHXJ07AkUbpSi6F5miuJhUwms8zuJjr3XZMXH6FDpfeBonX30J2dT0+924ag1W33w7lm29DtIiTFTeLND3L/HyEGLP9EOnhWlaZLuhnrXF4C0XJ1U0KSfx8tBreKzvRewdfRWxzPQ5g1DEGibEcra1kG0r4BZt6HDbWWSsIMhar0BBpus6E1o/GgnhkYkIUvnvOR0B6Pv3obog7q68/MYspvi6SsSXllUw/Hc7WdM/zsLDutQH2/IAbMv8xdSHhR0KD08LrvFjZTdnfGsxar0BXbHN6Bn0s6gWHXCpp4md52DjgaDXAq9LgotXIMky9LQOfZE9rDhkmLDiacLhBnb5vTjiEJCwpaBYk9hQAVzvmIJ1fC+UqWNQ1ARkkUdMtCMuOcCL8utK4Zv9NnDFaBOLOAk++H11qKpohEXynpOAOleo0LY3k0VPKovT+a0nbYxT8tzGGLyuogm9WIFjWImjWI7jcKO815gMEafRjhNYiRNYjQF1GbaNS7h7RMbWKRVi/ldPWTd7ggKerLNgR5XI6vGKr0YHFKpl0XX2GiXRmFgVbmejroFTs+Codxu5PbH/CeAkKzjBWjZppH9Z/LccfWV1xBUNcr4R9K3h3fgffd/B+tjRYvPYn7nvwn/b3oHhnA9aWoWWUoyT2FngRA6CXYToECE4JDYa+yIkB/WREma9ltLnNvP5qnIOqUgYmUR8ehXXYoXLH2ANPgvfgbLHof+KjzU92ngeLpFnluTu4ji9T9bkxmVjv3A/qhoYOnGMmXR07XwJuXQaScGOCUslMnUrEA8uxUDOiqHo3HV5VW4ri2KRwFqVb1hMQnXm95dOCbRgUbRWL7NYn76cY2l005+FqoosHTaZ8CORNMQWXaa0vlICmouJrhatqnidXJ+DP/gyfD3/CRTS6AJLcXrjb+Exm4An+p9Bfzzv4ke6XnLjtubbcPeSu7Gldgub9J8NihQV0yFnpEUW3CVJqI2lxljftsn0JLue0vGKzz0u4u6d1ZBkEXZnAvc3daJhRgT8jCTiOYcDzzrs6LSSWTwHf0zC0hEPmsYc8CT5ciFmdxpCzO2Hbpldf0aRv4IQi4vxMoGm8Iu05p+BqOm4aSyL+48oaOoSoCTJLGb2cYyeX9IKDFQCh1o4vLKKw5ifW7BmzMIb6Xq0Uf0Z289fJwnS9O389GVbwgNXfy0sA0HwYQe4vDnTnHA6rEEOjmoBrhoJ3lorfHU25sRpSYuQQjr4cQXKiRjU4RSYjdxCcCSCrBAr7UwoUVYA26c+X4oGNZplvb6MLQs1mmOiTaPrE7nSn8DZETi22MVZc8ge3QV55Az0dARitQcVn/wIxPXr0HcshNMHJjBwbAqqoqPFugt3+f4BPKfhUO46/Ea/A7IlxrLz6+rqmBCjraGhATrH4wATTDHsCCXmrb0q1IvRZLvUMXaxqYSlQi4ZCbOFoaMvPoupwZLfaEUlSytcfdNt8FXX4GpGCWcQeaQHmWNT7DJ933z3t8K2KnhRo390LO+OdOPloZexY/AlHBjfX1YvqnMW5KyrDDFmXwuN0hWpHlLgmcNmQYyRMFvquDyCbCiTY2mFPx4NlUVbSSC+tzaAB2v8qLVeOXWCpvi6SsSXEs3g9l+9BZ6cA5VZP6rlIKrkANuoAWNTSxvqVrTA1uIz6pmo8Lv/tbzgenTalpidaAVEpPUYSm3BWOIacFwFbDxXFFt2ns4dc//4KN1FpyarUgqalIQmTUKXxqBLE9ClEDR7BppdgerQoNl0KBYNikCpNGnISqys0ejrgWyjRdEF0RKAzrmRlB2YSlkxEpcwFBWRyNmRVBxIyQ4kybyAc6Olugar6xuwqsIDLjKAY51Hitb0BKXwdXR0YN26daitrX3dB0kSGXQAKYgqJrBoTGcxmMmdd6IQ1ZzUY5CJMWM7Cj8i5c8BPHrRihNYhUF5BZrG2nH7kBUd0emTa0oAXqgS8XithN1BAep51mi8LnQdN4b34nN938HW2JGiCPth7T34t8b3Y8hWTUuy4NIquLQCLkNjfssoxlgS4Zv3z/CAbhOg28X8WL4Pq2B0rL5S0HVYcxpscRViXIYeyyEbzkDNzv1avQ4eS+p8aKv1YGWtm4muZh9ZyecgKhOQC0JqpqBiUasxaNrCxiq5nI2JrGQyiFSqFomED0mapc8RK6F+etQva4mrDs3jXtiG8id/Oq50VLBVYKkmn4qTnMTgK/+EJ07+DI9beXSVnFRtghU3Nd6Mu1vuZr1vqI7nfKGUPdYce7KT9WSjMZwNT6dwqSrWZbLYLOtYk7Bg17EmJBULqqwJPNh8GDZBhcYJCAWaMNawEVNLroUaXIpE5DWMDn0LFo7DulVfRXVgW7EGKTMZwuk9u3Bq96sYOZWvc83jrKyGva4JqjeASFZm55mFsDvs8Pg9cJHpgtcJm8fGNskpQeVV5pRJqYqhwR70HN6BY9ledPmzUEvWxwIxHdec0rGlS8eqAY31shpo8eHIhkp0rXAjIansMeixKHpYeEx2WZWhlKQ4nQtnM83o83diwjUAjSp707UIpOoQTNXBqTjhEgAXRREoikwjNa8WjPqn+UjxGYzYJjFmC2HCEcGkI4qwM4Eo1epahFnCkEZKP/VavfBZfcZmM0a6jjYRIkvtJlEW/skjiD+1A5wzgOzW2zGZs0FMyKgEB/s5hC45u8DqkjmXhJSiIxzNQpk8hNWWH0HkJ3EyvREvpt6LrDRp1IlZwwCnwWKxYMmSJUUxRrW6FAUrpApST60zM1IFPSKPdoeNvW+Uvjg0owVLaSrhjX4XmuzGb05VZPTs28OiXGcO7mMur4QoWdC+9VoW5WpavXbRadGXtJyCzrjuyyMG08enEHn4NEtxLbi3+u5fevaF8gsELTDtHNnJxNhLgy9hPF0eFROt9UhZ1yBlWwfZuhzgpo1CqFfhGpcda/JijLY2h/Wi1BemVQ1PTEZZlIu+t3rJc3ig0ov3+b1YL1qgswVZmS3KCkHbot0lLyam+LpKxNdUego3/+TmBe/jVHncmRBxazaDTZkJuNTpiZVKlujKRuS07chqm6HyVqhMPCWZkFLF/L5oiCp2mxiBJkWhSfH8fbJQJRk6NT65QCl8Gc6FnqwNI6oNSbggwwlfOAX/ZAxQLFhZX4eN6IHl1A70pHw4IC/DXm059nGrMaTODj3TCv81S/y4ptmPTc0BLK9xQ5gxuWbOcyMjzLb+yJEjLAWwQGVlJdauXcs2+txnQv92SlbRk8owUVUqsHrTWWQXcKFwCzxaHVa0OWxsNYdWmOhyk83CTor0Lwv/mu3nf7Jl1xWfB7nP68hl+pGI7kEytoeNcnZo1t+1OpbBYdkA20Q7HJ31ECemHfV0pwh0BKGvCWLCksbPfvELZDMZ1Fe4cb/lNYjDe1HoWaovuQn6po8AdeuKqW3lz5lqWYzn/fRUDP81OIGQbEzAt/uc+N2GStTm6xKmX4cOZ/8rqHntn+Aa2smu03gJU6vfi7HNf4Cst7Hs9Rf+De3nZBUTsSwmYhlMRLOYiGYwSRu7nEE4kZszBa8U+mrYeRlWJQG7noEdGdRW+7FizQo0t9TD77ZCyqf4lr73hZqb8s+r/D3JaBriisrs82mkCVKCjSoSioaYoiAaySIWziBDAisqg6fmr3Os4LPHdIrQPRI0jwTdbWEj8sYx82HTU7AjDTsKY7rssg1pOHlqgCzBqUiwJi3gEwLUmI5UJItsau7FEkpNLTQnLmzujAXx5waLq75MdK3Ji658/cNEagJP9j6Jx3sfx+GJw8XHE3Ud16fSuCuZwi2qCMemjwLbfu91TZ6oJoos249MHCmKrcHEYFlPv1ZZxqaciht1KzrSKQSTRnpjTLbix31rEZNtCNoyePfNQTjatgKNW4G6DYClvIaSvovHjv8JRkd/CYulAls2P8LMPWYSD02ie89OdO9+FQPHOouT2IIhQeumrahavhpweRAKhVgaMG20X9rfcM7Pgo4tsTickxNwRaJwx+Nsg5LEoVZg9zIOB5fyyJQsGrtVDden07g1ncX1y94B181/DnjqFvw7qqYaoqxEkBX2CzVqhf2snEO0V0bsGJA6RXVgJd9TmwJtSRTZJZPIVk7BmhLhjFngjlvhSTjgT7oQSLrhzc1fM0Pve2GRjI65Y7KGEwjjjG0Qg44BTDmHEXIMI2qbvCDtFygCOy3OvLjvodNo3T0I2WXD6f/1CfD1S9Az7MDhUzrGe9K4VpOwFQLaIGDay5PQWLrhYsUKmfOkdR5pTUeGWgPwGcT4GGJiGCk+zVqlkIBrXmqIsdbWVuZw25PM4AejITw5GWXnp7kSLqstIotqva82gOv87rLJ9XhvD46+8Axrk5GOTy8M1LYvZ/bw1FLD6rj8pgdFaLF5aB9w6img60kj04fe97XvBm75S8B/6WuEtJyK+PMDiO8YNDKWyKX21ka4b2g4v7KRc4R+K13hLkOIDb2Eg+MHy6JiErU48KyDbF+HAX4VUvxsUyBHPkJWiI7RSGJ+MYJMp/O1rDHhRAJKTcroCSWxbzSKM1NJWLMavLLOtgaNR43GwZnVDCfJOdYcndtr4X+gDZcbU3xdJeJLphWo4VMYmhzAcGQQQ7FhjKQHEckMYmmyF1tTU7g+nYGj5GOO8jxecNjxnMOOV+02ZHkePp4KdTW2BQTd2Bd0BESdjRb+0tdA0VfzJ2cG8aVTgxi3GMLApeRwv8OJ+rSIg30RHOgPIzGjXkCAilVcHzZ5IrhmWRM2bbsFtfVN5/S+qqqK06dPMyF24sQJdrlAZVMT7G3LEaltRI+sF1MFqRB6PkhEkdMQE1gOK0vfYKPDyhz9LnbhcSYzjEhkD8KR3WxMpaZNHArYpWY4kith7V0C22gbpIzhQCVW2KD4RvCLgSMIcTLa0YP3cI9DXPsu4Lo/AqpWnvXvH4mn8BddQ9gTM+oAWuwWfKG9AbcHF/HbOfMS8OJXgd6XjMuUYrb+t4DrPwsEWs75vSD3slHm2Gg0ni5tQn1mNIyJpAq1pHh+PiiNzzABydecUe1ZvgaNrnMuwhSEnkv3eKLYqJhGMsZI5mZ/l6i8pd6vo9afRdCbgMcdhdURRVrPIEG1LUUJ5WBjpmS/cD1FaeZD0FQEkjEEE1FU5Dfal2gCMwM6mkTsLoRdXiS8fmS8AWg+P6xOVzFNsjWi4LpDUTT3p6b/zXIvYtfVwFrthK7FcWj4Bbw08BQOjO0tClee47G5ZjNLKby94SZ4u54GXvmX6T5xggVY917g2k8BFW3zCoLT0dNlEa1T4VNlkwuyvV+TzeEWOFhkqyU2AYs8veBSIOFZhR8fq0YkrsBfVYn3/N0/sX5sZ0NV09iz9x1IJrtYu4gN6x8Cv0B6JJm49OzbzSJi1Ji7tB+fK1iB9s3b0b5lO+pXrAYvCKyvGYmwif5+jBw4gIm+PkRyOcRsNsgL2HWTyPRaraioq4O3Oohx6ziOy8exJ7wHoex0LZ2k69iayeGW6s245Ya/QWXFcrweqO5xqCuC7n3j6Dk4gUyJCZDLJWHZch8aapxwixzUyTRkMruYSi+YLsg7RYgVDpbGR/fVSnrzpb0WDAg8eiczSMfmrvMjN35bJQfSw1KlCj4ogwtmoVimo3uFNNVINlLcotkoi5ZSeupcSLKOz39fRdsIMFAB/NWHBKStxrFd1yxQ4iuhxddDjLdjO2y4GRK2Q4StRIhp6SjUcA80Lg5x60bYq5rBxTVolPYYikIjK8lFkoVc7FuZsHLod4g47bajx+vCmF3AuJVjizeiwGMyp5SJMeoRvMHtwHVOCxp6T0J98XFM9XQXb3f6/KwfIaUWBhtmt0e4bKRCQPezhuDqfgZIz9fU2wJs/jhwwx+fk9vohYL6GEZ+1c2MOQhKdfU90AZb2+WpW6Lv9M5hIypG20S6vK9Yg7sFDYEt4F3rMYxWdCYVpAsRT80QSR5ZR5XCYQ21WeFFtEBArcbBl9Np9QtqXmhpaWM8a0rwAlCZDVn48w6RjRRFdF8/Xft8uTDF11UivjKRSfz0qx+CYMvBak8hgBhqcgnUZlJwCzkI+dW9hIXHCa8Ve9027LfbMKnyCKscwgoHdREpER5BRKXNhSqbF9WOAGodVay/TJ27EQ3uJczG2WLxnncNVAESOzt37sQjz76MIdmOrppmDLfWQ7YZJx4upUDsjoEfScNtFbGx2Y9r6u24Rj+GdaM/g7PvGVaPVGpdjzXvAla+FbDPbeJQQNZ0loJxOpUxUgUjcYROn4Kt7zQqwtMHJJkX0FtRi67qRgz6K1m+fb1VYoJqqcNmRLDykawGm+WS2P8ulmxuEtHI3qIYSyRoYlt+KJCUStgn2mAPLYcjvBxSqgYTXASnhDFI7Ta87YPvB3+WlVpqHvqVnhE8NDzFHp1Wyj7TXI1PNFbCeq4pKX2vGiKs5wXjMgmJde8DbvgsEFyK14ucy+LoC89i7yM/R3R8DNTOOWf3o2rbbXCv3oopRSwTaLSfWUTdmZ9MQUrFmc+Odn4Y8XgUh8JW7B4Hjo5lkZvDDdEiKGjyjKPR1Ysmdx+aPQOoc41CWqDGhxY+LJZK1gDYYqlCLs5jqi+MsVOjyER15JISMlkb/Ms3oXrTdkgVNZgKhxAeG0N8YhzZSHg6TFeCxgtIub2IeHyYdHox7PRg2O6GIsw9CVwRVfGJ01ncOGFM5ej/T9WK+H+tFvQ5crCk98Oa3AlL5gjo6FNAtbZDdF8Ll+86eKzBYu0b1ce5eR7uWB9c/S/BNXUCbiUJt5qCq34dnGSb7q9Cf/QkekKdOD51CMenjpXb2+s6ahUjokXbqlQcwegIS9ktgxqV1m8yIlqNW5HyLMNP/vEfWC0LGZy852+/Ak/Fws2hS0mlzmD3nrdBVRNobvoE2tr+dFH/LpdOoefAXpza/RrOHNgLOd8TibA5nGhweVE1NgXv6T5wJQ6u7KXS79tqRcLvR7q5GamWJUhVVyNK0flQCMo8TdZJ/EYdUYQCIfSJPQhxxqSwwFpLELcufxC3Lr0HLd6WswuukxF07x/HmYMTEFJyMU3QaxUQdInM7IPShudF4IwarAo7RKrDYqMdgltC+ugUEq8MQ43kszhEDs4N1XBdX1fmJpeO5/JNopPFptGhkeRsV9481CMvSM2iaWtwomlVkLkqziXuabJKKaokyMj+vyDQ0qND2PpXv4Ajkkb3Ki+++YFqhOUou18hRVNXrVASK6HE1kJMLMO2vBC7VhfhKDVhSkcwkjqAH7QdxuHmKfjsPrx7KoQH+4ZYq5XOulsQsl4L+4QDUliCJTddjy0u9nzDATzVobktCNt59Ik6OjkFJwUdE1YO4zYOE1YeOTWD5tE+bBYUPLByOa7fsAHCPC1LLimsdv0IcOpJoxXO4J7pcz9Bja7bbgXa3wK03Q5EB4Fn/idwZodxu8UNXPcpYPvvAZZLG7Wj6Xf64AQij/YUnYnJjdh7b+ucRlgX/flQy5OMwqJQZ8ZO49jAETaGw1NwqXa4VSc8qhNezY0argoezQ1LToIov34ZIXNAVOIQtXCISRwcLgvqfHbU+u1MVAklAqt0LEQJ6T2k7BHK+PFJl//7aIqvq0R8hbsP4pt/+Vfz3KrDKimwOhRI9hzsNhl2Sw4OiwynTYTT4oRVcyOleTGh+zAGO8YhYtKZwaQnjnEphFF5DCll9mrwTJyS0xBjrrriWOesY3VnNAbtQbaivRCqprNV/+eO9OLxPV3oTwpIYrqug9Lc1AYn9DYP1HworsUi4a/b63F3pbdc9CVoZvsr4MhPgUFybixZ7Wq7A3rHuzC25HacVrhiLVbB9KIvk513QcadSWLt5DDaR/thS073HrI5nVizZg02rV/PUq3OB/pJJpOn2Iq5zd4Il7MdgnBxmwvLcgzRaF6MhV5DPHF0lm21kPUwEWYPL4ct3I6MWI3Wu9bCvqpilnMT1bl9b3iKCa9wPiL4tiof/mZpHerO1/q4f5chwk4/Oy3CKI2EVjDniYTMBVnEH3rqMex//GGkokaNnM3twca734r1d94Hu8twaJzr8wklc4ZbI7k2FpwbS8RZnFIj8ohQcCe/Fx8Vn8A1fFfZY4V0F6bgQVywIUvfaZsCyZmBxUmmM0DOwrPrOXsAVpvRm8pipT5VJRvrV1UDiyXABNjM5zpGqbSv7MCpo0cRjseh2ezQpbnrpcg9jeobS9MGqXZEEGZ/vtNpk0a6pDwYh+flUXjPGL8L8m/oWerCjg47jigHMDL1ImLRPWX1narUhLRzO7KOrdDExYuaBdEVWJQ41iRO4trECWyNn8T62ElUZPNpjyUkXXUI12xCqm4z5IYt4Gs64LJYWSqwmE7jF1/8C0z0nYErEMR7P/9VeKvO/bc9Nv44Ojv/gO2vXfN1VFbeseh/qyWTiLz4Irofexh9g30YEQC5xK1OVDVUxpKoiSZRw0twLl8O1403wn3nnbDUz14FJkdVclItpC8WUhhpDIfD7PYCMSmGEccIhh3DCNnKowdVYhW2BrfitqbbsL1lO+w2O5RYFqP7JzB+ZBKpwQTsqsYEl4OnaOb8QoCcBw2BlRdZlYbgEvw2cCWp4WT9nnh1mDWy1fPZDtRDybW9Fs5ttRBcizuukDCMTqQxSUJsOMlGEmgze1ISFpuADW9pwtpbG2HJL/wthvSRTvR94APQs1kEP/7/oeqP/9jo+SUnyiJotD8aj2FfTw5H+6wYH3NjMyy4XeNxo86zurPiY6ox7PAdxIu+g7hG3o0/ihjHrL8PBPCdyvXI2dbAraxD21gNVgwqaIqoTIQxMUYeKTYFNgtlwaiw6yLrBWmHBfwi2rmwvy+AiTESYiTIsk4R/oAdTVVurKxxwxdwMFv+S5I6l40bi3CUSkjRrfhI+e1Vq4H2OwzB1biFOtCX307T3tPPGSKMhBvhqgZu+lNg44dm3/8iQ5Gg6FO9SO4cMYyrbAJrzuzcWlv2G1gsLKUvpxXroRYcWRRqev/1FqHTfEGjL5rDipSFR0jiMMpr6OM0TIl5gZUXWdHCvsSx79UShxXvqPbj4/WVCFimf2ckqMh4bDgrYzQrl4w5No7kN8Sj+GBTNf52TXmj88uBKb6uEvEVO7kfx//tg6wIPKFYkWCjBUnZAm2RB1VKYXHYvbDxTthUB+yCq7jZrC6IS3zItFsRq1cwLoYwkhxh23BimI2lts/zQa5XJMrYlhdkAWs14kkXhiZs6BoScLA/Pivdio47ZIVNdVpGzVYAbpeEbw5O4v/0jxdT/TZ6HPiL1lrW36Ts/VFUnB47g57unTg91oce3YbT9kacdjQgtYCgoRMW9b8o1mLlUwVbHFYEJJEd3KgRMaUldnZ2Ip2eXpmuqqpiJh0kxhb73cjlaBL0CkKhl9lGpgdlz8fWBKdrGVzOZXC5lsPpXAaHowU85c9cKCL9wGv/Duz/LhQ1hZhHQri6ApG6WsS0UWgzTFF42QF7eBkc8RXwB7ci2LEd9rYKllr4l6eGcCRhvCcrnTZ8qb0B1/oXsKp/PQzsAXb8g5FeQpC473gXcOMfA5Xzp0glwiEmuEh4UYSh4Mx1zX3vwJpb7rggVsiheASnup+FePD7aBvaCa9iRCdkCIhyDvj1BIRzOcvRJMxZBbirjUmCq6pkrGH7ij2IiTSH0ckIRkdHixs1zp4LLpeBkEmBz6ThkHis2rAJ62+5A1XN55bKmRuII/ZMHzIn80YVHGBZF8TxtaN4KvQsnut/jk06CzR7mplpBqUVtvpaWeNQcr1MqCoTc5RCWRhDuQzOxMfQlxjHUCqM8UyCrXLqnA06bzc2zg6/msPGeDe2xI7imlgn1sdPwK6Vf19lTkCnqx17PB3Y4+3AXs9qjMxRh0VYchk8+Jtvo258EGmHCzve/XtAZTXcQj4Sx0aBibSCM2WVRUSlJKLKKqHSIsJZIli7Tn0RAwPfgii6sfmaX8PhaJ67F1V/P+JPPYXkSy8hc7ILWrQ8+kTSKOSyY8znwpjfzZomFxAkCUvWbUT7lmvRumnLvIsHC2UbRCKROYXZaGKUCbEJ2zgkAaiXq9CQq0ZDthqtqSWoUypgXaDZPaUIiSURLCnvLEhCiz9Lem62P4bES0NId05O9+eqcsB9Qz0c66sMI6kLwEwb/OGuMMKjxvGBGphvumsJOm6sh7DIvxd77DEMffZzbL/2K38P39vedtZ/Q20injw2yho67z41iVtTCXw4PoVaXwu4kqhMzqJBdn8Hy5M/Z5f/uP1z+F7d/cXbBXkEwfBJrBhMo22sEnWxJdSqffrvWCcQdvVBlSKo1OxwwwGHbmWCjI2KAKcmsvYBDjYvWPyCmWoXYfVZIVJ/NI/Ra7PQJ431TCObf2oFci6igqapk6eMYz1tlAVR2jqCItYtNwHLKLp1B+BbZBokLTYc/QXw7N8BkT7jusBS4La/Bla9bUFXz4tBbjDOeoPJg4livzjfW1uZIUe5aKIUvvmElHG5zObyHOGsAni7yBY3itEmukwRJ4eIMW0CR5LHsDe6DwfihxER4kjyadar0CU6sa1yHW6o2IDrAqsgcE58I6TiF3EBA6VOP3Mg6BrckGHRVcjgEeckKPOkyvOqgqV9J9Fxcj+axwaR7FiBL/zJ53G5McXXVSK+iNSXVkO1VUFrugGOldsg1bRD9zQinc6wiWYiPIVEiDZjP0nX5fcLK/6LQeQk2C0eOL1+uKsr4WmsgbuiAhafGxmbhpiUxZQYx2hmHMPJYYwkRtg4nhpnTUPPZveuK25A9sGiuFBlCeLa9rW4tmUFWv2NTLQ56ABbQkRW8J8DE/jvgXFWeFyoJaKCTxJd3aksJuexay9atmdG0JoaxFJ5Akv9lVi6ZD1am9aj9hz6XFAaT3d3NxNiXV1dxfowisQtqa3FqoYGtHl9ELMZaIkE1EQCSiKKBLoRt55GwjOAjLf8c+BkQBoWoAR0aO653ztOE2BVgnDodXAIzXDYWpkws3spZcENgSzQ7bOtxGcxdtSoqTnyM8qHMa6rWQtc/2lg5QOAIDL3u1jsCMKRXUbtWGg3dJRP6jnFCku8HafVFXjGsQb73SvwmaXN+EhdRZmd8QWHCqpf/Eeg6/HCMwE63gHc+Cdl9Wjh0WHsfeQXzBJZlY0Td7ChCVseeBeWX3vjeaXQGNHKLkxNvYhk72/gO74b1eNpCPmPLifxmGxZCnXDe+CsvhZWSg9ULRDSCXDJcSNSmxgz3LgShS1/XWbxv1EiAyvicCIBBxJwIsm5mEiz+BvgqGqBp34ZAk0rEA4lcOylF3DilR2GhX6eyiWtrInziutuYnUdC02KqX9NtssQXRqv4fSaEF6qOYRnxp4rcw2sdlQzwXVXy11YFVg153eSUrnORM8Ua7RopDqtmW56VLN0g7UKt3FurMtk0BDqhzU2POvxFMmJaN0WTNRuwVDlBvQHVyMKCxNv5SLPiNxRBI9EYCaTxv2PfBsNo31I2Rz48f3/HyYD073ISqmaHMHmgy+hZnwQUU+A3W8yUIUpfxXSFTXwu5yotEiopAl79DnYc6dQbXVgc/sfoJoT4D9yCOLzzyG7bx9yAwMUgp7z7/AuF6xtbXBcey08d90Ja3s7m5COdHexGrHu3a8hMja9+k/GDY2r1zIh1rZ5G2uJcC6pR2SrXmw2PJFGbjyJ3FgSelwuacAwx29A1zDFJzAphJHkUkhwGeg+EfYKN4IVQRZFLWx0/pwZUS19DtSbkERXrn/6u2lt97G6Dusy/0Wvk6XncGrfGHY/fIZFyghqhL753has2FZz1h5jxPi//Aum/vPr4CQJzQ99F/b16xf998PJHJ46NorfHBqG+sor+MPBI6gOtEOsWw/eSnMOHV7xm3CLv2TtDB5r/WtEb/4Qrq/0oNbCI5qbToecCkUxdiyFZBcHddBWZuWvaTEoej+ylgnkXFlo9vLzrMIpiFjDyFpS0MQci5YFlSB8egsCWh0qFB+COQlVGR0W/dws98lyvSDICuLMEGtWCHYV/PBr04Ir3Fv+GP4WYNmdRnSr+Trq4IzXjZID9n0LePEfgFTe8bhuI3DH54GWG3Ghvk+l4qhUSBn7efGUlNnvjloanDdk2mvVjc2iQbAo4CXacuCpTY+QyW8p8HwSPJcAjwQ4jWouMwC1kKFRyZZs5ddHtRxetfB4yW7HKw4bQoIAlXMg59iKjPNalk6u5wUUpXl7lKTRmF1a5OKQrkPUVXiVOGqyk1g1cgxLuk+AH4lDFhzIVdSyNh0N8gQ+/qV/x+XGFF9Xkfg6H8gyNhmJMHFGoizOxilDtIWmEJ+cRDIUQi5XUj9xFih1iyZtnNOLOO/EqCKiJ5PDmJ5D0p5Dxp5GxhEDJ0VhsUWhC2Ho3Nntij0WL7x2Sr2qhCZWIMUFENL9GNN9UIUgdN4150oVrUgXaq+KtVg2C5pDR2Dp/Jmx8pWcMJwCFQ6arQ5a8x3Q6m+Eaq1hqT8kmrSkIZy0RP4yCalk+WUqgu8NBtDb2IjJyuk0KlHOoTnejXrbaVgaYsi1a9BnZH6JAxysJzjYjvGwnObAURMu+oxcOpQ6HXJdyVirQ582JyyDSwPiCAdpiIM4KsASdsCW8EISvODdLghOFxt5ir6Ej4JP9ICXNAiSDr5hFfhr3gOh/XrwHg+b9DERJ0mzGuzu2vVTdHb+EpXeEFz+cXBCee0Jp1H/t1XwV29DoPZaeD0bIIoXMad++CCw4x+NnnXGMwBWPYCptvfhtR2H0LXzFej5RYDaZSuw9W0PonXD5tdthyzLERatnArtQGhyB9wjA2gcyiAQnZ5AZ/yVyG18DxybPwvRdu5F3XR4jk6NYbLvOKJDXUiNnUEuNAA+PQk38wFNwVUyinP6l80DnRCdldBdVUipVkxGMhgdjSEpCyx6ntJs8LdtwJLr70brtptZXzMi2xdD7FlDdFGqSbd9AK+0HcML4k6MlTT0DNgCuKP5DtzTcg/WV60vSztm6ZCpMSawCmLr6OTROVOcGyw+3GOpxjaVR1t8Cr6JLnCZOQwPgm2AxQVMdQO5fKTNEQS2ftIorHecXYAouRx++dXPo7/zECwOB274H58HX9+EgXAa/ZE0hiNpjETSmBgaQ3RgFPGMjoToZquzddkRNKSH0JgeRGVuEjzVULm8mPIbgixjdaB5bALru7uwfKAP/nh0ThmjcRzSwQokV6xE5sabYL3hRlRWBtlxjKLuc9WO0vs52d9bFGIT/SUTVY5DXfsKZtbRtuXaYs8l6hFJwoo2uURo0bhQLz3qyxdXdSQ0IKHqSOspjLq68GL1fnR6D0Hn8s1PdR4V6QrUperYZlfLD1hUL+r3+8sEWdDth2sAUPZNFe24aaJOES6KdBVbElxCVFXDiVdHsOfRXiTzNWb+Gge2vLUVS6lx8gILS+ReOfSpTyH+9DMQKirQ8tOfQKqtPfcGx1MxSINxPPjsY/itQ0/B6VsCoW4jtMYtqLV/Fy7xUeg6j5D+Z2zBjFo42Jb7Z6WDU03r4Wefw9GXupHLVIKXWsCV2IlbnTwCy6yQvSFM5QYQGp6EUpJCzZ6bpCDmjmHMPoY+sQ9JPglV8EO2dsAuroMXK1EpU+sbDZUZHVVZHVXpDCozWVRmOQSUeU5cc8AhAYGbgsCFIPBh8D4HxNpGCEs7INQ3G2LNZQFH7iALQPWfhcVgWghu9bay0ohZpRCUzvjq/wFe/TdAzp/Plt4G3P63QO3a6ZS+rMpEkpZIQYunoSWy0JIZaMnctIhKq1DTOrSMDurcoefON0KrgefjEPkJ8FwcvB41Ni5KMSLjOjbG8iIqBg7ZCxq8I7EwJXkxaqnEsLUSo9YKjFhpvwqjlgr02moxZKuCwi8uUmrRFdRoSdTqadQgAwenQeFERHkbRmDDGbaAKMKWSWFl92F0nNiPmslhqDYHshV1UN3T5iQblq/CA+97Ny43pvi6yLxZxNdiyWXSiI9NInysH5GTg4j1jyIZDyOtJpBWEkipcbZPFriLguNhcbmQ03TIHA/FIsBdH4SrtRajUhb9XBSDeggT2jgyuXHotBJztofkqLFvEBmxAqpYAU0IYq3mxPvjMlbHdPjiNFtIzSGc4tBiMWjp2Tn/50Os0o6xGyqQWaHCVTkOq7V8Ysnn7PAkm+GRl8HLd8DqrGFCh89vnIX6WKShxg3hp8Xjxj6LnsWQkceQ4UeQsU4i44gg60tA9mfYatdc8FGKpnEQh/PCbMTY56ni9SxwVit4iqY5nWxkIs7lwhOeIP5t43UY9wfRrPfj/bFD2KQdR85xEqp15gRZgNu5Gv7gFub+5vNeA0mabdt/3owchr7jH8Edf7h4VVcsiJ2TTXCtuplFusgx7lxXzum7TdE/EltTUzsQix2CqCioHc2icTgNe8aYdJLxirrsVgjXfg5c0/ZFp65QxJT6zJWmDNJWmtJaCh1/CnVZrE6ruhpe6mvNImklkbP46PR+YSys7i6SnCZAFf3g+WqoWR+ivAUnrQkc8EzilECRDgGTAo+czYubm29nUa6ttVuLzY+pxuXo1NEy90FqYjwTh2DHjZ4W3AwH1qTTqA31Qxw/AW7mcWWGMQYarpkWV7kUcPD7wKv/aqTSsvs7AWqHQEX13obiw9DpL5ZWjJq9qThOfv9fkTtzFJpowdE170OXHsBoLIMFOkXMiVXLoT49yIRYY2YIPjlS0rxchz2nwJ3JwZXJwUZRN6cXx5euxPObrkVXcyu0eSJCNH2roPRGi4gqiqjlR5byWHLZFprA6IHd6N71KmK9Y/BIAbjzW9BdD48lCEFeIAWI5yAErFBsEiIZBUNjaYRTChKaDjIus7kktK4LoM25F/XdXwKfIqMa4Gj1Mjy3dCueTw0yt8lSmqQmtOqtqI5VQ5vUWO1VAUp3W600YoVaDwuM70yOVzBWlUZ2GTVRnhZoZJl+OVByKo68OIT9T/QhkzQWWCqb3Nj2QCsaV1G95dy/c1q86/2t9yN78iSsq1Ziyfe+B36O10ANjgv9uBZqcLw5FkXw3/4NlbteZPIkW7USvu1JLLG/Bl0XMCX/BTLaVpaOSQ5wluUeDMS7cPTlp9HfOd3GwWJ3YNm2m1DRfB3CYxb0Hp5EltLV8kg2AUs6ggi0WJAWJ9Db34O+vr5Zhi1V1VWoaaqBr94H0S8iqsRxLJnB4ZSIk1knhjU/tJKTkqiqqE4MoDbei/rEGNbHY+jIONCU88Kh+ZiRCG06FifSNOhI23JsS9myiNtSiEgJTIhhDHNjOKMO4Izez9LiSlc7HJyIFbwfq7Qg2jUvlihu1KsOOHMWaFkOWjILLSdA093Gxnnz+7QA8PqzJDgkS4QSjbGyywIXBwdjpNtyWiOiysegwkiltPKH4RP/AxI/OMeDC4Boo4ZdJZvNSFsvu372dbJow7joxQjvxgjvwgjvxAjsGIEVo7oFw5qIMVUgObfI10lGbTyCQhq8PIRQvAu53Ah4JQReDUNQQ1jla8AN9dezvo1rK9cWzxfsc9VU9B46gN3PPomh/XsoUsBEl1zVDNk5vQjTptZgvbIEx5dV48GPbsDlxhRfV5H4eqzrCdT7a9Hma2PGF5cCMhbYfWQMu4+OY/9oFN1ZmRVHWrUsXGoSTiWJoJZCq0VGvUOBT8hCzMaRjoSQjISLvarOBqVTgKcDNwedV6ELCmQph7Q1h6gji0l3DsN+BaFFzOEFFaiI6aiI6qiMARVRoLK4r6MiRpaphqe3YOHA8zkWEeJFDbykQ/D4wNcsBd/YAT5QZQglZ0EsOcE5rUhI/YgqhxFO70MiVe4gqOsiYrFqTE1VIxyuQyrpQ01NLesdRvVhbve51WjMhabJzGEtkTiJROQoEvETSKa7kVFG5nuDIaUcsEbdsEzaII4JEAd0CANZaPEU9JJeZ6WMBirwH+/6IF7asIVd9sei+O1f/RB37twBnlYG6aC+eiWUdQFkGiNIB7qg2GcYHuiAJeKCM1IJZ7IBLqUFFlslBBeJOzd7Tyl9kt5jwZ1/n91uJkrnm+jQSnP33p3Y/eufQRk4gG0V/VjmnpzWP8vvBW76E6M30yLIZseNyNbUS5gKvQxFMVIAHSmFRblqx3MQ8pNI3eYDRxN8irKcpeaA6rDGxsZYX7mCyBofHy9raVAaIaA+czP7Z5E5xutGlVm0t0yQlaU9jkONDrHLwjk0QGe/V2cQGZsXYcmCEag4o6ZwWi0INAFT+TEtSFjha8PtlipsVjRm9e4aPQpuZvE84W00CueZ2NoCVHecvSheVZA78iuM7vg2hiYmMYwKjKACQ/4tGHaswHBKYJEsqjOldJi7xp9GW6qHRbF+XX0fhu3T/a1EDnBrSTgyEbiVBDx6CiuWNuGa67bgiMeJo7EElp7ow5nOYRyAF0n6/pc+/VwMS5IDqM0Mo1YeYcfIWXDk7lcFvaqOpSxGgjUY91diwBXAqAZWeD7fUdOb09Gc1NCc0tBEY1LDkpSGxpQGaYGM76yehuoEnI1+uFtrIAZtmIpTGnUEPYcmy2zhmeDaUIm2jVWoX+abTrvLJoDd/2WkLmfyNWq169G7/XfwPJfBcwPP49DEoTIDnyZ3E66tvhbb0hvReMIN+yB5jBo/0giXRKcwgFPCCNR8FK0Um81WHi3Lb4FAAFbr+TfePhu5tIKDz/Tj4DMDkPPGH3XtPmx721LULp37RCQPDeHMg++GGgoxM5T6//01xDW92AD5xXAcvTMaIHtFATfM0eC4QGrvXgx+/gtQT3WxnmWe67Oorw9B0UWclv8KTu2a4n1VTcFIugeDqS7wzTasvOVmtG3ZDslqK4vwDXdFWDsA2lIlaW9U59a4MoAlawPgfSkMDPeyVix0DCtFFEU0NzcXGz1T/TOVBOyKJPBC2IjinUiWL3La1TSujRzETeG9uDGyDw6rgmO+ehx2VmNEdYFPAGKagytjR1DxIkhpjrKXpTv6FQ+EBWoNS8lwWYTEKHJ8Dk7VAbfqgk0/H/OnLISCgGJbMp++lwEv0pY1UvsKqX5WDTwtjlF6+0whRKNQetlSJpCoxjV+zIL4firPMFZh3JvdcN9YCZ7OA4V/P48DbVJVywwq5jKvGM/Nf3yZSVAU4BEFZDQd47JcLC+jI8I2nxPvrw3irVU+WEqySiitnBbgCg2ej06Vm3q5LW5cW3cttlrXwnsqhTOvvsayrzxSEE53IyIVHowLxnyETLyXajXYoLTAqzvQJ6o41WjBR37nWlxuTPF1lYivrJLFv3zmUaSlBAZ8J5CumUBgiR1tFUuZGKONQuzSebj3KJR2MRrH3t4Q9p2ZwtHTo4hNReFQMsYmZ9lYL6hYChkNyTQq0xmIepb1ICKLWqoZohxhIZuCNZOGqOQgCzyykoCMJLKN7Yu0LyAriWWuXmeDVtNESikTdeYSl7YD4z4BPUERYXsKaSmMjJUaQc//GHTyr7BXTDs12gKojU+gbrgTtYMHUCfn4GQ/Fw5ouQH66nci1boOoeQhNjGPRHZBVcvFisu1AoHA9QgEbmCRHhJgp06dKtaHFdzFSEzQyYqMOpYvXw7LAn16zglKz9r3LSi7/wNJbfL/Z+89wC3LzvLMd+d9crw5Vw4dqnNUZyEJCSFEBpMRAzaMrWEw9jiBxwM4zHiwMRh5xmMwFsJkBSShTlK3Oufqrhxujifns+M8a51Tt+6tulVd3cIWRvzPXc9aZ59w99ln77XX94fvoxnTaCSjNIbGaRgdXK+887FQTGKx3cSie4kZ00TCMSLeIF4rwr9venwitOkqCloY8r0bC3zgiS/gnD+H4XmMptPYQdCLMAp2PU9HH74Z9u+nO1mnnTkpmxNbvez/6qtgnFExzmoYZ1XUikYohKa3NgG+YjEUCXhFixNGIzQ7bYqFDVpuF09TZfQis3sPM/sHGG8+Sar0rIDw8v/Uh+9m/dCP0kzvl7+BmAZF7/suzdYczcYZGs1zdDvrvXpE8c4AxjsNrmuuMdpc2NznemSc80PvZzFzFy7G5mdt/dytY0FiINpOJn73S0GWAF7GJWmf/z1M6M+cf+w1vlL8Ekdjz1Ez58j7AXnPZ8DzmWzCVGgxbJmYYQOrU5Opdtdq4rfsKWFc8h6RCpSZJhy5kXDsDsLJ+whjo4SiHsp1ZS9a4DiUGl2Wa6I5LDddVps+Ky2flXbAahcKbh8QXsUE8PrgxqPMNM9KkqJK9ibSWoRct4bXLVF1yyhBU36K0LOZbjlMl+q8OrOff/fh72Fh6GIa2UMvfpUf/dP/SsO3eHVwL68N7uNYdhr3En2vkUiVaeUEM8GG9Lq3lua21d1tPxwq6eFRcmOTpLLTWNFhNFKoHR2j4hAtO0S6V0ZYXRUWoipzMZUl06PTWsHYOEV86Q2CLWmenplA1/dgqHtQ9TEURUWJaiQOZRgXoOtAlsGIcWWJiHYFnv01eO43LqZ9isjvQ/+IwtB+vrzwZR5feJwXlp7n5uoBvq30MNe1L7KTtsZChh/aT3Rfjkq1R/yxVVhaNHHfvZoJke+dQJloAhj8RZqgsX/5i3O8+eQSfj9KNXV9TkbC8uOXO9LqL73Mwg//MIrn8ecf/V7+xTd9eBsnggD4tyZjMrol2o2J6NvKk4SeR+X3f5+N//tX8WsVxu4uk5zo0AkN/qH798gGR3gw1JnYSlqgKVJLSgidRw7lJJnCZZ8rBKpna5x7dYOzr65TK1wETCLNUoDvXUcGGNobZbW4KIGYaJcKf4vfY9euGXZnVHZ13yIx9yVWiwt8JXOrbF/O3MKGkUENaqheAc0rkKLEqFYlGhRxnA3WWyvbZSO2mBoqpLyEBGXDbppBV4CzFBkJzNKkfQHU0iSCKzulfXzqWoua1qSuNbaMe+3C2FDbZLwCw36FcRrsOfABZu76OHZ8pAeS3mX6+jsxwfhZ+cxZOsd79w9NEJt8cJqNmcQVmQBFu5oG6VYT5+CQaTBqmQxbojdkP9JvwsEuHAV/uFaRcjwXbG/U4ntGcnznUEYSDl2LlTolvrr0VSnw/MLcM2TmPPYtJjjQnCJvTzBgj6PE0rxpLLOg9Ry34n6xKxgm409xPNApxhfITzzJdaPPc6L1ffydb/0nfL3tr8HXNwj4evWVV3nmE9sX0K7aZTl5RoKxxfQJGpESU8lJdsen2G2Ns0sfYibMMdyNQLPdS2nrE0GINLxutUa5UKFeqtKt1gmbDWxXAC0BsnZmTnu3JhZgnijwjcXQE3GsRAKjn3YnFtRdy6QjPCwKtAno+B4tp0ur06bZatKsV2WdxjX9L3GDt21aURMrYWAlFOpml6JWZ0UpUTU7tCyfrhlsS0/YaslQYSBwyOghCRuSFn1R6pCsFpC2suRz9/UB1z1YV2BTE9ZqtXjrrbckEFtcXNy2AD906BCHDx+WntwrLeK3ji/b1qoQnH+KcO45Ar8rF5WhmSKYvINw+AiBavRfWyUMlwhZRGEZRVlGUVdQFOeyY/cSt/M7/CgFpVfLts9d5iPVE+RaNp5nycWR+E7C0um0BAyb++P7siUci6lujilnEMto086copU5STNzAie+dFmGXqcTo1odolodpFoZotMRi5p3l8Sep8R7eJ7rObkJEk4zzZe5k0WuXIdh4nCEY9zOa+TpXWvi3SfZxfPcxHmZDvLO90lEOrelDQ4Py+P2dtppX4uFvr8JXrY15+K4sLDBl84+x+PqSxyNniLoawUK58T1jHFwxWbqaIPYShs1CGWkUxyRpt3FTXXRI+L6UBm0TAZtn7TZwdbbaMIjrL69PtpO1nYNal6UUpBgnQzLSo45bYhVJccGPamMjTBFmcvPD9N3GWiVGWxXGGiXGWyJviK3ib6QVVnOJiSRx82zq2SbHeZySWYH0jh9B5DpeswUqkwWaiwPDsuo7wuHj2xGfa8/c4KnjtxGqKoYvsf3Vzf4X27cz8DuGbpewMtzZZ4+U+CZMwWOLlW3pTGqSsh1Y2numIhzMNJmvFnAnS/grLdQagFRJS7TBWN6+qpyHYFwgOds7LE0Sj5CI21STOms2Srrnsd612PdcSUJ0UbbxThbZPr1NxlZOo7SPS+5OC+Yp0eYHzvAqwevY25iN/4Wj3pa17alO4o+v+XxiF9l8qV/R+Tl/4ji9+8Xux8iuPcf0Foeofb0IkHZ2SRzeDL5En+cfZxz9qJk1xMpSA9OPMh94/eREhpNW8wRgLsPyC4FZhfmnp1MOLfE/XonYLbjNScIXASVuWBNFemtV7F6qcNLfzbL8WdWJGgRtve2IW7/0AzVpLYtlfCerzzG3/3Pn5Cv+YUf/9ss3nv/Jti6Ox2XrJnv1DzX5eyXH6P4b3+N9OkzTNxTIjHWxfU1fjb8WT7tHWE3Kvdj8CA94dtNUxWs3SlZIxY5nNuRrl/M30In7dyrQiS7INkgt9rQTFJGRHfdmKdLg3PnznH21Alm5+fxfBHbCGlrbVp6C91YxdBX8WIh9USEVRWWu2XcS5hJd7KkFmdK0RlzHUaaFcY6DUaEs8/zGfU8oheWsqLeKLcLEiMEluDOC/HaLYJqG7XqoHoBvtbB1du01AZ1pUmVgKaq0FQUmqpKS47V3jZV9OqOz7UVhUEzxe6hIxzMH2Zvei97MntkZHdrCt27MSHnIa7XrSDqQtQqd67Gt71cY6Ddm0+/PKDzLw9arEauPD8IjU0JpkyDEdtgRPZmv+89FtfxpURjLT/gcxsVPrVS4quVi799Ulf5yGCG7xnOclMy+i7S+EMWjx5l9tEXaJ8pkTVGyFmjGKrJulLlFf08i33QJUxVDE4R5xxRbh4+yr1jzzEQvfi8nXgv99z27/l621+Dr28Q8HVm8S1+8g9/nIS7n4nqAcYr+4m5229YdbMkQZgEY6lTOHrPi2Q5IeNFmNgImVwPmSgg+3Tz7ZeSQmC1ZUdo2BFakQhN0fdbM9Lru5EokWRC3tgaDVEg6mOHATc4g9zkT6GJug1NsBzZ2PsyskBYeOR28sRdySQ9c7vVY28UjI4XmB3LpT6rY39cKcnF/7WYoqkocRsvptG2fKpGk4JWp2I6Epy1bI+W7ePpl186NiojInqW3s1IYmyb1pmIqA1EBtBkGmXP2o7PUqXF8fl1Xjx2lmNzaxTbIY3QooOOiY+peFh42IonH1vKhbHXG/d7A19S83/tFmLZDWLRCrFYhXrS5E/TH+K4flg+mw0LfD+/xR08s3medLsRmUbZbGZottJy3GqlCIIr3IBCGAnS7AmGmfYHscTe6w3amdOUMm9SzxxHSwoQuP0YO06EWnVIpm/WygM063HJvy0WzuK2o2uaZFAToEAJfEklrIjm+7LheWSocGvkKPvt83LhK2yhM8wr9UOsd3Mo4r1hSFJrcChxhr2JOcy+uLHj65ytTnC6PEnTicr/qRoGqiBCMExUy0Q1TDTRW5ak/za7XcxGA6NaQ69U0DNpUjffTHxyUu7PBdCzNaoTihSznUDSTs1zt3/GFiB1adtJRFlYx4BXbh7lqzfGeDU7j6dcvFb2rmjc86bDXcdDMjtky10wRQ+IZF0ieYdo3pG9tgP9Wbeq0yoaVJoxNvw0FSOOH9NQokJXySdidknobTJanQEqmFv25e3MC1XaQRQniBIENmpooxOVZDyBmiDQkoR6isAPCRbe4MVSndNmTh6XI9UiDS3K+ZiN31+MR7ouuzYqjJfqtCJRfvubP8ofP/BNEoyIaPt3nj7Kh4+9ILi9KTzwQf7L8F6ea/dATEbX+Pj0ED88lt+WfiPoxJ87tcEbr59i5dw6iW6GSVQm0ZhAJX6V2ddXfFphnUp7jWpng5pbou4WabhlvLD3fwVrZ2Z0XDJ55scnyU1Mkp+YIpEfYuV0TQofi4jGhbolYWIqNgcLdLqnaM0eJWxd/KE902Jx+gBvTR/k9MRe3CtoxF1qo90Nfn7xd/jI4jN03A/Q8D9ASE9uwrM0GjfnUW8bYNk/xgtLvaiYIGG5YJqicevQrTw4+SAPTTwk59GrmaiN3AmUiSZA25VMzBeC+GMoE+NgeJrJyrMkCq9uRskl+BKkLYJ6XKSDXcEqay2e/NxZHluqcG7YkK0c1y5LJfyHf/pJbv3sn4j8SWY++V+wDx3i3dja+bO89eSjHH/6yc2oaaLd5eZKgwOHzhAfdvA9jdXD/5TPjb6fzx1d5fXFKtMSiOlS1HnvViCmgDWT6kXEDuclmcVOVt0QAuAFzr22zuq57ZFIPVHBS77MeuQp5mKrLBgmBV2Rmn9XM6E1NhgdYNhMY4QRGkGcRXWEBXNGkmv5ek7oFWD7Xe6qvsYDped5pPQCu3QPRdR6ihS9wOulvdZW4ALo/+9g4szqATSFVh+UtVRVOjAUO0E0NkgmMclAeppEYhTFSuDoMUqqzTo2a4rNYmCyGJrMBQbLTiBBlgBeV2OLt72QHz/n8DdmHZHwQ1eDx69LcubGDEPR7ZErEckSshjXCpDEuurFapPfWy3xp+sVyQwrTLxbpMKKKNcH8iki18D4udUEGUn56CLrzx7HW2qTVHPyOr9gAnS9oJ9jVetF9gRwn48ucTJ7lJlkjTtiHvt1g6CTxOuk8J0Bosa9WOoRJvbtltHYr7f9Nfj6BgFfi2unqd//YTm5tU0ox2A1P0o1fQjfOoim7UbhIpgJCSjb88xmjzOXPcF6fH6TneqCGY5BrJkk6uZRlCE61jhucop2JLcJstx+GpS49CZss6eDJVgEIxcZBfVqmc98+tNSD0vY5OQkH/7wh0krMTqnylIXqHu2sp1ZSwVzItkDYvuzGCOxdyUyuFMtUKtWpVYq8tj5eT53dpZupUy8WSPfaTLtttDrVdq16rV/pqHiRxXaRldGzcq23wdnF5pH2/QJtt3fNEwyKF4Gp5Oi3U4SuhkCN03gZgi9lPjgd/UdxWLBxiGqdIniENMCYpEI0WiEuCGEsBXZ4oZCwlSIm6JXsXWlB1jU3uQsetHaKPxhaPFZX3DoKeiEfJte4dv1tzDDOXxfeDbnCYL1nY9PKBY2I0Qie7CsXdj2biL2bixrHE3r1W3J/xlAcLaBd6yCd6YulBXl+wO9g7t/me70LE37LeqNNwj7C8wL5nVUnHKWgeH3sP/I95HK3IB6icfxgmh1r3brK1SKL0LLIVr1mVprM9ToXASR5hSudT1G6xSmf2pzu+vEqK6NUJ1P4FUFu1WjpxHzP7A5Gry2T+eZI0lenmjR1S4W0k/W01x/us3Bs02SgnVYU/A0CETKkh0nHk2RT0QZTnik7DpGsIipFC8Hy4HGSjjBnH2It/TDvBDs5bSTYLUTXlHIfKvFTJX9SZcD8Ta7Ii0mzTrDWk1GIFN+iUi3iHqBYKT99nqDW831VOq+RdMz8DoaFWyZLig0E4MWDLQMhjKTqBP7+PR1t/Fvc2OU+1pJR6rr3P3EH3J94w3uGzxPxmzzfGGCZwuTLB++nSdufy9LVi/VaUrT+VklyiMlH7/QkYyCfp81byfzCVkViw585glY1xVSY3F27ctx63WD7B7spbTVixsUF+YpLM7Lvrg4R3FR1CFtralRUPUJVHMfmrkXRblYJyiYuacOpzl0zxRjghmvv5ASTqrF429uMicK59UFE86E/OEbid14K96BGynqlqwV2XBdNvqRNfFY1Kftq/l8/6zDN6266GHvStKVZWLan/LYsMc/n/lBzkd75CemopA3NNLBAlrrJRrVF2i0t1OL704f4KHJB3nf1MPsy+x7RwtJkQ63EzArFwtMB+e4keMc4CwGF6+BFQYZpIh2gUFU6Ozd+iNw649CYnhnVsJ6c9uCWTiBxos+98QifNct49w+lET1fRZ+6m9KLTd9eFgyIOpbmHGvZuIeduLpJ3nzyUel+PcFE1ICh+5/mMP3P0JmZJTap/8I84mfIZJu4nUVNirvJftz/5z19DCffWOFzx1d5s2lGuOoPIDOgxjsvwSImVNJGRFTD8RZM4qSLXCpsbSp8blcX6C0XiKxPsNM6QZGq3tQt3xG1SpwPvs653NvUEwuMRQdJK2msTomQTlAbxtEvahsEU9ojbXZxTy7mWM385LNtawnOBmdZiMyJHWfhjrrTHWWJbC/aoqzuAeIOtHsTI+SfmsfyYLTBKfeq1cUKbKyv/LjoFPF7ZQJOjUUp4nuttEF2PtvYC3Vlk6ghh6hKajUjRi+ESe04qhWHMNKYkUSRCJJEtEUMTdK59U6zqqAKhHUXJbkBw5i7R0DI/KOtMpWug6/v1rm91ZKnG1fnKOmbJPvHsnyXcNZxu13oPlW69I9X6N9tkT12CpGQ+RPbN+fmt/mOaXMrL6BqXRQAwPFt1DMAbKpZC/9VMjztGMScIXezpIC6Zvg+/+nh/h6219Z8PUbv/Ebss3O9iZmkZr1j//xP+YDH/iAfCyovn/2Z3+WT33qU7Ko/X3vex+//uu/ztDQRZ2W+fl5fuqnfoonnnhC5iT/0A/9EL/8y7/8jnLC/7KAL69c5vRdVy4y9FWDSnovpcxBitmDtGLbvYeCRbCpnWEpcZpTQydYzq3Rv09eZoaWIRWdZiy9lwOZvdwycJC7B/eSMi/RBfE8nnrqKdlE2plIo3vve9/LLbfccll6hwBe3dmqBGKdUyW89e253WrcuBgV25t5R1Gxq5kbhPzuSpH/a3aNVae3oJ/SqnxX8F85WH0Ur2ngtnTcpo7fSaB6w/jtuJyXm5XqpjjvtVjbQIKydkREznpRs9YWoNaWIO1iqmNUy7BLneHe1hEU06Zj6rQMW07EbT8uU/EaHZ1K06Vcq1FtOTSCd18jZmgKqYhJJmqQiZqkogZVJeC1Tpem+LkMlRtyMX5iZoiD2VjvNREDW6ityt+7TrN5pkfy0TxFvX6CUukoWr849lJTVbNXSxbfSzy2vy8evR/LGiZse7SOFmi9uo4zu8WrqkHFWuNk+9N0UqeJjjSJj4ii5u1REU2LkUrdTDJxI4pq0GnPUyp/lW53e32ZbY2Sy91PNvce0u4Q+pf/DcrJP7ro7e5bV9tF07mOViWFX6nhVSqyaN4XtSdX0GR6tyYYJTVRmzIygjEwgJ7Po0ZsMAxJ9X95M3fYpl/htb3mawrPl17lD2c/zTPLz9IJL95gR5w8o84gZ61FKkbv2I/Hx7k+fz03ZA9wG7YkxdAXXyFceB6tcTkxxrqS56VgL897+3g52MuJcBLvCsxgwqcynLQZTUc221h6++Okrb/tIlvUfrkLCzjnz+CdfRN/6RTB+nnCyjKqW0G3fTQ7QI/46KK3fbk2uxZ7On0T/3j3z3Asvls+nqnP85NHf4tbNo4yFq2SMbeTB1TdUc7WfwBN38+T08N8Yl+EotWb824o+/ztUx1urPS9yBFdCg03jaNU1RcJUm323/lPWNWSPDNX4qtnCjx7tkjtEqrvoaTFPbvz3L0nzz17coykItucTJW1dU69cI65NysUl3UC/+KXDYMWvnuGwDlJ4IlU5xDdssiNTchImYyWTUz1I2UD27TERKuurV5VS0yk3XVOlKg9tYh7/uL1Wx2LMr+rzcTqv+G6hS/KbYLY5I9HPsCvTPwgS/blOmqqu4bVfgWz/TJG99S2a1MxBkkk72A0dxcz2RsYtqzNtMcLKZAiNXLH+jSx5Fl5Hd74PcKjf9BjB+1bwx7hTPQWXvX2Ml9XiIZNbuYod+vHiPTJdkJV5/zMB/jkxHfy2+o0tUvCExdYCa/vqgRfWqFyqrrJHnjkkUmOPDKB5raZ/e7vwTl3jsiNNzL5278lI+U7mQDD5197WUa5zr78AoHvbUY4d992F9c98AhTNxxB3ZJRId9XXsP/tQcx/CXppJr78hDxj/wo+b/1NyVZ1GyhyeeOrvCZN85xqrTAiNbi/tDmAS/PITe/7bNO2Od5OvkqTydeZc0s7sDuGTDVtdnbuJ3B2m1YxUmx+Nh8Phpx2ZWfZ5f1DKOdL6F6dTbIsMAoJdJSmzBFgwxVslRkH+Xq7MNNLcKsPcpsZJQ5e0z2YXqa8dH9HJnYx+2ZtIzMiDXI0om3OPns09KpIKKEQjZDnPeiiVTenoNX6c018k/Z3CbAi9gurtqGFaFqRihbUeqGKIkQTqwQN+wQCEIwQ8MKHRLuOsnuCkl3nbhbJOFViHt1YoFPLAiJhQFR0QeBrCOPBsEWF/lfnIl6V18VkXwLXxO9TSAei7HWG7f1OI8nD/GZ9PU8Hxkn6Kc224HH/U6R93kVDoY+igiPq0b/eIhO7a9Z+sdNnJNtDb2s4G+AVlIwuhodEZUT80K/F2Cr7DlUQ4WuIpzROmqoX1E/cEfTAjyjRV0p0dQqdPU6ud1R/sHHeqLmX0/7Kwu+PvOZz0gv/d69e6VH67d+67f4l//yX/Lqq69KICZA1ec+9zn+03/6T/IA/PRP/7Rc8H/1q1+V7xdsYkeOHJH1FeJ9gm3sB3/wB/nYxz7GL/3SL/0PB75EDcfrswtMtxro1Soby+ucPrvMvNCgWdtArzVIOC0SXp2kW8VSLLrRvVQyByllDkgh0q0Waa1htI/T0E+wlDrN/IDD/IBCMbXzhaGK9LFuhBlPAIYBht0M7dk6YVXDsyxG9u7lgQ99iPTk5BVvLpcWlMqo2Kky3TMVQsff7o2bSPTBWFaqv7+bqJhgBKzWXqNUeoqV4nP8UX2cT/NtNJSeR3kXZ/nR2Cs8ODCJYt1J2RlnqdplqdyWLI+Clnq1UKVcKKB36sT67I6S5dFvybHYFveaaHLKvoZ9UqBj+TQtj/HYft5nfgsRkYd1ibXUNkW9KhfHLVUwNxUIlDKqUiEUQqoTR/BzM4RqArwULQHS2h7VtkNZgLWWQ7Xd68stF+cSOuN3YhFDk4AtFd0O3MQ4bqgcf/0pIQrFQKrGTYej2NosdN+UVLs7ma4nicX2SaHoeGwfEX8fred9Oq9XiQhKtr65OGh7ouQf2oObXaJSe5FK+QXKhecIay3UFqhNBVWkz7YUyZaltlQsJ4PtDWIINpZGF79SRekWSU8USe9uolu9qVDMiOJeInQ3a+ejlM7EcOtiAfs255qI5EWjaJkM+uAgxmgO1a3ROnoKZ7lXKyYzLFSF0L2GaVdRMHbNEDl4CPvgAawDB7APHEDPvTO9sNXmKp8++2kenXuU05XTeFs8tnk3zb21m9BDjVfypxgdnWJf6gYmwlHGywXia0dJFl5luCGindtTt9xQ461wilcCAbT28UqwlxUu7psATlndJ9IqoleW5RwkmAJTSpcbrtvPnQ+8h103Xr5ovBKxgLu8jDM7izM71+vn5mQT268WhdRSKYzpKazpafTJSV5cmeXc2aPEdKfXNJfJmWFm9kyQMByC5dc41+jwz2Z+nC8M9gRWU26Tj597nO9degtbOY2lnpHbg9DgufhHeT0xxQ+u/zqxoIGjWHwl9pMsqu/H9H2+PODzhbEE3X49z22FdX7S8rnzluvIjo5Lkp4XX/oordYZMpm7uOnIb6H0U3H8IOTNpSpfPVuQYOzF2fJl1+yugRj3zOS4ybaJrbssHi1uSymULIVH8ozsMlC1DcrLCxQWRJRsntLy4qbg+KVm2BFy4xOb6YvZ8UkJuJZPHufMi89KXbFNUxSGhncxpu9mJJwhbqTleR69IU/83jHMrQQUAvg8/n/A6R4ICzWTxpEf5PytP82Kke1Fz7oimuax0R+vtYqUqy+itF7C7BxF2RIBD9Q4TuQmupFbcOzrhKrsZfVpgoZ/v7fBQ4tf4JbZz5DdQoMfRHJw/bej3vA9MHYznt+k2TpLsfgWx44dZX5+UaYiz3TXOOguMNW+6HQ4EZ/kcxMPUdp1Pzdkc9yUTDBiC2F7DUXRJXnMypkGr35xkeJSW+wsVtTiyCMz7J0KWfzBHyIoV0l/60cY+ZVf3uZoEFHMN5/8EsefekIyBF+woV17OHz/wxy49wEi8asz5IbtMt4nvgmjfIqmo/LJlQHO5hJUb9pFIRGy3Fyh5lxOYpJ3M9xTP8I9tSMcbouKsYtAqmicp2w+T6h/lXwiYHT6QbIzD6GIiFLpNKwfx1k+zfy8zvnaAUreFFG1SlJfJaWtktEWyRlzRDWhT3X1lGIhFF8mRVnJEKQmiYwdJrfnFrK7b8GNDfBirSUjjqK90WijeS758joDxVWGCyvMrC2SLK2hCnbXq5jg2uxaNo1ognIqJwXT6/EkjVhSykCIXjx3JQmIS03oUomsmkS/iXG8UUb1VgmUDbp6hXqkRSXhUo+K+bgnUC1AmQBnAoyJca6tMNBWZZ/tKqQdhaQfYik+prpTC7A1kbLrXJaFsO28AN6I7+NTwx/gjwcfoWJcXMPeWXmd7179PN9SeJK4v90Z7gUKHd+i5mWp+XnawQCdcJBuOIAT5uiEKdpBTKZ+twNrm8TA21koKtP1BmasjB6tots1NKuK1+rS2vBpbvjgtwgDsX5wRZEFq9YQi5Ex2W4b7PCr/9vP8PW2v7LgaycTRbMCSH3Hd3yHZAb75Cc/KcfCTpw4wcGDB3n22We58847+fznP8+HPvQhlpeXN6Nh//7f/3t+/ud/no2NjWtmmfvLAr7OV1u85+VTsjhTLTsoa220jQ5K5+KkFkQ0woxJkLYgbZJNuGQ2XiO19DIjG11ynWEy3f1E/WmZFrdpoUe0O0e+cY5U7QTF8AwLOZ+FAUUCsvlBaER2XpAaXsiYqCHbCHs1ZRswWTMZ1FLoqTRaOi0XRVo6tTlWxePUhcdp1LhI8VJxZpsyMuatXaKTFdNlNEwAMWtfBi22s+9InOLt9qxkJCyVnqZcfg7fb+AHKuVuimI7SzG4gec79/BmLY3f9lHaHlon2CygvpoNJCzGM8JrH2EsE2E8E2Vceu5t8jQJjn+RxtEv0Jg9SqOr0PQs6p5JU0nTCKI0W05PxV0xuTn3CDOJ6+XnVp0CXbWLmUgRdSxM/9p9Y81NkFaX2idOJCCMq2hJAysdI5pJks5mScYG0MMUGy2V3zy3zqOrFREWlL/fLRGbSU2nLsFaH7i1XCptVy4K360lbZWEFZAwu0SNGhG1hK2uETMaxI0WMa0pab0Tbks6DqKVDonVJKnqHqL1NLSECGyT0GkS+g0Cv0rYqaN0r3WfQlmPlN3XJDHekeR6wpyWTmUuS20hhi/EMq/0eYJUYWwMa88erL0z2MMxrLQgSimjVM8TFs5Qf3WOwmsa3UrvN1O0gMyeFtkDDXQroFM2aNUGaLXGaJ2vSKKbazWRomQdFEDs4CYoM6em5MK44TQkhe8bG2/w7MqzOwoXx704h+tHGKseoRbJU85a5JwKY/XjzLTe5IbwJLvVy6Na5TAuo1kCbL2u7GMtcZhcOi3P+4vRKls+HklHiFsXIy61jXWOPfUEx77yOOWVXhqysFgmy8F7H+DQfQ/JBb63uroJqpzzfYAlgJZIXb5KpFGAXnN6GnN6CmOqB7TEMRFjPZPpSRC8/DxP/qf/QK3Qj3QoCgfvvp9bH/kIlm+z+sZJVs6v8Hu5NJ/dOyrZWLUg5NsXXH7ybJGJ8L8S1z6NoghKZoUX4w/z8QMf42ysl4I22lnn35z8Je6tvCoffz53L//rvp+jaF4UAt1E9mHIQLfI3to8ecUjbamY4XEiWpXRgSPsnf52MrEUaUMnY+hE1J53ueP6vLKFvKM6W2evo7HX1YhuSVkITJWBA2luv2+c6YPZi7TwO0RVKmurMmVRAjKZvihA2dJmhOVSE/pQoo4skc3hi3lhfolS5eJvKiybGWPfe+5l/3veQ25iaufo5fzz8Pj/DrNP9R4Lz/rtPwH3/O0rCmI3PZ/5Vp0nF5/mueUvc2ztGTpebRtLK9EbaNo307KPEA8VPrTxZb5j7c+5p/ra5uvaqsljuTt4avAGFjMpRtRlppUlRlkkFW5wNYs3hMxEm6H1Llp/inAMhaURm8URG8d656QZAoNIwKabCEk73/XxPZ9QeOXk76pi2jGsaALD2gLuhBKUDyXPp+D4Mt2zKEhznO5ms12H/7iyzl7XZVnT+KHRIVYvyfJJ6BaDdoJBO0kSg2i9zkCzwCFvnQknSqx1O7TuIuyKut+L55ISWcOwvoLJn2MoAuSIoETYJ+LpsdNdzWUVhCodfYgwPYM9sQ9tYJdMD2xHhjlfCTk9vypZFC9luYxGIkyOjpCP2pidLqVz51idO0ezLlJkL5+3FVRJWZ42B9BVk4qzTskrEPo71wLWowk2ciNs5IZYzw2zkRumlM7LuT9LQF600N/sc3LskQt8cs01rJOfJ1zun2+KSjhxJ+x6kNCI9vZOyLH0l94dv8Ocu8w5Z4F5b5XVsMiGUqWtXqFOUejsOSp2VyPa1Yh0VSJdnYwfI2HEiRtxJpjgjvYRUp6JqrTpmiWqqQWKlsOTqWn+PHuA85GLqa6DnRLfuvIy7119g1yrheNHZev4CdnaQZqWn6YVpOmG70wSx1SaxNQyUa1MVK0QVfu9VsZU6xhqHdsuYdhVQsFWLVLcMeh0krSbKRzXxg11HAw2wiRzZDnPgGwVJU5T1Ahic30Kfv/vf5Svt31DgC8Rxfr93/99mTYoIl9CK+fhhx+mXC5LBqMLJnQn/s7f+Tt8/OMflymKn/70p3nttYsT8fnz59m1axevvPIKN920s/6PSGEUbesBnpiY+LqDr6+uVfnuV87gCXfHFlO7PhlF5bpMjPuGU+yTdVi2rM8ytkSLmk6TF157jGde+jznV+cIwyFi4W5G6vtJdrd72NtGnUZ2idxIwK3TQ+y1s9TqG7w0/zIvLb/BmlljPdFlPeXh7EBGISzaEWCsT/JRuEj0kbiKhrJi2z1wFk+imDHCwCJ0hap9DMwoihGT243xAez9o0SuH0fbk6DQeIWTiy9xZuUUK9WAYidLsZ2h0MlR6uQpdZJy8r+aiW9hR3X25mLszccvA1kjKXsz/e6aaN9PfA7e/AM4+4QIW8rNQahQzXw3zfJ3QteSRabVoSrPnPxD6uUC0WSKjzwyw9DsHyHkgdrkqFpTNAbvoWvP4DdDlEaA2dKIdkzM4NpBWkNtSe2TolGnYLQpmi5uVGE4HyWdTRDLJknlcgwkByVZiJjYxYxR73oSiPUiaP1oWrMXTduMrDUditUmSxsV2oFKV3n3iRVq4JNw2ySdZi+S67RIil4CtGZv3H+c1H3SUZNsPIod1/AjDo5Vo6OskKLAsNkmZl5cWDbXTEqnYjSW7f5C56IZ2QhWpIqV7GKlPKzRDObu3ahBB8rnoHUxBUdQ0dcWIhSPxelW+zWRBmRuSZN93xH0qUOQnoS5Z+GNT0G7z5wo0jFid9AKr6M536H1wosE9e2MYm9nrqWxNGTwlWmVl3ebrOUcAuPizVvm0Nevo1m7EaM5xhF1lpuV09yqnuRm9TQp5fIU0XltgrnodZQyN9EduZXY6EFG++d/Pm6hvquoc8DKSy9w7vOfZePFFzCrdWKOS6zrEnM81KtEsERapjk5KQGWBFpTF3stL+pTL98fr9vl1GNPcerRp/CrXWJ6SrZ0Yoi0qOFpCXKVXkrRn43q/Npei4LdmxNuL3r87PEGN3Y+Q1L/FKrS+02Oxg7z8f1/hzcT++Tjg1Gbbx/O4LsdKs0qR47/Nh88+f+hhx5FI80/3PtxPpu/F1cult+dWaFHGpeMGhD3Q4xWSFgKUJs6tqMQcUJ0L5CshsuBx1rgEXohlqpwy2SGe/fmuXt3juvHUujXUCTvCwrz1ZVtoEzUllVWlyVgS5uD7EveymT8kCyYb3l1zjfeYK5znHqntI3YRdDk773jbvbefhfDu3eo1RKsgo/977D0Uv/LJuGun4Y7fwrsq99bRRT31fVXeXz+cdmWm8voYcjd7Q4fbjR5sNXBFBdm31azo8wNZShmO1jalet7y4JRkzG6WDJ7Ia6GZIMuerMqF+wiqhANA/bUmowtF7D6tNsCKxUGkyyPp6gmDenND0NPCrRfWq96LSZ8XFVfoewrlDyFkhh76ua44iu4V6oT2GJTgce/X15nXEQTTY3f3pfFiChk9ZCMFtI/5Xe2MMR0QyJtn1jDIFUcIF63sETdk7KKply9TlpkH7ZtjXZEo2VpVP1hirXrWF2/k0pzH8GFtGTVIzZ0kuT4UVITxzAiHQkyw0CjVdBprCm0NjTaRZ1OWccTGgo7mOBEiaVUYkmVeFLHSun4aYOWEdJUumS7UcZaaRTPotlUeS0MOeM5mJUWyWINu7az5IMgVhLpuYNTuxiYmmZgaob85LS8P+9oIsL76C/A2cd7j81477y++6fBensAI4Tpz1TOcLp8mmPFY5wonWCuNneZM+1KJoDve8v38D1r38axeIwXUirzpkKsGxLrhCTbPkNuSKoLYfPaHM2bx0LUl6suluKgKQ10iujBBpqgEFHLZOIuQwMqA2kPK2zgNEo49RKa38LEveaMoHdijYPfQ/y7f5Ovt/2VBl9Hjx7lrrvukvVdomZLRLq++Zu/WfY/8iM/sg0kCbv99tt58MEH+ef//J/zEz/xE1Kl/Ytf7KU8CBMUtbFYjD/7sz/brB271H7hF36BX/zFX7xs+9cbfIV+yNl/+DSzMZUnJkxeHDJ5ywq3BfPjmipz0B/OJnkol5TsN1ey6voqR5/7Mk+/+gUW6hVCa4KEt4fh+h7MYHuhYzG6RD2+ik5XMvLFklEZVTyw7wBL9SWZ4nRh8jhdOsVsfQ6/DzgutaxrMdWMMFnRmFwLGF9yGJlrYG+J4F1qHc1gPZJhLZplPZphPZrujSNinKFkJwivQs18QddiJKoylrQYz8YYH0wynk9gxXQ+W2/wmVqdQHidgY8OZfi5mWGmLxG7fFfW2IBjf0L4xh9RO7+bui8itRqaskZ211ex7ryHmj3Jn/yf/4KNcgdd8fnm0ZPsnYzD3T8DN/2NXjHtJSYu57Dr49cc2uU61WKJRrlKt9KS29SmAGkq0Y6FcSUmwh2srjYlSCuLSJrRxjE6+Kq4zrpoXgur3SQiJp1ii9RaE7NYIxQ3sS3Ti6+o1I0IdTMqW82M9caGGIttvce9ce+xGHeFhso7MC3wGWtsMFVfZXd9lcPOArdljjM+sS5rfoSJzLvaXFSCLgGUugkVfwC0AY/YoE4qEyWZAKtTQmkWrvr/wugw1dVBii+0cAq9G6Maj5L9/u8n88M/IiMv8vv7AeWVFp7rY6g+2sJXME78Adr8Exii0BgPJZIivO47cPIP0TxTpf7nf0776BuE7c7lsgmGwWxikBdGp3n+IBTG5lAjF2tywkDHqx9goDbDkXbALcpZblFPcVCZR7+EZMcTRd4DNxKO30509z1Y03dcMfpwLeZXKpupgd3ZWVwZwepFtILmlaN8Yq9alkE4MEDi0EEGbr+TyO7dEmAJcgIR3dt2HNwAr9KRBBZeuYNf7uKXO7ilNp21KmpHRIyuPge8noR/dcDkeKZ3XY/7If8gmuKDra9ivPEvUWpzcvu52Az/cOYneTx7h6w/em/xWX6i+zr33PQhlIMf2q7zs3oU/vBjsCGE1iG87WO0HvonlEKdSr3AU+sF/mPJY7HvKIl6bfZVz5Ly6zTNOFUjQcVIUNZTeF8DZbVk+XSDXpqrG2L4IcMRk5mkzcF8nN3pKFlTl+l5IsKWEaQXur4ji5lYnLWOb1B9fJZg6eI9thysc6zwDEvNU9tEU3cyKxaTdWIi0rn75ttRL6RxiXni1Bfh8X8Ga0d720Qa270f74mWX1JXfPm+BThzj1N5/tdInH1Gak9esDOGwWfjUV5KRxhOKlwf8RgzQhmANM0R3Mj1FIwDrKjTLAZDLPkpVl2FiudzIGbzv0wPcWsqvunwFY5eUS/e7J/H05PjfMteldyZP4C5XnmDtJEbeyyJhz/aYzeRX7MHxlzH4eXPv8bLj32Rivca2dYSgdpmPaVyfjpNZzBOzXBY75SveM+8YCoKOTvJkJ1iULZkP4oVl33ejGCIlOhGkZEv/AeMRpluPM0pa5DmWkVqXwqm0mSmS9R2JdAyvADTEaArkE17m7WyHybxwhH8cFj2XSNGLVenPDpPI7NCqPiXAVDpdCpPUl+6mfrSTTi1UVmDHvgbhP46qn6OMFzFa7m9COClpoRYKYdIrkMk15W9netiRL0dOSa8QGW9NSDT5rJ2haQphJEvP199R6VTsmiLVrTplGzaRYvA3XkeEbI1qZEE6ZEMmfEBcuMjZETdrpVE12JoK8fQv/rraCtvofkhaiQP9/883PLDV2XP3MnE/X29uc6JxTOcXZ1jaW2VQqlCrdLG6kaJukkiTkL2UTeF6b+ztYov7kuqQgyVtKLIiLulgN3vu2rIihXi6gXa5dcprl4UT4+m0vK6FjWIIlVZmOBmePLJJzc5GsQUOTKyyPjI88T0hjweSWsXQ+n7yMZuolJtc25plcW1ddYLRUl2EqdNTOkQp0NS7TBoeWT0LjGEXmxLvkbMx+7N34fx4d/g621/pcGXoI0VpBniy/3BH/wB/8//8//w5S9/WUaz/luBr7+ska9Osc2pf/My2S0pUjUdns/pfHVQ55lBg9Il9+7r4hEeziV5OJvg5mQM/Qoe7E6jwflXX+TUy8/zyqlnKEdSaMYUKWcPA63exXXBPNWR2mKL6ZP4YzX2z0xzeOAwh/OH2ZfeJ0WeXd/lfO08Z8pnesCs3wsGpZ1MQJ5Be4S8OkrUHURp5WnXcpTraVa6OpWtNIJXMMtzGGyXGRI6P/021CrJbWKc7dQvY026EGkT6Y9OIsEpzeKkblKLJWjGYhwcHeZ9uyfJ5bMyPfJC6uS11LRtNXe9Ren3TuL2dVOisZdJe78i0wQumONrfHbpAOebvYXw/d//w9zyLd/+jjU1hEm9rWaTtbUN/sNbZzk6t8JYw2Gi2+VGXSEbhHiCjdw30EMbizjxUCShXPsNoqY1KOk1KmqFRlil61dxvTqh38RtVwlbLcQy5PDMAfJ2jtrKBrNnTtJwOriCcTGTYN8HDpK5TqPaep5W6yyur9NwozTdGJ1wBMW8CVfdRbMbw11aRJufJ7a0Rm69wmC5xFC9gC5kDbK91MLkRLtXayVTCzXmlgc4XxuCmEI61WIsXWDMvjpTXlUVC+IBImpAtrOEJrzZPqxv7Kbylk2w0YtiifMm+8M/ROp7vo9qU2Njvsb6XF224mJjU4x1ZwvQlS6G8CbSlQWVddWkgC3TkLKtKrlWiZjTQAscNN9FDRzZxLhhOSzmXVpxk5SlsocKu8xl0noFXXH6n92V9QChb+HZMzB6K+r178O48WFJ7vJOzG80ceZmN0GWewFozc7hV6/iDRdpm6Oj2yJX4eAAC8U1jr35GivnTm++NBJNcfjm+9l94DZSsUH8qiPBlQBZAnQF9bePJvihR8ur0fSqNEXvVml5VdZiCo/edz/P5kfl62KaysenhviJ8Azml/7xZiRmw8zxy9M/yu8Nvx9LN/nejM4Pn/8jJl/9czx3EDccJzTzWOky9kAVVdwPYgNgp+Dc43D6S70dye2Bj/4HWVckf+0w5A/WyvzKuRUpkCpsb+jw0Ou/Q/71E8QYIm7nMaNJwoiLZ3fxLBdHaBHGVJqaSkWLUjaSVPTkJlgrC+CmJ78m0CYWWwKMCVCW1jSSTY/oWptE3SXlQMoPyY8kGL1ugIHRBHERXdpYpbG8INMWCyJ9cWGOytrl6aubpijE0mmGdu1l+oabGZieIT86gT3/KDzxS1DsnwfxYbjvf4Wbf4hQM+g6a5LgRzCYOmuvEDn9DJmFOaKti+dC11CYzdt8IZnlz/Rx5oJBPD1LoOUkbbluDKFbI7SIvW3kSDjohJ7RT0wMcEOiBwLFWuDpp5+W5QyCYErY9ddfz3tvGCF57JNw9PcFFavc7kcyLO//Jl6fvoXTTpOT828wVzxHKaxJwqW3KyXVFZ3h2LCUKhmJjTAWH9smXzIUG8IQIfarmYgoi/PwuV+H81/puzkE0FAIPEU6pa7uo1AgmoPcbsKBA6zrIzxbTvKZBZsXKkkCotyDwcOKwR3okvr8gmkZq6cjdn0efTCKV2zR3ahRPDvLxtxZCqsLlMsrlJsbdP2dozoindQ20yQiJhE7QDXF/G5gjiVoDIYsKyWWmouU3A3aikenM0irPUizM0S7NUq7PUrVSUm9ywumKh4ps8aw2WTAqpOzy2SjRXKJEpnIKhlrHaPPACtWyU7dkCCsU7Q3gZlT2/neqIjaq4yDfQEYZnu9IP0RKZmaFwpXK3pkGC02iqpFUfw0vpPBa6fwOglcQfDVsnGaFt2mTrep0REEjE2Rssg1m6sJIhnhrKvT0Zdpa+u0jBpRRSfvCZKlIQ74g+wOo5dpfC3jcNQo8JY9S0ebJVvZID3fRemTzAhn2NiRG7n5oW9m1023SRKYC9lkYk1+AXSJSPHQ8GkmJo5i2y10PcHQ0LcSSX2E11YHJbGQqGtdKG1PgbJ0lVunUtw6qXDTaIOp5DKus0invUC7s0ins4jn1qRzYGzsB9h76PIAyX9v+ysNvi61Rx55hN27d/Pd3/3d/83SDv+y1nydanb4tkePIrRLD3XhcC3gnibsrvjoXV9OsSeSKl8d0PlqXuetlCqFjS9YSlN5UACxXJIHs0nJFHUlIcfFt97greee4bW5eYpRURpkEW3nyDWnd9AWK29qi62lzjI9OMl1A9dxONcDZDPJGeqdUBJYnC0UeWvjFKcrZ1hunafsztNVl0DbOfUqDDWC7gBBdwjVzZEIo+Q0jRFbYTKXYdfAJDNMMLzgY59cwVspEDoNQqcla4UI2qiWA1oX/DZBvSoXi3LBeI1aYDvZVtAm+51q2gRISyZxVyyaLzWEMJFkPct8dA/R6wdksTJH/6CXmliehZn7CO78n3n8qVO8/qXPy/9zwyMf4MHv/gHCer2334KBr7//clzZsu2S/l19PyOKaqfws4N0h4ZwUlnCaArFSKArcawwSsyPY4TXvtirSpBWpaxVqak1eTPwY008e43QKqFEypjSOxkynjrMKLeSKI+hLnk4p8/QPX2a7tmzhJ1LGLGUkOREh8z+JtHcxbQ71zFQQk/qTl0Jt1aIcz4Y5nw4zGwwzGzYH4fD1LnoeR/2i/xvC7/DgVOz+C1N1v8008O8vPsejg3cQx6TdJfNepBtu2eqsvkiGuEJ/bF3Kxn9tZnQQJOgLXBRfUeCSd0UiwETMx7BSMewMik0U0XttFBaNahXoFqEcoGwsAbV0iYA3PwcAQZ9V/ZmLk1kahxrarIHtGb6dVgTgvq8t2gJ2t5mxEpGsMpd2itlWstllGaIqb49IJTHNKFT75ZYWzsnewG02mGTRrdER+Tq9k0w+E3ceifPHriN/+xotIWmG/Ddw1n+UapJ7iv/DE58Vr62qUb49Ynv4TcmvouMFudvtHS+ZcmRICRoXolmWujyHSOivYitvoiuLFx+vonaJkGDLQBaLE87NsR/iN7Kv2E3jX7N7b6VFg+96jBQD7awFJ4mcE5JlkLhfBneu5c9h/YxtWuUgbSJ2lyD+qoUCA7rKzSaZcqtBhXXlQBN0HYLUCbB2qWPZZ+gbKTwt+juvFMTnnJZpyZAm6GTUiHqdDCaddRyEXdpjmB1GaNRlaQEkW4bu9tG31JfFk1nGBifJGVVibVfxI4W0AYdnKxNM2aguG0GCw5Daw7Z2sVr3FF1Xhy4ji+OvJfHsu9hNUzQDK5NhyhCmxFLZ288w1QkIvWR4rrGH6yWeK568fwRQsg/OTHAI7mkXKiuFFb40yf/lFfPvipFhNtmG3vQxjZb3LZ8nA+X1hjpz7niGz4Wi/JfknFeFY66/olhKAYj0RHizSQDR08yUuwQ9YYxbv8YD73vdvbvmd6mDXlFE0s5EaUvnOylNi++2IvANjcgeHsnhZBFdBs6TkOD9ATRh78TbdfNPXp2kS6tX+5cFMtHQVn/2aPLfO6NFRbLbcRK4JsxeVDp0ddvnQe7fpvV9nnO199gvTO/Y6Q0qicx1Ri+msZVhwm0aRQtvelw9ONNqmOLHE28zJvuEoEzKJvfHYLuEL4rMg12nlVFrojQ0SvKhNC3t3zcYCRpMJJSGUqEDCV8BmMOg7EO+WgTy69QWVmjvFyiulyjttqivu7gOxe+lwgrRlFUIZkTQ7NsjKgtewG4xN4EbgKvm5QOsWs2JUCzapKUQrcbNCIG85EBTllD1Gydhq0ieKX2xlfYw3GU2gm6JZ1sJce+9gj72zMMeL2MjK123lriRHSBs4lVzqcWKHsbpOccDpyLkxK0zX0rJRzOjDc4O9qkawWbDoJd/i7yK3mUcu/4K4rP8PAZxifelKArkbydKu/n2eXrefpsnVNr29d4mhpyIN/muqFlDmZOMhF9BYKr12AK0/UcI0Pfwb79f5evt31Dga+HHnpIakj96q/+qiTc+N3f/V2+/du/XT538uRJDhw4cBnhhmA5HBwclK/5xCc+wc/93M+xvr6OdY3Ri78s4MsRqRH/4M969KAqBDGdMGYQRnV2GTof1G0exmCo4qKstykT8mxek2Ds2bxOzbg4SYkI/A2GySMDKR4ZzXBjIrLpCRG1Gi+//DJf+tKXZORRMEhO59KEc6dZnz9HNZXCjU4IMmqynRn0LVpVAQFrdoU5q8qs4bKsmPjiwg+vHlFRtAbJ5Drx+DyqOYenrdBRa3iXpExdsKgeZU96j1SYv6A0Lx5n3GSfQbFE53RF0plvNaEl1iPtSKPntB4YEwCmWtkELYEEMFVWNwqcWVmXj5PNBslWQ/ZXq1XZ9p2sJPZNP4Q+3CPV8NaP0X3rd1FEuVEyhZdM4CaSdGJxXNtk2Pex63W8SplTzSpvCkokRWGg1uLI3CrGuyC+6Bgm7USCVDZLPJvZQn5yMYq3DUQKwCjA41XIaGS6Y9vDrzu0ynVqhbJMd3Sq/XTHhqhTUYh2rXcE0ly3Du0KioiadaoEnQqhbGJcxXdrBCkHe8BjcGiDeLLG1dYpQRjFC0dx1ByNuEMlv0493aAV0fAMVdbftb04nrYPX9uHwzj1rk652aVSLjL53FscPlYlMIaoJyaoJadpJsbwlcvnDUfoNWkBa3rAqtYbV2QV+tYD1ytfN0KkjpqhNTC1Bhm1xHu1RW73l1hVXF6zEpzXouRdnXFXZdhRGfZNLFel247QbkbodnqUwoFq4KsmgXahN6XkRKD9BaTLvgtTNQVdE1pyCpqiyMWYKgiC/ECONUUqCfR78Zrt24QX2fGaOF4d12/S9euYWZvhw7vI7B3nxHOPcvqFrxAGVxbSFbUZ7/+bH+eFaJZfPLfMYqe3GL0tGeOXhgwOPPOv0Y7/DmroSRat/zLyQf7V1I8w2krxvXMu969727z5wrS0hT4QwcgKLYl1OnM+nkg72PoavYJtvYnNU9jeM5cxkIma0yXnes507uGN8C6+dGiQl3dbhCLVOQi5s3Cev7P2Ke4IF1nuZpmt2sxtBJSq27+radtMHjrM9JHbmTpyC+mhHgmINMH0JnTQhPhsvd9qy/i1Feob8/iVZSLddaJhWy6FhcZQ+RKQJkBZRYA1I0lJRNnsASpWRm4va1EqiiWP27s1w3UkCLsAyKxOC0OE4cUiWdXk8dilrnK7c5x7628w6JZIu3WM0OOp9M384dA38bn8fTT1y9MTBRActXtCswJUDZoqrfYs88WXeHPlCertWUHILV9raRZ3jtzJQ5MPcf/4/eiqzhPri/z2aoPnGqZkxRMWCYrEG18irD6KskWu4VLTwpBHGh2+v1Lnpn4kTNiGNUDt5u8jdff/RC4+ugksyi+/xfKP/A1Up8Pi6L2c2ve97L55kDs+PENmONZDSNVFKJ+H4tleXdH6MajM9+pPr0V7SggSiyhWY70Hygb2w3f9Dr6eZ+Pf/jvKn/pUT5zetsl97MfJ/diPodr25em+pTZeQbQOXrGNs9GksLhMrbIsCS1Eq7slkkaeidh+RqO7MbYwUbb9BhvOAgUxx9XO03Lq+P20xK7u0xq3cXenKFgRvNUxBtenmGiOoG05z2pKwGkj4JTps6QFmyW7SRQpJn2hjZkqKa1Ny1nHQGPKH8QhwqpYmxAwr/QiPKVAw/dSiOX+FYnuBeGFZCZUpNN31DYZNAyyqiYJXiw/RGm5eE2PK/B5XNFU1cGIulgJBzveQY800CN1dKuMZpVRrQKKuQ7aOqtKhq/wIE/xACXlojTARDjH/eGTPFQ/z3BpjEh5H9HKPjS3lzp7wQLFoxib41z8DG9EzvCceY5ldefie3HbEgwAQ7pFPholIbgDFJ+O26bQrUM9T7ZwmFhrcBvomph4k47m8GJxNy8sP8C5wkHCrYQthEwkFjmQPcWh7Cn2ZM4R0S+/nnQ9RcQex46My15ohXar41SW0hTmNFbP1rnx4Qluft8UX2/7Kwu+/v7f//syNVCArXq9Luu8RDqhSCMUWlKCal6kDwqqefHFf+ZnetSTzzzzzDaq+dHRUf7Fv/gXkqTjB37gB/jxH//x/yGp5o+fmeVn/t8vMhcO415BT+eCILA45adduEHVuU7Tpbp9LabxXF6XYOxkcvuqNe2F3BHo3Gbq+Kdfprh0RmrzjI+P880f+hYCKyHp1wUj0ZsnznFucYOVhktTjZMixbRvMu2q5C/xPnYJmTMCZnWfObtKzS6iGhUsq8GutM71WZf9sTUGtTcwlYsUu8IikV0E8Zspq+OseDrnanOypuxc9dw2Cu2tlrWzEoTtzexld3I3084Y48tp1DNd3MXtnhfF1rD3pCUYE5T2Wmpnb99jpTq/fG6Ztxod6XEc911+OmnyXgM61QqNYpl2uYxTqeBWBHirkOimGU7dja5HCX2X9rE/xj/72GXsTAJIt4Zuxhm5hUzlLNrGmwSNXi3PajLKa1NDBKpKouNwZ6VLPNmPsmV6IEnvgyWxrRyN81sNlz/3FOqxOEYqxc8dnOZ7hrOXpRj8t7LVc6d5+UufoFx+msR4nURWw6yn0cRvUMhi1jLY3Ryml5B084qdRrFTKO8gbUpBpNaVUJUSvlJm3moymwjwEjp2OkosP0hMHSa+mCZ6IiTimBd1RYa6Usi5OvQMde81PK+XMheGCm5jkM7aFP5bM3QKUzQiE/hi8XKJaWqHAf0sQ/pZBoyzqJE1Pm0f4FOdhyTBi1hABkM2/nBEvBhcH8VroYYFdG2OjN1kXybBDdkJbshOEbhlnpv7IqUzj3KoXedIp8uNXYfUDiDfDcZxgkN0OYCfHsOtrFB//FGC0vZUSnGWBapOIACZAGO2EO+MyCic2L4dqPV71cTXev3m2IpCPE0YTxHYid72QMVzAjwvkKmVl0gf/XczUU8jYgyilMj32oSBhx2zae8f5w8mVE7Fe7+5SNP+G6frfNvq77PL+AMseulOz9l38Incj5HsTPLAhseuLlgZG2tAtCj2YARrJI41HEXbwuZ4wcQiVOhctYWA/LmKjGxfMMVQSOa/TLz6b1GCNp4S4enGx3ir/uDmayJ6E3voHJ/en+fJAcEsJxZ6bX5q6Xf5mfOfIhr0FiY112KumWa2kWG+laZzCRNq2vaZGtSYGkswOT2ClRZptvlei+Y3o27YghJelXNa+dVZKk++RqKwhCbEhZUSZWWDgrIhr6thpcygUkFIrl923EW2gxbbTHmUwM3KUY6PUY6OUrbzlPUYRc2mpJgUMaWodRP7betyr2ZKGIhFDEYYyKhGSlPIC5AVjzERtZmMmAyZRi+FUkhj6L3+gv6XH/i8tvHaJmHHYmNRfpnr2nv4pspdnLTn+GLmq3iKj69laSfeSyf+IKHak75Q/Drp9jPsU04yHU1gdy0KZzdQWzoxL0asqRJfXZM1aLfcuocjqUViC4+ieO2LdW2i9ue2H4NUT3C6/ugXWP8HP4MZ86jsup4wEyWlrzIQXSUSrKO8TQ3YNhNzVXYX7H4YbvkhyO+9+NzKG/BbHxI3LJh+D3z/78s64s7Jk6z9s/+D1suvokbzGJMHSXzzd6DnJvCKHQm4OqKeuLuxCbJEE+y83iVyFBfM1RJ0jAwD9ij7rTF22ePbotqtoMmb+hmeSW/wlFmj1I0RdAdlC/2L5BTC97jL1djnasy4KmIWv2ChEhI3FHalLfbvThGZSmGOxTFG45tMyO12m6WlJdZWV2mcLRJZCBhpJIlgSsboTiC0ChusqTUi3TyNQKUmSE9w8DVVMg7rbo/J8VotIJRsmFgquuFhqEKguSZqRuhUFvG6ZcF4IaPbvfhoz2IiAtwn9RC9aObgCJ8r1fnPi0VebV4ESprnMbVe5qHVBg9UNHZ5Scxw+3rO1zyc7Ab+4ApO7jzt1Dlct4TXreNrbQRVZcOHVVdlRTZFjpddlc5OqblSZmiA6yoHSbb7zImKT3boDLsne5EuYV1fp+NFZK1d17PwQxVTc4nobRJGA0Pz5f2iE2q4ik2oJtHNAaKRcdLxfQynbyQX3U15SWH1TIeV01VWZ2s4l/ABjE0l+Mjfv42vt/2VBV8/9mM/xmOPPSYjV+IL3nDDDZImXgCvrSLLIvq1VWRZ6HpdMFHzJUCaKAQUtV6CLfFXfuVX/ocUWa4vHKP6mz/MgHqGFfKcDUdlO+6P86J6kGVBhrpF7PBSE1OSUPE4iMaopdPM25zMG7yQ12mKZPe+CfrYfVWfyQ0Hu9Bhue5wGv+qMojiphj1Wwy6TSbDkGEiDHkZrEsEgSv2OquZ44T5Y4yNnmBvrE2m72ZuBQoFBtBi1zM68AiHh98jw9uX1jy5gctCbYFTlVO9WrJyj+xjob5wxSJwkTO/O7GLGX+cidIgYwspxmp5VAwauoIgrGsNR2lNxWmNRGmkTSqCmtn1ZTF22fU43+6y0HHxrnIZRbyQnz3R5SNLPa/eyYTKP7rB5lxMEF+0Gag0mF7rMF4OiToGS7kkJ8dMFvKG9OT1pR7luDcHhj0Pp/iXIpIgBHQvW8AI4Ue27Zf4OUXh9X+fRLdQLnyDwOuJo4rd6Dfp/L/arCO+cxiS9GDAgZwTIDIIc13Id0Ufyn6gG8rtVyDWvMwEbCmbULAUNiwomqJX5OOiCU6goLcCoo2AwUrAUMnD2gHPB0rAckZnKauzkhFNpZLsZREJFiex71u/jBCTlUx3VwO7YUjCazLdWeJA8zw31k9wY/0kebdK2quR7GvGuarJmj1MRTHoqoaMICW8kGTmTjKP/DDExyVFe/f8eVovvEDn6FHclZVrSzfVNCnsLNgco7fcgrl3P0HTxZ0v4CwU8ApNgq6KGsmiRnNS10fRrizvIP6juKmKSLySMAkTggZcJ4wI4KcR2jqBoeL7otYwwHP8y3rfCXAFqHM8WrUmzbK46YoFnpirxTEVIsxXnreblsKT10d4ZVcvzUv3Qu453uJHZr/APbFPktB6ZCrr7i6eqf+wjEJdkymgG2ovVdNQMSyNSMIkmjRlH0kYRKI6esNF3WjBQo1q1WHVDWiGK9yf/FVGzJPyo86697G87x8xfccexvamN2nhnymW+HtvPsepoFeTNqS4/D19nu/qHkNrFXopZs0CQbPA+kaD2bLBXDPDciuxrbZFXH8jkRrTsQpT8TLDtiAa6P9O2LS0D1J3PoDnXrhHhkSGizgzHZ7T4nx1w+aZ5YDluriWA3LUGVJKzJhVbs873JBsMWOJc7QAtSWC2jKaWNBfg4lI0oI1xOnoFLORMRasYZqi2tRpYKk+LbMXhRP1bAUtzYaRldE3ITr/tYC2qKbKiNhmXZsEZhoUy9jnaoyUdTn/6EFIVa/z+tBpKsMdBiI5EnaOBX+Il1oWxf78IJa5h5tlZl59mvjaIn48jZcR1OTi3AwZHRrinnvvZXhoCFVE8o/+V1Qh8NwqoPYT4ERqtyrEbNsVuU38buL6Fu+XkeL+NkGpKMZyrtnyOtkLCvqpu1APfAvKvvdC5uqRgHDuRfidj6C4DbzsvdTH/yVe2ZcAS9RWin8v6iW3gqyKs0HD2+4UvWCaLhgBJxmYmZGi257Tpby6ysKJY7hNUXPks5i2WEmkGTb2c3O4i3u8QVJbHMc1Qp7C5Uk8XsJD3DWHUZiSUSyNGVQ5Hg9VHMtAuCWXKw6Oc9ExJQStp6/LMXldnvxETKYBtkVWRt2hWenKcbvu0qr1HjcrHbxr0V7cYqoJelSTwC6wdVoqVMKAomAc7TostBwqoU/narfcMGREa7NHLTHWOE26toDpCCkVf/NNYq8WR6Y5euBmTu06jGv0HMLiPrN7o837ln2+cyMkfgkpSWBp2DNJIrvSmNNJCUSVS4h06qUCbz3+GEe/+AXqjXVUw0czAhLZNHvvuYXJGw9jRFRW6uc4VT7Judoic40C1WKE/MY+cp1exM1XfM4nznMqdYq23iaihIwYAcNGwIjRG4sW6+NBxTdQ3RjahebFtzyOE3aSNOqDVBsZqu0Y1e7FqPPmuSacaLpCrt+0sQLXf7yX8fb1tL+y4Osvi/1lAV9CfPTMI++VF1X6voOkJkroK0+iiNqmvr2m7+HfJr6DL8dvpSMo2puebKosUL58VhAexN2KRi5t0x6wmM2bzCe2e1EGOwF3FTz2bLhYxQ7nfJ8T+JwVC8Qr7Kvpd4n4XUaVLpNalxEvQa41uE3A0Vc81hLnWUmfZiV1mgV7ETRB7bs9knWhdkz2ucMMRC9qVlwwJwhY6zQ4WjzDCREdE2Csdpa1+jmaTnHn4ylkH/VhPHMc35jAM8bxjAkCfUDqdVyrCaAjWCXvayj80PMVcg1fTqKnj2RY2p/GWOsQLrbozDdZbHU5MWZyctxgMfc2i/S/tm0mbkApN2SgI3STBCgTfSD7S8fXCtLElCj4a4QXtBv4+N2GTHk7nY3wzJ5hzmYEy6ZK2VTw3wXd+ruyUBRoB73UriucH4KOXwh7J5pNkpU66VqNfLVMplZl99I8MytLDFWKmJvEQSqKHUeNRvBrJVHYuf0DjShadg96fi9afh9qauKyaKRkb+tUJAAIW0WCdrHXt0pybAwmsQ/sxeprkkmR6IHLr9WdrF2vMfvay5x5+QXOvfKCpI3fatF4iqmpG8mGw2hVHQuhSePwXOHzdBU4ccN7ePLI9TSN3nV7z5LDDyy+yG3u/8uY3xPZLSgDfNH6EVqx9zEiNJYE6LsEAArw54vHTkDwNejbbTUhLnt7/A+5KfZ7qEpAJxhgPvn3UfY/SPpgltigkEkwcNwi//fz/5T/4n2QDaWnS3koZvOP94zyQPaS+45IL2wVcYqLLLz5GrPHTzB3VpAZbGeXtLSAXUmfPbHriRkPEMoqHXEnEBp7f05c+wy6urbtPWKVIOofn1Bu4YnIHbyk76FhRQmFxIkE0hpKRCGMaDKKagVdhrpFhp0CI90Cw90Nhp3itn7EKWBfJVV0q4l04KZnUPcsGq4p+2Ykj2cGtE2bgppmMbKHlcgkG05IA5W2HaVjRWhbETr9sWjhJYyZf5WtpxB2sRe2qb8lAFvQOxc1Wn0nmUYQmH1Wxh5DoXQm9cFdbyw+L5Di6Iauo+sGummgqyqB5+F1OridNo4f4GoKrqrJ/+CLxGrJ7im8iBf3TzqeUMiGClkUrLCXk9DTCOu9UKYq6yqGoWGrGqaiYAVgOCFG20dr+YQVkd7uybFgbLQEqcW7YDQXqc6idlEEyQXxjKDhtxRFOjLrfkhVNLUpNTTVUMEMTFnzZJoGlmVi2SZWxJKasS1FoRx4lANfarGVfJ+S61HyhB6bR9PfeQeNwCWp1QlHI5SnhmSZwAUbbzh861LAB1c8BrcQrXXCNi27g58zMabyxGfG0U0TRaQwq4qUBhF9GPgsHj/KmZeeZeXkMfkbCzeIZuiMJmeY1A6SN0cI0lUq132eevwFgqAuGSsrlRHm5m6kUR/YTC+0M4usGR6VTgojVEipPnE/QkLojvlREkG/92OkgjhxP3qZHE43CCl6IUW/14tjfKmZChJkZTXI2F2ikQpOcp5GYomncklG2m2+//v+FV9v+2vw9Q0CvjqnTnH+e78PLtA3axrR+9+DefdBytoGS7PHWWoqLIYDLDDAsdxe5sen6eZjvUm05aFUHbTVdk+k+Qr5QlFbI5uP4AzYrOZMHDFD9U14B28q+9yz4XFH0cd0As5pIcfwed11OOF5VyxuFWkEk17ADG2mHZv0JXnJbb3OQvo084kC5+NQU3P4YRYMU4btRDqlaLoVwbTiaELEULPpoNG+yqkt0kV0dwnNXUR3F9DdRTRnETW8AtsSJrYyQjIcJ+uPMuyOMmXPMDU4xuBkisHxFFFT4zMbFf7d/DrVrsePnXNkEzcO39I4n7Q4s9yk0/RYzWibgGsjtX1Be7Ds8YFEjA/eMSVvKOde3eDcq+s0a+62iWjQCCnVn6Hc7dFZj4y8h/LuW3g2BseTGtG4wd+eGuK9YpH2F4URfB9neQXn/HnaZ96iefZ1vNlFWG/LG/lOploBZsLDTHpY8V5vxn1UUeikqFTCOGVS2MN7GTl4Z6+4W7TkaA/wls7Ba5+E458Gtx9rFUxy138H3PDdkBi+hFI4pFDvslYTrU1BFEEv1jHWO6SqHkMu8mZvK70bq7zByqyQXn+t6ZiCzqaiCwr+OiWzRdG2WI/mWIpm2LBUGnqXe6uP8tHCHxP3eykiFSXJhprBNBwaeqxPdnChlibBipWmZCepmikqWpKGEscRBYHvwkTtwXA7YKQTMtoOGG6HjLQDJpoeoy2fjC9IqvveVRGlLM/hFU/hF07hl85usrVtmmlj7TqEff1NRG+7lejtN2MMiOs1xJmbp3viOJ3jJ+icEO04/sbOFP1Ck0uAMCkQvb/XC9ZDkf5WWlrgrARbL7J04thlIVKxv0OxXdyYvl8Kpm61qrPBEyu/y/HxCb5897dQSPcKyifrdT7qL/DA0m9zZ+HZ3mu1OJ8/+GOM3/cz3DOws0bYTibkAkQ07gIw65ZrdOZXaBfKCGzY7iiUCi7lgkujLtJpBBjRCAWJxQ7OmyHjFI+k/m/S+opMc321+a083/g+Agz5y1hRDSsmMgxO8txMmscn99Hq07PfaVj87ECeG/NxoglTRuF2sur6GnNHX2Xu9VcpHZ9j2ryO6dhhtD6QbgcN6slFInta6MMRNnxXsi8uewqLgc6CFmXJSLOi56lr1yiw6vgS5OfdIrv9VW5ihQNKhzFDZcwyGIrGMITWUelMT2xZ0Mtv0eOSKZFGDNx2jzDibSjsL5jAxooZJcjupWMN0fBMKk3YqDisrtcp1HzKaoKmHcexoiTTB0llDuFEErL+uSzm1ERAI2vStiw8EWNqefgtIazdT93VegvdrtOSP6mgKw81jVAXwsciCiVqGXv1RwKyiCikGPd6CWE2x724lUIgx8Kx0n9df5uEPF9DhO8vuwmq8Vg3kPqfohf6U1HRd3v9he1RoU3VDdgiz/iO2P5EBFzJLZMcfgMrvo6jimwEDc1TsRyFaAsMkSEUaISCez9UUUONhKKRFrVcIsIeaiihSNFVqQutNVeh7CryGpfvCcRzF8a9XjzubRefKTTLep994TmRbVFTVGooVBWVkqYwPxqhOhGlkjc3HW0xL+S9qy7fsuRyQ6UXAyp4NTY6S1TbZyl0FiXJ0FaTsVJNCKwPoGt5VG0QVctL4o9LTcwEpioyY8TaQtnsxTrDNNu0YvOcUQsU+8QtWqiw3x/lRm+GGO+sllhAjrYgR/V91nxXatZ1/cvnLlNXSMQMojEN3yzgaafBWkBPLWIll1lPmHxZfYinuJ+GkuTB9nP87jf/JF9v+2vw9Q0CvirNDn/yy38Tc6HNxJkF8sWLEZ1GLMaZXbs5NbObRiQmc23FzUR4zz3hjbI03KiFa+h4mia3ma02meI62dK6qJyQjF+iALuhJ6mLhWJo46o6rUwU8gmcfJRWbDt4GG0F3FPwJBi7teRj0KWZWKAUW2U5Uud8PGA1Nk6xlWW9qlHqQMeycEyLaGAxUVeZroRMFYX3avv3XUtpnB02ODdsMD+g428BgTua8Nh5gaR2FUDPDhWiguJYFQW4/XQTQ2dAFGJbOjG9jucvUHPmJfPi2coZWU/W9XcuqhZenOnOKNPeGHtSu5kaOEDCGyPycp1st7eYWHACXm/7nM3rnBwXgMukGr14QxVH75ZqwANLDg+UAvZ/ZG+P+XDb1whZPVfj1POrnH55je4WtjUtbNDpvEbgnGB3fA83ZR9GVVTKGQN7Js3I3iz2rhT6DvVrVzxsQYC7vEL39Cm6F9gFTx6je35W1ivtZKoRYKXcnhjxZu+hy2KMCUlTTHb39j49yfMvvSKJcISJ9OF77rmnR4185lF4/jcuilSK/Ro8ROPIjzM7+kEEQ/9Ktc1ypc1SpcVcsUat1MWuw5AnCClUhnyV6A756iIOuaH5rJktGsoaRxZf4JFjLxLxPBQrTnVskuDwPUQju/GCQKZViIW/8HBGQouoa2+L2F7NBOGMpzSIsIGhFFCVKioNfKVDFZ2CYrFKlFUlgmbWILaKkV0mPbBCzG72rkNiNEkgVE9EaxKn66Ux2gOYnQyRdpp4O06mHSHX1hnoqmSvIajQUWFFpJXYSq+P9Po1IyS2cZ7JuRMcOn+aG86cINnaHkFxdYP5vftZOXw99RtuRL3+ekbSKUZMg5xlkKpViZ45Q3jqxCYoE6Bd/raXHiNdpxGxqBgalYhJIRGlI5hX+4sPXTHZm7yFfalbsQVLmEjxzNgYAxH0gShNo8Z//OJ/5M9uvIfzk/vle2JOh8Pzr/F9zmN858ajvbRNRePpiY8w+aFfZPfgxNseH5k+WSjgzM/jzC/gzM/hin5hQVLrX5VS/xITNXWOmeg1o9e7RgLftjg09Ry7cj0G3oIzyZeqH6fkT1/2GS1T4elDEV7cYxGIuS8MOXLe4YGjLdLNNqbbwPSaveY3MYMWlt/CcJpo3Tqa00QRjoCwK6MSXSXA0VRcXdwDRD2gAItqDwzIXpU6h72+t11RQ3TVldqOpupjKz4RQVTiqbQEOU3XphjE8RQBMPqfpSjklDqjaoFd6oqsHVPV4GIQVwZ0e2RCihmDiBDZi8trEVukSxkip62HX0Mf323TrW7gNQpoTg1bdzFFmEOkLPdCJv1xL4SyNds6xKSpPEjbu1/QcUhQ7NOm0n2JpfZr1LyAhm/SCXQ5p6WtLjlTYzJ+P0nzPSh9sfiI+hRJ/T9jqMs7/t7iXFuwh1kwh2m2VLS1Ou2mSsWxqbo23iU1OcKEcO116TWOZJZJmb17jvCHnq7nebUyykJbsDj3QZwEZ73U8x5I6ztSNp/rb5Pp6r0DcNlzXHxuNFrjwZFz8lidbuT4amFaLuDF854iyLkEQE1SNZLU9IRcD9T1eO//94+tiARFA4j5EPcCBvyAnA+5QCcZaER8RWrNiWCL0Ch9N1q7EtTqQt7Gp6t2aSttHNXBFTVNuosrtusent4hn1lgIrPIdHqWpLVzquRfRhNrNBEtFEBNCzQJDIVzRqzhhERxN1TpBipuqOKLNNQglKRfhh9IEi7RtFCVgFENdVR01NDoj01ULFleoWOiiW3iXOy/XojO039co8uSUqYu6KPk8+K+mmXCH+yVj/TBpqz5DS72XqD1exXHV+kECi0fWqICztdRfbsPQC+C0pK1zkZylm7qHHr+DEPxCmO6xpDpYWm9NUcHm+e4hyd4mDNKb54XlnWrPLS6yK/94Pfz9ba/Bl/fIOCrWCqT+NU98ka4HmZZqAzRPacTn61iuL0FurjxLY2PcXb3btaGhv7C09oqkRgL2SHmMkOspPP4F4QzZaphKAHY3QWPezc8xtu9062p+MxbDrMRl3MxjzNRn4Kl4mkqvtoDgmLyydc1RosKExswWO3RQl8wVw1YzNWYzRZYylSoGgp+K4Fby+C1IiiOLoVFv5Zvm7B0EhGNWKyCEVkHcwVXXaYZLtIIVrbXk4Xwgco9/MTad2CHFi2ly3/OLfCFsQFWc8MS5ErzA2wXbo1YHKwFZF/aoOUEVEW0ZCpO2QsoNLo0uh7DKZupbJTJXIzJbJSpXJTxlE17vsmpF9Y4+9pG7+7ct8BdxFQLPJi+kegl4sROyiSxK0VkJoU1nZJMbcK89Y0euDpzmu6JY702u0Aoa2suN0ULNoHVVrDVNi3ITBPbdSva0P6erpEAWaLuYAea4q321FNPyVpOQQf/rbs99m18nmi9pxEi/HwvW3fwW+H7+GJzH67f9wYGgvZdZXgL0BIMVDtFqIpmnfXIOvVUCQZrZMcD9oURbvjiGfKPvY7Sv1bsG64n/1M/RfyBB2Q0JPQDyZTZenWd9jGRmhdIQfPPjWg8O2RK4dleymPALi/kgOcw3GljtQKstkakK250785z7eAhYsaiOcK7roQyndUUjFqh0NJ5G20faW0CrYirCTr/BoWYw2I6ZDatsRhPsmJlqCspGkKy0tHoiAW0IRgTt++zEgRMryxy4+njst1w+jjZem3ba8Q1e2J6F2/sOcjrew/y5u59tCJR6VywNFU6PeIBxJtNIsUNEhsrZDdWGF9fZ7SwTrZSpGYrrMdNPFF/I3X6Avb4CfaNHCZ+8DD2kUPYeycw8lGUfjrhwsoK//Onv8Dzu28gEHVrov7G9Hn/qd/if1r4PaJBL4L3nLKfV2bztFuqrEc5cO8DHL7vIXKj47irq7gSYPVAlrswL6N5zuICYWtnBrAL1jUSdK30JgGEOHSWpWJaCpr4wQTY9EUKlwiB+9KxsdO2+FCd4ZsK6FYgSe2W3xxkZX4Cx0xtgrULgG05P8Cn79rLG7t70T3DC7nzZIe7TrR3rFPcSWrAcOuYTh1TsAY6DUy31nvc3yb63msakgXyG9LUEDPmY8Q8jJiPFrPx7PfgRu5HjQ2CYaEpT9B0P0PV6UpQJcDVRpjgmV138/wN76Ga7Gk0qr7HobNHue3oM+QLa5v3jl5KYL+eq29i20y8wpHMEpOxyub2jU6M18pjnKgNXQbeti/lxKe//dJORnwF4O33U/ES3zr8BpoS8sX2EX6l/V20hTaaliAa9hxZkRCigSLHoo+JtMEAIqFc4r/jQyzT3gTTZtAiDNtSCkb0ochCEaLL/T6pxxiPTjAV3UPCTG/T8VvrzLHYPsV6cBJ7vEhyokZ8rI5mXkR3Qs+ssZKmtTEhqEppJlJUEirVBLRtWXAgkyOFk0ak/WXcJvnQZVCHuG1gWCK9Uid0Pbx6B7/WI/QR6Xgi7UP0Ha1NVa9JMKgpKoYEPCpCkERTAplirMg+lCyuf21vbzLaLOr7MHAUS1Y79oCpEKfvEPe62EGXeHAr13/r7/P1tr8GX98g4CsozbLwfz3ClL5dC0FMNJWVASqzSbpLW1LpBPHIex/Bu+8+arrC0skXKK+eQadLRUswb49S0DMy9UH3ffTAxxI3aqEL5AttIB8tCND6z4ntW03kdy+n88zlhpnPDtGwt1P/jjVd3rMRcG/B5+aSL6NRF6xJl4JaY0OtUVB6fVe5eNNXAh2zm8F0MhjdDFqwfUHvam2a9hr16CpesooV0QXPNZ0goOI0qbtdedF6oY4XGrL5gchVF5otCYIwhhuYNByNphvibYkUCghnBzDqq4x5KqOeykDg04ysU4ys0LJqfKt3mJucnjf9ZbvDLw5orIv8dSdA6foojg9dD+UatWeuZqauokR1OqZC1lPY04TJsk86UEkGiryBBAMGQczggB9ysB6iOg38fkF8UF8maIjxEmF358Wl8HDLVEEBsESfdlHyIU42ykZdZ70co9SO4icn2fv+H+Dgg+9HN64MCBwvYLXaYbkfrVoRYxm1auMXT/NI9dN8VP0yib7IdC2M8nv+A/y2/15K/pAEVxJkeQrDvkJ8B8+xiDK1EzWUwRbRUZX8ZJyJ6QHGU2NSjFRQSYuoRfETn6DyJ38quOzl+yK33CJBV+yeu3dMQRPkKn+yVOT35gu8Jmpr+pZ0Q75p1eOjhs0dh4aIHsqhGL39EkLwn/rk7wpOZQZjOW5eXMNdWBLS1RhqtMfoGM9AJI5i9LVgRHrLX0SOqBEQWh6h2UENyujdNSynKCNuitIg1JpU012KeYdmokOgN/GNFoHels5ofyGKcyKBey5Jy0lQi8UkW2YtkaA0PUJhYgzNDZk4fZ49J09w6NRxBsrb2RV9ReHs+DSv7zvI63sOcHTPAWrxa0hbE+QCgajbcIl0u5KUJt5uSkmHbK1K3ukyEjEZz6Q4NjLO7+gxWv15JqcEfGDps/zd2f9P0pELKw3dTPyRX2R9wWH2S1+k+NormLUGUccl1vWIuF6f2OAKu4NCx87StvO0IwO0I72+FRmgYwvRXptoymTmhjx7bhlkdAtpxju2+hr86d+CMz1h5nDmQUoHfoGziyrzKxscH/0MRcOg0Zmm2TzAfMJgIRHB6V93uucztSoIfNrEHMHqqWA7ChEXbFeRzdghxeftTETLVL8phbojVkg8ZpBKWnJsaR625mKpIhLWRRdzduATtusE6ydg7Rhhfb2XthcKf5jBfDAghY+LQVLOriLlKR/RyNkKedMjJtjl3K4kbPAcF98TkTW/1/xe7Z3EsNJ7rsjPlclYgSCHCtADTzZJVBGKdGILQ6RyypqlgCAUtcmuZMfskVn06pl6ZECiv8ZrULclI6Aay2LFK8SGCljTYxi7D2EcuI1w/Aa+EOT4zaUiL2zRC7uncY73nX2DTCFPLEyTDKPkQ4M4iV7UYeu/UOaIa58lqj2OqvSjYUqcDesezijXsVxrs7Y0h9Pe7gy5YFYstsmWl5+YITUwhWNmmFtps7jaYG2jRaXcoVXrghMyHtYZCZt0ghTdMP7Oz5V+WrxIXZOBSuGYkb/7RS4/McWKmsZY2uq1lCWvoVjSJCrGsjfRozrPz5elhtgX31ql3ul9wl5UvsWK8JCqE1EXaQy8RnPgNdrpM31Gp56Z5gD53IPk8w+Tzd6DpkVwnS619TWKS8ssHCuwNttluRHhTD7JmX5WTUvko2+xXGmDmYWTzMyfZn+7Si4/QGpwmKgxSnw9xUAzirZFH6+mNnk09Rwvxt+kpbY5VL+JI50jTAeDvJky+OyowTN5jUCkqeITDXzuX+/ygZU2N1V8CdIEmAsFQBO9crHfCvZ6/fbnPOm26+CFHVxBiWaCmoygJOK0FZ9Gp0G9WaHbraJ4dYygLuUSQtUXBww/KhxbIuugR+gS+C4dX5FMt6pmYGg6EVslmtCwDUVGnBVRWuK4uF1HasIKl6d8v4xu+7JXdQ9F9UDtPf8XXdoeXdK46wdO8fW2vwZf3yDgq7qxwVPf+V2Sic8a9MmnGozZJSa1DfR+TL9T0amcjVKZjRK6fYpdVeWZG2/mT97zTby6//A7LkI2QodYKBYwDaJCm6XZwW50sRptbKcjm+V06dgRVrIjLI9MsZbOb/s/hhdwsNSREbH3FxRGd6BOrCqdHiCjyoZapaQ2cMWkI/SBvKgEYgKQGU5KKARtvk94/TyjjmOWcayyHG+dlN/O/FChg04nNGiHhux7Y3NzLJ5vo3MEi79HhCzCOxPyCbr8nhxdxRRX6pgpegNVa2LgEwtUsp5Fxo0x0EmTchPUBYOSGlDSQ8nUV1VDGlco0r345UMSoUraV0gHClnfZ7I6y/6ll9m79jpJ99K6NgU1PoiaHMZI6liZFsrQGt7oHF1RLpbfD8kbOXtS480nThL0///gzG7u+Mh3suf2u6TvVkTrlvuAaju4akkx7XKzVzexZUe5W32LH9G+wMPqq9IbKOy4t5fPdr+FE+FuUr7BoBsn4fWidNt3O8TMQ2bcZmxXjqldQwxOCJranReX3XPnKf7mb1L97Gc32f+id95J/m/+FLHbb7/s9W4Q8kSpxn9dLfHnhRpOf6oU2V4PJuN8uK5w1xtVlKWLiypFMEwdzrGQLPPU888Q8XRGE0NMLxTxKw7EcyQHxtAENfg1EBAKh4KrB3REnrzXlVGwCx5tEVHTZcqIiYlBFA2RDPm1mLiBB0YXX2/hiRRcvUEgzpdKCzZahLUWgdfC81r4NGmPNGjtbdLYWxdUs0RPJYicNUiec4iVLk/VnR8c5K1du3lj70Fe33+Y9XQOX9QefQ3CvtKCgA+sf4V/cu43mXZ7aWAVN8aphQm805Cq1K5KD+2pKpV4hHIySjkRoRyPUk5GKCVsygmxwNbRbRXDFvWlogntMoNII4W2FhcKv9R2LaDlXVRDgIFe5EEs8uXuSQIDIewa9MhKxe8YCgdPQEdEVwNPOom6Yux7fKhwnp9ZOYEdBpQ1g380dIBHEyIVeQttaJ8JTzKb2odoJ95HoPfYxwxnjVz5s8Tbb6EK7R5NECr0rj8tNLG8FJabxnaTWG4S201gOTFsJ4blRLHcuBzbbqyXkvQOzFNc2kaDtlHvt6uNG73F438DG3SyfEfpvbyvcjdmP0osRGR/L/dFnkq+IllLr2YilStbhz2rWfatDDJViJOv+kQ7RexOEcvZGexss2QGTUiBWBEKiQxPTe7m8ZkpFgeHKKSzTLRCvm/O5YPLrnTuCRMLcj1jYw7G8BMh1WCDcmuNwktfItd6ngNDG6TN3s1SpHWdaEzyRmUXa84wZnQI38uiKEJCIIEeS6DHBeAI8Nse6jtk9OvvkdTWEngkIogu1F49kOitfm2QWEeLOarjeVL7KRiMY08OEEv2QJVsSbMHspImtmBAfYdkRV3P5+nTBf7sjQXOLT/DvvTr3DjwJkPR7bWlVm2S+MYRYsUjpPJHiF4/SORQDk2wrV7FaoU2s28WOPvaKq8UW5weMDgzYkhWW6Ezd8FMz2Pf6jo3rha4abXGRDvE1CLE9XRfINq+zIEnGI4/O2bw+RGdivA09O2Gss+3LLuynit+SXBZzMG+0SAwhGOsiac3cdSuTKsMFBfVLWE4VdqlNquNBMulFm7QkXNLJJni0Hse5PADj5Aem+T1hQrPnC3y1TMFXp2v4Pg+u1KzvGfsWW4eeI3CyiQry4fwvL5zTKQwltcxi6syYrttv5QobWsvgTmNpQ2SIC6om7bvvNHFHpgnkX+T6MAx7MwCigB3WyzwYnQqY3TLo3Rrozi1YTr1YWazMY7t1lhLdBitrHLH6mvctH6c3dUltGYH19FwHRXf7aXEymWfCqu7xnn/vxbSPV9f+2vw9Q0Cvk6/ucDnnnmTU9E4T+VibPQ9Nrbf5ab6cW6vHuWO6hvcWnuLeLdFbd6mcjZGW3Br962dsnnr9hv58iPfgT+1n5xtke6WiK+/BIWTHFNHeDp9M6Utof6b22/wQeMoD89MMDLwHhKJ61EUjWa5xPrsOdbOn2H9/Dk5rm30mLO6psXs2G5Zk3Fuch/N2HYveL4bsK/kcdNylweKPjM7RjZC5vGZDVqseGWK7jotZ4WMGTA9vB/bmKRZMulezNSQ5qkOlcgqpUiBglmTWkUqabwwQstVqbsiJ1lEyXrAyrmKZtoFEzQIfwubbxOuJUEZjc8vRBypYRYLu2TdBhlPKNm42IqLprVwjSJdq0DDKlK3atSMGv4lk9IFE7neImWhZ33FeImtNHw3je9mCJwMgZslcDP4/T4Mr57ipwidEb2EbpTQjAq6WcU0qph6BU2/SEMtb6midjj0cTwHPxRsewqKbqCK1DQhTRqIBUAg+637uc22eZFF/UVAQmmRCtvEPJtkc5JUa4pYa4pUa5KYk7n8IwhpxkvUUxuy1VLrNBNFAuFN69/odooYiecGVzq857F1rnu9som/zxxI8NQjwyzOxC++r9+1BeOSK9iofEneccFFJxiwBgyDMaKku3ES7RiJbpREO0qqHSHtxMl6CbJ+gsjbCIhfOJdLepWCXqZgVCjqFUqROuVomYK+xoa+hNf3dPdqO0xCoviBjuf7uJ67CcR6S33BiKgR9SNEg6jsU2GMRCAYpyKSgSrm27JOsTeOEg/EuNfsa9jnq5mPT0NrU9eaNNQW3bCF3mlhN5qkyk3itRah0wK3SeiKcZPVWItjQ22OTvi8OR1nI5MiUGOEavySPkaoxIh1LXxznKoEImB3O/zNp36Hn6j9Ielcz6ngdVUKb8YpnxWitBfPiaYdYWlgiOWBoV6fH2Iln2Y5F6eUENd7S0Z3lKCBEjRRg95Y9GJ7vmEzVM0yUhtmsD5Bti0IsLcztR4dfopXx/6crrEzcc87sRnH5Vc2ChxyepHWP4rH+Oe5DK0rOMrEr9+JP0Qz9W2EfWIMo3OMWPmTGO7cu9uJUKR+Rol4cSJOgoiXIOJeHEdlf/Gx6b9zcpi2fgGQNeS4ZYr+wuOmfK4l+wa+5vbZ9i6w4fXlK/rMp6Kf7A7z0erD3Ne4WV4Pwk5ZC/xJ8glesd8ULg0Z/bpwZvQ+x0RRLBTF7jvxRJ2aRym6ItN9L5g4FhOVA0yWDzNd3M1gs0O6W2TALZHqFFDbRcn8GQgpgEsJay4xV9NYz+ZZzg9SzAxgGjaWYKsUbKtaRO5P6KkoqqhxFI9FdLzXm5qJqkVxQkGe8M4cLmKeaCnQVcLNSJT4xuKT8opKTjAnawpj5lcYsX4TS2lQ876Puv+98rWeYDAUQCYbQR+JYk0miQxH8J74POVf/df45V5tVeK9jzD48z+POd7TL/tazHUrFItfZqPwGKXSV/C8+h2vp/QAAQAASURBVOZzXqBzvLSX1zeuY2Pjeh5gjG/STbJCtOqCCZA4nSJ6fR5zIkHQ9SSRStC+0Lu9vj/u1By8uoPqBLR0eCGn89W8zrN5jcIlUbGJZo/5WZRWiBKLCyC6YsAXRgw+PWZwaouGarbjcfvSEofWTjKCkEyqkk1XUKINtFgUM5nBSgyg6QO0OzFqVZNCIWR5uUWxKDTBQrRWHaNSQK+Le9pFJ0Ism2TijvswD93H6YbOC3MVXjhfoiWybkSNutHgrpEXeXDyOQYjK5RKY8zP3UCj0XfaGDq33XY7d999N5ahU1yYZ+H4eeaObVBcdnHbCRS1l0a71XS7RHTwNJH8aaIDpzGTK5uC8q6vs9IcYrExylp9lEp1FKc0SKYsCKBqDLbKJIMamlVH7ZbJl9YYLBVlFP9tTZS4BCKSLZjbRjn0xUevmTzpv5X9Nfj6BgFf9ZVZKr86t7kIWLR8Tpot5iix4a5jtqokBfV0q8akt8KMvsJIpEAsbNFatKnORgj60TDhGrZHHfzrAzo3h1RzCh1LsAEFpIsep7zb+eP4IzyWvYOg76nOujW+M5jlB8YG2LP/PhDF0pdYu1FnY/Yc6+fPsnb+rARkxeVF1rPDnJvaJ4HYyuDEtqhYJPA45Plc39G5fgOG1rsMNDxyl2hZCBNL0HMEHMfndNDlfNCSOl3jSowRx2SwHWJf8r6yGnBeD5g1fOZ1UYh/+bEVc0dECdDVNoFew7O7uJEYbmyQvST5p0shU33n/p9OwKn9AXcbGgO+g+d5dDsO9UqLRqVNs97Gb3eJKoK8QdTx+DRD0XvUzTo1q0LdqFI1q9TMGnVDULu+80tTZtX4cUJHALEsgZMjcHO9x06W0H+7c9VDMcuoRgnVLKEaRZR+Lx4rMmXg3ZnlRhloTpBvTjDQmGCwOUGim7v8OwgWwcg6G7FFNuLzbMQWKMQW8bRro6W+YNOrId/+TMAdJy8exxf3KvzR3SpnR3cWjUz5cfJuhryXvth7GfJuenPbtYKUqtbYBFYX+wrrRol1oygfC/HWvyxmBLoEYYktgEyAM0ERLLf3t12gDRbPxfyI7C9EFt6tCfrj0G3iey3aSpOO0qIjyrPdFnqridp1eH1qP09cfyNl26Cuwwde/hwfW/4tBsaKEh+LDOjVc0mOrg2zlMywlk2zmstLoLU0OEIxOYBrxnH1GJ6xPR36Uou3A8aKLmPrHmNFj5Gqj7XDT1VVA1a0QNa/THq9ObGjhDxvu7wWcQlUob3Ua6gBoRYSaCG+3muCC1vVQiIGRA2FmB4Q1RxiWpOoUiYRrPDRjad4sDQnZ/iSafDZyQFZF5eqzZBavxmrPSQdCOJfhKkOFVPhz2IzPD02KiN6wvZtlLi1UsCManQjBi1PpB+5NNoera5oAS03pO2GkoFN/iY9poYtDpWtjxVmlBU+oL7I+7UXGaUktwahybw3yRPe3bzsHaHpZUgGYY+AQdQLBSqRUERoNRmtfacptiKfoK2GeFqIHYFUTCMTMVADFaUZkGn5DKtqPyIDG17Iqa7QXgpRNYVAkD2IqKRfwHdPE7hnUPyyJCgSWomiaik9NMP0dTez/7abUGcyfOX8V/jK3Jd5vvIC9bCxuS8infFAe4bbG9fJNt0du8geKvMrmziNDeqNErVOHcfp4HtdfEHhrqh4eqxPvhLHvdAbgsDinUevLaVGVAgDq1V0tcmiEud1Rlkj2ksDlPdUUXOpkAlUphWFvB6S1H2ymk5aaAbu8Fsk7D8lxX+QY/eGn0d56GfRUgKo7vy7CQKajV/7d5Q/+UmZXaBYFrkf+zFyH/tx1MgO2QtXsVbrvARbhcLjVKsv9SnRe2YYWfL5h2SLJe7iqTMtPnd0hceOr20CDfFrfCQa5Zs0i1y9X/8uJDAaK1KkS9FNFM2S9ciKqI8WTdV3/G6CZr4tsmkUOB9VeWFA56UBg6MZbZvciKhxn26Ke7zHbMyQGUbCdD/gvg2fDy+53FkU5RsBa+055pvHWGye6olTRw2yQ2MMjk6RHh4hPTRCanBI9rFMltrGOm88/kXefPJRWltSvAMrQi05xPn4LhbUAVaDJN0tzmOROnjr0Enev+tFphKvCX0iSqVx5gVlfKMHpAzD4LbbbuOuu+7CbaisnKmwfLrM0ukizfLlE5+ZWCEycJqoBFunMGIlufZwagbtgom/aBGu6CgbGl49giJYJd2AmNMl3WnukH2zs2m2jxZXCfMDaNMHiB26FXtyGn14iPoXvkjpP/0n+brY3Xcz9q//L7RUTzbj62l/acCX67qsrq7SarUYGBggm70cNf+PaH9ZwFd5+TXmPvO7xIqHsGvT2zyxwhvQ7NRw3Leg8wysnEZbD9CaMn4iC4jtnCPn+m7FkO2CGXGP9O4W0ckOZWwWGmnmvDxuJkVq2OCloev4dO5eVqyLrHx3VV/ne523+KbRLOa+h9HTM2haAlVMdJeKInc6bMyf70fHznJucZFXMDk1vpfZib10ItsXR4lqg0ytw1A1ZKqsMNFV2eXDATQyO3j+OoScoqc9djwUlKYhUU9hxlVl3dbWG42IQIhDUte6FKKrbKROk58Kuf7AFLHs7Zxy8vxZocq6G0ix6R8+7/Cxs47UjdowWvyrsd/ljcjLElxMtw9ysHsLg7Up1MJFz/tuS+WwLW7witQdat48xOChLNmIxpPnivyrVpWFoMlYZYWHll7hvW++iFssCsZm2oGOoxs4QpBW0wm1HjGJAMACBwiBR8GY5phROkacrp4kiMTxTZduawNfes5VPNOgo8Zx/Bht30bE5BqhTTMQLUIrjEh2q6uZpXSJqi1iaouo2t4ybmEpF/XYRH2e3U1jO2nSTpSok8L3et61S62r1+lYZdpmhbZVomNWCERueB9Mis80LVPeIDRNQ1XVXq/1+s2xYGpDIbGwwf4vv8bYqYXeZ4hI1/5pXr7lJuqJIWzHIO6YJF2bTGCRC21ygUU+FHUh17bwKSkOG2qbgtpiQ2tS0Bq0FY9m2JH5/o1MDaW+RNI3yZg50kRIBhEigZBBCDmDyzlVYSE0abmiHPsi7ZthdLCigtxlDt+c6+XJX4sJZ4EXIefkGPAHSLVTWI7VWxDKtb+o1QzwfKFjppEIY+zqxtntmL1aznAVLTyDxiKBKKgREVZx7oU5PGOEwM7I6Fa726TbaeF0hAhrL+KmqjqmGkHXbDTN7vcWhtob66olmyGaYmJhYiqm3Cbe+7WZK4vym4HgrPNwQodu2MUJHBxE35XbRN/b3qUdOjRCTwqg+qFg/cpihFlsskSULIZyOTgLQpduUKITFHCCAq5fIAibkijBVQyq0SNMqHvJ9zVsakrA07bHMVMstr+2b3iHcpz/0/wNxpWCrEX9Nf8j/Fvv22S0S3B6iHlFVlJs+T+hIDDanSQY7X8XP0Sba6Cfr6OIIpy3MT1wMQNHanb1eofhsMB7zdd42H6DGV2ke4lzV6cVRDjemeZUZ4L1VgJdpJ2HAXof1Aha+3gqR2pgmOzwGMncALpY/IYmgafJCI/vKnjdAK/j43WEXpSP1xU6e57UWBORLTFvS92qfj2RJKu4ZNwjsFC2jC++fnMsmpxbrv7DuEGX9fYCxe4yFXeD5WSN2VyLxUSbph7KdM2IFyPiJsh0Uwy089hODsvLYgWarHN6Nwss3W1uIUIRxCeNPgHKxV5UZwowvmFGqETj7IltcFfqTYbjBYyoOOcU3gz38LJ6C63gOsb9PGN+moEwhrbD9xYRN0F53tQU2paOIwTRowb7nd9hT/UT8jVzU3+PytTfwLR1TFuTvUjDFSLjAsdohip1pTtnz7D2q/+G1huvS6ZMZWiQzI/8CPbtt8u55f8n7z+gLMuu80zwu/4+78K7DJM+y1eWBQoFECAAQgRoJYoSJUrqJU1rzfRoWtL0anVL3WqpZVoaimtI+RElUaJEip4ECRAE4Qoo7zKr0vvI8BHPu+vvnXXOi8iISFNVAEmhIO5cZ537Il+8eO++a86/97//P4qiO+YoCvC8s3j+qwTB6yTJ2v43mEwQJ/cTR/cRhtPbmjV7f1+wASKqHYettkuz78kKp+jnG2vVOXnhbaZvXn/H/k7xXkNdJ9J14T2CklIxShMYxQPomXkM+xCqttsH19Xg1bLK50YUXhqxcG6jvOtJzIm+z/dfdHlio0cqirBSaczQ2icYstq/wmL3HGvONck0uT2k+NOe960aBsbUYVaG7+er/SHWevt/xyDkcHqZD02+yPHxt0jbPXkvFaDr+vVHcPoDkCLumYdnHmSqNE11scnmjRC/fxvjSHh5FZcGYKtyiUz6MmlfJ90dR1nLoF1N0LdC4laXuN9E8wbXxHcL3zSIcyp5q0c65cljVk9HaOmYS6lpvmI+zO8pj3IhEX302+wWBYYzJsOtTSprNxhxGszef4TDH1pg4dAhFqbfXcH2v2nw1el0+Lmf+zl+4Rd+gVdeeQXfFzKVgm6kMDU1xcc//nH+0l/6SxJpf6fG+wV8ibhx7V/S6p6itvYC6fphco1HSFcfw+jvp4CIaks3v0wyegk3ep2gfhmtmqBvKuhbCmZ1IFEaOkL685Z2LLlJl9JCj/SoTxwotBo2tVaGtX6OV2ce4rmTH+TVqYf3VMOa/PH1L/B97c+Ty2xws1Rmy5ikGw/RCyq0gyJdP0/Lz9L20jRdm5Zr0nB0SQGUXQwFg2jIJh62SQq3VRr8CLXqoVVdOR+MYx61LI5rFrOBwpQXS0n5u91cWmFCNYzZDBM2RdO2kJ+/LePX131uDLtcmSxxdSJHNzVYkM/1A/7ROZ+52qDM789bvFGpsXyzRbhikuoW5cVeDx2MoIceCvWmLQ5qDuO+IilXbqaDNaKhdj38epOw2YRWHbXXHzSDv0sI2WdfyHKbaTZTZdbtCq6ZlTcES8+S1fIoulBEy8gsaqDZ9P3X0Jzn5e8vpmb53MjH0DE4Emgc9zWmthvwpW+VEnHRCrluhNT0gWiCaGgXJqf30o0UoilC2n0yUpiKFIYjlawww7lLpLQtXL3Hpq7iGz0Co4uqCvHceFsR6pu/HMkKJRYTmw0Wzp2iuLEDuhSiqQfRD38P6fwB2bfwbiGAhINPV3GlWmVP8ejcGj4d1aerhvJ7UFRNZkpF1tzw2ihC+Up01qQq+NU2sefLLLYxfWBgd1CvoXUbpAgpZQ1yBWE6GtEXFdK+K2ffDweKeZIWJcQCEgw1QRMVlIHuk1TGG5ig3rayu8uu2yElDqhZO8+/c37n//vmQ4C8gaBFIOf09iwELuwg3P+amjkQHDEzKMLbyUzLCvr62AFef+BJEjsnhU2KfsR8v86YEIhJ0sRCnGBPn+e3EoJK6yeKXCQHSSL0BgbbsagA9fHCJn5Ywws35fBjFz9ybvVW3BkKbukx0upJzHhw/a2nYl6cjrleUCRtV8gxC0EHMUT1RA8HXolCsVDMeiSGqETuPB5s52OXP6v8Hk8plyTguRFP8gvRx2hRkHtBSLUI2CegrCZsEba3RQ+kWjDRTA09Gby+2Q4xnHD7OXueu2/7bo/Fwm5AzLtbpeS/hRBJS5GQEyHA406I860eJWwECetBTOebaFWTip9SfcLFCTbxo47MLAm/KUe1qJkZ1owMy0MGq3NZ4iGT0UZVqoBOrqwyfWOZyc11RntNhnsNUuHdrU9uhRC8SIcY2QgzExJnUlTTR1jJPEQvU5EtACIJ5CUxXpKI9MV2R+DAgVnsgcE8ePxh9Ss8o74gX/o340/wJvff+r9vppf69tA0n1J5lUp5mVJ5BcPYZTfEsUqrOUK9OkF9axy/nxpk42IBxOPb5oGQilBlvfXzJMZ2HPLNJpbnyQqVuJ8JRVQ7iOS1KeN6ZOXw5TkmrkVaaQ6tIozlD6GV5lFuU+pNRPWyfo1W5zpfnEzzuQeOcW7u4K3/t12XfK9DvVAk1HcT2loUcv/VSzx+/iyPX7rIwVptUHETZ5S4BmqmBH11vUcjaeELirb0CVRkBXtnlp9jzxyqCm2hgpofojgxxsLMIsX8S/gld2ADkEB1a44bNx7FdTIYfg4jyJOKU2heiSTa/Xxq5GOHm+SN82S1q2SSdXJhiNERVayQpNpF2aZBv1MIX0OhAOtYeVzTJijZ1MdMkpLDvLHMw/EFrD2mbU5ssNgtsqYdZKP4CL3SAu3UMJt6mSVXl73kQpjLC/efdKq5gV44jZE/zYnwUX7xL/8Ef2TB1z/5J/+Ev/f3/h4LCwt8+tOf5vHHH2diYoJUKkW9XufMmTNSVvrXf/3XeeKJJ/jpn/5pDh06xHdavF/Al2h8//l/9JMY2S0qR34X1di9KCvBFP1r9zHUm2O4fRDL30/xCs0W3vAi2qxK7vgC5ZknUfoJ/fNv0/6t36T/3PNEm7vlbSG3WzzYJzPr0rGzbCUFqlGetW6ZJX+EN/NHODV0hI7gpm8r/Km+kFP+5m7SmhKSNzvkzS45s41hRHQzJer5UTZLEwTG7sVCXGzHN9dZWKlycC1ksieEN0pkVSRvvaQrci5oyl2zfTVizsYRN1UFV7iz9+40cmyZLpN6m2eMEUzFIIw81pZewV09jxH2ZZZSzIagSIWisf33c0qJ3x28Qs+y8VQDIwzJBu724vubC5H3C42MrJx5qhBvUPGMDGvZYyyli9y005LuUlFzTJInI6pm6sDcsa8kXDAizpsRq9seOsIXeUTKuyuMb6sPChrL3UIIhWxoCetazKYW0tRCWqr+Lp9C5PKTW2DMIEbUVodREeQqMQ8JCwJUymhURAWneoXw4m9Lc2AZioo+/STW4e9BzY7u2RcJXSWgrQZ0VU8CKQGueomDkwiSm4OH+/uTABZJpsBDcx1Ut4/m9eWsht86XfN9GUmCKQDWLXAVktkDsqx34euLBYNjGvQtm14qjSNGOoWbzXB+7iCfO/kszcz2dTVJ+KHV3+XvXPynVLSByIHX0tk4ladRL1OtTFItjFLPj9BLl7BUC1sMxSCtWuSUNBnVJq0Y2Ip+yzz099sbECUe8TYQC5JQZs3FYklVdoaNKip7t6owAwAt/u+/xRDnl7h0ihFt08F3Hw+2xVkw+Fkin7Ozva2Ptu//BjZQAgbsCJgM1t5TqBxTNErblC5BCbtAyEtqwBKJvI5Hoi8mEZUH4emYMGoHDFseRd3HEFUSTyX0dELXxnfTxGKRe5f8RUlTGDUURnWVoigx7ol+FLEWOKx5TTb8qqSO17NN1osNmpm27FFzdeHvp/HjG9/DR3pPyh408VludM9wpvF1+gKEbYf035LMBp3QMGlki2wVK/RSWVwrLRfyI/02U80aWd+TwCHtuNK6ISNGt0e21yXd60vFx3cKYeXQzWTpZTP0Mhm62aycd7Zl5WdfJHx38hyPxaelt9RvJR/hYjx/bzAkK+cJSjxQkbz1szjBzvQoTNYoTtbJjbalqu6t9+VqtG/maC1m6SyliYVD8n+lEFX6nF4ia4hRlAIaYjtlpLFyCXY+RrV8Xkkn/Hq2zJcKQ7jbtjpifz+9coNPnTvF42feJmm26fsBb83O8frR47x6/AGWR8f3/b1Ks8Fj507z+NnTsiqXu81H8VsJQWsWLd+SEWPC4ugCVyc/QKBNYHg5Mt0Y22thu3Vsr4HlNUh5VVJCDddrob8HYHXLXsMuEReGoTIit5tehiG7xFRhCM3OS2/MlfnrRKnXmF35KjPufj+8RnqKlr7A9XaRCzdd+r27UxFTuTzDB2YpmCncL3+DNS3g9IMa5xd6VIWQ2nZMxdN8/s9/jj+y4OtHf/RH+Zt/829y4sSJd3ye53n8u3/37zBNk7/wF/4C32nxfgFfN778PL/9iwPApVkthk58luL8N+5QlpERq2huCas1T7p5GLs3idUbR/OKkpqUlCyciQy1IYvlnMa6FxJdvsT4N36Hw289j+07t8wjX5i4j9+ZfZLTQwuSv/6uoQlfJpdJd4vxoEpOcbFTKvZwmsqozsiQR6XQJ2c1sJQmcdwlDLtEUU/O8bZXTxDanG0/y+vBk5xLL7Cez9zRq7GwFrCwUufA8lXs3iKWcp1sZoNhO02BAhkvi97JYnR18AeN/3IEfWne2bSHaOTmqZWP4eVneDBtMLGtUCREGK6uXSSz+Rbl+nky/bW75n893cI0MmhmhsjMsGhE6HaTCbPGmN2Q/iOamaBoCfXVNJ1rKVHdl55sLxy4jy/PP0Qc+xI8+YpBpOiSNmYK2kIYYoc+ad8nE7iy764cdSj6HbJej7TrkhUS3dvc9282Ys2QfTGOVaKdm6WTm8bJjOKmhvC1zF194vp6QFZfoaJdpav1WdXgkjLCuXiWGrm71k/E7X0IhRFUhrfnkW2AtfOzssyyK3c3v908i3fxt4nrVwc/UzS6s4+yeOyDrORyCCed1VyGteEi68MZatYgWyhWdeqmg7bSR615d3v1WwBQ3QGCSiwz18K9wB4wUhiOGhyqn8XqtVHEuREEJKF7d4qZkmDnYqxiRNi3CFrWdvVj+/K7/UuiSVkMVYmkCa1Ysgs/HyHP7cWmtD0Qi/yBCaeKqUfYekhWj7CMgESYi5o+oR5Jv7xI+OZpNtjjaNkFyEwRqCkSRZeVHjd02Wpu4LQ2ZbW2nESkpOPtYCUqjiWxqEv1uhQ6Pcod8djB7rfR72E8vhMC1PdTZVrZPLVcmq4J6V6DydoG460BiBIL64aV4cXJo3z2ie/iysn7iNO72eKT1dP87Yv/jJPhRfm4HuX40tqDuFd0jlcXSe+pAIjKez1/gMtjj1AtHgJ7mIxyF/EZJUQ1e2ihhZ7Y27LYCoaSkMlWSec3sVMtLCXCcNLovSyam0EL0qhqBgwhevD7t4vYG4NKpqg9D6ougj4oVO8EaBGqfIOf78zi+AgYYpM0YsEW0cViXS8SGwGq7qHonpxVwwHdF5d+fFE1V1SuqbOcU47SVy0JgjM0OKBexlbE9VZD9VRpVeKpGj3FoKdZdDSbliWMdTM4GPRilX6k0I9FlXBQKZSHskRMCYqY926LqsL2z5S7be887x4hjojvweBPYzG5XcHukfBr+PwiPvXfR8JLVJdlHVWYAxPLvqjMtlmw2PeBGhKpIVk15ohicoI0h8nsoyj7RCwqLW6oWywnG9T0NbYyq6xlt6hmmvL0nvRG+HNbn+GDnUfk74RJyIX+G1yqvkQQv7OX3DuGoqEIA2pFSIGL5JaKGcak3ADb88n6DhW/Qdbtojkx6rsUzeTnMQwc28SxDPqGTk8X/n8afUFdF15X30ziQpxXIw75A10Ksx3s0v7eXbdh0lrM0V7M0tsQ1a27vbYGynYtdnuWj6U1x86sYbstUs4GjiX8uwrUChU2K+Nslkq4lkEopNLDgGK7zkizxmijTqZTR32XHiThe9hP56mWR9gqDtPMl2kWypRGxvjeIwv88YlhRqw7+16djs/S+TqLb63z9tUtzhUUroxbXB1PE2xbkuwoax5cW+W+qxf4wI0NHtlyMCSv0peVNidp06RGL2lhir69JEVaGC87PRLHv1V89PUsvfQIndwIsWZiuz1styGBlum1pJ/c7ydE5VDQMhPLItYtQkxChC9kilhQylMqpaLwBdwgxzqmFqCKvlY9IdY1miOHyR16iuzhZ1BGF4hMG0cz6CY6tXaLzZuLNJYX6azexNlYJmpsSBDvmBE3xntcm+izVdpTYBDnamuMpHGcx8af5af/+x/g2x3vm56v/1bj/QK+hLLStb/7Y7zU+TFa0YT8Wc5c4ejUL5AbfxMnrdFLa/jWO2SQgpQEYVZvAnN71npjXHcqvJYkvIYQ2+jz9MppvufGSxxt3Lz1q1v5Yd46dpKl+RnSbDAcLDFEmyGlRUUZzIFh8Eb5BK/k7+flwgOYqy5/7Btf4dk3X8EUFQEzwbM02naGcHgMY2aG9OHDZE88gDG+QLuhsXppi+qVVbqrdbRAVJl66EGfnhVzebrEhfkxzk5P45nmvjL/fVcv8cSZUzx59k1mV5e/KaKMNnIc69G/gGblpVT0xb7LpWD/BdZXfKq2w0o+4lpFo1rI4KQy2IYpF3SaGmEqDUmtixRNjlCU5BUTLzFkVVCLAsY3l5haX2Rm5Roj1fVvqXqWaAqqoaNZFmYqhW2lSRk2hjrQ/Eo8j+7qKkGjIbO/RhRjCrGEMEaPAhI1jW+P4GQm6WWmcVIToAoFsP3HjuU2yHVvkuvcJN8ZzDbtAaC0Y7pmiivGNDfNGWIzh2pmMa0cKTNH0cxRsHIMGRlK8ib67iEy6DUiaopPQ+ljrr/FgfNfJ91YGTxB1TEOPIN56BMo6TKnixq/NanzxVGDnjCb2Y4jjZAnN0OONQYEm64KPTHEtqiKJWLEdONYyvl3w5iuH5LqNTnQWWWut8ywt0U27qCpPq6p7V+ECF+0bEAq75JLO2RyHmbFRyuHkBNNV7tPdQOLlc4YZ+uHObX1AKu9CYLtXqGdKNt1jpUvcbR8Wc5Zo8el5gJnqsfkWO3tz6QKJav7hs5zX+U8JyoXyFu74gB7Q9De+l6WfmcMv11BWc9gbmpYtQir6ZPuNMk6VVJOA+02H799x5v4HHaWIJ1HSVskKZ2OaXDNtHhxxuHU3BaeapKEBZIgTxzmBtthHs3NoXl5HL2AP50jmssJJ+Zbrz3XWuRvnf3nfCp4ST7uJxb/OvwUPxt/HM9MY+kaw4nOfa0uM50emcAgMISVxZ0LIDvYxFY2iHXhcjZLLHXdBqHoPvZ4ndRMTGqmiF44gK5V0GVvoei9aRC88lt0b7xG1+rjFXVy7S7FekiurpJuGJitFFpsDgQBxP7anh0VqlmTzaxNO5VHMx8BY0EmL0RfR776CoevfJaUK4CooDxZaMUZuZiOmosQihZ/aOfK1IZmqRfH6OTL9DJpfEtUSRwe157no8rzMkHQIc1v8AmuMHvHN2UoHobmYhoOlumQpANeGDnJ1wrPECiDa+an6l/hf7/8rzjgDnptAl1hY9hibdSiLdQg77HgFuRFDxMPGzex8MniJlk80jhRBjfM0A+zOGFODi+x8ZKUfL6Pha+aiAJHoEeEQvxIFee8ihcZaIHO963Dn16LGNlOyjc1+IWywi8XEjqiong70NsD9r4VoLe3b0YmO+5CVxaQ/hF0nt4eo7c953Li81rc482oy2LcxVBcDKWHqnQ4qKT4IfUYh9VBVd5NfL4evMGr/hsooU8mUkmHGlaoYsbJ9jkYSaEOAb931O2+1bqtSOKlxHXNDwdVaz/Ysx3K+8I7hfhfAcDE8DIp/FyeIJchLOSJikXpJ6WnVMyyoIQto2ZvgLYHXAoF3+UUyRkN9WKGVGqO0vd+htTMnOhglEPYEhOphI5L6HQJel3Cfp/Q9Yj9kMRPpCfnUirDYjrFDVvnRtbgat5kS2TI7hKCVjjXjWVF9FJuOxG3HabvUmw3OOa0Oex1GGpuoW2t069VUUVy7Z2WySJxk8mRGx5haHKK0ZlZSmMTFEbHKI6OYW73rydxwtZSh5tna5x/Y5VTIpE7ZnB13GCrsP89q17ETDviUx34zFJApecS+it4yWXqxqvE4RJWO0CrWqg1HV1svwd1QJkf0Qb0xVAZUBllxVee24OOb9kbLLWAku2xqwz6hx2uZuBpJq6q005pnDoUceqQx5WpUB42IgTQPLIEj1+Exy5ByVHREnGtmuCjX/kjXPnaG6Ki9aEPfYg/9+f+nHy8uLjIuXPnpIyleHPfyfF+AV/rW+f4V7/4k6S8mEx1DqX5MEk8UBUqG1d4JvdvmTLPE2gK/W0gtjP30jqOkE29x01ViCYY/VHM7jhGf4K2MslqfoHr6VGWmlu4qyuEooFf0yRNQRkdRR0fJTFF03SHyOsQBu7AqFiADuFMFCaYjo/mRZh9n0yvT77TJeP0yTr9W7MowQtTVTFyvS528O5Kd6IX6u2FI7x838O8fOIhbo5P3lHmP3b9MvMrNxmt13Atk046OzCPzWTppDO0Mjm8VJYf3crwA2uD/XIto/K3HrClV8dwO2J+PWR+PeDAVoCx53onTqTVsiZNGsVFdaWiy4vb7aGHARMbNyXQEmN8c0X2+OyNammY1dFB86gZ+BhBgBkIN/cuGeHoHoneMiGrLahPf7iKeYM2d00Is6EL/48oxIwD9ChEC4TZ9uACLS7U4v8HJtw723t+JvtXBrMcZhbFysr+nkSo0JlZ+laGpp1lI5XlupXirGJz00zRMtM8unGRH730Jebba7cu1L89+xRfOfpdWIUKfkpjNafRzIlGcU2CgbEIvnctlD46wlPnnp/R7xP3Nog66/R6KzT8dVpRi5YW0LZ1SZG7ZTyd87EKPmY+IJVzSed9zKKPmhdmkvfej3GkE/QqGJkaqrbLbfVCk7erx3h142HO1o7h3M3TTPaMBBRth+F0h4lcC10LqfVTbPQKrHRKd4C3yVSNw+ktjiVVZhttUjXQaxFmy8Hutki5VUk/eSegL6pJ3VSRRqZES1CT8jb93IAStZEp0VHT9KX/nS5Bh58IgGNRJ4/7DqqQcUojOpAlmkyDvrvThno1/s/XfpJPJ9+Q/X9ivXyuOsmXG4d462iKgjXGcPcAmdYsqn/nPUTTe2TU62SbK5SWFilvXZaU4N3Po9IrHkA99iDlDz/N1Pd+CLO8/3XENf3SK69w6aWXWOr3ca391bOUYbBw+DDzCwvMz89TyOelvHbsOCSOQ+y6xH2HxHWIHVf+vFnd4NqNq1xZ6+IqJ9GMAUBKEo+0d4pDSp/pygfR9cFCLXKb+Od/k3D5FZn9vj1CzaKbmZRV6WTM4LHZL1A2BpYerwdP83vxR/G1iEgTfR/3/n67ps2rs8e4OCZAnyIX+h9dfInvPf1F0r2OBGyG7SLKQHFaIUklgyH0tO0ExRbGrIKOFaIIBaLfZ4hkVBga4BYoLX2UkbVnMKIBWO5rHc6m3+aMcRNXSeGTwVPSuIqFq5p4YuFmGPimKRN+osoh5NzF/SlU9cG2Kh7rBILiJwyZfR/Lc0k5fWzXwXb6pNw+TzffkFYtwkerRYa2qDEmFm5s4sdCiEa5ZeQsxpBeYjK1wER6gYo1sY/S6oRdKaQgRBWEwp1UtgPGUnM8UHqWkjV663lnm89zrfPWoOfqmwiR0JO0V0FxNS3JApDZA2kHJwyppTqFMFdCEdVt1WfSbjFlt2TlXBcGvQmsuSVWWyU0VyPtD/o2xbB9H9vzMD33jnvVrfdQTvCEUvKD4B2MBtSG7VB9m8zmUTL1k2SrD6OFd6oi3yvEX1tNKVzJaVzJqlzJqXJeSu8HUHtjoh+z0I052Ik4JGYnYUYYjAvgoamshjHPBz5nCxqLFZ3Vsk4ze/cEtfC5ynea3Oe2eLS9xcjGEr2tTbx2E8Xz9km93y3sXF6CsVhUzhodnM1VTLclNSgVfQZMUaUfoZHu0zfakDQot+qMNGpScn2kUaXY3aXXvVP4Rg4nVcBJZzCnxpl+/AGM0QmaYY6Njs3iSsB6NcATVgPEREmbOG6hJi0so4uZtNH8JprTQN1hFUij8l0gZhMyabaYsLqMmV3sJJCVcjGSUGFFG2FRGWPdLeF2VLKesJRxSQUuqVAI9whaeoAhzp0kkqIkO+Hp8PohhW8cVzg1L6psu9/vwdWED5yLeep8QvkuecUkP8zxV57j2x3vC/A1NjYme7uefPJJms0mBw8elGIcQ0NDfPnLX+bIkSN8p8b7BXzd2Grw5JldDxczSHj6gsOTF91bwODqBFw/WCdnbDDubzHuiVGV82iwSUmtkVjhHmCm009pxMJN9m4hmsTdMmq/gOekqPV1qr0U7VYJr2ejuDG5/gBA5XYAVF/MvUGl6/dR8u5sg6RuKkMnk6EjZvF4B0Slc7QyWZrZHDXx/eQLg2rY7QBTUE28hJwTk+/F2MHA8HTCDfm/rw8u1CJ+qRDxb8oKOVVjJFEYixXGEoW8rFglaL0YrROB8BTx9p9KoRqzlPK5YkMn3mKqdYkTtQvoiaCm3dY/YKXoljL4oxa9iSzVokJVqE7pczj6Al2tgntbpUhkclP9CKvhYG12UOo9cD2MRFzYAjlbUUApCilFEYU4JBeFpOMAJQlIEuF3I+pKgzlOBM1xsK0mgZSo/cMMmWHbvqBLACcA5S0gt7u9A+J2/l9QNHpGirXMEE09R8dM0zGytIwsTStP08rSNjN0zAxmymK6nGamkGIqozMR9Bnv1hjeWqa4dIX22iLNTpWWEtGxLdopk9BSMHOBBFhWfhdo2YUAIztQB71XxKGB3xvG65VxeyVcp4DrZOm7ogJgoCoxtqJQKFQpDN0gN3oRfU+FKolVAmeOXvwsV6InuNjKc63a42atvwuREiHbDQ+UsjxUyTKXsij0eqwI5dC1m4StNfL9KuO9mhxl751v3EJB0y/kiIZKBKPDdIfzNCtpNgoZls0cW47JasdmrZOj6wnq43tXJxT1jZLeppRukM80CEspFiuHuZma33dOZsIe/9fq/8n3Lb44aHwXCYjaGG9ufpIV8zF66bE7JbiVELu4jF25RpJfoh1oOI1ZtM37MHwhfpOQcrYoNi9h905Tbl6j0NvvvSR0hTrTFVpzUzSHx1hT0jRlE/xuaFHEpG1z6NFHOfTQQ4yMjEjFzW8lxO22urTIq599iRtvC4rYQP03ibvE7stM5RSOPvsshz/zLLotqoYh3tJ1+lcv4Jx6E+/tMwSLK8S1tuBg7+4KLWH4wQ6Vw4Njqe2U+frGj3PTeJxQqqS6csRy9ohFJULrEmqBpDpXM3lemj/Bcnn0VsLn0ZuXuG/lmgRkKccZ9BX1erKnaGdbPu4PeowSQS1KC2CNBGhiW9h++SkV31IJDJXAVAl0jUDVCLbps0K9UdAdhWmq7heYU59m1npUKmOK6AQNzvZe51p4g9A0iU2L2LTlQPQmicx8FG6PaDDHIjMlTIU91MBHiYKBWWwcbYszfIsyhO8hLDXNSPYgE+mDTFgHMEUP7XaEScTNaItL8QYX4i1qBBzXhC/VISrKIOmyETv8erTCy3Fb7htBOxbnXKjoEkQKoCW3t4dIbN4rgfpuIX7L2DZNFtcloY2a2h4iDZBBkdeaXKJIWmYuSSi4HQr9KpleFTt/BSavESxsEo7t5zNqG2C/rcphXlUkRUz+TTOHkqmgpodQMmVIFcHIo1g5meC5PGFwZcTgWtbiWirN9VRGVkLuFvm+w/zmFgvNJifGRnnowfs4Vi6Qsw0UXUGJ2ijuFkpvE7pibEAgqLgWfmSyseKzet3lSkvn9+bmeGFmglbqnX0yxy2DR/JpHsqmmI99ciuLbF65xObS4sDTVPT7+p7s/RW9vqKSKESGZLUxCLF35u1tWxyn7+FYFP2xm+UK1UIF1y5JI+10mCcxbZo5hU4+xrEdLukay9o0iVfC81Rpfi083STkf6+HSSLseVwKQYtC2OZYeJ1nOMf9LDKhNqWpfOQrRL6KE1gsM0ItKkAnpNBqk/Y8+bnFvfvdIlTh9LzC88cVaQXjCR74dkxvxTx9CU5eMRnupjEUC1OycUTRYJBcEOU61VbJPvMs4//7X+XbHe8L8CVENi5dusT09DQ/8zM/w0/+5E/y+uuv8zf+xt/gxo0b/Oqv/irfqfF+AV9L6xs88fayzEwMVNJEliIm34/5wMWAYzcHpAnRP3Bt2OPSWAc/H0IxIVWYoJQUKHgu5cYmuZvXya0uUepWKfbrZIwGVq4HeY+wbBKOGPgVl9i6t3mk2tPR10Bfi9HXlcHYUNAa4mDb9kARJW/LJDEVNCtAsyJUK5Fj0AsV4+lpunqaNSXHeaY5r86ypI9S1Qs0jcx2lm+XbhdXLKKRFPGIPbibbIcexBxcCzi8Fkg1sZsjBpfHjTuyXHo34McuufzftoTIg0KVmH+Aw8uy9XsQ5ajJh5U3+ZD6Fk/qmxSUMfz4MG78AEFyCCdWpK/Mhh+y4W3iBUvEwU3iUNDjgjsyvNey01xPzbNiT9K2dPTsZfTMRbTsJVS9e2dGOJnHtx4hsI8SZqdJjP0VEtWNGF1ymVj3GG9HjHkKI8Hde6aEJVEk+NlC3t7vUtFNclae9DYFSYSguYSxT5gERLFPkATycZQEBHGflurRFiwe3ycKhJR3QJD4uAS0rIieMWilN0J/u3o3mO+VOf2DBHU7wE0ZcCq2pQ52PIoSNE2AKQ+9FKIVA/RigFkKMCXAEsbN9379KDTxehW8fhG3n8dxc9IMs+em8CKxSBj8sp0YVOIclSTH0PZcSPZLmIsMt1O8Smf4DXrDpwiy++WV9fYUxtaDqBsPozemJF1UCz3USNzYXdmroAmRF9F/JehJYtEpPLPEvE1/E9uuqtLUbZpmirqZpm1lcPIpWimFhq7SjtK0AlFNUG6JJOwIJgS3iSeIocYh4tnCjNxXDcn8zyse40afaa2JHbmklUBm1AW95crIFGenZtnK7rcaEdes/2n9X/MXr/062XBATdoKZ3m+/edZ8R/Y91zbrZFv35Ajnb1GcKTJxuhRWq0HCbeOoogO853vSHNplC+wUn6bG4Xz1PSeVDIcaiWcWISHFg2OLsVUWndWlVr5PO18nkZO4bWDPm8/rFLIlSjbFcqpISpi2BVKdomSVaKs58lbBXRDLAqEsI82qHwImljUh9iRs+hfTcI+8cUI5Q2LZN3kktPkii96OgfKiHFUJXS+jqpdozTvUFhokhlr3RXwqw2wrqhY5xXMKyp6VSEz5jLxRBM9Fcuvf+NMjq3GDMHMOP3iJDVmqQbzuMngniX17AQlehucLY7EfOXENFv5gUlz3unx+PVzLGytvOO6TVSQxBCLTgF4xMIzCQXo8VEE+HmXZUZaL3C09BTzmfvk/hNRj+q8HZzlZngTomDgG7UNrG6BLDF+X0uYRN5/NCvG0kOKgUdGCTC1iNfVw6yqFca1BmM0GVU7jGg9ioh+QB9djeTxravxvrmNSZsigehOi+fR4kcgOYSaCHez/dHTYMOMWUkjvSiPt4Un2uDLPp/p8i9m13i9GJAoJkaYwxaU3ShFIi0gBgq+/l5qpXS9396Wc7w9J8KN+BYt873QLu8WluZxvHKBh4bPcP/QOQrWbmJHKOJeacxxfWWO+vURMhsxY72aHCO9hjTTFf1n7xRCvW+zUGa9MsxaZUQaoq8PD7NZqhCnLIZbTY5cusD80iLzq0tU1ADr5AGyh3OktR628GwLGiheA8UXBsT3tulwFZPfGfoA/2Xse/ha6eQtteZ01OfTW1/jR9Y/L5Wb38wd5838Md7IH+dcZv7W80SYvs9YfYtHt67yQPU60xur5LbqGPUOZs/H6nuS7fFucYvKaeg4YshtA9fK0yhMcGH+BOfnD3F9cgTX2g9EK90WB+pbfHgLPlMroyQ6XyPgiwS8Ic1BdkOwVoqhw6jiMZx4VIIW5fYSxX6VvN8jK8SiPAPdTSgFDTJhBz0IBhf9bzHEkRZoqqw8+/pgDk2Dy9OmvL6ePuDgmrv7aNRXeaoN4879DLnPMBOPMh6X9qkVqzkD55jO26PX+br/Eqerp/nYzMf4n5/4n/l2x/sCfB0+fJh/82/+jaQefvKTn+RjH/sYf/2v/3UJyJ555hk2NgY0ie/EeL+AL9d1+dWf+jl5c4+9HlGvRdRtoTmOvDCoUYZ+6glce14+XwtdZpa+yMzSl1FjITbwzWfMopxKfGiUeL5COGUQlHr42TVCwWu6Rwi6YboXkelHpN2QrBOhdy3CfpEeOfoiS2W4DMV1RoLGHb+/klR4NT7Ca/EROV/RpzBHLPzhFE4lI/s5diLtxhxe9Tm6HDC3EaALeknoUGxdo9C6itFb5ko5xQv3P8wZUX0tj/K3z/k8sm0m+NURnZ+/L8ORfIqPOFc4sfh5ShsvUHCu7mtYDWOFVecTJMpfpBt2WOpdYNm7TM9vEt5OFRLKZ/r0YBgzKGqJIJuwVtzibOlFFrNfQ5FIYRCJEFdwJlG0/l3NjZVYJR88SMV9RKpZjrbz0gNNfNbbQ6i+FzQYU1Sp/ChUu1LvoPYmbD6aKcEa2WLUv0A2WkJXqqDUebk0x38c/QgvZQ9LEC3wtPiTY82Ioys+h1cETWX3klLNqbx9wOTMjCnpgCKjJjLpxh4wJre3AZqg/Dx06Qwnz71FxulJbrprmtSGJ2nki7hKIBd3tudiBuLGNpBmF0nV26uJqh5jbleuBhWs4NZjM/vOdxOR0fNaJl7bkLMvtuVji9A1QNVINFEd1qV0cVrPUzKGKetDlPQhylqF7F28ot4t/PQ63eE36Q6fwild2kcZ09wi2a2H5EjXj6PuARv/tUOKP0j7MAHKBlVjP47kLEUihOyzGhFrMY642QJvVkx+/lCJ9czgff/w0gv879f+BcMMekidqMjl3qdYdh8mljYQrkzOmDkhtR8KDxP8po8biIVJCl+zZO+UlKEQgF7xUO0Wqt0As0UsLXlFr0xMFOr4voUf2ESBeaupX40ieZ0UUtSGABBCOGL7MJJ7XhF0ZpF1FuqMYoEkaEvbitzyWQNz40hNaGdC2pmAVjaglQnk40g0T8gFnc0nmx/gM/UPMxoOVGd9JeBLhZf5bPFr5BuHeHTp45jxgI4VB8sEznMk0TquFbE03mNlsk+j5KOqQj1R3R1iWaKq5PoK80sRR2+0+XRmi6HyXeiKnsr6Ro6rjVnW4oO41iShOUZgTxDrA/ql+F7fmjX56n1pOunBdXW81uFDp99mvLE4qKAZColhERvCmHYPpJDKdtF+cCS2ZQUqQA0FeBJKFpGcC1qR47lHmEkflZ9FxJa7zPnmi5Kq915DADDZxyqsPpIIQwAhNULTI3QhdiSSeqlwYNoqmK5pAzNjo9kpNC3NRGeNqfolScF1tSLLme8nYA41slAiEzUyUSILNTZQIiHa0MNU1jEUcW1cwlAHs6YMhGTuFmFcwo0fxo2flEm7GHEc3P0aLM6rnXvzJXuRfzb281xK7fZapyKLh1vznGzMcbI2Q9o3cdSY5UKOK2OjrBWy9MRxqymkuk1GNlfJ9Jo4moKnK/Q1ja6m0dd1eqouRQ9cTZfsCkHfD8QQ1clYJ6e2eCD9Fg9lT3E8cwFjj+9gP7R5u3ac05snOLN1nN670AnTgcNYr86YsIzo1Rjt72zX5bYlkkbvEIJqvpUusZkpsiXUIDNFatk8jVSWZiqNYD+nEo9M3CeX9CkkPQphh1LUoey3qTgtmqUhnj/yNL+z8Cwtc5BkEPFk5yI/0vg6n9p6Tva7IqpXXkDQ1wj7mpz7fYu6n8d1TPROSLp/70T0vu8+B3EpJipBVE6ISgle2qbtTVPvHKDdHyJyxX2/RRK1SOLmtv7n/hBV6vXhSa7PHObazGG5vTdTaPseJ29e4elrF3ni4jmGtzbph30iX9wvE3T/W098iuuib5s0MiI5VSJXLjMxMkSxUkYrFtAKRWlyrBbystLW9TzaTo9Wu0lzc4Pm5hqXete4WK5xY7yPIyjL25FyNWbX0ty3Oc4j0UNMZY4yZI7t/m0SruVXeWH2HG+nLnPduUE32E1QG0HCjD3Br//Y7/LtjvcF+Pr7f//v84u/+ItScv4f/sN/yPnz5yX18MKFCzz66KP0er9/ac0/6uAr2Kxx+UMffFcI1Sge4sr8D9DJH5CPTa/J3I3fZnz9JckBj0zR25HFs2zJmZfD2p5NU/qC7PxMyxewhodIF4uk0lkSNYNds8lVPYrJGmZqDS+7hp9Zxc+syQUl95DvFjfNlBOR7g+AmQRnzmAWfjSi1XPHYUqoc11JzfBq4T5eKjzIsj3KujXEplkm11M4shJwdNlnqhbiJiH9sInprlFqXmasfpHRXp2it1/VSEqSP/CjqIYgSEX8s/mE/3ywuO+Cdqh3g4/WX+Jj9Zd4XOvB1FPcCKa5+mpAt9Zg072JG+0/lkVlrtxzmXAURowxoomTbA0dJXF1qkFC77bdITKmWqpDLbvIevompa0HsPrTKKpDO3udwG4hrGCzXpm0M4zm5waNy3cBWvWCxuVhnZWKIXvQWmIBpSjMdSMeqvssNPqMNPp0A4XN2GBTeA95W9xX/QoFZZljlWXuy2+QUgaLt3qS5T9HH+Xnwo+xLhcMg8XnSKQwG2ocCFWmQlV6At1rASFiSYuk4exFM8LVxNtXiIsmjNhoeZXvefPr/Ojv/iYjjYG9QSOb5xsPPcnVqQOUWjWmtqqM1YXinqD+2bhmnm5Owx/uE5WbaIUmRr6JlXclwBLWCO8UsasSNA3Chk7YNAiaugRYbsfE9/RtD5U7eyIzepGSOSp7NXZmW6hA3hbi0toJ6jT8jcHwNmj6G9IrSljFDhbPIp+3I02+83gw63ZMaqpOanoLa7KKuqfBMAk1wrUx4pVp4tVpVD8rqRg7r6dtv56y5/VujX2P1bv8/f3bwiD39xM3Mir/5IjFC8OD13m8cZWfOv8vmQ1eGXwPSZpO+CfoRJ/eljL4gw8BBneAmpy3R7LvsQCOwhoj2vP8Pdt3PBavtv06Qq3w1l/b3fJFAkBPUaZCSstiq2kwdC5kbnImcwVH9W49W4t1ppvHmGgfREsGlgxRtE7iXyVJBtct0cfVTgf0UiGh8H8TFPBIQQsV7DhmTnWY1bsM630MdQ1TuYyiCCNanfrq/TQWD+yj2wlVxZ5l0rV0ef137AqoeTl8PcOXj5T53aM5mbEW8cByk+87fYPhVoskcWTP2i51eSASL8+WbWn42/fHzqOcUWI6c5Rhe+rWz2vuGjd752lENYT1YKAn+HpE3xDiNgGBFm//TFSYhE/hPCe8w4zFI2iK6HcUAhxd1vVNWqqoP+mkIpt0bGElNuKfmM09CpgKHfLaf8LShJpmghOdpBP9sFjGfnPHlxKQaAGKWh0AMVYwkxWMZBWTVXS1dZdjMoMbP4ITPoEbnyRRds175fEU9lD0zC1lzU3/DL+Z+xxfmliiuWfhKmKhYXFyPS3H7IUmp4fG+C8f+15euv8RqdQnYmp9lY994+s8+ebr0r5EhKWrpC2dtKmRNnV0VdxtE/wRh/58A/dQE7d8m7dSXyFcztC+XqLTKONpNvn5kHqhwGVthovuLFt2SVbXe4Y1sJoJd6tyyq0qnKjKiV60gUqmEkSUei3GWzXGOtujX7sFzIYdodb3zsvVlvAHTJdZz5RZT1fYkPPg8WaqJBVg5SvoICRhRp06B+tLHNlYZLq1SdHrSgVVO/Rkf9J7sXcRVNpuMUWjnGelMsr10iyr5TFZrdsoD7FVKhPoBnOdLR7eqDK7EZCrZYnd0p0vlnTRo3UMbxnbWUTzajJJLq4xgr3gaSquoO3qg4RH306zOHWQa9OHuDF9iH569xgSMVLd4L4rZ3nk/Fs8fOkcxb6DHQQYRowm2EbbTKNbw9rZTvBTOq+MPMCXx5/gS+NPspWp8JFomT8VXua7lS0M4X9miOuZoP+m9szbQxcspBSXnS0+v/kKn197nuXeLrMjQ4rj1QmObI7zUO8I09YceWPAjHAUl5eyb/G19MtcSt+kZfakYqs8Zv2E2Q2YX0+Y20hYWIeJasLKpx/iE//o5/l2x/sCfImXFgDsd3/3d/nMZz7DX/trf03+/D/+x/8ofy7A2HdqvF/AV31rhY1nPia3+yZ0U9CzFfq2JmViPVOAqQyhXiDWKqAfQ1ePoGzfgMKwTbt9Dt9bk2quYXkcb2QOzzbpKxG9OKYXJvQjYcaoIC5HYaJjxAbpSCcXqxRidZ+pscglzWkaBzSVBU2lqMYEqS08AcYEKLOX8FLX8fM1EvPeYhGWtw3G+nvnUGZw9i6HdzxhAwz6ojHaGqY38iDR5OP42Un89Bh+aoRe36O1ukrv2lWi5TVO+LNMpQdgtOcv0ox/HjW3wWJ2itcq9/Fa5X7O5Q8S78nsmr7H7Mo15hYvMLd0iVxvQLsQTdzLwzO4Vo5HLlxmYXVdAstqIcdnn57mrRkhhryJkWjM+2Mc7x7kQOsQqf4IPV/oau2PjAqWMjB8FUDtbtBVLGVFFUtWszQFS3Xp6n3WzD5rVoszlYRLpRQruTE69m09MyJD7F/HcM9h9q7xkep1/lx3iSeD6q2nLDLO5/I/wqu5j6KaKakwJ27WlqHubsvHGqKWodd9knWXcNUhqN1bz1hRFSaPlTj0xCgzMwbtf/cztH/lV0m6g6yxb6Solo/RKJ3Asyv4GYu43EXPLWNkbmJmNzBzdcx8XxqJvlMEniF7rhw3j+MVcJwcrpPDcfKEoXjXu0eSrnhkkybZuI0RCOEEsHrD5MIRCqKiZQ2Rs0bQxU3lthALdre/Sa+/Sre/SttZo+1t4CMWydtmmNpgHji57dkfe8yM5YJb3R7ifFSTwWwk5CYcKlM9hiZ72Jk9QCyB1laa6nKB6lIRt52WfkIC4EWJKsUwQimeP2jIFyEe5eOAXOxhCGU+RaOhpehooll/QNHceZ8ZpU8x06aU6pIR0sGxjuYPQJxiaijGwNK3nQxRZQJXzUrwFuomb0xP8/rMmFwAjjtb/OTb/5wP9b+CKsU0VNa8R7nhfxg3KUurBaHYpQrJbD0jfbIGymc7QFV8IkVWNrREKHIJyXUVRRXy0wOgKWSohfn1H7QU/HdqaMoaZeP/g6UOpPr70bM0gr9Mwv5F2jtF1VT41wdNfn3KkJl3UR38weWAv3TFpyQuUH+EYtttjEQV6ms+ke4QGV1Cs0NktYj1PrHukojqoD7osRMzOFhhn2yskgkTMl4fu1fF6FblmZYkKn5yFDd6DCd+nDA5cI9EVoz+oE71qTTPNV/kueXnOFs7u++5ZbvMw9cV7n9pE23kSX7loz/AqfKYBAAihFhTccuncV5Q8wZ3FlP1OVa5yAcmT3G8fJaUvidJuW3MPVT1ObDsoLg6l9OzvJU/JCl5l3OHuWGPU9XufkzZkcfh/g2O9q5xbM8Y8YXYzyBEkrWrZqglI2zVh2guG7S7Kl07Q8vO0Mul8XI57IVDsufH2tjC3lwnX12nUNuk3Ngi69xd3XUnRFqgmipINb1hp0nqLkI2t4egTNfsArVUnmYqSzubol8wEZapkcAJlQgr55DTe+TDLoWgR8aN8cMFWt1jdFujBN0CSlhE3e7p292vMZneGsXWFYqtqxSbV7D8O0H63SJQVXq2Ti+Top+26VsWbc3kxug4l2YPcX3mkBTsSvasXfTAZ3rtOgs3L3H/2hnm3SVKpkvRcCmajpyjjMkXhz/Ifxr/Xk7ljsh7wbSzxp9a/21+ZP13mPC33tP7W9I1fieT4XPZNFf2KFCn4pgP910+1h7lge6zRNFTeEqWK9ZNXsif5q30RVbMdfqC6aMIFtMAYM2tw9x6IgHXeH2fcPCt6I6leOyrb/DtjvcF+LpX/ON//I8lXe5v/a2/xXdqvF/A11L9On/8Z/4KvWyXxOqgCHWrdwk11ji+/gwnlz+BHQ2oUSupTZ4rXWNdT0iCArFo5AzzkNwp3fzNxjQqj6FxEp2TiUp6mzMtKUtWA1e/yJZ5lZvlJr1Sk6HsJkXz3vSNREjw9iHf8yn2PbKOKP9HpNx3lkRtJhnWkjLt6CRD0Z/AIify1nxFuckvJl1WKEtlK3HW25HDpLvKaLQJFYuNyUmuTx++I7OUaTQxNvu4DZWkLZq53/t+MRPRR6YwEakcCzWGQxXjHQypxf+IPSfU08U8uBXHBIno44sxFoawxwxsW5Vtb2IIv6huVGc1anCGiMtKmmVtmJ62v/fGij0ea53hA803SDtneEFb5w3bwMJk1BhiWC1tjyJDFBlSCpSSHKropxLN9rHIrot5MHxPoVNP0WtlcDopFEfc6BJiQRXTTEkDnVp5junlL2MGXeJUQn8yx8bR4/Sm0ujZVYzsJla+iW6/800ycHTcXhq3n6UvAFZQlmDLdXOE4SDJIPoxhMS2KfybrB6m1ccy+5hml9js44gqX38MvTZPpnmAijvLZDiOdRfFvkAJuG6tcsW+yQ3jJuvJEq1wFbsfkuumyHYtcj2DXF8lL45TJyLnBuQ9j4Lfp69brGXKrOaKrOdyrOdSrBVsNoo6jazgyLooeg9Fc1D0PqrWR1N7JKojeHJyP04aCfelIk6kImb28OVFbAUKZ1yNs47GNeHZtAO4Ih0ttAniLEGcJokGQw8NDrSrTPVqGLGogAxR16ZYjhdoRELOfrfyVbFrAzn78kXmVYdeZ45vaB/k6yMHaWzTCYUQzFgjpJHTcE1V9lD8b+f+Az9a+xXZKyPic9Hj/NPw+3hAvc6fUb9Co1nh9faHUY15VH3XGFtEpLr4dg3PqtPVO1TjFJtxFldUNtSAca3LsNIl7TpMLq8wvbTE8FZ1QGMTwEwYewtAJmZF4+zwIV6YfIiLlTlsXSdraIOha2REJUBTSeuanFOaiq0q2KqKLRIcUUS6WcOq19DrdZRma7AovrXI2T1/BeAUymrhzry9LQC4pCvKp+4/3wdVo50H4j3nUFRR/TAGKJuOzI7Hgra357fEt6yrwiNOIVZ04liVlbnBa0VMpL7AhP0FSW324hI3en+GTnj4FnVy/3vY+8qDx+LfYt7mZ06M8PLooMqbCmJ+5GqHH7zWJxUPAPAOUFZuGUuLIQzTBRAUn3mwbBLLDvFeZc/Z9nVsm0C662cmALrcum3/3OVdi78n0wHiPaiDWRPz9hAJH7HPoiAgDHy0qC/VQ8Vvij3phEJ5zkDXDCmzjZAzF5RJIZahm39oYD4WlU/VFQ27qIqHhouWOKihT5xkiZMyEUPbV/59v4liXcE88Dbh1AjnU2m+4F7na1sv0wt2WRhCFOpYK8uB+Q9xrjHFuZFHpViViKFkg0/6n+V4cIYJexVD203ohKFGr17E65VoJpNspCZZtCe4aE9xPTV5V29PQf8+4K5yrHedY92r2yDrOnOqj5YdBjlGSTIj+EaFrl+g1c9Rb2fY3DIJT51i7PznyPUGFiKit2p97AluzHyMVm6UCB8trGFmE7KPHSWZLnAz2eJGf42Vzjr9xgrZ6jIjzS3GWi1GWz1GmwGjzYThFlh3ydN1LFNa3IiKWd3KsZUqspkqsp4eYiUzwlpmWIqcfLNhCEaPWOvEggapkBYjSii6TYa764y2bzLeuEqlXycX9IhMXQqG9VIZ6Y8Y6SaxHkm6tVCSDm0bs1zBswyqricZSELRcy8ro1QqMT42TkrN4a37rC23OWcl3JhMcXVylPaOaf12FFp15pcuMXfzMtOr126JoXXTOUkrzJbyHNM2ub/xPCWjJ8FZavwQyvyzUqGY0B2Il8i5z5bf4Qthjc8nbd5Sdtsk9CThg27MR1vjPND5ADf0ac7aq7yeO8dVe4mm1pEnc64vQJYAW9tVrfWEMcG+vEvoqQi7FAxG2ZfzqjHGgb/37S/ovC/A19LSkhTb+G8x3i/gq98P+JM/+UvoiYkWW0RxTGBs4ZsbREaV2KgT6W1irUMgFjGKS6B48mA3wxQPrXyUB9aelb8v4krlDV6Z+S3a9qB/Sw3TaEHhjkGQJwjzuGIkNq64ke67h+81qU3QxeJA+M2QcBiNR2OVp/2YCbOEurdnQKg90eUbU3BhokEnu8q4f5OpcIkxY5WyXZNZ87tFEinojkK+71N0fNL98BaNUcikJ4lBK/xxutH3y+fryjJl4ycw1ct4kcZyv8D1Xpkb/TItbz/9SfpCWXkuTdzHhen7qU6M4Bf2qygqfoS51UOt9kkafek+KhSehGnnUKhTjjTKsUYpVinHKrm70AZ3YkDgGYSAv2I5815icDoLryHR5N8mibYGPPK4R5J0Ie6S16rMTHSoHpjixfLDfKP0CGvWyL7XUaI+hnd+UBnzzqIFuw33WmSQDvJkvDzlXolSr0Re9CN5eTJ+DivKoSo5Em1vpk+IqXSx7SVGw+coamdIKgHhcEI4AknmnS9DQU/D61i4vQyOqGR5RXphZQC0SEtRiXTQJqV1sDM9MoUW2XIbNdWlrzl0iGmECs1IoetmyLbnKfenGHPHmfWmmHUnJK3z9hD0h2v2MlfsJZbUZdaTVdpRjWySIufqZF2dXF8j31NIOSaRfoJu+iEaRo6WGtNUYxpag7rep6mmcITQgTBcjvtkw7688Q7RYk7b4HCyxqjfoKOneGvkEG9O3sfFiaO41nbvWJKQcZscXrvAgdoNjKiFo4Xodofx4hbThTqTma5chO+EMMI972i87aqcdzVZvf5mQxPHqjtBuXWYkc4Mo/1JdG2YNw9meHPewt/2UhN2F4dqLlsVmzVLeK+E/JX6F/kr1/4tVn9z8BGmHufGI3+DX9+Y4NRr61hbPof9mJSsne5EzKhxmUnrTYwxg8bYYRbdNKurawPJ7HuEatgo+VEKjQ5HXvwaM9fP3fV514qTPDf+AN+YfIAVsSB8r5EklIMG0+4qs94m036dQhTIBNBoYDKsliWdK/G7xF4H360ReS1pLSEW9IOqpiKpQ0JZs5MyJeWvv+2Z5OvaLYrYH3goFhMph+8ZP0XRFNcCeL39KK+7H8HI5rFzJpohklo9Uo0G2XqXQtcnLewLzDSKkUGRc5pXp0f5pw9NcFFQhkWDvBPzl694fGpVmIl88xELj0G/B35XGt2LIRZz6GmU3AhhtkJX9ekqDp2kQyeu0jMcumYioCj+e1BUS6fTFItFxjLwwY1/T7k9qBYtGQ/y1tkhjOvLDPeFKfE9kpcCxOs2sWHhpPMkw2NYBxbIHT5G8fAJNDtL4kUkfkS/1aNdbcrZ77nosYaBYAeocjaEPfM7XPt/f+ELkygC1ZP7q6W6NDSfrubhqA5GZp10cRWjskYmtbrvNyM/g9sd5Wp4nOf1xzmdn6dzm6jTTlT8JkfdJY51rnC8e0lWtY70buCGFTKGEL5o4mXHefOHfol6dloyZ26NeHfbdVxmv/S7nPyNX6K0sS5f27VsvvjMR/jVZ55kIytovTXUsI4W1VHF2NmO35v8uhAridQyeTfPU+cDjtxsYIQt6W2Z9hSybkLWgZwjPAH3/65IXPUMYTCepWlm5CyAWiNVpJodo5oeppkq0jZs+qq4XwyovN90iP5ZUyUR3pGmkJ9USYzB47wJR4dy3D+U57HhHA/lLbzGFktvXmHpwg22giYdkZjbDpGImWKVw1zncHKDEaUqrz0X0nN8ufwEv1N8hjdKxyQF89bvCEXX9UUWFi8yt3SZSkM0I9wZphpSENWy4VGKxz+IMTXJ28ZNXui9wZv104OEj3g9ReWRysMcdGYwVmOWWeFc6jqbRk2uE4vdAbiaFxWtjUFFa+ge+XbRPnALaEmwFaAXsmDlBiBQ9v7G+If+GPZ3/698u+N9Ab5ExqlcLvPggw/y0EMP3Rq+7/NTP/VT/OzP/izfqfF+AV+Lm02+93Mf3AcCktiQVauBqWmeJMjJ7YKiM2b6jNp9CWIy1haa3sZtjWNf/Til+gmZQYyUiPMjL/Da9OdxjffQlyeakIM8RlDA8gukgwI5v0jJKzLiFxn1ilSCPFoyONlFAkpcqM6PGVwZNzjc3eJT1zY4FGQw8lP7XzqJ2VS6rKgNlrUtaloDO9PCGQavEpHOtJhghXEEt/4eMvZJQq42yci5/x7dnZE/0uzfoev+MoudnARd9b69TfzajSGrx3S6yUymyVS6jb3tzSSe1zOyLAVZXjEe5rmRJ/j61KO07dy+vzlZ63NoNWR+AybqezPVd4bh1cj0zpGyGmIVS92rsRQonMsd53z2KImWlSAumyhMeW0OeA3KYYimFlA0saBPScrVew1TEX5IdSytLU1Nb+YLXB3JcnWowNVyQSou7Y20K3zNHObWIw6uKxT6d1vwCMXKNkZ+DT23iplbxxAUwUwVO91EN9+5guX3dCls4baMQSVLVK+CMugTpIoT5IpF0ukUNTPFdb3PJkto6iXKyTXsaJ1eFEpwJUBWI1LltjAQX3CnWfCmWHBnOOhOM+2N7VNO2om+6rBkbLBkbLGhNsgqOgueRml5hfSrL6FvL8wkEE9BuxzTGY9olMfpad+F4j6JkgxAu6d3ODv6AmdHn6dn7VJJEtGUp4is/2D/Zfsa5bZ5a5wIPB63qhzJV0lpA5PX07mjfGX0IzxXeZzXrRlJhdmJvPAk8jsc6tQxNtfxelWKxTUqlSXKlRUMY5f+GccK7dYY6xuHWalN0whVPL0vz3Exe1YD32oSKhHZ/hil7hQj3QOMdGdJBzn5uW8O67x82Obi5G7WNdNvkW+9MRBEyTwsj/2P1b7K3776/+WgOxDP2bSzfH72Cd42jxLWdLx1FdNLYYcZrDCNFZg4oc9b5jDT1ml+yPgiz2pvyQSO3J+YnOYor/Ag1XcQKhBU30KzydBWleFqlZn5ecY+9gniTpfOl38P5/XXBqqQ26FOzqE88DTRkaeIcmNEXkS8vYjGj1AcF80TgjAxhvB2UkRfzC4bQNw+W0GVtl+VvXydqMfVsMFy1EXXYvJ6TFoJ0QOXwOkTC7nzP4TQowgriAbS1dsy1mYYYiUWhlXEzg5h5Aqk8kOMFb9M0X59sF+jA9SC/zfxHcbM7xziW/mdcZ1/fshiPTU4Ho90Qv7HlZinElPuv6jukmzT2sSCS/jlLTkh9U5IkCSSUi20eXYWIOm0Sh6fnOdTNCyKpnVLGCisXiJY/Abh6ht3+J75hkEvk5HDGR7GGRqin8/Rsyw6ioK3Dc4WWOQH+TwZHHwMfouP8hbH5P+ZnndLOj8rLAtSKVbKaax+j6nr15hr1Mh2e6i+P1goi8Nvh8UrCp/DFexDB7EOLWAeXMBYmMecniKII5aXl7ixeJ3FG9dpt8W1QCQkBX1Wo5gpMD06yVi5SM4Wvbcdwn6HyOkRCb84LxDKLKiRjRpaqIGN4eYxvCGElv+7CWbFqk+/fF4K+PSGTxPag37anS/Fbs1ti/g8jNmduuP1fAVcPcbTIgI1JNR9Qs0j1HwCPSTQA7JRk6neMiP9DTQcFMUBxUWjT8eEXzrxEM2UoCELYBjK/qWw22f+61vc/7UGGWHVIisuCl96XOd3HlGoWe9NGELYQAkbmJymUtAU8qpCUVUpqFDUoCQER4T4ihdT+TWPwkvv7okp/nLPHlzjPUP4w6UItCKhNYObnsHNzBJaE3dYX/hayGoh4eJ4hlUh+271MXAGwlBC/MyLoAVJW8MPLGlA76gGwtTlmw4FbFunmDYYEf3ljS75eJVhdZFJ5RojSo0yHSpKm6LSEZ2NXFVmea7wGF+dfoqr5eHdNaOAALf1NVf6Hgc2rzO8dZaj9VUKTQ+nMShDid7LpRGH6xM9VoadW71YIoY7KaYbedI9lYbVo50W6sYho62A2c1Y0gcF0CrdY1kplIZT5R2g5WOVAhRTJDseQbv/gyjTj8HEw3h6hmrXp9rxqHY9tjoeCyNZHpvdz+j5Iwu+hKnym2++yalTp+T8xhtvsLo6yLaIN9Vo3Klq950S7xfwdWVjBe/Zj+EZCq5U40rk7Amfyls/E9vgmeJGJfq+5tHUOaxkHjuekKSVu4YaoE6/SHDkN2nrXVqRQmt7UdsMxSz6Sd7b4SNk5oVMrpoU8c0KnUyZUC8T6yVirUwqKvLUmsGfevktFoTpcHoIfeQ4anpb4GE7BEklIKKv+LTpcdMO+PJMnufHCnLhP8kKEyzzoP82R6JL2FqP/OonGbr8Q6iJQWi2WD/xb+WNSIRQMPKaFm7DROmoGIGGrhXI5ocYyuYYShyym1uE3jKqUiWb9O4ggIgQPTVv5o/ypfKTcrydO7zv/0Uj7yMb13hgdY3ZTZ0kKNOLKqQaqyxc+00K7ev7PmVmzCM1HtDFYFPN8tXMA7w0+l2cDsZu9X/lbZ3vPjDEM4Us2XZEbaVLfbW7d20phcPz6ia66uLFOZy4QLSvynBnCLWz9aLG9VGDG6MGN4f0XbPDJKFIk4P9JY72rjMT3GBYXSadbmCnO+jC+fkdQhP3/ppGp2fTdNO4bYtekMdXhlAYRfHHUZJR9Fh4u3gSuPRTNeKxFaKRRVrqFlW/TSOM6cQ7Uizb+yPMcNCdYcHdBVqTwf6K3k54qoOb1KC/hV7bxHc8vnIoQ90GLVH5cHCCuXj3dwX9aVVb4XzmKq9nL/NW+iqFzgHuX/sQk+3d77qaXubt8a9xZegNInXg31KOTIZCg+FQYSiKqGkJl1IR69rdkwWmr1LpGMx5Ko9EHZ6hyqFQLBeho6V5ofgQXy09xtdKj3EtvZ9ZIBrIH2udZ6q1it4ISFtdypUlKhXxHe1PLfqi/602R3vxGbor99+F2rTrwyLUKl84YlLbrnaIKLgrVPpvUdcKNHOPidIT97cv8H9c/gc83b0hn9NUVf5lMc9/yecI35MXkYIhBBJCk2ysMxTFjEUhlVghH1lypJQhctmjFPLHCdc7tDZrNCOfTSuke5fjLxvbjCVFRuMCo45BdvWSXMRHW+dl38VOqPkJ9IlH5dDyE3d9d37kUvNWqXkrVL1V6t4aQXzv/sa7f0QVRUuhqjZJIvoHLamGqigpNMXGUA2GjRoT+k2GRZ9oIJJbKj0p1pPipj5EzbAJZUW7Iavc9wrhjZfxAzKuT8YLyHoBGc9nfKjN5MkmuhVLc9TNs6M0V6ZQRAIplUPN5NHyRYxSGb1cQiuV0Cpl1HSKqF0nuHEF9+xp2tcu8itPPc1/+sRn6KUGFdoPbLj8Py9HLPRiFFsh96Fpsk9PotqDhI7T8akuddla6shRvdmhuXn3zyCKAGJBLRbT+dgh79fJeFtoqR6oHRK3TrS1TrCyStzv3/07M3UqD/aZnK/KNWbDzfHFxgdYtcboZzKSwvWuX5kSkUq1KZbWyGXrpNIteT5pe8zS/6tHrFJa/Djl659Gv4vKYKII4HWOzsjpW8BLjTTytRHy1QPkqvPE/hCukiYgRUwGlYw8/8w/pMpcSEgUu+ieB4ErKWseHqt5j5tFj67hShEace33VEG/dtANF013B7RxwyVtOGRMh6zlYEuRk3f+m9Y5heLP6WhNwc5JcB6LCcoarpqjSYFlLc3NQhp1oiQrOzc7K2x6LnYyzVh3gXHBknAE/Xp/RHRJ/CWSYFnaySTxANgKI+/NoXHZc7U6OsPa6BTt7H4Rrx2a5qGNZR5YvITt+LiJTkexWS2M0stWBiIyiYbrxzhOgOuGhALACeuAbyV0hdjSZD+CqK6lCBh2W4w6TTTbplWsUBsqsl5JE2m7O1X0eE52NskGXyOtXGGtd1n2Mu9Eoaszv5JhfjVDzrl3m4oVhKS9gWl3xgvIay6llEMl36NQckmVAnmN6FdOUC8+wCaHydyYItsZJNuWTfiZdMSpQFg9bDCcrjKSqjKSrjKRXUVPPcVf/NTf5dsd7wvwdbd48cUX+fEf/3H+zt/5O/zJP/kn+U6N9wv4WlvZpPnRZ+/6f4L738lN0crP0yrM08rP4VvFO54n/HMKQoa9fV1KsXtGjmvzn6GbG1SJTL/B7MZvMdJ+CUFijrOQ2AmxBZ4tVOmgkYG6rVC3VeqaRUMxaGgqdT2hp3ske2TU3ylyRo4Re5gT6zaPv9ri4AUhDX4IbfgY+vBRFFFm3hMKfXT1AmntOU6VQv791HfxhcoHZMXgxOoa/8slOBwMvp8t9RzXhn4etVLHLnqYuXvfONUowRSVmG6RWm9EysEG1cMozYOkVIesWiOj1clqNTJqbTBrNXJGnYxSp2pmZIn/S+Un5CK5q+++bzWJONk+y0frL/Ns9WUONJbo+wZ9kf2PdfqxSTu06AQWZavPQ+U12Rgrv++kzC9Fz/IL4UdYS4ZIC8PdWOEENR5WNplKPLyoSBBmpRdMmKRwYlGRe+c71E7nxTY5Cj3VxcxWMbOb6LlNonIVPb9JztrAErTVe72OEGXpZQnbGdQtnexKhLURYK259Ntw+kCZN+Zgq+hQL3hSjvsOJtzOD+7VQJdAJSxIoHXIneKoN82cO0NZVMnuEmu2wpUs9JQ+etTECDap6zXqRpOa3qKLy2hrBiuycVWXF0ZfIK0Y3Nc/yH3OQU70DzIeiL6L/dGNEmphQi2KWLXP4uW/wIh1jrGkz0gYMRxFlKN7WxK3VJWLpsEF07w1XzONu4IUK4H7dZsHUwazWsSQ4sjq5U1ziDfNh3mLBznLA/SV/efHvHOdR6pnGa42SYcOpWKdSnmFXGkVZc/+DfpFumsP0F19kP7mUVnN7pgqrx+0eH3Bprdd3dDDhIOLLsPX+6xUNG4eyxHaGpPuBn/3yv+PT9W+OHi9ROcF/7v4RvAsLT3GNfq4eg/P6mKmXQKtwZp7DdeIiCwN3xCS9N8kiNkTgsaVi9JkozRWbKELv7VYlabVZmzujsgkHaeZVkeYj4tM3VzFvvIWyerFgQT6dnjpHM3KGNXhEaKREpEe0es1aTe2tnuvdkMzLDLFCRQtQxJb0iYi8YU0uYGhpjA0G0u1sbQUtmpjC180VZW9m2IIg1vZoynpOu8tCy4qR1fcmKteTBg7EoTlqJOPNukHm3SjFn1BNX6H1yjHfT4+eZnJ4gCU1zdTLL9aRu/czgG4M5R0Gr1YRC0WpCJfq/Iw/+6xJ/nlAylJrRR+k3/srQv8+H/5Fwz5fdKPPkr6sZOkT54kdeIEilDOXe3Se3Wd/ptb+E5AO0poRQndrEkrgWbDI76LH5VIEeQ1RVY6hNBQZTTF8H1D2FMKweLb9F96CffMGfzFRTTNYfLpBunhQbWscTnN2uIY4Yl5osNFgtEevcwaHbVHp1di6eb9suIuQtddosiUYhj3CtE/mkoJIDYYqXSbtFCm1d3B5UsUFbbnfWOnH05UzZQBPX9nn++cljv+ZbuPt58grA22jZZ9Racej5KqPcm4H6IYHUxnjOzWgxju/mtWPVWnZi9hq68yHTzP1DuIJ3QwuG6NcsM+iKOPUgpc2T/Wi21CkcRMjMFxjtg/wprZRMNGT2wyscloaFIKLfk4YTD+MCIRCpuxRxK7JJE7mMXjyCWOA+j7qEJVOxTtFgnXxg9zITOK3stKUaIdb73YuyLVO1V9DFUXFi93rumaqQ1Wc1dYy19lLX+NrtXA8k0O18vM9WOGuiFKNUXk3nnFj1NCnn2CmqAqloZRbYXp9ioZAUBFQkI3OT19kLMTc7eEUe4mXPJ0800eaJ3H8AKu6NOcsRZY1MaJQmUgniKq9X4sh7r9eAesDXovB0Pds337z1Q1QinqqEWFMLeKzxm84AJJskvJNcgw6tk8ulrngbUe482YSmcgeuSbGp4wVDeEKuPAXF14scpisWB9CIaMkux7LAB0qBo4Wkr6RooLo25GGGbImOUxLO4NWp9EF1VVYS8jPk+y7xzbLI9x4sfe5tsd71vwJeLzn/+8FNt47bXX+E6N9wv48ryAf/j/+jJeEuIrHikN8oqGHQtvkkG30P6IySp9skmDlLeJ7ayh9esknofiB6h+hCpNARMapSOsjX8A3xx8PtupMrH6DfKdG7s359sOH0lNGh3hlRPHePn4Ma5NTcrsshK30cI641tXObB+iUpnhUTt0srGNFIBDdvD1e+kBAhZ0Q+eS/jom7Gku6nFGfThY0QTx7CKB7clhnejSY0lfx3FdTicuQ9TExlinzfrX+aCdxkzc4BDyhTD5gFsIZ+fXcXLnsPN3MTPNdCybYxMA+Ue0viCNhZ0hwjaZdx2iV4ziysqZ01BGemjiLJTEmGrAVldqCD1sc2A5ZEJ3p44wmtj93N9G9TuxJi3xXfVX5Zy9h9qvE5OGLNux6ozzNfrH8CJpilZGqqexknK9OIy/Vj48rx7hlICK7WPrtfQhOSx2iVW+iipNkq2i5rro2d72KkuKbuDneqg7Wm+vuP1EoW+W6Yaj3NTn+CGNcEGY2wwziajUtZ6sh4ytyFGILdFz12gBrhGm67ZlKNntuibLTnf2jbaRHsqQsLPbLY/wwOd+zjcn2MmHGY8Loj87F3fW1tvUdWbLBlVTmWXeT17lU1jDSW5O89hxBnhyY0nMRKDjtHh+dHnUbQOQ35ExUkYDiNGlZAp5yCl9veRDo5T1g3yUoH+Nl8xaljqWSz1HLpyFi9aox2aVN0MNT9Ny7fphCa2mqJi50ipHqbmkEo7WFpHJjliM2KlrLBZUOnkNKKUQspSqBiJpNjcK4JQQwtsqvFhznOCV7XjnNUWpPfbVDVkphYyUQtlf4N8r2aX7PjbZCdOkxk7g7aHnhjFOtfiBb6qf4Q3eJy2UqDohJy87PPQVY96VuULj2RYqejkww7/05Wf48c3fgVBoBFx0fkQL3f+NJ14RHp+LRoxF/WIa0Yk6caTzgrft/HbaElENn2Y7x75DKaiERDS0XpytK0+Pduja/Vp63226NBwq/SjGr7epmMJZU+Plhbi3eNcfa+gzYwMrECn2I6Z2lIYaupkHAsjEIvKu1QDtQKqNoGqT1AwJziUyjNuir6KUQzh5/ceAdS9QxCR+gRJTA/hwSQWMSGe5tEN+7giwUKIpwS4Qqm7N4ziisrxQMBCrbQoH4sZHi1IwRC106R95SKLV1+h2/KJRSnzViQ8VFrjQyPXpTeWE+p8cfUg69U82SAmJ5M7kA2F2qyD2ulJs2MRil3EPPgxjNkPyX4oETeSGj89G/K1owflY9t1+ZO/91v8iS/+Fil/+xgTwhuW6CEroeZGUfOTaOUhUvdNkz45hzU9IittiZ2hseHcqo5tLXWpLncI7+JVJOxSMr11sp2b5LrL5LpLjOUuMv1EFd2IiVSd63OHWBkOCM07u/gb9XEunv8ggaD3CR5DpsGF0gJbpjCtVRjpdBhttUkH766QJz83LpWoIQUW8u02uXqH1HoPs+pJYHrH+9cS9EJMWNHpDWeojpRYGx9lM1uhphdoqgWaZGiQpyuqVLrCZPYmh+1zPKi9yTD7gZTdnCO/+gy91oMkXppJX3yu3eMyNNr0h04T5t5AM0+hOV2ibkzBiRjzw3smjDwFbhgGVw2D64Yhk0VXDZ1FYzdpZISCimyiuWk+vuXyg29VSW6mBNdfHidq2eT0/H18tvxRVGymwz6Tocto5FEKBY0xQBEtCqqJqppoqoGmmehyGPKxmL/ZEEvdpLshj7/n0n06zmWG4whTqrMakgou5FcGXp5CUTXECppknHUK3RtY3iZbRVgbUqlXRNI5lv1dAjjr4hYr6KQxDLkqIy3RD6yhRUJgZ4Cc5VlnmcSWjaqJbyNBFarDgYoR6VhxXwquJEokExixqqBosbxPpMTaDnff3V4aM6sD+qIv3r9MTETyNYUarFCFldvyswwA/rvuI+C0ZfL5TIYvZNLUtiXtRYyEIZ/o9flUt88J3/9WyJJ/qFGdnGPoL576dr+N9wf4Er1d5h6ZyZ24cuUK999/P47zzm7n7+d4v4CvlcsNfv0n3uTbGeIAEguxC5MGF6dM6rndE1Yon01XQ46s+NKHq3S7wdWe8DWX3p6F+WDsbDcZatR45myXD55NEAJ4wuOiP3mMYOoYQ+lDlM2xOxbEfuJxivOcE5WOOEPOHyLtF7CEypvoVbvbJUQJMbNbmPk1rPya7F2S27l11D2L1Nsj6JXxO2N47fHduT1O5InK0+7faaZVro6LfjdB7TNldmgn9DjigcYNnq29wR9rfoETzmX5m1fdJ3ip82doRpO37fwAVWkQWV0so46tNYgEH1+NaSsGScpDT/Wx0z0JrAR1RoCrVKqD+g6LVsHGCvsmQdvEa5r0Gttmw20Dv2OSCG6iLFJptMqj3JyYp16c4vrkDNXi/vNB9Msc2AyZ3RyAsdHmvfvfsioUzICinlDQVPKKUFzU7+6hFUMzSlinw1v5Mzw/8hwb6RVSQVZ6JgWaAPS9W7te0mu1Er5eIdJKzLWGeHJZLEogshyeNr/MM93LZLcviUIKfdF7lLf6f4xl/8Fbf7tsLnOifIrJvFiHLuD3J0mc0h30XUFHE4axW+4SVXeZurcuNd3EGSOaiK2ih130sYq+rMSK2cwIc1pLmruqob3b6xHZIPoEnDSOn6IXWTiRTRTaENqYkYXuZ8HLkTgZqd7WE5Wfu+xpARxLuiL7IUzDp2VsURt6i+bUNYZK1ymqu3TwOFGoeaPS26erZfht/c/wfPY+jDjgLyz/Bn/9xs+SSwaVk2XvPl7o/Dm2woVBVl8TBbREyuTX7JCrkUurv8EH1j+PmYRcTx3gc6OfwFATThQdPnZiis888TCTQ+lb57G/tETzF3+J5q/9GlF1YIMgzLRXDz3I81PTzJRu8EnjeVRNiAuotDSVm4XDuAtP0a/MslJdZqO6xFZ7g5bXwhUgxkzw9IiMqzHSsOQYbloUO8YdwjahGlMt+mwVPTZLnpxdK8aIDEpRmh/udPnR7grFeLAoD+NRguQAATOE2hyuMks3mqLva7iBMKIWEvseRf06w8ZblPWrqEoXlQ4hPheSCU4zz6I2sW0NcPdeaqFsVqlUbg0zyXH9xR7L5wbAQjdVHvruGR7+7hnMbbqfqAS88eaPsbH4Fp1r87SvDdOpDfZpxezxqcmLjNiDJMVbjTG+ujFPsN2nuxOipywTKRTzs+RyhygYQ+SMMmlTQUutkLiLxM0mr5tpfuqxZzg3MUg0VZoN/vxnf5FPvvg1aRHwnkLTJAjTS0W0Ygk1nyeJIpxmn27Tp+0YdNPjtPMHcFPDUiUuFgt2JeLJ3M/xcOY35MtUlTFeLj1BNNTAKt5Et3pYTKEWj1M3DnLmrIV7cQBeHNvAmVAZoiml0If9upzFqHgN3CjFqzzAWY6wA1F0AoaoYyGo8DkaiKTY3a9wKhHpuEfG65JttynWOpRXG+Qbbdm3J0KoYS6PjHFtYoYr8wtcmprlxtgEXl7jId7gEV7jfk7JxfhOBJhsaA8S2U8xrj/BsTM2+gWhjDl4K/FBg3a2jfPiRSrmYUzhN7dzjBPxdvoyr+Te5uXsGbaMTQ4EAfNByLwv5sGYCwJZgb9biCv6llnhSjzNaXeaftvi6NUbHLqxRCLRB2TGXIrH+uRGXFnx6ycWvxZ9kH8ffYLLyf5e7/0Ro+hdVL0lBZVKSZ9Rp81cu8fCVovDWz3G3FCCM+ktpVvoaRMtbRDpMY7no9fXSKrrJNt9u/r4w5TvtyiXfvV2NuAfuRBf6SXD4Lezab6QybC6p9+7EEV8tO3wyabDwz1fVpg83cQxU/TsFK5hESqa9DWNJMxTJI05CoRRvFAYHcDZwf+JWdTfxDxQ4RVrEF0NMRMPMxEm9yF6FKKGg4pdIuwDhXLrdtJX3M/EPUn8rriMCGEvSxkiFQ+hpi0e+Cc/zbc73hfgSwCv48eP8/DDD0uhDTFPTEzw0z/901y7do3PfvazfKfG+wV8deoO/+F/efEdn6NqooSrYVgahqmi7lns3yuSOCEKYwI/JvQjAsE1lv0R2x3GgnaVCXltPs2pOZtOSt/XZ7Cw6XKo6jG/2cNyA+JYlIpDmSESQ6h/qdtDmjCKE+sWLWN7W3Y27/h4iRO5SxitEsbCfHCN+DYq46S9wMmRT2Fr6TsMfkWEwncrTNgSIxA9Q1L8/Bbg87QeoSrAlTB4Vkn5GanglwoEF34g52xkW9j5dazCJkZejC303BaafW+PkchLSzDmt8fwO6OEnQpxp0zcKxKoGteHU5wfy3FxPEU9tz+jV/B7PNF+kydab3OydYakZvBi8BBdNYWiK4S6hi3AlQBUt4Er2+6+I8ASzV2KY6F0NeK2Qr+pUa+naNcz+F1j0Py1J3wjopr3qcsRyFn0kvzAi7GsTu6I7H35wVF++aP3sz5+H73UMfzbKByZqM/RzhpPbHY5sa4z0suST7IUREbzLnfCSACtKKEp+pXlLOhJsaykCZC1F/S0zRpLxfNcrZyinlndB75ElKKIoUgjSMax+wUyYYZGdo7X5z8gexCnvRrfdflFHrzs48eP0IsGNF3xtsZmNUZmfZJ4ndbmGq2NNZob67jdDpqiU7bGGbanpXFsxZrEEDLV+3Z3iJtfJrKqElwNQNYuuJJgS3B530N4cUIjSmiEu/PdSLTCK668DbQKGthmTKApdE2dnq7SMBS+OqrztRGduqXKSvZcco1P9L7M/errFNNbUpzg83ya3+AH8bD53urX+D8u/3Mmgw35N+rhFG/4P0JVP4kXWnTbd36HQjC8oQRk3Uuo4So1M+LlhQdY6dv0wt2FoIjZos8H0vDIuVc4+NxvYcaDT6YND1H8oR+i+MM/jDk1RRQnvHmzwe+dWiR6+7f4sPNFJv1FVpyCFNFZc3LS52x/6FL5j0SApTt77nxTVBxVWvmEeiGikXWlSmiEh2PGdK1EUqg/3uvzPzRaHNg2qxUVgXstTMVioRWP4CSCftijpOwqtopFxDVmOM0xLnBQehXuRC8xacUW7cS+NSIzw9GZMU7ODcnm8gemCtjGLkBavdzkhV+9wsb1ASBO5Qwe+2NzHH9mAk1T8bwNXn7l0wRBjbGxH2Ik85e5+OLXufDCc3Q3V3l6aJHHKsvyeG9HWV7uPclay6bd6+Il966Gq3Es+zhyKBQyeYqj45RnFnh5/BF+qphlxR58D8Lg/f/x+hmeXD5N3FgmWFkhEbSwP6CINRXdjDGsQPaztfUx1rgP38gTGFkCI0Oc0lAyDna6waqVYdO0peT7SeU0n+BrGLd0Zu8eIuHUtsu8yVFO+YdpRrvXt6H0GlOVS1jZFp2oQNct4LgVXLdCr5uSi9FbryO9OW3qmTy1TJ5Ovkg9JeTOs4SacKFPmGRJgi0xDnJpX/XCc1M014dp1Er0yycwp4bQihpCXmGjv0G85fGRqw/xWOu4fL6vBPxm6av8cuWLzHjjPNG9n8e79zHtj+37fBv2JlcKp1kunaJXvIKqhiwGWc75JfK9TeYDh4VtULawPdLxvZeRPc9kWR1iVt/EUkKW44pwPeOQuqu2+EKywH9W7udFpYKhdCn6fca6baabbUY7LsUeZFxVStwPLBsU6Zto2BH5tEch7TGi9ykFPkovwq3q9DdNIk+7o8IoVJEHD1T02QXiE4v46QEoMGPBGtKkH6dIfshVhOWgp1poKcEaiSUrXiTmAreM1ykRdVR0v4ftdFDDPvUM1HIKW1mF2CxScoelmrR4PU+JaOg1jG6dStckHdjy/pWy+lSsnhT5skT5bOcYSaDhF9kIDtDR76MwOsvosIIaepzrh3zFzPJieRRHt4gEdy+GBy+e45nXX+HE1UuocSIrkssVhVOHRzh7IMfNkktXqwk/AdLuQBugJ5Rt9rCNHrs0YBw9cD1BjweaAVvDWaojY6wPTdNOD5HYGj3bYqk0ymJ5lJXS8ED+fk/oPR+t46E1+1iNLoqf4Ps6oVBRfQ8h+uLGwj6HI5eFJGIugXHFoKJlKOh56fm2E0vBFZ76iT/PtzveF+DrG9/4BqdPn5ZDiG6cOXNG+nuJ+OQnP8nJkydlBUyMo0eP8p0U7xfwJTyVzn/oo6i5HNZQCa1QwFNSdH2DZk+n1dXxtTSBniY0xJzBHCoydnSUySMlJg+XKIykJLgSN+y1K03WrrZYv96WzZ270uU+oRWxuZBB6cSMr4hF78AM9tWDFq8dNThcu8mJ11/g4JUzUsY1Mk2ScokoZcsqqO84xNteEn8wIS5bWVLKGMcqTzOfHZUZc9GL80Y/wksShnSFYV1lWBcmxPsXhU21wxuZC7yZPcepzEWqxt1NJcQFrNSBchfKnYSK2O4klLsJY4RMWT7jJRdjKsYriQuZRi+t009rOGLRcY/Umuwrayqoaym0jTRKPc0l8yBfn3qI0zML3Bwa2ScHK8KMfQ4Hl5mNrjOrXua4cZqCem/J3TDS2HKG2OhX6PRS6B2FXCtBrYd41QDlHs27nVRAbQ/IEqNvRwOLAnGBXwv59IsxRy4PPIBE+FM+mfu6TJQ80jvVIxQupA/zduq72NQeQg+nWOgqHOzE0ufs9vAVn019jRWtxrLao5Z4hJFCVqho+nkMPw+hyCy/N2VHIZThGl26ZoOO1dhHc9ylOrYIdAG6DeywwnC3QtEZIu9WSIs+siigvPYWKXePUthtkS4UKYyOyUVnUczD4+SNIeyejX9zleCmh+bvBxnvGKInRBgYm6o0WRZgs+ZFNLyYhhOJVoY7I4nQkyZqUsNNbbE6ssLN7E2W003ZyC4WXztANNRG6Bd+GD99kmSPet9e6eH5mzeZcjqcOjhL08xL8P8Prv0E97evyecIXv/p8jRnjAUUPSJltQlCi1Z9hu7WYVS3jObn0IP8LSuLveGTsK7FtOyIXqrDSuxwPSyR7JFbt0Kf+6MWjx0Y45HDBynEGr2mJ0e71qa9eQ23u0gcLJNEQqp6f7JBVyI06W0ljpfbGt5VnVxlmsrUIcYWjjB14jgjM+KM9lheXpaCUatXV8ivKRwOxyklGUz1FHn932Krg33gBhqvb2b5vw4XaVVCDnohH65NcdxVGdbWGEUsOO9OVRPnRkMp0bSn8fJzJENHMaYeIH/gQcpDIwjh9lM3m7x6o8Fri3VeX2zQFz0ce8LUVAnATs6WeWy2xKMHShRSBtfe3OLFX79Ka1vIojCc4snvX2DhkWEajRd589SPy3dw7Og/ZGLij8vr/MbVyxKEtV//NT6cfZW84SHW1Ge6D2Pk/0esbpp2UKMT1OkaYl6l3d6i47t3NYHfCcMo8PZ9H+BLDz1C3xrQEx93O/xvw1kePrxA3OnQf+01ui+8QP+llwk3Nu7cV2ZCVEqksW2cS6Qht9pVUKXuxmBWwm+thBFqNoGRBktHS2mYQsW6EGOWIuKigl8McAo9esUWXl6o9kkJRvm74jLXbI6ztnqEWk1Ub7ZtF1Ia9913gMcee5rh4QV6Ucy5Tp83qg3earS52Pe4EUHvtuu7oLod5RyPJq/waPIaQ+p+OmGnbbNctTjXgzOaRzMrRCTe+XMf7c/x3239gOxhFSHoq6c6n2O19iVmv/ePc+ix76e8ZKFccfAEaN8DpPpKzGuKy3NxzAsktAwP88Ai5K9i+G+jRTW5E0aiSIKwR+oBjzQD5sKAYibAkKZpdw9PpDOFEJeoh2x/hLZv8mZjgrebY3h7FugpLdgGJwKk9BmyXMpmH60X0ds06W9adwVbwmwuLOXpFobZyo6xNXwI2+1y9PJZ0psDKwrhPfnrC8/yHw8/i6sPFvOjZpcPTnyDDxx4joK1m1iN/BT9jYdoXvkEva3Ju7J3GvYNAnMJwxCVncF1L0pc1q3LvDkseupD5oKQDzkOH+o7POJ6++5mfqix2ClyuT/EjV4JR7aPbB8fioY3NM65qXkuTMyzNjKFb9lUvIDDW20mr95EabyNw3V6mSqNUpeNsqD9J8xuwngtIdCFaq3C0sjucSN6eR++mvCBcwlHlhNWhkyuTI1yaeYw1w88TCljc8S7zIyzyqhfwzM1VrJlqmqRqDGCVUtJNeDNgs7qcImb5RE20vvFrtJJjxOyO/lNjkZnMHyfrp9lwx9h3Z0n6h3AdoYpuVnGPJPpUGEqVim8Q2tFnCTUCNmKHNYThz/7Ez/MtzveF+Dr9hAeLRcvXpRATIwdYLa5uUm0XXL/Ton3C/iK2m0uPf7EN/17okwc6CaBZsgGz0D0FWgavj7IKvm6QqAl0ovm2tQ8F+eOcWHhGK1cUXrRCBPVj7/R4UB1cPgkYpHsvEzkCc7tuytAJSJDqUZkFA9b0Pw0sUiKCGMVR8nRj0xcUW27LVRjGEWbQ9WnZc9FXjN4NKPL5msRi67P4tZNrN6a7AFwtXVem9/kKycaDOkTPNo7zmPufRzpzmLKBdlu1OJNlqKznDMv8EbxOhtjCnV6cgH/XiLtapQdlbEwYJyACTzGFY8hI5FeHZaQErZVemmNfkqTTah33TeJgutm6Xp5NlJD3DBnuawc4SJH6SgCeOyPiWSZee8S031h2rgs/XJ6XVX2ocV1hVxXId/T0O7i8RQpCc3cLsBq5kK0tEYx1Ki0oVLzyW05bBUnePvwE3jWHD/ye1/mmdO7/ZrVok3zhM7Ed22Sig2s7gxJZxbVmUPz51Cjqbv2zvS1iHN5g/MFjQs5jQt5laWMyqH+dZ5pvMEzzdd5qnmafNS7Y/84SYleXKIXbs9RmVY0xlYwTycSC9dvrsHbV91tYNbe14vWNwZz12xL1dCMYjNLgfn0GLOlOQ6OHuXIzAOMFMb3UV7FZbVa/RLXb/wUnc5Zmea23Tkm+NPkM49iZYdQLRPV0lDEMFTcyw1aX1qk78Q0EmE6nafuRWwtd4jvApJN0SfZuUbkrxBH62ia6IfZpXJrhsH8w49x6APPYBwc5VLrCl/YWOFr/TLr6twtqWTh4WZ3vooaNij5jzBfP0yxa/Hmgs3iqMGss8zfvvyv+GTjOfl8IXV/YyLN8qxFtH0MC1PfVmuUen0C30tJ8QFLbRHXXDrXUji1YXm+KvoYgTGDqQ2h3+WYCEjoKjEbWsxlM+a6kLnec+8tRCGzbo2Z3jUm+2cxBC9lbyiDpJBoxL89srrHWKqDly3jHfwos4//EDPpLP22T6/l0xegru3TrTmYTY+xKGZcF2bBCoZyhbz+70lpg54CLzE42zpI40KakaVNac/xr75H5Rv3Dd7sE4sqf+Nmm9nROjvtKb6awrNH0HQd21lH3WOGuy8EfWroCIwcheGjMHJcbof5Gc6v93j1Rl2CsVeuN6TM8u1xZDTHydkSJ6dLDG36XP7KCk5nsJ9G5/I8/YML+MbPc/XaT6CqFicf/WVyueO7x24cs/r2y1i//b8y5G9L0sdHqPt/lSXfJTikMPtdjzNx6AiKqhJHEfWrS6x+9RTVs9do9QYATQx3z7nrmjYvPvJh3rz/SSJNl0yKh8+9zqe/9jkmW1WpZmdVPIxhT/pBai0F44aCsazIvqu9EecTmAmxRgOyFYeK5qH6ou9W3Td6geizTOH6FklggKMSdYWPeYDpu7Lv5psJWZguWyjDOfSRYcyxKezJBVKTR/BzBd7e2OCLN1dY0kxZ0RKjXaxQ1++uqChqK7Nak8eSFzkWvMIB5SLmHrAuzqtmc4x6bYpafQrf2xXUCZWQrkgs6W2iuIPmtbF7bUqtLuVOJH2TCn2NjD1MeeYQQ0efRe2Ja/PgM/eDHi83zvPcgRFWYo2amxDFJverGZ5SDJ5Cp7Rn4SsYCBcTj7NBlUX3JivlgKKySaXxElfH+1wU7d17EpxCLf6hIOYD+DyouQzFPoVWSF4IU7zLfhbvUFpOi/sJou/Jl0DXaw0qWhJsbd0JtmJVo1WaojZ8H93CrFT3PXTl1xhqDMx3PbPA0tRHiFWDES2kvPEmcXOgzBraab724FP8s/GP4GzfP8Sa5KGhM3yXcYqTq5fR1nsS+AvSTZBk6CpzdLV5OvoCHWOGXrpJP3OTWCbzhF+mQa5VZGhri1nlVaYyFxkaqpO39q9tRN/cc+kUz6VSvGWaTG8oHF9UmV03yfZttoplfOEZv0edVZiQ13MBrZxHz+6yVQpYKycYoTAq3jYrXksYaSYsjii8eFzh3MwuWBctIceXEmbXRe9zhlT2AHbhOD3Rx20anBtJc2HqAH3rTq+3SrLFQS6zEF7iUP8y8941TM0nzqiDL14ISlHgLR7iNA/zNg8SxDmm+jEzYvRiDnUc5johY65GQQgkvUNsmQo3UwpLlvDmgyUNbmoxa0mCFw5ERh4wPH77f/gU3+54X4Kve8XGxgajo6N8J8X7BXz13ngO5d/+IHGgEroqbsMgEDSySEHRE3nxjvzBiH2FRBh3fAtGq3vDsSxiUV42NKqlwyyPfgLPGkixJnGXjnqBrXSNQq/LsRtXmN5Ylzx/PU4wRxMOLGyQHfMJEpXlXoHL/Vmudyv0dhqy94SiDW8DrRlUYxJFUIZE70EWDmYi5jxhXaniaH3+y/gv8cvZl5hZh4+dGlDhUtv3sUhLWLk/4fLjcHZGoeabFNsLfGbpWe5vTaJmRlD2aNaKS+O5gsLVoTbeyAqMtqRUaxRHeJFH02lxY3WFjd6GrJwI35P3EpaookUKmdCkoGYYUm0mDZWK6ZOze1K+2DDufK0o0ug7eZb8eS5yhCX9ADetadbsO8+bdL/LxMZNaZo4tbbIaHUNLY7w9ZhGLqSW0dlMpalaBepGBSu2mQwaTAY3sOKWzIy6lkc8UmJz7BkWMx9k6kaLP/v5X+XJM4PFpziEOo8W8Y5P0lm6n5I2TtEeIa9X7n7MIEw/t4SGFiPaG5TVt9CUDfqazSuF+/l66VG+XnyEM9lDklqyVxnyoc6FW2DsZOsstqSM3ZsSFFlFqvE859tPcr3zME5U2vOMADRHpDshETeVb05S2dG79AVA2wZlOyAtTnvkimnKlRylTB/DfYtstMKwFjMRm8xmn2VUP4be2YLQl30Joj/Bi1NsNkts1LNsVNOsb5q47p2gxE4rjE7Z5EoB9dU3WT7zNcJYVBwUxg8d5fHv/xMsPPIY7eomF55/jvPf+Cq15Zu3pI+vHj/J6ZMfZnGPF90HChYfsuqY127QvBhgLpWIyPPV+1LSPLkUtfir1/8DP772GxiyM0Tl58e+h388+xdwIpWnuq/zWPIqB7KX75Cx73ZLcsFYr4/jbFqo3R4pWozOlHBqD+O2D92zL+aOfY7Huhpw3TRY1WLhHsSIt8G4u8aMu0wxaL7jK+lKyKF8jUfKa4zZu1XiRpjmjc4CZzvHpDlsXs0xm55mNj1OdltAQlPWSKk/S9H4hnwcJRrn/Y/ztvVDuHnBKmjR66wxfOM8x9ev8Fylyc8+K6qVCtNbCX/t10IOlsvkPvYJcj/432FMb/e2iNtuaxm2LoDIwG9uz1sXIbxHL7SozgwdhpFjciTDR1k2ZnmpmuK1xSavLta5tnUnoJvO2XxMTTG04t1SPpu9v0Lp+M/iJL9Byp7hscd+A8PIS7q5e65G+ytLBCtdUupXKRn/AlXpEcQ6X16f40xLXHMUckPDLBw6yRQHSa+nblXAFVsn8/Aw6QfTxGqV+rVz1C6eZevyVWrrDa5beT73xKe4cGjQR6kHPiffeoHHT30dKxjcA4x0MOiFzHsUDIdyy6e45pNaigm3dinRwhdIqBkKbyARp5xD/PvxH6Axf5zKiMGMXWc2ucRIcIZee4OzZx6QlSoRY6OXODj+GlpXIdocItqskNRyUM+idDTUQJgu17HdOim/iRbew4D5thA9W7VCkWqhTLVYZqsk5hLtbJZ+RqGaaxPlLnHcvMYDZo05SyQed39f2F6ddTTOuhqL/TSj4TQj0QhFP4/tpdB87Xars/2RJKT7fQrNFoV2i1y7I0U/xDCDEH36CfTj34+RGqjDboUOPxu2+Q3LunXtNWKfEb/GydDlCcXmuDnEkLCp2fs5+zWi9bcI19+i2Vvi5777w3zhsTHi+AK2cwqSXeEowbI94FW435/g6dDje1q/J1MvjmEhMLHt+7L6sk8K5hbYGlS1ZGXLvx1sqfhGAUe3eWPqKcLRZ+6ge4uuo4naWWav/AqWM+hx3Al15ATm9BN4Fz5L0htUGf0ReOmDC/wX/Xu45u/631WcJh+/+Srfvfgq4/36vu/7xtws544fl9YFIvQASp0ch7Ua89ZrzJinMdTd4yeKNertMVpraW62XN4uxVyYVjg/pVAr3Hk1C7UxYn1CJngL4h6SbNDMOqS8AdCa3/bPEsbFY01wTHj1kMLzxxXemlNuJclEDLUSJro5DsfH+f+z9x9g1mRnfS/6q7xr59S5+8s5TR6NRjMjjbKEMLLB5Hyxjkk+YJtrH58LFx+ubTDX9gFjDByTMRICEQQICQlNkCbn+XLqnLt3TpXrPmvt7v66vzQjsK+kA28/61m1Y1fVrlprve/7f///4/FdHOvuJdVyCCtXCJeeJmpcIer1HahQUZgeH+PsqQOcO3iAC6MHmEuPE4mC3u3nMQ7ZxQyHo8vc0Vnh2Eqb0apAPBxF7QyhB0kStyDJ2jTBilvTPSLDQ00r+GWb7lCeailJxdSpCaj9Jtw+jKgGEbUgpB4IWif4yPgA/8fBG7ORf2ucr9nZWXbt2snmdjtbWFhgbOzLf8K+Wp0v9+ppev/x3dgJHytzLZoiflURGeoum9IRs8s+qSEX1ehjnkNX2XLKrjlnot/2vIgeiqJJd+P1HSxZ10zgopeH7mNy79fiJvoDero9z/6rf0xpI+Ik3yfU31UdT9dxdQ0RtBL4bZFt62feVAIjS2CNEFoTBNZeDLtEKpsmlzTJJjQylopuuPi1Frlef6B7IXWG/zT6O9T0/gJwkDQnjSwnlZAj59ZIP+tgzl3bd39IaH2EaOsKyec1GcWKzSTBvQ/g73orejRGydsZiXFUWLE81LCFq3RwFA9VkNXGmqz3ESx1ivkyQfwULWWGFV1jVdNY1mwW9AyrWiQp94Ul/SSj3VFGuiOUnbJ0Hrf+j9ajlpnBzC2QTNSpehbNXgmzM0CpVyQTZNG2gRQERe1KrshKtsBaNsdSZoBQ1AtsMy3wKNWmybavYnmThMFVmlGeVvsu/NZJiPuDoqp67CmdYWiiy1zpFFfUo5y6fJ7v+NQfct/0HGpul2SbDHdPoGV2kQhvpF/vX3tVItYkVG7JzzHvDbBBsrdltrVGobCCuHWUKEe9mmSllqNtGEwP6lJfbGpIp5rZ+TuYoc+drWkeaF7gwdZpTjXPY/oNzKiDqdyY7RD3wYp/iEvO27ncewgnvnav5rV59ieeZsQ4jxfbNMJRmuGg1F/rRqIeL0dkZAix6DkS1fcmLULX6lLEOq9WyWzIEiTVGimtRissy31a8Q5RkwQqO+8rVRTwG1MMGZcYMi4zbFwiqy3fFL0qHCLBdrW9Cape0a+YRf60/HY+XX6YutE/bkGUcW/9LO9dfJ2D1Qapti5F2YWI8V/sOc4fHD4pAxXfN/8JfmT2t7eYN5/LHue/THwTF5IH+gxbmiX/j6OKWgZFCp3fHb3IffFz7FMvb9U0CXNdux+5r0xQaY6ynCmzlCuylCuxni72J/I4xogiBusRw9VAMmSO1CJy7YA4XCUKloiCRdm4CXOll7DoFvP0ijn8Qoooa6OLYu44wIhDedyi12PRB5gEGJFoIUPdUcabhyn1dm/VD8bKCrb1yxR5YavOpqJnWTaKrJFjOcyzHBVZDEUbkFCtb9Qf533x87zoJPnf9hapCaZKJ+aH/izivsv973BKBbp7dtHdu5tgaABF11E1TZJoKKomUFLYcZN0sELaXyHlLpP0lrDdZSlWejMLNRs3NYGX3k3LnmAyGOK1TomXqwYzVQdfFqirkrjmZFDgYJjZIBaJSY+/Qn7fHzA4coKDyr/Ae6FKVNlY2esK9j0DpE6FWE/8c5TZp+TTS+p+/vTyCC0RyNuwnKFwsBhzdKTDQGId2qt4dZ96z6KmW7TTGs6Qgj8eExX65cMXukf4mPEdXEn0Sw5SbouHX/4cx06/graN9n+7BZpOPSs0Ii32eSu8zT7LYKJNBpfq8xnaS4ktmKK/N8Y9EOEdiFkrDnL+6kP4XlLWMJ08ucCBAykS9hgJQW6ykqdxNkn1sk6tIxzzmO7GJSw6MR30BFGL4eFpHXylieFWyLVWKNZXGKjXKNerFJv1mxKKxGqMtz/GORnJFl4XMwvWNLqzCZrzGXqVDJFiEQk690Dc4wHhdb+9LI02LMmcF1o2kahbsxKyR2QVb2Gm45AXaxZBVqHvY6T8ALrRn0f92jTzM59hNrrKej5guaCxUFTp2gKqFjPkpvnaxcMcC49jlo6gbGMcDIX2ph7y5GjAL+8psJjQ0b0rDLaf4mTzSca6dQ74Pgc8X9aLjd0E6bTpbNWX83RWLfz1uF+auc0E+5+QDZBtyJXaUKKss5UwWTWzLKhl1pQCLTOFbascn/06Ct1dEpUQhz7+5OcJq5PEOrSP1mjds4aRHmD0pR8iuvIi7sVPgtsf8/ShQar3Pswf5Y/xF700DRGw27Djeo8H9CaDVFiNuxJBIvcvDBnzZnlEfYFD1sKOfRc1gDPuvcy69zHvncKXwb8+G3FLkB4lr7KanuHiqEotraBEa+jBPGrUH+9ybeFk9TNawskS2wPbYl7CL311v8IXjym8dEDBFzoWG5byTQ65+3hP/a28tX0Ks+cQLL0qW1ibwtzzCMah9xJku7jpORzzNdzEabxsDb8oivSvfVcvSrLi3EWze4KwtYdEb5DBnikzWiM9ISDObWGCgqG1EkdMpmLOlRReGU5wKW/R3ra/uW6LXdVVJqorHAwcdo8MS74I0QbKQyi+Qbvm0K65NKsO1UqPoYN57nrrzfUZ/1Y4XyKD9eEPf5jv+77v47777rvpe8ROffzjH+fnfu7n+MhHPsI/+Sf/hK82+0pxvmI/5Od+7GOMoXJErTAYfZGs8SJ26hpWXPzCvTWT5pxNtz2Knh/BHCpQH7VYSgWsJxxQeqTCfkuHXfJ+j4zXJR2K0tgemiL0NATmeaeDFm1z1DzP5KL9fi7nPkig9etb8rWLHLj6R2Tbc3+zAzVFRkMhHruT7IFvRdPThJHHhdbHOR99gaQ2wICyh13REcrKHhQzg2Ik5QQRaj1c5xWCqafg8iSKv3Pgd/dF1L89INxWd6x1hmitfh1h/U72NZIUr5sERHmBIA5R6GGrj5HVP4Gu9msVnNjkUvQOLnEXM4pKS0S84ljCBMRQK5y27RYqPbraCuvaAhW1LmZqtEjB9FWKLYNi06TQMuVzUo1LTLSJJGEiSWzbKHYCfyMjKCBha5k8y9IhK7GcK+AYN0LwRuI5DsQX2B1M0ls1eXnmJEvtfkR4JIav683y3sYaRaUsHS41cSPcUVjPqOLYy3iJFdZ601T8OXx6jO5pkxys4wv4apTAbY4Rdo7hiXqgmmAt04hFUXPUbwnaDGqT5NRVOdm03ASVaIQZpcCFUp6Z4QLToyN0UtcyN8Ist8fE4hS756+yb/EyE51lBkopBkppClkTRfGoVxdJ0iEdh7S7B1nwTjHn3rlDbFo4YIfsJziQeJrETWroxD3kxGla8ShthmjFRVp+FidI4wU5Sf3fCQt0o4IATPGlWJI1stoMWWuWVGKOdHIJ1ejDcK3Yw4x8EpGHtdEEU+Dt7FxqH//X2Dfwh0Pvxt0oSB5xV/nehT/i25b+jGKwM0sltOh+ct8PciU5wT9Y/Rw/MflfGfJuXd92vUkGK5FxlL0IpijUiga1ooAAaUTbw/ohGDULrWLLDEPk6yRUh5TaIaM1UEKH9W6alU6GlXaGSicli9t3moKmFaUmj6KPgzaGYRjkMlNkMpNks1fJZK5i2A0Jsenz+Yh96weABDQqVAStQpaQtFy8CXpnEYXR4i5jyxXGljoYG7Uv58xRftV4L8+6J1jrlvGiW4vyDiVXec/uxzg48Bq/X4+Y2UBFvicI+HDTR3cUhEyeaGLc7CgWTVW0BKGgpfa1/pjqqxLNIJvQ8BFjqdnbqHfZqH0xuxQsMTbffArvhToVNynbupva6JM4QsTcfgjNPChdzV1mzEErIqn1x4kw6tH0H8cPP0tSq5LSfVK6R9bwJFR8835Y7GZ4uTbGZLtIsI0VMZVwKIw1yZ5sYg7dvMZXbQlvJkPglHjcfIRPlB+lLjJ7ssY2JNusMbI6R6m2TrGxTrG+TqG+Ltlgb2W6YBIMPLItj0y3L+QqBKWnDhzk3MmT8hrIhiEPjwxj7TlOpaVTW/VprEb0ROnSdQRDb9Y8JaapC5kEj5bm0FU6Ujg4iIVAcBcz3cHM+ugb97Sm9inArUpEYjnCWoow2oKuPJQIhX7fpwsX74vUuE89LoKVqiIDl45u0DNM2qbFqlWibuZpGhk6WpJIN0lqATnFoaD0yKsOObVHStR8XmeCWOJksIuT4S6MjaBevTNNderTWCuXSXXaeLkUuiBDqjW2tPD08QMYb/8mVpU8ubZF6jqYd2SKYNELDMSfx1Ku7NAU3LR2UGC9OkprJUWwHqJXa+gbuldb51YzaJVtdg+uUiy38Fo6fkfHygWYhQAzFyCoMW5m4llRc12zbCpmgbo9hp8/TC9ysTPPgrERxBEEXwvvYN/lb8Po+rRnPktw8bPoG1nYV3ffwW8c+wAXrX6wURBvHdTWOakvkd6AiOqxxwO8zNuV53cQtpyJ9/Esx3jd2EU1rZNNtCgFGrleBrtTJGoOUsVncmCBK4ML1BKzqP4s5ZYvHSzpZG04W6Lm/HoT3CFPHld44pTCpTFBwHXtGs4GKR5oneLD1Xey1xuj6VVY6F5moXuJljpLLt8jl/dI5x30Qo9wMCDe5K+IFXSngNkdwuwOYzaGsGrDGM4QejSAotzawXeUiNmUxkxKYzalMptUmUuqzKYUMmrA8Uhh1/I6J+aTFCsx617M+bTKpRGdq0MqzaRBxonJdiPZ8p2QwaYgXfFJuir6thq47ZY5FfKdP/Ae/tY6X5VKhX/zb/4Nv/Zrv0YikeCee+6R3qrYrtVqnDt3jrNnz3L33XdLra8PfvDLj9H8ana+VhZavPXnn5QLCyNGFijuilTu0qq8M/0cB8LHSCp9TPOWI7YuHLEEy2tFntt9Jy8cO8WLx05RKQ3cwP8lyCZGqgFHVh1OrrUZqrYwgx6G2h90pHpE3Jfo64VpeoHQkVlEEdoZxl4puif1NdxzqK0nsYIOedWkqDgMaQ2ycQ0j6EhI5JYjty3rFm9m2/QEiVPfjLHrQfkwrM/gvPirRG1RZH9rE2OEZimoCU2qc8aBi2CFDkUqa1uBdpQ28A7YtA6qtDM9fEGRHWuSLa0T2dT9u0l5J9nrl9kX5m6gP+8GFVYF05q7wFJvAS92CBMpwlRWttjYtmCLY7RuC73dQG/VUTcG+TcyVY9JFv1+XUSpi1Xukii46HqM1k6QnErjNgosMcgyg7TEwlKKQ6ZZzpVYzhalU9ZI7nRghBWCHscaLnev6dxTVzjSFFmDbbtMhCfYA7MzuNkZnMw0bmaOyPifLxchnH6xMBJMYYvxOGeVk5zTTnJeO0bvOlHhfNjgsDfJsWCaI+FlEv6CFElVAlEQXJDiwclcgmjxKom2S9cepWsMSQfQCIXGTIthY50BdZm0v4bhdjEDwap4+2FSOsUicts1aDZK1KoDNLsDshatpxRwrTyOlaObyknWzmxziuG1aQqNaUz/FkyZ4gcQQkvZGDUboecVzIKKPqCgCNIBITYpFqNRQBRHPJm+l/9e+jDPpe/e+oojnSm+dvkp3lY/Kx05EUQRilqW0mE5keHnxr6TL+Tu5221l/k/rv5njnf6RBJiPBGOs+jF0l9oE4kMsegFgeabXaaKxUE9b7BWMlkvmbjWNsc0jrHrYhVvUJvOsLCcl/U515uteYzaLUbspuxF3ZbQpNpuQWxK4dcwNgVXab9XhLhnRGz4QilehP2J9YhIEc6XqOfMMBMMMxcMseSXOeiu8KHwBYr0f4/Xo738dPAtPB2d2PG/xAJaZHeG7DUGkqKt0/CzPD73EN2gfz3mzAbv2vUE7cxzPOP0F2OHrZDvKLmkvzTfvM/8KsgHhKitqFMNDWLRxLanYPcC0j2XtNMj7fbI+B0ZQLvVbxQIpjaR7YwO48dvJY4FpfxunChJwPPsS/wcxm1IfDbYy6Ut2QkuDmVY72WoTWVpzQlx92v/2S73yA22SSR8Or0Ey94gk/pB5gu7WRkYZaU8KgkDbmaiJmvP3GX2zV5mV7XGW6LdjKRU7OijdL02VS/JtFOk6hno0a3hw7GaQLF2YTKAHRQIEmOE20TvN61jRiwmVJZ8n2U1kmQw4luzkXKtCTHvbY9TbwLCLzIbbcEeKeQ/1Fi21kYvmxJLtsz/ecJJ8gISaRmpZSUyuVKTSiywN/Lu4pJMx6bUTxQCLOKxh0dH6UhmPTEGCkhownclW21S80imNRKJEIMug7HB7niUsXiMIjuJFlw6rLDEQlinVlsntV6jVFlhqD5J4jpnq2coXBxN8trABK/lT3IpeSehYvHOtSm+5+zHSAlm2TiktKfFyJ0NXtAP8Xn1FMfGz3HEmMFsqaR6EXmnh3UbHgHHUqgkbF6P9vP51v087x4njof5T6QZQmWlV+XFC3/M22aek86dGEs+s/s+nj9xD/vTdcQoIywdt3lIeYF7OC2dLle1WUk9wFTzTq6unaAXFbdYR/fck0c5PMl0+ALnKme50FhkttujXI82oIPC2UJu564hNrdMjHiLJZgc7me2Lowr1KSKzbULJx9keLRxH4827+eAM8Fy4jKr6VdpGnNEiRrJTINkrotuCxI10LwcZmdYOlmGcLQ6Q5itEQx3EHUDEXPTKyr0iTqrRO0VovYqsdzuP47dhoS6T41NcH7PAc7tPSD72eFR0r2IXNch2+lKZ0q0gVZIvqtjuzpGILQV3/hG8LSQttWibVZpJRyaNoSpDh/9oX8Bf9trvoSG15//+Z9LxkPBGiUel8tlSTf/vve9jxMndk5oX232leJ8LVa7fNO/f4xVOcjdaIL56mixzVviZ3i0/Th3epd2vN5dM2RGrDVnM5kZ56VjJ5m/8x645z4OlPMc9DWGWiIq5dGYr1KdWaW6HuMFQtk+Jo5qRP4sUTAnG9tU0BU1i558CM3ow0oEsdORt5Y5/nBJivj6Qvzzic/R/NyncddXqOZhpazSGLDoqD5mKLDgMaPBPu6yvh1bKxHHEZX1x6ktfxY98FH9cKNFsgncuBZE6OHNFI7evEmoiSZISfpwyE1YpHgsaIANq0AqOUIuOU4mtQvVTOGZJnOJLjN6jXl1fQuKIC0M0Tt9Z0s4XQJaJmryVC2W9LcCTiH8TFeJaYtIuDmGZ+ZYLw6yWhphtTxCPVsgESokwxgxdibCECVuocbrmNE6Obocdeb45uXHONGcoU2SWXWYFzJHmQ5GoSMo+FUZMV2WWbGidMjWMgWJnd9uVhCxv9rGaMS0myFrdYeSv8K74md4e/I10iMxvWKAkxDSuAZRqBNFmmw4LlroogjueUMntkwpWq0KEUs12hCzFI/7iwHZ1FjS4m8r93pDE8uIKfZyllOc4RSXOLyVAdy04XiR45zmBK9zLD5N0WthOyHJXogtmtyOZC8KlW9nIqPTs/tkKSKi6gjnfMUgnNLRLmjoazt3PtZjPAF9OhThHY7xd0VS+0pSFQunJlIQjL/6moK+qqCtKte2K2JwvvUVHCVigoGY1pjFX979Dv70wAdYske28Pf38Swf4M84yM77XViHJH/EN/KXfID93Xl+YvIXeXf1OfmaoG+enrCZG7N3Zqx2HFgsazikVEQknLG+Q3bj9rbnQuGYajQcm66lo5RCzI06nU1zGwaN6TTBok26FTNgOwzZLYpWB1P1ZWZDQLpkVuBNkuDU4jRT8TDTokXD17bjYVok5XX4IfVZ/rn+cXarYhSFqWiI/xD8Q07He9mtrLJXWWbPRhPbY8o6hsiUbTPHSvGZI1/Lxzp38OqVIXpO36FQ9IjM0BxR8qMoeh1TszmZ38MoVcYaSwx3PIpejC74JwQDvhUTJUDIumHdkij1DU2c82RXCA8HpGUfkuoKaulbnzeRua0Eu2jEZcJcAyvbJLZyuKZBYAT0jBa+3UQ1HQ5MdZhY7I/17aTG2SMZOoqOupqluTTE+kqS2pq3oRPSt8XBMhf3THBl9yA9cWyxgxL10KMOaX+dZFBFD11qmYdZy76LeKN2J9V+Drv+cT7UnOIn1quSSbWiqvyrgRJPJ/vOuuELUiGDfDvBSGOc4fY+ct4EmjKIqvUXwNtNELIIOKvLGssJn1eSJa4qg1skNEqijj7wApo5R9oJGejakhY87ZskPQM1SBMEo0TxMBm7S9pqYZpdDKEd6abR3BSa6L2UFPJ+I/OJJAFRRw1pqzEdDdqaQkuJpAJcMwplvbEi7r3+EUi4r8jmbeosiQXv9ey4Xw4rovAAOm+LNe5Hw95WHyQW7eH6RVknFqycpuO1OVvay+vl/Zwu7eNKfvxNH4O4d4XDo280MYfqlo1lpjFUAeYXg06bO8JXeEf0Ag8rr1NSrgUVBNPidnh0LzZZiO5A838YAxE083l5pM6RF/4M84W+nI+Y+y8cOcLCkXHearzC3Zympg/z584pPhfdyfPRUeJY54AP+80qwwfrtIdXmGOSy9ULFNa8DejgNUcrfZNSQjE/LJYVLg/D1LAiHa6pISSUUBH6jduCTyLD9VDrTt7m72W3EbFYOEM9JzL/VQp6iOZnrjlW0ska3spoCcmTW1kUhzhRBz/0UBUNS0+SPT5M8s40RHUqT3ye2Y//Ji3Vo5UzcXbvoRkZdAMDT8sR6nlQC2gUMKMCVphDE3jPN7BQEMkIfVerRsus07RV6tkytcyQ7BtJHce8xji6afuXz/PUt3wL/G13vv7vbl8pztfK1RUe+r+ex0dlSPU5YDhgNbgSp1ht52TUf7uNUOFD5jN8jf4sd0b9KPemVVpFZhpHmOzdT804iG8NECs7ox9R2Og7Wv4scTBDHF+f+RA3hJR033gsBsUBdPsRNKNfCxhHDoGzyYx4G+0YVI4X3saR3AOoikrbr/Pc2p+x7u7EUt/UJMFHJAV+zTDECPvbRhhihuK5ADPY3A6leKguHDj+5ubrunTEAtNCNdIkzCxJI8Iwm1jWKqa1JjVoVCtGTRqoKQvVUnHxmNHzzNkTtPUkzsa5D8Xk9SWswtRIY3dH4XAzwgpG8aL9+LEoHDbxCKgoLdbVFhW1xbrSpKI5rEqoYmkrQ+Zuz9RtnM+hzjp76rPsa8xysnKJw81pskEHxRJORV82MQ5jAiGyGIqqkg0mTFWVWcBY1CJob3wsAkJmGkLwViP2RX1iLC8TUY8jP6nE0llLpHTstIaV0rGSKo1uyFNumeeSY7ycO8z59P4dhcFCM+Rk+zIP1V/m4dpL3N84TSq6DuaiaviqLs95oOoEat/xltoyEUQLEdGCQjynEVd3LhJiMZEPx6gTAep4iDIcEev9OLOAzomrS0z4ov6lUtblOq/vfMZbTqjQwxKLCs2LsDYcMeGQaWJ7XThlCmpdYaVY5g8ffT+fetujdOw+XCvdbfOhL3yer332cxSUNl4+gZe3cQtJnFyCXiHBFwbv4FOlByW8+Mdmfo1vXfpzGdkVhfYLxSO85BWp1Bsye9HPYGiY1jiKvofQGyKKdJnpFhgoCRkV40uokKtcIbf+AtMZh55hiGRc/7WtLMiNv7mR8rH3OyT3dxgo19E2heLEPRQY1KqjVNZ3U6uNEIb961HTfDLpOjk7wUh+L7vHHiCRy1ElYLrW4YWpNV5brLDc1umFNs5t4DFiMfa/G/+dI2ofEt2OE5yNR9FUh/2skkFkCW+ekBB72iZFlRw18vKdB5nCGD3B7Ht+mc9PB/zei3PMVPohbPFba/nXsQqfQTGbtIvfiZN+B2Wlx3i4yr2Xn+WOV15m/PwK+VV3a3ktGPrDgRBtl09m1CFT9AkF8Y9oWl/raHt//bZkr9VUFL9Efv79FObfghmvYiizqNZZsF8lEa6TcG/tlPWsPkNrJ6VLR0v04nGh4XP0YgvLF5BqOL8rxdnRJI6oQxTaQB0VbyZDNJ1BWb1GyCHuk065R2Wizcpwl64Z4cSK/Iy70XtaiU7uG3BTb+vrMUU+H1n4A35w5nc4bUX87PBuumaKrFem1Byn1Bol1xgm2xyQ2n/XW9uo0tEW8ON5FG+ZRKdCnTFezt/JdPIaqcJYb4G7G68y7s7QSvrMDHc5v6eFK/Q1NsxSYo4kQk7YIUcT4Y5MZhjDTM/ifDvDq80Ci26WjJ+j5BcoB3lKUZ58lCMTpUkENlpH57oh6KYmxoW2gDeKiL/RpmVVaSeW6SZnQZvB9KsUux77vSITXpZy15Qst4K1M6q38Ns96dQIWLrow01nTVyYSVDToKRjFNEnFZSkgDqKHLkmESDi+u43HSe2aEVFWlGBdpSnI1sWLzYYas2xt3qJfZUr7KvOYEchWukg+vAp2dTUwI7jukrI0wS8SotF6gjS9YYiYPTCyVBQw/4+ChbDN2sCAP0W9Txfoz7He7UXGFCuwawbcZL5eIDDyhz6dTqhYiUspqYoNmkG30Y3fJQYk4T5/2V1fR3/NTA26iGDhMH6/Xcwve8B1qoJmmqHpUSNSqKKZ9ZR9HXKjS5jay5jqy4Taz4Tqz6Jm6BwAw1WB+HKkMLFob6jNTuw4WjFMaUwxFU0WtuCYYJV+J54kHsFo22mDlYT0xnoO1nS0Rre2tY2svE3v64iHHUdRVnEUxosp0xmaXO1eZGleJmeGeAakay11JQMhnCk4hxmmCHp5Uh7BdJunpSXx3gTGpXi//XlXeq4Wk0SFnWNLo2UwVo+x3KxTCOdJ9JzxGqaWARTryP3uJXtq6zx9Df8LYYd/m2xrxTn67mrF/jVj/0WT7bvwYmvwTeO5C5z/8iLDKXWWOyMMNXYxWRjD8vda5W+w1T4gPY8H1Sf5x710o4o0LJ3kKvO27jinKAdrxCH0wS9CqG3c/SQi+CCQ6LkSgicmem/LhduGws4ARsTpB9e6wjdtQ8SbYg6KnoNtfgZ4vRrcnIRy049ULGciGK3zGHl20hrE/K9687zzDf+EARsMDbp2BO0knuIFFukGNA8n9L6JYrrlzD8QGpoaX6MKhiUAkhMPEji0NcQL7yAd+nP0FJJ1HRaNi2Vkn3bHmS6lsOcfp3BlZdlcb4wcSjNYpZWQcAHFTSRafP6C2TdFZTFXr/5f0MNMzVGM6ONFqMaYkF++49IAEmcIiJJFIvMlqiREdHgGz+oEKAoXVQ6xH6PoO0RuZ7MIQSmQZTSCBI6rmLS1pJS80Q4YaK+QAy+15uoRcr6LYpBg4LflOQM20VAr+3jZt9XqL+23d+rndv913Zu7+yvf8+tPicWGm0jRUtP0jJSWxouW+dDsIIFDim/R9LvYQsWwq393dgvcf3GfU0mWRuwnU5ebEsd8G39m7B6Ps/k/n1bzFi3MgH1FHTAAvIoRGxFvYUgqXhlzxGuDo1v7Uuh3eTeyQucmr6E7fX675XwwP7nRd3I1MAon7rvERq5FN8/93F+cO6jJDdWfZe9ozzhfYgqoxtpOYc4WCMKVvvkFjLTJc6KIIcYxkzuIpkdxgrWUFdP4zQWaRqC+EEUT23gROUBbH5OKqn3YS6pMo3MCFcHRjm9bw8r5UHB9EIi7nGSV7k3eoI7eJ2MFDvvWxgr1FtFFlYPcnn1KEvOAM2oLzrcii2cN2DRGkAhS0wvVvAieCCa4gesj3LMPC1f9yKblzt/n9e7H5JF8J7IwqYcxrJL7Ldew/TO0wlc6WRtOlu1OId/XWBK2C7mOR5fYios8FvBu6mRlRlokWXb/FX1zGnM0hP45QO0it8FG99T8Bs8Wn2eD0w9yV3nzhDNQmfN2iF2rlkh9ohPMKpTHUqzZBRYVNIsKWlW1RTrikVVSdBQdFqaznBs8S3OHt7jTmBs1CJeMZf5dOkxLhReImE4MquYikMmPJ/dvZj9ayOMdhVK2oIkiLldPY3IAic3ssfCajmds4czuALmvc28jk71aprV6SyNuoWvR3iGaCHaoIM21EUpuJLYyIkUepGCE6rUlN3MZL+Fln0C048ZW2+yZ/EsY5WAofYukv6Nc6+jdVhNz7KamWElPS1JDFyjuyVTEbRE7ekjRL3dW0dTFhyyzovsaVdkBk3fds59LWLucA39SJ0jKdhne4KLZMs6IZx3NMlQeMHR5HX2pZioYUl5BTKuWMiKvkTWKZLxCnJRK5q6rabuVqYlFFIFg0zBJldKkisnSRct0gVLzmXRzGmCqy+irpzB6E5hR0vYVk2iUG5motbQ66YJ9KMExjGCeDeuU6R34Xm6cy/gGja+IepqRXDTIdFtoV0H9fMMg/WBMquDg6yXSqjWEAVtglFthL0k0baNmY7qUeSLWMoLtLtlqp95QXDt4xRS/OEHUnxhRMEMLaw4gRFa5GKTidjEdMp43UES/hpHlHM8or1KeZvDVY9TfCa8l89H93A23keTJH4cc0qZluueu9TLsi8qN8K/g2gILz6IocygK3O05hOsvpbFb/fnwyijsnZiiBVBdFVTSdfaFGo18vU6+k1gjyIQslxKMDWkcH7M5cpIzNyAGDv750GNVEpeGkOUO+gujW2w/lSY4O7eXu5wJ9jnD5EJcqT8vGxmeHsNyarWYsFcZcFYkf2ctST7ZXNdzlspN0/ay29zpvr95mPrDb5/01yjRzfp0k54NOyQSiKikdSoJ3UaqRTNZIpIQH4VXc4N4noRwuhfinMtTPd99izNsW9+TpK8zQ/t4ZGpFf7dj/+dztf/7e0rxfk6tzzFsV+6k05s8ZnoPj4RPMLT8TG5KBdmxiEnog5HPZNhNy9rupb1iCVto+kRHVUwBNakI/Yh9RnuuY6tbKmX4VKzzKVWmWZgkRrskR7rkh7tkBrqyVqkN2tiMduYeSvrZz5M0OtTgFv5GQbv+ANSQxfkrJ6feycDl74JNbII9TbLx36D9vA1Xam/lkWCTUwsHg1ZLCqaqortPvGD245xOsIFFPkJl8iNSb4ak3/exViFleEhFsbGWBgXcMBri3iRrcjlVygV5ygW5rDjrhT8jLo6zU6edjdHulFmtJYgV41Q66L+zRGrEWK/S+x1iPxOv3bn7+xvjYm13dLoMFcOHGR5eOS22UBRn3R1YIzT4/slRHTTxqsrnFq4ykR19ZZuX9NK8uz+40yXh/nm5b/gX079KoN+n0xjjhH+koeZ48vLNisrUgRRBxoNhENlks6uM16c5GDpEgOpnfTQs80xXl07watrJ5ltjsuxzsYjq7hkFYeS4jOoRAwqIamsQ1yKMW2FbHeGe1ae5LjXz/j7scanwod4PHwUNbZICb1BAmI1IFIFi9o1UeqbmchQpumS8jt0Q4tmIr/jtX3MckKq813lTLSHXwq+lr+KrtXjacnLDBSfYah8gtOld9DVt9VixhH57jSD669x99mXOHV5iVNTPVIiC7yN3ezMHkVSSr98QNR/XNvZfc4437j+Ph5u3bXFpvpa8hIfL32Gl1Pnb39ccUzeSXPX3Ps5Xj1FWV+koM+iZl8naV9lv1+neBuuc+GGnTeSPGOnmTQNrpgak4aCuy2z+UYmILeF7ghD7d0Mtncz0DlCqZO/oR4kUkIqqTWWchWWck0Wc21q6YhYED4pFiW1w6haYSxeo7s8yOsL91Pt9YkTNMXn2NALHB99jKRVJdkZptjYj9Ut4ggZB3+djD1DeryOXdoJ6m91s3hz9+NX7yOVuYPMuEliWMEaiKk1G5x+ZZq56VWpH+jpPbqGw7zZZU3rEmkuimiqIxtav1dElPBmFisk/Yx0zNLbnLTNx6K3g/Qbn1NBL6NWSGtrZLT1jX6NpFqRAuxtxaQV5Oi2CzitHHHDIOn0SAZNSoVhMiP3ofjdPmxw5YykJr8dBawIWHl2mtBOEySzRHaWKJEltrNg51ETRZKJQVJKn4Z8O+uvXJaK4M02OLwgFJ8h4jwhp/G5IIid1PN8QH2e92kv7HCehMP1VHSK1+NjuPEhjmEzEWcpR1lsTCYJ+RUcnhL3+wbzp4AV36Nc5m5VtEscVuZ3rIP6x4Rkiq6eT1O7krpWk34TC3WVlVKKyUGLyyMBZ8fbzA/ERNu00PRIp+SUyLt5IgExNZuYqsq4N8ioP8iYO8gud4RRb5B8dPvfuKo1WLCEg7XKonC0zDUWzTUasYvlZ6QT379m+r187BXktfVmLNB8PK1LrDiocZc4bBELJsZYaKG2iaOWFFFfLQ6yMDjGamGQeqZI107KLOrtxMBVMZ8lTPbaFnuTFvtskz2dWfY+//PoZ57hueYpXs4c5/zu/VzYs59W6sZz8fCVGX7/H30dX277O+frb4nz1Vxb449+7hdR3Qx4ORx/gPU4yXkz5IwZUt2mMJ+K4JQf8HC0yHF1kry+QEadZ8nzuNzNsd5LgutLMdKDmQqHMuuMJxs71oVXg2EudUdo1g2Ge+tk6Mo6BX8kxh/r92tphdVAYS1QaYcCVy2gALFkKxVQ3ZIeUYgNlMn30Lj0PuKgn7HLlSa5y7bItfswkE7hKpUTnyRWlgnrVWKBZVJjYl1BSduoKVtS+EaRT+Q5xCJTJe7i/wHYQc9LbNFj12vDRNuwykKZfWR5ibI1R+aOBaKD3psvlhbMfs09pCrHSVaOYzf29zWnQk86YsIha4bLVPx1un6DRGySiROk48QtdTIcxMTp7GgCXnj9Phn4HGSafcxs1cwsMMw5DtDdisy/ORMOQSuRomGnadgpGoJcRL8uehUjCQCyvTbZXod8r4MVeFu7tXllbua+RBP71W+SO08+J7Jp2/vNfJcvYDGKsUGtbuBoJo6kQDclqYIE8Em2u41PyV6sZVSZ4BGwGxFZFVm9RjKNL7TrtlnCdRiurlNoN0h3NiLnor5CSxLoOSItJRkoNVHbJDKtoWDIa2DSwtJiTCHQK9g2Q515rc5kZg5X7/CWizGnpq/dl41czIVjFo8du5dzYw/SSo5y0NHZszjPdHKQc7uG6dj9c6uHEcdnW9wxs0DWXyQQjF2KyPL62D0PuxvLWjbCiMdPHefzd97F25sv8eOT/5Uj3T7xTtu1Ob1ykLnWkIRlino/eW4Eo9rG9lYvYEqaimPouIZOoIu6EvGaxExecxo3zrPM4gio3+br4owrAtbYz4AFsUIrFlkrS2auZIss+Vx3G/vkdhtKrnDnwBnuHjzNvvzUzjoNNynv01plXIrRSkjkdWbT5Q7OM8i6FP4U2at5hqmT2wpS/Y+wXK1ObNVQkynq20gHIkIq9jJzqTmmtIhe/WGChtC46u+rmpjHKD1JXHbx7Tvw7LsIzQ09sA1Tg3Xs9svcefEpHjh7lfsuhwzVd/7/yRGN1aNH2F1+D4e8a6LJF3Ov8czIX7Ccudq/h8TP1FUQnBpKWyHuKERdcDWVZt5kPWtTTSXp6Bq5js0D02/nQOWU/K5A8Tk98gTTw59mV9TioKAO9/v04Yc8V/LD3MxE0G/aEI6YwVXD4LLoTYMqaYq9NKlWknxnhEJvgqw3QToYRRNFb9dZOwUzRYOFks5CUWe5IK7J/jWYjRrsjyY5bs5ySLnEnug8ruvx+Pzb+NzM22l4fcbWpN7l0Ykv8K5dT5Kzbk0sst1EMreznKQxk6Y5l8Ie6ZI92cDS0ihujshPEPk2UWDjS2HnBI0wgerbZJ0kmnjNt3GDBOd1n5dTs6ynp9ASy4wVFfYXd7Enu4ex1BiDyUFKiRKKqtDxOyxVlzh7+RxTi5P0op4UVw60gKwaMu53GfebjHouw55N0c0SByVa4QDtsNzvo7Lcjq4jibqZiaxh26rJ1jFrGG6NoXqdPUs19i2skW03dghTx0KrMJOhZ4gaXg3DCzF7PXTnJowRtzDFTKPYBbSBo+gDR1Dzu1GtnQ7BShzyBSXgaVxSyuu8T3uW92kvSnj1pjXjJK9xlGl1Pw1tkCHV45DfYrffQUc4uz7xwC6iaBhnpYAXHUGEYX4Tl89LJ6xvabWHIxgiHfiA8xLfY68zlpjHVC9KvbtNE4Rg1QtpKhfSUr5HWJQLOH8s5K8OGzw9qMusznYzIoVMkGYsGGRXWGAgyJJzBxhyhxj1BhgIhKz1rcekhsxgCadKOFlrrGlNGkJDMxTiymmZwcq6G1lTN4/tZ7bkM25nsRAL0FwZGJC9um1743Gs9vW0uqZFc3Pel01s9x/fDCGz9TvHor6tS67XIddry77g9Sj5rgzmJDQNXde3mua6REtLxMvLqEGfCdRQfewCLO69k5WoQEfNsGwbLCV17mp6/Ifv/0a+3PZ3ztffEuerOz/Pr/9/ri+qj8hqq+T1eal99bxe5EllD81tk9mucI0T7XOM1iexrwOdF80OuWRA286xlBgmqzncpV7hPvXCDlpjwQb2dHiMRafEsFPlaGeG/a0FoiCUrFFzSY0pAYi3TAZ0hWEloKAGfbY0ETCLIAgzLHofRFPewR1JE0ssAOOIeueTeN0/k7ApWRYmaohCHcWNUZwQxY8Rsk5aaKG2BM1cP2oYa5qsL9J1q++YKaHUfNETisjpS0jRej7DWjFPpZzHSSUkEYRE1Qvqc0cjdM0+i942r9MO2gz1FhhdWmD48hrGtjk7zMR4eyL8PTGRQPwJUgWxrhJNILGEA6wJMg2LdpyXOPl2mKcd5HG9IgPhIGNRkbGoQDG+fRSqrrRZ0eqsqnWW9RrLWpWO6sjFrfgT0bPNXqwYhmox4+sRtteHoAkITXW4ywOZi7wtWJDDcoDCZ6xx/iCxl7qmX/seReQBo5t+9+Zz4h1mZJIMUqjaHrqJfTTT46xlBmnaN8Lqkm6XUnuRQvcqGecsieAMitqW15UIjMvTJiBz0sHTcdUEjmbTU0X9TgJPNGzCyJCkKlosoJRiNanJeipBbCJrq1R9w7EQUgM6oWYIwBf5ToeRSoXx1VXG1lZlrZ/4v6K+YLW0i3P77ubCvrtYKe0m2qZj07+tPJS4hxI5kiygb8LJ6Hv8cq8lPr3/nIC82t1XMTufRo0WN96fQI+LDFVWePcrHu84HZPaCKoLLaGnjir85d0qkyM2vjlMYIwQ6sNoDFButFHdZ2kb0+zu7OZg4xBD4S7yiSEKfoHcuo6lqDy/P8FvHU8y1LrAT0z+Eo/UX+rfG0aGXvGDtDsH8OYW8efm8ObmiFot6Yp3LIOOqdNJmHQtA0fX5XPdxI1OkRaKzIxDoeOQ9HzpFPqJPfi5UbzxYdYPTzBZspgOuyy2XaotjW43RSijrLdeDChqF9NskLUdsikdM5UhSKSp6wnpHJbCJu/1XuOoeRorfxF0b4cQ+VqjLGvFGuu7CLy0XCgKV/52Jp1+IZKuRrK+xYlMeqElKHFAEVUvEULNzBTscLGGLtgzxbW2cQ/0nctrwQ4l0igHHSaMS8wpo6xxTQ9P3E91o82S0eKqO0a1e4cQGZSvGUaFg9kX2JWax9EnWEyeYDk1wXo6L7nplagrmx50GOzOc7B2hon1K2j1LiP+Me62PsAu5eDW/3ku9RJ/MPAZLtgLfdjs38AGW7t4YPbvMSrFscW12mPu4Ks0D05jJxKYqomuaBTdHu+dfJY7ly/J/yhqkcXIbG8jSPFjkzV//4bW3WGW/SN0hPjXdRbHLlGwgh+vMTdg8uqBUS7t3Uck6kY3oa0bBBkHWpN80P0r9o02SAnSirrG5ycP8+TicdwNUoGCVeO9ex7j4bFnsDc0F29tqkRGqKqFohhEUUh9Jmb5pbx0wvr/OqJ4uMHgXRWsDbj9LU1eJjpKaKKGJkpooQQJdHKYagGdDIrIXvlJYi9B7CSIXZPIMVFEUMVfIRkvYysLJJRFbGURYxu0bud5E1nkIbqM0hCYFrXMqlJgJS7QkfV4Kn6oEQUmBBZqYGP4KQzJ8nJ7U0QANBL1mS5lNUvGypJUVZbtBT45+BlOZ88QaB5aGFNoQ7ElWkypBQNNlQH5WEClI7JtQYx1i/+TLKMPn0QfOoVWPrRDU0yhS0J9mYT2PMSnWQ4KzKWP8/l0nhfVKRay0/35b8MsYt7iOryz2eXhniNrqKYMg3NGkcX4UUreSTLNcT4eBDxmJrYyNA8uvs63Xfgs+5rLWHd8K+behyX0MFz5GI5xjlyxJyUf/J7K+pkM9clkH7atxDgHPc7eV2QhNY7DGGVfOFeDjHkDDPkloRB6y3PcVrsyY7Vq1KirDvXQRcTTHN9A99Nk4yLJMIfqWH062TewWIlwrYBWUqWZMGUdn+WHZJyQXEfMf6F8jxj/RB8okdR3mx5wqWRcOomYnmnhmHkcs0ikmbd1sHJOj7zbIdttkW03yXSFJmuHjNvrH/Vfl0XoZiayo56L3q6THR7lh//3n+DLbX/nfP0tcb7CWoVPfu9/wfIbpLpLpJ0Vks4qWiyWDjFhHFNPJFhOZXh28BgvlE8yJWultC0Sgn3OEm9z1nl77SJjlXOk4nX0RIiWEOxBkezDhMqUPYxjWwxoDfYrizscMQGr+VT4Fj4T3SshQHeoV7lDucqd6hX5XuFsBYI+3lEJHEElr8ned23Cge+E8qPyexphzEudgI7vMb7wOLtn/xIj+OvTmUuq9Xye5ZFhlkZGWC+XJQHEpglRRNtxCARJhrUz0lqsVKSzNbK8QK7T7EMW9Y3IvqgFchyhILsjkxMYOh3ToJbN0srlaGeztDMZOpksXuLmk5tgcGwbLWpWg0jzmYjKHHEmONgZouEvshzNsmwtM78rZD0Fa6pCTbAwqmKJIBiwQnShXeJ0SHhdLNfnrWdCHn0xJLsRgKyn4XMP6Dxzp4FniIxGxB7X5SPVKg/0+ue3oyj8Rj7L7+bS9ETGY1PM86+xcDNDk1Q4gaIfx7MP0ExOUEsVb2BV1MOAgeY6uc4ClnuF2D9PS1+RReX/86iX39hEDi6w9uNZx/ETx/CtA/3ffZtp3hymcxbDPYfhnEfdxvT55v6HguXBQ+ci3vdyKMUzN+3qMNIJe+qYIn+vW5mlWRQSBQpWASee4KLxKGkM/uX0f+PrVz4rXUJR+/bk+HEe230XrmmjKX1h8U6tSW+5irpQx655pLsa6Z5+U9ruUESNFQcETApXfCstvURLLdHQy7LV9TI1o0xTL/QzY7cwVXGw7AaZtEspGzKaN9hTTnJksMje0hBZM9vfx1jB78U4TZ/W1Rrrl1fQKy1JTNMV0V4hdlucplCcpFSax7KuRdvFrNZsDsisWLUyhtrT+2QBXEdlr3oE6RZOJqKX7VDVp1h1KlSDflDiehusxXzvX0YcW8jRzOxmaWAvV0cPoak5VLOOY68QbVvYl+Iae5XL9BSdy9FxvG1snCKU0VJCTusKV7sniMM+lEZT22Ryz2MWnsEz2ztZU7fveqzwYOtOvmn9fRxw+2RGgnv0c/ln+f3SZ1ky13a+PwIz0IlUG9fM4hsZYjUpm6idVRSbpJsg3bXIty2KTYNCy8IKEpihWKAnsGKDVKyib9ZgijhDwcQomJi2gWFpGAmNoegc987/v0m6i1TDMS6mv50p9whCPq7nZm7IOApIXFGfZdC8TMm+QjZ1lW6qRVPRcBTB0Krhm4oMbinpEEtQ12s5/oBv5gkelRlpMec92H4Cpru8vHQn4UYWdCy9yPv3/BV3Db6EEwtJlASOIqBQaSI1C1oBTS+hG2US5ghJa5SsWiZTN5hZrDK72qRW93E6OmagkPVmSVWewXM34bAKxeIohzKHSFkDaHr/PGmRgRrpqBv9G2UghFNhCCIIdUYSosg6I3UWXanc8jOiJknIBPjxLoJI9KIJGLEQaRb5DFEzGRMJbcDYlfWcatzGMhoYRouotoS/uoy3topT7+BoWdxEAccq4ojeLuGkivSSZTzl9oGTTfM0h4ZZo5Oo0bLWaVtVWiKbZvYzal0RuRTXtMiE9JCOmXDQhI6V7FtQbseSPGx3oUd+WOhX3UEvug8nvI+Iwo65M6pNESyfxlt5naa/TDupU08qVLKwWAhYy4VUM1DNKEJaEDuMGRfaWSts0LvHjGyUNs5khvjo4Xfz5NgdW2PYscZpDoWf4/7SW3gkfr987vdKn+E3y59kr5fhwU6RU70ie1tDJGtjxPqIJBYRZQ63MkdxZfC0rjaJog5G4ElyqY5v0QhLeGGBeCMo84aszCmNdlKlagv6fEXWWDWFo7XR2iLwfJ3DY6sKQ6ZGOnZJNHtEvYBOqFHXLapJY4dA843/VLBKV1GCRbRgFS1YRvNXZF9wHE52d3Oid4Dh5DDDbz3IxMAovblVli5fYPHyRVanpiSTYh89IRYxAhqlk0mmsRstks02theiKyrW3XdhPfAAysAAnuvSXl6gdflFuotTdLuBWH5tQWgyI+N85P/8Jb7c9hXjfH3hC1/gl3/5l7l69Sp/8Ad/wNjYGL/927/N3r17eeihh/hqta8U5yvo9vjn//qfkO1aDDbTlJs2StjGUev0tBZdrbMB+dn2mTjBTOIgZ/NHmUmWtp5PxfAIBu9trnFk9lnilZdRw46EP4hJ1lMiHCXEEfK0GZ/sWEhuMGQk2dwBBTob7ebPw7fwqegtTMcjpOhxUp3ccMauSsdshCpefJiq/88IY6FKHpHW/ggneoZn2t/Oon9cfpdFizuMP2S38gxOZNMJ0qhrXczlpnTkfEelvStL56SOlotQEq4s4q6EQ1S8EaquYPvbueBKRW101adDClHlsWmicDifW6ZUEvVbi5hGSOjaeM0ETl3FaYQyyhU42lYLfZuMo5HQNdxsklYhhZNJoAqNGCGmKTJ9gglQFeKagdQ5cq0YxWjiqqsEWoNYczFUMBSNcqLMuJEm6UcEbpe40caqttF8oXCtsJotcW73ITpmilBozTQaFNbXybYaWKJOpOWSrThoXj9jKGBilT15KiMF/NgiCAQ8Jo/v6XhCIkrowgVLvMN/keG4P8k3SfG48gCvxEfk2KirHnl/mVJvnmJUIW04ZHSXlOZhaxtOvkg6beD8+9t9jSgR9W7oCRq6xaqR52zyLq4mjzOX3MtychRPN2+InBXbDYZb6ww7MwwHF8kYs0R2jdhuyAVrP0/Z19WS0EJJiHHtuWib7pYktd/2nGAj21C9kZmyzc+JUVA6m+K9sVgKis/1myjS94UMtHWIjnWcbuIorrlZrL9hcYjhXiXZ+gyG8ypKfOuamB0fUyyc1MP00u/h0EKXr3viL3n0pWcwg34mt52Ax08qfPYulaXSzSfEUMvTyX0jZuIufnj2d/nI/MdJbJDF/Hkqyc8X8iwagsscch2dgZrFQN1isCYW2ULVZ+f3iuzoes5jNe+xkE2wZmdx4yKRVybySrKPfbEAuk1GSXVRjXVUU7TKRr+OYlZQtM6Nwc8YrMgi5adI+2lSQaq/LaA0foqE4F9/A3piLbfARHGG8fwK+nX8zeu+yllH50xPY6YnMic6WqxJGnBx/MLR2zwPoRL2oV1qv/5LrENE9DxXV8lXBYuquIgUlpIDLKQGJbRVZMgU1UVXHEY9jWF3kJI3sBXhFt+5Yq1QTVTlsQ13h3cck6t6LGkhM85e1gORKROZehcj+xpG7iVUvSNJBgSjWMK3ua97nHe075A1LMI8JeC55ApPp1ZRuk32LK0wUGtQaHfIt9tSB8wUSQtV4+Luvbx86DivjR2iomdQ2j5qy0fpiUBOn030dibu0f2+yl2eQXLjrVU14kUroK7CiJ9g2LcY903GfVFYf+NvF5k9eoUVouIMdu4S+cxplISzRTyw/bq4zUNZcjTv7+FTzgdZWc2i1q7dd6nkPNn8S2At02weptu8gzjYXpd37dvEMns3KgKsKdoEJhoqqcgmF6bJBEIBa+dies2Z42ztaVacPpxXXD+70sc4lnsrWfPavHqDxQ6qMoemTvedLHUGS53DVHY6y9vNI0uHYRrhLmruAerefmrBOF5sbzlYolesBlpuDjM/i1mYIZGfw0gLNrv+AGfMKFiXFMzLKuakgioFxrb9LimF4IhNfCyLcmIQr7dOcHEWNTQofM03Qno/XjtDtWJxcSZkdj5EaRsMByZa2M9yvpEFokJI92hqDm2jS8to0zZbeMYaR7WrvD0+zzv8ZdLb6smqisoL2JztJVHbBzkSnWKXeZJsYuc4HHWrBCuvS2csXLsA0c6MpNT4E1IxG+PrdlvP9jW0BBnG6aFBzpvvpts9RRGNcVT2WEs8gsYD7l75/lBIX9xmDIzDAM9t0fW7LCYCVkyFXmASuQKe+uZg/gmlQVpbl45yLxFSTRnMZzNcyQ9wPj9MPWXsqCETlg1ajLhrjPg1CrkhzIFDxLqFE8Y0goAVL2C259HdEMy+mQk9x2K7R7ERkO3qFDsxhVZEsR1S6EQSau9qPVZzl1kcPsNs7jJVan0kwPb9j0wGvRI922OsPMEDIw9wKn+cgbZNe3qBhdOvsnjuDF3/xvnSTNhkyiKYYeD1ujTXV4luqd0WM1Ky+NZf/EO+3PYV4Xx94hOf4Du+4zv4tm/7NulwCYHlffv28Qu/8At86lOfku1LtX/37/4df/iHf8iFCxewbZsHH3yQn/mZn+Hw4cNb73Ech3/2z/4ZH/vYx3BdV+qK/eIv/iJDQ9eY/mZnZ/n+7/9+HnvsMdLpNN/1Xd8lv1tgTb+anK/G7Fke+vw3X4O8iDqbrk6uLTRPDHJS+yRFwdlHQt2Hqk+gqIU+VGtj0jxrhpwzQyn4uGn5UOGYp3HcVxFIeU9xCVRfRsDFokRoUyUTNrlkhiFbYQ/PMuw8RtF9pa+vsWEXol382YYjNimdrP6S7R9H8I1KBk0RC9smtvHLFM2nMEIRoYMZ916ebn0ntbDPdpjVlnkg/d85kHiqP5FsMz+y6MYZ6Uy1BFOfatLREnTUBD3Voq2IXJxJO07R8Io76kJ03aVYnKcoHK7iEpp246Dcr/1VN3SsrtHoS8FK6WAJvSq+uk1Qya+67J/uYm/QTgta6St7U1Tzel/TScLz+gOz1HmK+r2AM4axTk9J0NWTBFqKyMiAlUNP5EnqNq12xMyax1zNJ4g0QhEJDgUUUqGbHmOtcJiL5gD1xI2FtAInPtysMtKscIg2pc68hL6YZo879pcphy7e3DTu0iyx10VoQ241S4GUKRfRoRoTWjqRqcrXpCq5IaQQQhQhRPUlWIsM5zjBGU5yJj5OozdDsvEn6MFS/3QqSdKZO7krbXFKvcBEPCVr2DadvzUGeCz+EE/p75RReGFW5HDQPcu7lz7Fu55+meTrBmrr2rX60qF9/OmDp3jxYA4jrKOEFXrJ/YTJt/GdK3/JP535TUp+Q773+YTFfyjmObeRzRWHl+0aclwQDphgdBMt09FlBmHdztEw0jS0FC0tS90o0UXoI90Gw4+HZTWwE01SeoOkViXJOma4jOKv94U4BXniBuO8cIQNJYkRp7FIYYk+Eo6VaGm0N6hJcVWXjtGhrbdl39E7MkPa0drc7dX5kXqdoxtsrAu2zp+PZfELOgdEBv86drpzjiYdMcFOJ+jN/86+dBOSFuXOOEPtPQy2+sQYOfcaxHLTfNVjLTUrWQf7DIQzdMz6lzWz/dexVGiQC01ystcphBr5SEV1IpZaEXFL6FKqWJ5GUtF4eCZgb6lJcrSLnhIC5w6G2pMOunIbrbVqsItqMCE110RfCybw4p0QbjGGuLZC1fRZijs0zBbJ1DJ3+me4Y+0ieaeGYP5WezF+L4FaN0jPu5Kld7tFqRj3YIx3MJJ9MCoG9r/e+dFbY6TOfwfJymG6UUwring1CpmJIuwNUeq00NZ6Ez+8cDpSegU9USOnL1MOVjGNOnMPfxe/lRzgye46Cj0Gej5f07R5dz3LntUkmqif3rAg8qh1L1JvXGAl6rBSKNEoJGWJtdVz0J0KQVijpzbwzRgrXSRjlkmqBXJhWcIER7whUkKA7xYmls8uIV01pBXF1FyFbqDJGqzeG6ysTaVDWquQVtdJahV6dkTV1ukqPrpbZyye5pg6taU/eL0FqsF6/iCz2YPMRQbLkcaqWWTBHmOqdIopLS9/hzciudhnW+zZJLmwLSZqFZR/+qPEly+hWBbln/xJGvt3M39pnpWpLtW5DKF3Y0AlVF1qA6+zPHKGBXuJ5ahObxtrbf+E9ZdPB+dj/sHLOned96SswHrGZn1kkFYxT8ftEd6SOVqRa1eR8RSm6TrH9ya5J3qc4of+FTz4Q3y57SvC+RKCyj/6oz/Kd37nd5LJZHjttdek8/XKK6/wgQ98gOXl5S/5O9///vfzzd/8zdx3330EQcC/+lf/ijNnzkjHLrVB3SycKiHw/Bu/8RvyJPzQD/0Qqqry1FNPydfDMOTOO+9keHiYn/3Zn2VpaUnu4z/6R/+If/tv/+1XlfN19fKr/NjHvp9G2qee9vG26ZFcb9mowEA0yoA/StEblq3QHcZyUgRBxNXA43XF56Ie4m8bH8cDleOexmFPlEDffuBMKE32Jp7jQOJpxs3XN+qp+jYdjfOF4D3sij7I/g34z1/i8x/pIcpmy2qPCaPFbmud/YlFdiWW8Bu7qSw+SLDByJPMzDB+8BNkB84Ta6Gs6wolUYAiRWFDyXat0OkUqEjI0QTt9s4oZCLRpFSeo1ScJ5tbu8GZ+5vY5t0kSkDinorei1B8RQTGEbGddVthLQ2WDjkFMmIIFFomkYEfJQhCk9DXCQMLXzQvQRgasr4g1WszsTAr66aEB+ukTFmzlu615OviOc/WaZdTeDmhMbZRuKqpaJogPBEitaJuySMWmZnYl2KjfRGtvlaP+HXHFh32zna3RIdrOYPL+5K0Mren9P4fYTUKXIyPci44xUXlCIv6mCR92G5G4DPUrDHcrDDcWGdPMEMhuUo6XSVtV0knquhKKMUuhSxBFJmyF4+lNpXQqpJNEG5sbqtoho5h6hiWjm7qtNsdOu0eiqjzEbkPeSkLwoI+GDPUAs4ZdR4P11nfiLCqioWXeQ/17NdKONemJeMOh4KLHIkvcFXfy4vK/RIqJWwkXuB9/DkP8wQJtmVsIrDOK6Se0LDO9rMzwrp5g2cfPsmvvfVbuTO4wv8+9cvs6/W172a0PL+h7+blSOjEODRTPq2kYO+79TmPg+RGVku0gW3bJTRFZyAL40WL/eUsxtQVOq8/Sd5vcN9DX09++G7aNZdOvd9a9Q5e3CXUHCJVMGPVZfMND9/UJa38rXdECAP3SLdbpEW2ZqOl2h3ZCykHkRmpFQ2WSipTeR+97PLedIOTGxLzLUXh1/JZ/ns2Q0/8L8VE0/MctjVOWl1OmmKx4+2Alxqp46jpu3DIc3XxEleXZ1jrBFQDla7m0tN79LQevnabuh6ZOesvLDbFDvr6A0KrqB+wkEQvAkWw8RHxCbkt6h03NfFuEOTdyMBuUM0I91TkKkUZqYDuBNK57dcb9oUWriWuZGCkf5A3ZDdvNGXHtd3XnuvXswnMuCyWF/Wp7d2UWrsot/ZQ6IzfVDS1YS9TyUxvtBnqyUWJvhA6izJgIxdqmqzF1CJPBnXEUjDSs0QClimyzSKgE8TEkajFFZ+IiFTBhyn0BAVxiyYJWsSjzTOZwiGjdHE1k7qR7esjbkhiFPwWViiApKKmNSGb/NwOxztEVcS1IQiUQpkl6SgxDZEB/es4ijEkAyhHAeU4IB9GFKKIfBiSVxOkjUFMZYTYHaNbH6e6OkEclG/4rTaupBtM0B+sazGrakixfo63T/8Vu9aubn1G1C6J626HZbOkH3gLqfvuJ3n//ZgH9hMLsGrYJgxaBJstbG9st5lZXODMiy+ylkpgmD627mDrPTTVouIMMNsc5Gp1jOnWBE6Y4CQa/xiLOzaCKU0iPqk0eUFZYSBc51h1laP+Mtl8iGqk6ASbxCADshdyD29kQlA5Tgk6c5+WHdJOKpDWeUeyyN1uAnW6h3ad97OutJhT16mrHbJxkqzIaoo+7rMt3soE2qIXBzRDha5s0I5iOlFMV5RW3+QzgeLRMRuSGMVuO+xdnCTfWSHh1lgyLZ45uIfcYJN7zCvco1xiJLoRWtqyypw3jvB0a4x58rRTacw0GFmTupViOjHMlD1GVzB73sLEvTJuKOxLp9iTtLccrH1Ji10JUzIrbrfO008z/6P/lKjRQB8aYvwXfgH75Ikd7xEuQ2O9w/SZS1x5aY3KnELg3qiIGItFz+AFvPQyU9YK563LHLrU5G1nDdI9k6YtiDtELdnNg25GOkk6VyQOI1rVdULvJtmxpM3Y8d0M7PIZOfgIB+76er7c9hXhfCWTSekU7dmzZ4fzNTk5ybFjx2SG6m9qa2trDA4O8sQTT/DII4/IAx4YGOB3f/d3+YZv6HP+iyzZ0aNHeeaZZ3jggQf4i7/4Cz70oQ+xuLi4lQ37pV/6Jf7Fv/gX8vtM0/yqcb78MOKXfvDboTgIh+/A3ztGIxtTc+aodmaodmapdmfouLfGjeuKjilgb4LKQImx0fHbR2isP8JSa89WzY+hhNyTXuStpSkODi8SJQUsp7+YV3AlRbuIhottRfGxAoeBSpehdZd8zacXvI9G8I82Jr4OC9k/4WOWyRfbd7O2Qf+73TQlYFdmgf2ZeQ63ciQXTsFG8XRq5HUGT30CK9cnMhAZqUZjUDpbwuly3e0ZlJhceo3B/CxD2VmyRl1O+IF0eEzcyKIX2XSjFE5ob2S4hEMkel1ux6FKIgqxwhgr0tADCyVOEgi+x1BAQgS40qaurXM+OctM6go9bZ79S/CeVyIePB9vCSxGukLraIbmvWncPSqqJjKJognoko8qerktHAgFLzSp93LU3QxdP8Xu59ocfmIZw79Wb9YeNHnxkTKv7xUF2i7DQchYGDAeuoyGghGrTkHQwt7GWmqKBX2EBXOEhprjkDvHEfcM+kYmc065lzP2O1krmLgpV66jdbFw7HnE1QZhqy2hm6rQSTIUrGIKK59CN8TCUGQHBZiv30Rkq91zcCLBpCQyiAGqEqBrIbaIQgr2LDWkF8VMKqNcUvdyUT3MFX2/JOHYblKIUkAVGxWZIRN9WV0jJZyxbc00/+bjjTCRvXqpq/GXTYP1DarhpBrzaMbn4XSApQoWyQmZFTvLSc5zvE/gcp0dD87xLudxjvhXSCgalhAWFWLLgnJacUHvEekdWayjVSD5BY3E0wZGux/QEAvC3ESP/IEOcSHm8epBHu8eo6oXqBsik5WnbuWo62kio7UF/VOtTTjgGqrRz5TdynJRiaI/SLYzgF1pka4vkO3ZpI1HUBMThHpPOlr91iO+nYMinVNVkleE7QZqr0sxSjKW2M1u8xBZkvK+dJwVzrlPcpFXiXtVRqowXIsZqolawv73GOmAwVNNsrv6v6lQaqheTTG/NkIwNEHiwCEKB46SO3CUzug4Txs2j9c7fKFSJ+Od4c74OfYFzxP5FVYChRVfZcVXWA113NtFi4M0sVvGd4cJhOi0K5zVQeIgc93iIyJnNRm01xlIVhgMKwy/3mbflTWKh31mx/Ywq1yj+O/FOleCEp22y4nWAspIm4RZltDITatFNlNhkSkhcJvL4O1LomdWML1ZbG+OZDBDyp9Hixy0UDiqJVz7BIp9AFsrMxC1KYZ1CnGdfFyjGK9K1TJL76AluqjGtd8u9Gyc6l56lb30qvvkdigYda8/H2YbK7OMnlxHS7SJdRfFSxI4OXwny5qZp5rNkSgmGMiFPNL5FCev/gbaNjhYY+/DTN7zL6kt2dQnfWqTroR4y2ucGCe5SDszKWeYK/EYZ8JBeptEA2qPVO5pHvKe4wPVFqNBQMnskbRDPr7va/n5iW+jo/cXp+9df4r/19Qvc6g7QxCX8KNx3KhIO8yx4g8w7e2mFuzGjXdm3wXQ2dV7uHoTO7dEIreAYi3hq2v04grNuEVd1OGqKnVNpapqtK9juftSsolGkMKSsNs0toDcCshjkMINU6ikGYyyFLtJMm2dQ5PPsWvhGSz/5qyNUSpDfXyMqXSStYEBGrkcuXyee+65RwbHxbrsZrZQ7/FHL8/ziZcXmFq/Nm+kvC5FE9p2hppggbjOBDlNSelQVjq8XTf4mniYvC+cKRcz/AJ29w9J5ufQBOpgw1zLYH0wzUpJo5qOJSTP7xbxuyWCTr8Xj4PN53oCi/PG5zet+wwZMKzrlKSszO09aJEl6oTxhmPF1rZwsLaHtGWtpiB1MZt07GU69iJts07HquEabQ77I7ytc5J76ntoN+aYD5aYiyoU15Y4OnMFU6B7gGeHj/Hrxz7IbHZYaq7eY13hHelp7uISe7qX0K+j8fcUndfTh3gxe5wXcydkv2wNSMKMcb/CPneZPc2r7OvNs1e2BXY5S5giwKoaMHAEho7D8Il+P3QS0n3Ra+EG1H77d1j5mZ+RpFWJO04x/p//M8bgNdbW21mn7vDa5+e48tISrYoI6orASZU4WCX2l0g4V3Fp4t/ivmgkfao5j2rWo5Lt96mezompLHuWU1sBJSufYXB/FrOwQGwsYJc6GKn+eQoqJ3nfP/xjvtz2FeF8CUfrV37lV3j3u9+9w/n6rd/6LX76p39aOmZ/U7ty5QoHDx7k9OnTnDhxgs9//vO8613voiaE7vLX8N27d+/mR37kR2Qm7id+4if45Cc/yauvvrr1+tTUlNy3l19+WQ5K15uAL4q2/QRPTEx82Z2vttfh//zCP8ZUfEw86UCJ3sSV1OL9ba+fgvdDucBYCcRCQ5WLjkpwTfj2ejOVmJLA4HtlKs2DNNr7idxBGRHPmh0eGHmRB0efZyKzyeR2c9PcDCNnv4fUel/jxlRfo2j8J3SlX7DcNA2mEjme4wCvOUeZ640x5e+ixc5JMBnBw47KCc+UcV7JtZeZJ8hfpB0ItrtrC5V+JFTUhmYxKhYJL6KXVAgSrswIeAJeeIuxOBN3GGKNIWVN0lMPsU6Z2pYTcr0t6hqfSSX5dCrFOeua4y6i0yfaGe6ujXJkbZz0XIP03BXs9jXdonZyhMXRt7E89BYC4/Y4cMupsWvus4wtPYUqWKfEIimXwjycIj/eIK8vY6q3JyfphnkaoagdGKYRiH5ka9uVTIs7T0pGXeX+zEc5nHhCRsIFvPB09wO82P4G3Pj//9e9yM6t5jTmyzpzsmk0UvrNoYrbnLFip4kRK5iKhqkoJFQFS4swBGx0A3YonJ5+E6mKYOdzIhKuBryuVnhSWaMqybMR7gKP6Eneapny+0SaQzqZG58XTmWkxkyruzinCtWno5RY4738BRPMvenjbpLl96NvYbZzkJ/6q59n78sz9NavwWHmM2X+ZO/DfH7iHrrGjZAQVQkp2xWGkmsMJtdkL5qV6OAqIstjUXdVGl5ELfBpCnHyyNxRd7VZh2VIzOatLWElKOYLFEpF8sU8WkrDNV2aepPlYJnmepWJK3nuWjpIKejTfws7a1/l0/mn+EL2ZVkDtWkl0pJGe6ipcGS+zbu9efaX2tL5F7NXY9qWbGN+58broGfCQgkWB3SWdiVZGDGZywl9w/YORrQd50qI7uoxRZGp8gaotA4yXzuJ74zAtjotkY/KKQ55xcFWfDqxKUWfRQvegGHx/ugcP2n9DvPaBK/FR+lsc86rUYbxYIQPRmUqaoOr2gpzamWrdldVfdqGQlXT6doNUoXXyacWyGgxGTXu9xvbIhDwRiYywG5jTDpa3fV9uOt78bojN76PED/RpZcQ8ha2JASw/JikF2O9mWIfQVhiXOIDuZ8hrfe15oSd7b6Lp1r/jx0ZD0HP30is4dkrkjHxNDnOx3nCjQyFotcxS1/AyL0gmfdEHd6EP8ip5kH2ruTRKh2avWm0nMIX7nkXj+9/RNa7Cbrqb1v+M35s+tcZ8G8UkF43kiwk8iyaOZaNPHvVLKdUlWxvDq1xBSW4RQDHTBOVD+MkD9BgD/OdAZ6eqbFevYyjTOOYAY4Z4VlZvGQJ14Se1qBpdukaPQIRZBGMm7cxPYg5uAjHZmPZDi/Eso5vu7UTGlMjOeaGBlgujdBJDZMIMqQFI2VSrGGqEokh6MwFGkgEpQWKSKyPen7Ip88s8/EX53h2cvP3ERyxgl7nxgtJzLF5pUdZ7TCodTlcTnDHnkF27xqXtf2lbBL/yd/G/8yvkkzNSqKdTYvtYTj1YZTjfx/G75cZ8Xr9RV57/fsJBJmDUiQMvpXzF64KCAma7pNKBJyKXyIRRiw07+Fq4356vaKU8hB7I5mPQwstSki0wo6fR4EhXWHIUOU9IaDHInMlnCsBERQOVp9gKsZXAnzNQ9F6UoNtk3Ld0VvMZ6aZzF2iYl+bw0W2OuwcZKxxF8Pt47w7zPAAOultiA0/jpj0apzvzTAw+Tgn5i7KrKTIrn72nrfx3z78TayXrgWgE6HLqfZF7muc4Z7mWe5rnr3p9TpPmZfDA6xk72D81Nt52/33kW3PwMppWDkLy2f6vXcLSYXUIPHgMTpXGzSen8KtGSQe/QcM/+ufQr2OgOxW5nsu67PTrE5Nsjp9leUrV1mfmyYKbwzEiUCpkM4ppvZQsCeIE2leLcxzfuAV5u0llugxtmpzfDLLcO3aWBsPdxg4VWVsjwjw7vxOEaCOnSK59Du5/+0/zZfbviKcL1FD9Tu/8zv82q/9Gu95z3tkjdfMzIx0gH78x3+cH/7hH/4bfX8URfy9v/f3qNfrfPGLX5TPiYzX93zP9+xwlITdf//9PProo7I+7CMf+Yjcj8985jNbr3e7XQlbFPsoIJHX20/+5E/yr//1v77h+S+78+XUeO7pe/9a0Xs/FtEe4YwZrAYGa4HOWiCK08UCTFB239xEzZQsvJdR3yFKQYEHoxT3aQEOKyw7yygdD63tM+bv5r7ch0hoKcI44HT1SabbT3MgU+VQdo3dqfoO1sS6k2SmO8ZL1kkW3DK9JlTDJLOJAa7mxjCMmCNxm7u6afJefmtR0E0t0E1Nk9ZrEl++kClIStVEJ5DFoTczLYhRohS6n0MTVOmRiSro5glw7C6xLaLCVdJxk3zQJht1ScY9UkoXT2vxUqbL0+mAs4lrA70YUN/iOLy/3eVd3R6564paxd3mVA1qV5I0Z5Nb+iBybhvN0du1i3ZunF6cx48TZNQ1ct4MyatXCWe7WyE4u+RRPt4iNSKyjNu/X6EVlahFI9SjIarRCNW43yrxMJ4imM36kBTZb2RQNp+7tr35fP+50XiSvx//GgeVfsDCj5Jc7n09V50P0dETzCeFtIBCU6RQicm2qgxUlsh0RGalz2wVqzaaNoSileRCqJ5U6ZkRheY6A7UlCvVZbLeDGkeShVOCsuKIqpWlJiYJe4TIHkaNfEyvheU10P0mLStgoWwxOVrg0q5RpkZGdjBaCjMlVFE4YlUJVxxs1jCiEE+NaJg+DcujZXl0bR/XDjBUhSQGB7tl9nZKrLvzfDHzPKtmf1GSDdL8g8q7Gao9yB+pMS+JG2rDCnrM3aHGsbpJYjteSYlIFAU8tEvktNDMDpHVpmI3CRIdMka/aYpHPVKphAlW/TyvZ++mXhrlf134Xb578Y9lJFPkD/90/QEWrhR569xZ7A3RW0c3ePnAfi7dNYSyy5OZl5K9Ts6uSmdFRH/FFS50yTwnTc/J4DhpnF6mv91Ly6zxjfC3bdcYMV2tu7PuSu9sPdZFgbqkVR5k2C9T8vPkA+FAlRkIC2RDQQGvbLF+XUzOcCkzzVq0gluro7oeWtcn4cLxQ/dx6qF3se/EUYwXfgme+S/g91kNw+G30it/Hb2KyvLiZSbXLjLjLzOX6EqHa6GkUM3eOtqt+yqJbkYGlDqytrTAoXSd+4oznCpfILGNxl7QlS+5p9ASD1EuP8p4NiAVP8fszEvMz63RbOVwnRyOk8H3NVz0LUesvdHLJupTZe5PkRqJ/8b4VT6oPcdFbQ9P6ffSNg0M05H1jAnDJW1A22oS2OvkdaHB6Mvs8JdkioWiC1a/LFFs4dXTVFdGWa/swa2NkWjk0G9CWe3qXQnrmhxKcXZPkZW8gApee58Veex1ezywonPPKhyoCxiky5mBeSYzaxiVPNnFMQy371TpSpeHMr/GUfsxWmGZJe8oh+0nZVCnHozwucaPSPr5TVtTI15IBJw3BASw/1wpVLgz8JlQWli6y6CmM6ok2RXnyF0HV2tFTV7yn2GxfYmaluQL97+PK3uPytdM3+Gh+T/mfbXf55Df5oDvMRDeGrK/aaKG+EpyN5OJvSwjarJ20euMo9cLkqnvZlebF6/S857E7M5uvV5sOxxcrlDqOLx28Ch//M4P8OyJkyhKFzVqoURtTKfG4elJTl6Z4Y7LSxyYr2NuG2eE1ZNwbrfC2V0K53Yp8rp/MwXIRmhiRgamdFhsgjBL28/TC7NEQYooFNpVKeIwSRykZNAho3gyoyWcLdsKCQsJavki69kC1XReElkkQod3VJ7la1Y/z3tWv4gloGcb1qaAE72dyH+bJNs6kzf45UMJXi3pEv7eh8L35QNUVQQx+3NJutbh4GyNPas9yr0uqtbBs2pE1zurvkUtyHIhKjNHinQs6tVdBtNnSSo9KSiccwtkYptu0mQ+VaBjKBRbERMCWSDogyVkMGIu32DFXqXg12laU8zlp1hLr1wjL4uh7JQptfex3riPaV/o8vXPe1HrYaQhmc5IBj8taVBJasyJ+W6j+HRieZHv+5OP8cirL8jHjmHy++/6IL/38AdwhKRONwBRstAJULohSs9nV7zKw/FrPBif4Q59mjGjcoMItBMbLBl7UEbvpnj4bai7H8AoiHtwDXX13IZDtuGYVYXK2Y3L/1jVUcqHt2XIRH8CMkM4nfaWk7U6Pcnq1FWqi/MSHnwzOZKs45IVUgnYNNN3Uxt4GHSbXabCoUTfERbzSSVs87r7FI3GRUJn43dVYgoHGtLpSpavredXfYU5V2fJMVnp2dS6aVQSPLD3AX78/T/Ol9u+Ipwv8dWihko4YcK5EWZZFv/8n/9zfuqnfupv/P2itktACIXjNT4+/j/V+fpKzXwFQY8nnryGyRW/Zi0UhBUqk67GtKfKgnJBficETgX97IQZcSQRccQOGdb7opvbhQODro7T1llzNJY9lZVQYU1RqOgRNSsk2CbcvN3Ewt/0C1KVfV8nw7sa93N31GctrEQ1Pq8/Rj25jmLHkr5esWISMRxqVjnWWGCPsihrdTZtnQKXrVEWsnkqUZZmbWiHHozuZUi0DkghQWFdJebphM9rphCgdCir7f5kobdJJR2ClFjsG1LDp+cn6eoJfNPEM0x8Q+/XZGxg5fvOSb+KYlOoN4rahOHrRP6rxNHU1sAlptzAPIRhnWRMGWKv36HktSj6bYpeg4LfoBDUKfo1Sn6dkl/DjH1CT6ExY1O/msKtX8smWDmf/P4udtmjdjklI/ubRQfJAZf8iS7ruwvM2mOsmGXqZoqxqwscenKKuNqvEWk+GtP9Gp/N9YiqpTGTB9Htg2j2QbAPEFsHCbQMXhTjRRF+LPpY9r54bkcfyX58/ou886WfZbh+sX/9xWWawbfTDYVUgMZ8weCFXRavlAzMJZfxM1Okll8k8s73ayqimHwvotxLU261ybbmJGxiuwmq/qVslleGTvHpiYdYTFzLjhyrTHG8N0swYvPqkRNc3N3X/cl16nzz2u/xQPFJwgxc5RCX3MNcbRzlYu4QvcTOhZlw7gRUcUQ4YxsZspS3GZnVyChphoMUa9YqjxWeZTo1JzMluSDDPZ33MO+8lTPtGHejgF1eNyWFaBziQoSiBNhOyERFYaKiM7FuMtQwtorNBVn7sumzYPdYV9s0A49O1Nc089R+xDEsW2gHbb638Un+15nfIRf2BUW/GJ7gN+MP0TbylMwOo8oqJ2bOsuv0HNb6tcWOsw8q99us7y/QDbLSsRLOQa8nHITb11WI7F3CbpNItLHtJolEC9tuycem1aYVx30hdV+V/WqgsiZER8NbZ9KF5aMEo0FROmbjzgjjvQkmvGHpmF3PJifmDz9qk9b+nKL5x6C2WdB1Lqf3c7n8INOWwkw0z4w3R/s2kFo7TGJ1c4TdEq1gjG44LgNHscy6XdtX4fSPtNfZ3Vphb3eBQ8UrDO1dQj/SRcmFO8a5yDrO2Oj72TP0XixrTEbrLWsYRVFpLi1x7otfZPHcq3i1aRS1hZ9XCXIKcVrId3iopotu9J2stNmR2dcvxbzQwPNsIs/C8xOyNtT1bGp+kfzAQd5534MMl3ZDVKC6ELIy1WBlqsnKdJNu48YMS2TCSlHhatFkrmSwWNTpJnY64LvWFzh++RLHr17m2ORldi8voOpJqcdk7ruXwje9j8zD+1AMFafZZfG1q8ydW2PuYhu3F+LE/fs4ra7iRGmy6jKHk09yIvkZTKFVGCssekf4Xe8b+DN1PzPb9ITGA3jYNXmLULzTNcq6Quo6ZkTB+FcLY9b9mPWgvy3uTk8XwYIWoX+Z5Uybp+6+j5XB/noh3W5w57nnMfwGlWGbIW2VQ/4Ch9wV9roVVvFZRMCLx2i5h4i7Ryn29pLybn7/tBKKFH4WjqpoYruWVDDclxmZ/+/ccTniwHwabWM8Fzp5B5erlNo9lotlnjtxJ92EzdHpqxyfvCyDRjt+J1Ta6TGWht9CtXCERiZPLdnl9T0eF8ZEfaJw3pp9By5sy14LWhiBcOiEnl9flP1LNkmUYROrQow+R6gJkfkMkZpBVyyOdVd4W/MSj9bPMRz0KIQRyTgm6Ghc6O3m37z1f+GJkftJBgrfOe3xrTMe9sYl/8Wyxi8cNFnSehRra0ys1phYCxhoCTbNBGEiwLOq+GZj576LulbXYIY8rygjdDZ0TEVGelSpkym9wtLAU0T015691DvoFL6ZWN0JARfaosftBO9ci8k/V6Wz3mQmf5Yr5ZeZK5yTBGObdkQ/wLvH38P+gbcw14w5X21wpd1l3lGprOoE69cEKsLBBMH+DHF2G6NyHDPci9jdjZnoRpy6cpFTT/we2dV+rV7DTPG7h9/Dp/Y+QHAbqnphotDhlDrJPcpF7lUucZd2hZxyo7j1cpBjyi0z55RYdoXOaBbbUMkoLoXePCWjRdnuUs76FLW6RE2JdWQnMFl1Uqy6aVadNCtOhqZ/82yYYpgkVJ1SpUa5UiPb9Uh5Puvj40yeOM7qyAhBGBGKoH4YYKdqZLLrJI0K4aJD83KC0O0fr2aGlI7VKJ8QDrZJrZNlwTW56oecp0Nd9bBCi4JbIO/mZRC+6BbpZrr85x/5z3y57SvC+do0z/MkPLDdbstaL8Eu+Dc1QaLxJ3/yJzz55JOStn7T/mfBDr9Sa766QcC3vPwynfZZOu1X6bVeIwx21nepxgBa8g6UxAli8zAhFmHUZ17ra4D0aWolLbfIN2w4G8IZkTSm2wkPhABuWEHzF9H9RTRvAb07jx4uomj9G/9Qbzc/tvjdjHv9ero/LP4VvzH4OJ3EIGjDZL00w22TvdWIgnuN4tnC5YR+hpPaBSbcNRmh3+6IneMg5zjAMgKH3P+MH6l47ggD7QlSYX+Qq6kRTyZ8LhlCzXljt0WEJWMQ5UzZYtGS2htGCcUEZvVexOo+i+Gc20FP7JsHcVNvwbXvJ9JvFAm9pYnUe9ih7NUp+zXKXlVOtne8cI6x80towY1RpMU9Wf7qoXGe3ZdkWelAuI4qGO+27U+xGfF9n4+4V/g5YrLJxiz9/QjjPkFzf/Nd8dU0gT6Mao1jJPZgJw+QSR8iYxVIm2nSRhpbFxpA285TFBG9+jH4/E+htvuQ0xZ7ebX5HUw6d9IRGmib5y8KybRmydfOUqi+Sr61LEWUd+xDKks0tFsuvpSlGdYTIa/tHpYujW/ZOKkDnC2e4KUwv5WNtU2Vo51ZjrYuceDEIiOnZjCivlCWrxapue9Gf95m5LkXyU9eZXJ0F2f2H+LM/sOcPniEtbxg8ttp+V6PoYbIiq1LZ0xAFTePWojWtkyVFQaodm2Zja3GSXxdJxxPEY4niQ0NxQ9BCFr7EYpwzLZtq26I2Q2I3EDKBNzKoqQOB20+pDzH/zb135hw+8REl/V9/Gb6+7is3kXacTjcfZJ3xJ8nFYWsxMOshSWCpYCh2TlGlha2iu0dy2Jy/z6u7t9Pd4OUSJimhliGI/WxhINlp6ok01Vsu41pdr9kBk/x78QyRUCZ1zacMeGUbTpprdscs3ilrBoMYzMQpkl5SWy/iRrOU9EDpgydGcOQzFg3/XyskPFLmO4goTdAwx2gJchD3AFxQm/4X+Omxl5cxmbPMDF/lt3NZca7FUxJIx9eE/HdhCJNxDinItyTMf6u66bMmom6lpU6aIrpoKQCBAP8m5Dp2blfoYnhJrD8Lm4H5tqjdJQcQWwT+LZ0shw3yVV3mHPeGMtRhiQee7Uae7UKZaWLFiQx/Cyan0H18lhh4gYCBxF4sOMahr9K7K0QukuoQb1/Xwom040MvghFidMhFoz9aynG9n1K7S7pbhfd6+44T8IiXSfQEvhaShLKCGW1tIBMqjAz8C4mhz9AsFGDVWxe5NDiJykE85SP18iO9KFRL0cH+BH/B5mLBnjLylm+fvopjteWpa6ZEIGNjZjIiInNiNDqb0emeBzL1zD7Ld5qbG0LZREvofCFgYf4veK3UNH7NS8T0TTfEv4Wd+iv7TgefV4h+YkMzOXxDeF4pAiMFJ6RomnHrNsay4ksK/YIwegAqYlBhsbL7Cpn2VVMMlawsfT+4OsEPT567r/xmVc/yrEraY7MJim1Pel4DTa7ZHvXani3TGhDCqKBzczC3r2Y3/O9RI+8m/mXq0x9cYletR8UFtP08MkS+x4ZpbA3Q73rc2GhyfmFBudEm6/TdkVtca8v96B1Za/rLZJ6HVtrYugtmVkKtB6e6uNpnoTg/XXMEAyrWoZsbpisVSBn5cgYORKBjtWMKM5qDNeKFMKcpPJvdn2u9AyapiedLdeqEuk7YZ5KqNAIDC4qJS5Ew4QbARuxcjlqzDGU6dAow6TxLGF0Sb4W6KO0i9+Ln7jGiL1pqW6bu+p1HvYs8vUrvKS+wDOZ13G0a4F2OxgmrdyPWrqfRnqUVUXDvYWOoZBt0CdbaEvX4P+5pMfBZIXxsE7G6cj7TMgYCLKP4TDPQJihtDiNefYviNv9OayXKvDksbfw1OgemUX3Io1epEvm5q6SwFXNG4ioxDnYpyxxt3qZu5XL3KNe4pDaJ2Labu04wavRfl6KDnEm3M15bwzLcxj01ih6VQaDCikhMXSL2tes4TBotRlMdChbHUwhoePqNDsJGp0ErXaCipmnXhQ1pzZWxiVVbJEqNEnnG6SyDfyWxurrJWqXs7KeXl4vaZ/0gQBKGbrOAO3WYD/AtLmM2xC1jwW0/yYBvk6qys/+2M/z5bavGOdLkGq8/vrrrK6uSpjgdhOQwS/VxO4KuOIf/dEf8fjjj8t6r+22Sbjx0Y9+lK//+j7zycWLFzly5MgNhBuC5VCQdQgTtWk/9mM/JvdTZOe+Wpyvc2uX+cZP/UNJYrBpsWLgW0fxEqfw7FOE+vD/WFXxm1kcYTQrfO/FJt9TK6Ojsqa1+A9Dv8druZdv+TEj0ilEFmVVIxekSHQGMDvDFH2Dw0xxnMscYHpHvVWFPOfZz/OJU5xJHqRjJMjXGhyZFpS2hwj1vnPvelXOsM7z+RJt88Z6Kk2JSBoOOaXDqFtjT2eVTOhKpqLZ4Tpzo3VW9bkNZam+Zfzd+Ob9LA6/ldAYQA9jjs+4HFxq45jrVJNzLKYnqVlrcjDcUqDahNFtbG+6utkwoBwEDIQ+Q4HPaMfj8PmAkXMKRk1kUjxad/RoDEW0VXVbU3ZuayZtzZDbeyZDvvMvA4br/X1+bS/86QcV9OGYUSNiRLaYon7zW1845WLBvOIa1LpZuu1BouZezPZesk6ZZDeP6SQFLQunkn/OPek/wFL7jvece5JXV95PvNIlX7tIoXEVPdyZhY7TaWopiwVDpZK26Zo6Gcdjz3qDYtfnmXe+k5qhYs9fQQ18UvkCH/5//gSUJ/j9F+f42DNTLLSvRYT35aZ4+/jTPDgxx+H938vY6Dejadci08HaGs2/+iLd5y7hLTmo6QnWRvbxWsnitYLGa3mNKxl1I9N5zfTQodxaY7zeYqReZbDVhypuHYeA7ao2NVKs+DbrkU01SsnJ8s1YMuiQDxqSOTDnC9HTgIt33cV4scVPTP4yd7T72cXluMB/S36YLw48QCYQMI4O2V6brNO9ZY4p0eux7+ok+ycnSW6gDsTxLe7dx6UTp5gsjRO3I5JOgrSXI+nl++QOQi/L7KJZbTSzvdF3rvVmG9Vuo9gdVKuDbrQxtM4OVtNbWS9iyykTNadLgcKyp1CT+kBvbnxSNrRjDL9A7IzR7uyj7YxLGHRfP2CnjaCwF+FoqRtNY1cUo00+jnvhT4Tyu1y1GvvfjXXkQyj6tjoDXKL2IlFjgai+QNhYIuyu49s1vKMOzskI93B8OzZ+pC51SzQFrQlqU0GPBjFTd2EaB9DdLKogspi6THjhc5T2XqB0pJ/Fc+o6C08XaDkpZnbvZmrfXuqFa0EeratAI40XlNH1IjnFRuRWr7dAdWgYLrMqXFINArfOQG+dkU6F4U6VoW61v92tkPVufU29GROwLOnsSJmHTadncxt822R95Dj1gb0oekio+ZxtWjzWOcxblEv8lPHrZJUeDgav5wboDbhSLmLz81+qQ3s7ExXSn+UD/DHfQHej5u5U/Arfwm+xi1k5TG+ub9UXDVJ/bJCsCcbYN1gyCWhvNouWz6MVCv0+n0dNp4l6PZpLs6xdPU1+zUG/7rbxdMHnCIlALDCvWeLUKcr/y0dIP/ooyjZIdRTFTL++zvOfm+HVqRpLesSSFrFqxdRvsp8qEUWlK6GDoo2mVO49MMKeXeOMjo5SW2vx0osvMbM4uUXnHYugiZ/H8A0KiXOk7dMY1hQNPaauatQ0lZpuUwlValFEI6HcXqT3NqZHOmZoSs0/2YvmqkSuRSUcZFnZtQ0SmcK2dYKRDM2xknSs7dZnSDU+0Wf0Raeb+1qC1AfJiRKAnqjzVAk0ndBIsphKovuXZGDV6j6PGrWvnVetjJN6ACf5AKGx64b1k6xdcoTD3CbX62y0NgNdl3/YPIgXpfh1HP6KYAsJcFyd4x59Fk0VVXQ33qeaWEtcnefA2VcxBKOxmIsL4yyffDsXh5Os0JJsqbqmYRrCATRZCNLM+WmW/CSN8PrxLyYft6Wu6t3qJe5RL0ud1fS22rtNq7g2i70si92s7KvC6UGlZuRZE+gaK49tRYwmmhzW5zmuzHBEnZX36rX/Bq6p0szorKcTVFMmroB9i6DHxnzZXkhKp6s1dy0Bo1miPlroptqYqiWPTzC4BoK9WRPjl4TuyFKCWNW2tgXhmRYLiHyCZqLNrt1pvv8Hf4wvt31FOF+f/vSnpc5XpXIj056IpAvK9y/VfuAHfkBCC0XWa7u2lzhYofu1CUcU8EFBNS8OfrO27Omnn95BNS8Gm3//7/+9pLwX+/l93/d9X3VU8+vVVd7zRx9Aj4Se1wCFVol8p4AeCRrdeCNqKWpoBCV7iGsEeLq/0Qeyd2UxcH/bMQVL1hs5DTEJ1SBjpsiYabJmmolgmL9//mFGa/2MwuVsk1+Mm7zaSuELBj/BrGatkrHmSVvLRNY6Lb11SwpfNVLJ+BlyfpoJP+Yur8qdfpVTfhUxLGxalZzMiJ1XDtLNHSKbKaEspOguZuXgK6y8/jqZpceZtpNcLOzifHEXk7mxm6b1LbVBlJxBTc6h2vNoiQXSYUxSaIQlD7M0+K1Eev8Yh9ZnecfpCvvWRtGvW/j19BaVzBWc1EV0+wJ5EZULA4aCgOEoZihRZjA9QiI7BlttFHLjso+TA32KaLGobS5CYx6aC9CYg8bCtsfz4DZvOA43hNULGdpn07KIS1Dyt++ApTvTNNQULdWmqxkopk7CikknHNJ2i0yqhm5cp82xYaGfkIX5/TZOpznIqmNi9a7ybvUvOZWbRtvQihNQybXTfRIEIRR8drfChXGFq8MqjVRE0lMo1nVGKlnyrbQUu5X/Q1eolC16o0UeestD1P7kCdqLK2imybu+/4fZVypz7r9+N8+eHOaxxoO8tnaScKO4Om1pfPiuMb7l3l0c0nXcmSbeTJPedINqtUedmMZGE3KQjdCj1qtRCZtMDV9geX8TLz2Bbx3ENw/AdayKIg2QanUYbNSYaK6yp7lK0r/xXHUjnWZg0AsUXD8mdH2MwJH1EHbkkAna0tmy9QTJwm7ye07xyoF9vKos8qPTv8J7q8/I73Gw+CL38ix3E9xiha+GIalOh24iQd5scZKLjLJKgTqzlsp/LA5izfR438s+p6avDfXLefjs3SqPnVT6NM2xSlKIG3t5Um6eXeuDTFQGsLTd6PooqsAt39IiVMPZcthUUcuW6uCnOwRJIaBapa7XaCot6lGXeuhSCSJZb3orMzayLd5GFv5WllBE7ZRJRjHJKQYFTWVIixi3XUZTkDSzmGoJMx7AjMuw4uA+9kXi6RW0poKZOkD6rd+HlhoncgKiXiCwtBs/d0jdW6PqLlFxF2Vr+f2aPzWKSLk+qdglN9ElUQyJOjFRWyHqqNBWiTuCsGNzkaWQK59iaPxRkqm+3mEUBbQrZ2mvvopwP+2hAqmJMllzjlznE2i6h+DVWXklR0u5G+++9zIX55ltaLhuRhIL3GACzhzVWDN8askQ21onZVxbVHqxxnycZYE0TVXH1D1M1cfUXCzNI6t1GVLrDCgVioLmRdFIqSYpMYarHm3bpZMI8a0IVfcxBEOp6mOoHobQJ7qJTuLNrOPbPD73EJ+bfTtNrz9/JvUuXz/yF/wT588ZaPUXiKtlk/MH0wRChf46UxQLTbEl5bmmJFBFTalqbfQ2mmKhav1tVU30n5N1p/2makk0NUEztvmVWorfqYnMrZg3I05ceJlHXv8Mh05OUjpSl2vv0FVZenGA1sVBbM1EF4EYt4PSbWM7HrluQL4TkPBD9EjMk2/OQkVhNZvk8lCBdsKU13u657F/rS4zYsIDVIpFUo88jP2OdxAPDHE1sDjXjjmz1uX0fJ2Zam9DtuBazZfIEmRUl4LSoyBYIQVVv6EzPjrCvUf2cGD3GIZpbhOs7we3xeNOt8vFS5eYOv8a+xqvcE9whhP+JcxtMsrrDPNq/BamGkWi5f59EWiCDKnMSkpIXfj4WkhgmgSGTqAr/ceiZksTzHdNArVNoHQI6f314JBi31WRZU3K444xiFULQy8wkNxPURsgSRpVyGm4pmTibMTrzKvP0/OFw3WNxCJSczjJ+3FTbyUQ47+UdYgpeD7D6w5DqyqlFhRbIVmhE1aqM3TMYmzXEIZhSAIT0fRQJfNkF301YFoN+fXBmMdWuvIcC3uX+hLfrj9GTnVZTB5nSRtnrR3ibcyBuu9z+OJFDl+4uCUGXR3Zw9qJdzKd2cXrcYIX0Hn4xDDfev9uHtxfQlUVVlsOz01WeXayItvVteug2HHMoLfKW4PXeYBzHNdnGE80KFo3knR1I4vL/gRnvd285O/nJQ6zYhTobSNzEuPKPdmz3Jk/x+7sHLlsDcW6yZrej+mdS7N0rkyzeS0I7iRSTNsTrFAgEfskIjFHuliRK+dK+TjqP34z91J5f5nv+re/wZfbviKcL5GVeu973ythftsFjv8mdiu60F//9V/nu7/7u3eILIvs13aRZaHrtWmi5ks4aSJ7Jmq9hMiyYGD8ahNZbrktfvoH/oEceLqJkJ4V0rX6vdzeeE60cFutlqVZZMzMjpY1ste2rexNn99s5gYeX1w+recXafzZFIovmIJCntIvcEVdlhNBEKvMRRkmozLzYWErCiQicOPaOmOJSWxrSdK2toyWFN9s6R2CbYLP18OLhpUU+4OQw5019rsO+3yfvX4gaBS56gzy2vQQK+4guv0gmimKmAXmMKJYe5FDlz9JslfDVzQuFYf5/IHdvDi+i3VtQmoc3UhhG6EkKoT5DGFxmChnoFpVxqq/ycHWSwyFIUMelNt7sDuHCHtH6biHCK8L0VqJmNHdBmOHSoyeGKM0kZMD5v8Qcxp9h2zLOZvfctA6M0ssP+birfQd1jCZZOXIwywV7pWaKtEN+iYxuiAayS1g5edJ5uZkr2dW+yyAMWjrYF1UMS8pWJdUuYgVZqQCBk62yO3pD+YcXHa8AAEAAElEQVRiDfspLcO/H8vQuM19ZfoqB+dSHJnJkOkZW3S+c0M9Lo+3ODKTZXzdlouJF47UuLLbwRR0GFEWPd5Pr3EHtdqALFTeNBGGsREC3rHUkLvp1aT4GPnnMUtPoBp9Bzbys3iVd/z/2PsPcMvOs7wb/626ez29nzMzZ3qVNNKoV8tyNwaDgQQMBAJ8lDjAH1IJIYFUPmr4pxCCnRiCwbYsV8nqXSPNjKa3MzNnTm+7l9XXd73vPnPKFEk2JDblmeudtdvZZe213/Xez3M/941T3g+JKGpGJcyauPk4fuzatHuyVqFnaY6e8iI9tSJZV1gtXOelPBfVahBTYWDnHgLdpFypcNSDE73d/MjCn/H9M1+SUs1CWewQu3iWA9SJo5sRCo7Okh+hEkZQnDo9zit46cvM95vMJRyqXp2uRhe3LuzioeA0d3EQc1mRUShx/mYuh2F18NBhuPONAnGrdVJ3dJ2nbr6FR+95L2M9NvmFPyfg/Mr7ds2tNDLvxTV301UK2DjrMTTvMlry6AkV2TAt2JWV0GEmMs9sdI5CbI7SyliQxuzXjVAh6mVI+3H2ugvcbs+ywfHod31OJ0c42DaAEnXw9SqWUqeOTSnwZPVMVGZLy3SVG0VOC+jQQzqN1rZDD+g0QnLS827tI1V0PYWmJgm8KG5No7HkUZ5pYpUUvKaB29DxmhqBq5JOddKRG6Qt1U97vI+M1o7SDPEqNq5v0/ArNLwKda9C06uS0DP0xDcQF6IXkirtMFY5zJnK61jLfXxrdgqqHpLJtrMlFdIMeliwRynaQyvJpLWPDeMLGMk59PQUZmYSNTOPG1VADzEUh+lSBwfHbyWspRnWCiTWCCAYhkV7+zgdnZdIp+f/0skRQtQlcDXURohqiZ+bQtHp5qulB3i6vBdLNJrJ76nJncnL3GIuEgk1ejMRdsfPkh37EkrgESTasN/zK7Dx/mXgFEdVI615/S8xLjZs/vWFab640LJfiIQBXVadILTAFMp3YgZSCXwFp2kQ+FqLni/N3US/s8OW8YvsvHCWnWNn2TxxCeOqBPNiJsfR0a0c3bRVCm1Mt3e1fNqkwJGg+f/lfqZvJpJenXctvcQHFp7hgcJrRIUQxnJcjPbxWMd9chxLjv7lMWpkorix0qfW6llrbVW/1a8mKlPrLq+pVH2zEQjT99Rt9LXdQ1vQx9LcPPOOx2JbF83Y+vaYVNPi/nN1dl5W0eqtM4pmqGy7vYc9Dw6Q7VoFFqHrs/S/TmOdLgipWcrvGeS/TRd49MjUCgh7SD3Ez+p/zi71ojw/lUgz2/MQs/3vY7bksnTmPEOHXpUMhivKiJeGhzm+aydeLIURJigFceajaXZt6eXuXhNvcVqKYAgxjMmFIlORHqaifUxFeymaV7VGhMI2R4jWVPlIX5GPds/SVzuGMnUIvPWATBzjlhOh7CsUchGKAxqWWE5f9f0HgUK5lKVWSuOXIghB38YiOKKMJUV3fHZm57gpP0Vu2fqlGCY5HQxyKlwewSDnwn5hTiDvl8l+RRj5WERDi1jYbIEyvwXSIq4tf3tbN4Z84pf+lnYoQ7ywMFTeuHEjf93i2wV8BWHAnX98p+zNGU4P05/qXwFOaTMtx9XASQwBvr7ZEIeMqGaePXoK88UK3fXW559RijxrnKS2Ine+/ofZCA0u+HnG/HaK4epElSTgQTS+gyZ71aeI6k8zbni8qe/juNLDaaNCo73AvDpFM7ixV1OP57HBcSUY6/Sgrqa5GI5gzL+L/mJL+MNVbS6ln+Ji5kkmOxycNfSIjkWTkYt9OG4/08kBZsxBGuFq3+CViAQuO2JF9rZ7DCkusWkHKjHqQRuemWfrnQP0jWYpzNSZPldiZqyMJ0pRa58jrtOzKUvvaJa+zVna+5Oo34Q3jPgumlWXymKT8kJzdbvQpLzYbDXXhyEdC4fZfP7PpEKgiPn2vZzb9F24sTQpfZGMMkVanyOjzZLWZpe3cxiqjVtXqS1EKC6kaMxG0errs9uiz8LZGGJvbtGwonmXTZfq5MuthZ4Qepnx+lnqeRdu115qdoLi5BL1oE5FaTKmlTmjFrFCjXgzTrIcI6jpNNUYTS1KzYzQ0KJ4YRxLjeK/TSPyjUKjiSm05sIaeuYIXscRArGoEgDQjjB4qZ+R6SxJ1ybmWxihu3IEi0mymsgw07eB6f5RJnuGmEuKPp/1x3hGU9lpqoy4TbLTl/FPvdkyib3qcZVInCPDG3nEep7/Z+JPSASt93ExuYtX+x9hKhlj0ZlhonCcCXeGpRi4xduxF961IneuZ94g0vFVVKPVK5OP5hmIDTA0N0RuyuZ+XuYmjrfsczUT5bYfh7t/ThpOV778ZQqf/jT2yeUGQQHMN4xwMrA40aNyaJfKpbb5lgH3ct+EGn8/D1W3s2d+itCd5nJklonILOPmHPPG0g0z12qgE3U6MJxOsDoJmh1UvQ4irsYntMf4Hu1pdCWQQjF/7t/N/+t9F5WgnTZfJaOoxHQNM6IRT0fI5nW62j168y5dmQboU8w1J7lYvMSFxctMNYsshh7Nt1gTastS8h1GQOcVULZ8PSUVId/qKFKXAUACQ89gmHlMswPTyBEEPoHjtIblEMyrUBILa49Qcwi0Jp5ZwdWr+KpNqDgEqovnmTTLA1jlIeyi8NUaIXCu9YXToiVibReJ5S8QbbtINHcJ7QZV6qtjutbNVy8+wIW5rQypJYa1IlHhBL8chtEkm50hIUznTVuqO1p+hIYfpebH5Gj6Uek5KIa9PNZeFibmnV6R7kD0D7m82HMrCb2dW+ohNy82eM6BJxSh1NmKjah8zLK4qXKWilZj1thAIRiS9yXUAvf0fJYR/XmUK5X9m38IHvl1MN7ehPcvEgfLdX7l/BSvi6rTW0TUsiTQ2nPuFHvPnmTrpbFWNWxNzOfyHNm8QwKtI5u3M93e+X+kBUARlTbZrycsIlYZL2LiusJ8EQv4FTaMtJ8QvoKKwAak/Ab3ll7hXYUXuKN8iEi4CtLHzW4ezxzga+kDnIkMyfkk4nnEHPEYoYAZwTFihKLSiL5i7C0ozMJoNxTJF3GsLfcTitcX9L+madI0IjRFfckOUSxfluDENNKFwpZQQ+qiSoNyYcwtXrdl4SKeZ06b53jsOJZSRgmbdDgx+q00YdjEVWrYVHFFZU1t4FDDDit4oSUFQnZ238V7Rt7DB4fvJblczSkUCtIO6cjhw5QqFYrxFFO5DiYzbUxnO3CNZTAQhGybdLj3rE370vJvSIENezrY9/Ag3RtawjLCHLjwp2dpvrkg789+eBNzG1L87lcO8+jJsuQQiXhIfYOf1T8rQZiMoTtbx/q2D9C0Ay59/hkan/oD4uMnV6qLZzdv5vS2rVIwbCXE9+pYMsmn2k00q0EyGqEznSFy5ChePWCqcwsTj3yMV4pwuXDt8S36E28ZiPODQxfZ2XyK6PhLxGtLRK/Thy7WTtVcBrdrE2H/LSj9B9D9JJOnjnPipcPMny/Iyr0INRqQHrHo6SmTD2yyDZu81aDNra/r7b8SgnR7MezmVDAkwdipcEgCtBkE6+j6v5+P7ErxG99/D9/q+LYAXz/8wz/MnXfeyY/8yI/w1y2+XcDX+eJ5vuML37FyXQCwD278IB/Y+AG6E6uVvm80BDWzWq3KzyfG4uKirBYuLMxiWU163HbudbcTF+IdBLyhX+CYNn4VjVBM/usXnpqi0NndjZHp5Pxcgcdn0yyuAWKiL+MRDN4t3EXsGofDV1jULjLfnKUW9SgnPUpJQX/xsSI+jhEQvA1myfo+A+URtkx+B+31DfK2hlHl9f6vMJ1/gVvnHe4/GbDxhCG4XuviZM8IT2w7wGTXBsKyy0UlReM6J/+MqdPtKbTVQ3p8lV40brqth30PD5Fqj7IwXpVAbOqsAGMlXHGiWRNGVKNnYwuI9W7O0jGYQlsGY74fUCtYVBYsCajWgiuxda8CdleHGdPJdMTIpKHzyGeJvPSF1sk6FqP9p36Kth/4u+vojd7lMzRef4P60fPUz87hFq9S3FI0aul+tCQkgwnsuIvbC05PSKU7SrEjQUWN0150uGv+El3LHiOLaopPmQ/wVeVeKl6GihWhvizv+42GoJGLU1w69EkEHqbwgpGZ0QqqVyHi1kk5dRJ+QwIpQWEIVJ8zgzWOb6jQjLb2WaKpsWssQ99UG00tTU2P04w3aKZnaSZLsnJcj3py613VIxcoMVRzK465GTsyih0dIZQSyashMvfZ2jRbzIDNmkJnucLBaIb+ypv8wvgf0u20KNlHo3H+Qy7JYdFgf4OIBTqdkU1Ulh7k8lRL2jhqwA/c2c5P3ruD7JpMrfitClEhfek0D/McG7ncuiPeBvf9I7j541JS2Dp6lOKn/5jyl78Mbut79k0D873v4VguxZ+mDjNrLhALDKpag4p+42xzTE+SFEd+s4Og0o5VzUvRi4qXXSfaIyTW/77+GD+ifYWY0sqqv8DNfJq/S8Mbos9S6GiGsmH/nUTDVGgkNLykjpo2iCQUPGeCyvRLpKcPYutLzLYHTLWrzGWUG1bVRUQUAcagS1bL/HWVs6vE/76pCH0NqzTQMi6WBsYjuLVrWSGK5hDNjUugJUY2cZ64viQXnrap0IxdKxYkWqobjSzlUheVSge+b0qDeGn9kJqnPTeBo4R8feo+np25gzw2G7QlhtQixpqevYjZIJeZI5Odw4zWW31cor9R9DVaOZZsMbIU5MhQcDKUHEHzfmc7KKU1GTXH2WOfYrAxRZe/RN4vCy9xSuatTHgfwApankfZyBjvav/3dPotIQJBX6uM7CTo3QW5DSj5DSjxDhQhj61orWqY2ApdUXl9dQjgfOUx8vrK48TfqtLb7mjN4VC1yRtli8PVpgQoWUMjo2t0eC47zh5hy5uP0nHiHOlxS77ndd9vVxfBvpuwtm6lONTHMWuKsekTFBZniNoKcUslUxOeVCqOarBktrGot7EotqaYg+IrZXozsNlROcme8jFJxxJ3eIqBF0uTt5q0V6u0F0qIfOSh9q1czPQyHIf9OY3RmE+kXsUtFHCXlnAXCwRVoXy4WsVSjYBkn0V6oEmi2xatNCthVzSqEzEqEzHsUstHy9E0qske6skRqumNlDIbsaOrwkWBIlQJi9iJJRxD0PnWnzcWkhnG891czneyGCTRZpsYs02CZcVYeWwoCvcqOu8NDHZKWYprF9oVo8Hv9f0Jz8Vel9d71E5+oetnuDO5Ae2Zn0Ozx1g0Evyh9z5sIlJ4TYiwCR9YJ3Ckx5mhtRgWzWaTEydOSNA1MbHqu2iaptQJaDM15g69xqVTx5jt6GO8fxMTQ1uY6uiTSYShBY8Dpy02z6x+1vhgkgPvHmLLvk757ktfGKP+yoy8Lz1whPTiP2fM7+J3vQ/zaHCn7K0S8VB6kp+1///sUoQEvMix5SkOf4T59H7mFmtUXniFjjcOk623QJOt65zeOMT5jZvw4wlC3bhhkiBbLNKuG2z63o/Ru3kzbW1tLNVdXhpb4AuHz3DocoWStT4hL3rSB1NTbMmf46bkCfYrF+hxQlIVi0RFVB+vOh9KVesYE6Us042M7B1z0yEduwrkRiuoV6lk62oa00+SqoUkSw7xcoNYvUrMrqGvobeuDdvVWRDPbbVx2elkzOvlbNjPkp7mFs3hn/7XX+RbHd8W4EvIt3/0ox+VAhi7du2SvNi18TM/8zP8VY1vF/DlBz6vzb7GF8a+wJOXn6S5XC4WajC39tzKhzZ+iAcHHyS+xsBXVkuazRVgdb0hgFcrrkyMLdl1LVS51dvEDumLA0WlzjPmUerxJcm390XG1Fo/6cYti80DA+x6+GGGzCL6G38AJz4LviNpiU8q+/nv3sO84W1ZMXMU/9+CxsPoxBPneDH7Kq8kj0oFputFJAjIBgER4fpliKZ2qAUKS9qaM0oII4U9HLj8ATJWS+WqES9i3TRG7+42NmRGiM+6HHr8MOmDB9k1dmY9bUTTMDZuZFJLcqymcDY3yJncABeyfXjy5L4+Mr4igdjO7hQP3z3I3Tf3EjM1Aj9gcbLG1JkS0+eKTJ8v44hekzWhRzTa+xI0Kg7Vgk14A+WhK5HMRUi3x0gLkNUek2DryuVIQiwuVk9i1unTzP6LX6G5rPZpbtxI5oMflMIUjVdfwT7Xop05qi6lb8uRJDPZEeYyI5TTbbhOGdcuUxX3mQkqYkTE4xL4V+0HQS/9DvUFfs74U3qVVl/AyWCIX/O+jxeCXctHVkDSaJDWXDKKqD44JEKXdDNKvKniWTO49jSmV5EgqlWVeme9JWKvNSMxjg7WGRuaxTVbx4/uiJP/fuzyfhpqmlxW58AWm23DZXylSs2tUXfrVJ3W5ZpVoVJdpGZXqSuObAa+9rU0PHMQN7JZDs8cbalghiFpv0afNc/mxiX+weVPsa3eynJeNqL8djYhqYGakNGuKnQsuXSWoKsU0t82wqY738vog99JW7pr5Xt8c6LEL3/hBEcmWqoqG9oT/PMPbOe+LS0BIRGu6/Lss8/y4gsvsImLPKI8T1u43H/bvhnnwV/mcvcWDp9/hWc++wdsGC9x65hLe3l1MXR0WOFrNym8Maq0lE+lAajQJdWww0G8YDsRuw+z3kGlZMjq1fUipan0+B7fG36Nj0Y+R1JtgbgZZwsHwx9mqf82gr4YjZ4oxZxByfeo1lysso1bcgirHnrVJdEISDUD0o3WMN9p23BgYasWil+ioU1REvTIZIli3qaasalFqtT9wjpxnasjbybpiSbpjph0GNChubRrwmS5ieq7qM04ejOD6kWl2asELIKW7eZolPppLA1gFboJg2sXSYLWa+bHMcRou4yWnUEXJt1yaRagex67zpfom2/N76W0zomtKaw1HoNrw7ZjzM9tZHZ2I5a1en6KJ4p0d58nkZvh+dnb+Pr4fdJ2o08tM6rP0a9VJLV75fHxoqQldnRckiqYNwovUClYeRaabcw32jmxuJWzpU3U3WsreNcLTfFojxXoiC3RESmwoZonN7MdxWslI0Y7vsA9kU8R9Tzp+TU2HOdyf+z/qJBU2FQIzsfRzpnEzgVkJgTYWj8Pe/kQZ3PAzGiez2//YSb676FDHB+mToeh02Ea8rJRmmXsjz9N4egil2K9nMkPMpNYNdVdZ4Hhlei05+lqztLlzNPmLL0trDU8X1Yn9FgUP5Vs2YY0G/jLPUNXIobD5uQio+lF+lOVlR5dEbW6yfxsiuJsiqAakd5vVqSLSmyQUmYTpcwGPGFgdWX/iF+LUJNRLuFEC9QTord49fsQvUsd8/NkCmWSdUuqpVSVKHNqjAUjJs8fFTOOE0mw2cxwi5Fh1EjhqVGawg8zECI9Qs1SpSupk3eEVUnruY/ET/NHXY9x4OZ7+Yk9P7G6vhH+Vf/jfdAsUmvbxR/Y76NYa1WHhfGzaIMRW6G+LQDX2bNnV7QHxNwqFK/37NkjgZcAYFeiODvNsace58QzX6dRLuHoJpO9wxT33s6lvo1USiEHzjTZNe6siKg0MjrRAx3cvldj4xefpj61Xd6e1P6MzPZZlPt/iQvGKL/71Hk+v4aOuFud4a7Fp4lUCnhXmUWLSaW7arFttkis2ZoLgliOyE3fibNtP0ulJcphjSW1SkGpURbS89f5iahqSDJZIx6fI5EokEgW8HSf85VhzhQ3caYwymyj64ZgbHvmLLeYp+ms1YjPCcsEV/aFXh12JI7VMULYvwtlcD9q762YsR4MI4uqXh8syomzOiu/y3DmKOHkYXlZqVxCucqW5srDnapBgZvo+Y3H+VbHtwX4+oM/+AN+/Md/nGg0KpH22gWguHzhQgvh/1WMbxfwtTbEYvHxS4/z6PlHeWP+jZXbI8KHw9jOZmczmWqGSrkiF2bXD8kXuO49bUGS+93tZMOWr9Z89yLj/RYT0wuyZL82coUCffNz5PdupPTwZlKXn2HruecZqK86wx83Tf44neSriQSOqhD6UdzKLsLSfhxrcOVxos50PwYP4JOOvUEh+jyadnxZwEKhQ8mTjHRLoYpDmTiPGxUer5+l7LsSlO22bUa9gEgQMq6rXNQjJAv3ctPkI8S81meZTV3g5aFHmUtdkk27vtHNSHKIB/wUAxcrtB8co+Po5LpsvGIYhL6P6PK5mO7l3PBOLu26g1NmngtL1zaxihPoxlycW0bb2DuQZc9AltHOlNzbS5O15cpYUW5tYbK4JjRdJd0eXQFUcisAlrjcHkU3rr8IW6uKVWq6FOoOS4tlZo6dZuq5l1mYnKVsxCWAugK05OVIEkv/5qTF4mpIVvHIhU1yYY02KnQq89ynvcR+7QTmMvHokprndbWHqtfylhPiHG7dwGt+Y7RCM54h37uBdEcX2a5O8r1dZLs70TMJvjj/BJ889UkKVuv4DNwczuL9uKWbxPKAhNkS6fj5h7eQS7yzzysMJSvHjjD3wlMsHHyB8vhpwlRAvU2j0a6TNhxymkN3TCWrQ8auEr9K8bGhxTnd924mU7vR3zhC8tlDtJV8xFpIzWTIfvhDZD/6USKbWo3fN/pOP3t4in/zldMsLi8wHtrWxT97/zaG2lYXvefGz/E/v/o/maheJG5cJDTnGTcUJoVM/nVUvPZcCHnkDdg7diUnC9V4nFc37OVzA7dxIdJznd7I1aTBcHuC/f1ZtnWn5PG9uSNG24VH4el/3epJlNWqYQ4FP8TR6d1Xq5VjRDS6RtJ0b8zQszFD90hGVm9FNP2AsudTdD05xheqnDs3wdJ0Eavi0TZXYdv4DFoYxY7kaMTaRIr/HX2vNdNjNlNkPrVAMb5AJTJP05jDVeYIuVbU5koICXRhNJ9y09LyIl/rI9nsJdHsIupeW8n09CaN5DyVdImZnMebXXkW4jkpFCCoW6EiRBci9NoF9ldPclPlFBvdObrwSCg+gwuH0QMHT4kwc6KHymm79XV0dPHlDwzxudwhab66qdHH916+l4brM6k7FPRWj5KMMCDiLKLbC5xlgNf0WyirGakqO6wuske7TEqsdNdULFXFQhfGxhGNXtNko36YuHZYVsY88sz79/C1xjBfKo8w76RWQNWe7GluyZyW6rIFLyWrZEU3TdERqmppSm56RTRnbUQCuNXWudnWMUSVVylzZ/432Wq0kkbTsQzH+rMkklU0TXSJCpbFFYN4hVAzCDWhjKbJfpmWrYpgYwhSU3CNQqcowppjCuZZlchZBWNcQblKFMZrC3FGA+zRFuhyskJkQXhltpZRhWYnJwt7uVAfYbrZSbkWIyz5LOmpa6TBRaQUiw6lTpsqRpV4skJbVwejfVuxwwX+7PInKdeWiNsaiVInfTPtdDeLGMuL0BufrdfsR9VlU6rA5vQCQ4kS2hp6sNj/Z+tbON+4lWK4h7aBIVLdcUplm8pETYolrNtHQYNAvcRiziI06ihXVZFj9TrdM7MMTFymc2ER7Trmu28XooITRJM4egKLGK6eoBZTGO+eZ7DjTnYa96Irrd90LRoQ7Gojt7eP9oE0hqnB9BH4ow+CXSYYvoeXhn6G514+iCN8Wn2fSMTE9vwV4C4UrwXgEgWCt1vL+Z7L2OuvcvTJrzF+9PDK7UF3H+5972W2YxTrzSqbzzSILYsU1SMKx0cU7meOd822xHbCTSbz3TMrZsXn5yq8lt7H2eSo7P0TMdy4xB2V19nFGJ1RIe1eozOj0Xbbh9Fu+jhLX36Vxd//T4TVVitB2NFG8MAOlC3DcMHHDHqw4hXmYxMs6SXZL1ttJqnXBEX6BlWyaFUCsUSiKLj4XG52cqYywvnKBhYs4eDdCj1w2VY7zc2VN0m5IlEfkjY99u7oYPtwgkT5dMvM+WqwJNpdevfBwH4YuA36b5Xmze8ohDLtwhmYO044cxz/3CGUwkk0Wp+/lvgoyV/4b3yr49sCfAmBC1Hd+qVf+iWpAvPXKb5dwJeoYAmQK7I3Ylwxg67rdS4nLzOeHKdurKrexN04Q7UhBmuDJL2381sLpKlqPmexX7+J9rHN8mTkGD7Pmae4tEwFuZKx65ydo39qiq7paV7eYvHcgZD3BVU+Uq3LqpQIQXr4SjLBn6STHI9ESPkabW4X7V6OTk9hKDhEj18FJ89h6w6edO5litW+K8EFfzcG74kabNvTTvzAMCe183zt0tck8Jxvij6VVggVxn1xhR3GApsiAV35+9im3Y9x9mncs19lzFc5XP8QhfL7WlrIoucmf5RXBh+jHFt9nish6Aq9Spb+okr3WIleYSK8GNK3BLE1VihKNIr2nd/D1MPfwcEZjxePz3Ou0kCIoF0dcVNjZ1+GfctgTIyeVITCTIPCTI1EJiJBltgqy1lFWbkUDb01h2LDkYDqylh7vVh3WarbFOuO9Hz5xk+DrX6BjF0n49TJmQptuSTtvR209XSS0HVigYJg8Jl2gFJr4C8VsItLNMoFrFoR/CpaWIOghudXMdQqt7VdZm9+Ri4CxOxzstzJiwtDVJez3HIfaoEU8DASLqbYJt2V64pnUruYYmE8KiSq5ON1TWHX7mH23X2AyMBG/mTxdf7o3J9SsFpqVqGbx164H7d8E3HTpC8bY7zQwFnmsguu+/t29fCxWwfZP5xbTRSJN9gsrhEymbzq8hRhdWad39qNwg/TuGEHTrCPqvddhLR+f0LSOWyWUFQLY6CN2I4R9M4kej4qh5o0V77760XFcvntr5/lf7x2lFCfw4gtsnPYIpkscLl6iYXmwg3/NhEEDDseabsdM7gDt9ZL2elgxsni1Is8PP4Kj1x6lazTmkM8ReXF3l18fcMBzg9mqRtjUsVUjczhpzJY+QekvUXW0PlwZ5a/1zzExpf+Dcp8q1+BVC/c/49gz/eJjAKO5TF/qSL7ImfFuFDGuYqSK76KfF+S7g1psh0eYTBLaeYCM2NnZXO577qYrse2mSX6iq3qjB0xOffww1x813tYSnWw0NRYWLJwyw6ZZkBKVM5kBS0k3fBlL8lbhtYkjJVxzSqW3qCpuDTDADcQfkZxacGQawqa0fofua94LCYmmU+OM5ccZz51iUpE9Me9zeu1bG0wFBNTiRBRokTVKBE1KoVksrULJJwyMXF8CoPZqRR6A0xPpZJOcK5LgBEw/SibC3vosruIKBqBWaWmFnA0WyqMCoaE6RukGgkuOllejKSZ01tzoRBauD2YZqNexRPN8WsaIPOBz4C3RK9Xp+CO8Bm28rIZo7ncj2ngMqzP8WBvji2Rbs6fGZNWM4auy3WB+H01LYtao0mjaVEPQiqKRgVtdYtORVHRQ507LZ2djqAJwtbYE9yV/u9EFJtyGOefuj/MK8oBOhSHNr9B2mtiqiqmYhBBl+Q1ww8wfG+9qbbvkitN0VYYp23pMrnidKtPak3YqTTVwTbqw1EaG0KCzjqGWZJG46bZMqNdbOYl8BNVO3GsNr0onzv/Pp6euItgDahM6XV6owt06mUp8571HRTXxA8MPMXDVYSnlitNfZeCOJe9DcwJn7rsq6QyT5JthqRrETqWehhcUonZzRtbTUjAtcjm9BKDVwGuRTvO2Uo7Z6t9FIPNqHo/qt6HonUu0zNXIwhtDPscqNM0ki7lXGpdsVEc7zktz6jRzeZcPwJyv/7y4zyazXBKy+A6Cmm7Li0Msk6NPe559qkzDCYTKHoe3wK/VGqNYpFQeJq93e8iliey7YPoAwckXTQMfNzxF6idfwI3dPCjEQJTxQ+qeFqAFYlR1+M0POFbJnrNdJrRCNF0lva+fjr6B8l0dpHp6CLd0Umms5tIInFDcbcrUZqb5dhTX+P400/IatiVGNy+jWy3xoXFHPX53UTs1u8pwKfXWGR/vBdVUblcO8WrC1+UHpIiYqk04cAWXjBGebWSXukJu62/xPf1fZ2R8AUcxcYxVFxTwdNVFBsST6okn9BQ7dbjrR0BlQ/7eH3XnpMUIeVfGcRf2iTViisNn4JrU1I0HOXGlHcRdihsQmJo5QIDxXOSgSJfT41wPLWdY5ldDA/2cGCkjQMb2tjfFyFTPA4Try2PV6G5PkkvIzsEA7cug7H90LVTnhveSQRNl9qTb+K8+jLxe3YRf9d9fKvj2wJ85fN5Dh48+LeCG/+H38ev/cfflou/lkWyAAnLja3iAUrIUmSJ8dQ4k4nJdW7tbVYbQ9Uh+uv9GMtS6dFohbbOMvneCEF7huJSjr2vbaG33uJ2X1Lned44ja24OKqNXp9h/9EphiZmpSzqGxvh+O0u7zZr3NtorixFSqrKGcOQ2dec79PhRjCtH0fxWw2ShjKGoZyDVDdK2wBazxBabw96Z5w3LYfPHZ3mi29OU3VWF2btehlyr2JlX0ERKw9xgjNSPDD4AI+MPMJtPbchTiXj4/+Zi5d+lzB00fU0m0f/Gd1tj1A9+3XOHvwTBsYPc7L8IU41H5TUMeGZlhs4j7W/wIV4kwuVi1wsXaQqMzzXj7aKAGEhfYssA7KQ/iWFjsEt5H/kh+G2+3n2a+M8+8YMk/jMagGzRiDB6NXRnoywdyDD5q4UDce/ClA5LNUd7Os0wL6TSLhNMnaNDC45UyUWgOcliboBG6ZfprM6Je+P+yG5lEl8y14afTsopPqolGvUSks0K0U8IdwR1AjlqMptC1q/fYisZWdU5UD7eUaSl+Rtfqgybg6ywEO0lw8QUxsosXGs5AXc/ByNRIN61MKOlwiXj2HfUSmczbB4IoddiuBqAaeGqpzaWKYpzFbF5Oy0YS/ej1fex9akww9s8fnwrnbibQOUzU4eOz7Pk68cxloap5clepVFtsbL7EnV6GERrToF7ls33svXCXW8sIMg7MQPO/DpkNf95euuFcWvzINbkGIMajyPEm9HTXSgLCuHvsUOQ8+1gFiY1ZlNFaXQxWVlmnFvgku1cS5WLsrK942iM9bJcGaEZNjB9EWbylICze6RlLNJcrg3kLJPRXS2tkV4aPEkew89Sfr8MogS80yqh/pdt/O5dzX54uLTeFeooOYAG9nBL86+wu3lo/Imy0zj3PGzpO/4SVGqvPF+DEIK03Vmx0pMnp1n6tRpakuXCfwZAm8Gwqu+izBkqGqzZXIO3RWeOlC97QClex8icAP0ho3RtDAdF9MTfjIqs7EYk9EU00aCOT1OQY3heia5WguQCVpjxgqIuUK8oCXD/U7DVR0svUbDqFCLFKlGCri6jac68j5Pc25wWTxGSHM7N1aH/MsOoVwa6uihhhZoEuTofoTAS0gjViFPQ2AQBiadbpRtVoK2wCSyvCishyYnvC7O+h14cqYVPaABe13YY4vEjHldM9RvNIRjU1kNaSoh6VAhG6hktGkeyvwm3eY5+ZjP+nfxy+7HqRK/fn9ooJBzA3YUJ9m+dI6RpfN0ly+iBev3tRXJUcxuppgdpZTbjBVdzfbLz6yEcu6ei4TMmT7T+DSWaZoj6XG+f9tnGMm0+ivnG22cXNpCyqzJ23KRlmT91eG6gqqfoGElWWxkma+maVSiUAkwyi7JqpiPbyw0daX6FdVcNqYKbM2WGIwuyOrnlViilzeN2zjs76dU7yPZMElepyor5nLfm8TRp3GjDdxEhDCy/nGKYxNzLDKGTmc+Q6ajAy2Z5WzB4pXJGq/Xs1RlpU+cSwMG1CIDaoVBrUhE8RkZGWH//v3SKkhbbgsQbIJmrUpjfo7GzDTWzAxnJo/w/PwzBI0aqQYM1OKMFOJEajaqZROLdZIa/SBm9+7Wc3gOztjXcc5/DdxrmSdro2HoFJIxlpJR6TVpmesrQWYsLgGZYFO0gFkn6c5uuRXXxf1XQlA7RTXs0GOfYer8+au+GRXVGEWP3oIq/FZFj5oONyd02f9+0ijx68N1wk7YkDjLDo6ylVOU6im+dOFhXp7Zv9JLubv9OB/c+NWV46v1Eq3vWPgIpr6iEX9elQlyUY1u3hbQeFeGaG6UdM9+TpU3MfN0g91Fj75oN/qa/mTRt39JXWBMm2VWLeIJRVhNw/M8mey9zoFC6LgUghjzsT5ZNb/Y0LHXqCeLfOGO3gwHNuQlGLtlKEemOdECYRPLgEwm5a56fkEh7bu5BchEZUxs46u9hdeLZtkmjGnEzW9OjOuvHfj6xCc+Ifu9/vE//sf8dYtvF/A1O7/Iz/z2n654eaxVsFobQnGugM7l+BzzqTGa8YkVdbIw0KG+Bb+6HafZLxfmA2GDR8Ik7/Y2YQqHdTxe1s9ySJtjQjGoWAbGkiIzxiJbGBg+RsYlbboYBFIyWwxF/BhDsX9yKGFUkEcgFLlbA10oLYmqQ1eC6GAKM2VKep2QYBcTk6YKJabWVpy05puXeP7yMc5MKVhVQUu8kqXzyEdn2YfGu/JbyY12ER1qURDkc6kKtnWZy+O/R7MpTtYhp+If5QveAaqBkFt1+f+pp3jfzDGOnexnvHnTMiBssjf3BDv3m6i73s9SxybGa+NcqlzkQvkCF8sXGSuNsWRd62N3JVKNkP5F6CsqjMT62LjtXiHFwcThKFbdo6CGLCVV7P4Yk6HHmbkq3jtc7ZmaSj5hkk8YZESPVK1IcmGa+NQ46XpJgqi0I7KOdfLJKG0jQyhCXWqpykS9jamu27HNTGtB65dJlY6TqJxG9ZawdRXL1KnFUjiyUfadNdcYepSYmSauJOWI6SnieoqY1to6hia9gqIjr2LlXiMSXGbLuEdmuWIRqEmK0/uoue9Ba98KynpgIpTjnPgMTnYCKzaNnZxgPnqOp8rwgvDVWhbFSNV1Nl3sgsZeBvuKvDv1Ktv8SeKWT8QJ5KL6G1kWhtF2AqNbgirXbsNt5PACAa5aI5DyHy3ak59SuVSbZkmt0TMUoT8xT+3s89QXTuMnHfQZhbx/M/nv/hjJhx8W8lV4RQu/YOEtj2qhyKXqOJfcy0wYs0yYLWXBaXMB/waGxqIy26/1kKeH2UKGhXobNbuDlN5LJpJiumTh+Nf/2zg2m5XLbFYn2RytMnrLg2w+8D66s/Fr+gWL/+uPKT/6BUJneUGoR9Duu5en7o/zVOUr/NjCLA81WosfW9H4b30f5ncGP05FT/FgxuRj7TEeSGpodh27VsSul7HrJQrTU5IGuzS3RHGpSrV6PSU/RWboVb2HtGWw7eKLZKqTeHqcam4Aa8sm4ukGUbWCS5RmkKXpZ2gGaZqB2GawQlFx/MaYGGIPCP0bz1BoGgo1Q8ERJu2+Q8x1EdaIddOkoYuu0zXLiVYGjLimkhBDFbRcRcgJSaApq56CDhcItcdA9nZ6gYeDjYuFrVityyLjjYMbeviKL4enecSNcQbiL0rw1lQ0xr0NFDyRNbdkFaUWdaWfo6+KXilfbsX4i4RRH4SFd1FsblxZGOaUBtv1aaLJE0wnJ5iJz8j3KExzdd/A8PXly8tj+bLhG3Jryn9RWeHTZb1KCDlFpOJpQ0vjK3EivkrUU8nVVDrKcWm2vT/5GW5O/LmkEC4FeX7b+nu8Gm6lLESBwpCeyix7Fs6ze3GMHUsXifnrE0SFSIrT7SNMtPdRbOvEiJt00qBLD2iLJ5mggzNukvOuwUSoUL7OpCGmx05fodtX6fZg7+BLDO/8PHqklQypTW+jMrkFzahixBcxkmXMpLhcRTXePmEVeApOzcCpGzStCIt+yBweswpknDZ+YGGIHv8IHfqFdYBrkk6eSTzEWeceIks9ZBvX/vZLCYVqGoJIHSOcwbAXUAJLmtiuRBiiNR30ehmtuiCV9N7J3CmmjXjgkOjuJ0hkqNZq1KRYhDAVCzFUIRiki+wfdq22YupsGT6vC0uRgdb+i1kat53MMTQbvwbMq5pOX3YTOxK3k9Fa/a5eYLNQPMbizDGUuoXhuxiug+lZGF6TqLV0TYWzYcYopFIsJU2W4po8971VqLqBbgpGQkjge3i2cw19urUTQiIZh1ibhZHowa3dhVXcSYeucGtCk2ugM3GFH781TlX4dshVjccWdZybjGlGnCoHz7Tx3KW+lX7a3e0n+ODGr6wHYWI/NX0Sp0OMJ9MoF1q/8UDVmR/dy6lNIywmVvt2BVW6O8gyGLQz4HeQkc0dq6F3x4luzTOrTnLwtSeYujyOH40TRGKEcQGsr38EuKLvP5JhzotyoW5IVWthjyJrpMtg7NaRPHuGs2zpy6CHDcKp1zGmDpKYfp307CHMZYGutTGbGuZcfjcncrt4M7OTk7FhakGI7fj4TR/D8vmOwTZ+5eYRvtXxbQG+BOXwk5/8pOTT7t69+xrBjd/4jd/gr2p8u4CvYrXKb/3H/7hyXXyb4mAXB30pjFEMYhTDGNUwuuKxJULRyxjpI+jZN9DNBdqtdnobvfTU+8h5Ke50t7IxaHFxJ5Qq/0WZ53CYpBSKLNj/uSbnq0M159HTb6Knj6JFVulTvqBFLT6EV90hOk7/0l5PeCM94o+zs9mB67UyVXG1wK3JP6Et+gaPBzfz5eA2Xg+3Sld2CQ71Jpq5gBKZRzHnwZglNOYI9VUDx6tD8Q00ux3V7kSxutHsbgyni1S8Gz0Zpen7WE5AxFBlNicR0UhGdDIxg3RUJ9WsYk5NoFwaI7wwhtmoYvpuawQeumESZLvxFZ3QqhCpT+NETEqZfuqxJEFQJ1ymA66KqrzNvomlSGTzJNvaSWfyJPQUUSeGWTUxyzoxNbkum4auYg4kKZh1Xrp4iHmlTKxtmq1bn0TXXaLRfkaG/x+6uz6MOvYM/mc/gWZNyj/1giTqd/x7guEP4840cadrONN13JkafrG1KG+oJZ7LfInDqRfIB3VpNdBj6wzXVQa9OhndQn8LZbsrIdo6pChnoOAGaSaCLcwEuwgZpjPsJCnSktf4oQkj6wZF5SLKQJO2vW0EmTKOsYDtzFAsjmE70/JzXi+i0T4GB34MM3c/49UpCeQFoL8C6ucaq5Tea/6WCIN+L/3NTtKNDjSnE8vuYNHJM47CONevqLb+tqUoOoLGQBiQTjgsuWP4aol7sjPc5z2DVl9+7baNsO8HID8CrtWqAIqMstfEr5QpPn2S4vNjeOXVrHys3Sa3qUF8oMljmQT/KZehqmp8uNbgh0olupeb2+uewUwzJcdsM82MlcQVSaCVEAubOAlDIxdRSYoqrW5KA92mn6VU6cTyErhGEsdMSuW6byQEU8BUfSLCn0nRMRXRG9vyLROXDS3A1mvMBwucUeocjsKsmaaqpbCUGLavE3qB9DZkuQodpgyCjEmyLSp7Ou/uz7E/k2B3Kk70m7CREGI7ogdUKKROnSlSmltf9ROtIbneGJ29Fvuq/4588SV5ezl1K0uL34//+nO4E6/wlVvgUw+o+JpCu5vje5Y+zEi9i7QfRVdVLNXGUhwKWoUxfYZxbQFbVOMEUFNET6tDKVSY8Dbg1UcJ7N7Vbyl6iQ3RMba7Cmm/1eclQtDopuPTkm0xF58juEHC4C8UoRBWMrmp4fIvi9P0eg7Nks4rS3nK8zGGZz2iV5mDV6MGp7vbONrVzaGuQcaTnVIIIhS+jIGJ5qmyp6q3sUS3NY9+TYO/cFuTXcHSgFcMMwjRfCHlLhZUaqu3S1Sr4yW0aKVl9RCquI08niWSXWvOw6qPonqrQ3PkVtVa1xH3X4+O6gYYXogmjsE194nUZ9PTaXrach+dmNgSKKpIOIiKiEg82RhuQyayLEHR00SF6urerpbIiy+q8mEMdVk1Mwx9XMXFE2p0YYAaLovCSBn51ffyzawQhICHr0HTFEmCQErLx5UIeS0r1QlVTUPVNQm45GUx1kg0Bg0Xr2QTCi6wXOMoqOmIPNe5pQW8IIqrJAjCiEwaC3P6XHWBtvIsCdvBWNOfZqViVLsSFNtNlhIGogXbayqSbXGVpPN1QxXJ6KRLNGtjpnz0qI4WMVH1BJ49QGN2H/HqMAcSBoaiUAwCPpkPObKjRC5xgQ2cl2OQcQw85uodfPHqSlh+mo8Mlhk6N4azZNGMmZQ1k6IwEi802P3mUToXWmsm2zQ5uWM744ODZB2F3sFN9N19C2fqBn92rEBxssYd6NyJzo4QJmqnOVN5jbLT+nsBens276Pvgx9C27iZYrkkPdEWpmeYGxcqyQVEh971QoiRFcI4BS8qt8UgLtekrhCZTxsE+QhhxiSSMEj4Idurl7ipeoK9jZNst04y4K+qUK58P2GCOXeUOXsrs+4WeXl+Ty//5Mdv5lsd3xbg6/7777/xCysKTz31FH9V49sFfI2Nv8Bjv/5vUYUy1vJoVbSWp9NwdVoVJ4AgFBxpVZrhhb5OIMaVSVr0VJn93JF+kLiWlJnYNxovc6x6TBpmRm1rhdojGpqVCBhCbkwsXmVtRMdR4/haXAITzw3luKKUKICEupx5F6pIasJYdqWXt0jgKJ8ndHDCihw+a7PfImOcQA9TCAcQ+RlDofgY4voBri+m79WQXiZX6mOqIBOKGtnyiywr8bVe/QpZc6WKL7eJMCQXiGmnNdkZ2NILK6qIrLpOgRSFME2F+DpguxoBijA1VcUJVXidLG/VVf+oq0N6QoWC6tM6vRdiUeaTEeYTwu9KX2kSlovHwJHDCATgcjDD1nVz+fpan6q3C0818XWTUGTuhe+JKU5aCkFZNF17iLWTEUtg5rpRfV2e9MV+FadtsWQWl4Xoh5kyMFMRzHQEI2UwOXmJmekZuXdFD1I+P4lhCHXB/SSTW1d6DKyTp6g+/RTRrEuiT5zQl0FLLAed22R1BasiNJAJmiX8Zkl+7rcL8XU6gYbja9iBji9EB5I+SkwcOynw86hODt3NoDkpuXha+VvhPaU38fUGNdHnozdw9QaqVsXQG+iGu1aP4IYhzFg1LUk83oaixZiujDFtO8y7Ks23OJG3RfOMJAcYSfSSVztR7Cz1ao6ZQoxzxZDzpRDLv/7fi/rLkARZ+jLYagGu7pa49ppHCuGGMnNKSFlxaChlblb/lH7lcMt+4O32r1D0mzcpnk9QnRRV7dZzaxGf+a0uf3CryeGcSUcpQV8hz9ZimlQ9g+sL+XkxT8RQlDiowjg3hqYKvyDxe/rG6SOa7hNqwqTawxX/RP+MFhBDJS2orkToJ0oGTQKstRW9y/gcw+f48rgkbb7/YpGMaLKHU4rr9IuRoSNlEnh+yxPMXzs86dc3e7HK3IU6c+MNKgvXHt/pdpV8r0auEzKdIaoatP7e9eg4/VW6Fj8lrcX9MM+i9wkKahrl4inOpxz+3U2vUDQFhS3KL0x/nAO13bLfpG5UKWsFiixSDBalUXQ1UKipBg3NZCLMc8zrZlFWDNfLPPSywMPOy+QrC1T1HG5bJ6Gxnm4oFCQFmGtqTZqqja1GcAQ/Qv72hRm0hY6ocjTwlSaO0pSAUPyNrQp6uydpUK0RIHGAfOKQgUXYMR6yezxg33iAZq//QdaicGpA4fiQwokhhYmO1rnrShiuQmcxQnchSlchSnvZlFWBv42/eSEMgE3PR3NamqeuruHo11o6iFD1QPYiC3V3MaMK9pDrmrhNX/6+3zZEJTeTIZ/cx23aDiKqSs0PecUpE934FLlNT6NHWxWgKikusIkxNnK6voWJ8z04c6tzc79aYo8+TYe6nnaueDoDk0vsPP4aqVqr16oZbePCyAeZ67xJZm/EoR7oCp6m4IYWQeMIRv2INLsWoSkGG1K72Zy+haSRlWflCyacjCocjassit4zTdh3iCe3iJWKpBplkmqDuFonpjXWVWPXFwmiFMIYhSDeAmV+nJRvMuDpDHoq/Z4qKc4RpUq3cZZu8zTdxhm6jHMYwrV93fMpzOU/SPfPfpJvdXxbgK+/zvHtAr5ee+kpnv+tv3gFUVU0dufuZUtmv7xedQu8Mv9FCk7Ln+Jv41sfIiO4mLVZyDhyu5hxcMz/Axnlv43/ayFAdJse0q2FjNohAyUDrZ6lZvVw2R/iXNDPubCPhqxZXRsmDpuUaTYrk4yqk4wyyyBdZP3b8cJWL4QInyanA5hVNTKoDBHQLqHz9StGKiXS+qdJaF+RSm4icVMJ9rAQbsMWlDYqJMIFkmEDN0xIOl/ZGqBY6KNeV3G0GK4haKZJbDMtTZ6/4dA8QsPBMxyC0KJtdpa2pTlMtyoX5S9s2MTh9gEKgUHRj+IrqhTk2YXGTnS53bhq+7oSNiEn1wAtMcrLUCumN4nrDeKG2DaJGw0ShkPCdEmaPjHVxgzqaHaZaNAgEtpEAxs1GWFWz3Kp1s/F8jDjlQGc4NrPnPRqdNlzdAkpcbtAd2AS1XpR9YFlwYOrvLu8BQJvYnlMQrieiikWfkPJ7WzP3k7KyGMoF8gZ/xZTnZILkqr/nVS8vyOVPQt6mX/d+185mWipDL/rdDeDY2K/XbsEsLWQ5lCe57if8bBrJVk1qi0yqi0w4yc56fe0+sIk2HfZrs+xVZuXfT03DFEtESbUrk0lEmU+3c7FVB/TegdeDdSiI6s6bSh0odKBQmfoSxGmdqdIrjxJdGmcSHmKRGkR8yrVXl9XSXTYpLsamB0en8t08zVS0qNPVFNE8i/V1Ek3TNKNiKQnX01lE75+s3mLubyNtexlEFNi7IhtZnRSRT91iabnYhm6HMFbVDUNNUJc+N/11TGGxwXak7dHC1sIFu9iQU+yEFFYiPoUNKiqqvSCi7oheavCDvs0W9yT9IaX11Wnyn4Xs+5WZp0tNILc8sEQoEVq6LECkdQsRnqaRhlKi204SorQbNG/roSquTIhllxWtxMVI9/S8CwdT1p86HjierO1dUITVzFxVQNNNYkGogZvghqhmjCYbE8ym4vjmBF84aOliMqkoJ4G6IKN4S9vxW2+L6mAMbuO713ECmYxXYWoo5FtJog6uvRIVN+hWqJMnKo6oUiuCc833aQ/2sugOowgtcpjOj5NpfclqWDaXBL0+yhOxcRrajIh/dYvIHwHA6KuR8pyyAVN8okGHYNpur77p4nf9bEVkGbVapQX5qjMz7W2C/NUihewvDFCfYpoW514h4W+7DWpVfoZeP2XiHhJKa3/Us2jFrpoybO4xjQLqslcIs1Ero0LXQOU01mEFr9+oYo63Vj5RvMRhzubi+woNIg4UZQgKhU/RaapY/4o/RefIuK0VFsryUHGNn6YYm4LgV/Ctw/h2ydWvdmUBHp0H5opxJOiZDWF5HL7hygsioKyE4YIJqt4z+J6cMNqZgPPqOPptZVtqF0/cWqF+jIYE6ytOFqYolNJsSkSZTQWJR03iJiQ1y+T90+QsY+Tqr+J2ZygtvtnSH7kV/lWx9+Cr78h4MtzXf7jP/ppKeNrGCGLQTsnrTbKnqBJhSQVRyordSo14lc1ckc0j3SsRndcYVv9vcSFU66wykg/z5nc5xlzfc7bGoU12XVhQjqq+2xTfDq9LKo3iBq2oQYp3GaUZtXEFRQLXDzdI1A9etwcKUlXhAvqHJe0luO7ozpSDKQQKVAz13rIKPTo3YwmRtmR30E+mScWj0uJ2LdTILoSTcfnmQuLPHlugWZllYQlpuHtaOxFZ7Qnhd6vMK98lrrdapRNJrbQ1/e9mOb6RmvX8bn45iLjxxYJ/NZPpitbYFT9KolgevWBZgr6b4a+/dC5VZzh1j2P6O2wq1VKh96gdOh1muVyK8MmqBSJHE4ijpUwpAlwUyiS1SyMukuksZ5eciUsQxgAezQjDo5hY6dMnGQndpiUpBhRr9NNlVxPgkx3HEUXKlCBrBaKIqS47DY93LqL23TxGh6eOAnLul0ot63h4VtlPNeWtJrAMCGbJRAnWj+UMs6t5wzwBMfet2UfURyLhGJJSkJizWVTVATfJsTMJBr5NcWXi5JZXaOmqtQVlbqg0tr92K6gBL71MSHrmkoofyOSIiMWYYq/PIKVIe+/zlh7u+DdN9w4ZSeF7UVwhSCBUPmOuwwnbfpNh4hroblVVKcGYlxVoct7Pnk3SuB0MGEMc8oc4bLVy3StG8u/PsgS6nEj6jwbtTk2avNSDnxAXaRdqch+Ts/tQfcOEPX3oi2LDogTX0W9xJx6gpOTZ2k2PY5ndvBK/jZsTAm7DmgL3GfOkldM4n6GqJ9CdaOoXkzKEXthmYBzUkWs1TOVk8NZVgf9RkIJREXGomiGVESbqRpiiXlAUamiydFQQhoqUlzBU0ALfD409jx/5/Tjsl9HqC1+dtO9fHrLQ/h6hM0SaGnLgEuj4zq9XGXNZSpuMR+rs2DWKVInqNnojiLlzI1QxRTVyUAllo6TakuRakuSzMUx4gau60lrDjEcx2ltbZvS0gLVQoFmvd5aFiutjLhqhNLAVnjeOb4hQZjrG8u0ymuPVfE9CH0YXdDohKeXrFiLng1Pshhac16rn1BZsxUC7EYoqkzrP7OOyz3K4+xSWlLYs2EvX1I+SEnLEnoW5bBKLdKaw6Iu5IIkYTQqf2/1AI67Gc44vTSIrACrjfo0O7RFEmuqoeK4O++3c9zvlgIdrdf22arNcVN0nnREFM2LkhXghyl8r9XrcyWEzYGjG9iaga+qksIdVRUpkIGYU1wfo1Sme3aG3vlZ+hZmSTTXUy89TaPQ0clSZxeLHV0U011krAzvjf4Jg5E3Jb31UHUHr9UHCKwmqnttH2GgG/jRBEE0ji8o2YZBIMCn7nApNcHF7DiWvvx3IXTX29lQHKSnJo42dbmfRbx/IaIgqOhiztLlVvTGiGKcJRgeZpMNQ6/S33mqtYj1TU5N3cbFhZ0rVLJ0UGGfe4qbvJOMhBMrEvYi5t2NnLfuYMy6nYrfQ6BYuLo4f5TxjCq+1mwBAEGe0EJZub/6cFOtBnqtjNYokesp0LmnQCJfkXPi24XjRHDshBQHse0ErhvFbmSx6nlsK4nnRmUlxddEpTKQn11WGYV9hqjOqiqu2Ndaa78QOqh+ESVszY/CXkEnRdT10a80UInziiIqKy1migBjYk4QtEjRt6UEoVRavlGIfvVdbGIHG6TupYgJ5jgUnqYQFOTzi+fC91A9B9O3MANLSqkrgnbpBThuS87srUL4gUbVUM4XQUeSoMtEyXtE8lXSqWmSyrUtCMLDa7HRTrGcJZxp54GF7ySj5rEDn5frAWW/pWwcuGN41uuEfmudIfZpI5akmM4zme5lTB2hYIs1aOs9xuMeW5lG6ckSDrXxkenHuefyc0TqLtWzKu45QR1vHVfzuXbOdMSoxloAVdHbMeL7UdTN1yhffiMhD8NlVlMgLR5aR/iVREcgaM1rwJjYCpB2vd3sh4qkKQow5vpxok6SdidFv7sq/hNTS2y+rYu7fvBO/saCr3/4D/8hv/qrv0oikZCX3yr+tufrLx7Cd+LSyRNUJicpn5/Gmi5TtjxmoyZFIQ+8BnAJHYf5MMmEn8UN8mwPk3wkFmfEEhOYRFZotwTUS1+nfOSLUlbZz8Nke8jLcY3XHJ3qmgxRjxFwa9zj5oRHes3vVHVj6FYbhpVHt/IYVtvKtuaYvBy9xHOpI5yKjklVHhFiM9wcYEttEyO1QbRAx1Lc1sCVj9N1Xe5rsd/XjrW3CWNE4QX02+Nz/N7leZmdiVg+tzdUZsZKUl78Sghh6IeFbL0RYXBDlanUf6eeexNNj7Fp4y/S1/d9Usp2bdSKFq8+dpHTLwvlNWFWqLBjT8D+/JeJXvgsyhopVU9kNRO7mAhGuFhJUFkqUissogq1LrOTnNlJNtIltxmjA21Zpvm633PgUXTmKNgzLDbG5bYeXk/dTkXROqSyUqZzmJ3338zeh3ZhRFo9S4Hl4UxUccYr2OMVnMtVQvvaTLVQmYwMpTGHUphDafT2Vl9d9fEnWPi3v4pSn8GI+yRvHiV9124UdxFn8TBhaYqILayy335acZUYtYpK1Y7R7BzF3nyAitlF0eikoHcy7hk8M/85FtWvE6otAL3JcfiRYp26+xCfNz7EoqNjex6W8FuT2TgVN9Bww2+96pGkhuoQFZLAwoTXs2RPYVVJUr+Bz4oAml3xOXoTc6RDG7MZIS2UKXVBdXRlL4imemiaJ6F1vzXMYHUbOVt4b7XCNsosdLzJYvvJlqJeJcB3IoR+EsIsVjPNRGEj9Xo78VAhHkJsDd3ynYaKJ6schpBCVwU9RHgKKSv9U8L/qNFchMnXyF96johTlqdKV9V5qW8Pnxu5gzO5watoPSGm4cvPubV0mR859CV6awU8TWdpaCuN299PXm+jo66TriloV/kwCcDZSPhUEjbFSJMls0YNaxU0LW9v7HP41ye2cY4P8gQxbAm2v8gDHGPbdR8rlAtPel2cWaNcmMCW1azN2gLGW1BQxXnlUiCoiT2y11iE6P/ZoBbYpc+QvYoi9FYRqzfomp+jc36ezrl5Eo31YEuAtMX2duY7O+UotOVlz9KVUFwHrVFFrzVJNhbl3HB1+GYUP57CjyflVtAk3yoENVMIiFxIX2B+jQVJzIsxXB2WI+7fWMHz6kimFtm06TVSqZZQk1tMETnTxcbmHAPqpXWAa87ZxJh1B+ftOyiSwjWF3UEF1ygTaGvk/99JCOGMegVzcQa92Up2ClDhZtsIezJE0i7RSJ2IGNHWNrq8vVHv6trwBbXbbgGzKwDNXt5adgLHjhNex8/t/0bEQ5N93ga2+D3LgBkuq4sc1i+yoN7Yw0+GAGieg+rY8vhSHQtVbm1UVxi3vzXNUFEDzJSLnghQIxqhJtIZaRphHsdI4ElRKZVIaPCIs5eOMI0TerzYLFBxlquaMhWziOueIAzmpGCZIhMvEUT/R0XN8Fqyi1PR1AqtdtiFOy2TXtFIt/JRArT6UYbHv8jggmgHaFUNp9u6GRv5MG5i57oEt/BcjCTEWgzmbIcF26WpiGRCSCZucFdHks2qhjHXxPCCVr+sSEIZKpENWWLb8kS35fGTBk+cnON/v3aZV88vSYEiUXPOmBoPb+nioS2d9KcMFpYWWFxcYKm4wHxxnnKtQBhcXyCoGphYShJTS9OmZbl95yYe/NBO/saCL9Hn9bnPfY5sNvu3PV//N97H+CwXfvtFChGPCW2JSXUJdw3tQw9VesM8WqSTiVgnr7shx0oN2kKFf0yMW5Z7K45GYTG/yM1f/W+oy+bXesynY3eFzCZQ9nwU56Yf4Dlnki+MPcYLs4dxl38U4ge8RTO4NemyM9WQP761UffhaFPjcEPnnH1FCL8VG5UYB/webrc20dnsWwZtba3+mzUZXaG2KJrCBRATgExqgUlwJrTAlu9TXM52t/OVjaMUJcUC9ike/yBrsi2fJZVKcb7k8/kjMzx2ZJqKvfqj3orKezC5L2KjdL9ApfdFYgN9bNv668TjQytVxtrSIpXFBWbGFjh70KGyeKUC4IL7Bp3h42xJzUiPlbi++vxOEKXo7Mby70FV70RZNolcG0Ho4pYuExTHoXS5JYahJtBSvahipHtR46sVOdtvShB2ZSzaMzjBtbLophmjLdtPXu8m67fTZvZIFcIroZgq5mAaczBFZCCGma2jOrPX8bRaHvbbnKxkpUyFVA9afhgy/WvGgNwufearzP/Of5GPbf/Jn6D9p396ZdJ//fIkv/7i73DOWgVd7WGC71ajfGx+jFyt9fqurnBxMM5kb1T2EK7flwpeYOLYacqLCo1ajER+Kz1b7iakHXs2SmMM5sdnuNy4xKK7iKtoslqhGEmi0XZKzRKWcGdRdOKRLqKGoO+0BPUdXSgretiKTyMIqLsC9LVye+8kRAWuPVqgO7ZAjwBbSQG4pulNTRMx7WvaDMQsHbgxfDuFWhglMnMr6tIWWVFxAuHBEtDQq7KnxvFMAich0t/v6L2s7DOEnDfL1adQbsX1jLrAA8bLbNPOEVPL0uw2rpXRlQZvhjt5zP0YHoN0CAolITlFOJiJZwvwFF90IOF7TeKTJ8iOHSJWWl3AVnLtXNg0yoXhPhxdiJ+Ibi+hgBaSJUlXkKUzyNAVZsgsL+zXhpgP5tUSc2pZjkVRCfwGxR1EUkeIQRm6WNBohL6C8MQWGiPCR070AYrbxRDVjFQ2Tq4jSb4rRXtfhkQqJp9DVMSLsw0WJ6ssTlSlV1+woi4ZYqZmSfVMkGgX9DMhLBNSd2PM1LuYbfQwb40wXc3SlA0UrRDLsg2o7NB0tqHR7sv0yvrvLaEiEt/1qENBrVOxa9TrdTkyVPgIX2GIVtb8CNv4Mg/gYFJTyxyJTzPbvJlafeuKeuwGZYYPJI5w2x3vIdM1TDQavYZxUKvVePbZZ1laWlZ6lXS5gOkgzTG/h9lg9ZxoZBXaumw2x6bY3rWFrflehiMapdlZJo8dwztyhPaZWQm4UrW1DAjxljSULVtQd++WQ9m2lYKncGa2wqnpChfGpwjnJ2i35ulozkta59WRizgMxxfoj1eo6fv47PCP8mS3Jumnsm9sfpbdF86y++I5SWW0DQ1b0yQj4QrVLBHPkLj1Ps6O5nnGeZHjzkFslufaUCHX6Gag0E93UYgQ2UQcYW/g3MD/TyFtRLhp8AybY+fIVsXjVmPOGeW8dTtj7i6aZoRoyiKW84hnIkQSSTTTpN6oUSiVmC9VqK85j4lIC8aEvsTB2AKzkcpKglN81j0XQx5+Q1SVNaZTZquPuNWdzERyhJnMFkaNGiO1MpnFpkivSCVRP2Ggdok53UIxyhjxEmashBEvyq3oT1oLGq8XggbrNePUnQRVJ0pVVC6MHja130o6OUw82Us0vn4tZVkWY2NjnD5zhnqtKk2ABU0yna8QiyyirlHhFb+0BaWH8+FGLrKRSQbk3H3ls0dcm5GyzQeXktxeiUvStYhjSYcvdNS4FLXRfEGT9GV1TdAlxVZUtXTfIxLYtJtztMVnycfnSCcWSMTLBI6CXTUkhdGpGNhVk2Yxhl0x8UUZ/21W2IGmS0poYERQjBjb1VH69V6iepqXjSkKVpJoUxCqW/OCqHA24pNYsTlRClyzg0UfVYyjXg9jQct7TkQ/NW4K5+mtXkArT8gkoIiY7bJ1oUrPUqsqJyqT81u2Mb3xbjQzRURRiSVjpDe3k9rUQTQeY67u88SZAl88tUTZEswYFU1VeWRbJz840M6msod1piiVe9eG0ZOQIEwoKC4kNf7s0BSfeX2SqdKqJcCO3jTfs3+AD+3pk8BOvqcgoFQqMTs7y9j4JOfHpygtzaNcx0qgkejh3/3C3+dvNO3wX/7Lf8nP//zPE4+/82zQX7X4dgFf87Pz/L5wOV8ze8e1KJu6htkyuplNu7dg5tfLRS8dnKH22AU0J5AnoE9a89x09D+zebqlNCeoM23bamh7M1g3/TBd9/yIpMSJEIfLhSMLPPPYMd7wX+Rsx0HmUi2vpis+W/fou7lpOsd8dI5Xkpc47i/ir5mBBk2ffTGfvXGf3LI0+PXMAHU7j95cUzlrtsnb5NbKo/mrKoezUYX/sDXCM12tH21XM+BnTtc5sNBYBWq4OIpLGFXx4wYn1AivNnXeFJWB5efRwpB9vsWt9hIj/jma+oSkN1m1YJ2R4pUQvRp67B5UfbkvIrTo1BfYmDDp1GdIq68S015GU1ou7CKCMIml3onb8S4YuQ+jP4vZm0TLRfGXFil8+tOUPv3H+OWyXHzE9uwhcfvtJG4/QGTLDgpnK1x+aZr6pQoJsc81RcpXi++m4VVYsqdXQZkzh79M61gbiUiUrnySng6NnoxFtz5PpD4JtVnZl/F2EcZyOJqBNVHBX9Bw68JnPsnRoV3Y22/lQ9/3oyTTmWv/LgyZ/63fZOHT/xk/H5L4O+/FuGc7jfokZ6fP8PjSBV61mtjLh0WfEfDutMvOmKAItk6ibQWX0Yt1Eo3Wt2ZHY8xvPYC7/T5iqUGpJChGJNKFoui89vnP8MKftBpxN+0/wHt/6ucxoi2alF91qL85x8zLZzh97gXGq8dxg9UTh6abbNz/LjLZEaxiHbvSxHFFKsCXi3yxFeBCgAxLDTlNlON+ikl3NWOZNKrc1HmUHe2nSOhN6fvTlViQNDfPSuPbSXw7jWel8OwUdjMrVdFCJ07oJOR94jFChOUbDZGhbCoBTTXAVlrDUX1cVZi6tuTKPcWV3lLCP00X9GV8TAGnwhk+oj3FXvWsfC431Hgp3MeLyn5cRUh6fBPCBGFIvlBg07nzDF6+jHbFfN0wWBjehrPhAPnEiARcgjJ0dSypDaaMOpfNBuWURyKr05OMYwrwZBiy8r12+3a3iaGuldVeE74bMH+5KivmwgBaGEFbtWt/S9muOImsydzFCp5AwmsikTHp25Jrjc050u0tIOO6ZQrFFyksPc9S4Tlse7a1ezwDe+4WlMU7yJU30mPFrulZmyTgEB6naDKhlDG1Cu1qjbzSQL9qASw+WyaV4B5eY2/5axIM1GM9/Pvej/G/J/I0K6senFp8jAdiX+VfvPEmfvxhOn7u54nt2LFyvxMEnKxZvHD0GPPPPimz/5Zu8NTWm5nKdjCyOM222XH6SossBAmOe92My36k1vvfr5zmJ4NHuclI0GwOUD81hbOc6Fs5PBSFQi7HfFersmXs3s32m25ix44dMnlWmJ5k6tQJJk4eY/L0CZkMW/f3KMxH2pmK9DId7ZEjVDX+Y+SzfEB9VD6m5PXwovKLWA/ez6vdKl8vVJm2XXlsjkxe5I4jr7Bt4jxGo0wjHqcWjxG4jlyArxwbash4V4Mzg1Xm2lapjMmGxuaJFKMTSWJOC8xGExnM5LA8Nw7plxjWDtNjnl73vkspnfmOCAv5DMnOn2brzh8mEl+lIBeLRc6dOyfHxYsXpf/SyvemaQwPDzM6OipHW1srQVdxKvzO67/J/z73Z2SrAT/2ZY2bL7SSWadzA/zGTR/DE+yV0kEGm1PydnF8bE3Pc1v7BDnNonwpRuFsQoKKKxHvsYlvAWWgHysyQkPro662Udd1LCGQotWxRReltoQeW0KPFzDiBdR3Uj1zRIKpHbwO1LATVWmXBFs/aGDb4yjGkgR6WrSMqlu4bgLT3MuGkffT1/egbBew/IBF12PBEcNlUW49FlxXbq0gYKAecPeJKtvO16RKpYhCX4y5gXGC+T8l3pglatTQ83GCwWEco0qzflp6hV4dht5JxNhO6GygVuimMJGkfqmKX66huB5+6LfsHlppYhBKw34V0eSohG9tNSCorKoew9YjqGoePexGVXIogkKsiRaFeZrx6Wt6qCpBhKN+D2N++8o8Pdi4zG2l1+nyFnGynbj5TkLdIL+0xJ4jb65XRty+nfOjm9ZVlW8UKyJl8gASjAeVqKYTCXUiwkbCaxmd6wKoiQSWpqIkdZSMybyucrbkc74gRNZUSWUWKpZ7BvLcv72HfUPtmKaxkiATWzEEc+H0xUkOnb7I5SnR21igf8MWPvF3PsDfaPAlJoOZmRk6O1u+C38d49sFfInMwG/8h/8oge6WbVulcWFvb+91FxRB06P06HkaR1o/MqPLIBj7A0pPvrAsYBWS2dDgzPYh/sh4hGeD3ZKLnosb3LGxnR2pONqxMt5Ei+4WTRjc/J4hkvtcvjz+Jb5w/lHmmqtZ7bWxJbdFGh+/e+jddEVjWNYMlj2NLbbWNJYttjPY1jS2I57j7Q9LLUyg+x3Mh20c0tuYV9ophu3sm83y4QspUg3hLdZawLmBTd2rSHDS8Kqtrd+6vhjYvBnt5lRylPnI6jEb8S0218+ztXZGNshLrx/dIJXpoCs/THuyn7TRRtSNM7ekcrouqiCtv02psD2m0WUoGG0RYrmzxILnMApPoVhrFgzRDGx9P2z/MGy4D/QWBSZoNLDPncPcuBEtKaThQy4dXeTo0xNMnWmBQEHzGuqKsbE3Qdzy8GZLaOEiurKAxgKasiCWIlTcAgtWnXkrZM6KsWgnrrNwDmkzG3THqnTHm/R0xGnv6ULLtSpVV4aXzDNZeYrx2f+F55URSszuE1vp+eokEad1Ikl+6P1k/8HfxY3W5ffZFN+vNSVHfeYEjlZep9xe8+GpqsELNR1nOYvQbwQ8Ele5KWwjEnQR0XuIxHoxU70Y2T7UZDuxM18n/cbvogtqm6iwZjYzvvlHWExtX0czE6Ny8Rylw6+IHwxKMgObd8n+IXHflYWM6IeITomkxLU0KS+exM114qVy11W/EoerONbUwEAJDCw/zoKXo+qnMEONWCDofQpZxZVb4W+kfRNUP6Gc6WuiAix6pQICtQWcBIdeUoxVWw5VtVHECfltstHXizRV7uMV9nJC0kdFVvwwO3iWA1RYrZheCUExE3RKIYmsCLXMSFpaHVw5UeqiAb7ootSEfqdGJBqlbaiTTMkjcfBVlOPPE9ZXfxNaxzaMkfvQundJ1dTrGfW6CszEFKZjKsWkTkdngl0DOQZ6UzKJocb1d9wf+k5DnCrL8811YExUutZGLGVIkNUCW1kJzK73PmzblueOcqFE/WIRJueILLokKnnUqyizTnSJ6egcR32f5+0oJ9y0zHBfHSI5MZjW2N4Vk+qKt27sZNdQJ7pQbBNm80c/w+uf/ySfaj7IkXB0+a8C3q29QXf263yuoyTBz6YZ+Id/7tFehcoDD/Lc9/wAzyVzHK/U2Hv+OHumxuRfzqbzfH3bLXTkstyUTnBTOi63vU6D42++yZEjR5hfqOPO2XTMzUuvrQ2VqwScBEV18yiJ2+8kftutqDt2cGZykuPHj3PxwgUUq4HWqEkqoWk3CJ31PVtCdrx74yj923bQv30XvZu3YWHw+niB1y4WePVigWNTZdmPeqdygt8yfp92tSBVfw/WvodDyofI9y3SrJ5m6dIY1K+tnK0NQX9tRuI09RhNLUpTiWJnfKzBOS4lztNcltvW0Nit7ueOhZ3snL7IRvMleswz656roO+g3vcezJs+gjFgc37sX1KtHpP3xePbSCZ+ipkZlbNnz7K4uB5kijWHAFqbN2+WpsUimXBNWBXm3/waT3zqMTY/d5SkY+OqGp/a+m4+u+kuIulLBKkj6KkTdFRD9l/ooGt29djbsrmHAzuz5PUSxTdepXKwhjUhXqd1PJtpl9xonexwU/Y5Xi/m3EH+S3Q/z2bnySg6nZhsdroYCixiySJKm2i6LKFGl9DW9X1fP4QYidvM4TZaXotOM4PTTGNbKSJmN13d2+gbHCCRjRJPmytDN68PIoSnYvnrF2geXhCKHa3dljzDwqYv0Og4cQ2RIXCTOOURGktDNBaGaBaG8a0s32goorLm1dGcIqq3iGo0IeYiNPA96tiNAlZNyAC9zfytJFC1NBnDojc2Tlu0RNzwsAODsXKGY40+DmZv5nRyC+FyC0W/vsRgokhME1YMllSxjjg2w9NT7Dl1hnSttb6rx2Mc37qFy4MDMoEhJhj5HGLcIGH1fzMUobitCN/FJhOJCfa07eMXvv/n/maDL7HwF2XCvwVf/3ei2WwSi72115U1VqL4p2fxy7acUHT3RcqPf5Jg2Qcl2e/T+XcfwfzAJzjvtvPC+UVePL/IKxcK1K6iNWQDhZt7Mnzg7iFuG01xtPgKXznzJZ6beR73OibPYvF0a/etfGjTh3hw8EHiwsH8LSIIXGx7TgIysXhfBWfT2Mtbz1ulvgUCANQNXGFEWdOXt63h1aPytqu8Na8b4n3Won2cSW7leHyAsraaeewP4ZHQ4N2KSc8NGlG9MOSSH3K24UtFIBFdXTFuvaePrpEMakJHjaqoiwdRTj9KePILKPVVsOrpCeayNzMW3c1Zv49qw5bg2rXFcEkEDbJUySplMkoFIXIvRmp5m1Su1wN2bTT8HHPNHmaaOeasKPNWQO06vRGi382M5VASaZxUDDcmpNqFWP9yn55oKA90+R6FLLkmEUjwTmxQZIgs6enkRc4mxvGFRYKgCNlZdpV20tvsaTXqC/raW0xRQojidg5xJ68jBKxFnGWEr3MX87Svf7B4n2uy16EupPvVZU6faN5ew93XImhCrc5TpLS+GgpQJWSoTcHOFxBCgixRoRWebXL7TVSCBHFDACg5NIdQEdvl67Ia5azeLwDWNwGmRMh3Jiqavt/aB8tN663PHcj7oorN7dGT7DdPYS5Tl18MdvFEeCehlIS//nP3MMsjPMcQrex5lThPcSdH2C6TNwJktgvVKkEflCNLbFmBTL6tMMCfP4lz7nH8xdWKQC1hcnxHJwujm8kafQw4PQy4PaTd+DXUu6vD18EXpqIJlTClQVpkWg05tGwEPbYGHF41RPLwnQI3UQmbvVCmXrbp3pAh35uQx6ug5Ylzg6DMSJC1PCqlMlrBp81K0Bvk5L4QgHRtNBSLxcg0jdxpwt7XUfMX1i0CVTWDGrmLpfBeLtc3cXza4chEicWac125+/72kIXmDIVyjNBPr6hkflR7jh/VvsSwOoc3eCd/tO8H+f2Tv4XtVYk5Oj/35za7L4V4qsbjB+5memSA1PJEqm/fzW333c/NuRR5YZR7Zb/X6jTfeJ36q69Rf/UV7JOn1olsiLiY7uZo+ybG27u4vfsk35V4hfiORwj2fZwFepg4dZxJMU4ex26sn9MEOBSiGJn+QTbffCv773+IVObaCvvaqFkurxw9z5tHjlM4f4zvCh9lX6LF1phxtvL18s9SbNbxms8TUiLe3Q8Dw0y196Lm2mi6JmfnPU5MikpL69gQnovv29XDd97cz45cnJmxCpfOzfHUzBNcUr/CPuUsD9cb7LHXfyf19D7CbR8ifvtHUbP96+6rVsscPPhpTp06TKHQju+v/kbEaw4ODq5Ut8Ta6rrHaLNI+dhX+NprJ3jygsFth45z+6xQsYNz2T5+6467mRy8jJE6wXdte4R7++/luanneGL8Ccp2mbayye7zGYbmVs/RnVuiZHeeJN5uM6h/jNRzUcqf+zyBNEwWLhERsneOkrutE9OsQm0eanOcbS7yK9kYR6Mtav6mRpy7xr6PaG1X6zPhsyHyKpvavozec4HFbIxCNIsXiONUb4EhcV7RfFSziWoKauPbszIEg0B6qgmAVs9LoBa67ehaD6bejW6KY/oMinkWLX4eM3uZaBAhf/G9ZKbukXO9iEbmPNNtB5n3QqziMFZhGLcuzilrbRSEsImYmz1C1UWPCqqeSTobJ9uepq0rSyoTl4lq0TsltnqzjPPmGzQPHqTx6qs4l1aZQ60dqqJ3deIsFQm2vhe7Zys1t0ylfoo5tUCl8fYgdW1E9YB0POCc2c/z2l5KelYa3h8wTnFX/jSTnQM8l7uZc4lhDNviQ889wfc9/hi5WkvqvpyKcmkgy0I0Ki1bWp/7iqqG2jLkltvWdSFgI/ooA2ldYxDq4rKBagqfM5MEMRJ+hLg4f4ZqyzBe0PuFNbziUJdauiHNK0JZQoQIsb4Qoletz2SplvQQnExOshRtUZ+3ulv5zN/7DPxNB19zc3N0dHTw1zW+ncDXW4UwAi0/MU7tOSFTLL7wRazX/jPOzLi8P9Kh0fWj303iYz8P5npQVJpv8NKjY7xwZJZxPWDcCJjRRQ+Mh544I42P9dQplOW+HBG9Xifv2fReHtj+LmkY+4WxL3Bw9uDK/XE9zruG3iWB2M1dN0uVqLd8/0FAo1KmurhARaiLLS4yNz/LG+PjNJZmSdYqRBrWO1r2ahEPI+lhJl3MhIuR8oikQhK5NKn2TtKRIWKlYZSFNsLFJG8WDR53A54VtMU1z7MPjUcwuE+6jl37/p0g5JwdcMEWrdqtSJkWscQStl6npljUVdEx0KCfKXZwVjbHp670EMhelggXGZCLpAxV2bsh6GBvF8K4sEyaMqk1Y/W6gGqeEN8NdXJhgmyQkNuEUHSyatTtBUlXXLJnZLXw6tDVCJFoHqIp7FiEaszAM76xLJilWZzNnOVC6sIa0JVjW3Eb3c3ubwrEJGhwD69yC0flJC0mtSPs4GlupxKm1lWl1DVDud5lAbS+ycbwQGmdhFcBkwBQLoGy5vI6MNVSJBNmobqiyx5Nw1cwQk2qcxmKRiQTJ9aZIpKNg1KnUT9C0zqJqjpSfCOVHqa3573kcns48uVHOf/qi1IFrGd0E1apSGlm+po9KipTXRs30b1pC70jw/TXXiZ2+L+g2C167GJuH/+k/lG+VhmW17d3RvmJW9sZyWoyC3/o0CE5B4oQNKeNGzbQU36DzeP/k6jlYAfbqCr7qSj7idhC3H79OxAn2qpXQJ86jjZ3Gr8wxnQuxslt2+iZmWXDhQtE7dbxJ8xWD46qfPVmODmokPSS3FTcw/bqZrJBklQYIx1G5TYZxkgsq/S9VQgyclVpLg9r9bJqUcdCM64Pym4E2EQCQuwPCbAqFXldhMDK+TAlgVZPkKM7yF5Dp3Q0n3rGb3lalSwink6SCOnuPJkHh2BDg0LpBQpLz1Eovoy/7MFzJVLJHeTy9+Aad3ChNMDRqRqHLi9JQOZdxweuW6/ySfPX2Ry0Fn1NNcqPbf/nPNF2J6o3T3rhtzFccX5QeNepLv7e5yfltyeUBS/s2M7mX/xFtt3cMjMNmk0ahw7RePU1uZBsHj8uAf7aMEdGMG7ax2x7O89Vl3jJGZDiHoJm1GkvMGKNc7N9AtNqSLGctWFEovRu2Ub7yCaaRoTxxQKzc6sJK/GdCDCyc+dOWQUS1CRxrpi7cJ65i+db2wvnaVbX9qmGbEsv8GD3mFT8Ff24z1d/lJPN+zhheLwQ84XnANt70rJq1nRbn0esNe/a2MaHNnWxWTNZvFhh+lyJ8kKTlDbPxshLbIq+RJd5buWVxFFwOBLh8UScZ1MZ9m16L9+95bvZ27FXgvSpqakVOqFgC6377IZFLjdFe8cSN+37LjZs+AHU64ky1RZoHP8iT7x+nMdmsjzr7eKOqeP85JufI+02pFLg1Ls/yJ5/8o8xEw6/8cZvyPOyiFwkxydu/gTvHXkvr86+ypcvfpmnLj9FtOBJEDY8G1+Zj9u2dvPuv/sL9Gzagi+SC5/9HIX/9T9xxy+33oeqknzgfuLf/zE+Zb7OH534I7zQI6HH+Acj38FH01tQqoucP1fj8Mksi6XW3CIikh0nv/lJUgMHiXk2+aJLW9EhV3Qxl5X5xP92RMWKqNSjCaqJNhqxmLwu1Ch9o4aqv4Ms63VC+J26lT4o7qZj9gBdtd6VOWsxbHI0WGBKLa2ArFD1yXek6envpre3h56eHrq7u4lEvnEVWHdunsZrr1F75hlqzz9PsDyvXglz2weJbHl/az9tAPO9w5x67SXe/PpXqS0J5eis9EsMfSH2de05+3oh9mVNS1Ax0sQSMW7qsOhNNTmfyHEwM8zLya3c88JrfO/jXyBhtfqrDm/dzom7RxnlElsXzxCpN6m4UcpulIobkVvLfwfUeKF2aZhS+CYV6aI/MkS/3k+H0rbuPCHm6BNKDaEM8URoSMBlpI4TzRyGuEhIrfId+8N+7sndwz/68D+Cv+ngS7z422UPC4VVZbi/avFXAXy5c3UK/+s47nxrQgpmnqX+xp+BZ6OnTTr+/g+S+fjPolzF6xVZ3Ne/dImTL0xLupuIkf1tqAcWeHrxSZ6aeBrLX81IBk4Ot7Ibr7ob3e3nluE8d25q5+7Rdnb0ZphtTPPY2GNywp+orrqV9yZ6ee/QI9yXPkC6acqTZnVpQQpatC4vyuv+daoyV4dmGKTbO0i1tZNq6yTV3k48GyOSAiPhoMVr+OFCq4LWnMZfclALSSLVASKVIaLVIXTn+hnUSqTE09Eqj7sqRxtCwn2ZdkHAftXlNjz69AYlvU4lrGMHrhQIITBJVIeJWEJXUbDpA8nPbiQvy/4aEWL+iBMhGZqMhLNsUk7RrxyVogbXC0fJ4RgdWLF2GvF26pE8dS1LVQw1Sz2I4Pn+iqKboNRd2baGharWMc0mhtkkYloYRhMz0pDbuOgfa3YSaXRBIYtVUKlVHErNRUr2vMxPXR1xPUU21k4qnicVaSdpdKEGLbpVKOTFa4sshU2ezEZ5reNVlrJvwPLn76SP9yQfYkd8xwqn+0ovztWXVxa6PjgNH7vu4zQ87IbY+nLrlkoE0+dwqk2sIE0zzGAHqW+qP0nkBUS20oxrRGIaRkShWZinvDhO014ixCIMmyS1GEN9m9lwYC+Jre1S0eotj1VNW+k5EoJlztECjdfn1jUpG31JEvu7ie/tQI1eu+BqNi9zafw/MzPzZ4Rha19aS0mmD2apjLcMyNdGrqePntEtcuEktu2Dw2jiIW/+MTzz61BpVa3o3A4P/QsYfRjbD/jvL1zid546R8Px5eLzY/sH+PmHt5CN6bz44os898xzZLwovWobuzs2k6zq+KVrF0BqXMEczksFTTXtsfg7/y+Nlx5v7edohuwP/DTZn/hOaewuj9lGg8aTT9L83OfxxYJ+OabbNb6yL+S5nYrgZnFv5l7uSd1DkiQV2+FMtc54uYHeCGl3NNpdnQ7foNs3yfomCVfIs7/19yNSJgKQ1ZaBWUVprrssekhv7JKOTGYIkaNBtYNON41xFaAgomKOpImN5qWKaPPEIo035qW8ughzIEXqwUGiW3LX+n4FLuXyYdknJsBYtdaqalyJcpjg6cpmnhrbQr1wMwStxaCoHojeiYft5/h14w+IKzaFMEktjDGotmjofxj7EJ/b+JPsHc4yV/lDXpr4srx9Q6GNn3rUpX+2db5WEwmie/YQWhbNY8fgKuVIY2BAUggTt91G/NbbMLpaDBjPcZg6e4LnvvRvWLjg4JUDNMFbXvv5VJW+WJnRVIn+XbfQ+a4fQxu8dR3VV4D/EydOcOzYsXV0PEGRNRpV1KU5qeqnrFnaiD6StoEhukY20b1xk9y2p1X0L/40XH5JPkaIXDxT/nFqpHmtR+Wl7TFc12frUsD7zDgbPI3ieJVGuXV8p7Q5NkVeYmPsJbqM82sOAQVl6A5JJW+MPsiXFg/xmTOf4VThlKQbdzW7GPVG6Wh04F+lNCtaBq5Ut4Qq4tmz/2KFiihM6bds/hWy2VugMo19/DGeff0oX5jL8aS/j6YwELer/NSRz3LXTOtvlM1bGP73/5boli3rXueNuTf4V6/8K86XWu97X+c+/slt/4Qt+S00vSZfOfOHfPbkf2ZiTmXHWJaR6dVqs7Gxmwe+54fYuedOmRytPfccxU9+ivpLL3F0WOG/PqIyl2s99oG++/jF236eiDNOofCCHLV6i35plfoonnuAyvgBwmVPvGg8ZOdu2Dm6SCKcWamitYa4PAvW9c+N4ttu6AYnIhs4ExmgETVaio2RBslIlVisDkIBOgSjZmBWTfxmG7XY7cx6G5idW5IsIhGx0GS3N8Q2v29For6e8LB2R8nv66Oru/v6VM9vIppvvknhj/6IytcevyZxoQo7l1IJY+NDRHd9t7zNGX8BNTiKdtNejtULnB07K1WvW6GhmbtQI3vl3BFLNunZ2NpWF+coz89SmpvFXQZUNwojGiPa3oEVjaNPzTJ0foyUbRN1fF7ccyt/+KHvIRZzuKt4lLuLb3JX6WXavSK2r60AsSvArOxEVi470m7j+mGqMXrjG+hLbKIrOiI98mzF4WDyBE+lX5Nbb3ndIMJvDhCtbGVTvYtRbG67eS8f/OAH4W86+PrN3/xN+QbeKn7wB3+Qv6rx7Qy+hOpU7YvPUX5FqDnp4FZpHvoU3swR1IhO2w9/nPyP/STqVVRFu+lx+PFx3nxyQjaOi4W2t3ue2U0neGHpWdnAeyU6Y53ca+9nx9g2SlY/b7abHHQcZiqrC0g19OnWHW7rVNieDug3LCasM7zkHuVY5LLM+K48XyHCpqkEwzMJzDVqXzIUhUgmx1wsxWw8RTWZIdnWwUe3bmLvQD+p9g5iqfR1wb6o/LlzDdzpGs50DXe6jjtTJ3SuBRGSQpCp42TmqccmKOiXWFJmqAfKsoRunMV6Gyfqw5x1u9f1XcRx2KAtsVFbIqeuTmyGoRFTshhLg6j1Vr9MqPiERpWEopAOhKqQhqmsSnQbakBOP0lCO02JCNOaxmVFY1IRJfq3llDXNJdIpEYk0iAarV0jG2yajRvSx1b2w/KMIACj6iYwrB4i5b3ElzZhzSmUq2WK9rwU9hBm3FeH+LuM2UEu3kvM1onZC3x16DSPjy7gLgusbM/v4qdv+klu775DAqhmzcWqOctbl2Z1eVtzlrerl68WNXinETF9opkEkbhOdWmC6tIUhE3CoEH/tk3c/N53E0tHiCUNSR0xotenn8mM9bETvPHnn2XszOuSNicipiXZmNnHjpvvo+3ARqnsJHzVrvn7IMQ6W6T+2izW6aUVd0olokmwlbi1B7NPAKj14VhN5sbOMX3uDLPnzzBz7gyOO0/nniXatpZW+i6sohBuuZm23MP0jm6je9NmYsnU+i/4zJfhyX8JC8s0v3Q/PPBPYPf3XONNN1u2+DdfOcXnj0zLjq/9hsnHhzvY6qs4ExWhxHHNezW6opjqacylzxPhOJpY4N/yccrFrcz99n8jqFZlpjy692H0nvdIlS9zJEP+o5vR8+u9zqzTpyn+8Z9QfuwxwmXpcdtUeG4HPL5PZarH4P0b3s8Pbv9BNuU2yfuPVxv86WyRP58rsuSunrB3JqL8nVyW9+pRklVX9nz4RUtuW5ftlnb6W4SYUv2EipdQ8GIhTlRQjyBV0TEWfJTm+uNTfK+R4TSRjVk5hPKXt9Sk+swkjcNzK9+/AGTpBwaJbMq+Y9qj7SyytPgcr0x8kf91dpKjk3fhlveuKBem43OMjkzjdOb5gQtP8dG5J+TtL0T38suJf0ixpPPT9h/xQ/rXWvstGOZn3J9iSusjnXsBN/tFNs0G3DIe4/1TbWgXLl9DI9S7u1tA67bbSNx2K0Zfn7zdtSymzp5i6tRxJk4el8fs1Yk0kaioxbs5qfZyLjLEktkm56cD6lk+of8p+9XT0L2L8OYfotp7P3OTs7KiNTt2jtmLYzQcFy+dx03nl02El583DGiPRxndMMKOPfvoHN6Afr3FcuDjvfCbqM/8GmrgUQnzPF38aSadvTRNRSbHhOHxlUhrs2yKvczW9CvkgrNrv2UYuhN2fBi2fQBS3a1jJQwlC0j0bb156k0WZxbXVfZdQVXr0Ll1563cu+9eksn1v/sw9Jma/t+Mjf0H2WMrX6nYxZPHb+YLzbupSsmlVnxk4XV+4MgXiYi+NV2n/Sd+nPYf+zEU4/rVCDdw+fSpT/N7R35PAi7hUfb9276f7+jq5fLYr0pxCS22lfnMd/HsiecJXrnIhqk46jKvvNZjsPE9D/D+e/+uTFj826d+mS8vPCPvy9VDvn/aY8cAOBsDWSlau69SqZ3k83fBo+eof/J1ZvvuZ2b7B6hXW48TFi4bb+5k9wP9dI9ctY4UMqSCrn81MKvOrtwWVueZqoUcDLZxgi2S7SEiqjToUhaYDXqk/cL11q6C0ilAsKhm9WQ6iZ9xab42J02oRYjfb1okR7a3oVylsvtOI/Q8ql9/ksL/+B80jxxZvUN8V64rQdfA7/w28f37caenqb/2GvVXpgnCHbIdYKlwjGcLX8HVlv36QoX+eArLsZkWVPnYPjQBwNTWGkWc83be28fu+wdkX6qoBJfnZhkbG+fJ104ycXmSlFsh45ZJ+vW3TlWGofRiqyXSnBvaxEK+k3IqR04z2eomOOA0uLu7TrbPRXUKre+kvkCjNEdxYR6nXsVzlBYwW1M1qzhRKeokFG9n22ymB+BcRwF7jVDLgN3N/eX93FwYJqwXmQgmuazUZQXP7Ovh137uF+FvOvj6256vb0FYFfxXPkPh6RDbbmW6vLljWIc+SehWyH7Xd9Hx0z+NfhUd1HN9jj0zxRtfvUSz7jCbvsD0yHHOpQ9T9lYV/tqibTw8/DDvyj9A35c0GlNLNIIayr4Ybt5rSbBPzzI/M0uzuIRuV2/4I/bUgMtdTc7315hpt1b6hERWep+5lYfa7uaOgbvRsm38btnjj2aFWafINqr80oYePt7XjnZ1VtjyJLhqgayaBFkCeF13MaUp+DkNKxVSjVgsqTVm3QLFamkdbehGIX41BaJcJMt5pwsrXJ3I++IzHOg6xO39r5KLtfafUKorj9/C0qn34dZayoiKZmMkFtBjOkY0K84MuEaFur9EuVZc8SK6UgESmetYzCMWE8CqIQGVYVTQ9TKqVkZRCijKtVLzV4eimEQjPURj/cSEKmC0F91JUpu6hDWxRHRpiGhtGKMh5G2v/QYlZcywad/ai5Y3WCpNMD9zkbnJMeYXLmEJY+GrQmS07UgCzegkwwixyAieksa5zsL9nYSqi2yeSVQCpdaIJk15YhG8+ljKJJrQiS2+QvS1f0e0fFR6aLmJXp6Z7uPotPBHUenftpPJk60s8ebb7uSRn/qHGGsWcW8XtWKBI489xtGnvkazWZH7K6LGGUxuZ0N+D51bNmH2J1GTJqHtEVRdWenwl7PnIoTEf+LWbmK7O1CXG8NFRnlpaoKZZZA1e+4MixOXV4Deyn7QNOJp4d01R8fuAl17hD1Bi3oSiw0xPPTjdHd/GFVdPj7HX4av/zJMvNq6HsvB3T8H+39UpDvXPbc4NXiLTekJ54xXKZ0vYghwclUIcNHMBJwqXWQmLFA0G9z1wD0cOHAArXgRnvjncOZL8rG+o7B4MkVTv4WuX/5Vojt3UH91lvKXLxA6AYqpkXnfiNwfVwMQv1ql/OgXKP7JH+Ocb4k+iDjdD1+7SeXVLQq3D93Dx3d8nP3d+1uqgkHI04UK/3u2wOOLFdzl052uwINtab6nO89DbWnM5QZyAYz9ioNfaOIVbLyiJSuSEpwVLYI1hu03CsVQMQXY2iDAVgazL4Uiy4zgztapPD1B86ho8G89PjKaJX3/IJENb52svDpsz+GPzn6JTx58kpmJYfzaqodXZ+Yy37Phy+xpP0my4bHrZJVEs6U5O73n3aQf/BckU9vkPlqo2ky/+lk2v/KLRJ0ypUKCx2ZvQ50P2LF0geiaPkn5GaJxWbFSl2mhxoYNdH7iH2DecTvTZ06t9GsJkBRclclPZHPyN5cfSlDwf59ozmLb1n+Nqt7Lp586xGdP1ZjyVgHIBua4u3GQjqWL2N61iSdx/LcPDNM5sgGzs4eC43Nxckr23K28ZiLB9u3b2bVrF/39/XJ9Uvd8nixU+cpCia8vVRgpneL3Tv0rRpstCt0b3oc4uPh9UvczEZmlPSesFl5io79a4RJm8+W+AyR3fwRj2wch1bUipiIUCQXgEnTCqkg0rIm29jaC9oCD3kGOekdXpOC35bdJSqKgAK70RS+eIzjxKAePvcLZzBLdvROyD6bhRvnc+fdxeuEAH96Q5L0vPIr2XMu6J7J1K72//mtEt13f0+3qmK3P8u8O/jvZ9yUiowV8OOvyrsGH2LnjP6Bprfey2Fzky4c+y6mvfJXceRtt+aQ9l7M5vrnKRL4ugfM9aY33pKpE1+Sd1KJCqjpA57bvpGvv9xKJtBQZQ8fh8o/8PRoHD6INDBL+s9/nxGtFZs6vVre6RtLsvr+fjTd1ol0nmXXDEL91q0xjcYIjb77JwZOXman7sjdZ+NYJBkJXV1cLZPX0SMAl1qzifHt1+DWH2gtT1F6aWUncGt1xUg8MEtvZ/o5BmJjDSn/25xQ/9SkJqlpPZBDbubNVRfY8Ilu20P97v4fZ37fmo4Tyt3X+z59nuLJFAuWZxkWOTf05g9PT9JRWVRsFra/Z0caEojDfsR87cwBFa6nvirza1gM97HlokHzPKnC/vNTgd58+x58fmhILQlJelTs64d2DJhmvTGlujvLcDKXZaWm581ZhmxEJxpRYnrZsB1u2jrBzdIR8d69MlDuBwleOXOIrrx5jZnqCdqVCm1IinpmlnJ/lkL5IeY12QMZS2DGf59bCTewLb6E90reuXaXp1ZhpjlFQT/Lh3/lPfKvjb9UO/4aAL/H1hXMnUQ7+N5qHxik2f4SQNKHvYB//DO7FZ0ncew9dv/ALRDa1ssJXQnjQnH5llte+eIEx9wzn2w9xqeNNanoZ3VNIWBpdXpab4jsYVQdI2QaVyVlKEzM03Sr+Mt3prUIRHhaJLBUtwYwXpawlqWlJqnpr9PX3sHdTFFKHOFp6kouVVenhZKSNaux2itE78c1+vrMrxy9v7KXD1AmqDo6oYk0JkCWqWvVrvCWuhKcHVCM2S0qNGW+JuaBEWWms+p9c730rIjuXuq6R85XrqlbhzOl/xuzCcxye3s+rs3dxrNSPv6yaJLycduXPcXv/S+ztOIahedLrpHL5VhaOfYdsCBYRyUzSsfvPyPRPEIv1EhHAKNKDqmXw3EVsWwiOtIRHguv0YV0dup5ZllvvXR5XLvcRUbtRCjG5CBTg1Jku48xUUITJ0lUhF98JE78tyoJb49T0JWqhSyyTZdOGLbjNEKsuqlSOpCaKraROhTUCb1aO0J8h8OZaPmjX7OS4NINWtR4Mo5tYpJtYPElMgKdshHg+SqIzTixzBWQtb1OGNH98x2p2vof/+v/Ae/xfEBESv8IPxsujPvJrtN31vZx8/mm+9vu/ReB7kpb3oV/4p8TMpFQHvdEIG2Lrrt7W8PBqFor3Dn2+4jrxfZ0SZBhdCWlj0AJaZ5k5d1pm9p3mtUA61daxTB/cTM/oVqbOnOL5T/+hvO/u7/s4+977MJOTn2Ri8n/gui3/FnE8bUx/gO6jr6Oca1H90GNw4Cfgzp+FWEutK3B83Mkq9njLhNu5XJGf6+poJHRetize8F2O4bNjZyf/6P3biYcWjz32GBeW5cPFYuYDDz2E+r//FOur/5WuvSWiudbzhdlBlId+BXZ8h6xsi0pQ4TNncS61quuRzTly3zmKnrkWCEtLhYMHKf7xH1N94utywSKiHIen9ih8fa9Kx8Yd/NCOH+KhoYek2qKIguvx+bmirIgdqa7u27yh8eHOHN/Tk2d3UgiL3Pg7DF0fr7gelIlt4AZEhE/exqykDV5d8XSmalSeuox1YtkXS2Srt+VJ3T9AZPCdnT+EbPahSoOXFqd59tKjzI6dx1m4mcAavPLuCDogu7WX/UM5bokHPHjud9j02idRfR/LVDmxNUUp26qERCLdtGXvJl3cgHHax3rpVRqvv0a4LMJ0JWrRxP/H3nmAyVWW7f83vc/ObO8l2fTeeyUJvQqIKKL+UQQLtu9TUaTYG1hRLIj6gVRpgYTekpCE9F432/vOTu9n5n+975md3U02IYFEULyva3LmzExmZ86c8t7vcz/3zc5yDbuHhdlVraE+vZBU+1lcXLeGs9o3EjZp8Ngs+C1iQmPwZxaDrYox4ykbM56KseNxFZdmt299/e85dPinJMNWShzfwt8Wo+3wITZ1xdloHkW9Rowf1NcW4mdKdB/TA5uo0rdTVJxP0awLyV90DXrbYNIqJs4aGhqkY+KePXuyUjIBvd1Od0klr+cU0mrNycoZC416LnQZuXHfrynbqcZSJJy1oDdj8PTLXlNoedM1mScLFvNs/gK6jbnYdFqWGVJM8vdgam+mtbERZQDpFIP5YcOGZeWEIgNV/lrpNFs7t/LwgYd5vv55WYkSsOksnG8uY2prlO0d1axUZtOaMQ+qcTZw7diHqXCq8n1LqgzHnyPotwbloDv/+uvJ/+z1aE5REpdMBnhg3bX8pWk/3RnlyZyS2dw861tU5/T3ZvVhX91brPrHXSR3+dBmgs697ihl07qpHRZAozXhds3C0V0ETxwk8dLu7ESeecIEcj9+Dc6zz5afM9nbS/0VV5JobsY6cyaVf/4TXa0Rdr7SzIFNHaQyPV/WHCPjF5YxbkGZdDA85jsoKTrERII3QktvRGZINfdG1HWvuhTyaTG/dfXUYr58zjhybIMnnN4OSihBcG0LwbWtpDOSUX2hFefSCjl5djwSFm9uloRLEK9USG3Z0LlcuK76MIqnF+/DD8vHHMuXU/qjH0ppr4CYvDi48U02r3xcXh8EiszVLCi5XDpqGiod5KxwEd22SVbHRO9lsqNj8GfWaKkvm0Zb6VLi1r5zBVSOczPtnGpKBlTaBQn77SuHeHRLs3QIFRDtI19aNoJpVblynw15e2l7bhUtD/4Df6+HsNFAxGrCbzWQPCr0/hhoNFjzCsgvLsFRUEgDQdYF9tGgryPgCBI3DDHpnU7jTKXIS6YojEB5MIcx4YlUJydSxGj0GvU37NYdZPL3P8V7jf9Wvj4g5CvRuIW7//d7TM1bRo1DdRHqjbbxVsdTBBUvOqsFrdEkZwg1Wq1cigtaIpakPe2n2xYgaI5iSGqwR3XYInp5Mx0t/TsO9EYTRosFo9mC0WrFZLVhtNow2+yYbELiZUWn08u/Lc6hTb1RjngiHOqO0OaLkRJ9QYKmZPIh8stChAoPcUS7C0X63aio1FZyXno+C3qn4eqxCMftIREggkcbpEcToFsbkPeDwi7jqHOCcIc8mkwNXBfyDzGJMBTC/jidDX466/101PtpP9JNXIQpSqeyNPsMCnuMSdoGZJiJTrFFxjCLHe2MKu4gVRyluamY5m3jUOLqycNauIfCSY9hdmcamIeERuZXSTJl6iNWg4mWXm+XJ0lRXRHEVCVZQaJtIUJdEWkIIsZXIkur/36KqC5OUi8SngzEEili4uLyDs4OIjcqaggS1YfQm1LUlFaRZ9ISj7QSajuIt3GvtPsfyhXRYcgl11hCnrmEXFMJLlMhpnyHvMAZitSbvF9glRWG40FUMMTFURCjniONbHjwQRI93YzJ2cuonO2IiFWBuG0eQfv1hIMFhDo9GDBJrfm7tSkXDk6xRJh4KkJcicqsNbPOht2QK48Hb0rINtvoibbSE24mGDs2Q06vM5KfW0FhQTUFhTUUltRgz8mV31sM7vfsep03Xvi7fO2MJZcy6+zL5ePi+ZQ2Spv3UToa/kDFoSZKOmLyEBBWwenJH0G75BaSqdxMVctPrNEvq8bHVIn1Wlm5E71aRnGrdKCzG/GG49z5wgH+b32D/C9mg5YbFtXymYU17N21g+dWrya/ro6pW7ZizUgFHeesoPjS0eg3/RICGXOB8plw9g+gYob8zcSgxvfcERmyrDHrcF04HOvU4zi7ifNfZyfeRx/F+9DD2UGHNDmo1fDcVA3dE8r52PiPc2ntpYNcVveHojzc7uHRdg8d8X6COcpm5sriXC4vclNkOvVctaMhtmvg5Sai+/rluZbxeXLGXGT7HQ8RJcXOQFiSrS1yGaLNX4fZ9yL6tgjxnrmk46qCQaNNUTsih6vnjeCcqjxKTEaIBeDpL8GuR+Vr0rXLCJ/9TbojO/Buf47oph0Y9qUwHdSgDR+lIDBqcBZEsBXGsA7PxXTdH4mUTuO7L/yAXTtXUdRjpqTHiSt07MnBGoujxYJ/4kLKFp7FtPHDqS20o8vkEIpe3va6g1kjjNaD20kcJdMUENepZOV41plGsz3mkqRHwKUJs0C/n49pn2OyZi8msw0mfxSmfxLy++zz+9EUivDEjt0c3LMHe2sjxgFVvJDVjm34CBZOmczimkq04lqUThPZfg/G1bejj4azDouJ8olox1+FftwVhC15vN7Vyyt7D9BSd5iC7jZyIoNdGbUOJ8NrRzBz7GiZwSWUC8dFOk1v/Ws8te0P3N+7h7YBcnwlXEncOwtLeDwrxpRx4bQa5g53037kr1KKmDKqhM2+x8Xohb8kZ+J8ThXhcD3bd3yGcPgwisbMDuPZ/KPuNeKpOAatgU+N/xT/b/ynSEbr8XjeoKP7df7ZvJXnvFoMUT0TjzgY3ehAI5xxxDXYFWPzMB895XqWVS/j3Jpzmehx4Lv/AfwrV5LOVE50Bfm4r7pK3hSPh/oPXyUjVlwfuYqSW29VP5s/zu43Wtj1Wgs+fwy/Nk3QAOYaO/oyK72plCRWgmy1+6NZwnAyKHCY+J+zR3H5VFENPbXzfSqcILC2VRKxdDRDwgos8ri2ChKmy+xLW7fiue+vBF58UTrLCoj4mNxrP4590SLabv4WobVr5eP5n/88+TfeIMdKQmK+65UX2PLsk/g61fOaTq9nzIKlTL/gEuyKi+6/7CYdTUoZZP6nxqNzGOXfTDQ2EtqwgfBG1U0xmcnvElvG5xxGQ+UyevImqE3NQiJaZGDGhSMZPqVAZnCdDAmT76co+J54gq5f/Tp77tVXF2OepKFR52eHbhgNqXxCMaM0RsvxezAcVUU/GjG9QsCalLegWJr16N1uKquqqKmopSKnknJHOeX2cvIt+XKyN3bER3SvB9MoN5ZR6mf7wJKvDwLeL+Qr3NhD4y/XYTe45IG31/cmu3vXSg32UD1NYmJClKffiaPcmYTYCYU0ymktwWUuwWHMo8HtYaN7N5scu1EyFrP6tI6ZgfEs9c1iRKhaNsD3ZMhWjzZATJSrU4oMANUk4miTmeWg9YQcDEgyqu0npeq6VhqQaDPPiZmalKJBSaZREmlRkZf3RV1LPKemzqv3dcYkWkMAnTGMyaYh4Z7K1mQFG8I59A5wASpDwzkYWYEehyXO/oRCW484caq/ibusheJxWzA6ujGZi7DZqnG6a3Hlj8RqExcKo6xaRkOiVypOxBsj2BIk1BYi3BUh7I0RDSSIJVPE02liKSTBemedUkJeo7rzOd1Wisry5aC4zh9hc7uPjliCiD5A0r0Rxb2ZsNFHUhtnbIuGz835KguXHtvXKfbT3lWrOHjXz+mJhPBaTQTycwkqx1bHtGhxGYskERO3PFOJJGgy48MirPt1KgkTF89UmlQyBVGVdB2POGrpxan/BzbdamldnE5rCSnL8Cc/Soq8/s+pA73NqP6do29WQ/b+oOet4jPpJQkS1awdL65m2wvPEupVB99ifxF7zVDGJSIzLs9UKm+5plJyjPnHdQM9EtjJxm7VEGF0ziwmuhcNIigaAjj1j2DXPY1Go27X7hw37ba56IIzsXrGoksOEU+h10hyJS7kwpJdm2OSfTl9pO7o5YFAhO+vP8LGNrViVZ5j5juTcqh95HfE3lSNDII2G4eWLmXBTV+Usi/iIVj3a1j7S0hkKlDjPwRn3QruKhKdYVkFSzSpVUrRW+G+tFZ+phP2ULzyCt5//IPQujezj7e74IUpWjZPd3L+lI9w9Zir1Yt2BslUmtd7A5KIre72Ec0MNMRWX5zrkETsnPwczJlByclA7N/xIz78LzcRO5Qh1cIfZFKBrHSJSudApERwfSSmEi15C7EnGJGTVSICwBjdgbn3FRGsRcIzj7Si9u+ZjWk+MWcY1y0YTr59QIWwbTs88gnw1CGMmuPjv0g4PkqdGd+4UQ50B/19k4Z4rUJsZIr4qDSJ8jTuiJmqHSE6uvU0h100U0Ov99gmfZ0I9C0aRUc6h9Itm1l8cAN6ER2AhlcrJ/FK1QRs+hjV4pgLtaOJHVvN1WjTmHNjuMtzGTPtWoqHj5SGMPoMYWn3hrnzmW08udtDLDOzbiPGRH0rl+jWMEezjQra0FQvgOmf4kDlclZ5wjzb7WV7oP8z61IK80IeJva0kW5pHNR/VlDgprwiht3+mqjrYIinKG2PkjBo6cozkjBqiUWtBENT8Xmr6ejQkRTnmj5otXjdBexxFdCQW4TPYpfXhAqzkRV5Tlbk5zDHZcvKW+VAvHkj7HmKpl1reNpbyVPKXPalK9BZ6zC4N8j8rT5rdafRyUXDL+KKUVdQ8FYdbbfdTjzSReCSFOG5ihofo3cwrObLlJV9dGhXxCHg8axj567Py34yUQmdOPEenI7xNPmb+N76W1nXpjoV5+k1fMgVwaKFhzxG2jOTs5OcBXx18icY5ljIttUvs/W5lSiZvEePI872Wh8NxWHyLHmcXX0257rmUvbSHnmc9pEC0ZPmOP98oiPGsuePf6PD4iJ66YfpHTZWkqpWn1rB8oTeXvKr12oocZkpc1koc1kpc1sozTFTotNR4Evg7oywtjPAL3u8NIlgbWBcoZ1bLhjL7JGn7s4tWh3EhFFgTQtpcc0R+1muGUN+L4FV9xLduT37Wtu8eeR+4lps8+cTP3KE5htuJN7QgMZiofRHP8J59goCnm62rl7JjhdXEctUyMwOJ5NXnMfkFedL2W4f4m0huv+8k1QwgT7PTP51E9C7j5WOCyt76UYqHBXXryfl8RC2FNJYsZT2olmkdBmzE02I8TNymHLFDIwOyyAS9tiWZnmuPJqEKSmFdk8Dnff9BcMDT6MLq5Oah0dYuX+hwq5CIXXWkjSUk9LYcYR7cfk9OMJ6ebOHjdhjTpwhLdboiX9fMXGotzuxu1zobQ40FiuK3khCpyeGlolTprB48WLea/yXfH1AyJdnzQY8j3XI2bkt7U/Rlu5AeZt+JQFF6MbNCjq7hVx3IaXuSuyOHMw2B2a7qFjZpYOgEorjX9dM0hORhM48xo2h2iF7UsRNlMWFzCOd6luqjw1aplIoSpJoUiGaSBJXUihJDZaEDYdix6k4cKcduNLCUenYQU6PzsuqnHW8lrOJZnN79nFT0kCNt4jhnXnk+w2SXGliUdKCIZ3ENvhXQRxgzeYy9tlHctg2nIS2n4hNztjWT0/CgVCA3rS6LwkHOyUm5C7CjlxIoSyyeVanc6DRWkkdFcR6stBoE+jMfhloabKlceVVkpNXidWR6ZmSvVMG0CVZ9cJKmtsa5GyYdBHKq+Zvb9azcnub/A01Bg+O4tfB/pbMqxIYV5/iyk1Gzrn1j1inT1dlsXGVDAkJm5TsZaR6yd4Avif+RujVxyVhjpstRCYuxp9bRE+gle5wq6wcHQ1Rmco1FkuCkpchZRb90FUEJZWUlvmiJc9RWoDeYc4SJX26CVPzbzB0qr0OaZ2Z+NhPsWqbwpED+6VnwbJPf44JS1a8o22d/QzJJAc3rpMX1db9e+RjZruD4soRFFUMp6hsOIXF1Rh1ZmkQIxq7j1kOuF9Xv4XXN90vJ1NGlc9hRs15slIkn4+HsSUex556CG0m9y2WGo83cS2J9OAeEGH8kjT60CVsaDOueO8E4nO8TJLfp0LMO/waH933PGYlQVqjIzxyHq+MLyOUkZNM0FUz0zoao8GITtuLLfwnzOFV0gs0rTESzb+aWOmnwOiQ1dp4nU+NyDBosU4rkn1RxyOCfct4UwPexx7B9/g/SQXU3p+4Dt4co+Gl6UbGLbiYj4+/lmE5wwZ9D18iydNdPh5q8/CWv7+S4dRrpSxRELFpzqFDk+V2EBMdB71SXtgnnxSTAqJy51hcgSHfkpU/9pGsrf6wvHmTR5HxVJSC6Dr0PWsJto8g4Z2ZdS4scOi4YdFIPjyjEptpwHlAHGsb/0jisVsIt2kJ9boIe1wkPYOrqhqzGevUqVhmzmSfQc/LDQ0YzWHKCny49WF66lsJtBiIB44mu2lyS/NxjRrDU/E17DQ3kbLo+MbMb7DCtVD2eB3a8BYdG9YSjIVIZMKdB0JBS48xl4C9GFtpFZUjRzFihAWl50bMOj/Dh/8v1VXXD7l9fZEEf3n9IH9ZV49PlO3FNYAko3UdTDceATe8UjGNPa5+eb34pWbk2DgvP4dzC3KoEtLITF/Wnj2b2bJlDc3NQTkB0weHo4dhwyxMmrSAXq+HQwcP0tQUIxgcTJqFeVF+gYeqShcjRkykoHAmfl01L3qCPNftY603SGxAJcau07LYFGNF70Ym7XmEtZ4inlbmsDUbeC3ObWkW1uZx0dRKJlfreL7xaR498CitoUx/kDArakizfGuK+UoNVd//MfHKJPsP3EogsOtYV8QToLn5/zhw8A5p6uF0Tmbc2LuIRI7Q0+dKGDzAjoiOx70GvJmqVh/cphz+d8Y3OH/Y+YOOBxELI6o1W1c/TTwj9/Q7FLYO66WuQI+SdOHSDWOYeQo5HTa6m3ppSxrosrqI6t/+HGQz6iiymbDG0hi8CRwKOFMa+djsuWXMXaxey5JdEVkRidX55PLoXs04aR4jzn3E6DvSF+uM3FTkpqrIIQmUMP4RpEYstQ7jCdUQqZiY/DlCQET6ZBz9UqEu4oefxzKlgLxrr8E8cqR8PPjaa7R89WukgkH0pSVU3H03frNRSgv3rXs92yfpLill6nmXMG7RUhm5MBRET27Xn3dKoyCd0ygJmKHQ+jYTQ0cIrd9AzwvPE9h1iLa8mbSULSJhUK+h+kSICuUAE6blkDd/GsrIGrZ0NPOnN5pYu08hlZkktjmb0OatArMqM3eE01y2NsXZW9LoRYSkMPYZp+HRxXraHXLGX0J0e5QmHeQqU1H0MwlqjZgTMazRMM6gD1vIjz0SxBAJoY1F0SRiaBOxQe6lQ8FRM4LP/Ogu3mv8l3x9QMhXPBblr//veiLJAIl0/wnG5HSSsGnp1Pno0PsIWZKEzAoRU5py21gumXsZZ49adsLA43hTgO6/75EnLo1ZT97VozGP7J956YMgV32hon05N9lgUa+PpDeKJaQjL+0gL2WXS5HJMxQimgQ+Y5SEE/w2qNcpbAkl2dmdwJs0EDP1oM/Zgj5nK1p9f1P18JxaLqm9WF4MCqwF6qA/LQigSgxVMiiIoHpfSSp4O0J0N/noavLT0+yntyNESg6CxCEhTh2ifymF2aHHXWQhp9BMToEJZ75ZyOvl9+57v4FkMx730dH+DH6/CBgFo76A/PxzMBoKCCcU3urV84bHxJ6gYYBtPSxAz1lJA5poGs9J9g4Jh0Rx02tSaNIilDlEMhUgbYigtSQxO4RfegtJ7S4M9m4M9jDuwmpGjr+RkrJzhryg9PT0cP/998soCJ3RTMGU5bx0MEx9awAHGiwGD5qy12mybMhWcCb5yrhq/wTGx0ZgHjVOuDBk+6Hezj1O8bcQ234/So/azK51lGCa9FF0eSMIJX14Em14lA480VY84TZJqI6G1eiUcsVcg1odc5uKMfSZTMg3BX2eRZUtFtn6JYx5FjStb8ELt2RNKNLWfHanp/LClpCUO8269ErmXfkxWRl9t+htb5VuVTmFRe9I2njorfU8fdcP5b42YekKln/683KImezwo6z7K8a9v0KbVGeU46lq/MlriabEIEwjK5ZCohJ3t9FlfJxe6+uk9UImZqLQdCEl+o9gTBUel/QdvWTAeqJlH6F195H2qZb1O/OGcfeky5jlrOQjaNllOMghnTpx4khZWJAcTWlKlYgYNHXk6P+EWbdDrivpHFmFDClnZ1373gnSIvChZRPxwy+T8vRLeeuKVYMOZcxULjdfwkTDOLSGTAU1Q+J60ym2RaJsDIXpSKWI6SCu1ZBrMTC/wMniwhw54OsjfbKn65WmbLVOGPqIqADTglL2GdKSbAmStdkfoj5y7AyvWathgt3KaFOAoGcVGw9twNcxneQA58LaQjOfXzKa8yeWYBhQiUu0tRF64xXC/7yb0IEOkuHBEzOir8YyeXLW/t08cSLBaJSH/3YfHQf3owsHZLZS8qhAY9G/Yi9Mk2fvZEK8i0qzH61JYVdVAc2G6TwQ7mK3TpUb1TbbmL0rF32q/3OJvdsRjpETieFIKHSPmsXjw5ex3ScmRY49J5TY2qlxNrJo/BLmjJrM6GInxiEMFqIJhUc2NfHLl/fTHchUG1AYqetmtL4DT74bgyPGwuhbnOPUUTj1wzI6AZ0eRQnT1fUC7R1PSoIhiEciYaSnu4pe7yR6ui1Hmzn2fx+NhqIiO0VFYRzOvWg0m8Xwd9BrRPUpJ2cqrpzpGOzT2Rav4MWmel4MpunSDrjWptNovHF0XVH0XRHmFjq5aHI554wvxmUdTHpFdeGlVb/jwc1/ZlOFyJjKKCRMbi4ZcQlXjLiCckfpMa6IJcWXMbz265iM+cfEFRw4+F1aWu6X63b7OPm5fb4tpAeMIaQroX08O5US7jn8FhFFrWoIs4frJlzH9ROvx6AzZH+TPvmfXLb3ENj8MtZDb6JPqr3YvfocNrmmcsA+gpRm6OPaHfVTGPZSEOmlKB5g9BUXUjVmmKxglbusOIXSIHPeDHlj7Hq9hd2vt0g3XPnZtFBh0VGj1eDMmNxkPrTsxTTV5MiKviArYkK5qyvEPV1enkzG5BVffJsrMHItJmwD1EHiONe5+8mYJGd5Fnk/Feig94H78T7+uJS6G2oWYhp5DhqjWqEWf09UvMUkTO/f7qPzZz9XHQOnTUW5/v+x9bWXaNzVXyErGz2O6RdcyvBpM0/qmqP4YpKAJTsjUnkhJIjG8gHutieAVKFs28zuf9xP26EUAcccoha1XUiTSlDcsZGitpdpzOtgd5WGLeW5HNQtJeETOX/qb2ixNDCi4ADDnRocGgeKp4Xxr+xg8h51fJbQweppGl6dlk9uspLyUDnm1Mn32omcupTBJF28I4Eo6UgUXSKGIRHFKTwrE0G00QjOhcv59Odu4r3Gf8nXB4R8Cbz15KNYXW5iNg2bw7t40fMGe3z7+sedKR3lvlHMcy7m2rM/RFnZ2/fihTZ30Pv4QTmwShXo0Z1fRkgTPZZc+XzSzUkQETGjkZO2ZkiWg7y0XS7NQ1i6CgRNKfabk+y3a9jnMjBhXBmfnliJfQi3oUhcYVODhzWHull7qJvdrb1orQcxuLagt+9B05ebhZYJuTP4yNjLWF59FiadSe018ETprA/IPi3Zr9UQIHFUxoqAyHUqqnZSWOWkUC4d2IZo+j8ZdHQ+y/79t5JICBdCHVWVn6Gm5gtoter7tfkiPLG1VZb0D3X2E8m8tIYLkwaGJ3SIiEuT4E8ajTBDxKLVYLJr0NuTJIwhAqkeeiNteLxtUtompG4nC6PJgs3uwmrJwWp2YjE60aVMRHxxTBoHFn0udq0Lp0aLAQ2thi4ezF/NSzkbSGXkMFOCo/lo9/mMiww/8R/TCfvbY+V7fZI9Qe4jm17E++A9pPzqd3CedwEF//M/GIoLshdcUUXqbmqg/dAB2g8fkMvu5mPtrwWshhyKXTXkaovJ1RVnZHxHXfS1Gpm1ZCi0YNavx9L8K7TBI/KpiKGAF47kcTCQz6i5izjnhi8NbVf9L0L9ts088dPvyt6KKdPOYdKUFdKFUNv0HM7UXzBom+XrkukC/IlriOedg7HKne3XEt+zryFcHBM9Pa9ypP63+P1b5WMajZ7i4ktl5cFqrTmpzySa5bvuvBPvI49mm8h1n/sSP1KG8cI+NQw3z2rgq/OHM8kS5Lk1L+IPqQRlYs1YFo+Zg0ljkEYW2vaXMR38GbqIGv6rmIcRLryJuHmW7EcTs7wSWg26HCManfYYUjgU0RffNdV7hPiR10i0bEQjguLEOc4Er07UcGhMBYvS5zE3IOrQ745gp/VaOia4eWGUlTeUOLsCESn9PRq1VhNTnFamOm1McVhIhPfzwN7/4/l9dUS7F6CERmdfO6vGxY1LRrJwRL48DoRcKyRDjdfLpejxGASdFsukPrI1G8vkSZKAdTXW07x3N/s3radl3x402WygzGbV6WUsgTDGKB89jpKRo4kGg7Qc3Eb39qcY2/4U+Vq1orfNU8KrndVsqwmxZZRX9m8WRqxcpzmfscNmUDSsVkoHY5s30/nzO4kKJzfx0dxunNd9huYF57CtIyyDoLc3e2nyHFvhNuq0jC11MrnCJW+jypw0aBRWdft5oceHJ55E2xHFUOdHkyFhooJao+1hgr6dKm0nk9nNJHaTLrPSMbyaLm0LyoBqutMxkaLiiygqvACTqUBey4RJhzDraGoSBkgWamtrpVGGWFqt/QRKEDmffzs+7ya83k34/FtRBuRfys+TEkkvZvb1jmBlbDmHzKOJ5jlJOwb3gFWZjZydn8OKfCezcuwYMsep4vPR8YMfSJdPAd+4SjZ8ZjZP+dbQGekPm55bOpcrR17J7KIJNNb/gtZWYeCQPkaKKKpZQmYo+ruGgjDnyctdIG3g46bh/GTzb3ml6VVQrOQaajEohTR7I6QSLqyUkW8YiTeopec4kkBjKsZE/y4m+7ZjyZhFhU1O9pfXsMUdRTF60Bq8aPVeZlsKuHqfg7KXdpP2ZtwONRpyLrmEvE99EtMItUIoekOFYVRfVStS56XJm6AuruAbcEkvdBgYMz6PYXNLsFQ70RiOP5Gzt6GX767cw7om9dqTq9fxWXcO5yV14BPxE4NfL3uqew6QOPQiyXYxaZRxUS2qwr78QziWriDRkySyvUvKAtX/FCG643GijWvwnrWQQ3rV0VZ+Ta1Wuu0K0iWOwVOFMALp/ssuEs1B6T6b9/GxmIerxi5ymyfCNAebZc5qc6BZ3pqCTbQEWmgJtsjJaFPKhDlhYlzbKKr8E9Fp3DJQWrQcGBLd6JMeFF2KmNlEj9HBdqWMQ0qelBUK2ExNaApXobGrlbBhbWmufUnDmCb12IwbDOwdN5bmiRMxm8AW78Ya68BKRN7MaR261Cj8lQupG1PFNoOeNZEE3Uc1h5vFUR5XiImHM33fhniMyWh5+uwZvNf4L/n6gJAvX8wnw4tX169mR5c6cyygSWsp842ktnsKCwoXseTiiRRUHjsbIuzMj65Ude9uprfDI8lWSBcjcVQQpoAurSU3Q676SJZY7wskHASNaK61yOwiY6mDfQ4N3w70sj1z8Z/nsvPDkeWMPAXnIaH/fvNwjyRjbxxupEPZgCFnMzpr/0BEm7JQGpnGuPaZVHVWHCNp1Bu1cpsIktVHuERV692aLQxEPN7DgQN30NG5Uq7bbCMYM+bH5DgnZV8jDsFdLX5Jwp7a1oInfGIrV/nZBWnQajDIXDANejTSPlsvamnJKBolji6VRpNOoRXEOKWgScVl6DEpUcJPSjdGXVoNOhW5OOp65r54HEU+r5igpTBIq9snpWoaTZKqWD6L45OoaUxhCQSxaNLknr0Ea3mxzBQxWg0YbUZMdnVpNOnQnUTfjOL10nnXL1T3p3QardNJ4Ve+jOuKK44JA89u40iYnS8/z7pHHyCeMXcYcpsZjOTlV5BvL8etK8IVz8Oachz1eyex6Z7HqX8AnUa9EHvjpWz2TCWQP5LlX70Jm7v/onamIS/yvTHa1u6i7rk3ZWVPmJCIvjGjZhc5hr9g0qouWCltDrGKT8PM6zDV5Mu+tJN5/97eN6lvuFsuVWgpKjqf6qobsNtHHff/+Z54ks6f/ASlV3VVzLn8QxR+9auyQVrgtQNd3P70buq61AHppPIcbj5nBN37N/HWW2oviXAUPf/88xk9OkM2RN/fpnvV4OeI+r4MXworvkc8Xonnkf1yhldAyBBdFw4bFEKdVoT8Ujlu9U44iwWeX0nv6kehq3/wKsJhN03PZcyEKzjXuExKmo+u9CkJBX80QTCWlOvGFJiUNCYxwNZreLLSyL0VejxitmQAhJviFIeNqZJsWSXpchn0JJQEzzU8x99338/2+jTxnkVZ50KxS547vpjrFw5nnD0tezZCG9bLZTzjJpmFJo3ZncBWacb6kf/BuvxKMYqms75Oxig07d1Fy77d2R6SLLRaOdCrmTiVsjHjsLty6WlpzJphdBw5TDTQn+0ozg8LCuuZnqdWN70aG+td+ewtUHhI0cg8RKs2zY2Vw1hcfT65uQulBE4g8PwLdN11l+w/ETCUlpL/xS+Qc+GF8rjuCcbY2tjJqo1/Yn+XnfrAcIJxE2m9hlSBGaXQQirfJA1g+mDXaGRMwKXFbvSeOH9544i8HvShXOtlgr6NQk0Qt7uNouJD5Oc3YksZKM5dTvHoL2K1H3/SKBqNyiBd0f/7togFSR1YRfDgQ3R7NnHY7iSWo2AxHevAG6MKXIs4YJ7HumgJ6/3JQQRdyFyX5DpZ0NnCmB99D2tDvfytcj/5CQq++EW0JhPJVJLXml+T4c3rWtdJ6a9AgaWAy0ZcxvLi0fiaf52VIhoMIrDbSDw+2AlP2Mg7nbNJmRYS1kzBE3XR3BvmtfpdbG9tJBl3kkq6spLXt5MEigqV6LcqFT1XmfvlbgsFZmhb/wpbnnk8O0Foy8vDMncUb+bV82bXBhmwLmBMwtUNZSx4pglHqJ/1mMZMxTRuBWldrdBSD/rbojIlnP98DhMHWkM0HPRm5+PENX3C4nLGzC3BdIJzojinvbyvk+8/s5e6bvVYGV3s4JbzxjArzy6dTRMdAQIvv0DopcdIdhzp345F4zEOX4auQI1vGAStak4hDEwO+bdyKLCZqBLOhhlPPGsFU865SKoh3inEtuvs7SD0QB2m5hSKNsXqMVvYaNiFN+AlEU1gTBkxKSaMilESLXk/85j+FNsYxP7Wbe6mzuChLjaKmH9qthJmttUzu9rDpcMmMtI9AtOevSTvvRclc94SUsuCL35RPfZ7DpDefB9seQBNQj3XpNM6IqlZBIs+xP4F57Dakua13iD1kZjaB3s0lDSaYIKZRhNPntc/rnqv8F/y9QEhXx2hDpY/ulweDGJAVhaspaZzMsN6JlNVXsKks4sR/eUDK1UDyVb4BIPVPpjSekqN+ZQa8mVVyxkzYQrrhrTs0Bi1GErsUt4k3LwMpULiZZMnR2HOcNuhFh7vVE++wjL+9toyLi08+VDRoxGPJulqDLBjbw9rD3Wx0VdHs3MDGtcWtIb+rJB0LJ/84EzmmpZwzqjRjBuTj7vYmnX3Od2Qh5WoGsbFwE2hp/116g/9HiUaQZMyU+Q+jwL3eWiSYvZekRlHYhmPKqzp9rOyy8/GQIRYKiVN2v8TDlJJFnVa9EICotMOeV8sxbo2EiJVdwit34c+pWDMceKcOB5Lfi56rfp6gzCHIE3bnh20798tiaPN4WDSkrNwO6wonQ0k2uqJtdUTbauX2/5oGK0OXEVVuF0VuEwl5KQKMAsXr0QAt+4JHPp/otWoM7YRZTa9iY+RzhuBuSxH7teqjNGKPteSzXJ6NxADfSFhG+hCKLLBBm1HTT0uy98xpzIySZ0Z5nwezfwvgvnUsqIGwufbSn393XT3qHlBAvn5y6ip/hxO58TsY7G6OtpvvU3avQuYRtRSfNttWKcJKcpgxJMp/rqunl++dFCSFoHLp5Vz9Tgba158VkpbBcaPH8+5554rM5kkBPF6/Wew4R4QEwbCeGTKNaQXfBPfmxGCb4iAbNDlmHBfPgLziGPl0CeCkAcLl7GOv/+V2BtrZdVeoMcBa6ZZyb3ySj405zppFDAUGiIxHm0XtvUeGkSjuDjexUSIRsN4uyVLtKbl2GRVY+D5rTfayyMHHuGBPY/Q1l5J3LMg61xo1Gv42Fg3H7V4sO3ZLt3KYgcOHBs6P2oENlcvVsM+rAVxmHAB7eO+SPPhepr37Za9hX09N9n/ptPLjC7FYqdy1BgmTZqIt7V5SKI1sBqWX1klK1lFNbVyWZCoQ/f052RwalpvJjjvGrblpvnx3pdoiAmrjTTnOhMscyYxmwrJy51Pbt5Ccp2zCT39Ct2/+S3JTpX4impGwZe/jH3JYrmNwuEjPL/x42xUxrHZeAW7knmDrWkiSXSdUbRCYtUbl7+bGNxPEtWxchc2fRerdtax5ogjK+ku0AQlCavQerFoIkxkH1PZRbHbDtM+CVM+BrbB0ryTQsQLB1ZL04zEoVdZE6+VPVzPp6YTRFTI0uRbephX0cy8ylaKLPtJxvoH7H1IGas4aDmfLekprIvk4hmgZBQRARNbGjh3ZA3nTxpDrfXYCUpRzXjswGM8fuhxPFH1eBK2PhMddmaYPAw3gDfmplu8d9RNTzSPiGYyvfEyOoM62n2xrJnCiZBvN6pGFm4L+Q4dBwIb2N77MhpDL05Lki9Mv46rRn8Y3VEh7QORiEXZ8eJzvPX0Y1kTIrs7l7HnnktjTZLVzc+zrSsTOpxOM75Bw1UbrIw4Esj2/GhsBRhHnIVt0blYxpRIKaGY2B0Y7yCULjtfbWbPmlZimbgMvUnH6NnFjJhRJKNKtDqNnBCUS7261OqFK3OaBzc3yXOWP6r+32UjcrkxvAfHw3/tNwoxm8m5+CJcV3wUXU6JjMvoC2tP9qgRFEISGEh42O/bRH1wZzaex6JzMDJnGsMckzG7HbIfVFxDVDljpt8s1yyVIfF4XI7TPH4PzT3NtPa20u3vlqQqGAoSi8TkOMOgGLAkzZwTm0Z1qkD26L9m2MPhjDT47SAmGURlV5yDxVLcROU30hukfX8vsYCTXrOHI7l7qHNvJ2Tqz6+zeV1YG5dQr5tOKvP7z7TGuWlhNXMXTpK/pZis6/rVr7LOiKbRoyn82tewz58HiQihXU+yccdrbEyb2ZAzka2OMUTEtW0ArFottaLnT6ulK57kcKQ/eufsHDt/nTo4Tum9wH/J1weEfMVicb78z/9BadZTLMJx0zrZ6yNukVjobcOCBYQNbo7didmrITduJV/jJL+kCLvWjM6TJJ2RdBwNrc0gyZVKslSiJXtojrJtFSf2+1q7+XFdGwFFNQ0WIclfrykmx3DyMy5KUpTpg6rFe4MqIextCx2jOBNENFRgYG/+Ifab1hIwbZVGE/K5tAYlPIy81FyWlS5lUVUJU0uc8mAWOUd9JOjoZWrgunidmBEXRhLHeb3siTmNR5bwDBK/gvgWSWFvYdajmHUkjWnC6S4CqWbi2jBxfZS02YHFPR6jo4KkXosibxqSWg0JDTJ4VmSiCGvezQ0edrf4UDKlffHblOdrUHRtUtYiGtHFTJRT6yJX40ab1BOPxYkFwyQ0GhStnqRGzImLuACdzBQR98Xj7yuk07gSXopinRTFOymOdpIf70Y3hAekT++k3VRIh6mQbnMRPqMLrUbBIE34FfQY0KJWecW3FFVHMZ9qNOowmkRgtg6TxaBW/0QVUJ8hl4I0inBogw67SY/VqMeSTmPyxjF4ohg7oxg6w1hTSLmpOTOISqUVemMdpB3tjKvegr7pSTnARUgop10Li74OjuLTtqkCgT3UN/yOzs5VWdqf655PZel1JB/cjOdP94qSuRx85H/uRvKuvfZtc4U6A1F+sno/j25WpZEOk57PLxlGZewIG9e/KScrxIVeEDARhpslK546ePE22POkum60w/wvEyu9Bs/jjSg9amXBNruEnHNrpDPjqSLe3EL3g/fT/chDGHzqZJQwc9s8SkfkokWcc+nXqHENLcMUn3uDL8TBcJSxNgvj7JbjOiMe7D3I/Xvv56mDLxLsnpJ1LrQkoswINPBhfScjWw+Q3L/vGBmtICnWWbOwzZ6FtURLatUXae/w0xTNpcU8mdZ2P8mMy1z2/9hssn/E4HCx7+Ah4pEQ+nhUmqEkhsiQG4poDXQdHIRgJzz+WTj8kro++gJi5/+c72/7OY/XrZYPjbekuTo3gjW7OTSSxLutczC/FCL092dJ+dUBnGfREjZfex0vmh285QtmiZPACKuJc/NzWJ7rxBRMsr3Fx3YhV2zycqgriMPgZ2bxFmaVbGJYjqp86AgVsKp+OW+2zSCZUvcJty7GWE0Lw3QedJo0pbRLEjZeW4d53HnSKZHKOdnsryER6oF9K2HvU6QOv85GZZh0KVylzKKXfmWJcNi7cFKpvI0rdWb3Z6GEEL1VXp+QKm4mENgpzZX6IM6dh+Mj2O6fzhbTdBqc/blMAsMsJpbnO6WDopAnivmeDm8Lexs3sL9lN+u7dnFQacev6+1/z4SLRO9MEr7ppJPO406MWS0xQulmSabMpjBnj5jJRaPmU+G2ykqWeQjZ3u7u3Xx3/XfZ3bM7GxR9y+xbmFCgxt4cD2Jf3fnK82x88lGCPWq10mJxMLZiPlZtMWvs23jNuYk6s3q+KPCmOW+zhmU7wJQhRFqrlZzLLiP3Yx/FWH1sFplAIq5wYEM7O15pxiOiNE4BEU2aNy0JthgUKavVptPMCCssCiex220YXE50JoNK2uRNi06vLkWxNFC3ie4jrxHR9GTNJiyWYspcUyk1V5DWpoiSIEwcXzpOkAQRbZSINkJcmyCpSUoCdarO1Jq0hoWJMYxIlcix0Pa8ZnpLFZx2Z5ZUHU2yxM1kGjpipd5Xz6ojq3j64NM0hVWZpIAxaaGmZxzju/I5DwOFvV007D3CPyrm8ELlDJQMCZvsqeM6cydzp4/APHky4TffpOePf5KGIwLtk6fyf5dfw+r8YhkTOhC5CS+zfDuZ6d3JFD+MqTkLxzmXos30RfYmkqzzBnndE2BpnlNKd99r/Jd8fUDIV9OhLv78f7894WzGwLDgbKaVw4lTsUgjjOReL9H9vSckC2JGxiiqWBmiJe6/nQOQwBZfiK8faGZnUJ2Fneyw8uNR5UxynNiRR5CX3pYg3fV+PA0BPM1BAp0htCnZPiQHvWKp02iwWPQ4c4zYHAasVgNmYY0t5EcZMhSMh3g5vZ4XzWvZbemfRTanTMz3T2GZbxYTwiOGdFo8LdBrpN5caxRubDoUbYhIokEuU7oYFkcFDvdYdCYjGqPa+C+XRu2A/qgB1uYmHfFEFw0N99DS+o9s8LLTOYVhNV+Qcp/j/S6i0f2lvR387c2GQRIdpybCsnEJKNvCi43PZWUsi8sX89lJn2Vc/ji5nmhpoeETnyTR1CSlQ5V/vQ9tUREhr4eAp0fOZgY9Pfh7egj0evB7evF5e/F7/cQTCZWgZYhaSqOVZE0RFEiQNrmuI63VSStZg80plzq9iWRLG0pHF5qUBo3JQqK6ikZPD0lxaTJZKBk9Hos7X7owJpIpSfgTSkpWXgbeF0u5Lh5PJHCEO3GH2smLdFAQbced8B3XoU2QsQ5TkVz2imiH49jAn04Y00kMqRg5+hhujR9bOoJNE8WWk4etfAI2Zy52kw6rSS+d7+R9o1iq60IKpC7Fug79KVR6Q6HDNDT8XpoTCGMC+XkOabCv1pFXsJjiW76DsbzslL7PlsZebntqNzua1e08vMDG5+cW077tFToyM6Kiv+aCCy6Q56osGt6E526G1i3qurOc1OJb8DVOJbQ+k4OTayb3ipFyJvydIBWP41u9moa/3oNpd7+0rzkP6peOZvonv8aUYfNO7T3TKda0rOHve/7Ousa9xD3z0XZNZmxPG5O6DjG9t45hnkY0R02SGWtqsgYZIng2ZbPSum8PzS/8hebdO2iP2rNh7n2wOHMkYRJ21OLo93V30XpwP0os+u6I1nG/XArW362SY1GddJbBZX/g8UQX31v/PSmzKrMV8LURM3BEdxIMqfJYAXF2aYuPYVfjItZYRnK4vGrQW482eJkYX8ls3W4umfF7rNbBzyeTQbq6nqe57Qn8XiGXVbef2Ca7u0ezvm06W7smEBea6aNg1ymM1bYyQtuJQZPCQIKxHJBErDLfiWbGp2Dih7Ph4wTaYe/TknClj6xhe6pGVrhE+HEHuYMqQ+dPKJGEa2ql+6SyoxQlgt+/HU/nOrp2PEbY2s5AL6r2dBHr4/PZHpvDkVAhqWgKTVRBIyqA0SRaEfR+lBOhgMbYhdG1AYNrMxpdpgKa1mJLVFOaLKDaqKXcZWHKqA/jNyS5e+cPaQ2pREdYwn99xtelcdXJQBiCPHbwMX6x5RcE4gFJFi4feTk3Tb2JHNOxx6K4LscaAzKOIXyoh/2717HX86Y0VxIwai2Mzp/FuMlL6amJ8ez+P/FidAPtuRpM8TQLd6U5fzOUdmeOGY0G+8KFuD9+Dba5c4e89okxRfP+Xhnc3N0UlOZbipImJW5CoSKWx6n+9WhTvGJJcCTj2GpJwbyogYlxLRqNQkqbJK2Ny2VKEyMdq0cT3Icm3t9/nbS6SeQVkRSV/Xei8klr0aYM8qaRSz3alBGNXA54PK3eF13akyx6qjM9UYfS0Cjic0SFL1Pl0w0ijGrVr68S6Nd72K5dzxbW0kT/udCAkUmG6QzvmojrYA2apFqZEuZmaaWNgrI0I4uthDo7+EdniudsVVkSNrb3COOTTfTWllFXWcOida9yyavPYci4O74wcx7PXP5RamuHMSvHxmybjuH7V8LaP6HzZ6qhsqe5mFjFZSSWLCdqCxIKHyIUOkR+3hJKS6/gvcZ/ydcHhHyJn+8XP/4tVquN8mFFuHNdgwKDRViwEMqKBtVEa1A2rgsL50R7WMrijoGQhgknuNLB0sFBfRWpPlvrwRWf1ICKUG88yU/DPh5SovJC60zDl6MmLg9p0crX9FeQxP9LxRSUaFI+rhG9Smdoe3UYeqRhhLi1GlX5gEB+PJfZ/plM984kJ1mAyazHZjeS6zLjdJiyZGggidIMsdQeRZ40et2QcrREwsfBg9+nrf0xuW6xVMleMLfrxA2jsVgH9Q330Nr6YJZ05TinUFNzk2ySPh7pEj1yD73VJENxhRuVgKieiL6IGtshzBNbWe9ZnyVdSyqWSNI1Nm9s9j3ijY00fOITJFvbMFRUUCWyPcpObvAtbbhDIYK9PZKcBTMkLbuUxK2HkFdo9U8uJkD0sulMJgpqhuMsKMKem4fdnZdZ5sqlzZ170gNK8RlD/gCthw7Sdng/HYcP0lV3kGjGAGQgdJoUNrOCyaLFMnYBuhFnk0zaiPlixETOmj9OPBCTVcZEpnIpluIWI42oO4RJExE3g5aITiNnWkOpNKF48u0MIt8xTHqtSsZMugwhO5a0WTOETdw3xcJEX3iEmO4tmBDFbIpi1sfIc9YwevinKC1erubhnQLEIEdUwH68el+2UX/5mEJWFAbZt2kNiqLIXpsVK1YwderU/p4bMdjf9Zg62PerA0VKpxIb9008r7ukxEecOOzzysg5u+qEDfZvh8i+fRy69zekn3sVQ8aYJ2qAPdPyKf/4dcxf/LETyqtEg/sTh57ggX0P0NQaoebAaCbUa5jYXccoTyOGo/poxfHUT7Zmodit0hSjee8ueRPSQCGVHAir04mrpAyjyUwsGsHT0kws1G/c0wdxNjW5cxkxaSoltSMpHj6CvIqqUyNaJ0LrNnj0U+A5rMpDF3yN3RMu4Cuv/a+0SLfoLdw25zYWl07htZZNPNPl5Y1IER30Gz9pUwoTD+5jwba3mL9jE8Pnz6Jj6UG8xt3Y7WOZPu0RaVokQn7b25+kq/tFUql+UikmnoqlccZ5eCL2rJHHtkYvO5q9hOJD9C2TYoyxh/GaZswiH1KYw+CRJGyS7gj2scvA2yhdUPenymSF6+nUHBrT/b05DrNe9uZdNKmM2cNyT2lyo88l8ODLa9l9919oi6bosLrpmTSeLncerf4kPWETiggcfBukjVosVpGzZ2BycT6j8vQkvPeiVzbRqvGyJ13CXn+/XX2+Huba4sy0JdkT1fGU14jDUsq3Z3+bheULeSfoifRw5+Y7ZQ96nxvjV6Z/hQsqzifZGMxavwsX5WNKHFYtzYbD7DzyKn5fRpJqszH13IuZcvYFdH/72+zY8QJrp1nZMMNJZ7SLiUfSnLcpzdTD/e9lHD6M3GuuIeeii2Rl7FQQDQRpf/oZ2p9eSaCzi5jJTNRsJjVsOOnhw9it2Hmmw0RPQj1uXJoIM/SNlOn8gkVj8HVj9HTITNFsOHdOHoncIlKmfkYtrrFCgaP6KWuk+2tJ2kEOFsxpA7q0ni4UOjUatGkzxpQZg2JCK1oUFLJEUVwj+nyZj4dRZi2jzer+cyiqsDt6/FdH9SHqcrdxMH8LbU5xLGdknmkt5d5RjOieRrVngvw8bwfxP7sdGg64texJR/AGlezkvpJrIlnrIO02MW3PDj711COMbVCdjsWkbEvFIpqHn0fK4kBnTGBytmO0NWIwHcFgb0Lv7EJr86Lp04pnYNacw7wlxy9E/KvwX/L1ASFfQ7nexJsD8gQnyVZ7GKU3OnRVS4zTM4/rXEYMpQ7VblnMBGVIVHqopZDUHQfimWdK9fxylAmvUb0QXdCS4IsHYuSKpN9ThPxLgrwIq1ezHp1ZkB9BbAaQHEOG9ByHFA0iS5nXicbtHf4dPFW/klVHVhNO9ksSlHAVCd9UEv6JkLLIANN5tXnMr81nXm2+lGCcLnT3vMq+fd8iFhM23BrKyz9O7fCvyUbogYjG2mWlSyVd6sldWBpL0uWed1zStbPZx1/frOep7a2y8iPgNOvkrG+edhetBXtoNDdmSddZlWdJ0jU6t99tTSBWd4RGQbw6O6XEQ1S8DEXvvEH4eBAW6qIhezBBE+TMQ0fdQXqaG9GIbLlTsH23OJxZMqYSsjwcGWIm1sV98ZqhbH2lU2ZPl3RVbMs4LHYcPiR7F46GzW6lePQESmpHSSMDUVHQx3QkOsIyODiZWYrqpXAflC6ElY5BExsCva1NbP7ZJxhhPgJ6HSHMhNzjCE3+FKG88YRiiiRooViSYEwhHBdLdT0cV+T9cExditeJ+6IieCag0yhYjVocFkuWzPURuL7qm9Wkw24US5Xk9VXiBJ7Y2sLTO1rlQMKg03DNtGIKurfQ0aqSq+rqai688ELyRJhvHxIRteLyxp0QV8lGetSF+FKfIrhTHRjpCyzkXjlK2ku/GyjBIHUP3kvPA/eT09rfE1VfZUZ32fksuOZ/sVj7z//COezBnf/Httceo2afgbFHTIzp6sJ4VDyCvqQY28xZWGfPxjZrJgm7TZpiNO8RZGs3nQ11x0gPnYYoZdYAir2Y+l7TkOYyoqLlLC7BG1eIChtwu4OzL76MaTNmnFYjoWMQC8Kqr8O2/1PXK2bhP//nfGXHb3mj10/MMg2tcx7hdP/AzahJM9Pcw7T0m4yJPI6rw4fzKT2WrepxKMw2QgvTBM6NY8kfTiLpIZHol9MJR86ioospLroQq3Vo2Vlftb+uKygJ2eaGXl4/2EWrd+Dxm6ZYE2CWoQG3NvN4Ok0hnQTSRg6myvHgHCTRq8m3MaLQTkWuFd1xKlxyMicJnkianmjfDTx99yNpjmrnHBJCyO3Uh8gxBMnRB3EaQnIpKhVRgx2/PpeIdvB+7iBIEa2UaLoZWTANm6WC1kQrb4beZFN4E9G0+j1Fz+wkq8IsKww3XIROu/RdxTsItAVaWdu8Dm9SnbTKT7iZFhqLS+nPYhTXYWHBrneZ0LnM0iJdbrNUCm/dATq2biDmVX9rrcFI/piJGBvboLcXbV4e0WUzORxsoN5fjy4YZXhbmqquNPo+jm00YB49Fsv4cWjFBPQAJJNJ2UMVCoXkMhwMEgoEjgoNGBriPLVfKWRbspQYBmzJIPMDb1HrPygnEOT76zV0FhloKYGQJUFMGyOui8tlTBfDaDJS4aygwlFBub2cckc55bYyKppy0a8LonSpv41QuNjnleKYX3aMeZKYAFcCcSm7TvSESfREiXerPWdxj4iNSMjThzjrCyMuEWUhIELkA0IVpNWgsRuI2VNsdm5nvXEDO9mOMqDDcqRhHLOMC5iqn4st7UBJqpVCRUT2DKgYinaQYCBGe1cIbUDIJFW0unWsH21mT7kRYgr6wwF0reHsmHOUv51P7niSSd0Hj9nOoqgfHqMneGEEpaI/I2wglLiVmK+UuL+EmL+UImMeZ33hq7zX+C/5+oCQL5FV0fHrrZncnX/9TykJUIYEHXJo+UGVjq029UipTWj4tt/A+GCacDhBKJDA74sRDibVvqW0OgEmDndFzFgXWHCX28mrdJJf4yS3wo4uM0g7k4gmo7zS9ApPHn6SN1vfzLouadIGlOBYor1TUUK12YvSsAJblojNHpZHjuXdzSAnkwEOHvwBrW3CHhgs5krGjPkhbvfsDOn6Pa2tDw0gXdMYVnMTbvfQEotYUuHZnW1SWri1sb9qM77Mybm1Vg7vfIg99h202FTXMoHlVctlbsuo3GPd7WIHD9LwyU+hdHdjrB1O1V/+gr7g5CQppwPC7vqle3/HvrWvyXVBbpZc+CF6//RnPFu3EBPOcfm5aOfMJmazZqtogrwpiZMY3WQGrTa3u5+k9VXQBlTRxNJoscpst57mJjY99SgdG5+R14WemHVQn0ofckvLJRETN/G5C6qq0emPs7+k04Q33k/86W/g0qu9MClnBdpl34Hxl0vHs3cKQbwFOVNJW4agDUHgBFHztXXQtXELoUCYiN5E1OkmWVpBWKPPvkf0BBMw7xZiK9oMGnTJCHoUjNoUJfluyosLsJkM/QSOKLYjq7E1voqNCDZtAlPlMuItczGHTVg0GgoXVeBaVjWoGf+dQFwm29a8yP4//5L8jYdliKiA36qhd/lUchcuZe8bT2LafpBRTRrMR6kKQnYX5hkzKFsyH9vs2cQGka1dclLhaJhsdvINfsZbDlJp85FIaVnZMobumG2QdLB42AjVCKN6GAdbWnnl1dfk5xWE9corr6ToDEySHBc7HyW98ktoYgHCBgffHPk1HspfnH1an46xIt/FpcVFLM11YMsEMScSXjyetfR4Xse36RUML3lRCtJEZqRQBnhh6PVOiooupLTkchyOAb2BpwixH/9l7RHu39BIm6+PiKVxaGJM0rVQq1fNIN5uIB7BQDBtIpQ2EkwbCaVNg5bJkyAyYh+3a+LYNLHMMo5dE8suLSSEIOW0QfQSNdmaOOI8Qq+pn8wW6VNMF/3fLTOI+U5NTnzakU6jD/Ri7G5DF1OVGkLmHc8tlJWk9PHOoe8SQk1h0esxu3PkpJhiUIhqowQJ0pvqpSvZJW9Gv5bqumJqer2SwAp4TUZ2D+/mSLmXfGselbnDVGKVIVh9ZEtIMY8b1J5KE9nVjf+lRjlZlyVhc0uxzy9DZzu5752KJiURE6YfYhnZ51ED6+WEdoq3bLt5OWcjGxw7iGX64QWGRytY7JvO4tQsSnPKs0HT/aHTFrR2gyRxIiRe9Luu9wbZ5A8TVlK4AwqzDkSZfCSGIcPjIvo4fu1+Sk0NVM2ZxfORXJ7eLUxe1G0wznqEywNPM2V/PcbDGjRiMDjwu5ghOcFJelw12nGTMJRORqepIdXQg7LjZTShXaRJYcsZTvW3fsx7jf+Srw8I+VKCcdq+p7qeDYkMORInElnVEmYLYvpNXDn0Wsy1LnRuVVanHVBJetvKkljXa6W5Riip8LP6dv7QJIrlIocBLvXqmL47jK/lWEMMAVeRVWZoCXv3ohon+eV29MZ3N+N2OtAZ7uSZumekdOKQVy2FC1i0bkzR6bQ2j0eJ9Q9mxEVxYrkrS8amVrkwZQYUp4qenjfYt+9morHWrMlBr3djNvjSlTND5oQdj3SJ3LD71zfyj42NWTmXqCaIPoSPz62mvvEV/rDzD7RYVdIltPmSdE26npHuobNFovv20SiIV2+vdCeqvPfP6HP7+xzONET45Kq775JN2aIyNfuyq5h92YfR6nRygBl86SXav/8Dkm1t8vX2s86i+OZvSjmkeD4aDBwjbzxa+hj2+4bMCRsKRlHhkZWzXEnQxCxq0+Y1THTWU2H10xW10R510J4sxBc+9j11er3M5BNLQcK0cqlHlwyj9dWjj/vRinAgjQZd8VgMpePQGYzoDAb5evFaMehW/78erd4g5WPZ98k81nc/+3+Oua/PvKf6uNi2qVCIrl/+it7775cSPzFjXPCVL+P+8IePsfkXhi2BiI+D9Y9wuPFxQrEo0aQJRVuMLWcFJutMwgmNlHtJwiZJ28D7Kgkc+NyZgiBhNoseu8UwqP9NVuKy0ssMoRvQJzewgtf/Wh3hjibe+uMPsT6zBpd/6M/tM9rYVVCLYfp0Flx5LnmV+bTs3Z2VEfa29UvA+mAwmSWxFxMGFm2c88v2U2VXJ092+4rZYTqL/GFj1T6tYbWDpINiBv/xxx/n4EF1FlmYloi+OdFE/6+AaHx/vtvPs91eDrfs5xe772B6YI987rHSC1g56dNsaf47SnAzBRY3dy6+kymFU46RVHd0rKSt/QmCQfX/CmiiYN6mxbRLQ2xEiuh8HTl5U9U8qryFOOxjZXD5O4E4R4jIkt+9dpg3Dvb3wOZbtBSnuym2QI7dKn8bf0KDJ4qsWnli0Cvin07itCEivXLNkGuCXGMae2cTjtY6XMkQLmMC41g9MYdXDiAHQoQjm8xlmM1lmIwFx/2OwrAjFuuWvWPi2pFEyNYK6KKILgpJDMrZTKFVPBiSPUxwFZDvtLAxuoGt0c3EM1JYgybNZHMusy2XUqEffcy1RsYu+GNS5qv44qRCx05widxGndNIzKGwLb6LxpBq1GDVW5lePJ1KR+VJEWepPGiso2vbJqKe/jYBGwbyxk3CMXN29jFhvy8qz/XeI8Sbm6nqSJHv7/+BEk4r1rHjsDndpLZvR7tvP6ZYTN5ihVZ2zclnzYgYjZEWwskhXKCFs2aXhXFHnJT09Fdwu215vGmfSb2lCls6zk1Lh/PJ5RMGBaGfKiQJ291D4KVG2TIit6lRi21OKY4FZejsp5Y3Kfrydq3diGuViKDRssW2lzvK7yGmjVOuK+Ws1FwWBaZT2pVLOmNoMhB+PWx369jq1rEtV88eh1YaeA1EjlbLDJeN2S47kzVJEhvqObg2RCKqTqBr9WFcta/iHvEyPrQ8e2Q5a1pmo2Rs7sfkHeHqsQeZHomjW9WKsrEOEsd+Fn1hoeyDlTLtmTPRWGwknv0b+mmLMU4QlvfvLf5Lvj4g5Ev8fC03P4Iux4BlfDXW6SPQO1UyJeR6fSc4OVB9owXfqiPyJCLkTnkfGytPkO/o76bS9HaEeLSui19E/Hh06m40uinOiq1hciL9FxKbyySJliBZMry4ynHCvI33A8T22uPZw1OHnuLZI8/ijfVXkMqtI8hNz6WleTT1nUcFABq0zKwREsU8ScbGFDtPqvF6YBXs0OGf0NLyQPYxl2smNTVfxO2afeyFMJ1mfZ2Hv71Zz/N7OqTERqDYaeajsyq5amYlXbHDfPel77IrumsQ6bph0g3Uuo9vzRrZuYvG664j5fNhHj+eyj/9UYbo/isgzDDWPPg3Nq98XK67iks47/Nfo2TEsZU5QRq6f/c7eu77q9iAaCwW8m+84aRc+PqCm0Pe3kzFrM84ZCBR88jl0dbdR8OmjzE3v5HxrnZJyoMJAzsSE6lnFBFBMHp7h5Qrvl8g3Ly0Mhsujd5owiACk02mwYRNEDjDYBIoGrSj8UZCkX3CZxSNNo3OYMbtnoo7bxoGo20Q+TuGeOr1aHTCS1JDIJHmn9s6eHJnO7GURvYAjCk0Y/Y3k0ilSGoNFJVVkltYkpFYqlU7SeCCfkLCflnRE8IinePOBIR7pSBkDj1MaNrM/L2vUd7bzX5XFTsKajlcNoolc2uZ6/DjrdsvyZZ/QKZYHwThPbqPS6DK7uf88gPSFj2lNRGY801siz9/3B4tEQb86KOPymuRTqeTjpHTpk07szJDISuKxlnV7WNVl483fcFBbTw1Ri0/bvs7C3b9UXXmzBtB6znf43N77pGTWnqNnq/N+BpX1l4ojTOEqYuaM5fpM9HoyctdSFHhhRg2hWn/2Q/QdqiD/GRemsCFCpHpKWnPajDkSSKWJ+zsc+dhNL4D23hgV4tPkrBVO9tOuudSSA5LcsxSii4MLIbKuepzCex9YxUd3/ku6Ta12hRaoOC/VKFPiWk2lZKbJwKOF5DrnoPBcHLnWkWJsnfv17NZkmVl18jKoAh99vRu5rGObh6LjMZrmopiKB30f6v0PpY4UyzJy6Wz9y0e3HsvjZF+C/Fh9kKuqv0ky9IL0DckpEmG7Bc/CiLAXVq+D8uRSyEnHAhhOvODDT+Qtvh9odA3z7qZKudgI5XjQVzn6ra8xfp/Pigl4H1VqtFjJjLvi1/FmT9YiSGMP15qfImNax+lZNVWFuxKYTpqHC8KL2+N1LByhpb95ZlwvQzENbLQWqhWrCxlFNWnSW9qIt7ZOygUuaqxDf2zz7G1YAR/mvcx6rBl1TG3nD+WxaMK3tVxKMZZ0b0e/C83kmhRJdZiMtw2qwTHonJ0juNf32R+aPcuOX55rv45uiJdTA6N4jtN12NJm+nKDWC8uoKxZeMHfcZUOEFzZ5D1XX42BMNsSiY4oEtJx8eBKIimmOSLMT7SydhkHSWGvSRtrcQdbSSN6ngplTTga5iDZ/8KEsHMpLUmiTV/MwbHGsI6Bxv1F/JqhzsbdzBnWB43LRvBzFIbPffdR88f/ki6T2ItPudRlEVfXIx15gwcy5bhXLGC9xr/JV8fEPKV7O7m4PwF2XVdbi62OXOwzZuHbd5c2ZcjDDF6/3mI8FZ1AGCdXoT7ktqTluJI7bo3Rke9n876gFzu6w7y9DgTh0vUg98dVDh7S5jxvrRa0cqEFovwYkG+/p0hwlBfb35dVsPEMpmxBtZr9cwsnEe5YQFdHcNYd9hHd7A/d0Igz2Zkbm1+loyVu0+uCdjjWUdH5zOyn0HID4+GGGw+vrVFkq4DHf1N9qLp+9o51SwbW8QB717u3no3r7e8rj6Zhqm2qdyy7JYTki6B8NatNH36M9IO1jJ5MhV//AM6x7vrnzlZdDXW8+yvf0Z3oxrIOvGsc1j08f+H0XziXrvogQO033EHkU2b5bqQSBZ/5ztydux0QIQ5H2MUkiFpYnAtPncqmSTXGGJhYT3DHap0KZ7SsqmnnLe6y7MyJKsuxricDiqsPkkShKSsPujmYCCf+IkCLzXqhIq8WPYt1SeOMdFSz+xqV7bs6RMuopnbyVb63m+Qn1ojKu5aTBYLhgw51PURQXE/6kHrb0KbFu6aIjbBRSxZQVLjIC1cQ4udKEZB9rQk0hriaImnNJLsiWU0rSWiQFTRICKCwoqMl5LPK31xCtlYBXVpTsUYq+1mprkXQ1c9Ye/by9YEBIEVUlTpOFgzjGGBl7Ht+JNKWArHwuV/gcLRQ28LMfGyfj0vvPCCjBTJzc3liiuuoKSkhDOFQ+GoJFvPdvnYGhg8CB9rM3NuQQ7nFbjkfblv1r0Gj18PgTbQGYkv/TbfSjSyuuF5+X+mWVNc6Y7Sl0stJNXFRRdTWHguRmN/hT0e7mLHXedifjKIzq/u6KkqG74LI0TGRAf1hDgc4zNVsUXkOCej1Z7aRF99d4g/vlEnQ++1Gs2QocFy3WWhyGk+bs+XooSlcqGn9VUif3gW0wsqqUnmpvF+LIkyzorbPUcSRvF5LZbqUx6oC1n6jh2flZb1grCOGnkbZWUfkc/5Yj7u2nyXdCIUcBvtnF8xm950HhsS1exnjNyPs9uNALOMrYxXtnKo8yU2hyJyQkTAnDKw1DeLc3sXUBurQF+oki1ThmzpnG9/jY8pMe7deS9/2vkn6YRp0Br41PhPcd2E6zDr397EoW+fb9ixldd/+TO6QmoPpjD8Gb9kOTMvuZycwuIhjUBe2vkE3Q8/yPjXm7HF4JUJGl6ZbcVYkem9OkoeWGovRQlF2f78s2x7/plsMLRQPkw46xwmzpyH/9bbiWzbJuXgRd/4Oo6rP8rDm5u58/kDWeXJwpEFfPv8MYwsenfXTqni2N8r5YgJYVgiICaBZharJCynf/sf9h6WhEvYw/eRXQGn0cmK6hVcbD6HgqdSpCNJDMVW8j41nkY9rPcF2eBVZYQyu/AoVJlSTDL2MiZ1mOGxt3AmtqBwrBlVH/SRXIyhUkzBMgzBUvxdtTT6CvDE+/dxJXEEJbqJgM7PgdpzWB/NzfrAibHMl5aNZLpbS889f5CKjHSmjcBYWyujTmL798vYE4GcD11G6fe/z3uN/5KvDwj5UkRY8lNPE1q3TgZypo5qwjaOmYx57DUyT0acR10XDsc2p+SEJ/loKCEztDob/HTUq3laYX88m3+zdoyZtWMsKDoN+lSaSyNGPlOQS0V1DjmFljM+4/peQoRYipPak4eeZK9nb/Zx4ex0bs25THIto7Uzj3WHe1hf1yNn5weiOs8qSZiQKc4Znocrk1dxshCN439f38Cjm5oJZEJrRW7UZVPL+PicakYVO2T2yu+2/47XmtUeKTGOqwxXcsOUG7hgzgVv+zdEeG7T9Z+V+5J1+nTKf/97dPZM+O0ZhKgCbH7mCVnxEtUoYZ199me/yPBps07+PUSY45NP0vmTn6JkAnxFEGbh//wP+vx3NiN+shBVrWd+9TMOb1ov1+fNqmFiai3W4GG5HsXK9vh4bCkvY0yHpGOiwH5/AWs6q/AmLHIwLrbDyTo+vj+RxpQTx5IXRatPkxakJakh7jMS8xlJp868Rf/7EYOI1tHSQX8bPHYdNKxRXzz143DOj0UK+JDvFYlEeOKJJ9gvBh+ib2LcOGlMYjaf3CD2VI6nHcGIJFvPdnk5GO6fXBJn+elOW4Zw5VBtOc4APNRD+qnPodkvcuOgJ8/KH0rsPBgyyRSjMqOeWyZfwdTqa7BYKo77WXz+7WxZ92GsLys4X7RCOCOtnjyK1Mdq6S3cP0iuKKDT2cnNnSuraCKCw2I5+V4moSIQvOpkr2fimA0E9+DpWSNdGb2+LRgOJnD9XY++S32PxBIX1s9dTF75We+IGB69PQTxisc7MRjcTBj/W9zuWfI3W12/mh9v/DE90R752g+N+BBfnvblrPW76LFr9Gzl+fYmXvHp2JSsJqyxDerN+5ByH7bQa2wIaegUF/4MxrvHceWYD3NOzTnSyfJU0ehv5Acbf8DalrVyvcxeJqtgp+KyKCa5tnzmOna1N9HjUD+DmJAZu3Apsy69Enfx4ApfH9pD7XSEO+TfzDPnDfnbelqb5XVoz2svk8w4FzryCph67oVMOOtsUnX1NH/+8yTb29E6nZTdeacaFJyBP5rgty8f4t61R0goaUnOr55ZyZeXjyTX9s6URoNcgw968b/YQLwxQ8J0GgLTtKyp2MlzbS+wv7c/0kH8PosrFnN+zfmy2mjQGVDSafbXeTDevx9zWKHVquX66RbaLP2/sZj8Ga73MkZ7iNrkW9Qqm3Edh2iZzeXYbLXYrLVY9MMwJyowhkrAo8uETkcygdPqtvQkUxyKpWiTfcOZiZRkB8noJnypNnbkz2a7dRgirl1gVlmOJGHTHAm6fvlL/E8/rf5hgwH3FVdgnT2L6O49WGfMGPQ7vFf4L/n6gJCvgUjH40S2bye4di2hdW+SaAljnvFZtGYnqViQ2NY/YaxyZKti5jFjSCbTdDWqBKsvvNjfday8SvR2dY6188QoI+16dZdZ4LLzo1HlDLee3gv+vwtEaKqohq2sW0l3pL9foNZVy0XDL2JF1Xm0dBtkntbaQ93SbatPFiggzvsTynKyZGxalXvIIEvxf17d38lf32zg9QP9unfhuHXN7Co+NK1cmn7s7NopSdcbLW+o75/WUBGqYGJoItd96DqGDx/+tt8p9OabNN1wI+loFOuc2VT89renbNn7TuDv7mL13XfRtHuHXB82bSZnX/9FrDmudzwp0XnXXXgfelhWecRFsvDLX8J15ZXH9C+dTgji9PoD97Hp6X/K9XELl7J8QSW6V74HvUcGvTZVNZ8X6gvZta8Ls93Bld/5AQVVNdn3Ef0/wv1x0E08lswsBzyeVhRp0Z7OviYpqyHq88nM/03Jx6N1h/E+tZJET5cc/BqqKrEtXYrG6SSded/B73Xs38u+b+ZvDP33FUyF7eTU1mFyq9XZlKLBdyifrp2FxPw69f+eRBD8O4VJm6DW0UOBOSQzoESFsSmUQ2vESfpM5frJ86WOwuqaQURLmGMMabZy8EV4/DMQ7lFDpC/4BUw8fl5NS0sLjzzyCF6vV8oMzz77bGacRjdDIf/Z4AtmJYUtsf6eHuGcNt/lkGTrnPwcCk0nJg/B4H7aO56io+1J8uvrqD0cQijU40Y9m6Yu5ZveNjwxHw6Dgx8u+CGLKhad8P2amv/OgQO3oQ3pGL7tQsKPvCivewL2ZWfh+ty1BF1N0rjD41kzyB1RwGodnpUoulyz0One3bUrGm2TRiGCbHl615FIqBM+mjg4ntJhe0UnHbu1hS6Kv3sbOYvO5nSgvf0p9u77howbsdlGMGniHyVxFT1PImNNyPwEhuUM4ztzvsO0omlyXQz3lN6YtHyX1u9HfHJgLNz4NpZ183pxmjcdBbTpVXdRe9rPh9P/R0XsFdYF9eyIiJxGdT+zG2xcNPxirhh5xdsqKY6G+BxCFvijjT+SZKjPaVfki5XYT65yqwQC1F/1EdrbW6gbXkmnRp3oFL1xo+ctZNalHyavvOKkP4+QB29a+Th1mzdmHxfH7bQLLpUSQ1FZ9z3zDG03f4t0LIZx2DAq7v7tcQOeG3pC/PDZfaze3Z6NJbjprBFyklRIl98NxOdt31fPyjWP8WJyDXus6gSfgJD0zi+bLyeDBfES+/g2fzhrjvGWL0RASVEWTvHbTWHKI2k6TfCz6Z3YbdsYxV5Gsh+rDETpg8hTrZD7mrxZa1XCZRt+jDPzcT9zIkWyVzUAUXoi9DYF2LO3lyOdkaxkOZ3yk4xuwZusY7NrEnsco6WyQGCKVs+nc3OYqukm9Nr9xA+o2V9am528z3ya3Gs/jvY0Tz69E/yXfH1AyFcqnWb2+r049Fpy9Hpy9Dqceh3W7ijG/b044mnsyQjm/c9hajiADhtpbS6KsYi4rYqwpWjIQUhOgUVKB4VskHIrv475WdmjuuUUGfXcMaKMiwpc/9FVrpOFaPQVLomCiL3c+LKUVAhoNVrmlM7h4uEXy9ysRFLHhjpPlowd7Awek8E0ozqX+SNUMiZ6CUQekqh0Nfdmsrk0sHRUoTTQWFCbL/vJtndtl6SrbyZR/N3qcDW13bWU28q5+uqrT8rxLPj66zR//gtyMGNbuIDyX/3qX3Iy27v2NV76890yB0xvMrHk2k8zYenZp2XfiuzYQftttxPdo86Ii9614ltvxTJhPGcS2194lpfu/b0kFhXjJnLRTV/DvO8ReP1n4CxFWXQzjz/6Bg07t0kHxStu+b7MXzqTSPb20vnTn+H75z+zEmUhl3FeeOEZPY7FJabH8xr19Xfj86mSUCGRKi66iKqqG6RleB+xlORNWhn3EcZ+8ucJRPjLG4d5cU+bNASx6NKM1HsoU9qlHffwYcOYNHEiOtFL1Uc2MyTR7N1PWcP92IOqIUVUsXMgsJDm2DiMNU50hcLsIiXJp5JUhiahQ5FP8VlTipQ9FlYPe3uiNRBKAl75Pqy5S10vngCX3wf5tcfdjhs3buS5556Tn9XtdkuZYWnp0LP8p4KokuL13oAkXM91+/Ak+iv2Fq2WpXkOzsvPYVmekxyD/m0JSUfH07KPKxjcN6gKVWGcQdVbG9B7VHfH0MxP87lUG5t71EkX4bgqelGPl6EmtsHuPV+W728yFjG18o9473kA3+NPqDlwWi05l1xCwRc+j764kEBgNz09r9HjeQOfb+ugVCSt1igJWJ9xhxhQvt1xIKWEvRsksevxrCEc7jdlUr+jjdyOsRjvaSLdohIx1xWXU/i//3taZNuiulZXdyf1Db+T6/l5Sxk37k7QWrh/7/38dttviSQjUtL36Ymf5lPjPoXWm5JOd305WzIPbyBE31ypPSshFDEYh1MJnmmr4/luH9ujFmrSh/gEf6JAOcyGkJ41QRPeAU1+412VXDHycs4b8ZGTlhD25eH9fvvvZQi5kPOLSo2IOrlmzDWySvN2iDc0UH/lh+VkW2LFWRwqzefI1k3qkxoNI2fPlwZNBZVDEyShsDiwYS2bVz4ho0z6ICb/pl9wKeVj1H4ocR4XZkQ999wjn7ctWkjZz352Ur+pMHT57so97GnzZ9UvN583huVji075vBuMB3m56WUpK1zfuh4lY5IiJlonhkewyD+d+cGppMaU8/JYO88rAbYHE8SPatgyp8OSYE2L1nHN5qk4Q/kkDQFapt6FtiQ9gGAJslWL1TrsXU9UHA+RYJxdr7Ww4+VmqbpSEScZ3UZv4gCbc0ax29EvkZ2Mjk+ljUzsPEBs92OkMpmPxmETGf7sQ7zX+C/5+oCQL18iyag1qpHCqUI015vj4qZgi0TISURxG7QUFTgoLCvGaTaxMxjhxW4/sbRqpH1FkZsvVxdRajZiehfW1/+p8Mf9srlVGHVs6+pPZRczu2fXnC2J2KSCSfKk2+GPShImyNiag910Bo66KA6AqGx9eEYFH5tVRWWeOtO0rXObJF3rWtfJdZ1Gx/y8+dh22rBELbL3QxAvx0lcIAIvv0zLTV+SmmrhGFh2151oT8Ks4nRbyJ/7+a/gLjm9Nsdi8Nz7jwfp+sUvZA+buCi7P3IVBV/6ErozeOwe2baZlb/4kTTqEJbzl37jNlyFRShKkqd+/gPZQC5c1D70re9SNmrMGfscUor5z8fp/OlPUbyqdERUAAu/8uV/mYFK3+fwejdKEubpzcjrRGN74blUV92Aw9Ef6H0iiNDcW5/anY1RKDCnmaQcpFznk/u6cPkbNepYYxbZ57bnCXjhVvA2yIfiqeH4kv8Phi3EffkI9K5/0cypr1kNJm7KONXOuA5WfB8MQ//9aDTKk08+yd69qtR5zJgxXHTRRVgs7zxzMJBUeKnHzzNdPl72+AkNyIJz63Usz3dyXr6LRbkOLG/j3JZI+OnsWiUDkMVv3G+cYSAvbxHFxRdLoiAHcPEwPHczbP6LfE2qdAr31M7k7iZVljivbB4/XvDjrETuaCSTId7adJkkPqJnasrkvxI/fERKkgIvvKj+XaMR90c/KmfE9W539jP29q7LkrFYTHVI7YPJVCzlieLzCkdZg8GpSgkDuyXZEjevbzPp9EB3Py1O50QZcJ9rnUXsvtfo/evfVav0oiJKvnsH9oXvLLR4qO+9e89X6O5Wv2NV5fUMH/5V9nj2cfu627My+KnuKfxPzucoac2RZCsVOKp/R6vBWD6AbFU70ZqOT6g9iSQv9/hZ3dFKyrOSi9P/wJoOsj+q5elQMa0RX/b3tmlhcX4ZF9csZ1zpcuz2MSclrRQqElGx29K5Ra4PzxnOt2Z/ixnFM972/4bWr6fx/10ng44LvvoVlLOWSGOOQ2+p8m+B2hlzmP2hqyiqUdUfsXCYnS8/x5ZVTxHoVtUkeoORsYuWMu38S+T5emDWX+v//C/BV16R63mfvk5eO05FQSHUK49tbuYnz+3P9oXPHZ7HLReMZUyJ82175d5ofkMSLtFzLtb7MD5vPAsql2OxjqG3Ps2YbQoTMiKcpAZWlei5d5gJnzUgK1qj2cMo9lClacFhrVIJlm4UpufHk27XS0fFvGvHYR7+r7su9CEZV9i/oZ1tLzbhzdjsi7JxOrmfnshONjmrB5GwqRYT1+mSTNz/MOFdb+E+71wKvv8T3mv8l3x9QMhXIpVmZyBMmzdKc5OfwL5uIsIGWg9NGmjXQcwowhg1RI39N9GI/m4hgvv6Km1iKStvBnXdNeDx7Lqh73XqY7r/8KpZg79BVsOePvw0baH+C71weLpw2IVcOPxC2dgrIA7DQ53BbFVMOBiK3KWxJU4+MbeaCyeVYslY8W/p2CJJ1/q29VmZgXivaclpbH1FzO7CiBEjuPzyy0/Katq/+jlavvY16RToOOccyn76EzTHcVY7XWjctYNVd985pIX8mUKyq4uOH/8E/0rVFUyXl0fR1//3jFZ+uhqO8M8f3y6/p+hhu+irN7P12afkbKu42AtCVjl+ImcKIqOt7fbbsyYkppEjKb7tNqxTB9t8/6sh+lUECesbSAqIwXl19Y3k5Lz9Z0ul0tJw5ker99GVmbSoMYWYkj6MUxtj/Pjx0vXPZhuiVzEZgw33kH79p2hi6mx0RJmFX3sd9gsXYp126jPSpwTR+/TEDRDpBZMTLvo1jLvkuC9vbW2VMsNeETCr1bJixQpmzZr1jj5jVzzBc8ISvsvLmt4g8QGX/xKTgXPz1f6t2Tl2GSZ8IihKjJ6eV2WFq7v7lWwkRp9Dq6hsCmJ9XNe+PU/BU5+HqE/KLbfM+BjXd7xMVInKnpy7Ft/FmLyhJyVCoUO8telSWYmqrrpRkhABYYDQ+fM7Zd+qgIhLyLvu/5H78Y8Pkk9LE6nwITw9b0iJote7IZujKKDR6LDbxxKNtmSlhH0Q1u/SkVAQrowrofi7rd+8mfgRVVqcc+mlFH3zG6dtcicSaWbHzutlJVGjMTJm9A/IyT+bX2/5NQ/se0BmNzmwcZ3nQyzrmIl2oJpFp5Fh4wMrWyI65p0gnkqxvruZhiM/ozT0jHysNWnlb6EJtIRa0Cn922qESWGBU8e84knku2bgck3H6ZyMXj90/7D4TZ6ue5qfb/q57K0WuGDYBXx1+lfJt5y4X7f3H/+g/fY75MRa+W9/g2PpUnnuXf/4wxxYvyZrMDRs6gzcJaXsfPkFaaAkIM7LU86+gEkrzsPqHEz4442NNN14I/FDhyWhL/n+98i58ELeKcQ1/e5XDvGnNUdk9qI4xMSk6leWj6LAYRqkptnYtlESLiHPDCb6VTIlwoUxZwwR0zgOpGtpSQ92epzgVbjxcIAZ3epvLCIM4tVNaGaGsZUL6WAtVkuVrPz2IRVL0vO3PcQO+6S2OO/qMVjGDgi2/xcinUpTv7ObrS800nZIVVsJ6A2dtPs2sDsnj83WCSgadcJglmYPX9I8yszxY9BdfR/vNf5jydfrr7/OT3/6UzZv3kxbW5vMNrnkkv4Ll/g6t956K3/84x+lJn7evHn87ne/k4PRPng8Hr7whS/w9NNPy4vZhz70IX75y19iPyoJ/d+BfPl7Ijz2k80YgnFm2vRYtRoS6TSbQwrdGg0FFXbV3j0jIRRyQlHCCqdS+BIKvqSCP6ng8fnp2n+QxsYmnre6OFCizvzokwlKujsxpVKEXS4CZiuB40hCThV2nZBKDiBohqOI3EBiZxhM5Gw67b+N5FGENr/V/pYkYi80vCBlIX2YWTxT9ocJ63eroX9wkFBS9ATjFInYgMz33NS+SUo0NrRvyJKui2svltKSXWt3SUmSgOj/OOecc2Q/yNvB9/RKWr/+dSnZESSk9Ic/QKPXn3kL+WeekBfEE1nInymImVJxoe4bKInMkOJbv4PpJHri3gmEG+LjP7mDziOHB5kvXPK/t1AzWe3FON1IRSJ0/+739Nx7b9Z+v+Dznyf349eccWJ9KhA9QYKEdXQ+m5WEiWqGGFCL5dsd44Fogt8MaG4XPUljdG1M1LXitJokAROZV0O+T6gbXv0R6U33okkrpNM6gsp5xGtuxHXF9Hccw3FcJOPw0u3w5m/U9dIpqpthrtrndzTEtWzTpk2sXr1ayh/F9UbIDMvL+2flTwaNkVi2f0v0fQy84NeKbZSfI00zJjus0t3vRBCVIFHZEhUuUekS0Rh9sNlGSqdCEYJ80sYW3ib452egUa3e+0afyye1Hg5G2jDpTNwy+xZ5jjte35OQIApMmvgn8vOXZD5jmtAbb9B5513E9qmyR11BPgU33ojr8suH3P8VJSK/V0/P67IqFg4fHiSXFI6zgmzl5c4f5EqYisXo/vWv6bn3L/IcKsLni797B47F/cHS7xZe7yZ27LxBkkCjoYBxhb9hY30jP2v/NR2oZQ4RjPuZjstxK07pgmeqVMmWUTgSVjpkPufphte7lR37byUR2i3X69OV/DG6BE/wAMboDtWxU07S6pljj7PAFiPPoJXVMFfOdFyuGdLd0mQaTB6ES+Ovt/6ah/c/LF1ahWrkC1O/wJUjrzyuHFVATDJ5//GgJNlV//gH5lFqdqUIMN/w+MPsW/v6ICMjUd2adsEljFmwBIPRNGT/c/OXviyjVkS2lCB1lgkTTsu2a/KE5cTRMzsy+ZQmPZ9bMpzpo/w83yCs4VfTG+snHkKOabaMpMu6nF7jxEG2+AIVNDJe38YUa4xZORZqcioxeauIr00S25fpe9SAZWIBzqUVGIpsQ/Zk9fxjH9E9PVKK6r58JLap/8KA9iHQfsTHtlWHqNvpJZ2RTubrD1Nme5HVulIeTi0ijno8LyuO86cvXcp7jf9Y8rVq1SrWrl0rc0wuu+yyY8jXj3/8Y374wx/y17/+lZqaGm655RZ27tzJnj17sk5Q4oIsiNs999xDIpHgk5/8pBywPvBAf7bSvwv5Ej0Hz3ztDSYYNeg1GuJGHbE5JeSPyye3zIbuJIP+RO/YP9o8fO9wK71JVUf8oVAvn31pJcbXX5NZSn1QNBqSY8aizJ1HYtp0EqNH49cbVCKXIXR9pK7vft+6N6EQOQ0N9uJb9ZGyfsI2kKDps5W2oytx4mZ+FwGI7wZC4y4ImCBiG9s3Djq5CgImiJiQWoi+rT4I4iYqXWLZZ3F/Se0l0qK3wFggM34OHFCzT8Ss+Jw5bz9oFfA+9k/avv1tSYJyLrtMSmTOpBnFO7WQPxNIxeN47v2LzAcTzdPCOSnvE5+Q+WDadyHlOh4S0SjP/PqnHN60QVb6LvzKNxkxYw5nAsHXXqP9ju+SaFHDtO1Ll1L87W9hOA29QWcK4fAR6hvuob39cRkaK+B0TqGm+nPk5S1+2/35cFeQ25/ekzWkseuSTNU2UKP1MGrUSM4//3x5vh4SXftJP38LmoPPydVU2kZAczWGi27CMqX89Ezy9NarMsMWtQLJ7Bth2e2gNx5XZigmB3fvVge2QkYprnMnIzMUl/R9oah0KBSka1dwsIHSRIdF9m8JS/iRtreXWcqMyOA+WeESvVaxmGog0CfVExWuouKLcdiHtsR/W6QUtRfytR+JaW8UVxU/qx7D//nUPk0x6P76zK9j1B27rfbvv43mlr+j1+cwc8ZTWCz9xFT06fifeVbKERPNal+IMJcpvOkmWeEXx+HxEIm04PNtklUup3PSkNI50U8qq12HVaKWc/HFFN38TXTH28/eAVqaH6F+05+xeIbj9E8n7C/i9+6HeMOpyvOK4nl8oftq5hbOyVq/G8sdJx0j826RTiu0tD7E4cM/I5lUyUKdeQW/Da/AH9iEJfga2lQ/iSg0u1liDzLL7JNVHwGLpSpDxqaTkzNd9oCKY05kVH13/XfZ06PuB2Nyx0gyPqFgaAIkJPON131auj4bysqofuRh9Ln9cQW9bS1sevpxIkE/4xcvlxNfQ+0DYn/vvf8BOn74QyllNE+cSPmvf42hqPC0bjthlrJm317ueHETjYntGJzb0Rr7DWK0WjNxywyCtkUkTCNkxIaAsDwZafAw1ZqQFep5BRWUyNDqoa/d8ZagtKiXpCoDy4R8HEsrMZYMJmFpJU3vYwcIb1FjiVwXDsM+7/S2AZzU+aB1Kxx8AQ69AC1b8CWL2B6+gL2Rs0hmgvEspghh/WGei8TY7RjFpc5Ofv6t63iv8R9LvgZCHKADyZf4KqL5+Ktf/SpfEzIqMYvi80mzgfvuu4+rrrpKaubHjh3LW2+9xfTp0+VrxMzieeedR3Nz80k3L79fyFes0U/X3dvlffMoN7lXjUZrObXKxa5AmK8faGazXy3Di4yWH4+qYEaOLXtSExeakHBRXLuOyM6danNzHwwGrFOmYJs7VzopmseNPeGFTUgX/MkUvmRyMEkbUIk7mrANfExU9t4tTFrNIFI2pFTSoB+S2Inl20lyTgatwVYpSRRErDGgNqALlNhKpNxiXP44/m/P/7GpY1OWdF1Wexn/b8L/k3LFQCAgJwzERIJer5eTEWLfPhn0PvgQ7bfdJu+7rvqwzMM60W/2XlvInynEm5vp+N73Cb76qlwXBKXo29+SspXTDWHMsPu1l2RPW/nocaf9/RMdHXT84IcEnlNJhL6kRJIux1ln8e+CaLSVhsY/0Nr6sBycCAj5V3X1DRQWCBMW3Ykd1PZ2csfKPTR61HNZkTbITH0DpRaF5cuXM3XqVKl2GBJ1r5J+5ptoMoO9ZKqYSOkXsX70OnQDJEGnDCGve/LzIGayzS645Hcw+rzjvry9vZ2HH35YKjTEZ122bNnbTqiIybMt/nCGcHk5EumX0IlvO9tlzzoUlpuNJ/1biOpSe8cThEL9ZgR6vYPCgnNlH5eQFwp3udOChjfhn58GXxNprZ71Y5bz2dBuGYY7MX8iP1/8c4ptg3OcxD6yectH8Pu343BMYPq0h9BqB/9WwkCo9+FH5ESL0qMOQM1jx1Lwla9I199TJddi4qb717+h589/ltdBUVUruf3203LOSCsp4s1BonW9eHdvgzYLOkUEhqdY5VrDXwqfJKSLoE1ruSrnUm6YfAM5lQVo3qPJxD7E4x4OH/4prW0Py3WdzkG6+EZeSC1lVdNrhHtfwBhVJxIENDoX1c4alphbGa+vRzugHmsw5OLKmSYrYw7nFJ5v28cvt/6GQCIgw48vH3k5N029acieQGEqVP/hq0g0NsqYlMp7/yzlgicLsa+0f/d7eB95JBtTUnzHHWhPQr5/ohDscLhOSmXFcSRu9d59rPN0sCmko0M0Z/X9fcVIMjCOeGo6kZrppHOsmDUKk60pZrtzmJdXzNQcG7Z3MEkabw0SeKWJyM5+Z2bz2DycZ1ViLLMPkv35VtYRXNcq153Lq3AsrTizSqNQNxx6SSVbh19WnV8Homg81C4jWr6MXYdL2fFaK5GA2ntpMGlQXB4WnDOc8XMm817jA0m+6urqpJ321q1bmTy5/0dYtGiRXBfSwnvvvVeSM6Gf70MymZRVMaGrv/TSocuWsVhM3gZu4IqKivecfMkm9scPobXqca6olpbwp9Jw/ZMjbfy5uVsKfoSU7+s1xXyqrOCE5EI4C4XWb8iQsbXZGfY+iCZ+29w5WTJmOI2hn+L7RlLpAYStn8B5hyBufYRuIKk7HTu82FbHkLIBlbZjKnEDiJyQWw6U94jvJBwLnzz8JM8deU5eZAZCOFddNuIyWenqG3x0dnZy//33y/3ParXykY98RO6PJwPP3/5Oxw9+IO+7P34NRd/85hk7sR5jIT91Biuu/yI2l9oE/36AnNl/+WXav/99kq0ZGcjSpRTdfDPG8n/xrN87QDqZpPeBB+j6xS/VnD+djtxrr6XgczeiHarn6d8AsVgXjU1/pqXlARRFrboLx63qqs9SVHTRCZv4owmFP685IuWIEenal2akroup+hZG15TLPKy8vOP0MwjHxa33k159O9qEOkiJMY70Wd/DvOAUB9eit+z5b8PGP6jr5TPh8j+Dq/K4++GWLVukukNck8R1RcgMj3dci37fdd6g7N9a3e2jI65WDPsmlxa6HVJOuCIvh3zjyU3IifwnIQHtaH8Kr0+ttAuIPiMh6xOyQlGJ1OneBRk9EUQf3NM3wZ4n5Wpv6WQ+bglTn4qSa87lpwt/ysySmcdUqTa+dRHJpJeyso8yetQdQ761UG/03HefrHj3KTmss2dT+NWvnLScLLJzF203f5PYQdXl0HnBBRR96+asqcepIp1MEW8KZK3f4w1+Kf8aiHpbE7+pfIzdqOqGcbnjuG3ebYzOfYeVxjMIn28b+w/cSiCgmoAJieHIEbfRbhjHI417WH3kn/g8L6NNqT1MwmlZY53CmLyJLLL2MDyyEkN6sAuwqABZ7ePYGYyyuv0Q9XEtVmMuX5n+FakUGagSEYgdOiQJmPiNXVdcQfEdt5/U9S3Z00PzF28isnmzdM0s/NrXyP3kJ0762ij6D0Ohw/0kK6wuIxERdJzGr8CGsI0NYTPd8f5qdBo9cctkouZZpDpq0R+JZU05l04s5rvnjZWB3qcLifYQfkHCdnT1+aRgHp2rkrAKR/ZcFHipEf+L6qSwfX4ZOefVnNL48m2rWy1bVLIlKlyi0jVwZCZ6YYcthhHLJekSDsEDkUwoHNjQIfvC+sw5yka5ueTL720v8weWfK1bt072eIkGZeH01ocrRbaPRsNDDz3ED37wAylJ7Aun7ENhYSG33347N9xww5B/67bbbpPPH433mnwJiJ/wVAbP4vVPdnq59VBL9oJ9UaGL22tLKTGdWp+DeC8xyySzxdauI7x+/SCJooDIw1CJ2FxsM2e+pwNCMUMcVFIDqmrJIeWRfZW4o58b6Aj2TiEuFcdU2zLEzaZJ4vVtpL7zBXqCh5lVuoTrJ13H6Jx+EiAmGcS+LCYDxCDyox/9KLkD5BUngpitFXbjAqIRveCrXz1jxOsYC/mPf1qGVL5fe/UEcZF9Un/5i9onZTaTf+ON5H3i2lOaPf1XQlSh22+9LWulb5k0ieLbb8M8+v03MHsnEGSgqemvNDX/NStrEqGeVZWfoaTk8hOSgDZfRObsPLVdncE1kWSyvoVxJg/LzlrK7Nmzj18FiwVRVv8U7dbfoUGddIs5z8Fw9Y/QFg/dozUIPYfh0U9Cm6pKYN5NsPQWOI59tjiWV65cKSXyAqJHWUwEiomVgQgrKV71CMMMHy/0+OU5qQ9iUmd5npNzC1wszXVg1+tOema+u+dl2cclnAD73fw0srJVUnwJBQXnSPe/fwnEkGTL32DV1yEZQbG4+XFZNf9QeqSj65emfolrx1076DzS3fMq27cLyVGacWPvlFW54yHp8UjL8N4H/iFVHQKOFSukg51p2HH67+Jxun73O3r+8EcpRRNmPcW33Ypz+fJT+2oJhVijSrbiwvpdBOUmB19TFGOYsGsP/tyDvFJi4qHOV6UBg1Vv5YtTv8hVo646Ye/Te42hpIglxZcxvPbrmIz5tESC/G7vM7x05J+DQrEVfSFJxxImFS9gvsXDBOU1dP43JKkeCBGV2ZLQUBfTgbmWj0y5hTGFs4+RXjd99ga5LxV961vkXvOxE37m6N69NH3uc3LyTZi0lN358+O6VCaTQULhw4SCgmCJSpYgWYeIRlVp60C0pHJ5JVzIvkiSYFRM7KnDbeEdnTCPJWqdS65rNrNzC5nlsjPbZcMWT/Oz5/bz5Db1vGU16vjcklr+3/yaIXNA3ykSnWFZCQtv6+wnYaPcUo5oqlKP9cCaFlkFk59jWhHuy0ag0b3D63ewCw6/pJKtwy9DZLCRDUUTYMQyqF0OFTOPe648xpxjVw/bXmhkyvJKqiee2JjlX4H/kq/TTL7er5WvU8XhcJRvHmjm9V51dqnGYuSHI8tZnHt6voOUKO7cSWiNCHpeJ+WKx0gUJ0/uD3oeO/aM9hmdbogA0uP1sx2vEtd3X5C5ge5ipwJB0qotJtyxMIGD+3GGA4zKsXPdBedTlnNy+TFdd99N969+Le+L3qb8L3zhjBChf5WF/JmCmDkVhhx9jmli8kDIMm2z33uZ5MCA0a67fiFdvrIh0l/5Cq4rrzhj8tH3EmLA09JyPw2NfyaRUCUpRmMhVZXXUVb2kRMGfW6o65HW9Pva1YqyWxNmlqGRqeUOLhZ9OifIwEt7mkg88A2M3apDZhojytjr0F98M5iOc9ztegyeugniAbDmwaX3qDO4x0FHR4dUXXR3d8vj8ayzzmLu3LlZYtibSEqiJQwzBPESlf8+5Bv0UkooKlzz3faTjv8QA+Te3vUZ44znUJT+aoOoVsg+rqILMZtPn2rhlNG1X+2T61CrKGsrJvEFnYeERsOKqhXcMe8ObIb+ibzDdXdRX/8btFoLM6b/E7tdNVw4HoRio+vXv8H35JMq4dPpcF12Gfmf/xyGAftEZPdu2r55M7FMX63zvPMouuXbJ1XtSsUVWc3KVraaAmQTZTPQ2g2yXytZ1ENd7IeEzfupT+XxmN9JU1Dtr1tcvlharx8tu3w/42gpopCrDqv5sqxOarVqJXZ3zwHu3vUg65qeJZmpcKfREbNOJ2o/izH5U1iSozBTt5+i6FqZExiNikrSYMQ0TsoKFpGfO0f2j4kquecv99H5k5/IKlbFH/+Afd6847r9tn7zm6QjERmYXH73bzENGyajCUScQVBKBfskg4eOiSjog/hVu/RjOaCfxaaghgbfflLhnWjor0gnjLU4XAuYXXYWCwsrmJVjo8JsHPI6vKWxlzue3sO2JpV8iurXN84dzQUTS07rdTvRNYCEZYZqphEuWQkzVecQ2twh+8DEc5ZxeeR+ZPTJ9RTK6tbm/t6t1m1HVbdyYPhilWzJ6lbJv7QIcabwgSRfZ1J2+H7t+TpZCPv5XzV08NvGTkkAhCzli5VFfK6y8IyaTyh+v3SXE1UxKVHMND73QTQnW+fOkSdGUR17P5sCnA6IMNOhJJJ9ZiXeZH8lrjuepCEapy02MFfm+MRsmNUkyXSNuG8xUW01kWvQy5OSaDrv+b0aEFnwpZvI/+xn/2Ms5M8ExDbzP/UUHT/5abZPRLhBFv3v/0hHs/fycwVWraL9hz9E6eru/1xf/1/0+e/9rN+ZhqjStLY+REPjH7ODIIPBTUX5Jygv//hxqzNJJcU/3mri58/txxtRj6dqbQ8zjS2ct3gO8+fPl32Tx0N88xp45maMKbWSldLnwfJvo51xrbCuVF+UiMDqb8DmjN1x5VxVZniUZGYgxLXqmWeekdcgkVEm4iGqqqpojyUyDoVe1nqDg8brYrCmGmbkMD3HdtKRHXLfCe6WksL2jqeJx9WmegGzqZSi4osk6bLb/3XOo2+LRBRevBU2/F6u9roq+JQjzSG9lmE5w7hryV1y2Ucot237JJ7etXLwPWP64+j1b+9gHD1wQE5k9OU4aUwmDFdcReTiK7E/9ySR+1THUJ3bLQPaneecfdz3SkWTxBr82VBj0b8lSzUDoHUas+YYYqkvsNDc8n8cPPhdgkmFVeFi1njVCIQCSwHfnPVNllUue18MLE+XFHHUyNukwUYfhAPw6iOr+evehzjc298bltSXELUvIWpbQInVLUO+l+SkGJfeJYPbj3S8iCPdb97RB3FOEE6K2nVdpJ7YjcmXQ80DD2GqqRnUi9z9m9/Sed9vSZSk0S0YjuGCqUQSjSrJincc9zsZjQWYrSNpM05lH6PZEctla/c+4v51GCOb0aaj2dfqTZWMLFrK+cPO5dySERQYT95tVkRqPL2jlR+t2kebT33PaVVumQ82ueL0ZnEleyJSjijNNjL7rNhHHWdVko4m6Xlgn5w4MNW6yLtmLFrTENf0YOfg3i0hIx4IESQvyNaI5aoMW3fmnJXfK3ygDTeE2YYgWH0bQlS1jjbcEBa+wjFR4Pnnn5fW3P+Ohhsngxd7/Nx8oJnGqNqEvSTXIatdYsD+r4bIzZC9YuvWEXpzvRp6OwDGmhq1KjZ3rrQA19n/PXtWTicCsTj3rXqOt5pb8VlsWGpqibvyOHKSxKzc66Foz07KO9sZM3M6E89ZniVm/0kW8mcCor9REFcR0iwrTA6HJK/uq676l1dsxbEjKnLi+BEwVlVJ6ZNtzplxTXw/Q2Qytbc/QX3D74lEGrKW4OXl11BZ8UmMxqF7unpDcX7+wn4e2NAoxxd6FCbo21hSonDZxRed0MZdZOFEHroP46GfYdCqxC/lGo32gh9ATgU88gnoFANHDSz8Giz6xnEHF/F4XJKu7dtVMicmDaecewGvhOKSdAnzjIEYbTNLsiVI1zi75ZQG4qLnpL3jKVnlGmihLhwCiwrPk06FwuDgtBlnnAnsXw1P3igb8RW9mbsKivirKYXNaOd7877Hsqpl8mXxeI/s/xKOjIWF5zN+3C/lthKZSj2hGN2BOF3BaGYZkxlxYtkdiOE8tJvz3vwno7vVCIoUmqwRxJ4R0znwkRuoqS1nVLFD3qxGPalIUpKsvluiJThocl9A5zINIlu6PHO/VX0qwYGDd9Dc/ACbwzqe9NsJJBPSWOLKUVdKYwmH8eSUDe9nvJ0UcSD29uzlkQOPsLLuGSJJ9ThIYyBmm0XEvoSkcQQWnY5FuXbZz2iPbGHVru/iSHUwzJSixiRiWI5qD4iDqd1C8ayPYnKUE/Lto3fXC0SNPaROYE4p3DxtthEyG0tnHsFhhrMjns+mgMJbvgDx8H5M4TcxhTdk+9gEbKYi5las4OMjL2JywbuXgEfiCn98o47fvXo408cKl00p43/OGUVJzul15016ogReayK0qSNbpTXWOGVfmOgDS8dTsjcs/5Pj0Jq10Lypv3erTVS3BsAsqltLM9Wts8Bxeiu3yWSIcOSIdMu1WCrJcU7ivcZ/LPkKBoMcOqQ2u06ZMoU777yTJUuWyJ6XyspKaTX/ox/9aJDV/I4dO46xmhdSj9///vdZq3nhfPjvaDV/IjRH43znYAvPdqsnu1KTge+OKJMX8PfDLJowCojs2JklY1KiqPT3MKDXqxLF+SoZM48b928lUTwdiEQiUi5bX18veo8l3wABAABJREFUf7MLLrggO2nQ1wPSEIlRF4lxJBzjiFhG4nJ5MsRMVMlqjlMx+3e0kD+jvVW33U40Y/0t9kVBfE5X7svb2uL/+c90//4eaYsv+s/yrv8Meddd965cuP4TkEol6ex8lvqG3xEKHcg26JeVXkVl5XXHlc3tbvVx21O7eatenZm1a6LMNDRz5dxRLF26FOMJevyiBzqJPXwnjsTf0WoyxgFaPZpUEmwFcNkf1AHHcdDV1SXdDDu7uuhxuEhNm8Nui5P94X5Zu8B0p1X2b4nztThGT1XyJbaLsIf3+VRbcnXbmMjPP0tWuPLyFg0KWn3fw98Gj18PR1Q584bcMr5s1xDQaVlWehWzXR+lJ5SkpaeJg01r8MfsRBiGL2bBGz7xuTCLdJqZHXv55J5nqfa34zNa+e2ky3ijbLCLmrh6lul01CgahqNlODq5LEOLMdcyuLKVO7SdfyLRy46dn+NQ90Ye8RjZL3qYRPaaq5Zb59zK5ML33rntvZAi9iGUCPFM3TMy72t/74A2EWMlAdsSYrZ5pLXqNWaSw4w7cYA9DX9GEztMjUnLh6umMdqqx+/dTFLpt7sfCiJSQBAsm7U2S7YUUw1bQlo2eIMyH2+bPyydmvWJBkwhlXDplH5XPqfJzbnV53DBsPOYVDDpjIyx2n1RfvLcPv65RTU5Mxu0fHbRcK5fOBzLOwzOPh6S3hiBV5sIvdWeJWH6YhuKJ0w6nkZv9lBg/A66uHrdz6JkkiRbyeFLiZWMJ5ZWiCVjMjw9psSIJqPElfigdbHM3o56bSIZRaN4MKV6Maf8WNMBHJowOZoIdm2/pLPTMJ6PLFCNet5L/MeSr1dffVWSraNx7bXXyupWX8jyH/7wBxmyLOQkd999NyNH9uu/hYXv5z//+UEhy7/61a/+LUOWj+eCdU9TJz+v75CZWiJ49DPlhXy1ugjbSTZhvxeQEsUNGRfFdW9KI4+B0ObkyFl+0StmFxLFsn+PHqJ3CiGNFY6GohdEDAZF72Jtbe1J/V8hqThyx/fY+/oamotK8F/9MdpHjJIkrT4Sf8fETKy7M8RMWsg/+yRr/vHX952F/JlAWlHoffBB1VUwEJBBl8Kmv/BLXzqt2T4DEdqwkfbbbydepzY9CxdR0X8mehP+i36I8NTu7pdkYLM/oDprajQGSko+RHXV9XJW9Nj/k5ZmHD94Zg8dAVUVUKr1sTzPyycuO1dO3p2oCuZ/chu6Hb/CrnsGjSZJTD8Fv/1bpPT5ct+QYy/xj1yqSo09kUYeTDdwKK+Ihvwy/AMmKPRpmBnTcFZUx9KYlsK0VnUXG/A+mgHvN+gxrYaUJopXtx6P7iX82rdIazITWWkNTqaQx3LcmoXotLYB79P3HuIE2//Y0c+L59Tvc/Rn6X9+qM/X/75HPz/wb2Ver9XIMV5vJE53JEFXKE5POE5XOE5PKK6uh6Is8TzItfH7MaDQrLHxjWI7280mkqHhRFs+QloZ+jouHHzz7SbyHUYKxFLeN6n3M8sC+ZwZh1FDZNNmlMIKDjTG2X2wh31tfg6FY9SRouc4nrkmvZYRRXZGFzsZnamQifsFR0UViD6irds+zbNdbTznN5JIg1Fr5LOTPssnxn0Cw0mYDfw742SkiAOP053dO2U1TEgTxcBcQK81Y3DOo9W8iKSx/1i1pAOkgxswRbZQrfdz88z/YUJvlCN3f5VYZYyUTYOhLY0x6KTs2m+QO+U89HobnbEE632hLNnaHYxkf2Vdol1WuGzh9ZBQjTDk5zbYZdX13JpzmVk8U8bB/CuwvcnLd1fuYVODOnlUkmPm6+eM5qJJpWiHcCQUhi1DEZ2B68d7TutPMHm7jdLWYWjTffulID16dLRhtdzK9pwEG2xO1lnNtGlSkkAlM3mNJ4c0di0U6lMUGtIUZJZiPU+flmPY4yGoQGdSS9IyiRsWP8p7jf9Y8vV+wfuVfL3pDfL1/c0cCKsnKNHQ+aOR5Yyx//tVIaREUcgTBRlbv0Ed8A6AGICqxh3z/i0liqLPQ1S2wuHwkLddu3YRCoXk/nX11VdTXFx80iSh7du34Hv8cTm4Kfn+93FdNriXMaQoNEbigypmp0LMKg1ajA2HMDYexuXrYXxxER++6mrKPgB9R8muLjp++lP8Tz0t14XzmegFc1500Wmb7RSObJ0//olqBiD+Rn4+Rd/4Bs7zz3tfVK3frxCXM49nDfUNd+P1qiHmIhusqPBCqqo/i9024pj/E4ol+e0rh/jj63Vy4kpDirG6Dq6dXsRF5y7PKiaGQnS/B9+jr6MLHSSamiEjUI9GXANv5ml4uDjM9oIcosb+gbhJSTO3O8mSjiTzu5I4T2W8InmVQjh3L/6SdQQKt5DW9/eamPxVONvm4GyfhT723kU7iHAPP2k82ZtKXnoHrPc95yV9UlEgkzWH+JXh11Rqu0ii4fc5bv7otmFIupnRfC210WrENxYesLkaDXlaA06NVjUx6SN92qFI4AAyqaRRfIOrkQL6QiuhChsNTgN1ujQHvCH2twc40BHMSsKORp7NmJUrlttbaff8iNeiHbRlMp5mFc/iljm3UOWs4oOCU5Ei9sEX87GybqWshtX51AkpgWLnKAyu5exhMlEGEN1UFGN0J5OtMb4VKcb5lVvkw6Zx41Du+gWbDBY2eENs8AUHZeMJaJMeShKbMIXXExiQc2fSmVhYvpDza85nfvl8uX4qSKQSkpxkSY6o8gxYH0h8xHOySjRgve+10WSMupYcdu4fTiSqGg7Z7F0UVa5Bb20cVGU6NSIE+UmF+ZEI8yNR5kQiOFNplLSbQPIyQsq5pOk/Jwa0Ib5WdSeN5qGNSATExILYTna9gWKjlkJ9mny9Qq4ugUsbxUkEg+b4n1HY8Sv6fGKaAgJKPj2xfFpDeRzqcVPnMdIbTPPJOWP5znniHPze4r/k6wNGvrriCe443Moj7epMSK5Bx63Dy7iy2P0fMViTEkXhopgx7hhKomiZPClr3GEeP/5fKlFUFOWERErcjn5+oHvm8SAIlyBeJ7uPie3U+o1v4l+5Urp3lf7oR+RceMEpfZeBxKwuLAhZ7B1XzGS17KiK2X8KxIRA+x13ZKtS1hkzKL71O5hOsjo5FEQ10fvYY3T+7OekfL7+6tqXv4zufXCe+XeC17uJ+vrf0uN5PfOIhoKCFVRX34jTMf6Y19d3h7j9qV28ckA1MjGTYL6ji69+aAFjTmDdn4opqoudaCJLp6VxXlBReDUa5blYmJdjEQZ2cNlSKVaYrKzQW1igM2JJayRplKwjszxmPdW/Lp4LpffRw4t4NK+Q1PQ3tRtTxeQqS8lNLMWSqjzh+wz1d8R3kCOCAY+nh3g+lUrhV1L0JBV6FAWPotCTTKlLJYUnpdCTEssUvSkREXzyEFcrNxp5y0Mrl7mDblq5zCdCteF32HSqDHGH0c5Xipz0aM3c2HEl53rnczpgKLZJCaFRSAlrnOjsxuOaI4hw733tfumsuT9zO9ITUrehgDaKqeB5DO430WjSaFI2anVXM7/4bMaUOCU5q8qzoTtdeUr/YVLEQZl4nVskCXuh4QVJaARsBjuTS1eQdp7FmxE3nfEBY4R0ivJkD2PCsCO/hI7E4MG+2OI1xjBFsY2EfOto9+/LTgVo0VLjqmG0ezQVTjV3r08iNxSBOhGhUtJDE/R3inRKT9wzn3jPEkipRFDv3IapcBVag29oIqQ3YdaZJSESN4vOwPhwmCn+HiZ426kIDTbKiBgsNBeNpL1kPN68qRQfrqBwjwgAV/fTtAYi80wwNweTzogm6SEd7/j/7J0HeFvl9cZfbcmyvPe2Yzt29t6BkEHYo0AplA2ltKWMUtrSAW3hD7SUlg5WW1ZLoYyyV8hihSTOXrbjvfeUZVlb/+d8V5Ilx85wvH1+z3Ofq3sl29fS1b3f+51z3gOHrQa2nmr09FSIRtNUizkwMpH+qVCnwOJKQps1FtXGaBS3huFQoxbVbZa+3jUBXD4/CX+4gmu+JjxjRXw53W78u64Vj5TVC4c8+ipcmxCJ+zLiJ9xgt6/VtnnnTqm/2Ndfw17ZT4rikiVSb7Fly0+pWS4NLCwWy3GFVN+FXj8YSBjrdDrRz6fvQufXrFmzoDnJuh7qRVP743vR9emnQowmPv44QtafjaHC0m3CR88/i7z8fLSHRsKRkQPdgmWolSkmrTCj97z1xZfQ8tRTcNM5oFQi8sYbEPW970Hep0fTibAcLULDr3+Nnn3UcBLQ5OYinurKZo/+DWU8YzQeEjVhzc0bfPuo3ikt9fv9pjltPdqE+986gOpOaSY8WmbC1TlK3Hr5OdAfp0chTYB92iL14PqyvSugrUSQtQdZnS24ecZUXJqbBdUgBtdmc4UwzmhsfE8UmPs7u5G5RFzcRQgNmTfoyTYaCnRSup/XhMJkE49bPMYU/utWk0203ThZ6JAigtTHpP1ROl7fdYRePaD48IlFj4ikyQoceg2yT++FzNaNbqUGP48wYIs+CJemXowfTrkchYdvFwYtyUk3ISXp+mPFJSnDAcSvKloHeZDqtM0Sjja04cW8B/Fl19ewyaVJN3vHPFibzofbGXhOUR1PdqwBU2mJM/hEGb0/E5nOzn2eVMQjJ0xF9Kfd0o53S94VaYlVXb3jgDnRc7A45WJUIgPvNDajS96npYTbAaWtDKqeI9D07IbSXiOi3iMJCR+1Qu0TQlplryASSx+R5L/d97UWmxrv5bnxeb5NnMpqpQzfWhyFG1ckISJIL/4Ovc7XlJpqKEs2eZwJPwOsfYRawtxeZ8LE+b2urp7voaWzGa3bdsGxUwalzQCX3Iam6S/DGL8dbgw8FlAqw6DRpsEhS0KnPR4N3dEoaY/EkQYDipvtA0aPCb1agfRoPTKigpEepUeG53FaVBAM2rGRqsvia5KIr4NdZpy9Wyo0nxmsw++ykzAvdHyl3w0FtupqX1SMrO29KYp0cttVKrgy0iGfNx/uablwJSejx+kcMDpFy2C/Fv0JqYHEFS2U0jRgo9dTNGWovfMuYZcsU6mQ+Oc/w7D62NrI07GQ/+SpP6GrtXlAC/nJHDGz1dSi8eGHYdqyRWwrE+IR94tfIHj16hMOhkVz56eeEiJONHcOCkL0HT9ExDXXQHYc+3Pm1DCZilBZ+YywWfc2tAkLWywiYRHhywM+J3LH++cXJfjz5mJY6FKgkiE9qBtXn5mFlClpUt8+0aTdiXaHE4WmHuR1dgcM3WJddsTXlCO9pR4LI0NxxWWXCTv5U8Fma0Fj44dCdBmNvU5iZCoSHbVWNBOOiFgJuVw1cMsEi8Mnmo4VUr0CixZ7nx5UJyI8SCUJqmOElNq3HeMRVMphbGkiNbW+yee29lqIAY+Fh2JK9Az8bNrZaKt4WMysz5n9AiIjV2Ikqe4owC+33IS9XZI5S7wuDL9Z/ntkhc4TkbGCeqMUJWuUImXWPk2XvdB76q0hk9YGZMUYhtxoYbylInpxuV3YWb9TiLCtVVt9qXahmlBclHERlKpovFRVBJNLBqWlCCpbCWQ4dqCvlClF/7gwbZio6fIKneOJJP/tk30tvcYnhIaQw7Wdoh5sZ7nUxJi+f/eun4rLZsdCXrvL40y4CWiUGrr70IUDU9ZIYovWwdGitQe5yXaby9BjLhdrmgSiKJb385E5NEjcfwf0bdPhktlRP/tpdEcdgkaZBLk+HSZnApp64lDZGYn8plAUNsnFdWcgqDYzJSLIJ67SPUJrSrReXFPGeiYXi69JIr6IB4prkaJT44bEqJPu+TIeoVOVUvX6S+ELWLq7YWpvh9lkgsXlgnuQ7wlFnAYSTQMJKcUouDG6LBbU/PAOdH/5pehRk/S3vyJ45coxZSFPwqzS48I4WGFGfczSxrgw69qyBY0P/R/sdVJRdvCqVYj95S+gHsDCvGvLVjQ89CAcdVK+vGHdOsT+/D6o/JrEM0OTISD669mdaDDVoKj+A1S3HYDJrYUJwaJg362fC4siRhJVXmFld8B6CrfIWcE6nKlXwbnjSzirykUmwhlnnCF6TZ7stYHsk1taNgkrfepZRQNSCTkiIpYjNvYiBIWchTazSgxi+otM+YSVySqE5KkQqiNBpT5GUEX3E6FSn0yz1ZHCYQO2PAh8/RexWabW4p7oMDQbonF7Wg6izVtFlHDRwveg1Q5/P0mny4l/H/wLnjz4vBDw9E5dlbkedy1+SAzA+/8ZNypbpRoySl2kFEZ6XEkOc/2chhQgTIvU++rJvEYfNHjtz3hhIqci+tNsbsbbJW/jzaI3Ud/dW4s0P3Y+wjXh2FG/AyZ7rzV8siEZ56WfJ5aMMKlv3HgfK2040oCHPziMqg5J6MxUVOJXihexSO51jpSJ6JY7cy0saXNgDgmGmdIDybq9u1ysLRa6jw18/dNoEqCkNEFHCiJ3LEF4Y4QnVZNqZ+WoghP/gg0bYT9G5sYYND5xleETWnokRwRBNZwTNcMMi69JJL7GI3Takc3/qaT20UIpgYNBpVBA63RCbTJBZeyCxmaFxiotWpkcIWlpCJs+HeEL5iM0PV1Eq47XdHWsQFGT6u//AOYdOyDT6ZD89FMi3XIoGCkL+YkmzEQk65ln0frCC+QnDZlWK5paR9x0I+QeC3N7fb2IlHVt3CS2qbl47K9+CUM/Tq5M7zXDTDVxdinqRMLIG32SxJJDWvs/9ryWltNBeDPYXXDZXFQxD7XDgZwwNealxCJMpUScRoXVkSEwlhbj3XffFX28aELmG9/4xkk5lFK/p7rGL1Bb/w5MnVsAvyatnY4slJqWYV/TfFR16oSwGig6MhAGrbJPql9vZMp/HRlM9SDjPJJCTV7fvg3oboJNJsfvI0LxusGAS2PCsUJTi7DQuZg/75Vhtdk/2nYUv/riRyjolNLgMrQqPHjG45gVP7jvt9nmQHGjKaCejNZt3f1HEHQqBbI9roveKBmtI8dZ6uJgUxH9BfC2um144+gb+KL2CxEd80INrNenrcf5GedjeuT0MR9ROSmcdqB6p9Rzq2QTrA2FeNF5Dv7quAQmSGnwZ0WW4qa5RQiNtMBsqxX1WJSWOxBKZQg02nTYZYkw2hNQ3x2N0rZIHG40oKTZ4UsTpKvGT6DF+ZC+Vz1wQSeunEC7WoaSTAPkMyIxJdaAtCg9gjVjf3w1GFh8DTMsvgLpT0idyICC3P4GA4miU4lIkZBSqXrTcmw1NVKKomj0vB0uozHg96tSUyTjDnJRXLwYilNoQTCSOE3dqL7tu8ISmeqLkv/+LIIWnNxN6XiMJQv5gYRZudmGBtvxhVm4UiEaiY+WMLOWloqmyOa8PF8DcYqCWYuK0fzXv8JtNp9Wjdh4hRwFpeiSA52etL1AseRAu0c0ScKq9zn7ad6u9Aq5EOxhKgVClUqEqxQwyB2Qm4/AbcqDztWGYJgQrg5GdsJ5mBK7GhEaHYIVchF5eG5LPp7YUgqzSxIouQYrHrt6CXKSY7Bhwwbs2rVL7Keek5dffjlUWr2vhurY1D8L5PZ8pOi+Qm54Hgzq3pn4RnMUdtYvwI76BWg0x/T7v9Dgpb8IVX8CS6sa54LqVDE1A+/cJtW0ANgSpMP9URFINShxVXg3slKux9Ts+4f8z5KpwjMHnsGLR54X0VaNzI0rE1Lww5UvQ6sh78Whg4ZudF55jT0K6il10ShE2kDinM4HIcT86skyY4LH9PlxOqmI/tSb6vFOyTvotHVidfJqEQVT+NUxjVs6a321W87yz9Ej74ZZp4A5SCGtQ0PRINfjjdK1+KJmGdyQQylzYF3qVpyfsRE6pUW05dBSOw5FikgTbO6J9aQJhqGgUY7Wbvvx0wQjg0T0Kj0yCGsb7Egskco+NJlhsNV1wW32CLRwDQxnJUM/LxaysRQ1H0JYfA0zE1l8ncgCvb+FxNdgoFScUxVSx2uAeqqQLbvl8GHJuGPb1+g5cEDU3fgdoDA8EL3Fli+XXBTHQESMeqJVf+dWcbxygwEp//g7dHNOvymnsaUZG57+k6jxIjLmLcTZ370D+rDRs6oez8JM1Nx88AEaH/0dnK29DTkJ3dy5iPv1r6Gd2tuDcLxA/5fJ6To2+kRCqW8kqk+kin7udFDJZAhVKoRwojVFnnoFFe2Xtr2PpddI2+rj1Ffa7Z2oqfkXqqpfhMPRIfZpNPFITb0VCfHfhEIhpYq1d1tx33++xIYyi0iuUcCFmcEmWKhe1K2CyhAJu1KHli4ruv3d1jzEBjViSfxuLI7fg9ggyVmRMNqCsbdpPkq6lsMqm4pog7ZfYRXjWU+kWp9hgbIkdj4NbHwAcNnRqFDgZ9GRqDSocVOkFevm/hmxsecP2Z/bXrcdD27/LapNNWJ7ls6B23MvweLpD55UqtxQQamLFa3dKCQx5o2UNXahstXfc7NP6mKUXogy/0hZcvjYSl083VTEiYLbboG14mOYKz5Cd3MezM5mSWTpFLBoqZXCwJ9Zk3Um/pN/AQ43S2ntQSoXpkQBnRYtajpsx3UTjA3RSEYXwuSitx4rKVwXkCZI94auLdUwbqwU2/ol8UJ0mb6shcsk3ZcVoSTCkqBfEDfhRBiLr2FmvIivE1mg9/fcyVig9wcZRxzPXKK/hYTUWAr3O00mEano/orE2DbYKqULiBd5SAj0ixdL/cVWLB+wlmdYj7GjA1U33wLLkSPC1THlueegmzH9tH9vwbbPsfm5p2Dt7oZSo8FZ130HM9esH1Ofz2CFGa3LT0eYCSOQwQszEsvUnLn91VfFORTz43sQdtllwrxkNLG6XL7oU6dHLLX3jT75CSnptZKIOkVvhmMIUcql6BMJJZUCYZ5I1ECCSqyVCgQp5MN6TlLNVW3dq6iq+idstmaxT62OQkryzUhMvBpKpRQJ33m0Gj97bTfKzcefDKLGu+nhFiyO34vpETsQqe7tT0T9ctTBqxAVfSGS486EQTf++jGOeer2A/+7GWgtEYYo/wgLwXPhIbgyWo7bznwHev2U0/r1bZY2/GHXH/B+mdT3L1ThwhXhTlw+59dITPwWxgrUz67IY+rhX0/Wbu7/ehikptTF3pRFb00Z1fqN51TE8YLdbhTGFuRsam4/AHPLbph7KmGWd8OlGPj6p1AEe9wEk31ugqXtUppgcbMTFvvAk18UTZccBD1GF77HeuhPMU3QtK0WHe972rDMi0HohVNg3tOIrs9r4PI0tVeEqGE4Mwn6RXGQjeHo66nA4muSiC+qLcjLyxtRC/SBFjKoGI8D9RO52HV/LUXFhIsi9V7yQ5WS4ouKiRTFU3QzG0zz3aobb4L16FEowsOR8sLz0B6nB9HJWshvfu5pFG6T+uXEZWbjvNvvQXj8ydvzjydGU5iREQdFKofyPHF5zST80/Q80Sd/seQ1kPA3lOgZZA2lF41c5hFJfcSSUukRVIHRJxJYtA5RKES6yljG6bSivv5NVFY9C4ulVuxTKkORnHwDkpOug0oVJmpQn/lwB74ubUNuRgqSokJ8EarIIAfk1s/R3vo+2tu3+xwWqelzRMQKxMVejKiotVAqJ5877YhjNQGf/BTY97LY3K9R46fRUZgWE4pHzv0UWtWp38Np6PRe6Xv4w+4/oMPaIcxVVgbbcXFUMBbOegrh4Ysw1hGpi11Wnxjz1pMVN5kGNGqhyKvPAt+TvjjSqYtDlYo42rhcVvT0VPtEVjcJLbGUwW6X3Ar7Q+YCtO4gKJRJMKmmo8GegsqOKOQ3hyG/UYm246QJqhSSmyD1k6M2CPuq232C7Oxpsfj5ebkiEjoUdO9tRPubReLSp50WicirpLFK9+4GdH1WDaennYfcoILhjGToF8dBPs4j+iy+Jon4oijVI488csLX+QupkxFVQ2WBPpEQKYpHjoiIGKUp9uzvJ0Vx1iwpKrZ8GXQzZw5piqK9qQlVN90EW0kpFNFRSH3hhdNq6Nu/hfyVWHzplVCMgdTK0RRmAaJsEMJMpDDq1CctzOgy3CNqoQLT9qSI1MDRJ29K3+lcxGnQKNL0vOLIK5S8gsrznL+g8gop3Th2pTpZyAyjofFdYVPv7a+lUOiRlPhtJKfcHDDYo8L11rYv0dDwLlpaNsPl6p38CgmZI6zhY2POE5E0ZhQ4/D+4378TMmsXjHIZfhsZgaq4JDx57v8QHRR90r+m0liJB7c/iJ0NO8V2gsqNK8OtmBaRidmz/g6dTmrEO15xOF1S6mKferLqtp5+X0992Sg6IqJjfvVkiWG6YU1dtNlahQAby6mIkktzg0dUSS6CXrHV00MpqgNPgKmtTgT10OKCyx2DBkxDnmUhPmnNRVWHo18HTC9xIVqfg6Bk1S5ZtlOaoH/bBzJteWJTEf6zs0qkrJI4u3F5Om5fnYmQIeid1XOkFa2vFgAONzRTQhF53TTINUq4HS50UyRsazWcHVK2lTyYRFgS9IvjIdeMTxHG4muSiC/6+Mhd60S9pEbDAn2iQ4YXIkXR0+jZVt7b+JSgCId+iSdFcflyqJMHf0O2NzSg6oYbYauogDI2FikvvgBNevqoW8hPJoZKmFHGSK/A8hhPOJywnkLT2v6gdLwBxZIn4uSrifLtV8CgVEA+wSLWwzXb3tT0CSoqn4LJVCj2yeUaJCR8E1GRq9HcsglNTR/Bbm/3/UxQUDpiYy9GXOyFCApKG8WjZ3y0VwD/uwWokcxR3grW4+8JyXh07bOYE3P8ulm7044XjryAZw88C5vLBrVcgfWGHqwyOBAbvQbTp/3Rl5Y6ETF5Uhf71pN1DJC6SE1xsz01ZP71ZGFB6gmXiuhwdPkEltQPS3pMboJOZ//1doTCrYTO4oa+q1uIrCCzQwgum8WAL22zsMUxC1+4ZqETx55XBk+aoNQTSxJX6YNMEyxu7MKDHxbgiyIp1ZrSS3+0LhvfWph82j36LKUdaH0pH26bE6pkA6JumA6FXhJ2JMLMe5tgpEhYmzRZJdcrEbwyCcFLSYSNDRF9srD4miTiixk72GtrYSIHxW1fw7x9O5z9pSguWyqJMUpRPMnzhn5v5Q03wl5dLSzJU1568bSE3EhZyE8mTkeY+UPCzD/6FGAgcYyg6o1O0T4NR6pHBLpltrZuRXnFkwGNj71QVCs25gIR5TIYZk64VOwJAVlyf/Yo3F/+QUR+y1VK/CwmBhev+BWuyrmq389sf9N+/Gb7b1DSUSK2ZxrCcHFwPaKUbqSm3oYpGfdANgxNc8fD96HRaPXVkEkpjF0opdTFAcx1yLzB25PMW09GqYun0+ZgJFIRKQruSxMU/bBoLTUdpoboA0GpxhptMmTuaMiMdgS1NCKutQxh3T1Q21ziHHS6ZdjnzsJnztn4zDUbR9xpwpmQIlGUIigEVp/Gw+RoOtTXl61Hm/DQB/kobe4W29Sy4FcXTMPKrJOPDPeHrboLLS8chsvsgDI2CNE3z4AipLf1gdvpgnlfM7q2VsHR6hFhQUoEL09E8PIEyLXjQ4Sx+BpmWHwxJ0xRzM+XomIkxvbtOzZFceZMX1RMN6v/FEVbVRUqb7hBNOFVJScj9cUXoEpMHDILeXIyzFww8hbyk1mYET4DCY+QClVJxhNkhc6D9fEB3Trb279GReUz6O4uQkTESlHHFR6+dMykPDHHx132OeyvfxNqiwVUffKniDB0zLkK9y97ADqlNBlltBnx5z1/xutFUmpbuCYUl0UoMF1RA4VCjZycRxAfd8ko/ydjDzulLrZ0o0CkLvYKs5r2gVMXSVxM7RMpozS5U7kmnm4qIn2vbbYmXwSrx1zhi2RZLNV+Tc+PRa2KgkKdCgsS0W6JQ70xDI6GFsQ25mOBbR+y5FLtqJdmdyg+d80Wgqs4eAGiYuI8katgn/EFpW6ebuRpMJ/dKzur8KdNRb6o5pqcGPz8/FyRvjjo39vYjebnDsNltEERoRUCTBkZOOnrdrphPtiMri1VcDRL54pMSyIsAQYSYUGnnwo5nLD4GmZYfDGnnKK4K8/XX8xW1ut4RsiDgxG0ZLGvv5g6JQXWsnJUkfBqaoI6LU1EvFSxsRPeQp5hGGaksHdWwPjyGYhslqIlX+q0eD5zEX677ikUthfikZ2PoLlHSsU6L2UFzpTtgsbVDrU6GrNmPoPQ0NNv8TGZ6LLYpdRFb7Poesnsw2hxDOjA1+u2KPUoI2EWeoJB+IlSER0OU28dVkAtFqUJSlGf/pDLdZKboDwJRls8GswxKG2LwOHGUBxtdiPG2YBV8gM4U34Ay+RHoJf1ukc7IMdRZS4qwpfCmLQKhrS5yIgOQVpUEILUY2/CpsNsw583F+Pf2yvhcLmFSdK1S1Nx55qsQaeOOtosaH7uEJytFsgNaiHAVHHHGny4XW70HGyGkURYk0eEaRRChFE0zJu2ONZg8TXMsPhiTgdyvSMRRsYd5q/7SVFMToaruxvOtjaoM6cIcw1l9ODC/uRiuGmCWMgzDMMMNV3Gw6h7/zxMKemE0g20KOT4VUwsvvIYDqSFpOH7WSuhaX4ebrcdBsN0zJr5LLRaqV8Sc3rQMLTBaJFSFv3qyUqbTbAP0NMiPlTrE2W5nigZRWXUfn2j+ktFDDHMgsXaIKJbAyOHVpsEtzIZ3c5EtPTEoLIzGvnNoTjcoEaHuVcoamDDInmhEFyr5PsxRV4f8Jt6NNEwJa+CJnc9DLlrIQsafxOe9Dk8/GEBNhdK71lYkAp3rcnCt5ekBvT4OlmcRhtanj8Ee4MZMp0SUTdOhyal/3G0EGGHW0QkjF5PyNQKBC+LR/CKRCiCR7f1QV9YfA0zLL6YoU1RLPCkKG6Def9+avIhntPk5CDl+eegjIgYEgv5c39wDyISJqaFPMMwzGCpq3sdVXvuRW5+F0J7pNSyMpUKen0M9Bo5ehyNcChkUAenIjx2DeSaMEAdDKj1gMYgrQfalrPh1WDT38qayXVRSlv0pi7WdvSfukiRGUrX8zf3oHV0kBllZY/7UhG9qFQRUKrTYHEnos0ah1pTFIpbI3CwXo/q47gJJssacbE+H2uUBzDddhBqP1dTt0wBWfJiIGstkLkOiJt53MbH44kvi5vx4Af5KGo0ie0p0Xr88oJpOGtqzCn/LpfZjpYXj8BW1QWZWo7Ia6dBmzWwMCURZslvhXFzFez1UmRSppJDvzQehpVJUBjGhghj8TXMsPhihguKeHXv2gVbeQXCvnEpFKGhp/w72EKeYRjm5KGhUEHBT9FY+yayKhxIqg3MRjgtVEF+YiwY0HhEme9x8EkKOc/rFeoJM6AfDEZKXSQL/D71ZF0DpC4atEqRrjg/oQWR2jqUt4ficFMojjbJBuxnRoRoyU0wGNkRSqxQHcXMnl1IaPkKms7AsgEEx/WKrYxVgC4MExVqQ/DfXdX448YiYVNPnJkdjV+en4us2FPrX+myOdH673xYizuE2xT1AdPNiDrh99RS0CbSEe01Jp8IC16ZiNCzR99RlsXXMMPiixmLsIU8wzDM4HA6e7B7z+WilUC0IhsKUxsc5looXSokx3wDIZp0wGaSFmrcbOvus+3ZJx530XT98BwoGUf0J+ROadsj6mhbRdG58e3USEPZuk6LL2VRSl+UUhepXmkg1Aq5qLnqa3QxRdmEsNrPISvZBJR/CTj8om0yBZCyBMhcC2StA2JnTDox3Nljx9+2FOPFrytEaigZpnx7cQruWpstbOpPFrfDhbb/FqLncKtoOhl+WTb0C05c2y5E2NF2dG2uEk6KhjOTEHru4NvvDBUsvoYZFl/MWIMs5D/+6x/EmqC6rlXX3cIW8gzDMCcJmS/k7boETqc0q67RxGP2rGdFndcpQUMrh8Ujxrr6EWqD2PYXAEMNCTCvGOsrzo7ZPo6Q8z6nHBtpYBTZKmsxeYw9utBjcyDNr/FwQphOCAfYe4CKbUDJRqB4I9BWGviLDPG9YouiW9pTz0iZiJCj5cMfFeDT/EZftPCONVm4bmlaQP3d8XA73Wh/uxjm3dLvCL0gA4YViSffxLq4A6oE/Zio/2LxNcyw+GLGCmwhzzAMM3Q0N3+KQ4fvREjIDMyc8RQ0mtPrcTRkOB2AvdtPnHlEXb/bfSNxfbe7hzk6p+onpfJUt/1SLil1c6ijc62lAEW2SGxVfBUobim6mLykN50wdvqki26dCl+XtuDBDwpQUG8U2yRuf3FeLtbkxpyUuZfb7Ubnh+UwfSXZ8RvWpCBkbcq4MwZj8TXMsPhixgJsIc8wDDP0OBxdUCiCx93gb1DROX9xNigh57dNv29YkHmE2HGMTU6mXq6zRhJbFOFq61O7ZUgIrN3S8tjuVHC63HhjdzX+8OlRtJikerDlmZH45fnTkBt/4vfS7Xaja2s1jJ9Wiu3gZQkiCiajyOQ4gcXXMMPiixlt2EKeYRiGGVM47X4pk/2JNf8UzJPcxjANUym6lbJUSiUkwRWTy9GtIerl9tRnpXjuy3LYnC6QdrpyYQruOTsbUcGaE/68aXsdOt6V0j6D5sYg/PJsyBTj43Nh8TXMsPhiRgu2kGcYhmEmBTREpXqsE9bH9ROp6++1FP2astoT3TpTio4xw0J1mxmPfFyAjw41iG2DRonbV2fihuVp0CiP337BvK8JbW8cBVyANjcCkVfnClfDsQ6Lr0kivujjMzY3QalWQ6FSQamS1hz9mJiwhTzDMAzDMOOFnWWtePDDfByulerBUiKC8PPzcrF+euxxx6o9+a1ofaUAcLihyQhF5PXTINeM7bEOi69JIr7IWvzP11x6zH5/ISYJMzWU3n1q73OeffS8UtX/fs9jhdrv52mfWuV5rvex9Del15AwYIYOtpBnGIZhGGY84nK58b+9NXhsw1E0dVnFvsXpEfjVBdMwI3Fg50hLaQda/5UPt9UJVVIwom6cAYVehbEKi69JIr6sZjOe+e61cNhtUnh+jCBXKPsVaELIie3eSF2vuOsVcIGir7/XBP4Or3D0Pi9XHD+kPZ5gC3mGYRiGYcY73VYHnvm8FH//ogxWh0uU2F0xPwk/Xj8VMQZtvz9jq+lCy/OH4TI7oIwJQvTNM6AIPXHt2GjA4muSiC8v9DG6nA44bHY47TYhxvwfO212ae2wD/waOz1Hjz37bJ594jnptd7XefeL19LP2GxwD5dl7SCgyJskyvwjdn2igAGpmoHRPIVKGbD/2Nd4RV+goPQ+JvF3uqmfbCHPMAzDMMxEo7ajB7/7uBDvHagT23q1At8/KxM3r0iHVnXs5Lm9sRstzx2G02iDIlyD6FtmQhk59iagWXxNMvE1FnA5nR5B5yfaPGIuQAB6RVuf13j3Sz/vJ+x8rw8Ui97f4d1P4nOsIJPJe6N4/gItIErXTxTQTwBW5x9C9RG2kGcYhmEYZuKxp7INv/2gAAeqO8R2YpgO952Xg/Nnxh8zge1os6DluUNwtFogN6gQddNMqOP1GEuw+BpmWHyNPVwup0fQeUVbbwSvV9z1isBAQWgf4DX9RAH9BaWfWKTo1FBDFvKrrr0Fs9aewyYqDMMwDMNMuHqw9w7U4dGPC9FglPrELUgNx/0XTsOspLCA1zq7bCICZm/ohkyrRNSN06FJHTtjcBZfwwyLL6a/NEESYIEpnIECrTcKGBjN6xsRpMdypRJzzr6ALeQZhmEYhpnQmG0OUQtGNWEWu1TG8o15ifjJ+hzEhfbWg7nMdrS8lA9bpVHYz0deNw3arLGRFcTia5hh8cUwDMMwDMMwQ0d9Zw8e++Qo3tpXK7Z1KgVuO3MKbj0jAzq1VA/msjnR+u98WIs7AIUMEd/KQdDMqHGjDdgPnGEYhmEYhmGYUSc+VIc/XjkH7/xgOealhKHH7sSfNhVh9eOf4d39tcJgTq5WIOr66dCR4HK6Yd7bKPaPFzjyNUg48sUwDMMwDMMww4Pb7cYHB+tFPRg5JBJzU8JEf7B5KeFwu9wwfVUL/ZJ4IchGG047HGZYfDEMwzAMwzDM8GKxO/HPL8vw1GelMNucYt/FcxLw03NykBA2diznWXwNMyy+GIZhGIZhGGZkaDJa8NiGo3hzbw1IvWhVcty6MgPfPXMK9BrlaB8e13wxDMMwDMMwDDMxiAnR4rErZuP921dgUXqEcEX8y5YS3PTiLownWHwxDMMwDMMwDDMumJEYitduXYKnvz0PyRE63LwiHeOJ0Y/RMQzDMAzDMAzDnCQymQznzozH6twYqBXjK5bE4othGIZhGIZhmHGHRjn6LoenyviSigzDMAzDMAzDMOOUSS2+nnzySaSlpUGr1WLx4sXIy8sb7UNiGIZhGIZhGGaCMmnF12uvvYYf/ehHeOCBB7B3717Mnj0b69evR1NT02gfGsMwDMMwDMMwE5BJK77++Mc/4jvf+Q5uvPFGTJs2Dc888wyCgoLw/PPPj/ahMQzDMAzDMAwzAZmU4stms2HPnj1Yu3atb59cLhfb27dv7/dnrFaraJ7mvzAMwzAMwzAMw5wsk1J8tbS0wOl0IjY2NmA/bTc0NPT7M4888ojoWu1dkpOTR+hoGYZhGIZhGIaZCLDV/Ely3333iRoxL52dnUhJSeEIGMMwDMMwDMNMcoweTeB2u4/7ukkpvqKioqBQKNDY2Biwn7bj4uL6/RmNRiOWvm8wR8AYhmEYhmEYhiG6urpEltxATErxpVarMX/+fGzevBmXXHKJ2OdyucT27bffflK/IyEhAdXV1TAYDKLL9mhCQpBEIB1PSEjIqB4LMzTwZzox4c914sGf6cSEP9eJB3+mExPjGPpcKeJFwos0wvGYlOKLoBTC66+/HgsWLMCiRYvwxBNPoLu7W7gfngxk0JGUlISxBJ10o33iMUMLf6YTE/5cJx78mU5M+HOdePBnOjEJGSOf6/EiXpjs4uvKK69Ec3Mz7r//fmGyMWfOHHzyySfHmHAwDMMwDMMwDMMMBZNWfBGUYniyaYYMwzAMwzAMwzCnw6S0mp9okBHIAw88EGAIwoxv+DOdmPDnOvHgz3Riwp/rxIM/04mJZhx+rjL3ifwQGYZhGIZhGIZhmNOGI18MwzAMwzAMwzAjAIsvhmEYhmEYhmGYEYDFF8MwDMMwDMMwzAjA4othGIZhGIZhGGYEYPE1AXjyySeRlpYGrVaLxYsXIy8vb7QPiTkNvvjiC1x44YWiQ7pMJsM777wz2ofEnAaPPPIIFi5cCIPBgJiYGFxyySU4evToaB8Wc5o8/fTTmDVrlq+x59KlS/Hxxx+P9mExQ8ijjz4qrsF33XXXaB8Kcxr8+te/Fp+j/5KTkzPah8WcJrW1tbjmmmsQGRkJnU6HmTNnYvfu3RgPsPga57z22mv40Y9+JGw29+7di9mzZ2P9+vVoamoa7UNjBkl3d7f4HElUM+Ofzz//HD/4wQ+wY8cObNy4EXa7HWeffbb4nJnxS1JSkhic79mzR9zwV69ejYsvvhhHjhwZ7UNjhoBdu3bh2WefFQKbGf9Mnz4d9fX1vuWrr74a7UNiToP29nYsX74cKpVKTHrl5+fj8ccfR3h4OMYDbDU/zqFIF82q/+1vfxPbLpcLycnJ+OEPf4if/exno314zGlCM3Rvv/22iJYwE4Pm5mYRASNRdsYZZ4z24TBDSEREBB577DHcfPPNo30ozGlgMpkwb948PPXUU3jooYcwZ84cPPHEE6N9WMxpRL4og2T//v2jfSjMEPGzn/0M27Ztw5dffonxCEe+xjE2m03Muq5du9a3Ty6Xi+3t27eP6rExDNM/nZ2dvoE6MzFwOp3473//K6KZlH7IjG8oUn3++ecH3FuZ8U1xcbFI5c/IyMC3v/1tVFVVjfYhMafBe++9hwULFuCKK64Qk5lz587FP/7xD4wXWHyNY1paWsRNPzY2NmA/bTc0NIzacTEM0z8Umab6EUqXmDFjxmgfDnOaHDp0CMHBwdBoNLjttttElHratGmjfVjMaUAimlL4qVaTmTgZQi+++CI++eQTUatZXl6OlStXoqura7QPjRkkZWVl4rPMysrChg0b8L3vfQ933HEHXnrpJYwHlKN9AAzDMJNpRv3w4cNcbzBBmDp1qkhlomjmm2++ieuvv16kk7IAG59UV1fjzjvvFLWZZGDFTAzOPfdc32Oq4SMxlpqaitdff51ThMfxROaCBQvw8MMPi22KfNG99ZlnnhHX4bEOR77GMVFRUVAoFGhsbAzYT9txcXGjdlwMwxzL7bffjg8++ABbt24VZg3M+EetViMzMxPz588XkRIyyvnzn/882ofFDBJK4yezKqr3UiqVYiEx/Ze//EU8pkwTZvwTFhaG7OxslJSUjPahMIMkPj7+mEmu3NzccZNOyuJrnN/46aa/efPmgNkA2ua6A4YZG5CnEQkvSknbsmUL0tPTR/uQmGGCrr9Wq3W0D4MZJGvWrBGppBTN9C40u041QvSYJjuZiWGoUlpaKgbwzPhk+fLlx7RsKSoqEhHN8QCnHY5zyGaeQqx0g1i0aJFwZKKi7xtvvHG0D405jRuD/4wc5afTjZ8MGlJSUkb12JjBpRq+8sorePfdd0WvL289ZmhoqOhNwoxP7rvvPpHORN9Jqh2hz/izzz4T9QfM+IS+n31rMfV6vegjxDWa45cf//jHoncmDczr6upEax4S0lddddVoHxozSO6++24sW7ZMpB1+85vfFP1t//73v4tlPMDia5xz5ZVXCuvq+++/XwzqyBKXikr7mnAw4wfqGXTWWWcFCGyCRDYVDTPjCyoKJlatWhWw/4UXXsANN9wwSkfFnC6UnnbdddeJnkEkpKmWhITXunXrRvvQGIbxo6amRgit1tZWREdHY8WKFaLvIj1mxicLFy4U2SQ0Cfbb3/5WZJRQ8IGi1OMB7vPFMAzDMAzDMAwzAnDNF8MwDMMwDMMwzAjA4othGIZhGIZhGGYEYPHFMAzDMAzDMAwzArD4YhiGYRiGYRiGGQFYfDEMwzAMwzAMw4wALL4YhmEYhmEYhmFGABZfDMMwDMMwDMMwIwCLL4ZhGIZhGIZhmBGAxRfDMAzDMAzDMMwIwOKLYRiGmdTccMMNkMlkYlGpVIiNjcW6devw/PPPw+VyjfbhMQzDMBMIFl8MwzDMpOecc85BfX09Kioq8PHHH+Oss87CnXfeiQsuuAAOh2O0D49hGIaZILD4YhiGYSY9Go0GcXFxSExMxLx58/Dzn/8c7777rhBiL774onjNH//4R8ycORN6vR7Jycn4/ve/D5PJJJ7r7u5GSEgI3nzzzYDf+84774jXd3V1wWaz4fbbb0d8fDy0Wi1SU1PxyCOPjMr/yzAMw4wOLL4YhmEYph9Wr16N2bNn46233hLbcrkcf/nLX3DkyBG89NJL2LJlC37yk5+I50hgfetb38ILL7wQ8Dto+/LLL4fBYBA/+9577+H111/H0aNH8Z///AdpaWmj8r8xDMMwo4NylP4uwzAMw4x5cnJycPDgQfH4rrvu8u0n0fTQQw/htttuw1NPPSX23XLLLVi2bJlIX6ToVlNTEz766CNs2rRJPF9VVYWsrCysWLFC1JdR5IthGIaZXHDki2EYhmEGwO12C6FEkIhas2aNSE2kSNa1116L1tZWmM1m8fyiRYswffp0ERUjXn75ZSGwzjjjDJ+xx/79+zF16lTccccd+PTTT0fxP2MYhmFGAxZfDMMwDDMABQUFSE9PF0YcZL4xa9Ys/O9//8OePXvw5JNPitdQLZcXin55a8Qo5fDGG2/0iTeqJSsvL8eDDz6Inp4efPOb3xQpiQzDMMzkgcUXwzAMw/QD1XQdOnQIl112mRBbZDv/+OOPY8mSJcjOzkZdXd0xP3PNNdegsrJS1Hfl5+fj+uuvD3ieTDmuvPJK/OMf/8Brr70mhFxbW9sI/lcMwzDMaMI1XwzDMMykx2q1oqGhAU6nE42Njfjkk0+EEyFFu6677jocPnwYdrsdf/3rX3HhhRdi27ZteOaZZ475PeHh4fjGN76Be++9F2effTaSkpJ8z5FbItWCzZ07V5h3vPHGG8JhMSwsbIT/W4ZhGGa04MgXwzAMM+khsUXCiIw0qOfX1q1bRfSK7OYVCoVwPSTx9Lvf/Q4zZswQToUD2cTffPPNIhXxpptuCthPdWK///3vsWDBAixcuFCkMpIhBwkxhmEYZnIgc1M1McMwDMMwQ8K///1v3H333SItUa1Wj/bhMAzDMGMITjtkGIZhmCGAXA/JZv7RRx/Fd7/7XRZeDMMwzDFwrgPDMAzDDAGUUkh9waiO67777hvtw2EYhmHGIJx2yDAMwzAMwzAMMwJw5IthGIZhGIZhGGYEYPHFMAzDMAzDMAwzArD4YhiGYRiGYRiGGQFYfDEMwzAMwzAMw4wALL4YhmEYhmEYhmFGABZfDMMwDMMwDMMwIwCLL4ZhGIZhGIZhmBGAxRfDMAzDMAzDMMwIwOKLYRiGYRiGYRhmBGDxxTAMwzAMwzAMMwKw+GIYhmEYhmEYhhkBWHwxDMMwDMMwDMOMACy+GIZhGIZhGIZhRgAWXwzDMAzDMAzDMCMAiy+GYRiGYRiGYZgRgMUXwzAMwzAMwzDMCMDii2EYhmEYhmEYZgRg8cUwDMMwDMMwDDMCsPhiGIZhGIZhGIYZAVh8MQzDMAzDMAzDjAAsvhiGYRiGYRiGYUYAFl8MwzAMwzAMwzAjAIsvhmEYhmEYhmGYEYDFF8MwDMMwDMMwzAjA4othGIZhGIZhGGYEYPHFMAzDMAzDMAwzArD4YhiGYRiGYRiGGQFYfDEMwzAMwzAMw4wALL4YhmEYhmEYhmFGABZfDMMwDMMwDMMwIwCLL4ZhGIZhGIZhmBGAxRfDMAzDMAzDMMwIwOKLYRiGYRiGYRhmBGDxxTAMwzAMwzAMMwKw+GIYhmEYhmEYhhkBWHwxDMMwDMMwDMOMACy+GIZhGIZhGIZhRgAWXwzDMAzDMAzDMCMAiy+GYRiGYRiGYZgRgMUXwzAMwzAMwzDMCMDii2EYhmEYhmEYZgRg8cUwDMMwDMMwDDMCsPhiGIZhGIZhGIYZAVh8MQzDMAzDMAzDjAAsvhiGYRimH2Qy2Uktn332Gaqrq/Gb3/wGixYtQnh4OKKiorBq1Sps2rRptP8NhmEYZgwhc7vd7tE+CIZhGIYZa7z88ssB2//617+wceNG/Pvf/w7Yv27dOrzxxhv4yU9+gksuuQTLly+Hw+EQr9+7dy+ef/553HjjjSN89AzDMMxYhMUXwzAMw5wEt99+O5588kn0d9s8cuQIYmNjRcTLi9VqxZw5c2AymURkjGEYhmE47ZBhGIZhTpPp06cHCC9Co9HgvPPOQ01NDbq6ukbt2BiGYZixA4svhmEYhhkmGhoaEBQUJBaGYRiGYfHFMAzDMMNASUkJ3nrrLVx22WVQKBSjfTgMwzDMGIDFF8MwDMMMMWazGVdccQV0Oh0effTR0T4chmEYZoygHO0DYBiGYZiJhNPpxLe+9S3k5+fj448/RkJCwmgfEsMwDDNGYPHFMAzDMEPId77zHXzwwQf4z3/+g9WrV4/24TAMwzBjCBZfDMMwDDNE3HvvvXjhhRfwxBNP4Kqrrhrtw2EYhmHGGFzzxTAMwzBDwGOPPYY//OEP+PnPf44777xztA+HYRiGGYNw5IthGIZhTpO3334bP/nJT5CVlYXc3Fy8/PLLAc+vW7dONGFmGIZhJjcsvhiGYRjmNDlw4IBYFxcX49prrz3m+a1bt7L4YhiGYSBzu93u0T4IhmEYhmEYhmGYiQ7XfDEMwzAMwzAMw4wALL4YhmEYhmEYhmFGABZfDMMwDMMwDMMwIwCLL4ZhGIZhGIZhmBGAxRfDMAzDMAzDMMwIwOKLYRiGYRiGYRhmBOA+X4PE5XKhrq4OBoMBMplstA+HYRiGYRiGYZhRgrp3dXV1ISEhAXL5wPEtFl+DhIRXcnLyaB8GwzAMwzAMwzBjhOrqaiQlJQ34PIuvQUIRL+8bHBISMtqHwzAMwzAMwzDMKGE0GkVgxqsRBoLF1yDxphqS8GLxxTAMwzAMwzCM7ATlSGy4wTAMwzAMwzAMMwKw+GIYhmEYhmEYhhkBWHwxDMMwDMMwDMOMACy+GIZhGIZhGIZhRgAWXwzDMAzDMAzDMCMAiy+GYRiGYRi/Rqm7OrvRYXeM9qEwDDMBYfHFMAzDMAwDoNFqx7cPluHCvcVYsbMQm1uNo31IDMNMMFh8MQzDMAwz6XmvqQOr8gqxpa1LbLfYHUKI/aKoBj1O12gfHsMwEwQWXwzDMAzDTFo67Q7cnl+JW49UoN3hxMxgHTYsyMYtSVHi+edqW3DOniLkm3pG+1AZhpkAsPhiGIZhGGZS8lV7F1bvOoo3G9vFgOiu1Fh8OD8Lsw1BeCgrCf+ZlYFotRJHuy04Z3cRnq1ugsvtHu3DZhhmHMPii2EYhmGYSQWlEd5fXIvL95ei1mpHuk6N9+Zl4WcZ8VDLe4dGayJDsGXhVKyLDIHN7cYDJXW46kAZGqz2UT1+hmHGLyy+GIZhGIaZNBzsMmP97iL8vaZZbF+XEIlNC6ZiQai+39dHq1X418x0PJqdBJ1chs9FtKwQHzd3jPCRMwwzEWDxxTAMwzDMhMfhcuOJigact6cIRWYLYtRKvDwrA7+fmgy9UnHcn5XJZLghMQobFkzFjGAd2uxO3Hi4AvcerUa30zli/wPDMOMfFl8MwzAMw0xoys1WXLKvGI+WN8DhBs6PDsXWhTlYGxlySr8nW68VNWHfT46BDMC/61px9q4iHOgyD9uxMwwzsWDxxTAMwzDMhG2Y/K/aFmGqsdtohkEhx19zU/DP6WmIVCsH9Ts1cjnuz0zAG3OmIF6jQmmPFefvKcJfKxvhZDMOhmFOAIsvhmEYhmEmZMPkaw6W4yfUp8vlwrKwYGxdlIMr4iJEGuHpsiLcIMw4KIpG0bT/K6vH5ftLUGuxDcnxMwwzMWHxxTAMwzDMhOKDpg6ctasQm9uM0Mhl+E1mAt6cMwVJWvWQ/p1wlVJE0f6Uk4wghRzbO7pFlO2dxvYh/TsMw0wcWHwxDMMwDDMhMDqc+GFBJW45UiFMMWZ4GiZ/NzkG8iGIdvUHRdGuio/E5gVTMdcQhE6HE7flV4rj6HKwGcdYgfqz2Vyu0T4MZghpbGzEq6++iurqaownWHwxDMMwDDPu2dbehbPyCvFGg9Qw+c7UWHw0Pws5et2I/P30II3oFXZ3aqz4+3Qca6jWrLN7RP4+c3zDFfosZmw7jP/UtYpaQGb80t7ejrfeegtPP/00jh49ii1btmA8IXPzGTgojEYjQkND0dnZiZCQU3NLYhiGYRhmaLA4XXikvB7PVkt9u1K1avxtWioWDtC3ayTY2WHCDwoqUWOxQyED7k6Nw12psVDKhyf6xgzMF21duPVIBTr8opBnhhvwh5xkJA9xGiozvJhMJnzxxRfYvXs3XJ4oZm5uLlavXo3o6Ohxow1YfA0SFl8MwzAMM7oc6jLj9oIqHO22iO1r4iNFfdeJ+naNVArkfUU1+J+n/mtBSBCenJaKVJ1mtA9tUkDD23/WtOCBklrQMH1+SBDOjgzFnyobYHG5oVfI8cCUBFybEDkkBizM8NHT04Ovv/4aO3bsgN1uF/syMjKwZs0aJCYmYqzA4muYYfHFMAzDMKMDWbo/WdWEx8obYHe7Ea1W4vGpyTg7KhRjjf81tOFnRTXocroQrJDj4ewkXBEbzgP+YcTqcuGnR2vw34Y2sX1lXAR+l50ErUKOUrMFdxdWI8+TDroiLBiP5ySzKB6D2Gw25OXl4auvvoLFIk2wkNgi0UXia7xqg3Fd8/Xoo4+Ki9ddd93l20cfzg9+8ANERkYiODgYl112mSjI86eqqgrnn38+goKCEBMTg3vvvRcOh2MU/gOGYRiGYU6Fih4rLtlbgofL6oXwOi9Kapg8FoUXcVlcBDYvnIrFoXqYnC7cUVAlDDk67DzuGA6arHZctq9ECC8a5FIk9ImcZCG8iClBWrw9NxMPZiZCJ5fhqw4Tztp1FM/XNAtTDmb0cTqdIrXwr3/9KzZt2iTG9pRWeOWVV+KWW24Zk8LrVBhch8ExwK5du/Dss89i1qxZAfvvvvtufPjhh3jjjTeE+rz99tvxjW98A9u2bfN9oCS84uLiRAizvr4e1113HVQqFR5++OFR+m8YhmEYhjkelKjzn/o23F9SC7MnivR/WUn4ZtzYjyKl6DR4a26maMT8WEUD3m3qEEYcf81NxbLw4NE+vAnDfqMZNx4uR73VjlClAs9OT8WqiGMjEAqZDN9JjsbayBDcXViFHZ3d+HlxLd5v7sCfclKQxlGwUcHlcuHIkSPYunUr2tqkqCWN5c866ywx3pfLx3XMaHynHVLB3bx58/DUU0/hoYcewpw5c/DEE0+IMB8p41deeQWXX365eG1hYaEoxtu+fTuWLFmCjz/+GBdccAHq6uoQGxsrXvPMM8/gpz/9KZqbm6FW9198abVaxeIfWkxOTua0Q4ZhGIYZgWjGPUersbHVKLaXhunxl9zUcWmYsLezG98vqERFjw0kGX+YEoMfp8dBPUEGlqPFW43t+FFhlajnygrS4KWZGcgIOrGIomjXC7UteKi0XjTj1snl+MWUeNyUGDVs7QmYQEiKFBcXY/Pmzb5sNcpOO+OMM7BgwQIoleMjVjSh0w4prZCiV2vXrg3Yv2fPHlGI578/JycHKSkpQnwRtJ45c6ZPeBHr168Xbxip7YF45JFHxBvqXUh4MQzDMAwzvHzY3IFVuwqF8FLLZMIk4X9zMsel8CLmhepFT7Cr4iNAs99/qWrCBXuLUWKWalqYU6//e6i0Dt/PrxTCi6JZH83P9gkvc2cHDm7egLce/TVe/dW9+PqNV9BYXuqzmyeBdXNSND5bNBXLwoKFAPtlcS2+sa8EZebeSXdmeKiqqsILL7wgAickvDQajYh03XnnnSJoMl6E16kw7v6j//73v9i7d69IO+xLQ0ODiFyFhYUF7CehRc95X+MvvLzPe58biPvuuw8/+tGPjol8MQzDMAwzPG6BvyyuwesNklvg9GAt/pabitzgkenbNZyQGyOlt62JCMGPj1bjYFcP1u0qwoNZifh2fMSYT6McS+fI945UYnObFBG9IyUGP82IR3drC/bu2o7ivK9RW5APt7u3uXJdUQG2v/kKgiOjMGXeIkxZsBjJ02cJw40350zBv+pa8WBpnUhFXLOrED/LiMctSdEiVZEZOhoaGkSkiyJeBImsRYsWYcWKFSLqNZEZV+KLOliTEt64cSO0Wu2I/m1S4rQwDMMwDDO8fN1uwg8LKlFrtYsUnR94UvM0Eyw174KYMMwLCRImHGT8QEJsc6sRf5iajEj1uBqijTjkWnjDoXIUm63QymV4MDYY2Ye24b/PfY2GUmlA7yU2IxNZi5ZBFxKKsr27UHloH0ytLTiw8SOxqLQ6pM2aK4TYN+cuwOqFU0Wa65ftJjxQUocPmjrxp9xkZAaN7NhzItLW1iZqug4dOiS2aaJh7ty5OPPMM0Vm2WRgXH2zKa2wqalJ1Ht5IQMNarj2t7/9DRs2bBC2lB0dHQHRLwpjksEGQWuyrfTHm1/qfQ3DMAzDMKPTMPlRT8Nkt6dh8l9zU7AobOKaUiRo1Xh9zhQ8U92MR8rq8XFLJ/Yau0VN25kRhtE+vDHJZ21GfPdIJTodTkQ67bjqy/fRdGQvmrwvkMmQOHWaEFxZi5YiJDrG97Oz1qyH3WZF9eGDKN29E6V789Dd3iaiZLTIZHLEZ+fgx/MXYXXmbDze2oNdxm6s3XUUP0mPx3eTOQo2GLq6uvD555+L7DVvg+Tp06eLFMOoqChMJsaV4QZ9cJWVlQH7brzxRlHXRYYZlAZIhhuvvvqqsJgnjh49Kp7va7hBLodkM0/8/e9/F3bzJOxONrrFfb4YhmEYZug47GmYXOhpmEzpd7/JTETwGGiYPJJNo6l2iaI5BA30f54RP+EifoPF5XTisf35eKLTAbdMhoSGSly84VUE95ggVyiQMmO2EFwUwdKHhZ/U73S7XKIGrHRPHkr37ERzRVngC9KzsOGMS3BQJ0VlqFkzpYxm6zkKdrINkqlP186dO31tnTIzM7F69WokJCRgIjFpmiyvWrXK53ZIfO9738NHH32EF198UfzjP/zhD8V+spX3Rsro9fSB//73vxc5p9dee63oG3AqVvMsvhiGYRhmaAwTnqpqwu89DZOjVEr8MWdsNkweCchG/zcltXiprlVsT9Nr8dT0VOTox3+t22BwOhyoyT+MI7u248+yEBxIny72zyzYg3N2bkDmzNnIWrwMGfMWQqs//QipsaUJZXt2CSFWfeSg+Ps0UD48dR62Lj8PVrUWKrhxT3IUbs9IglLOUbD+oEw0ElzU6snbIDkpKUmY4qWlpWEiMmnFF33A99xzj4h+kTU8ORmSJb1/SiFFz0ikffbZZ9Dr9bj++utFw+ZTcVRh8cUwDMMwp0dljxU/LKhCXme32D43KhSPTU1GFNc74dOWTtxdWI1Wu0PUNP1qSoKwP58MZhyUFlh5cD9K8r4WqYEtbhneWX816mOTIXO5cFVDMW6bkoz0OfOgGkYPAFuPGRUH94ljKNu3G80uYMOZF6M8Zap4PqWrDfeprVg9by5CY7h0haDoFqUWUkkQtYYiKNNszZo1yM7OntDn76QRX6MFiy+GYRiGGRw09HjF0zC529Mw+aGsRFwZx05/ffub3VlYha1tXWKb3BGfyE1GtFqFiYbVbEb5vl0oztuO8n27YbdK0ZL6mES8e8416AoywCBz4x/T07Aq+uRSCocSl8uJuqJCkZ74RmM73p+2FFaNDgqnA0v3bMV5LZXInr8IGfMWIT4zG7JJlipKdVxkokGBjfZ2yaGU/BeopotaPE2UBsnHg8XXMMPii2EYhmFOnWabHfcUVuNTT8PkJaHUMDkFKTp2FO4PGqY9V9si7M+tLjciVUo8kZOMdRMgLdNs7BTpfSV521F5cJ9I8fNiiIxGw1kX4J9RU2ADMFWvxUsz05E2Rs6Twqoq3Hu0BruUki16bHMtztn6FmLaGhEUGiZE2JT5i5A6c86wRufGwvlZVFQkbOPJO4GgrDJyLySDvInYp2sgWHwNMyy+GIZhGObU+Li5Az8+WiNS6ahhMvVQYve4k6PA1CPMOAo8hiQ3JEbh/ikJCFKMr4hCV2sLSkQPru2ilsu/B1d4fKJUv7VwKf4JPZ6ubhb710eF4Mnc1DFnvkJD6P81tuMXRTXodLqgcLmw/MCXWLBrs3hMKFVqpMycjSnzF4u6tOCISEwUKioqhOiiVlAEmdZRn67FixeLvruTDSOLr+GFxRfDMAzDnBxdDid+VVyL/za0+Uwknpw2MRomj7QVP9nRP1sjiZKsIA2enpaKGYax3ZS2vaEOxTu/FhGu+pKjAc/FpE0RdvAkuiISk0Xj5NvyK32plnenxuLe9DjIx7BAb7Ta8dOianzSIkVzsxXADdWH4Nz+GYzNUjsjL3FTspAxn6JiixGdmj4u02zJMZxEV0lJidim6BYJruXLl0/4BsnHg8XXMMPii2EYhmFOzPYOk2giXG2xQeZpmEyDabZPP70+V/SeNtkcUMlkuC8jHrclR48ZgUJDy5aqCk/vrO3isQ+yiM/OlQTXoqUBRhUlZguuP1iO0h4rdHIZnshNwcUxI1/fNdj/+Z2mDvyiuAZtdieUMuCOlFhco7Cgak8eyvbkHSM8KbVyygJJiCVNmwmlamzX8rW2tmLLli04cuSI2KY6LkotPOOMM0ZlLFzZY8U/a5pxaWw45oXoMdqw+BpmWHwxDMMwzMBYXS78rqwBT1c3CavuZE/D5CUTuGHySNJqc+Ceo1W+aMvK8GBROxevGZ10L+qXVV9SJAQXRbg6Gut9z1EPruTps4TYmrJgCYLDI475+c2tRtx2pAJdThcSNSq8ODMdM8d4RG+gmsafFdXgw+ZOX5SXROQsQxC6O9pRtleysSc3R4dN6udGqLQ6pM+eJ3qUpc9dAJ1h7IwtaaxLDZL37dsnRCZBJhrkOB4ZOfJplHs6u8V15aPmTlBy54XRYfjHjNG3r2fxNcyw+GIYhmGY/sk39eAHfvVJV8dH4LeTrGHySEBDuP/Ut4mUzh6XC+FKBf6Qk4zzo8NGrOlxTcFhn+AytUtppd5ap9TZ84TgojQ7XbBhwP/hqepmPFRaJ0T64lA9/jkjbVw7OtL/9F5zB+4rkqJgChnww5RY3J0W64v4kp1+1aEDQoiRIOv2e+9kMjkSpuYKww4SYxEJSaPyf5jNZtEgOS8vz9cgOSsrS9jG+7dwGql+gB83d+KZ6ibsNpp9+1eFG3BbSjRWRYz+WJzF1zDD4othGIZhjh0gPV3VhN95GiZHehomr58AznxjGUrXIzOOg109PrH7YGYi9MMgdh02GyoP7ReCi2zXLV1S5I1Q63TC5Y8EV9qc+VBrj1/T1+N04d6j1XizUbImvyY+Eg9nJ0I9QVJSW2wO/Ly4Bu81dYhtcmz8c04K5oQEHRM1bCwrEUKM3tPmyvKA58mIhARs5vzFQpRRJHE4oT65O3bswNdffy0eEykpKUJ0paamYiTpdjjxakMb/l7djCoL+V5CmPV8IzYc340LRu7uJ4DF3wNC4jHasPgaZlh8MQzDTD6ol013d7e49nsXYtGiRZPKUnmg+os7C6qww9Mw+ZyoENEweTxHMMYTNpcLj5U34G9VUppnuk6Np6alYW6fgf6gfnePGeX79wjTDGo2bLdIIo/QGkKQuWAJshYvRcqMOSddt1RvteGGQ+U40NUjIkMkFm+coE2kP2jqEKmILXaH+F+/nxyDe9LioB3AqdLY3ITSvXmiuXP1kUNwOXst+LXBBpGWSHViabPnQTOEBhcU3dqzZ49okEzXOSI2NlaILop4jeRnU2exiRYL/65rgdEhOUdSZJdcPuk8ianPA979AdBeDmSfC1z9X4w2LL6GGRZfDMMwEw+73R4grPpbnE7nMT+Xk5ODK664AophnpEei9AwgmamKfWNGibrFXI8mJWIq8Zrw+T6A8CRdwBdGBA/B4ifLT0eJ2xr78IPC6pQZ7UL04cfp8Xhh6mxp2zn39NlFFEYinCJHlx2u+85skvPWrRMRLgSc6afciSGanZuPFwuDEMiVAr8fXoaVoT3n5Y4kWr0fllcg7c9UTByqqQo2LxQ/QmbT1ce3CuEGAlfi0lygSTkCiWSp8+U0hPnL0ZIdMygJ5UOHjyIrVu3+iaUwsPDsXr1akyfPn1EGyQf6jLjmepmvNvUDodHoWToNLg1ORrfjItAkLMH2PQbIO9Z6cmQRODCvwBZazHasPgaZlh8MQzDjC/odkc1DB0dHQMKK+9s7/EgQWEwGMQ9gNZHjx4VgmzOnDm46KKLRnSgMhbMBShtzGv6QA2T/5ybgtQx0gj3pLFbgCNvA7ufA2p2Hft8RAaQMFcSY2I9G9CO3Xt/h92BnxT1prvR5/LXaanC9OR4mNpaUbJrhxBc1fmHRDqcl7C4eGQtXi4EV1xGFmSDPM9fq28T54zN7UaOp3HyuDtfTrPXHX02zTYH6B28LVly/9SdRL82qrGrKyoQopiW9rqagOejU9KQMX+xcFA8mc+IromFhYXCwbC5WWpfEBwcLIw05s6dO2KTSS63G5tajUJ0fd1h8u1fGqYX78+6yBDJybP8S+C924F2j3vmvOuAsx8CtGMjrZnF1yQSXzRTqxrj9qQMwzDDDaXM0LWZrssDCSxv0fjxoOspXd/DwsLEuu9C13z/QUlBQQFef/11MZBZsmQJ1q9fPz4jPqfIJ82duOdotWiYTHbnP02Pw/dSYsZXw+TWUmD388D+/wA9Ut0R5Cog5zwamQL1+4GOqv5/NjKzV4wleCJkmrETvaHz8Y3GdmH6QBHJEKUcv8tOFrbc/nQ0Nngs4b9GfVFhwHPUh8ob4YpMTj2t89rhcuPB0jpfj7Jzo0KF++VkNGFptztEpNhb65YZpMGfclKw8ARRsL601dUIEUY29rWF+QENq/Vh4aKpMxl2pMyYDZVGG/Cz5eXl2LRpE2pra8W2VqsVDZIphXqkGiT3OF14g+q5appRYpZqyygtk9oL3JoU3VsbZzUBm34N7PqHtB2SBFz0FyBzDcYSLL4mifiij+9Pf/qTmKnIzMwUObmJiYmTauaVYZiJD13renp6AoRUX4FlMvXOmB4Pul56hVR/Akun053yIHP//v145513xGOaNaZlomKihskltXi1XnJny9Vr8bdpqZg+XhomU/1M0cfArueAsq29+0OTgfk3SLPpwX7pW92tkgir2+dZ7wc6q/v5xTJJkHnFGK3jZgGa0bXWr+ixCufJPR6HuMtiw/FjnQsNe6QIV19zh/jsHElwLVwqol1DFYn77pFKfN4upcz9KC1WpEOOlb5ko8WnLZ0iCthoc4geeJRa99P0eASdRBSsvzRRqsuj9MSKA3tg6+mty1OqNUiZOVukJuqTUrFtZx7Kysp8k000abRs2TJx7RupiPnzNS14qa5FuEESBoUc1yRE4pakaCT6R2jLvwDevR3oqJS26Tu67sExGXlm8TVJxFdLSwv+9re/BeyjL8+UKVOEGKOFBhoMw4wuVEC9/9MPRVrPcDGeL+dOlwt2lxs2z2L1PXb59vXO6Q4MDWA0chnUvkUOtVxyx/LuG+oBn0KlEoPUZpsLe48Wi33nnHOOGNBMNHZ4GiaT6xi9i99LjsFPM8ZJw2RjPbD3X8CeF4GuOs9OGZC5Flh4C5C1jopoTu53dbdIIqx+n7SmxRiYAub7/VHZvWJM1JDNAtQj2xDW7nThof35+HunHW6ZDKHGdpy35Q0kNVSJ1LTkaTOF4JqycDEMEVFD+reLui24/lAZynts0MnlItp1Qcz4qaEbbkiYPlBSh9capMkMMkqhKNjp9MNzOuyozj8shBg5KHa1NMOp1sIWnQBHiNRjjS6DM3JycPZ554v06ZGgsLsHz1Y3438N7SLtlEjSqkSU6+r4yMAoqIh2PQDs+mfv5AhFu6asxliFxdckEV9EV1cXSkpKUFxcLGYyLBapr4qX+Pj4gKjYZCwIZ5jRoq2uFnnvvoGCL7eKfP3JiluugEulhlulltZKae1SacQ+t1IljQZOgMxhh8xug9xu9axtkDk8a1qc0gzyaGGNioctOlE8ToAd6QnxCI9PQHhCorCLDo2JhYL+13HYMPn35Q14yuOkR7VD1NB36VhvmExDnPLPpShX4YeA2/MdDIoE5l4LLLgRCB+i5qym5t4ImRBk+/xEnh8yORA1NVCQxc0E1EPbUJiuN7WFR1Cctx3Fu7bD1NqC2thkfLjmCnSGRNAAEN+W9eD+edMREho6bJEdssA3OV1ikP3SzIzxEyEdYajmiaJg9Va7uIbdnBSF+zLioT/NMRtlCGz48AMUFJdIO9xuKDtboWmpE9dNMumgiBhZ2SdPmzHk1yeSGV+0m0R/rq1tvWYh80KCRD3XeVGhUMr7XLXLPpdqu7wpvwtuAtb9dkyl9fYHi69JJL78oaJvyt8lIUaCrL6+t8O8N6c3IyNDCDESZCM128Ewk43mqgrsfPt1FG3/ypeHnzJzDtJnzxt0ofrJMXzSYyBtRMXSVpsdZpsNPVYbemw2mG1232NaHM4Tx60oIqVTq6HTqKFTq8Q6yLst9qmhGPR7N3zvC9lutzfUob2+Dm31tejQhcAeESsGOdqaUqhMHb1HIZcLAUZCrHdJEGtDROQwnxuDo8DTMDnf0zD5W3ERws3QMJZrdah+a/+rUj1XqxSNFKQsBRbcDEy7CFCOgMlDV2NvqqI3bbEr8L7sE2TROYGmHnEzANWpCRWH3Y6qw/tRvHM7SnfvEKloXlRaHTLmLkDComV4zpCI/3lMUmgQ/NS0VKQNoekFDS3J8v7hsnoh1sk44R/T0xGlntztGE6E0eHEr0tq8YonpTdVK0XBloWf+iQHmQd9+eWX2LVrl8+hderUqViyYD66qspErVjVwf1w2KXeWd4+bWmz54s6MbKzH6gx9slO2Lzd2C4iXd5m63QVPi86VIiufuvbrF3Axvul7y0RmgJc/FcgY3ykcbP4mkTiy9jSJNIE+rtpU1SstLRUCDFa+kbFqEO5NyqWlJTEUTGGOU0aSoqw4+3XxcDHC80oLrn0SsRnTcV4hJpsDlRnRQtdD0/mVkIp0QOZWNCi1+snRL1qT1cX3n77LRSVlQvBmhWkhrOlEe31tbBbA6/B/lBdRngcRcoSRaQsLC7BJ850hpARN/GghsnkPva7snqRIkQNkx+fmoxzoseGs1i/1O4Bdj0PHP4f4PDUvKiDgdnfkmbPY6eP9hECXQ2BYozWpsZjXydTADG5HjHmEWSxJMgCjRNslh5UUA+uvO0o25sXUOtDPaFoIE0phakz50DpZ6TwTmM7flJULXooUXuA/8tKxJVD0B7A7HThnsIqn6X69QmReCgrCaq+0Q1mQLa2GoWZDbULIKiv1S8pCnYSEx50vd6+fbtokGyzScKKGiOvXbsWycnJAa+l61HV4QOe9MQ8mDsDJ4oSp06TbOwXLBbXopOhze7Av2pb8Hxti2glQFANG7We+E5y9MAiv3Qr8N4dQKc32nUzsO43Yz7a5Q+Lr0kivlwuJ56/81YoVGrMP/8STFt5VsDFNfC1roCoWF1dYDqERqMJiIqNtqhkmPFETcFh7HjrNdEPRyCTIXvJCiy+5ArEpGVgrELXBTKqOJ6RRd9Jm/4g0UTXjOOJq5Fy0BoL0EzzG2+8IWycqaD9+uuvF2nf3e1tQoT5omQUMaurFY5z/o1U+6LVBwdEybxpjOFxCVBpAwfjQ0FVj1XUdnkbJp8dGYLHc8Zow2SbGTj8ppRaSGLGCwkVElyzvjn2B3BUj+YvxmjpllwBA5ArgehcOGJmoMlqQFGVGYeP1MBq601p1odHCHdCElxJuTOO24OrxmLD7fmVvs/5wugw/H5qEsJVg4tQ1VpsuPFQOQ6aekSPsf/LSsL1iUNbQzZZ6HI48dvSOvy7TqoTplTfP+UkD9gPjZyvd+/eLaJd1FLDW3ZCDZLJB+BEopraCjSUFaN0N9nY70RLlcfO3UN4QpKnn9giJGTnHnNelZmtwrXwtfpW9LgkaRGnVon0yWsTIhE20DllMQIbfyXVYhJhKcBFfwMyzsR4g8XXJBFflNr03/vv9c10BYWGYe76CzD77PPETOnxoAGXf1SMnMT8oa7mXtOOlJQUjooxTB/o8klii9ILSXx5ZwtpEmThxZcjMjFwlnE0oJnP/izXvQKLrmUkwE4EpSwfzyGQjH0mQtRqKKHB0CuvvCIsnSnqd8MNN4jr6kD1OWTKIgmzWrTV1/keU7H88aCGtwHCLN6/vkx5yuc0Ff7/srhW1OmIhsmZibgqfgw2TG4u8tjEvwJYpcawUKiB6ZdKs+bJi06qjnBMQkMzY11ADZm7di9kPcca9jjdMrQ7Q2GPyIFu6pkInb0eMkpZPMm0SopwUi3f78rrRVPbeI1KmGKcatPjXZ3duOlwuehfRY2T/zk9fVDpckwgX7R14UdHq1BjkaJg1yVE4v4pCT5zCproOXDgAD777DNxPSciIyNFg+Tc3NxBX5c7mxolG/u9eag+cihgckhrCBEprBnzFqEpIwfPNRmxocUoUkyJGcE63JYcjYtiwoTp0YCUbvFEuzzuoQu/A6z99ag7hA4WFl+TRHx5u58f2rIBez96D12tzb70lemr1mL++ReLmdETQYMvioR5o2Levg9eaMbaPypG/zvDTFZohpBuSjvffg0NpVI9CQ1yZ5y1DgsvugyhMXEjchz0vaW8/uOJq76TKv1Bg2q6jvmLqb4CiyLjzKlDKUD/+te/xDWVBOpNN92EiAjJbexkodQgiowJMVYnRc28wsy/pqcvNBEQFhvXrzALDo84JlW9xeYQBf8ft0hCZhE15h1rDZOddqDwAynKVfFl734yzaAo15xrAH0kJgqdTdSDa7tY6oryYVBYEavrQqzWhOQwJ2I0Rqic/bRYoF5lsdMCa8hiptHgYMC/td9oFuYYZT1WUZvz/ZQY0bvtuINnD6/Ut+KnR2tgd7sxTa/FizPTkTJWzhuaXCrZKJ0zdjOw7IdA1tnjSphTewfqkfaSJwqWqFHhj1OTEd1YIxokk/M1QdfxM888UzR8H8oJc6u5GxUH9or7XvneXSKydjRjOnbPXo6GmCTf61YZNLh9ShKWhwUff7LGYgQ+/SWw9yVpOywVuPhJIH0lxjMsviaR+PLidDhQtHMbdr//FprKS6WdMhkyFyzBggsuRcLU3JOeuaQBnX9UzBvC9hIdHe0TYhQVU57i7CrDjEcozZcMNHa+84YvJYMmOmavOwfzL7h0yC2aKXLSn7DyX7yF1MeDJk/6i1Z595Eo4Mj28EHXzxdffBFNTU0IDw8XAmyozI56TF3o8BNj/hEzh1VqWtofSg3Vl/UKsoK4VPxJFoI2F0TD5J+kx4nB95hpmNxZI6UlkVW8tz6KTCqyz5GiXGQ/PUEir601VSjeSU2Pt6OpwnMv9xCfORWZIqVwqVSDQ0M4ihr0rSHzNoz2h6KCVPPm3xiaBJmiN5W02+nEA8V1eLleGuTPCtbhyWmpyNJrB2yc/OvSWvyzRhr8nx8dKlwwT9ehb0iwdEpR0by/A21STysfaSsl97zEeRhPfNXehR8VVotWD0RuXQWWlh1GqFqFlStXYuHChSLNeTgNQV6ubcbfKxrQ4EmYUDrsmFa0HwsObkNkR4tozE01YuSgGJs+5Vg/gpJNwHt39rZmWPRdYM394zba5Q+Lr0kovrzQR1qTfwi7P3gbZXt3+fZTsT+JMLpwy0+2l4lndp1cE7129jSD63/a0MAuPT3dZ9xBAzqGmUjQxAZZxee9+6YY1HpdoeaecyHmnXcxgkJOPRJM3yEalA9kYkELTYKcCJpQoYH8QHVW9H2klEFmdCHzo+effx7t7e2IiYkRKYhBQUNrLd73/DK1t6K9ziPGhBujFDXrbKz3tT2wqdTYuuw8HMxdILajWhtw6c5PkKtRHOvIOEz1ZceNWJRtkQw0qCmyxzUUwbFSI+R51wNho5/aOxSfVWNZiWh4TIKrva63X5hMJkfStBlCbGUuXApD5ElM8ND9mSy6A2rI9gOWXjMFHwqNJMj8G0NH5+Cjtm78+Gi1aICrk8vw68xEke7mP4FLxgrfPVKBL9ulyNu9aXG4Oy129BsntxRLgouEl80TFdSGSucMxfR2Pgs4PRMTMy4DVv8KiEjHeKCmpgYfbdmK1+V6HE6Uaokj4cIfc1OwPm74auuqLTb8s7oZ/6lvFenI4u+qlLgpMQoXK2xo379L1InVHS30uft66w+nzFskTKdSstKh2vobYN+/e6PVFO1KW4GJAouvSSy+/GmtqcaeD99G/hdbxACSCI2Nw7xzL8aMs9ZCrT31fhs0YPSPivUdIEZFRfmiYuSww1ExZrzisNlw+LNN2PXem6Iex+seNu+8izB3/YXQnkQDc4fDgabGRrSU7oetcidazW4UWyPRYTSJ504EzWIez8SCrj8ctRofkPB67rnnRL0tmW9cd911o5LOScKrs7kRn1fX4QGjG41ylej5tKxoLxZ98T6UxzH+CI6MQkSfFEYSZiHRp15fNiDdrcD+l4HdLwDt5YHRioU3AzkXBERrxmsUva6wQBJcu7YH1PXR+0htKUTT4wWLBzW5cww01GuvCDT0qDvQWyvXV5DFzUR37Cz8y52M1+WpKNKnYk1UBP6YkyLs4qlZ7vUHy1FpsQknu7/lpuC86FGceCWhTvVDO5+WIiteyL5/8XeBWVf2NrbuqAa2/h9w4L/0xkgpmou+A5xxLxB0ainBIwVFzSm9kAx8CLrmBy9chldD4lDlcUSkusxfT0lA6CDNUvpjr7FbOJ5+2NwBp0ctZAVphFX8ZbHh0CoCo1pmYyfK9+1GGaUnHtgr2nB4UcpdSA1qwxRDGzLOuBD6C3474s3GhxsWX8PMeBFfXro72rH/0w+xf8OHsJi6fO5ZZMwxZ/0FIv9/MFBUrKGhwRcVo1kZ/1OKBo7+UTFKuWGYsQ5ZNx/c+DF2f/iOcKfzmtksuPAbmL3u3AEnLShNsLGxEQ01legp/RrKhr0I6ypGEupgQO8kRQ80KEEaipCBev10aMLiBhRYZNQw5owOmNMaRL3wwguiFo+ujVdfffWwpgn1h83lwmPlDXiyqgk0R03Nb/+SkyrMEUR9WUO9L0ompTJKjy3HqS8j5zOqdexbWxaekIDg8MCISb/QfaM6D9j9HHDknd7IhCYUmHOVVM8VPT5bNXhxOqgH10EhuEp27UCPsTMgDTRjzgJkLl4mjAw0QSMwKKX3nNLx/KNj9STIjv2ce+QaHNFPQVFYLrqzzsWj7ix0u4AUrRovzUxH7mg1Tqa+UNTPLe9ZoNXTRJiiW5SOSqKL+kMNdO7VHwQ2PSCJNu+5tvJuYPFtp9xfbTgnbMhI4+DBg2JsRd+j2bNnY9WqVeKeQWmij5bVi7RPGnmRWcpjU5OxNnLw41IyYNnQ0in6c+30uGASZ4QH47vJMTgrwnBS0U3qOVezbztK33sapeXN6HL4Rc1lMsRnZovURHJPjExOnRD3ORZfw8x4E19e6MZ65PMtIhpGN1jvLFvOilVYcP4liEpJO63fTwMK/6gYzfD6Qw48/lGxkR50MMzxsHSbxATFno/e9Q00DZHRWHjxZcJMQ6XWBLgI0sRDfV0dOqqOQFazCyHGQiShHvFogkIMa3txQo58fQYSrU2IcPQObtwyBWQpS4Ds9UD2uUBU1rgqBGdOHZqkIhMOOodycnJwxRVXjFj0khom315QiSMmqX0A9XV66CQbJpO5R6/ZR29tGaU0nrC+zCPG/KNmYdS/TCUDDr0upRY2Hur9ofjZwMJbpLSwcTw7brdYhFEBCS4qAyDjAi8avV4MPkUPrtlzA64voxpBomijLzrmEWS2LjG4/3PKNfhd2s1wy+RY3rEff+94B5Gx2b1pi1HZpMSH/zhbS4G8fwD7XhbHJtCEAHOvBRbdAkScQnuPks3Axgd6z7+QRGD1L6Vo2Uj8L/1AYydvg2SvGy05F5511lkibbkvOztMuLuwWpilEN+MC8dvMxMHtnfvBxJy/61vwz9qmlHRI9WUUf3npbFhQnRNP1WBXfQp8P4doqm42y1D89QbUKqch9J9+9BY5tf4nDKyYmJFaiJ9H5Jyp0OhHJ9jQxZfw8x4FV/+KQ/kWrP7/bdRdzTftz9tznwsOP9SpMycfdqzEHRq+UfFqqurA6JilI7oHxU7VQcwhhkqKFWC3EL3ffI+bD2SuUxYXDwWXXKFsI23O5yS0KqvR2NtFRzVuxHSSUKrToitEL+olheLKhTlEbOwyTADm/W5OGCYih6FFnK3E/OMBTi79Wusa/saud3lgd+b8HTIaNZ26jlAyrLjupMxo4ytG6jdKxlAUFQmaupJf15lZWX4z3/+IwxTaCb74osvHlarfpfbLWayH/E0TCYr8D9MTR6SVDFy/zT59S/ziTKqL2ui/mXHmsJEaroxJ6we08KaoZZLaY4umQpdCWfCNfd6BM9YB9Ug0uLHyiQOCS0yzSDh5bD1ClOKoIv6rUXLkDxt5tClag4n5KraUoI7ixrwgV0a71xT9x4eKXkCKnefz1YVBMTNCqwhi8wcGhFD4weRWvgsUPyplDJIRGZJUa7ZVw3etIEEDk0CbH6w1wiC+sRRk9/MtRgpqKciNUemJsmUSUHQOIl6dSUl9boKDtTcmtoF/L26WbwzsWqliIKdHXX8tNUGqx3P1TSLfmIdDunzDFMqRI3fTUnRiNOcohAis5dPfg4ceEXajpgi1XalLvW9xNTWKr4jVCdWdegAHHZJ7BFqXRDS58wXKbfpcxYcN73fZmtFWfmfkZx0PfT6KRhtWHwNM+NdfPlTV1SIPR+8LQp9vYWS5FZD5hxTl60cshkIuqjQgMNrZ08F6P6Q+PIKsbS0NI6KkaXyOK9rGOvQDYCMaQ5s+tg3cx+WlIopq9ZBGR4lBFdXTQGCO/KFyEpGPeLQDCUCBxwuyGELz4IsZTEq4xbg3/IpeNEcDOnWCWjgQJb5MJaUlMOs6MH+9ChYgqejwR2HKHMT1rZtx9rW7VjesQ8at/enAJc6GPLMNVJELGsdoOdmpaOGqJkpB6p3ATV5Uopc4xHAf/BJtSNUYxI3E6A+S7SmAdwAdSRUv/Haa6+JSanFixfjnHPOGZbUGyqWv6OgEts7pEmCddQweWoyYk51UDUIqNbY2NwohFhHbQVU5RsR1/YFoiFlXhBtVh0OdMQjvyMGFlfvMVHUOSCNMUF6HBode9zGwaOBubMDJbt3iPsoDSb9eyJRPZy36XF89tRTMrwaC1Aj5hsOleOwqUdEQmhQ/l5jG0KN5VhgKsLN8hrM6DoKGaXx2fsxCVLppUimV4yR26IQZCc52WA1AQcotfDvQEtR7/6s9Z7UwrOGzunSbpFSGL94vLcejn4/iTD6H4YJElp5eXn46quvfO1BEhISfA2ST7Xf2t2FVSgxS/c0qs16MCsREX2iYEdMPXimugnvNHaIFgFEmk6NW5OicWV8xOAcK49+Anxwl4h2ifTPpT8AzvoFoA46bnS48tB+X08x+i55IafEpJzpUlRswWJh+uN2OGDvaEF1zUuobn8ZTrcZ4ZqFmLecavhGFxZfw8xEEl9eqI/M3o/exaGtn/oGotS8kxzdZq09R9SIDam7U2OjLz2xqqoqoNErRcVIgHmbPFO64kTIBx4QU/OxzlRddUDCPE862nogbvaEsVIebah55K73/oeDn2+GXaWBUxsEdWQMFMF6GHoqhcjyiq0QHNtDx6EJhztpIVRpS0Uj187omXij3YYXa5tRYu6dwVPZypDesh1X1YTj7I7F0LilqEinwoSPw77CB+FfokEfBod2BuTB8+GSxWJl+16sayUxtgMxdqnejHBDBlfSAiimnivVM5BF9ET+ToyFqBZ9F0lk1eyS1mbJTtsfe48SdpMcmggXFIoBjCpCkjyCzE+UhaWJ7/P+/fvxzjvviJdRHQctQ3mdfb2hHb8srkGX0yWMEahh8tUj3TC5vRLY8wKw99+976FMAVf2uehMvQBNSPJzY5R6mVH06Lj1ZbHxPmEW4dfHjNzVRup/M7Y0ocTTg6umkIR473AqMinFF+GKScsYt/cvSme76XAFWu0O4W73/Iw0LA4LRrPNLtLcNrVKKdRUB/REdiJiu/xNPfYDDSTIAlvVCNQGIN4TIfNa31OqoP89rq0c2PVP6bzxCiH6ubnfBhbdCkQOY6TD3AZ8+bgk+Jx0TZdJaYirfwGEpQzZn6HIN10DqK7LOyFNpmXeBsmDPW96nC78oaIBT3vqOqPVSvw+Ownro0Kxpa0Lz1Y3+VwqiSWhenw3OVpEyQbVXoKiXR//DDjoEUAkri9+CkhZPOC1yWUywdlphLOzAy4jrY1wdHSgsaYCVTWVqG5tQqdNSo/2EmyzIyG2BfrVnXBHSd83VZUMiR3rMeWuJzHasPgaZiai+PLvG0NmA5SCRUYdBKV/zFpztnBJDIk+Nt94KKJi5eXlvqiYt0u7FzLq8I+Kkb39uIWcvOr98ulp8aY5HA+yV6bGkCTEqIhYMzR9giYLlENfdPgw9n35GeobGuDU6GBQ23wiy1ur1TeqRTVZruhcKFKXAEmLgOSFQHi6ED67Ooz4a3kZtnY4YYdnltBlhdb8NaY1HcJVjTNwpnE+5JAGFC6FETK5FjK7dP66ZC7sCs3HG6EbcERXCrdcBbs6GzbtdDg0uZhhtWJd2w6RnjjT5C0ml7AakqDMORcKEmJk1atiO/lhjWqJc0EJuzMSpko7zHVu9LSo4ejxzg67oQxyQhvmgD4rFPq0IKhVHZCZAhvW+6BBJFl8x81EWU8wNh1uQBOisPacC7BkyZLT/pdabQ78pKgaHzZLg9aFIXr8dVoK0kaq8a3LCRRvlAw0aO1NETMkAPOvl2y/QxJOUF/ml8ZY560vqw9I4+uLSqPttcZP8LfJTzwpd9IT0VZX4+vB1bduJTYjyyO4liIycfxb4L9c14r7iqTGyTOCdaJxcpK2995Lw0dq+vvrklpYXFIq659yUsQAP+A8oGiV7363D2g4BDj6af5ONVskyIKiep0ZvVDqmje1UDuCYy46ji0PAYfe6HWCpONY+SNAN3gDMZpszs/PFw6GbW3SJBuNKWnyZdasWUNWA7q3sxt3Flah2BMFC1bIfVbxChlwQTTVc0VjXsjg6ipdFguce9+B84MH4Opog9MuhzNlHZwJZ8JpMsMlxFUnnEJcdcJFjzs74SSheRI9Ks1qJZpC9GgMCYI5VYaEZU0wJEpi3t6tgPXrKIQXRyNr1dmIv/0HGG1YfA0zE1l8+TvVFG77XDRtpqaP3hBw9pIVIiUxbkrWsPxdOiWbm5t9QqyysjIgKkYXJTLr8Bp30CzRmJ1VpNkz/1lAWjql9zIQmVSo7E3LoMUQD5R/ARR9ApR91tuvxJveRANuEmIkyIZzBnCcfj+pPsu71FRXw2I2Ih7NHrEl1WqF9hPVcgdFQpa8GEhaIIktasLpKfh3uBzY11KA5yvLsLVLhw5Z70SEwlYNXddmrO4047qO9Uhr7n3O2VYEa/57cIqUGRk0M9ZAN+9SOI29KVbWaDcKp9TjM8MuHOnMR3VXNVzyYNg1uUKMRcriscZYLKJiKzr2QOfqjbDZlUGwpJ2J4NxzpXoxQ+wwvrsTMKpFS3ev1bcXtyEeDnUqzHVAx65a9NQ54HTL0GoAmlNC0blkKlqzY9ATrEJCnQVxu6sQ+2Uhgrt7BxXarBSEnTkNwVlhUKIZMhp4NhX0uvn54YIMLQiHMmkeInJW9kbLgk9twmtjSyd+dLQazTbHyDdMpij+vn8Bu18MvNZR6hbZxFMKrUJ5WvVlXW2tPmHW0dAr0Ch7g54fCF1IaEDPMq84o/rOgcwu6H7UVFGGEk8PLu+9UCCTiZQor+AKiRr6icnRwO5y4/6SWrxQK0UpL4oJw59ykgdMQzvabcEP8itFWiJBaYnUF4wirf1CKZktRwMbQwtBFhjlEMiVUiovnT+JnnujZ/JrRKHj/PRXQMWX0rY2TLKmJ4t65clPaND5ROOazZs3i7R2gvr9nXHGGViwYMGQt+ahCOVz1S14tqYJPS5puE/vHLkhPpydhGStWqTxkRhydngjUCSQjHAaJaF0jICi/R203Qm3tfc+NBhkGg0UoaFQhIZATusQWkLEPnmotHaFyFAXvAnN7i/EJI7bpUDrkWjU7QqByy6dk1Qj9o37fjMk79npwOJrmJkM4ssLnSKVB/Zi1wdvo+pQ70wUNX4kEZYxd+GxHcyHEKvVGhAVo/fcH7Jb9Y+KjUbfHEFPh+QK5XOJ2gd0VPb/WgrJe0UWpVzQbN/xIlkOK1D5NVC0QRJj/r1vxO/L6k1PTFk6aWrF6Nyk88FfaNFCUS4DTD6RRZGteHcTlLJjo1oyij4kLzomqkW43C4UtRchrz4PGxuKsaMnEibdIrjlnvx1tx0Gyz4s1bbiGlsucgpjIW+QUs+oftJRuxu24g1wdVZDbjBAv3w5zLt3w9kiDWq0M5Yi6MzrYW9UkrIT++R6JfSL4oH5BhQ7ylHYVoiCtgKxLuoyokczFQpVFhZb7FjbsV+IsXhbYDpcfWQutDnnI3zGRVLx+1idnBjxqJYnstVw+JioFhRqUdPhjp0LY6caRYcrUFicj4YgKxrDZWgKAxojlWgOBeyygQf4RJTMgNQ2BRKLO5DS6ERKkxuJrUBQQjIMZ69DyNrV0CboIGs8LKVlNRyGu+EgZObWgaPeXiFGNWT0mdKES5/aoW6HEw+U1OHleun3TNVr8WRuCmYYhq+Zs+89pusTRbny3wNc9t4B6txrJJv4EZggovoySinuNfzoFWZU3zkg1Kg8MqrXkTEiEvqOLjSajThacFjUrHmRK6gH12whuMiZTR82sdqnUMT01iMV2NYhTUzdlx6PO1JjTjjBaXW5hOX509XSJEZmkAZPTUvFrBOde5SSuusfwJ6Xei3uaXJRFwZYOj0pf32ghskiVdGvhowa9g73dY7Oc+ohtvF+oMljVEYpiKvvl1w5TzAOItOxTZs2iQllgjJ4li1bhqVLl572uEWk8XV3C1FE4uhouxHP9bjwjlwLm0w6rkirRTQqb9VKn8lZ+ftx5+v/Qmhjb/3loJC5oQjSQBEVB3lYmCSghJAK8QkosS/MT1iFeATXcZq2u1xWVFe/iPKKp+B0SudjTMx5yJzyU6hVccIsjurESnfvlHpvnnMhRhsWX5NIfH16pAFZsQakRQYNewSIZgDJnKPw6y98DlbhCUnCpj73jLOG3SqXTteWlpaAqBjlTPtHxVJSUnxRsejo6OF5TyzGQKFFM3fUL6U/KI/dP6+dhBbdPAYLfWWpnwkJseIN0qDH5QhM3ZhyllQXlLkOCI7GRIA++46ODtTV1QUILWr6rYATcWgKqNUKRaChC+HShEFOjkskskhs0efh545Ff6O0oxR5DXli2dW4H02KqegJXg2HtrfHULDbiFWGbnwnKQ25xRqYtlbB5Uk/czttsFd+DVvJRsiD3DCsWQPDunVQz5+Psk4bwuUuKN56Da3PPQeXJ8dfN28xDOfeCluNEs5OT0RELoNuRiSClydCnWIQ57HVaUVJRwkKWwuR31qA3R2tOGrRId0RhLO6GrGuPQ9zu6QmnF6a1eEojZ+DoKlrkDb7mwjRT/Co2ElGtZzBceiKzUFteCIKVTrsbmlAeXsF6mRGtJ8gO00pVyIxOBFJhiQkBycjSBWEso4yFHcUo3aANEO5C0hodSOl2S3EWLojDNOmr0LW6ksQNH++mMByGeuw491/wly2E/GyFmQZLFAbKdLSz21aqQNip/lEWUFwJr7XEoxCh1LMbN+aHC0Gzn2boA4pNDg+8Bqw+3mguaB3f+ICKco1/dIx0y+Jevf5+pd5Uxjr69BWXwNrdzdUDidiO7sRa+xGVFcPFDSYBdAYqkdNXBTCVq5E1uJlSJ+3cEjrn8cS1Ibg+kPlqLLYoFfIhXgKSCE8Cb5o68IdBVVosNlF1PWnnqhrQF8ouodRBIlcC49+JASBgCa+KKVvztXSPZJMpyhK7J89QhMW/QqysEAxRmsSRsNx/6dUSjIAoXREYSzhaY2w7kEg48xjXk717RTpKioq8o1TFi1ahBUrVkCvD0z3c1mtPgHlizZ5okwu32NvJKoTLu+20Qi304m9U6fjjTXnY+fMub7fmVNRgis2fYgz9+XBKZfj5XMvxX/WXwwX1U52GXHHay/grD07oAgOlgSTEEm9AiogKqVTQFH4KhS1WyFXuaFIyID88qcgS1k0xNlPn6Kk5FH0WKRIs8EwA9lZv0JY2IJ+X08R77FgwsPia5KIL4vdidm/+RRWhwtJ4TqckR2NM7KisCwzCiHa4Yt+dLW2iJqwg5s+8fUtoZSOOWefjznrz0dQyGmIi1OMilVUVPjs7Glw7g99Rl7TjoyMjMHNLlETR3Jx8jfE8DVz7APNvnkv/OImMFuawRtOaABUulWy3iVBFmAKIJPS5kiIUXoiHc84iIJQmik1lyRx5S+2qDaQoKiWV2Ql+2q1As0OKMOixRoMS3gOIhZfhuCZ6yUh7Pf/i6iusbJXbDXsQpulDU4lpZKthkW/Em6F9P2Ww43lIXJ8Py0NS809ML69G9ZqJWQK6ebptpthK/sMbnM+9KuWoW3pWThqiMfBWiMO1HQgv84ovqf05xemReDczDAs2vMpFK+8CLfn/9IvX4GQy74PW7UCtvLeCK8qMRjByxIQNDsaMmXgYNrpcqKyqxKHWgqxtbkBFW3tyG6pxqrOYqxq340gV28qj0Wmws7QdBRHZ0CZMR8ZyUuQE5GDaN0wTVKMSFSrotcQY4CollOuRGNILIr0odijVuAztxkVsl5Xyf7Qu1RI1ichJSYLyYZksQixZUhGbFAsFAM41plsJiGQKWJKS3F7sRBlXd5eRH3QWdxI6VAiMygFuVOWYNrM1cjfdhTlheXC8fWGq69AorJTipCJSNkhqR6tHxMDSlusDkqCPmEWolLmShEyEmeUwjyUny9dDynKdfCNXnc7shifeYUkuobRFW4osdfVwbhxEzo3fALr/gOS3bj3OY0aKr+UKnXmFIRffTVCL7pYGPNMND5q7sDtBVXCrjyVGifPSkeOfnDCuc3uwL1He+sNl4cF4y+5KUiklgJUO0Wiq+lI7w9MWS01NqbJwhNl0ThsktD3ryGj74M32uqPLkISZPFz4Iqei+7GDLgQjJDVKcdcRweFzQzseAr46oneXmOZ6+BefT+cumS01tTgi7w8HKmuFk/RNzBXq8V8lxs6SvPzCihvml8npfENXM84EHaFAlvnL8Uba89HSbLUq1XmduGMylJcU1aAefYeKL1pfB5RVRAShp/JglHouVSeFxmC3+UkI1p9nHFjwQfAB3cD3U1UgwIsuwNYdd+Q1ht3dR1BUfH/oaNjp9hWq2OQOeXHiIu7FDJPBG8sw+JrkoivmnYztrzwa2xpi8BnjhmerzegkMswNzkMK7OicUZ2FGYlhYl9Qw31RDq0ZSP2fPQOulqk2WWlSo1pZ67G/PMvRURCIkYKOpVbW1t9UTESZf5RMeqh4x8Vo0aFxww4adacBha+mbZ9QAsVVffzNQlN8avRki7wA1lKjxg0eKBjptREiopRdM4fGoSRZTmJsfQzB98TZYiFFn1uXoFFYoty4UlYE1JUi2q16pAia0CKvBEGZ6DIJixuDWpNetT1GNBoC0fkoosx75KrERoTF/C6mq6aXrFVvwtNPU1ivxsK2HRzYTWshVU73ff6eI0S1yZE4UpnDzSbv4R5XxtkuhzIPHVgrp4OGLuOoDA7FkeTsnGkR4FDtZ3oshzrfKdXK9Bt6z0n6fSbnxCMlU35mPvxy4g2SYXXhvXrEX7N92CtlMG8vwlwSOefPFgF/aI4BC+JhyJEc9zvQrGxHh9WF6KjbCey6g9gVdteJFl706eIQ0FJ+Cw4FnsNWmhik5ETmYucyBzkRuQKkSEfazc7GuzU7fVFtdw1eZB1H+tA2KJUY59Gif0aNQ5oNChQq2Hrc/2TuYEIoxuxHUBshxsJsnCkZ85H5sJ1mDJjBcJoJv14UCowTXxQujG9T5RWN4DAEe6u5kafGCtqLcTRugOotDXAQQfSDwaXHnqLARGuCFy87GIsSFuA9JB0qCilmGbe28pRW7kbn+dvR0x7IaabSo5JPw0YhPrcFj0L1ZieSnoyWXAfeVsSXSR2vVBvM2qGPPvK04vojwD0OdiozcmmTejauAmW/N4el4QmNxeGtWtgWLsOmuwsWIuL0f7qq+h89z24zZLYlev1CL3kEoRffRU0p2gBPhah/m9/qmjEYxVS/dHK8GA8Oz3tGFvywbzXrza04ZfFtULQhblt+H3JE7io7sNesU7mGeRaGJNzev8EfRcpDdC/hqwxXwgyt1sFk/NcdDmugAtSiqhWdxSRU7dBFpEkTZh6l9BkX58+bxqfzyCibwTKXzCZmhCiO4CQiFrI5G50uYOwybgchwy5cHkmaZKrqjDj0GGE9Gmx0y9yuV/dk38EqrcOikSUKTQUr2lD8S+HEo2eei6dXI5vxUcIu/j0oONPNttcLvy5slEsdIsJVypEHdglMWGBYyMyCfv4J8DhN3u/85c8DSTNx1BhtTajrOyPqKsnYxM35HINUlJuQWrKd6FUjp/JDhZfk0R8obMW+PNscZExRszEh2HX4B9NU1HWEjgrGqpTYUVmlBBiJMgSwoY2FYRSEIt2bhNNm30OUDKZyIlfcMElSMyZPuIz6zabLSAqRpEUfyIMOsyNVyJTb0KMvQYK6m5PRcDeFIi+VtFCaHnTGuYC+kiMeYz1UkSMFoqO+fdgoRoXYdrhiYpFpA/74ZAYprTRvjVa3maSRLCo1apHsqwBGaoWRNtrofDrfSWQyeGOmYYOVRIOlxhRVGNDh10LpUaL2WvPwYILviHaJBAN3Q2S2KqXIlt13XUBv0quioEh5ptoVM5Fl8cKns7Us8KDcYvNiOm7dqD78zxyDIAqdTlk9L5RawanGZt1DrwqV6DBcqxrk0Ypx4zEUMxKCsXspDCxTovUo95owceH6vHRoXrsrQoUkTPdnVh6+HOsqDuAaGsXQi+9BJE33QZrFdC9ow7OTltvSuLMKAQvT4Am5eSuP3U9VuQVbYOl4ANk123HHGOBiOZ5aVKFYWtoJrYGBWO3sgMKhQVTI6aKyBiJMVpnhmVKg/+RgFJJ2itgLN0Ea+VXUNftR0h7FeR9vp90ZuRr1Dio0eCARo39Wg0aPUXrGoUGibo4xHYrEV3VhcjCBsS2u8US3elGSHYmQlYvg2HJXKijDZKQIkFl6Rj4sVdw9XVro4EbtQGgJXXFSTVbtjvtKGstwuE9G1Bw9CuUGMtQEeZAa2j/10qlTIm00DRkh2ejTZaALaZQ9CiTEaaNxu+npuACvUOKjHkXipQ10zWtH1cxOo9FT7JZvRb4VE/WN1LfWiqlFe7/j2Qn7a3Jyb1QinKlLh/T0XRKR7IcPOgTXDZPvY1AJoNu/jwY1q4Vi3qAJrZkRtD5zrtof+UV2Mp7622Dli4R0TDDWWdBNh6aJfeBagTvKKzyRahuSYrCr6ckQjkUE7WeOsCy3f/F91XLsD8kV+y+su0L/F+cCsHzrhrWrBC3pQfdnx9E145uOHuka5ZC1gCXOwxuaKGR70Wk6v8gl1kDDtlhU8PerYStUwZbl1w46tlM1E5CAaeVJqMGfm/cYTJULM3EvtCZsEP6myk99ZhV04FoVZBPQAWk8Xm3xXNh0mO9/rh19BU9VtFI+dX6NvR4orXUUPnmpGhcmxCJ8FMUzoe7zLirsNpnmHJOVAh+l52MWOoDSDWcH/5ISt2mSabldwFn/nTIol1OJ9V1vYCKSqrrksYnsbEXInPKT6DVDuyGOlZh8TVZxBc5S331R2D3C72DgdgZaJl7OzZiCb4oacNXJS3HzMJnxgTjjKxorMyOwpL0SOjUQ5MrS6dTbcER7PrgLZTtoQGrRFxmtjDnoCaTo5KXa7egs2gb2g5vhrNmDwxdJYhGa8Dg04tTHwt50jzIqMeWN398ItRN0exgxVe9tWKUruUPzYQL90Qy7Vhy2qYdJLSampoCRBZFtByO3nORolqxaEaqvAmZ2jbEO6oR1N/sPVn6egwxnPHzUFhhws4PPxQ1G4RaF4S551yAeeddDLPKLoSWN42wqqvqmAHsjOiZiIw8G+WyXOwyKURtBxGtkOP7phasPbgb8q1b0G2Uw5l7ESKjc31RoHw48TKs+AoO389RVDk71oDZJLSSJaFF26oT1NvUdfTgk8MNQojtrgycHMhpq8CKukMiKjb1knMQecutsDe4Yfq6DraK3lYMqqRgURcWNDPqpFNphONWSw1qD30EXckGTGvcDoOzd8LGKlPh69Bp2GJIwJcaoNldCbnLJGqdSICREPOKMhJoemqiOkhsTpuok6prL0FP5TYo6/YhoqUUKZ0NCHMcm0rUqFAIkXVAS2JLg4bgKMQExyFJE4lkdQiS5UFI6LYhprQCoSVFQHsDFCoX5Gq3WCuDlVDoZJC5rZD1J0pOCZlke03fLX+nNqq7zFwLTD0PyFp70pbU5Dpm3r0H9Rs/QP6BzSjSmbBzdgKaDRYYVZ2wD9BHTK8KxtTwbGSFZwlhRuussCwEq4OliBWlaQlB5k1bPNxrbtAXqpOJmS7VarUWS6/3F5jzb5Bs4k/RgXEkcdvt6M7LE4LLtHkLHE1SdJuQqVQIWrZUElyrV0MZefKTaPS9MW/fjrZXXoFpy1ZfmqIyPh7hV16JsCsuP6XfN5pU9VhF4+T8bouozfrd1CRcHT8Ex27vAQ69KaUW0oQm7ZIp8PjsB/Dn0DNEv0JKa6R6svmhQx/RcDvdIlvAuLkKzjbPd9LdA1v5p7AVboQmMR3KGT+ATKGF3HIYwd0PQxNshkrvhFx5/OGwy6WAwxkMpzwcLnUU3Lo4uA3JcISmYL9Vg501DbB4JhIT1V1Ya/sE6agBgiIlwTL/xpOalOn3/3K7RfPkZ6qb8XFLp2/kMk2vxW0pMSJapT4N4zNyuPxrVaOIglJrgTCFDA92fILL9z4syc3oXOCSJ4HEoYl2uclFtPkTUddlsUitdkJCZiMr6xcICx26iNpIw+Jrsogvb/d3uuDteBLI+2dv7jE56q28B45pl+FAvRlfFDXji+JmHKjuEPUwXtQKORamhwsxRjVjOXFScf/p0lpbjb0fvosjX2yG03NBComOxfzzLsKMs9aJAfOwQAMhGlx4UxBoTYMPf1MKDxZVGOoRiwp7uFjXIQYmBMNgMATUiul0I1g0TsdPs0w0y6zUSoMgStGgNW0PxSwzfe0pnVKkJ34qmXb4D0Q1oUDmakmIUZqiPur4h+xwCKHlX59FRcb+aZ9EMLqRpmxGtq5D1GyFdZdB7meZLiCRQw2Ekxb2uhBGThGtDw5v3Yi89970pbhqgw2YdvbZsMyKwp7OA0JslXUGGp+QaJoeOR0L4xZiatQiFDhT8VqjURSVi+edTlzVUImLD++H8eBRFLr0sMTNxJzIHMxR9aZl7oRDiK59cCI9Si8EFqXzzkkOxbT40NOewGjotOCTwxQRa8Cuyjb/fq2Y2laFlS0FuGBFDmZ+51o4jTIhwkRKorM3JZHSEfWL46EwnNoN3m63orRgM7oLPkZC5WbEmwNNI/L1GdgSNh1bgwzIV7RDYSuCzBONlEGGlJCUYwRZlK73nOm0dqLGVCPs8ynts9pYBUvLUYQ3FyO1swmzrRZk2+zoO19Lf6FAo0aJJghNWj161EEIdQPJ1h4k9ZiQZO6E4XRvYRQBohRDSpmjmfiAx6HS9kCPSWTRgIfSIcs/l8wDjn4i1UR4kSmA1GWSEJt6jlR3eJIRm579B9CwYQPe6miHUadFaagGeWkRcMqaoLZWItZeBaOrGY4BRGSCPkESYl5RFpaF1NBUqGRKyYnVFyXziLJ+22B47L7JUTV9ZW+kjKJmY6i3nMtshumrryTB9dnnIjXMC0USgs88U6QU6s84QxgL9Ae1kqjsaMIXxbWiTjNYrcOUyGgkh4UhMliNqGANog0aaFUKUS/W/t/X0PHGG3B6sipI2BnOPQcRV18N7ezZY7aO8ut2E245Uo42u1M0331+RjoWnq4Q6qwBdj0H7HkR6GnrNYWZ/S0ptTB2GnZ0mIQlfa3VLnpM/Sg1Dnemxg5JpM3toompcnRt7jU/clmNcFDqvdMKRcQUyKlUgD4TexNkqihApoRM3QVNajOUYQYog1xQKrohd3dA4WiF3NIImbFacmQ00jXx+NeaLgTBpIxCUOI0hKTMhIzqxY9+IL03XkORtQ8A0y456fu4w+XGhy0deLa6GXuNvRNkqyMM+F5yDFaEBw/peUamK3fuO4iDDillkdx0Hws3Iu7Mu07JUv94GI2HUEx1XZ1S+rJGE4cpU+5FXOxF46Kua1KKr6effloslE5GTJ8+Hffffz/OPfdcsU0F+ffccw/++9//ipqR9evX46mnnkJsbK/DV1VVFb73ve9h69atCA4OxvXXX49HHnnklPorjBnx5XLh5n9OQ5RbgQxdDLKCkzHbbEJEdZ70xffOZFKomCx/lRp0mu3YVtoiibGiZtR1BvbWoJvLyqwonJkdjeWZUeKGczqYOzuw/9MPsX/Dh6JxJqHR6zFr7bkiWmGIOP7A/oTFtyLXe98xud7HQI0byXzC3xAjJF48RU0OvemJZGvvH52hC1tycrLPzj4uLu7UL3Z0nCSoaFBm8i6N0j7vtniuUUprGhCZR4x5BZlHlPnW/o8HeE4ddOw+er9q9koOVGVbgQDLa5nU88oTFbNH5qDRE9Hyii0SXv692Ag5nEhWdSLXYEKqrAGRPWVQm+sHiGp53AfJhZBm1vxs98ml7MDGj4XLprfpt9Kgh3VeDHbF1qLQFNjolMQAiQASW4viFmFuzFwcNsvxr7oWkV5Ds3oqmxUL8oswo7IOHS09KAqKRXloPBYrdPg21JjhkQFOuLFD6cKR5CDEZkdIgisxDKFBw5t612i0YMORBnx4sB555W0Bt/wsYx3OSTfgsm+vR3KIHt15DTDtqIfL6BGxChmCZkULgw518iAacbvd6G4sQO3B96Eu3oDk5n3ojQ0CrapQbIlYjB0ROfhaK0NH936EWsqgdzmhcbvFOyderVDBLlegQ+aGy+XAdKsNs8VixWyLFZH99GPqkMvRpFDALJdB7gbCnE7EO52eBJ6BcTkVcFrIBE0mmny6bHLIQqKhTMqEKnsGFNHJvYKpr8ii78BQDpJF3eVejxD7uNeS2gvNIIv0xPOkc/0kZqtLm5pxw5d7UBwh1S9OKyvGz198EonNjbBrlGg7cwYal0xBTUoQii1Voq6syewnAP1QyVXICM3oFWVhWZhmbEX44bcgo4J6r5CjZrIU1aNJoH4mroSojJ56rAX+CKZjO9rbYdr6mRBc3du2BZgVKCIjRWTLsG4t1Ivmo83ZhZaeFvG++K+r2jpR2aRCU5sB3V3xcFlprHD8z0SrciNMr0KsQYfEICXmlu3DjF2fIqxCcrMjZNk5CL36asRcfAEUIzl5dwJeqm3BL4prRI3PLIMOL8xIR6Jf4+RTgoaPVTuAnc8ABe/3njsUIaX+V3OvPaYOutPuwM+KavB2U4ev+fffpqUgdRDNv6kOqztvF0xfFcPREQO5VspQcTvtwgBJfpI1iKoEPaJvnQW5Vnn8SdGOaqCjAq62crQW70ZnxUHo7dSZrwNanEK/K300kHsRkLEKCE+V6s36HGuXw4lX6lvxj5pm1Fik8YxGLsPlseG4NTlGtJEYlkyqj34Me8H7eCr5KjyeegNschVClQr8JjMBV8ZFnJbQs1obUVr6OOob3vLUdWmRmnIrUlO/A4VimNthjBATVny9//77wqaTBsGiu/pLL+Gxxx7Dvn37hBAjUfXhhx/ixRdfFG/A7bffLowWtm3bJn6eZuLnzJkjBtD0czRwvO666/Cd73wHDz/88LgTX+2Nh3HGJ1cds9/gdOI7nUZc1tWNEM8gx6YNgWvBzdCu/LHPaEFYazd348tiSYjtKGtDjz1wFnVGYohk3JEVjfmp4VAP0iXIbrMi//Mt2PPh2750MeqZkrP8DJGSGJ16gpojr+2sv+sguRz1ZztLYX5/kUW1WiGJJzXIovojsrD3ijEyg/CHBLuIimWkY0pcCHSOTo+IauwjpPwee2slThaaaSZRQv8bRTX7+x+HE/r7MoXocyXvI2SNCEYR0lGMdJQhxZfbHqk0Y4auCWmKJsQ46xHUXQNZ36gWCTkasJHYotRGamZMEdp+PhdLtwn7P/kAuz96B1aT1OPDqpdhX1oripO64PQLNFEqHAktWhbELUCoJlTc5N9obMeLNc0oaTND29KNhLI6aDutqFcY0ENRRBJyAM6GCldDjTRIv9QpBzozQxGzJgVxqadXlyBscN3SzCwtCpX8lG5gTV0WbDjcgPc+z8eedidcfjODOTonLlw+FefPTERMQ4+UkljZO+NPFvUkwnQzTpCSSJMDfWuaxOMOUTfY2VCAjtYKRHeWIMivObAdMuzWGfC5ToXPg9SoUanEgCzJ4fSJrFlWG7JttmMEFF2VemQksqgCQwYZRZHonA8KHzAS5daGwd5qQvfeAnRt2wVLUZUQW3DLyL8ZQYsWIuTss0VKmTJ6jKQKt5VLEWYSYxXbAiPMNAijmksSYjQQo4mRPmxqNeJHhVVosjkgd7mwoLIQF1k7cb7dgZ6NG4UphA+5HEELFsBw9tlwnbkI5aoOn9simX2UtJfA7JBmz0OcLlxkMuGbXSak23vFVXV4Empzz0XQrCuRGTkdQRQlo+bg3nRF0Zfs0MDXNEOCVD/mE2UzpWjfEPWCtNfXo2vTZiG4qGce/KLr1tgwNMxPRdGsCBQmuNBkaUFzTzPaLe3UmlV8D122aDjNaXD2pIm1236sWFRp2hES2gqb0wKzVQG3I1hanMGAe+CpgKz2alxQvg2ravZD7RGsRnUQtmUtw4H5q6FISEKkXo0og0ZMakZ5omnex+FBasiHwRjLa65A5hf/qpPuZ5Sm9seclIEbIZ/QfOUtSXT5mzqlrZSs4k+ikfb/GtqECOtyuhCskOPR7CRcHhdxwuimec9emHfuQPeOnXCawqHOOhfy42RmKGOCoEkPgSYtFOr0ELitTnTvaoR5byNcZr9JVo0CoRdmQD8nZsBrpTAyKi4WtvGU2UGQVfwZK1di/vQpUFKUjCLKlNIvFs/jzur+JzD8oWtfeBrMIcnYq4jBJ45wHNXEoUobj57gBFyTHIcbE6OO70Z4Ohx+SwgvMfFKEysrf4TC+XfgruIG7O8y+6Jtf5iajIRTFOtOpwVV1c+hsvIZOD3p7XGxF2PKlB+Py7quSSm++iMiIkIIqcsvv1z0dXrllVfEY6KwsBC5ubnYvn07lixZgo8//hgXXHCBmLH3RsOeeeYZ/PSnP0Vzc7NoejeexFePowdfV2xCef1ulLcWoLy7FmX2LnR7HLQ0LpcQYDd2GhHnuUnR7PKmkHCUxmQiKSoX6bFzkZ6yEnFhGbA5XdhT0Y7Pi5vxZVEL8usD6wKC1AoszYgU6YkUHaP0q1OdCaF0mtK9u7D7/bdQW9hrN5s6a64QYbSWkZtXc2Gg0KLUGL+BX2B/Dz+RRWuaeRvMDI3TIVm1CyElCar2lgaU1LajuNWOcpMadnfvqF8GF5LQgEyUIwsVoteU/HiChgZbtFDTVKqZoEUfc+xj+p/8Byt0XFTTR0KMLKZpTalO3sfHrPvb578+9nUWuwMNiEY9YnwpmK0IhwHdyBL/XzkyUAW1n6U7DaCdUIjarVO9hbvJNEOhg1upg1uhhVsZBJdCC7tbiY7ODpiNXbA7ZHC45eiRKdCu16JdRw0jVVCpwhCqS0BoUAIidElQqkLhlGnhgAa73Br8zyrH4R4HFG1mKDus4nf2JdRpx3UuOc5VhSDEI7ocMqBFr0aDVgm7TCbEkssjmnrX6N120oDODZf/2vdaCoJI2/4olHIER2hgiNDCEKntXYdLa324BooBBkTNHd1499WN+OhgHfYbknxOWkRurB7nTzXgPAMQVmyGuYRGmtJ3QK6xIji+AvrwQ1A4G481kbCbRXStXS5HjUqJaqUS1Z41bdcolWhSKqF0uzHHYsWZ5h6c2dMTMGgnumVyyCGDrp80uC6ZDFUqJYrVKhxRa3BIo0arUgGrUouEiGzhtCiMPSJzRIqcjlKWPAMea0EBjBs+RdennwYYHkCphH7pUoSsPxvBa9ZAGT7GG96SYCnZLAmx4o2BdVc0GZBxlhQVyz4H3boo/Ka0zjdYzg7S4v4oLfJee0WYCeXk5OCKK66Ao7JKvC+09HXv082ZI4QYLeqkRDGZ0lyyAa68fyCq7AuoaEJLfG4yvB+sx+shwSjucw+kfmb+tWTZYdlIMSRDSRNLPmMPz3qgXodUE0jNzH2ibJaUWtyP2PTWAVJUioRTs7kZxqJ8KL/cg4i8YkRWBIq+ihhgV7YMedlyVFIZWkArCTlclgQhslw9GUJwuRyBf1MmcyM1SoHZycFYOiUGqzKTERfa+xqz3SzSZWmp6KxEWXsdSlubUN3ZgTaTwyfKvGuXIxiGbjnWlR3F+WV5iDO3+9oA7IrNwQcZy7AnZqq4/vWFdFeEXhJilIEihBoJM+9jgwbRHrEWoVef9ERoi82BWw6XY0dnt6jf+XlGPG5POXHj5GMw1knmK1Rj7m1pQuftrG8Ci74rfb6nQGWPFT8sqEJep2S0cGlMmBBhoR7DCJfNhp79+2HesRPdO/NgrWiDIiwDyvg5UERkQEb31D5QDSwJLRJc6rRQKPT9ixW3w4WeI63o+qoG9mppco+QBSmgnx8H/cI4qGJ6zwOakCXRRZlTBLWtWb58ORYvXnziFjZ0/6a0RRJm5KZ88HVpEuMEaYy+Y5XJIQtNAsI8UbK+C002DzYiJaJd9wD570rbVO95yVPSWMqT9vhMdZNww7S63DAo5PhNZiKuij9xFEzUdTV9iJKS38FilSbcQ0LmIDvrlwgN7e1DNpGYFOKLolhvvPGGSBukyBcV869Zs0a42oWF9c5Wp6am4q677sLdd98tUhTfe+897N+/3/c8pZlRXc/evXsxd27/JwSlMHqtr71vMKWijbb4IqrbzIgN0fouxKJBnbkJ5XV5KKvLQ3lbIaq6qpHV0Yhv0nF7UuqMchleCTHg5RADOhUK6NxAmlyHDF0s0sMzkRE3D6Eh81HRFITtpR0iOtZiCoxkUG8xioqdmT243mL1JUeFCGvbtwkxWiPitF1IDLUjSmU8thbIW4vk7zooGimmHv/CQ0KO7Ki9aX0eUdWb8ud93AiYKVd94K+EAwpUIhElSEUJ0tGMwFlTvdKJKeFyZMWHYEpyAoIi4yWhRaKKZrZOYvZXfH5VXagv7YTT4fJFTAIG/X5rEgA0vnc7XZ6133P9igc3HE47uu0d6HF2wOzqhMXVCRv8nBD9ULnliHZZkOGuRTKqkSAvh17WDnk/FtkuN4klLSwuPawuPeQyF5Qym2exQiWziH1DhcmtxWF3Gna7c/CJajEKnclwCEeqQBQuJ9KM9ZhqqsYcZSsWh6ciQjMXMkg3V5fbBIv7IEyuEtjdcjjcGmmBBnaXtPbus4u1uvc1bg1cx1QrnToyOKGWmaFVmBAWYkNoiB0heiuCdT3QayzQKbuhkZuhpubR5naU7zLi/epUfBE1C/ujMwOEWI6sCpfKD+BsBEPjXAkXvDPKdriVX6JR+zHKdNVCVPmLre4TnJ8GmQrz5MFY4pSLNMKMrhaEdDVB1s93xgE5ynWJ+CxiIbanX4bE5FnI1JigsRWjoqMABa0FONp+FN3+DpweSMAtN8ZhVbEaWQdaoW3sdYWkuhr9ihUwrD9buMyRW9i4hKKNVV9LqYmFHwXUW+0OmY7bp/8WFWppRv+7SVG4LyNBNEym+9XLL78s7n+zZ8/GxRdfLLI7CFtNDbo+3SiEGA1avcgULkQujUBYuhEqu6f+hKBUwQU3wTr9EpT1NEkRsrYisaaIGYmf/lDL1ZgSNiWgloweR8k1kFF2gjc6RpEyyk7wNyPxQMYLRkMMGgzRqNAF4yiZqChcKLJ1otPagYx6YFGRC4uOupHoKSEi6OpRlAQhtvKyZWiLVIn6whhdDMJUsXBbU2DsjEFjmwFVzXJY7ce6kM5JDhO99hamR2BeShgMg+yJScKMahmrjFXC2Me7pt6BlNYocygwp1iDc/bZMLey9z2oM+ixIWcavpyyGN3KaNhtWvTYTr1mlFyMSahFUi2aXyQt0vvYoEG7AvhJZZ2os6IIExldnH0qjZNpiEitBSjKRQN0bwSHHIAX3QLMu/60WqzQ4P4vVY14vKJBlK8myIFHKvOR+9lWWMrboTCkQhGZJWq2ZP3UGCpC1dDOjIJuagTUKSGQa079fewpbEPbfwrgtgfem9RpIbBna/FF7R4cLZVSSqk8xdsgOSjoNFLlmovg2vwbfNrcjhfjL0aDJgqplnqkWuqw2NWCRa5mRHXXQkaCrZ/vT+CBBksiLECceR5T2Ul/jc7pc6Xo5Yc/lmr0SMiuvAegzKh+TEGKui24u7AKezx1Z6vCDfhDTjKSBoiCGY0HUVT8IDo79/rqujKn/FQ4GcrGaC3kUDChxdehQ4ewdOlSUd9FKWAU6TrvvPPE+sYbbwwQSQR9Uc466yz87ne/w6233ipmMDZs2OB73mw2i9DxRx995Ksd68uvf/1r/OY3vzlm/2iLL6fLjdxffQKHi5osByEtSo+0yCBhaU1RKdomgeR1Xes2NaLtqz8g7OAbMHhm5Cj15zVDMF4MDREz0X1RuIEkhQ5p2lgYVHPRZZ2JypZwFNTZ4PAU+4vXyWXipnbG8XqLkRCiBsXeGi0yw6AbdT8NQ60uJawhUxCUcxaUaYukNEJvk1z6PRQeP6Z2yk9IkcgiwSX6AJ3CaU4zklQfJqJTniiViFbF+D2WIlcdVjlKysp8tWI0I+37NTIZEhMTfcYdCQkJvkHSMZ+jw4W6og6UHWhGxcEWdBo7YdW0QOaWQ+5SQeZSQe5SS4/dSlHXdLK4ZHY4VCY4VF1wKE2wq0xwKfu/mMudGqjtOsQ7jUiW1SNBXol4ZQlClYH9oQgSWG2OFCFEguTtiFBWBwgyer7aNg/V9gWods6HXR4q3hO53AEZuuGGCS5XF1yOBmgshdDYqqCSO6GSOaFQ66AxxEMfFAG9Ugat3AEn7Kh06lDkDEWhIwIFzmiUuyLEIK4vSV1NyO6oRnZ7NaY7yjEntAQRSTrYdBfB7ForrIYJpawGBsVbCFJsgUx2grSQ4+CWKUUEj25y0jpIKjb3r7tT01oLp8UCZ1c7XOZ2yHo6ILMboXR0QTWA+D0eLrsMbUV6lJfGYFv0LHyVMAv7orPg9BNiUeoWrNd2Yp01BpnW3r57R3SleC/iM2wz7IPTTxBTA2FvQ+E0XawQWWnGJkS2lEJZtx8yfzMJ//5x9P3UhsBhaoG7dg9U1o6ASYu80JnYGLkUWyKXIjwuFysjQrAiXI9oWRuK2wtR2FKAjj07Ebm9CLPzzYjyCwpZlcD+KTIUzAqHfcksZCbO9Jl7xOkHUYM51qDbcFM+bIUf448tLvwlfA1cMgUSLY3489FHsAJtHsMOsrFfhsLiUrz22mtiooZm3s8555xj3gN7YyO6P3wZ8kMvQ68rh0Lt7p2L6oiBI/0S6M67BZqp2QO+f5Su55+2SI+pgTRlXPSHQWVAjD4GYZow6BQ68Xvt9h5ojbWI7KxDcncnplLUzmZDlDNwoEuRYnOTGs21OnTX6iA3914rnQoZmqfHo2vJNGDFQkQkZojG4CqEobjehd0V7ciraMeR2k4xmPcnRKv0Ca2FaeGi/YOmn/vcUEPvkYiYGatFE/S2oiOI3bAP03Y2IMji9p3XX02X4ZP5clTEUHqjXkTPtIhBuDIFekUc1O5IyJyhsNu16LEq0d7tQGu3Tdz7T4QzVgv7/5P3H2CSnWeZN/47oXLsrs45TE/OMxppFEayoi054pyWYAOGXVjYPyxL8C67YD7C/g2G5QODCV4sG5yzrJw10mhy6p7pnHN1V04nfNf7VlXnCQrGMjzXda73VFV3xXPe897Pcz/3vbMCdBU1bdDcn6JB15eojtUl4LZEgRR0SJ+LoEdHEVR34esmQJe4Vpej5Wa46ROw5YFrUguvhwWTu3yZ1IsvcvzSCF/rvJlmM8C+BYOdCwb6lcQXhMjo9gjhB9rRK1+ffrrCTJrZvz2HlcijOFXs/PLxmcegX5tC2Rni0AO3vuY1n/A9+5epKH87OstAprhe1S2Dd84+wc/PPcquG99fBLXS088qrmPWUhnLW2K1dcqGIebnlcBMgOWL3ywKbYkQ1GChZHgNc3TTtqXE/R8NTpK1bAnm/8emBj5SH1maQ7K5Kfr7/zdTU9+Qt1XVQ2vrz9Pa8nE07Y3T+/jDin/T4EsscEXpV3y4r371q3zuc5/j6aefltWsHxb4eqNWvkRj/q1/9ASFFSBobQgAJADYEiCT4MzDjtgzVJ36CxRZ/gZLdTBe28VzXj/nC1EGjRQDDu2K2XDbcuDLbMGR3U0i2UksvVotKezWeFdrlnvDk+xSB/HPnyvywzfIdMvMTd1ujIpNTMyk6OvuJ5tK49ULBFwWDY0Rqiq9OPKLRZAlKA8b+XFdMZSiYt8qILUR5a+2ODGtWLxebwiRjtHR0SWTZyFCsTJElqyzs1P2K4rRoboYvjDP4Jk5hs/Pk88Ysi8h65kiFRzAVq4kg63g1Fw4dBdOhxuX7i6ODrfshzStArlClkw+RTITI5PbeFHv9waIhGuoDzhp1aapLQzhWzyHI3oO1Vy9uBIAx6rcglF3AKvuIFbDQajqQlE1VE1BEQIJuRjq0OOo/Y9C32MoZcWrEm1iprKFo74gXybGOdUimHKwuz9Ex4QPVfTsiOxYaw2H3vk+9h26h6H5NKdHFzk7FuPs2CLdkwlJi10b4VyCHfODEmiJnouu2BjVO7cSuPMOAkcOCx4JiReiZC7lljC4I2IQ2JbAUxdFEQtJYa59Rbrmle5LvcJj8PrCdnixHCEMLUBB8ZO1/GQNL6mch2TGTSrvZlZ1MOU0mXYazDozpO1FDp4f5ci5GDnFw9H6nXx/024uB7soSmAUY4e+yHs1hTvyDegl+mzOY5Dao+G/oY5GF7gmzsDYsaKRsahcrO1VEB5P9buXBVLEKCgxKxfwYoUvsuWXf4DR8xD6XM+qpxjwNPJo5c0SjF0I7WL3QpS9Lz7H3uMv0jo1LuG05XExv6+NMzs8PN64QH9utQJjOUR/30ovMrG1BdvQXsU5/KOMS6ksv3RxmLMlr5336FE+NfoPhPofXk23Fv1vXfdyxn0T33i5WMW6/fbb5TVOhqAS9ny3qDwnBHRKYTqqic/WMftMFDO9fL1wtrZKU29BTXTv2L4KiAkAMZeek0bkZQqgqOiIyo6o+IjHEvkEhn39iQth9VDpqWSLI8y+rML2i1Ei5xegP4u94mOquoWvPkegKYO/GdSmbaQrttGntnE03cj3ZyKcnV1//tWH3KvA1uaawA+tj+rVhOhbmvv2N5j/whegb9nuY6w9wGMHdR5uj0uweaUQALc50EKdu4MKRyt+rR6XXYVqhUhlNeaTeWaTOc54bcari1UJdS6L44yY66693KtmgZ90PMEHtceJUEygFBQn3VX30t/+YZT6PaXqmnOJ/rguyXo1k+vBoVLP1mlyAwso7gZZ2VJDTeuU7uIOZFVSSZTKlwp499cSvKsFvfL1F5wozGWY+ewZ7ESBuJJhUJ2m3aolaC+DBkejX1ISvXurry7QsUFM5wr8w/icFD1ZMIrXdyFm8dH6Sj6WOUX9k59cpu6KXui7fxe2vvXqzB7Rfyd6ylYCspUgrax+faUQ/V3C41MoMa6rmrUWRXfWRH9aVMFGl+iiwpj7T7pqsGc+z9DwZ7Gs4hxWV/euYl+XqygU9O8h4v+WwdfauPvuu+Vi9v3vf/8PjXb4Ru35yhZMtn7yB+vuF9QKcb7mjSIV7UohmIrvCXbzceurbMp3L4Gw9Nb34rnjVyUHfXbsKAOipyx6mcHUBAN2lkFHsQekHIpt05jxsjURYkcWdhoxdigjBJT1GVLx/AIAqUL0Q5S6haBDJlasZL0i3x0xE0eu3jtV3hd/9xqzdK80xLEhQJjY+vv7V1XFROiFAM5cBc5cZXE/aJGp6mchPSUfF5UyIXkvkgOpVEqOotr7akIsqByCruVx0eZJ0qrNUFcYIZzoxZ3ZQIFQLPDWKhBep3KU6Cu5NHeRwYtfQet7jI7pXrryqxMiC5aT4Vglg8lKhlNhvNtvxn3wXsYISiuE8+MxUvn1x4K41lXnouwZ7+HARI8EXJW5BLam4bvxRoL33oNfePdUV5MfjJN4epTspeUeEVdXmMDtzbg6i1W41xRi6pSCKBuDtHwmxdR8lOn5ReYWFliMxUgm4tiaE38oQjhSQ01NLU119XJUhdCEEJ3QndJ8V5hBl3tNliTaS2NWSPttEBUJm/c8b/GmMza6BQmHh2/svJlnmg4xqVZilT5zBIUP4uSt6PhL/W6CkujVnsKvfQenuqJ3x1+3DLKE9L+ocL1SiXGxCBD+cpd/gD30XDGrXoq45pPUxEciN0sVRdV0cqtL401dbRypqaDeVVxAikX+pegleqI9dEe75TiwOLDhwt+tuSUdToKxSBGYCVqcMF1+o4Vl23xubJZPDUzKnooKXeOPtzTztprSNUwkBoRBuqAnCuGOcq+NsD9Q9vOQfbvcf/uhTva7R+Dk54uVfxFiMSsEPQ5+DDrvlLRnc3GR+cceZvHhhzBePImywuA8EfFyaVeYY9tUTlYliRvLvTDXCkFFFEC43KuXt/LSYmBtlcyXsTnQZ3NTr8KeAQtHYfkCpVSECd60h8COaryRBLnp8zhmL+BY4UG3MoatGkacnWQj2/G37qV1x03UN2+6qkHtGyXE0itz8iQLDz5I/JFHRfZuSaXRevvdTN6ziyFnfBWdURjGXy0EMGsIdjDiez8jNMv73lVp8+stDSiGT1bM5pI52T5QHHPMJfLMp3JUxc7zQPpb3MtRnKXE36RdyT8Zd/PP5p1E2XidU+xTcxIRvWqBEu1xxX5FLom3rwf/hW48A3Pojmp0AbZEonPtcwVU3JuruOxRGO6d5/C0IC8Xc2bePdUE727BUf3DUcUT11chzHbh6BnuS+8mgIeMs4D3w51E1CCpl6dkj1jZ2kNxqHh2V+M7VCeFja52PbmYzEip+G9ML5AvLbmF19nPNlfzwTrB7tCWEydCqv+pP1w+z4Uo1T2/By03vvIPJV5L9JkuDBZ7zUQVU/TSly02RJLsWmsusXbagM5ohlv5u4SHPxiallUwNzk+aH+eO3mEitA+ukRfV/DqlbTXLUR1UCSoNqJX/nsFX0I5TvRiiQNbiGEIcYzXO+68805aWlr4zGc+I1/jS1/6Eu9+97vlY5cuXZKNyWsFN4TKYU1N0STyb/7mb/j1X/91Wam4ZuPkGwx8Cdn4//ndC9KsdTKWZXIxu2F1YG2Up4nlH9/msHqRX9K+wc1asWnbtBWecBzhmZqP4mzYUayYicqZz6Rh8jEKl79HdvosamoWbyG3YdeLeP5XvMQVJ7sET9XY/lriaZvhwUnGx2ZJGw5ShpNg+0523v9BOm84/Ia+0IrTa2EyTd+paS6e6WU6OkbeFcV0rK5GOXQHpmVKuXZRvRIJBUEnEjRF8RziOBPH7Pj4uNyE0pI4pzYKwUmXFTDTxGnEaWZSemqJsYGpVYIZ8j0K+jkRRqlnjHo5Jl0NeH0+WREWm6jaXWkU21BySJoaC3Pj49PHiedXC7V0Km4+kgpwYGoSj5XmIm2csTo5Y3dy1uokynpJdI9DY0dDgEojTqj/LIdPPsXWmaGl48l0OnHfcgtVovfnjjvQwmHZ05btjkrQlR8pZfwU8OyqkqDL2bixv89rCfH7iHOvZyouq3M9Uwl6JuMMzKWuTg1SM6jOKKpjHrdnkVAojtO1QF6ZJWHMYa+Qd1/3r4pKva9eUgOb/E2SJlje6t0NGJemWPjrv6Tw7GMyMWIrCvO7b+Jo605OKF5OKvUIVy3R6fImHHwE6GD5wlWwJ5lTFpkONqJHmglEPKvFQSrduF6B3L5onE+98ILsR0o+9Qhe7yz+hiz+hhy6e/lzClEC0e8kgNhjkcP0eNvp8rk5UhHgSGWAw2E/wRWUMSHMIChxPfNFQCbAmegj24gWpyka7aH2pQrZtkjRjyzo/NHN32PZPL/SPcJzi8klNbE/3dpCresK362sKB5flrGfu8RZtshzejMDS6bxhivA4Oa7ONOylyHMYtWqVLkSY7nPzpOz2d9nc+Mlm70DNu4V/VFRv+irUji13UV0Sx0Rfw3V3mpJ99toFAv/jRag85l5+nqPsfDoD3A9d5qaSzOoKw7tmdCyYEZPk0iwhHGYDcTj1WRSNdi5GppyCtuUSXZqwxzyjLPVHiJY2FhKX4oVrZK/L3mSXYe5rZlMStGS7PkLUkXS0dSI/9Zbce/ciaL98CqphZkZ6Re2+M//gjFb6rPTNKnaWfGhD0kVT/HdZo2sTL4IGmOZzlgeBTAztWpi1b+C6WwRTtMEon+PO/WcfDq/wy/9+FoCLUtjm6+Bjolz+E8+iDJ+fOn95BoOMbnlJxmouoO5tC0raWXQNr9ifyGdX+VHWA5xFHSgsgeN3ejsRaNqjSSThc2Mw2a2wk2q1oPd5CficdDUvYinZ5Eyg/2JGp3PbnKytaOSP9nSTGVJjOP1CpEUffHFFyXwKrObuuraOTLfhZIw0SpcVP/sbllpM1MF0idnSL08iTGTWaWoKKth+2uWBD7EdeGpaEKaIj+9sFx9EtL6n2ip5s1VIbQrAbZsHF74c3jh/xSFtkRsexvc9btQtemVfUDxA537Cjz0X4tATCS/j/y6VDOUv1R8bD2VsazYuMpyZn3Egk6e2ryVT3t/gUvKdnnfQW2G/9NcSVttR7HP/fWmhFtm0aNUsKiEGJscz8LNvwR3/Ab/rsFXIpGQDcHCa+vYsWPy4BYvIyaPpqYm7r33Xkn/u+GGG17xc//mb/6mpAYKsCVeR/R5CTqhoBHec889Umpe0AeF1Lz44L/0S78k/++FF15YJTUvqgp//Md/LIHhRz/6UT7+8Y//WErNrw0hpjCXykkQJgCZ8PCS44r92cQGioErYr9ymf+kf5M7teXq4NPmLh639uEnyw1qD1vUMWpYRF/RKyLFFtBwYKw73xKCdqZ6mbHDzBh1zFnVzNkhZgnJcU7RSQXiMsm+uz3A9ppm2oPtdIQ7ZA+KOHZmhwc58b1v0v3c01hCPUhk+usbOfDAO9h+5E4cLvcb5jeYGohJOuHg6Vlis6sXgrXtQeq3ebGDcUYmBmRlbK1HVn19vTxXhP+YAF0bAS3xnVRVVcljWfx9fW019eoCzulTRcqXoI2JjNeaMHQfscBm5j0dTOrNjNk1LGaR1bVMJiPP1VcaBaVATsstbaZuUhmspDrYjJGM0D8YYyguLrYREvp6oKVjsE0ZYbc6wC5/mhbbTaFvHt/pHnzpZaCa9Xgxb72VTW+9n+Btt6GWGp6FclX69CyJZ0aXL4q6gu9ALYHbmtCrXp+MWDpvcGlqGWB1l8Z4dmPaVdjrkKblHTUauv8yc+ZZRpKDTKfHyVpXp4MIWq9uVhN01NHga2RzZSt76zexv34TjYEGHKIf4EohlDAnT1M4+T0KL30Tpz2B7lk+xhK2hyesfXzXPsLTxnby6OxA4z04eZOto5dO4LRlM5SzGM5b5NccFk63hn+lYuMa9Ua3wyL9wvNSpTD55JNYJbsAEVplJYF77iFwz134mhwoA48VqzpCpGFFjLjreLTyMI9GbuaF8F5Mzcm+gJfbSmDsQNCLc03yRSQxRJVgqUI23yP3F3IbS6MLNb+VgEyMAlD8MPvIxDn2tekFfqt3jLhh4VFVfndTA/+hYbl3YuXfCrAkqH9lCmBiYYj63ifYM/iSrCyUY5AmHBRoYlr6pT3t9fCk18Mxt4vcmu9JVKhWAqg6rYLOngQNx4fxH+tGSWdX/1533SWpib6bbpSiJ9eK3MCglIMXW/ZskdpeDmdXF7mbD3C0vZIn9SgD8X7i5gg4olJ9cG0oqNS4G9levZltlVtkVXOLu5qGVBRVqOBKCfxzxaz+RpLeYsG50pOsbhdWsJPs8DTZ8+fJnD8vAZdU0izNf3lVw2GZEkiooZBU1fTdcrMEY476oj/k6x12oUDi8cdZePCLpF8uGtCKcHVtkiAs+La3o/k3NkN+Ym6eX7g4SsyEgFrgHv1F8skzS8BsZURMk/fGhc1AgupSslaou56p7aJ38514W25eAmiV7iur2hmmxdzkHKMvniJ2ZhR7No9fraDaV4NbVFZWRAGbbkzOYnIGk3MYlI9cUYn/KC7ejgNnKb12lAKf1wsMdgWINntkAslrwQOmkwM+z1KvWnWpwibER17JOSvaBE6cOMEzzzwjr30ihAK2SOZv3rwZM55n7m/PYcxlpKiHBGCl64ikT44kpMdi5uzsslCHpuDcHuH4Ji9/TJqedHGtJc68B6rDfKK5mgOvxMw6PglP/QGc+kKR4i4oggd/Gm7/jSKr51qRmILv/moxWSNCqIy+86+uX5VSeMVuAMyyiX76w3NM1RTnAVEoPb54F39V8dNkNA8eM8NvD/wNPzP3GKqslG2g0hhqvjaDQqzz5i4VtQHKYEuc5xtoBEhw+v4v8O8WfH3605/mU5/6lKQBvu1tb5P9VmJx6PF45ELy/PnzPPvss3zzm9+Umf2/+Iu/kD0w1xsf+9jHpNynWJCKD7h7924pEy+A10qTZVH9WmmyLHy9yiF6vgRIe+qpp2T2Xqgl/uEf/uGPp8ny1UL8tEJKeqWBb3KWXGKW6WiM8cUcE0mLybTCeM7NhFXBpB1hwo6QxMsOZZD/qH+L+7VjS0/5pLmHvzDexUl7szTRDZCRYEss3oS7u42Krtg0+BQ6Ii66qn20+w3aFKHi0422eJzBhcscy/l50drF5fx2olnh2bZ6olZd43Kxqvl68fun6ahslVlrYQ7aRDXGiWFGnjlKrrQwdweC7L33Afbd9wDe0GvzZXo1YeRNRnsWJNgaOjdHpsxRl70LCs1bK2nfU0Xb7ip8oWJ19cKFC3z3u9+VgEdcNERCQRyzIiGwNkQFTFR1Bcgqg63agI5z+nQRZAmwNX5y4346kfUVFEJBGRPUsarNV1RdFCBQvJ+VVEcxTi5MMjw3zPTiNIlkArWg4rScuEwXQly8XCmN2l7mLF9xs33EbDG5rr0g2oTVHPXOPM0+k93eRW42TlLbcxqrd4HUpBNbGG2VwvRoJPZto/49H6X17jejrJDCtoRnizAZfm4MM5Zf8mvxH27Af0sDWsD5qgH06EK6VMmK01Mah6PpDTO9uqrQWe1na32ArXVBttT58fmjXFx8kWfGnuHUzCnMDegdEXeEpkAzYUcdqhkhl6lgfjHA6LSX6UUxH61fTAi7hy11xdfZLl8vwFbPAoGZU1ft1bJthWxUJzPnJJvw47r7o4Q/9itknB6e6Jnh+2cnefLSDH7D5p04eScOKsq/rQIxn4NRFKZj+VXH95VCsQzcuQXc2ajcvFqW8OYmqg7vouqm3QQiXim7vypiY0v0RAaeXtXrlNY8PFlxkMcqD/NY5CZmnREJWg6HfUuVsa0+N+oGCzBxmZtOT68DZILauVGIBWe5f6wMzMRiVFQcX2tECwa/cWmM78wW+2l2eDX+Y10ah7Ha/Lc8ikqVrOTZtjSpFgvm+1JpXKXjUKjWftvn47JxI2pmt1TNfK/ybbbby/1EBc3JfMNuMp13oG65n0hkCz7HlW1CVlUqH38cM7Zs/K4Gg1JlUqhN+m65BbXEFhHfsQAwZcCV7+9ffkJFQd+1m5ndN/Ji4y6eTLq4OBFfR4mv8Ntsa05TU7WA7p4magzTt9B7ReAsAKTw+JOG0eEutgTb2GJCMDq82pfsCsb1hZRGdlEnFvNyPtfOWbWDnshWLle2Mqb6aLWSvKXvOe7qfQ6/6K0phbOjA9+tt+C/5Ra8N9ywlAR6PSN76TILX/oisW9/B7uUfFN9PkLvehcVH/ogro6Ope9d9BF9sm9cMuL2BDz84672JbqufK5SxWxh8Akqz3yZ1pGT6KX5SIB0Ibj11aCf6AbVPVExExX1Mhhr1Wtp688QPheH0Ry2FUar6EBZV1k00atU3DsaJJXQ2eyXiQZRNZstUR1jc2lqumNsGk/jKOGXC7rF39hZTpQSrCKsgIPC7gpsf3Gxrw0m0HvjS9Wx8hxc7kMr96SVZflX3t8QdjHc28OTTz7J4mLxHKyoqJA9kzt37lwlimXGc1KEw5jNoAYEANu1Sn5evresIRN/sWOT2BPL198xj8JDzU48+2v50JY6Wl6FifRSCAXRx363OC+We+Rv+c9w+D+CcwMwJy5SQs5eVLuElYhIPgjAduuvFEU8XmUIj67h4b9leORvsKzi+VAfuI1O7TCuWJTheJRfddzEC55O+dhNi6f500t/THt2/MqegBVlMNZUfG8icSjWqrOXYGZjpVRpXSGSKEL1WoiECCq8WNf8K7eWvKHA1wc/+EF+53d+RxoeXy3EIvMf/uEfpK/Wz/zMz/DjFm8Y8CX6AURWZK2xb/n2qzTnjdseJuwqCcRSuOnQ5thG/xKt5ai1nb8w3skLlvidrz/bJCZIMQE2BXU2+bPscE7TWuhnZn6CF2JuXjK2M262rP4nJYfmG0D39aL7LqM452RlzWXqHJhtpL1Xx5EoTtSqrrP51tu46e3vI9JY5Lz/sCKbLEigJSpcIxfnMVaoI7m8Oq07I7TvqaZlRyXOFY25AtCI6qxIRJSzbe9617uWEgSioiuqYYJaGIlEJNiqqarEEe0tLa5fLo4beeoIKf6mAyt6tQ4WjWpfYYhMqaARvjT5Ei9PvcxkanVfmENxscl3KxFlH4VUI2NzGn2zmXVKYyL8doZaR5YaZ56wuUgFSZyKhSubpXF8nPrxCeqmptBXVP/yAQcVjUmqGhJ4qvKydUVQ0qYdrUwF97AYvJFQeiuRCSdaCQfYXg3nDVUEDjfiCXmvOwsayxRK1awybTAub6c36DkTIfx3BODZVh8sAp+6IJ01YiFrcnzqOM+MP8PTo09LQYKVUetuod17A+2BHWyramN3XQdtlZVXbFaPpvJLwK97Mi6rbZemEyhGlt3KAPvV3qWtWlm/uDS8tWgth1AE6G4+hF23m9RLJ5j50z8ld7HY3ymompGf/3m5mBOL6FTOkADs++cmebZ7hlsMjffiZMtSXxjkG7xU3daEUesjuZAjNhkjeqqXxYFJkrECGWcFOVe42G90LQ2coHMdnVFW08QWMHFOlqTYBSBLrk5KnA1u56HKmyRF8YKvaNRd5dBl8/dtlQEJyK4kg1wO0ZMkQNjKbSA2IPsW14ZX90qa4kpAJhb+ayuQ4pIqaLdlit9KIHU6pfOCfSM5RZj1Gnhj38Qb/470C7xSeCyLtyZTfCCZZvMK0afpUD19XXeQ2nwfFcEmIq4IR39wlN5LvbK/8z/cuY3m6PPF7094DK384sUxIZQThYKiWLRc5VwR1RhRhZE+a489hrnCdF7xePDs2oXicknVOqNkPCtD10ls28u59r1827+JM+n1C6PmSg83tJbFMSrprF4PCMX3OZ+dX1JbLI/9i/2yr2xt6IbN7niYGxYq6JpWqRmJ45+bwB3I464ooIcNxipq6Ha3cdrulPTnS3YzxlXsItyawr2uOPf3PUfr8SeLPSbl78DhwHPwgARiwgLBtWXL61o1NRMJYt/4Jgtf/CL5oWVA7T18E8EPfog/aOzin2aKIOLdtRXSBNez0idQ9BF1fxte/OvidaMcIiF34yfIbbmPsfTMKpn8cp+ZuA7ohsXeMS+3T3ayI7uJKlcXWqhFii2tDMPOkAmmYFuEyO52qtoaUa/gQ2ZlDBLPjpF8bgK7NM+KvqngvW24NxWvV2IuWupLS+aZSGT5cirJKbV4rfdlLer6kyTmMldkHqyNBjXGIcco4VIvulDLFmI1otf/Ssl3M5Fn9nPnMKbTqH5HEYDVLgOevnRWKgB+eSpK66LBO8YK3D9pyCTWkjLj1kpJS3RvqUS5ipjKNWPwWXj0k8vqk4Iq9KbfhL0fWQYeolomql2XHyreFuBEVLuEz96rDNu2mJr+Nv39f0IuV5yHQ6GD0q8rGNy1rof18xPz/F7fuGROeBSb39RH+FjqGNpSBW2wuHa9nhDVPlHlE0rXAmS13QJtt153D/q/256vf6vxhgFfuQTZT23BrV7lQBYLctHMr4kqhF0UBMjMb3zwCwGMmm3Fg7zsoyVOWt0F8/3w/J/B6S8VRTKEz1L1Pro3/Tyn3YcYL9Eah+fTTMQyJK5zQlx6aQVCbo2Io4DLzlLI55jIuUmu6EWRH0efR/X1YvsFGOtHVbK0THvZORCkOracXUo0O3HetImW7bslfVFUzcoUxlcb8bmMBFsDp2eZ7FtcVQXxV7ho31stK1wNXeENjXJFD+J3vvMdksmkfB+33XYbR267Dd0uFEvp4jeRynsi+zMDgocvKhlXqmpVbVkthiBuv4oeOLFQFGBLAC0xCmGHcojPqBrVNDgO47O2k05WMTxrbwhOvHaO6swUNblZGqwF7jy8m7ve+Q78lZElH6KFHzxM4tFHyZ89K/uRyjFU18iLew6wsGULNS4V3+Iclek+Osw+2c9SyzyGVUPCfBdp854lufi0EuOUNsZlbWZJMl30vK3tT3N5PCTwMJt3MJ5SGI2bDERzTCU2TlAI37zNtX4Jrspga1ONX1ZWRNZWNKoPLU5wYvYol+IvMZEThJrlxbFta5ipTozkFozkVuzCak+4cjKiLuSmMeyhscJDU2lsDHtpCLtpCLlxp8ZKgPtlbFnVOierSitDGH9fsFs5ZXVxsrSNU4XHobNZvPcVYHFLrR/12SeZ/bPPLC3m9Lo6qv7jLxJ+17tQSosQQbF86tIs3zs7wdTFed5m6tyBjjA6EJFQTZKZXnxP/z1KellW3tHSgv+ee9Buvpt8dSvJaJaE2OazJBZyxTGaxVzjqbNRiCRG0YDaRZ13mHrrKBXxZ3EvrqYnzrtr+EHlYR6qPMxz4f1kS8IaHR6XBGOiKnZL2E/4OvpFRJVALO7Loh5iEwv+3AYG77qqSwDW4Gsgmo0uga21f2srLpLhD5AN3C1va4VxAnN/jaMwJHul1vZQCd+qjlyOzf3PUtX7GGp5rhaGtjvfXRTQaNy/DjQJKpWg4g8MDOB2u6X6b63obRbVHwHCBP1I0HdWhljUSBn7+4vN/VfJHtumKWXBo//0BdIvvoi9RgDI0HQGmrfzeMNeHg93kRL2CqUQb3VLbWCVEmF96NXTgQ3LYHhhgOHTz7Jw+jh2dy/BgVnqp/JSbEa+X1E08FZwOdzM2ZoWLta0MeZplEbta6NKS7KXXvYoYutnkzrO4+Z+HrTupcdqWvq7nXV+3h3KcHv/i5gvPIMxsToxpVVV4b/lZgnEfDffjB5Zf96/Wln21NGjLHzxS5LGWwaA0xURvnPb3Wz58Af52d0rbAOExcqJfyiqXiZK71FUQHb+RNEQWSTpNnqdQoHksbOkj14mO5wAUdkSUuVrIm3M06/18VJlHy9X9DPqnMZeUYoSldWV/WVibHU1U9/tw3whip0tXj8cDb4i6NpScV3X5kfmYvxKzwjRgolbVfgfwvC3Jkw0VZBAraz2WN4viopk8M1205AvXtdytsZ5o55sRTsfvKmDd+9vInSVHlbR6zX3uXMUJlOoPp2qj+3kZQ+yn+vR+eX+5t0BD7/QXMMDoQCFC/NSpCM/tPy4qJ75DtbKTY+8ymNf/O4XvwGP/c9ib5YIcd0XyoiZRXj4vxUrveK3vuO/FStkr6HaJXy6Lvf+PvF4cd5wu5vYtOm/UVO93t5irYH2/69ndKmf9ZCa4E/jD9E5/gxMX9xY6EMAerEGFR6I17IHEnZAK0VAWg5DV5EBx7938CUqWkeOHOGnfuqnluh+Fy9e5Oabb5Zv7sc53ijgSxjrfvZXv4fbYdDVnKZ9k0pdg40qaBqCwiM8tQT9ojz5rs0oCEqaBFl7l4HWtRRjxPM+/+dFVa1ySViUgIU537a3Ly3+hRJjUQQkw/hiRtK4+mdSDM2npET+YrqwYaXkSiGqbsXLzcoT3qRRHabWexHN34tViNI0GqZ52rPkhTUXynGhPc5QXRq30yPpi2UKY3kUF4eNemjKhsdSDv70BPHJKLqSlWbBDiVHpFahsd1BfbOTYNBEEcA2nyyWziWYKu6b2QTzUyPkElHZk+FWLQIuBU0s0uTC6jq+B6GEJ1QHy/RBcfEUDa2vIsRCUQCtMtgajC33hlmGHzvbTCX70QodzC/6SZZ8adbS33Y1BGmwYyi9L+KfvkzQSODyeNn35rex//634w2GyPX3S7CVePQxshcurHqOSy3tPLv3ENFbb+PNN+zj7TXCH2gZPEpufT5PcihK9ukB7AExYRd/V4dymYD+VTzqi9LXakRr55LVSrfVyoxdQdTysGB7WbA9LFjF0VrT9F0ODznCmoFgKQZcKl6njqo7yNo6KUNBsOxiWYtYtoDiFpTYHrlpntV0CqsQkEDLTG7FSG0C24XboS5TYbw6ybwlkxOiL3Pt8e8if11VrZy7hlz9AZxth3C3HWYxvJ2eeaPYh1au3k0nyF4B4Aiwt63Gyz0jx9n+6JfR52eXZMer//MvExC+UStAfCZv8sTJAZ58spvqRRf3K+4lSmJOqFtmp/HVpbnhHYfwbNt2zUWUVHpLFJZBWTS7DNJK9+XSV07eeNUora4TtHtO0Ow4LQ28y5FXXZysPMjXK0RV7DBTrmp5v3hHewJejpTA2MGgT5oWX+9Cfyg2tAqQiX2hwHilEEIeAkjpvp2c1e8jXhKVuTeY4hcaPDT5qqnyVi2pAxZfKAfd3ykumIUBczmE7PTBn4E9H7ymoa1glfzTP/0TY2NjMrMvrsOrhK5i48WsuABjg8+sZkeI+aTrvmJVbNNd4Cq+ZzMeJ/nUU/IcTj73HHZmuY817fRwtHYbzzbs4VTNZvKledRhm+wIKBza0cxN2+o50FJ51QXutcI2DHL9A7JHK3tB9GldINfTg71CRTbm9HI53MLl+k4u1nZw2VNDUtngeqbm0NxjaJ5RPL4Zuuqc7KhtZGu4k52Km85MCq9YIHZ/Bzs+wUm7iy8Yd/M96ybyUqZGSKDr/MS+Rt7XpFN/4WVSzz9P6tixVd+NCNf2bcWq2C234tm/D3UFbfrVxrnL/Xz/r/+OI888RjiZWKrABd7yZirvO4Q7/jjK+a8t03aFgNUNH4MDPw2B1SqDlmmSPnaR1NHL5EeTkkaoejY4xuwEeo2Od28L3v1tmEGF8cT4qkpZeRRMCWGbUg6n5eCBhdt4//x9hMziMTXlmeflLX0Uupw0B5tpDbZKgCao2NeaP2ZyBf5zzwhPRouf/e5IkD/d2ky1c/3xJdpdhCXRxESRYtyxfQ/dNPNN0R5QMJfUod+2p4EP39givUo3en0rXWDm785jjCdJOhV+/oCHS0FNziv3VgX5RHMNN4XWV26Ff5gAYemT01ip5TnNtSksq2GeHRGUK1QIrxpirjj+9/D0HxWFNFaGWMu94/+F2qIIxquJbHaCvv4/Znr6O/K2pvloa/1Fmpt/Gu1KqrFiLSN6MEtCGPbEab6gdfK7Hb9ASvfiNnP8xtDn+Lmxr6J5QqUk/wrqoABS4vsT1Vqxzlwp/rFSQn+Fjc1SCF+0t/85P+p4Q4AvQaMSvV1CZVBwa4XRrKBUCZGAJ554gi1btvDjGm8U8DU9PMVX/p9zKCtoQS4lSYvrFK2u47S6TuFWE0UKkMiOrAJaO8H5GrjqojJz9P8UFwoCZIgQryFc0kV29hr8W6nilykUgVk0LXsAemeScn86kSOeKZAzXpmPkuhDixCjQRgE58aoTUapNmNEBEBwZ5ipSRGryOBSLLyiJG4XR68NlZqbStVJUNHx5lUc2QJqIYtmF4GW9hpMeK87xEJM/CaC0yzK6mJSKle2RMP4q/QuEvQqoUJYBlsisy/CNl2Y2SasTBNeazuFdAPJzPoLmENTZOVkd1OIPU1hdtR5SZ8/yslvf41EaeEu+u4O3P8O9tx7PwwOF3s/RIVrYJkeaSoK5zdtkYDr+P5D3L59Mx9tiLArsP44lMBrKE7iqQ3k4m+uxqWcxrr8A6xLD+NY07vTbTVLQYknzH2csruWQJcA8E5NdCbaMoFYsEWHzDUufGquSHn1d6P5L6Hqq+W3nbkGKnIdNJmttBKiVs9Qo8WpVxaoN6cgMUdiIcVswmY+4yToyLOroUBdbYioo5akoKdk4viy41TlxtAwr6uqVQahYhEowJTw8msQlbNS9UxUFQQFZFoqMS7TKsX5tuq3NQs8MHiUD1x+nFCpwpJq6cT+2CdovXEf+gtPy96f9LFjMuuaU3VO1W0ntfOt7PQ2sWkFXeucZjG1Kcj225o52BG5bv+fjSKfNZaAmRiTC8tATYypeF7mLDTyNDrP0eY6Tpv7OAFtWYpdRL+zi8eqbuYbtYc5E9giPedEuBSFQyEftwuKYmWAnX7Phv1iVwpxfIqeMQHEBK1QLBpXVq401cWnh6b4zPC0TBo1uBx8ZmuLpESuC7GgEFWKk/+0LDEtkmNbHygumttvf0WqYaJvU9D6hYKvsF0RAGzD65RoqO9/YlnGfsUiriBYB5ntJMZcpLrHRMPH0mOimvRC3Q5eqN/JhUg7lqrhcyjsVpJsGznP1t4TbF4YwSUqtNIG4pAU6xCiHcIG4prfrWnKqmxRDONCEXB1d6+qtGU1B32hRnrrN9PfvI0eby3jlnPjuasuyOZ6F5XhBLp7jKjVQ9/iZUkxLZRYHGujxlNDyBlgRybFbQvT3Bidwiw4+ap5hAfNuxi2l/vIayvjbO+YZ3NNnLqheWrPTVJ1bozAUEm5sBSW20l6Vwe5A1vJH9iO0tKIrjlk4k/4n8lR1XGoDrmV91fe94P5NP/lkhDssdmiKfzN5GWcX/0K2XPL1WB3RZ6KrhTBG7ei3vaLsP2dS2qPlhAnOn6J1Eu9FEZTEmwpoodo5fdvmyh2DL3GgXdfG95Dm9BfQf+sUCEVPWYjiyMYJxdoORvAmy0u2McdM3yh+rs8EzyBtYG4iqD3rlVllFWzYOsqYCbmtr8fn+P3+iekRYOgHP/ZthYJxMohqP2CaSISEqIS/M53vlOqX4uIZwt869Q4X3hxRCaqyrG9PsiHb2rhHXsb8buKc1usYPBPE/P889AMv/1CnF0xi4QOD725nnfsb6LTe22xLyEKlblYrIbl+haX8q2qV5feZb4balfRGa8rxPL95c/Bw7+92gtw+7vg7v9erGq/wjCMFMMjn2Vk5HNYlnhOhYb699LR8V9wlRJZS3OHUBlcqTo4d3lD78vRim382ub/ytPu4vs54NX4sx2b6PK/yuqfqOyJOXMlMGu9pVjV/RHHGwJ8CZGNy5cvSzPiv/u7v+NP//RPpbqMUCwcGhri61//Oj+u8UYBXzPTZ/D/nw8ynm9jKHeQ4dwBcvbKi7uFtyZNx74mdh7aTGXDlZusX3Wko0X/CLGVG5tFBuPWXy1magVlcWWIQ05UzER1SIA2WSFaub9MvTOyKSYTefqjBYZjFmNplemck+mCh1nTz5wdJM71TVjCPLJBidKgzNGgzFOvzNO4Yr+KOOoGF4O1YYlFkdMnL1iKAEqi4VWApTX7puahb2SSoYkZKeuteyvYf9Ot1DS0FhtmJcjyrt5/nYxhk/kkJ2dOSul3AbbEAtGyNKxcPWamWQIuLddBLru+H0wcHpuq/exuCrOnuQi2hJCES9fIZzOcefQhjn/n66RjRaqZL1zBwfvfQVdVHZmnn5GgayUVR1CRjm/dybN7b+CF3Qeor6/lJxurZH9CYIVseDmkXHxPVIKulXLx2Y4gp+pdvJjO0T+bZGwhLWklIsO6VRnlTvUUd2qn2Kf0oq34HaO2n6etPTxp7uNpazcx1svNuzSocFqEdIOAAFv6JGnXZWKuARZcU9grVD11S6c2U0tduk5ubqt44VWMAmo2jZZLo2Yzcl/NZ5fqtE7VoMadpN6doN6boMGTwKevpzwW0MjhpGDrJPQIk+5NzLibmVbrmSDCuBFkPOdhPK2xsEFFciP6pARkpU00n4s5QPRUCPrkyHyay9NJSKd4V/8zvLvvabwiqyrAVKSdb3Qckb9hR2yCzUGV3Qe3s/3+O/B1dpDJG5x4fpTcS1NsWhTvvPhpJ7B4xGXB7gh37W3kUPuVe9tebZiGtRqQRQWlMYMye1FSE+vNo9Tql1ep58Wo4AX/jXyr/hYeqb1BiniUw2fCbkPjkMPNrWE/W6v9kvIoqI8i0kZaVowXs4tSAGIhW9pyC/IYvLHuRg7WHZReYpdTWf5T9zBnEyXD5NoKPtXVSGgl7VFIJvc9Vkxe9T6yXP0WFK8DPwX7/wMEG1719yMSnX//938vfS+FWI+gIAoK7lW+UPIvfYfEN79I/KULZKdXH1t2UGGgoYFv193MI6EbqA56OCQohG0VHGyrlMmZ8m+c6+sj/sgjErSL6tRSKAqeA/sJCiB2zz1SNVDQ6fLDw1KsQ1TGJdC6eFGaES+9NUVlOFDL5bou+lp3cjnYwIDpXpOmKEZHtY+9cu4Ky2SReF9ux8bzqgBeolKzspdMWBeMJ9cLBGi2zcFsjjcnU9yZynLB2MYXzLt5zDqAWUp+6moCNXwcR8UxVOcCoZTN7sHitmfQJryGOS5k9s+2K5xpVzjfqpDyXKX3DoV06N2kQ++Qt12Zs1Qt/C0uK4Vu5OkcN7njFOzrEQmV4v+kvRrnD9Uws2Mb1YUm2mJ1NOeacJUo2+Uw7Dwz+iRz4UViLRapDjeKWysCvhWA8ErjOrCIhnY+i/1sFGKlfuywk8CdzTj3RhjPjBcrZSuqZWIUFbON+i3XAjMhACIrZYEWLGcLfzbpojdTfJ2faaziv7VU8/Sjj8j1pgixBhX2Qyv9X5e+V9vm5MgCD744wnfPTUpvVBE+p8ZdBxowWv08HE+SKqlCtioanz2dpWoqK8Wdqn5mJ67WV7YGNKJZUsenSB+flqqK5XC2BiUIE/5hqvMaawFRwf7Of4a+R4u3RTJd0O96hLKh6BVwwA0fL8rK+yLX19c19Q36+v83+XzRxiEcPiT7ugJ6Y5G6LABWWXlQsKo2YuyIPrSV1SyxH6iXf/mlySj/o2+chGnhUhV+va1OVgwF/f61hEg2qZaFy/cKweu/VfAl5Do/97nPSerhm9/8Zuld9Gu/9msSkIleFyEo8OMabyTa4cRv/wBFKaC7B1kMTHBUy3BqwU9lfDuRdOOqvzd9WSq2ONh3sIutO5vRr3aCi9Lvyh6kq+2LMb0AEyeKZedyFkbIzUqDY+cy4BK9S1eZYF9ppG2XFAYRhpBlkZBxOyIl1MVidcquIM+1s3aabREmR4VlUEEO3bFA2jtGwjdAwRXFcMbJ6nmMEngVF4K2UNsq+qIYxQVhYmxCVn3FwkeEsFUQx/+VfOSEmll5IVde1CULSak0JYxLBYVJjGFXmIAzIC9yq76DQprTM6eX+rbOz3VTyFZiZptlVUtWt3L1YK//vUXFRAAssVARgGtXU2gp41eOrGh0/sF3OPn9b5Mt0VyClRFu3LyLyOwCqaeewowuUwEKLjcv7djDU3tu4MVd+zB8Pkkp/MmGKikPLmYdUfVc8o5J5ZiPZfEPxNk0mCKSLR4fAlo9RIEvkmP8eqiZ4oJBgtvVM7xFP8kt6lkCLC/ihGjHHJUs2l5Z9fKTIaIkcJLltMclpbmf8XgYWkNfaSkUOJLOcHs6w+5Mgfl8mIlCNTP5ENGsm1haobBE8bMJ6Dlq3CkJtqo9aarcaSr09fK4QiFyJutn3KxmRG9lxNXGoiIo2RtdjGxJS1zecpI3n7Edsi8yoYZYVIMsEiBq+5gzPMwXHHLRdrUQ170ar4OwkcaxGMW9OM+RiTMcGT+NUwAE4FjtVj6/7S0MhBtX9cOJisLW+qDsKetwOZh+ZgzvxQU8JSPSDDYPU+Axr82OXbU8sKteAjH9Oul+ryUEiE9PjVO48AO0/kfwzjyLZhV/A7FMm1ddHHdu5enQNp6s7mDWqaFaCRQzIUdnPoGrEEOxkhhqEus6Kt9u3U1DeD8X7a2kXHsIuav5oy3N8thfiuQsnPq/cPwfITayfH/Hm4pVrs1ved1Uu8T8IwCYAGJCuEco+66cg6RC4YULjHz7IVKPP45nvNRDUopoZQBfU46tTWO4g8uf3/DXo229H0X0ibXftj7BtiIEsBIVcCHYsbI6I0L1+7DzhVXUQXHkTHkruVy7if6O3VwKN3PZ9pK11h/HNQGXpIkJoCXGnY0hKTn+eiSvRFVMAG5heC5opwKolUezkCUyeZam4ZdxjA7x9fwt/LPxJqYoLnIVbDoic7R2RKmtmccU/2sWCI4u0HhxhuaLURoH4uil80SE+HjDzS66O52c69Tpr4cchnxN0aOUiHyCvLfYp+WJfw/f4r/I11n3nSRcvOdkgENn43iT5TlHQavdhbPjDrSa7aS0LBc9A5z39nHB20eve4RCScjitYRqKxyJH+DDcw/QlC/SG+f1Rb4U+QGPhF+QryEUQ68E5MQmjkkBwMrftaiiCVP5jXouyyG4DKZ7JznXJmx0PEaBLVMTBHJ52jva2b9zPy7dJV9HnKPiWlq+noprbDkhvZDK87WTY/zt+XFGwzpWrXup4lyvavyXjnre11gpjcHn/lFYE8RQnCpVP7UTV8crb6WxTZusUPg9NkW2Z15coGQIUOfdWy1pic6mNZVycfEUImsP/xbk4sW11Zt+Cw7/UnHeEOuvx/5HMbFTblcQifCbfuGKLSWLi8dlX1ciUTw/3XoVXdxAtRB4mjizoWWNjGBTEWQtga09EFiuCG8U49k8v3ZpdIkyujfg5c+2NbPVd/1VMGEUnzl3nrkXjzJ39EU0wa556wPs+/3f50cdbwjwJXyzvvzlL0vJeSHl3t3dLamHPT09HDhwYMlb4ccx3ijgyxjoY+pvxAVzjVQ7UZx6Nxl9gn5DZTDdTDqzA1ZIuitKnip/L+2hXjYHewkps6v6lMqiGj/UEA3ka6s/onokK0hiv/TYun3fBn+7Yt/hYSaR49tnJnjq0jSnR2Mkc+Zrf7tqBt0Rx3AsoOiLKI5FVIcYY6j6IpqWYGdsGy2JFvJanrw7T8WOCiLVEXlhEX49i7nFdUBLXFxeSYgLhriIiMgWciTSToxM4wqw1bjqty5HxOcsUgfFgqUEuIRfypUiHY9Jb7XTD3+XfCaDZlq06S66dB96z6VV3k1GIMDJ3Qf49q4DvLxtN3mnk0oUtuVUamIGSdn8XGyAFkp+ZQNiMeW+FScfwEltiQKYxOYb5PkKeaJLCwxxWbXwqXkJmOqZp40x2pimSo0RIU6VEqNKiVNBAocwH7lGpBWFkx4PX/P7eMbjJl/KwAlRiQOuam7xdbDTbsYZ8zA7l2Bmao7ZiSmM0mJRUywizjTV7pTcGoIFIo44Tnvj33PMruK81c5Ja5NUbjQXM9RPX8At6R2g+QM4G9sgUkfeMsnlsuRyBUmxeTUhvPdSApzZLlI4i6PtJF26T2xXol1WZRb5UM+j3DvyskxMiHi+ZR//uPU+xkSz8wZRG3SxsybAvYqTbRMZKlPLv8HLGHyVPJe9KnfvrJNA7KaO1w7EZP+YkSlWpXKLS+PKypQcM/MspqaIZheIC4GbVxGK7cBl+vHlfHgKfjwFHx4zgOXOMxC4SFpbFh4RUWO1sMvez051P/uMNC3xbxJJPIFqFxe6hh5ksf5dxFo/iBXuQHeoaA61OOra8r68raI7i/uqqlw3g0FQDwUFUWSH29raeN9730fvky8y99DD+F9+nmB8Wb3QUFTOVm3iaMNOovtuYsuODlndOlhjUj3xdFGwQ9AUV/rsiDm5886iYEfXvUtZdvG7GJOTSx5akkJ47hxWYnWf3KLTz+WKZi5Xd9DbsJlL/jpi1vokUcCls7tUiS/PX0Ks5kceQoK+7zGMc1/n8YuTfCF/hGet3UsPN7jzfPBAPe+/Yy81geX3Kyp7QkUyKXrFnnt+FT27LOnvu+km5o/cwX+q30Rv3sSJxe/PfpW39vyV9OUyFChU3oBR8V7MmWr0GRWnEURRVFnJMKfOkR98EnPm4tLz5iMhph84yMxdu8h6tVWgUo4CbNrFcd1jVxrNArujm3jn+BGaskXQFdOSfLnqEb4bfpq8+q+wlrhGBPNBOuIduCwXk55JJnwTGKohe8PFtVT0XtpqgLgSJkUQS/XLTcm4YVYVByoezcfNnRHeuruOzZVBvN9IoA3npaek9oFGnJ3BdbTR6z1PRQUsdWJa0hLN6PL1QwiSSAPnvTWo+alStasErEQPuOjtqilSKVdF/5Pw6H8vVqzkF9AIb/pt2POBJYZNJnqWvku/z0ymWCHUTGgfTtE0nkFbe8kJtxQrWWWwVbcH/NemEG8Utm3zL1NR/nvfuLQfcCoKv9Zexy9uUAWzMhlJOc6cPUv23Hk5FkaXBcHKsbh1C4e/+U1+1PGGAF/iqQUAe+SRR3j7298u/bdEiGZgcb8AYz+u8UYBX0Ldxv7DTeTtLeSsXeSsneSsbRuAsQUc6kXS1gJjeT+92S2krNULqCp9QPZNiF6xWkffMl1HVFkkla5Erbsa6Fm5LzIy4yeg53tF2ftyFkY0Rh762aKvw+tEs7uaX9P0QEyqEx47M825WJohh8mIbskL18rQhWyxomIoa7POdmm7nkWihaIniqBMgrNYCZyJ2zE5Klpqw/YNhw0VlkWFYVBhmfgtm6Q7SMwXIaapLOQW5SJTiDpY2SZMCbIEhbBRlDTXP6GaRXOPo5YayzXPGF53jrA7RMhZzPytrKrJrXS/Iyf6/V+m9/kT2CmV+sUMHaksTfPTaMbyhTTuC/HCtv08duNNnN62HVNk3iwbdSaLNppEjeavWHcJofBunNLYN1gWR8Hiy+R5lDgRZZpNyjg71CFp/L1bGcRzpYu4M1AUCxBNvGJ0h0u3S6M7jO0OMyCoLcNP4Z84w7bE/JJXkoisotAbbGDeu41UqpOZ0TgLkxNyESPCrRWodhWrWTXeLPX+HCFlEXUDmXBhNn7ZauKiLQRAWuQ4pbey3cwwYbk4ZS/PGboCO9xJmiaO0xzrxWEbaLpO1423sPuu+2jctlOaw4u+hbWb8DXMpRNSyCWXXCSXjpPLpMllM+RyeXIFk5xpkzNVsrajSJldEWL2F059SdspQVlKArJlkCb2a5ILfKT7Ye4YP71EAXu49RBf3HIP8+L7Lj7ThtW6vRSl6m+VRKTi4+NYfI083xfugB4H9+6o5a27GzjcGcGhqdIkWYCnqwKpNfsbSY5fT4RUFxWmSWUuRdg0qTAtee5VCDpixVZGg7t5wr2LbirlQgy1uHjWLZuWRYvW8TztU3miAY2H9nkwlVHcqdNURU+S0ofEFXbptcTr3JLJygrqplgjw8k305e9BZNX7v8j5g8JyAQ400vgzKFJoLYE1kqPGRr0x8dxd3+DprFR6iYm8eZW906drN3KyOaDaAcOs6OjkT31QYI+J7pjDQAU++RRhp4tAjHRJ5aYLDLJMyrZBRcZo41sMkR2NIa5uFosJqM56a9qpb/rAJerWrloB5hS1gMoYW682VVgb2cN+3e1S7DVUeWToPM1hWj0FMlFUTHIxteMi6vuMxMx1MZtKJ1vKi46r+daJVggl3/A0IlH+FKfzpcLt7FQElrRMbm3LsVHbt/F4b071gszTEwsATGhamjF45zYsoP/+bO/QsLnJxJf4M+++3scssdQqneQ995FLtOBba3vIbREP7Y1h6PBjfdgB87mSuLf/jaLX//GEvhV3G5Cb3sbFR/+EO5SH9QrDVk5vbRA/NFhCuPFRJzi1gnc3oj/5gZUly7PZwHm1gK7MnhbCeTW3r4S0Ft6jtKYzqfp6e9hcWGRgqYwFQ6TdDhQLZPmmMWWaIDq7Or1jqmYTHmmGPONMemdxFRfWXLWtjSclotPTvw0N6R2kFPy/K+mz3LSv3pdK3wwV1b3nJpzqcq3suK3VAVUdDpjTeyf2MTm6Sb0ElvFUAsMeY7R736CBVcfeuvNOJpvRC8938bPpdE09CKbTnwRd6rYf1jwVJAJ1TEVnGW8xsYS55Rt0zCVo2MohatgY1W0YdfvQanfiyr0AQTguobQz6uJyVyeX780xmMlxcjdfjd/7LZo7T5fBFrnzpHr7V3Vb1qORMBPtDKCvm0rbXffTftdd6F5Xr166r8p8HWl+JM/+RO5aPjkJz/Jj2u8UcBXKp/F9we1kk6VUd2kNA8Z1Y/JVnSjC63QiZ1v2xCMoQyyYOQYylUxlG9ZBS4MPUGqdoLwNjf7D+5gf/NeOWm8qhD0xXNfhWf//zDfu5wtFXzkw//pVWdPrmZ4PNazwMCZWYbOrjY8VnSbqi0e/FsUJnwpTs1OcnZshOmMAEVJFC2NoqdQtSS6Iw1aGqukpGabTiwjjF0IYxVCxXHlbSMM9rXpQmLxUqEuUK0We82amaODWVoRPWjzsgfNLSggikLCdHLO6uAlNvGw2kW/2Y4pXmdNaKpNdThHZTiJ3z+P0zeBqU0Qz8eI5WPEc/FV6lOvNByGjT8D/iw48jpZLUw03EC0qhFbC2CpPmzDgzqvEprO4s3ZaKaLguWVht3JFX15tSiyyvU2nLhLC/IFUoxpJwh4X2ZLIE6137khgFq9X3pMCJNcQUpX0DEFFfPpsael2bEQRljpn3R/1s3di4JKOEdQXU1rieVdJAyXrECEnAX5KTaKgjPEmLODY5kmjmUa6bZb6LMbpSraVq/FjQv97D/xGJtmBpYoLPPuAE817uOp5n0MhJapwR4zz8GFy+yZPkNzaoy8U8PR0EDnvW9h+1veiu8VGoiL6V303MQffpjEI4+QGRqm4HAUN5cTvbMOvTWEWqNRMHPksukiaLNVcpLY6GTR9jFrB7EXTPafO8+mqSJVLq/qfL/jMF/cfDeJjYw+y6HkqVOz/ARu3mqGCZYEOtLY/ICCBGLDYgZTMzgD3aiB02j+fumb9krCqTqpcFdIg2RBJxL7cnOVRneFvL/8uEgyLNF3Rd+qyCYLMNH7GORWgAbVQb7lZrobj/BQcCfPFQyM/DQVzFPBgvydM3hxk6VWUbnH00RdOoM9+hTdC8c46tB4QdgcrKjwiQVZG1vYZu5lc34PVYUGrIKNUbBkL5uQ4Rf78nZ5u07xobRiM65bzFopqqPn2TF9jgPTPfLYKkfG6eJMzS7GqveQD22jCveShcD1hMtIEEqPEkoOUxnvxrs4ipJdDYBFFW0oVM/lxk301G6nJ9DEmOJaNwuJV232OdlElo6ZYdoun6F1YQKXmUO1CrjqawgduYXQm47g296OZiZRRLP/KuAUW769cn+jcYN5UKyChOFyesZJetZFetZJIanjDBao2p4kuNmJ0nmkSA3tuKMoZHCtikY2Tvbi93nohVN8YaKOE9bmpYc6HPN8eDO8557bCdW1rvtXyzD462ee5lN2WCq5bh3s4/c++2mqYiVBFEVDi2xCq9mBJhSLFRXFnMPR6MF3Qyf+Ww+ibaAoLaptse98l4UHH5S+bOXw7N8vQVjwnntWmdhf9eP1LRJ/ZGipL1dxavhvbSBwWxOq51/P7FaoGAo1Q6FqKADtnXfeye7du/nHZ15g7PwZfOVEg6Lga/Riek3y43ms1PL5VFBVRsM+RioUCoExAvYglpmSlMcy6CsCP3EVNYWD/NL/Oiyd3xr/ODcld1NQCvx+499yLFD08HytETB83Bk/xJsXb6Ett9z/Oeqc4uHwCzwWepHYGgEocTDXmibbcnm25/Ny3JHPU21a8sifrHXR3+Yj7yrOR+5Ygfi4wXnLQbfTSY/TuXquWkETXQXyVgjELO0rpcc1Afw2+J9V/68RnMsQGpjjfLaaL2y7g6Tbi24YfPT7X+dDD38bvUR7z/m9zIUqmK+sJFpZSbKhlub929iyewuhcEi+lmjHCIt1wY843hDga3R0VDY6/luMNwr4WigY9H75Fxl0VHPa2chLjnr63I3k1eUJ1GHZ7IiZHJg3uTWaZVtMZG5XV3EUYhTsKWYMhf5sHVFzZQbBwPL1YtVPE95by749t7E9sn1d39E1Q5xIwuzxmf8N0+eX1f0O/CTc/MuwYhF6vSEmR5H5nl6Yo+fSEP0DY4xOTZFWEmQcSbKOFDlnCtOfkfsJM465kb/ENUKU4EMWVFoWlUaBClNUpywqTZOwzJZbhAwL1fCRMStImBXM2JGlHrRiH1o1c1wnL1xNoak5TGN9tklUWtrCcKCzWfZoddb4JZ0wljGYL5tSxpLMiy2RLlL90nnmclmSVrYIMLXM6lFN47UXCBuzeIhh6jlSHki6wXwNppC2rWKbHmzTy6ZcMx+M3cLNSWHZWzz+4uEU0d1p2BYm5K9eqsAJWqX2KquiolleAC2xCcGRlVURh6VSP++hacpF44yHUF4h4krLalaLL0aTP4VPzWzYSyHDV02udh+9ehdPx+v4+kQl/Xkx4Re/I5emcIMrw6HRM+w9/igV+SyxUAeLoU7i1duIeZul8qJQ5RTebpqRxTRyxDWdOZePpK6TV5CbamRpjk/QFR2gPjGFauVkE7y3poJgSyPehircDTV4mupxNjbgqK2VCyeZjT5/nsTDDxN/5FEKI8t9RUKO2nfLLQTuu4/Am+6QJssb/GjFRWpiumhsLLLoCTFOYSammbo0QP+xaWZTJgmPQjwA881ORmv9zOtOYhqkNZOclsfSsuKDLD21y3Lwptgh3r5wB+255fP9GFm+gsnL5OXxralJ3L5LeDx9hFwTBCwVr+XAYzpwWToeS9D/dNymjst2yFGAB6em4nTouBwaLocue9PkbV3FoWtSNMYh91VZZRNKeGLUVZG+ElTrpHBVxUqOYCYnMPNRTLuAmC7L54E4phftCubsGhbsSix0DEVjwlXDtKsC21XA44pR6Z6gXe0nZCXR1CAjah0XMnA6EWcss+z9I6LeV8dtjUe4vfl2DtUdWqIUr/pZLBvTXAZmxdFkdD7DybFFTk3EGBwapenSCW6ePM+e2T4p916OuK+C6fbtjFU7ma2uoi60mRb/HizTxiyYK55zGeyJfSUVxxsdwh8fJpgYIZAYxp1bTa8UZ8uYr4YL9dvpq25kMFRFv6uWnLJ+IV9h5akxndQaOvWmSq2h4noFwE/0C2uix1nJL49CpkbJoyvlce19Qgxm5eMGWjaHvpBEmU/ATAxSV6Z+O/wGVduShNrEnClKpi3QcXsRiInNtzENdynSUbqPfp8Hj0/wjYUOUiXvStG7+bZgHx/eV8XeW94C7giJo4/xWzN5vlpVBGUPjBf4zdNRtJkezJkLGNMXsNdIbWsVFdJXzH/rLUVvsWsoSkqq7okT0rhZzBEYxpJHWcX73kv4/e+X88lGkRuOE394iNxAMUGhOFR8hxsI3N6E5nvt/XbXG+IzHDt2TDKrBDMgEAhIw2RhaXThwgWskg9a3uniXF0rFxvaeVdbE++vr+Qfx2Z5dnCYtulxOmfHCGWXabROp1MqIu7YsYPOzs515svidfvm5vji8R6+c66XhVwMh5rhv2e7OFKok16T393zMqcqemTiUygNC9N10XLwakO1bHZl2rgvcTuHY/txl1Q9DcVgIHyS2dBLeNRB2lIxWtOLhArr++OiIZ2Lm4LkfMVrrydj0jWQ4mTew/8biTCiFS01flghxGc2Tdh0TooROQZWnHLzwTCf/tDHeGHPQXm7bm6Et5x4lLDlISMqWopC1BmlP9gvq5WWujoZ9e6ud/O7N/8uP+p4Q4AvVVWlv8iePXvYu3fv0iZ8e/78z/+cz3/+8/y4xhsFfMls3h+uBrhCSjkTbGYu0M6wr4VuTzMnHA0c1euZcVRKetv2mMnBqMn+BZM9CybuNUlVm5SkKE7kPYzmgyRWPO7XJvD6z+CsnSS8JULL5vto7bgb9XorY+KQExnmZ/6kSEsUoTqw936Y1I0/S9QTvCK9SOwLtTFBRxK3U8arm9B8qFQImp8AToU8FUZ+mXIkRtMibJlUyn0Tv71etiCjeElaLjIi560FCDdvkSqPM1aA8YyDwZjNpbjO+WSQASOypIi1UQjvL5FINcTSc40ohs9p0OVc5GbjFLdbR9mpDOJTcly02/h748182zy85D1zPaFjyP4oIcnfGRtj6+ggW6dGaUiu9grRqjX8m92c3L+XzzbfyilnCMVOSwECLZ9AXVhAS8RQSJUAXBHIqXoGVUtjC4ECG3ZkOnnv/L3cmNy19Nwnfd18JfIIp72XNtSWEDx8kclaSYUMuoKr6JLlx4SZ52RykpPjL/PSxEuM5lfLzvvTGk0zXrbO6uzMGNQ7U9T6MtT6BG1QvP8NpkBxLMvGYQU7Mc1wIcgj1kG+b97IGbtTNniXo9qtcos9z4GeF9h86SzZQAuLoU0ScCX9jUWbh6XPZcqFoABfioBhSmkUlR/FWnd/UQhfPFYeS3+74n7xd7qVRbNy6HYOzc6jm8IiIV/aCjj9LlzVYVx1ERwuDU0TAMcGpYAh2vrtNIadI28JgYEMBSNDwcxiGFkKRg7TFFsey8yj2qL3TjTXC5l3Wx7VQglOfEpdiGyJ3rzSKJIW4nHdLv6dvL+0WeYucuZbKViHlqruujKBT/suPu0xVGW9QMmPW8w6KphxVZBwuzBcFg5XCrc7zoyucspyctzQ6c1piM6Tcoj+hx3BCg5WtnFT7S6ag524XDW4XLXoejUDUTg+tMDLcovCxBg3T5zn8OR5tkWH5fdejlxjC4G776bhrW/BvbNIdTtz5gzf+MY35ONisfqmN71p6e/NWEwKcCzJu1+4QGF8vfJf1B2kr30nffWd9ATq6VErSNjr5yA/WXYqI+xXe9inXmaPOkCNsohpa0SNFqbzm5nMbyFlV2HaDgzbgWk7MRH7TrmZlhNzAxD3isK28KcmCC/2EV7sJRzrw1lYXTGwFJVEoJWFcBfuBptNNSdx5JKo03HycxbZRYc0y45sWSTcnljNQhQ+l2Ug1nLzVS1ckvOTfOvRJ/hCd4Hu3DJI2qEMcr9jkG/vfjNnItXyPPvlSzk+NJDAmh/Azk3ibPLhObgJV0sDud4+Us89V/QWW6EMKcK1dWsRiN1yC54DB67qLVaYmWHxy19h8V/+BWO2JI2vafK4EdUw7w03yOMmP5aQ9MIl2w9NwXeojuCbWtCCr9277JVEOp3m29/+ttQPKNsaiZiamlr6m6amJg4dOsSmrVv59Ogcfzkys26mPxz28Ymmarbnkly8cEGCNrG2K4eQp9+2bZsEYu3t7Wja6mtzwbR4vHuGB18a5oXeOT6Jh7vFsYvNyzvD3Pq2riUjcQFsBAgTNGoByMQ6RuyX71u6X4zpORbjI8SsArmy36Jt02SY7M3a3JMIsydnE7Yn0JTViRz5WsCAw8Flt5eJcJBAvSoFoIqPOZjT92KNxwmPnyIk1jpohHa+l9Atv4o30CAT1OVq30rK51oa6Eb3i81MJtAvj+C6PIK7dwxv7wSuufXv09RVFlvCzLVVMNMaZLo+xDnnLl6qPEDO4ZIKhvtGL7EpeYKFqhkSnsSGVFSx/UTXT/Abh36DH3W8IcCXyECcOnWK06dPy/HkyZNLRnfiTZWV4H4c4w0DvjKLdP/f/0LYnCNUmMadHEW9SobFdAVJhDqYCrQx6G3mnLuZ42o96XQ1uxfUIhhbNPGsKQ4VyBMz00zmvMwK09kSGHMoaVqcp2l0n8BTMYBa30Cw5RYibUcwarYRU5UloFQGUWXwtJhdoG76EveNnmVncnFp0vi+38vnQiEGNzBM3CjEQi5gqlRYELEtKq0ClYVsqSJVBlOmrFrJ+0zRuLxBCONAX3Uxi+mrxvZVEc/YDPSPc37KYEBvosfRwZDeRJSApGSJcGs2t3VVc9f2eo5srpZeS2tDeIoMzyYZHB5iaGyMoZkYg4smw1kPUev65VG3K0P8pPYI79Sex6UU6ZSzdogvG7fzuLVfAgkhOhFRhPhEkiqXQcRlEfGoVHk1Ij4XzmieqRPTJE+P4lyR7bUF+tu2leQtd3OsYz/f1t30OKwleoL8rudFL1dK9nSJdpaWSi9b6wJLindiFPeJ/4hfmCL59Bj2WPE1BGEj2p6jd+ssY/6Z5axgLr60LzahMPaawrZxWgrBAkQKFtW2TQ15Kq28vNCELIugqFSKTdwWIK56B6763VAntl1kQx0cHYrxRPcMT/RMM764Oiu+3RjkprkL7BqdJWCGZHUrFt5ExrM242zTWDXPtppuGtST+BePobzWz/dvLAyrlqR5PynzPuySDYCoQnm1x/Fr38WhjktVSAE3RQJD9NMJ+ClGkRKRInilTVUtVNWWoyJRnqAI2diirUFs4gwp78v/Wb4tIa2lY1iiT0UYbLuYU6uYdVQTdYSlebB8D4qGwyrQmRlla3qQ1syEfCfiQhrXAhi6B5+RwG1mrv3ZVY2MSyPqgFFN45LiYFDVmdB1JnWNaV2nymmz3W2y3WPS5rQwTAe5QT/u0yoVF/KEZlZnubVtXYTvu5fgvffjbm8v9jitod4duzDAIy+PUBFd4LASpTY6Q2ZkgcKKRv9ypHQXQw319Nc2SgPjC542ptT10tVO8uxUhtitDrBX7WeP0k+bMrWGnacU6Xpr1W6FqW+kE2p2FEdBJXYHiz3C7hC2M4BhuoifuEjsqaPEj76Ekc5haU4swcAIVuDadwDX3gPoHV3Fyt3gZeyeMyiXz6AOXEDJrL4uWrqDXN1m0nVbSNZsIVnRgdueYa/51zQpL234e1mGQi6mk0150Wob8FdlUROD6xM3zTeWwNibiuIEJaQmjHpzQ3FygzEy3dOcn5vkW0qUR+xqUZeTf2ML8YZand+Of513xRdQdr0Z361vwtHauqF4g1CKTJ86XQRizz8vZfpXfeMeD94bDuK/9VZZHXO2t2/8PIWCtAlZePCLpI8fX7rfvfMm3Ac+iBkrXddU8B2ok7LxesW/vuDJyMgIX/va1+TaS3wOUZkqFIrXQgGOdu3aJdWFGxtXM2meW0jwy90jTOcLvKOmgp9vrpam6ytDVMuEObkAYWJLrhCUEjYNAojt3LmT1tZWWVxYGcPzKb700jCNz8/wJlO4Ndr8ARms7RFp3nykq/raPYtiOS78/h7+naIqtOqg0HwDlm2iT19EE3TbNSHScXm7FdPsIm9vIme186I3yyORFwjXn+b2oKgOy1ZsjqZ0Hoo5SG6gHFoOFaWY6CxRswVFu0zTFslOue9evj+IB8/wDPbFXqlmmjl/jnz/QPGzrAxFwdnZgWfXbty7dhbHLZvB4ZDWU6KKKcC0gCRph4sXtx/gcrhG/ut2n5vPbGvZ0A/0jRZvCPC1URw9elTK3f6v//W/+MAHPsCPa7xRwJfonfvdP/5dqeAjqgVieeEnRbM7Q4uvQJ0ep8Kaw5eZRE9NoFxB4l0sSgqhZhZCHUx425k0t5BONuONB+laUPCs+bcsBvOGRTSvMWdYxC3xHBZJ7xAT4fP0V3QzEpjYWDF7g9iXzfJzi3FuzRQv/uLlXvL5ecIflJ02lYWMBE1yE6IUS83xFgGrbKG7JkTFQaiyrQBUxbG8X95Kt0Uf2hV4/HMjQ7z83W9w5vxF5iKtTNhhxs0QM0qYzBoKZ2e1T4IwMdne2FGJ13l1emYstshQXw9DI0MMTs0zvJBjMOVk2gwSUpJUaykieo6I06DKbRPxKNS782zKXaAmehItX8ooiUXIprvh4M9A261F0ROxuDQMeTEV9JLYDx7Cji6s7sto6ODslpv5QeU2hgI+zGYfVq2nqEEuomDhmcqwvaCyPyIAVoCtdUG21AXWSdIL64P06VkST49hzKSXM6QHavEfacJRde2GWNFAXe5Tm0tMMzrWx/jUAL3RywzmRpnR4mRcYlEtJzBZyRWTWOE1NuMLfyan4sc0PKQyToyCB9vygOlFs310aC52zpi0Dedw2Q3kPZ0YjrXnvkWdb5yt9b00BUcJJE+ixlfIiV/pOBW/neAziVFc1IXksqLKbLxISGQMm5RhkhG0M8XGlGBBcPiLYjDiWiqEMER9ybaLm6hxFvf1Yi+i3BzF0XJI5T5xWxH7lhPFFlpqQiVNgBvxXMUqrBzl85ZGeb+64f3L/1sabVVm0TWHE83pQHM55KiLfjOxX7bk/Q0AAQAASURBVBp1txOHw4EnCq7xLGpyecJZ8C/wXTXKP6d8xMzli69Ly7K3+jwHa0+zs6obp3ZlRbV0wc1iLkQ658coeFEtH241QMBVQZW/jvpIK811m0j6qvl+3OKhuTgvJDIUVlwdaxSbG1WDA2aG5myKQjZDYPEitYunUNOznC600kt7qSpqE3YrdDSGcITEd5Qin5jAER+jNjNFY26G2vz8qirVlWJWU5lQdRbmXTDqIjys44ovzzm2apPvssnstcjutrAqiverpo0rb8nNmbXQ4qBEVewZFWtCw5pyoMVAyRYrzSIKisZoXQ0D9Y1cqhSsiVYGNOHRs5ambrPZFWWPb4HdwTR7KwpsiWg4vGXAtAycVt0uS13PXoJL3yuaO4+Jhf6K7yHUDFveUtxab10yB14LOFIvvST7GBOPPY65MpFbrk6sadJXvR48+/fiO7AXz77duDd3ogoqqeiFyywWzWrP/otAWPJctLe8haxWS3ZyCDU2hM8YkbTFjcJ0hFHd3mIvWn55gWzYEfLaDeQ8byKbacdMr188aszyrbpF/p9AJ/Z4BjW9/L5vUHr4iPMp3rw5hGv3O4vfydX6K8Vrzs+TeuGoBGPJF57HnF1tOq7X1y9VxXyHD2/YG5a9dJnoF75BbsSFXrdvST1RdcxS8e7dePct96/9a4UARs899xxPPPHEusfEWuzgwYPs378f31W8nrKmRUasH1b67V3l9coUxosXL8pqWzn8fj/bt2+XQExU2FYCsWzeoOcfzlM1mJAz9B+Qlb2twtLlg4daeN/BZqoDrtXtGMIzS/ScHv1LiK+vNK8C9rU7Vnto1WyXc3z6/DypY5PkBheJNT7L3KavYbqKawPVuZ1U5K2yf7dcZRPX16WKW2pKJsPT17iEiuttfRRJHyxTCNtmln3lVkai0s1iRxXpTY2YW9vRt20mUFG7BNrcipuhniFOHT/FbLniClKRVVQshVXV96IJfvPyGNGCYIzAL7fU8itttbjWAN83UrxhwZeIhx56SIptHF+RXflxizcK+EpG57n7K3fhsDSa4lVU55sImQ3C4nBDulkNi7ToCeodCSr0RYL2PL7sDI6rZOOTioMxdStxexeasYOKwhac9uqMV9Y2WSgozBn2EhhLOhcZCV9gPnSevK+bgJIp0fiWKX3hpYrUMsXvatpfWcuP4ahEDdbgqqpDC9WsB1FiE6BLCDK8TiepqNIK3y4xGYtwZFI4x/okV36hop3s1iMMaDWcnUjIDFM5RA/KwbaKJTC2rT5wfdKzsu8mUbzQXq33SYiZXPwWvPRZGDu2dLdVf4iU6w4SPXFiTzwN8dgqxbGX6rbxQsMujtdsJe3xYIpG5CYftn+52thoq7w1FOCjbdV0Vl7dnNvKmVIiN/nsOGYst+RV4r+pHv8tjdekpYhpKLkwz+zQIDNDA8wODTAx0kePMcRYdYbR6hQ+V54t+QJb8nm25gtszxo0Chpc+asQE76qEhOby0usopVYuIlYoJq4J0TM4SJmpIsVtlKlLZqJkSwIMZL1SQnddFCTbKUu0UF9vJPaRBvOkqFyOQylwFxgiJD/GJv0U+ywR+koiH6TFT+RorJQ2Uaq4wiFjttJBeuYl1STONF8TKpYrqoMl1T+hLLlqwlHQSWgeqkO1FFb0UClp1KKToT1AMGsQiBp4l/I4ZtJ4ptaxDk+izExRX5ySrZ+mJoLQ3cXR02Mbky9vO/CdAewg5VYvhCWJ4Dl9Mq/NxQnhqmST+Uo5C0sYfL5KqNKV+hwqdTpy5LqSdPmZMHgu2qGcw6bxIrj0YFFp5qhVc3IKqf4BZKWg7jpYtF0k7JFL50ifeNEP11BKY3i2HOrmLUemXCwKpyrEjDV2QSHc9O8RY9xR6VOuLIaRdBRxSbmmZLQizh+RcP/wMAA/f39DA4OSjXKlREJemir9qBEAkx5vJwznQxnDdRCksbsNE25aTkKYCbH1DTWFCTG3CTH3Zi55aNK0Sx8dTkCTVlcjVkMv4LlVMi6NdIeTZrrZl1iU8m5VOwNEhPChmAmXc3AfCsDC+0Mp9oYSdVjbOAF2Bhysrelsmi83uhnZ60bv+CQCqAiwIuYh8Qm7Enk7dL9G94ub+K2Ael5EFLoMz0QHYSSDL8McQwF64vG08IvUiQqzDx2IYsVW8BKxLCSceysUJC1ZbVTsnxLo7ytKcX9Dc7xVxqWKiiRLuyCgSrmHtWU+ZKNQiQeDLsOw24mb3WSt7soWB0odgq3fhGnegFN6eaPNn+Mv2q8V/7PnRUBfsrt5xvP9/BIX0JWfEVUEue92lN82PU8LdsOwo6fgK57rujbtPwebCmsUa6KpY+fWOWpJq6Pogrhv0VUxW7Bs3s3Ztwg/vgI6VPTS75T5mI32RP/jJWYlLd9Nx+m4kMfwn/HHShreqJ+GCHOrQcffJD5+WVbBBGCClheqK+lBL6eIXrKxDktgJhQ6RZJ73KI9Z+gJQogJrz0xHwl+jMXv9kn/bvEcuDP9RxfMXKIetgWbZIPNM1zd8UU9alLKFPnilWutSH6PoVx8krDYiGuskEyohwLCy9yqfv3SGWLdExHqpaayx/AN7sXd0dY0kQ9O6pkn966sEwKpx4k9vT/w2JmlkVVJeFsxXAcQJnI4OodIzgwizOzPgGRcENfgyK96frqFfobFGK+60uG6pYuiwcCkNVX1FMdqF5VbVP0Cr6yWM3RZHGu3ex18JmtbewL/egNld+w4Ev0donmxbXR19cnS8PCd+THNd4o4Kt/8iLvffiDFFaq7xhQv6ARTjtxWh7Sbp2E1ybtLDbBS/8pVciql/7BtolYFm35Au0Fg7ZCQW5iv9EQ5J7VIbLborydtXaSsndjmttRSg3E5chbNvOmvQTGUmaeRuc52txCyv4EQW0501EOQxV6YSESuUoyRgC/Pke1PrgkeZ+pvgnnvb+FtunItZWmXqcQp8eJEyd4+OGHJbVBZOfvu+8+du3YzoWnHufk979JbKZoFi7kwVtuvhtjx+2cito8c3mO8cXVx3iV38WRripu21wlqYri9spImSYT2QLjubz0v9gX9NLsvj4+ffL808S+8L+xTp4nPSGUGZcn2ITTwwt1u3i+YSenq7skhcpf68HREWQ2oMtKigifpvLu2gr+Q0OEnddR4jdTBZIvTJA6OoGVLi6a1IBDAi4BvFT3+guzKQDrxBgzw8tAS+xnE3FSLoPJmjRUxaj0Jthk5tmaKwIuAdI3DH9dsedCbjuL1EGhRrYBaM0WTF7on5M8/Sd6ZpiMiYuoBWoej5LloFOlK50nHNNQ7RqUNbYDhpZlITxOLnCOWv0ldlij3JhNE1yJuCXfXucFj1uq3B13u8i8yiSAFHrJ2YSyFkExis3UqApVo0XaGaCVk7MhxuMBbFMoTnrxWAX25rs5kD3PLl+C9j37adm5F7c/WFy8SrqdeD9K6XZxFNVvM5akMLtIYXYeY3aBwvQchZl5CjNRCjNzmAurpcNXhq3YCNVrU1RemnwUQgo53aTgd1DwuzCCfgyPG9PUsAw3VsG9NJpGed9VHA03dsEjPbValQDNmgNH6Zwv2DYjeYujBYPjusllp0lcUAxLISqhHQWNLQWN9oKKc4Pye9Sv0tPkpLvJwURkNUisXCjQOJ6ndjKLO2mSLwE1MQr4FlDj0mKgUo1S7UxS58tT60vhNuOohQS6qETnU8wTYpw6xqlnhir5/Qr5/nnLR9R2kbCcpIXUhBPskJNC2IPmhn2jl7nl3AluPH96lSR8waWTb3OiNWfx1C5QRYYq89qAQir5WSop1cmsI8yoWsMlq4lzuU6GjAbG7SriiHO9+D35HCnag8O0h8Q2QltwhJArgWbaOHPFSporb+LKl28X98UobuuvHeP8K0aRLVLc1YqVOsGAEKbRAlWJRXFGqCde+bh/NSF+k4Tm5Re3f5LHIjfL+/5T9iS/GUigBWrl+5g2A/zzZYUvnYsxlSq+RwEgj6hn+bD2OHe6+9C3vRl2vrtIb7zKonylZ5JgQciq2PPPk+/rX/4m3GFcO9+Bo/HwUp+qe2slwXtacdR7pfy9oCQmn3qqKNlfqqJVfOADhN/7HvTK11+KfG5ujkcffZRLly4t3SdA1r59+yToqqkp0tL+NcMwDJlgEUBM0OTEOrccFRUVS0CsJlJB7KunSZ0pPu4Ifptw/vNSQ3aj40FOb+JaJsyQBbCu2nLdZuvp9DB9/X/E7OzD8rauB2hr+SUi8fvJHJ8n17uwdJirXh3vvhrpHeao8602Lha9nqdPknn2e2QuD2Jm1l+3hEWBe/t2PLt24d69C/fOnRj1VTKhubJ3bal/LReTScWJ6ARTsSnihbhcfxaEbcwrWMblPDeQqPwpbC0oKDZUpp+gw3iBCqdfArXbmm7jfVvex4863hDgSwAvUZoVJ4oQ2hCjyAz8xV/8hcwQfuc73+HHNd4o4Esc1O/8lzuJSnLSKw+voUglsYAFYdOm0jKJmAWqzTy1VpaIYVJrFlX9/Ja1Ya9UEYx1kpc+Y2Lbji0v5huDMUlX1MdweU/R7D7BXuUiDVcwdM5pHiyHF3c2uiyM0HQD3P4bRYrdDxGEid9WNPWKiVZES0sL73znO6WITDksy6Tv2FGOf+cbTPYtXyDa9x3kwAPvolDbwbO9czxzeZajA1Eyqo3t0bHdmtwqIh4CFe7ibWuOKqOHTi6ziV5qmGaUFuYdOwiH9rO79gZuidTgV1WGo2l6JuP09Y1jPfc0dWdfZNt4t/THKYfqsQg1pWV2XK2yOeq4kZEdH6OvYz/P5TL0ZJYvAoJT/ZONVRJ4+fVrZxCNhayscolql10oXYgjbkkt9O2vXcqs5dJpZocHmBkaZHZYbAPMjQ5jljj6ulrAGYnhrVikypukzc6xqZBf5b9VDlvRUKq6VgCtXVC765pWBTPxLI/3zEjA9VzfLNnS+w1aCu22xl5FpSYpsvbr+xfcdpqaGoXWfbW01o8TXHwBRZjMzi3LNIswXQFijXuZqt/BUFU7k7qDWHKK+Fw3scVhYplZYtgsCjU+216izJYrvsviLiuqwpZJwFov9LLue7Hhgt3Kt8xb+LZ5M9MsH5/CiPrt2gu8Q3uebcrIqz5dBDgXFZSsrpJWdDKqTtahkXOq5L0KBZ8iqy9X0ZRZFUoK9AQ4kjbOdJkeJ3pNTbwY+JQCzhWf3bTcpKx7SRkPYFLu5RCknn5yylnO2SZP08HzNDG7Yu5x2BbbrQw7zAI+t5uLDRWcbwwwHV4BuGyb5jmDrWMFto7lCadfOXpQKNDq/z5NnqdxKlIuQvo6ORTR3r582ylHA22F/5eRVUmMu2WFKz3twl7Rj5EMeDm6Zx8/2H87ZzZvK/rolSKYitE800117iw+rZv6whj1hRz1hkFD3qI5D9V2DueKxNyVIouDrOLAdlgoLkP+1qKClvJqJPwaGa+gwV7fwSPa7JwFsanohobD0HEaDnTLicN04rA8OGw3qvD3EpVDsakOFAEeNB1F9N4WTOzZOazRIcyJSazFjPxeBGu+PKpOBWdjNc5te3EduANHYzOKoGWVns9WdXL9wySfP0ri6WcpjI7L/9WcJtV7UoRaimwP2+FHueO/wo2fKIKujcIysVNx8oNjFIYmMMamMKemUcwEKilUJSlHhQRKYRbFikrxIYdTqNau52QNeBr5yR1/QK+vDbeZ49OX/5ifmCkZ564Jw1Z5wtrHg+bdPGPtWqKAinP7A/oTfEB7klpHRtLPZK9Zw/6i0bWQ3Za9c6VxA3BWmJwk8dQLpE/EsJU2lFIl15i5SK77W+hBS/aJSYrijYdQfT7yY+Ms/ss/s/iVr8oFe1lFNXj/W2Q1zL1793UbC2/4VVsWvb29vPTSS3KduBJ0HT58mFtvvVUKYbwRQiRkRTHh/LmzXL58mYKx/FtHWGCHfYlWYycu8x3yvpD+N3g9TzDi7OSZeD2nzA7O222M2dXc32zw4QfuYn9r5XV/f4aRYHDoLxkd/Ty2LUCeSmPjh+ho/884nZWrrtep49Okj09LZootxJMWhVHxBBgTFMZ7V6niLoWq4AoW8FTmcEcKeA7fjev9v48SWW+PsFEIqqbQenj55ZfleqocW7ZsYf/B/VQ1VpEoJFaJjWwE3uSYj0n64VzgveR8h+XzaPkxAtG/xZEf4P1b3s/v3PQ7/KjjDQG+BD9XKCuJTYhunBfqSaVy7Zvf/GbJ0RUVMLEJac8fp3ijgK9CPs3R3/4lTC3LmMtgzKMw6lOIajkW1ThRLU1ey2KWsgxhw+CGbJ470mnuSGcIXuPnF4+m8Uhp3BTeFZvIYKt4cxkChSR+M0VASeF3pnG5TQxlU8nwWYCxHevAmMheF4GYzbSVos93lnTwLDWe02w242wuwKZ8GtcVetREZJ1BplvuwNj+TqpaDhASE8LrAMbEKSGOWUGPFdQh0dB71113ceONN65rsl15wbjU083jTz7OxeFh4v4QcX+YfG0jZmMrUaeHqVxBUuNEOOwcom4hQNYmLtNJL1Ws5uavDdHsP0wbA9lOEuMh2o7Nctfzx3CvAFwTgWrO13bQF6zAdBncFejh3vpJArllR/jnQ3v526b38Ez1Lby1NsJPNlSxP+i9rgm/MJWS/VzpM8LEs/RZGv1SZrhQZzM3Wq5mDTIzPEBsuqw+ZRPQc1JxSVS0wqEkdc40Ndb6LKCIvKC51WzF03gIpQy0arZdk2YjX8m2uTAR57HuaQm4zo3H5IFcZSk0GSqbFF0uTPUVlcFyeDMzVAeyNO6spfOQj0D6JEr/4zDyYpEmVQ6RGRZJgM47ofMuaNy/VGkTmVExP4i5obzFF+dxTZ8isnAar52WFVSHQ5PVHDWTQYktoixE0QoFNMtEtS0UjxtHbQ16TQ1aKCj59kWhgtJYvr10n40ohLyUruNb8c18P9lJYgWgbLcneDMv8Rb9BJ2BPB6/H0VVMBSTnG6Q0y1ZqcqK0SHuE6Mtt4L+CsROcuDM2OhJG0ccVNGOM6dgjekosyrqooJauCakRPdYOLwmutfE4TOL+z4T/DvJuR4gxw1Lf60ro/i17+BRn+AC9XzPvJHvWzcxai9nxkX7mVXlxqzzoFQ5OJw4x30zL3H77FnCuSwFy0Pe9lCwV4xWcSze5151X/lvxP8ZJbJ0m+tl7gr9BW51fWP8ysgnNQm2xJaZE4vi5e9DC5g4Gi3skIJpqJiLKnNGmPOVXVxs38yFji4ut7RTcJQW06aNkiigLmZxRWPYiQJ2tgwibKls2qjM0aDM0qGO08m0vF2nLEhhHkEHv1YI+tu8EmRe9bOgeUk43KRdOjmPgu01UP05fN4kHn3j83mjyBgu2YcntmzKg3PRIjSboW5qkdqpGFpMQY2JonTxuxnzVXG+qoPzkQ7ORTqY8RUXl6LjUBcdhqoi7SkEA0GT+6VNKY7uQpr35b7GR9Tv4lHyknb5SOYAn4/fS9Jbi6MijBKxybunyKojFBinUa3gAB3sSzXROl6J01jT41rIYM73Yc73ylH1piETpTBfXOcI2mOoI026o51Lyj0sGg1MN6T4yxtuIOlwU5eP8g8XP8m+2PX5Qo1YNTxo3slXzDuIUlx3CCrbvepxWQ27Wb2AugLYrwrRAyzBWBGQWXoNicWbSE5vx7aKn0v3xVHyJ8lcPEame2CpwlX8fwfeffuKQOzWW3B2dEg7C1ENE0IL5XDv2EHFhz8swZj6CkCSWKgLYTaxUF8sgbpydHV18Z73vAeX65Ubkr/uUcjA9EWYPAWTZ2DiNMx0ywTzZTo4z2bZ+2mWBFRERHQHbZlmOqxaWo7UEZj+bRKDx/mWeTMPau+gO7dsVSAErD58Uyvv3NtAwL0xddu2TSYmvkz/wKcpFIq2A5UVt9LV9Vv4/VtW/61hkOvvJ3P2LNmz50gfP01+qH+96I34/Rua8O7bg0dUtERla9s21MwkPP57cOHrpT9yFxMVt/5q0WdzgxDCekJAQ6z5xfVQhMfjkT15Yt1fUVFqTH0VkTNzfG1iit8fWiBasHGT4R7fFD/VUMEtzcvKrf+uwdeGC9RLlyQQE1sZmM3MzEhO7Y9TvFHAlzF0gs/+4bUpEUL2O6dlJRArb4JCFdRNal0aLT4flf4ATp8HZ8CPMxTCGapA8fpIZlPEU4ssxueZm5lifm6WaDyOdYVDR7MNGtKTNOUmqTHnCekFvO4anFoLFlskKNsIjAkgNm8ICtEQM75jTIbPkXZP4kOhw/LTWbDYnolSZWxsdhvT/IyENzNfuZV8zQ6c9bupbtxJWyCE7zqqOSKEupGoyJZpDkIxSVS7gpEIk7kCY9k849kCE7niOL5iTF2BAiRETuqYlACri162Kr3U20PyorkyxGJgPFXP4GIr/bE2kkYtEc8ondXDdAX6qdTXq4Mu2BXMJhsxY9WYfXmMy3NylekOBNl1/zsZO3ArX5xNoIwd4+PjX+OB2WdkBl6EGWpBu/HnYN9Hiv1xV4ncUIzEU2Nke5b9ZQoRixnfGMOzF5gdGSSbTCwthoreWSmqXUnqAnmqnHHpabNRTOkO5kP1OBr2U995L4GWwxBue0X9epl8kU74WEmdcDaWo85UJdhqNFRaLRXHGoUnxTbxJ0apNKao7wrTfqSViupFlKGnof9JSC0bMssItWB33kmu+VYWwrtYzNqrAFZ5W6mQ9WrDqev4g0HZ2C024WFT3l95WyhwXSkhkMnlePTiAN86PcEzvRkKKz5/u3eIQzUnuanlJEH3egngjUJV3bhcdVLy3C3H4r4c3cV9p6MK9Sr+f7mBAWY+/ackH3tsuedk21YczU1Yi4sUJqcoTE1jr+mX2vD9RJpxbb0XLbIPpexrqJpEG02+u9nHV1wuphYKaFMZVLFlls83pwa3tzh5oNPFXW06AbHGsW25qFlI5RmZTzI6n2J0IcX4fIrJWEYKDojqe1lqRHybooJV43cSzNTjmGmRDm45JU+3b5B5RxaH00VXfSXbGyvZaaWp7jlP5rkXyfUu071ktLSSqKwkkc2iz85RuRhF3+CaaPgCjO64gQstuzgebqK34GQhXlgnLCbC8mgQVFG882juARzaGRxmH27VSat3L62eg7R49uGzXHgyk7hT4yz2HcefnaRGi9PmKxAuzEgVXTGnXyvSio95vZpFPUzM4SfhcJF2Oci6bAouAzxZfM4YPn0Bl3b9IC2fd7CQCxEtVLCYLYK1WC64BNwWc0FiuRD5kvfR+rC5T32Z39EfpFkt0t2PWV38d95Jn0tFdU+gucfR3ROgX7n3WbVVmnK1NMYraZuy2do/zeaCi8jBwwT2NOPlDGrP17BzSVJTLua6w2RmStcdTcc6fDf/cNu7ebDZJ6uITXMF3vN8kkDWpqY6y+aWeZq8PYRTL6FFLxfNoq8QOUeYHzju5guZm3k5U5RZF9GuTPFh7THeoz1NWJSXV9IqS2HZHpLmO0gY78KmSDtzKJcJ6V/ApZ5cyl+aeYXUjEt+ltSki0Jq9XmteXV8myP4djRJn7HEqXHixy5jF4rHrRb0E3r7/VR86MM427uumBidnJyUC/Vz584tLdTLIZJU73jHOySN70cS+TSIniwBsiZPF8eZbkl7WxfiGlrqzcpW7eRSOsz5oRnJnCl7jomIWAG222MccH2X8D2/hn3w45waj/PgiyN89+wEuZKZutep8Y69jVIpcWfjsihKNPoCvX2fIpks9nV5ve10bfptIpE75O3C2FgRaJ07T+bcOal+aW/Q4qNVRnA0dYHeiOISv2EritMvqYiiN8y7txrVuwL8jZ2ARz8Jw88vf94j/xVu+JisGIvfTgiTiN9SKEaWQ9gAiMS1+A3F72lZOQwjhWmmMMwUppFcsS/GpLxP3l66L7XuvoL4OzO9xIg663oHv3rLp/lRxxsSfF0ppqenqb2Cod8bNd4o4CufzvPcJ3+fPH6Zkc2bTvKWi4LlJWd7KdivrzSnUIdyunWcHk32QqMKzTVDegTlChnSuSSGlcNSDGzVxFbEVtwX94ksRaO5QKdVoMbw4VJasLRtKIKCsgEYS5px3Nop3K6nSfkvMeKymVN1woaX/QXRCzQvs55XCtGx1u9tpj/YxVzlFrLVO3DU76auspF2n5s2twunqjCXN3i2u4eHjp1gXtFIebx4GlvIBYKM5wrM5q+P1lnp0NjkzLJLHaDV6CaUOEHQ6sPpWA88nM5qgsE9hIL7CAR2M5Fu47n+DM/2znK2f5raxSlplnr79AUaFycxK2xmdvmZ3htAaTOpdE2hKasvAnnbwRztzLr38kKhkzNWFwklJKVm31IV5meDOW649CWUk/8ImYXljOieDxazWdXLKlaiaTh+ZoLEE8MwW3wdMV2MZS7TvXCUhfw0brVAtQBZ7hS17hR1/jxhLSYXp2tDEA37nQ4uOZ3MBusINN9M17Z3savliHS9f6UxFRN0wmkpB//S5TmqckigJQBXg6lK092VoZo5QvFBQrF+akIGTTd3EtlTgaPQV6xuiYvsihBCEwuhnUx4ttKvtDGachJPJNYtEDYKseQKKAo+28ZvmvjyebzZLN54guzICBmnk6zHTdbtJhepolBTLY0kU7nckmzy1UJVDZzONG53hmDQxOc38HiyuJxpdEcSRVnEtsXirfg7pAseTszs4cXJA1yKdi1Rl8TxsyPSzQ1Vp7mhborqqma8/iZcrnrcEliVwJWrDl0PviY60coQvQWzf/Znsvek3EdQ+ZEPE/n4x1FDIaleVxifoDA5gTE5SWFiUlKkyps5t1wlzrt8zB54F1W1N1BlFyuj4lM/V63x9XpwJ4e4c3GGJsvBi446Hk95GE7bq0Rxjmyu4v5d9dy1rZaQZ/2xmDNM+maS9Ewm6J6M0zOV4OLULAu5KKrgTyoGkVQj70xVEDJEDx203VbLbZvSZJ54gsTjj1MYXa4+C9ApKpqCrlUQSl8rGvjLYbrdDNS3cKKqg95wM8OeGmbUoLAOXve3lT4nWxqChKo85AM6oy6FnkJezqOrwjbQ88M4cr3o+V6cuX72VDTIfokjTUdo87Txfz//f+U1ORwO8zM/8zME/f5iIiI2BosjxXFpK90uzyVXCXHMmZaPfNwmHzfJWwKoamQdKlmPTqE5gtUSxopoGO4seWNOLq6uN1TbgZa10bImak5Bzat4UibNiRxRM81Fp5PzvjCnK1oZMBbIWuufW7c1WnMNbMo205ZtYFKNcs4zyphngoK+sYVLyPSzOZ3hUH6R3fkM2wLNVBz6BPau95M+283cX/0Viy+fkAayDx++Xf7P2xX46XSIyYtRpgfiWCt6RkU1uq4jSPOWAG2+M0TmHkIdP1b8njdQx+yxmvmieRdfN28jWeq9dioGb9WO8RH1B+xT+iTusQJtpNwfIzG9GytfPIYcgQzBzn7c/n4U0deWXSyCPrEJBUgxGiLxAIWkRlIAsSmXpMeu7CkW4QoX8IhJ2FLk3xnpMliz8Tfkqdip4tsURPGEMVxhuvP1HFuoYHQFqAs5bZzymm4Qqmnmvre/j4r6tiVhmx9qCIGrMtAS1SwBtgS9fCP2jRD0WimEIcZwy4YAU2gbdB9/hgvP/4CBTHAVfVcoJYoeMbGJdeRiOs/XTo7zxZeG6Z9dPt72NIf56EGVTtc/EI0+Lu/T9RCtNR+jYnIL+XPdRaB17twSFXRlCLqo6M2SFa2du+So19UVxUGEKMtAjPTLU6TPzwmea/GfdBXvriq8B6vQWhwl0JPEGHwc8/hnMZNjGJqgm1cyEd5Fb9zGsjNoWgFdNwiFXAQCDnlbgqsS4LLta1/fXmmIxPWUcRcfve+z/LsFX8KDQfTGXG+Mj4+v82P4cYg3CvgSPUd//XMfwenx4nR7cHi8+N029foMNfY4kcIwjnyCvO0lZxXBWA4fae9mot42BjQvPYUcc6kkDsON03ThNN14bD8hKnBbPtSCRiH3+nZRC3W5MigTowAHAVXBi47bduNUVHmf2AQ1S2Q3hESswhRB/RQtzoepcIyTVwySqir9uxylOS1v60SVIB4lR8hOkVI9jLlrGHfVMuGqYdxdQ7+nWW4T7hpiekBKel8rPKpCg8tJo9tBo9tJo8tJgwsarCFC+Yuo6fOkEmfIZIY2+G8HuaiXxIiKMeHEHnPSFNlMW+smPJYtzS2NubniODuLlVhDW9J1fDfdJI0vHTfdyLnjRzn58NeJN9jYHTqhmjRNrmH8Uph/dSyojfiD+9lacyNV4QP4fJtQjByc/TK89NdFpbFSpKsOMOQ5zPxkK1WxOgJasSJm2gYTqdPE848TdAxR68tS683g2+D1RMRVlZ4S0LrkdNDn9lLZdBO3tN7JkcYjNAdXG4NfT4gFyvmJmKxuPXd+iuRoikZDo8lUqTEV6U+y6hvPJyTQCostOURVkxfX1lockQSu3GUC2V40CQmXY4Ia+mmV2yj1q6gjK8OdyeBNpfGlU3jTabkvx9LmyuVWvRvLa5PdYZHbLt2HcSoV+Jp2EdxxK77Wvbg99bicondNJZWaZnFxkFh8hFRqjGxmknxesAPmsFlAVeNoWvY6vzOFfN5DPufDMISHVpiE0cjZ2GaOz9Qyll6mcepWgY7MMHfUKbzvLTfTuXc/yg9Z0lcYxM5++k/JnD4tb6uBAJGP/QyVH/2oXDBcKZLpDI8PjvL9uTiPG6qcAwQt86Y5k48MZrhxBRYw4xMUBp6gMFqkjooL3lCwnmcbd/Ns017GhGph+TvA5lDI4OYmha2bIFNhMWssMJueZS4zt7TNZmZJbaBQ1qjU8e6XbqT94hTVc2dxFlacx2LRJb7PDSpaitdLfsceBrv2c7mqjYtKgPPRArOJ9RUiUbmOKGmq1BRVapLOsM6+za1s2tQpZZoFvUeEsCU4m0hzPJ7mRCzFy/HUhkkk1ZjHketDz/dRzSy3VzSR707hnfXSUNXAT//0T8sK61UjlyxKZC+OYi+OYA6exRy5AAvDqEYU3VVY6TV+5RDZ9FBTUW4+1IwZqKHg9ZPzOMm5dDJ6hlx+jlxumnx+Vo653AyWVczsmzZMFhQmMwrxqMWIoXHZ6dhQ8Eac2aJe1W6F6SzU0JlrZpOzC09Gxey5TO6lJ1E9Cr6bbiS//0Zebqjj2fgIF+YuMJ0bANcEqmPj6lTIUc2Oqu3srdlBfbCLvxxSOEelNE7+ha/+E+9+6mGC995L1S98ArV1E+OXFxi9GGWkO0psZnWVwuXVadpaSctmP83BfgJTD0PfoxBd7ocSkbJdsu/zC+bdXLTblu7fpYzyX5QZNlt7sSnO57o+TXB7FM9dt6HUbrv6byKuFWVAVgJldmKO9PkeUqd7SJ0bITu2LOggQwNXyJL9wPnE8hxaiDgY29fKxchWkkrx/Bbufdvp5RCnaWZy4x5Xh2+ZMimobivok1e8r3y/M7CeRSE+x+TZ5WqWAFtC7n0j6wd/7bKsexlsBRuur8VBVLyO/Q08/j+hkCalRzhV8d+4MJVmUl1cJTghvMMECBM6CUIu/6XBKA++NMLTPf28ufVh7mp5Gl2slywFf18zFd+xsfqLYl8rw3bouHZswbWnC8f2TvTNLSi1QXmOFKtKq6tOqypMYq2YjGFkE5iksfQMtrYxY+W1hqp60HUfmuZD1/xoS/s+uS/vW7M/n9Y5O57npeEs/SNJFmMW01oVecvBHclh/vH//Ef+3YIvUcESNK2Pf/zj0uhuoxBv6stf/jKf+cxn+Lmf+zl++Zd/mR+3eKOAL0H1+ruPvueqf+PT8jR4EjR44zR44gTWVGEEHWkq56dH9XA84ObFSouMYxlsqbbolalii93BZm0TEb0WVXOTzWgkF2ziCxa5tDArdUh1OCGM4HC7cPmEt4/wuFEkeMvlTIz861toFepPupJDVdPoSgafncBDUpo/26pBr7+e0+FO0g4HeV30EhX7V0Tir3gbedtWLKryC4SNBE6rILO0aSG17QhR4fLT4XWxOeSnxZeikn404xLJbDfJ3GUs2ei6OpyZIO75MM4JF45+G603jTW3KKXprxS2bmNU2JhVYFTZMgOsNVXj7GjG0dwsyYKil6ooWFF6nmAFydYuhlUXcUshhwehGVnFPM0M0sRy+b8cov6IVoPbdGMkdZiPEc5PUKEW0OJ3oS68FcUoLkhtRCXlUSq0b+C+Qk9a0uNjzK3T5zAZ9TjkftSh4XMG2BTuoivcRXu4Q/polU1Wi75CxawbBUPKH9v5QmnLY+eKWzpjcGLWy4npaqYWqqjIu2R1q3KNt5oIlzlH0OgjmO8jYPThsmbIBTw4IioBf45G5gnaq7PdKcXNsF7HkF7PiF5PWnWjGQbubA5XJoc7my1uGbEVb7uyOdn8rbrcKG4XisuD6habu3SfW942ggXSkTlSgQkyrmnyBRexWA22paE7cjj0nBx1PS+3V1pUEhcjh0MYd0bADmNaAQp5P5msm3TKQSKhE4vZJBLJK9K6Fy03g2YlA1aExAr7CDd5OpQ59oXzHOiopaIqsor6KPbFAkH0Q77WkDYDTz3F7J99hlyJ7qtFIlR94hOE3/8+1JJibqxg8Oh8nO/PxngyGiezolpQ69R5S3WYB6pC3BT2Y89lSD41SPpstOjcLo/lAvlsD5mxp7HGBnAsJuVSazhQx3MCiDXsZiS4TOHSLYO9M73si56lPXOetD/LXBDmggrzAZukR8H0ugi5w9TPmmw7PsuuIRvBsCtHQfOwULEFT3aOQLJ4LtouJ8q2HYxtu4HLNZ1062HOL5oMza+vxIheJdEDIiTe9zaH2NUYwmvEGRkalHSm0dHRVZQmkckWyczOzk65if2yBLf4nkeyeU7E07wcS0lAdiGZWUN+liZa6PkhnNk+IskZdhoav/XWT9AlxG42+v0KBbLd3aRfPk765ZdJnzyJFV9NZVVcTvz7t+Db1YqnPYKzQkNNTa2ooI1elWa3FKJCHmqUwCwXbKTX4+OiDheNGBczk/QlJyhsQAkTM0+j06bJadDktGhyWNQ6bOkddKUQlV6fbzOh0F6Cwb2EzDCu099EOfUghVyai3Yrz+rbeT68mR4rTUYbQxOAzLXxPGmrQbZ629jTu0jji4O0T9vULEDwzjup+sTPS5l3EfG5DCMXo4x2RxnrWSCfWX3NCNd6ad5eSUuHToN2CufYU9D3JMSLlVUxpZ62O/mScS8F6wgfwUdtqdIdJ4VT+yqd+tdQykIsIvkgDKH3fVSUa1+VPYsRjS55iwlJe5FAXPrcwHxjI72tLYw2NWGXnt9rFthfbdMVnGdx5DwuK4NXLVATdOG2s8XjQRiDvx4hwJuwbRE9w6J3tySZvy6CjaurWQJwCVuJVxPz/fCt/wQjLxRvi+/27X8Ble0knhtn4rvdDGozDIcXmExNo+sFNM2QVaOmxgjNQScu8yQJz0lsvUTlnAXHoIr46Wy3jSUO7JALgk5sj4KlG1h2Fnv9Wf3aw9JQTTeK7SZnqqSFU4SpETaT1JoLOEwD3bTRKrrQNr8NPdy5AYgS14wAmiZ6zK/eCjKTyHJmZJGnL89KIDo0n6IgKJkbXCgbU3PcUOXgz/7Hh/h3C76ED8OnPvUp/v7v/14q0hw4cEAqHIp94ZUkOKFColM03gmvr/vvv58fx3ijgC8jlaL3wEF+XELKWZd8hAzdI6ldYhQeQkVvIU9pLN0v/k74C7lCmE5/8T5F5H5fX5VD1SqgGVl0I4NuZuW+ZmdQ3RkUfxbCaYhkUAIZVEcW1ZFB00ujkcE9kcM1msU9ZOMcVlBTr+/7sx3FiVawqiy3jS323aKvY8W+uF/eJxZ4Nkm3j35fG3P+CIZXo8Y5Rbvaj1tZnU0XM4AjE8Y/vwvPwjbcsS5cGY2A/i382kOoSnpJ8S7l06X6WdKnk/TrJHwapv76VkfmMxV0D93K7ORetFg9DYaOv+R3s/SesXEEJ/DXXMJb1YenqheHd1FWP4Jxg8qFPJGFAsGEsepIEZ9hMeQgWiE2J0mf9kNTzPz/2PsPKMmy9CwXfo4P7zIiva3KzPJd7bun7cz0WI0sEkIgJF0E0gUW4keYhX5+hL/CXe7FX6GLgCUEwsgzfnqm3bTvri7vK7034e2x/9o70mdWV3VPD9MDfFV77RMnIsOcOLHPfvf3fu/reQL8dFIsdlMs9FCrvZsUsy8BmCHAmARmApCJbXsLpG324ZBFNJYmEe8kEt2ovwp176jD6kYTmuWbxyoIpNCRqEWrVCqy32ybtwVAu1XyuFKPMeVlpPrdZsSVJiNqnkPaOil1d8ZNZFnerSZtc1sUyt+Jshj4PuUvfJHVf/bPtpS3qqPjvPMnf47n+of5ZrG2i0YnLBg+3RHl0ZhLt1pivdnOTu3MUpUrJU7OD/LMysP0OW3xDUGUPmue5xX1OcrVa+RKAdkyZCqgN6M0ghw1PSmNoyNOk4jbJOo0SbcqxJwGlufc1ejTMFNcOPmzlBODrKsBN62LXErkKdGP3xITuv2Tj+GOSNtLa6ANto73JAmLArXbhBAEmpqakkBMKMMJae6dIY67yIZtgjGh1LrzexDWFmdFZqxc541ihddLFSqbvhM7QnVXSfoLnI6ZfC43wGcLOrxzlvobb1I/e5ZghwGtfHwkQvj++4k8+CCRhx+SlKdNEH3baJb30xk3Wr04y3V7jcuGzmXL5IppSgqzd8A5Ffd8jtk2x1o+h5v9HG4cpdseRI0PQIdKoIkamGfxlCls4YNmCi80jVYihR0yaYkV/wMW1OTxbHlyTEl4SZLDf4D46T+DHulq07ELDd6eLvDq1BxvzF9iRptCza6iO9NozvyBPmPhVsDwMoyIGrLEKA9+709z7COfQ9+om/Q9n5XpShuMCYriZGlXfZ8oA+g5nGTgWIaBvjq55qsw8QL16y7lxg/iBT3ycXkcfhWXL+DItYgHlWv8uP4sn1VfJ6TsBHeCgtINvfe1PaYyI+0s5CZo2eXRttezrb0duDbuyhL1yQkuLru8o/ayFurYeoXc+gqjV2/SNz+PJtgsWQVtDGKHDXLZdLsmeadnnCtet7Vjn3twzdV7CPHtTlohFmIZirkx1P6HyfU8QC4zSi6ca/tLvct4Jb5vQYmVGSNJqdtRt+RUcG9+Ge/G5/EUD9cw8YYewE314/nbj3VqJdxWBV9rEryLQfy3EhL0SMAT3bEd259h0mPt23syTeLvFC9E4Y01yq8uE92RxawqTSr9Af2fPEZPbwhe+Pvw9r/dMClX27XkH/0rbZ++O0Sl6UhhrHdmirx8c42L8yXKzYMXqoUY1Vhljnuqt7g/XeOjD3bQ3RdGEaJXwoT8f/aaL8Fz/cIXviAVD4UxrbidzWal3LzwSfqOFVD+Dwa+/GaT64+2ZTffd2wqpgnVvE3ltF0hLhvtS4e4Lu+/t50d2xJZusszSj5McI739e2aiduJbAeKihbvQ8keQU2PQWoET4C0oE1XdkTzHOxGAadZlIqQjudtmca2AV1Y/o0AeR9kCONNzWtuATjda2zdlvs8Ae4EYNt5e/uxW8BPqtcpKBuy6B9UBGpAfUijNqbjjukoQx5BbL8ym1aHyKpHqOhj1AJUNyAf1ZiM66zENFwLPKHqLLxNaoGcuKZrO5zuN0+GdoJre1t+f7v3uZ7JzdqjzFUfxK0PkrHDwv1o1/sRE2YiS3QkLpKMXCUau4UmKBGCmioWLB2PTLNFttbA3CN8UouGKXakKGXTVLNJAiG+IsQV5BnXfp/tYXC77bot/++8vf23QoRBFBD7fhPPc6hUMhJoCcBVLuekDcPOiMZKWKaH64ZxXRPb1t4tGXrHUBRvF1gzjHZvWQqhsEUkHCUaFcIdaeLxThKJHpLJAaLRPnlh3Ruinq1YrvD1C7P8zhuTvL0uRNS3P0MHVcatEoPKOuED/GpuFyJDdjtgthO0iWzaQr3Jf372G3yt0ODC0Kg83tFmg0izQVdzhZ7adRJCHa40iVpvEbYh0goIt9jYhpDdnthGZC/2K8STJwgd+ji6mFRuhFeex7n1DZy513erWd5liLNg60xVFIyhIcoPPs7NgWNcDXdxvgrnZ8vYB1xmFa2CGp5FC82hyX6ebCzM0cxRjmSObPVD8SGp4nc3IVTiNo2eRb/XS1PUcQkQdujQIdk2KYpbnycImGzYvFWu8dzqEi+v51nxI1u+T1vv3W+RLk5wdOo6T5+9waPzS/QePdIGWw89JEVU3q/5bsWucDV/lcvrl7mSv8KV9StMliY3fnO7I+XrHG3CiVaZ43ZNgq5+9w5LcyIDL7JnVrI9qa8sQ2PbvFe8ittznNbQvZRXX6asrFFKGNSi2gFy+yqx2DiJ+GmS8XtIRI8Tsgb4xxPz/PrcIkbgctj16Z7Pc2P9FnV9CcdaxQ6t07QKBOr+8d30YUxLcjzeyzEzxXE9yagWwfJ93FaL2nqVWr5Ko1jFa9moiqhsdiV9T1OPofAMgd9eaFCVEnHzt4ny+wg+RwtDXlelDQKutEX4oCNPkre4hzOcpClYFpLi6XAPV3mIc3QHqzRWTfI3olLts31BAC3kkT5cJ3W4hhF579c9T9FwVVUSyZvSmy9AlF/aqoqrKAiBVVdV6PQ9kgS4ulgIVmTdUsVQmbQMZi2DZUvH1TWShjAS14loGmFJFReqmkKJVgBNMfZ9Gwzt3I0FanRsO4QjPBA9UW9l4HsG8XiObK6fcKKH8wseL080WK6oNL0QTdfiRH8P33d6lKePDhEyYyh3xfU9OERGXcjoCwEN0YtI+1FO64cYcTrZwosKhMbT0jcslCugPP+34Mrvt+/Tw/DYn4HH/iyE2vNk2/W5ulTm3GyRs1OrXJ2aY7UsFtZ0BG9JmHHsDBOH0/4tHlKv8ohxhQfUG8SUA2j3D/wx+L5/DP+zg6//0ePDAr4+8PB9GrfOUHrjKyhTL5FuncVUd1/Aa1qca4lOvm7ZPKe7zIqL7AaV7GT2JE/2PsEjHQ8TU3uYrTeYrTeZb9os2i6Lns+yr7CKRuldVNF2RqReJVEtEq+WSFRLxOV2mVirTsxuEfICcvoYveownVqGrG5IEY3d0cRSL2OpFzCDC9RKsxQsjUafRjUXohKPSZGSttFreKOFUBsWes1CbYRQGiECO4wbhGQNnRQ4EX0g9t1ZAv29RCDUxYIGvrtIMVRlaijD9e4MliOEG+p0VMqcnr7B2PQNrGoZN/BRfWFwGkivr3AgfJI8DN+XwCkQV6GNi5wa78Uc+zR6/8MoqoZjFagbr1JTX6LZtYQzELQRzc4Qvj3TCsakgjmhYk4oqBWlXROkaXKiJcQDFNOU9DtJvQuHZd2OEo3iSHGJME3TpKoarDXCLFRiNJsJWV8o1ep2TGxsfOpKhY7yDY5Ov0o2fxNNrKiJEKDvVJr4qE46vEKouYdCIjj/hz+2IQP/8XYNyQcYwtBybf0brK5+ncXFqxTyIrslAFcXnkCkO0KMEZsT3ZGREQkwDgI8YpIspJZFv9l23t7crtcrG9sC8L3/oVsIdgigZpoBlqUSCplyIh6NxolEk8RjQgymC8NK8fLVBT7/zhxnqxE5wZERBBw2qnzqaJpP3TuE4jZ3Z9TKZerFIq1CgaBWQ3dddEesYNfBa8giflUuPthojo3qq2h+BNM1JXASQEsCrkaDkPi7DzDUeDfm+KfQex9qe0rJ9acWijeFYi6ixTW0WAw1GkOVfZQVT+P15RbPzda4WHRp6CEauoWn6zzcE+FURuWmHeL8fJn12v73G9ZVOm2FLluhX9P5zPf0EjmyzrXCNa7lr3G1cJWp0tSBACOkhRhPj+8CZILKGxFCOXeYPAkluU0wJuqx91IUBStlMysmiv8FRdET392ZMzTeEjTCt1i7eYuzI8M8f984F0fHWekYxdf2A/eM2pCWFc/kenk4FeNoNCRl3u/kU7kJsEQvANdsZYcwyY7IKhlGWwMcKvVJQYyxZo4RvkJC+z0UUT+MitP7w3DPj2LEHZTaQpvOKPyMNrNokm5259+NWIf0bFU+VAhgYIUlvVjWKIvJtxAN8B0UMb6KMs7gfWZfTEMKgVy1DJnNu26a1A6g/WlBwIjrMO46HHEcxj2Hw8K7LmhDgBbHqXmfxKVbUuiFkV5Yf5GQ9kb72Gwsmspemqrvvt1e+Hz325uLZu1tUWYAgRDC0A18zaSsxFjzIlRcwSYQ51eAqfmkQwHJUJvNKP6uWa/TqgrTbhfV82USSwgmyOcWH134vpsqaiyMEg3L/Z7457uy9tgPPNlE3bhYLJallJuLwN8+288DQwzBdtAGS/Iz2DaaK67DClZyhFBiDKsSYK42CObyeJOLBGtVBHZQW8jeyDxA+OiPo3kRyt2vsnTy30jQ1Nf/EyTiP861azNSsn1nVlssZo2Pj3Ps+Anm/CT/6S2haLu6lRXtjFv82EMD/KGHB+lLvbe5ibi+CDVyIfkv2Go75f6FsbUYKwRmb1xao/bGkhTr2Aw1GhA9ZRLJ3MQ4/0/xV6+RD+Ksazmq4V4qjsJEI8qk382VYIgLwQitPe6xMeo8oF7nYfUaD6tXuUe5hbUjOyvAqROK07JiNMwYNTNERbdQR57k5Ef/Ot/p+F/g69sc/6OBr8JSjclza0yeW2Vpsrx1fRLDykhujmO9N+hRzmGuvSUnWhUtwpwQsBDiFbEhzkR7uWCmWbQ6aRpZfC0Dyp3BVVhV6RcCFhtCFpuCFv0hk15TJ1evweI8rYVFWkuLUoZaqIN562v4+QJBsYhSKqNsSFNLX7JwF9X+xwi6TxONd5E2PUjO0EjeoilaagInvJ+XLzIQNfHRKwrRokJX0aXfaZAxmrtMUfeGyAb6gVDyaoMx4fvTCqJtHyA/Qp0oq3onS3oPS2o3eaWTOnE0T8Vygq0WcoLtzNEBUbMUVuM2amOCjuU30ZxlorpNp9VWG+y0qnRZVZJW80AmXd0+QcX5MRztvh0fepHAuUYhWOCcYrPoBbiBRiZWIZVeJZYtYnVXUCP735i2AuZkG4iJpi+I7Of7u/oJwRNHE5NZQ0rwm61629dKRoAWVQn3RwmlXPRgFVVzUEXNhh5Is1V18BTqkadRj30KdfihLc+tDyKE/Hip9A5ra99gbv4lFhcaFATYKnRj27snoYJeLUDWJuDaS/P64N5TIFURDwJptVqBSmWdWq1IvV6T+5tNh1ZL/I0qazDfbwiAL021XR/N8dBtG8u2iTlNEl6LaKuOWa0RLtek6Ihp27J9EEdgZ5bJ1jUqiRhuIokaiUqApMfj6IkEZjJFKJMmlMkQyWQId3TI/QJMabEoSjgsFw38hkvt7WWqryzg5Zvbq7jHO4g/3os5cjD1aHK1yu+/c4UvXFjk+ur+80ywcI/1JDk9kOT0BoXwcC5GrdDka796iaWJdh3Liaf6eOJHRtE3aIV1p87N4k2Z9dkEZDcKN2i4+2WixULXUGJoV5ZMtGx42y/oIIqiYKFsZsVWN+pyrGaT7Ooa3fl1egtFwsvLO357G5HNMhWLsprLkXvm43jPPMDvLdzgrVKVlaADz9y/wBFSfO5LRHk0leCBZJRho85C+Xo7o7UBthZrB9fedGudjHlDHCr0crjSL+mDGW9TbtsnlvomCfdXpViIPDcOfQzlM38POu/gFyoobJUFgvw07sR5vNmLBCu3UKoLqE4B3WjIrP5ssovFcI8o3sUMr6Nu1Em3wcgGgNlzexdgEfWsW/dtMzt23r7dZH7dVZhzVObsjeao1PbYZLRfKiCrB7J+TdaxmT59hk/sgxv6/gcMFVUVCnyxDSqeoNtF0BwXrVFBL6+hlVfQXQ9N1C95AbobUDOTLEQ6uBaKc0bXmWpVWG4WaZew33l0U/yAZB3S1Xbr0BJ0Jnvp6hwh1NMkM9/g+KWfQMhGNYdu0f1HnyAWH9013gtLJgHCRNsJikzTlKbFXUOjvLyi81/PLLBWbZ+vAox+/GgnP/7IEE+N52QN6e1CLNQIwHX+/PktRd+YpfHQiRHuHeuXlkHU1qC+1u43tv3SCkFxBUUov7KbflwIYrzpH5HtDf+YNJT29tCtM5S5X7/BCWOCU851jq9PEyr5eC0Nr6VyPaXx4mGVt/t01nWxaK/iH/D7+dHxH+UXP/KLfKfjf4Gvb3N8t4MvISO+PFWWYGvi7BrF5fouMGGOxQkdT8NQlFJYkXLr0s+q2WKh2ZTiDncK4aOkeQXw8hhegYGQxalUN49nDnM8MOkqFYiur0nZaHdlt9qf3F5fFxbyd/2ZRLZFy2XhUBz7sILd26KWKtAMraDspXcECmatl3DxEKHSYazSICt+g6n0darpawSpW2RNl7TeppmFmj7Rhkek7hFpeERl72K9i4DI5i/rdnPvBbWDq+FhziePcDZ9nMvRUWbNLiwPCcYSdZ/BVZeR5RYDqwJ07F4RDSkl+qyL9Jntltbmtl4riHVJc+Kg6xRN7wEqN3LY8872BPNEB4X7fJ73XuXFuRc5t3pu16p7LHyY3u7vpWUe53orQthbZIxrjHONMa7SH8zuM/MMHJXmagJvMUkwm0KbSWJUdUxhHCxply1ZHyd6Rd5uYLhiYv4Bi7AYxlbGQvax9uRcix6wT25v7N+4X0zQ/ZBCoXWGpdXnmZi4wtpaTNIJ6/XdppKapjI4OLQFtnp6em7ru/XtCjGEi5obr1rDr4lWxa9W8aqi39gntmtV3EqFVq1OTWTQGjUadpOm69HyBSVJw9YMbMukZVqyt82N9q2YmwaBzHrpro3qOpKRKgg8rqQJtV+zZehEwwY9iTCjiSi5aApfM6gJMQ7PoyJEHdbXyb34Iv2XLstaERFzfX1cPHWSUupgs08RAkDtpDbuoz5GY4RWAoKzJexb26u4wu8m9niv9Lux/TyFwqvkCy9TyL9Cs7UgH7Na7+Ct5XtZrucYiC8wnJhhMD5HNnOazs7P0pn7NKFQ79Zzep7PG78/yZmvTMvbHX1RPvUnTpLpOVjZ0fM9ZiozbTCWvyoBmdgW9WwHRUeo4460RWd5WWa0iq+8TPX1N1Dn5/c9j2ACFPv6ME6fJvf00xx69FFJPfrt324brT799NN87GNtQ1PxXr4y+wr/bf4671Sa1LUhfD2N5i5JWXsh3qE7U2jefglsEQNWP2PBMIerfYysdHG43k/C25Eh1lXM/hjWcAIrNoF16e+gLJ5p35cegU//UrvW44CBNvA8KfHfunmz3W60++bkFA01TiMsLB6y1GNZasLuIZql5WYJvN3nu2ZVMCLrGNE1jOg6RkT0+fbtiABnd0/BvesQ613iNPehuAHIZgUo81TmPZXiba7BaVVh0NQZNAz6NIOeQCfcUvE8QWYXep5CBEtDFXS6mEU4LgSYXOYLVWYqKg3fkIuJ4vrVyzpjyjw9WlV62ImFV3FHIwizQBfLQSde0BbVEvYivcEyg8Ei0UD4L7XZ520gr1AnRtGPkFeiKN0DNLu6mPNbLDdWWa6vst4sSPCZKwY8eC3gnokAS2AIH5o6XOpXmOzViXR00991hOEjjzDUd5JspFN6CypoMmMkhBzaYg6b2+qO23exDGTXYEbUzj3fbnssSFB1gr4HsRPHWb55i8krF1mwdVYUnWJMoRCFQhwKUYViDEpRhQP0ofbFE+X7+IX5n0ZD40zHNb58z9t0xLKyBk0sqnRGOmUvmlfyuHb5mtRPEHPQnTWeR44cpRHr5fOTHq9OboO0vmSIn3wwyw8fC5GlIsGTV1lmZeoKq9NX8CsriKXiMHUSqtDEbqD77411sBSkedU/yjeDY7zlH2Xa378oE9byxEO3MCJTuNFJ4rU1PnM24GPnA0kZF1E34bl7FL7ygMpSpv2dRfQIMSNG1Iy2e2O7F+2+rvv4zPBn+E7H/wJf3+b4bgRfruNJBaXzF9c4P1FgJfAoR1RKEVUKJzQ6TLmdFzLwd/F8KV2jz9Lo86r01ufoW79I39o5+ptL9LZW6G4Jvw6PQtNktWBQWzHx5ywilfc2OdVSKfRcbqNlZa9l2z3ZCI3YGjV9mkrjCuXKORxnv+eM4E+rygCp2EnK892ULg3TXXYYsgpoCPPnbRNDEb7aopG6ST1zlXziGtPRKZZ9jzVbo9FIQKWLeOsQR8JDDIVsOpUiaXeFeGuRUH0eozYvRTzea9QDg2mtm2uhIZlZPFm4xnFvgZRaY90ZZt4+yYJ9kkX7GK7U8NqOcMihd1Cn/2QvPSf6sZZqVF+cx90E1ppC+YjP13re4L8Vv8xyfbdMbb/Zz7g+zqAzSKQSoVquUhMTdzHBiqWYS+eYS3eylMxgKU1GucE4VyUYGw+uY+0T8lBolXqprx9mKX+Y68URnMQAx3sCHl4+T/bFr+Dc2jCbVQJCGYfoSJhQwgG7iO8o+I7a7n0LP9yHZ+bw1YSsu5aAQoCMugAXtQONJN9riBW19Y4Olru7WO7qktub6lwbH4oO16FfKICaBn3RKGY83s6+7AV1W0AvJgUI9sq2C1VHr7YDHEmAVNsGTRuAad8+0epi3+Zjam1J4w8whAqouAA2rHarmSr1sEE9YlIPWTRDbUDm6CauFscNsgSkpB+P+Ji66mOoHsa7ZIzvFALECirkZhNy56K3RP3mmTME587LrI0hwP3Ro9Qef5x6NCKzf5tiImL7vUSnnuJUMMRgPYO2sdjhGXWK/d+gOPAN3FDbZFxRDJLJ+8mkP0Im87j07Ftde5aVlS9RKr296zmFUl5n52fozH2GcLhtsTBzeZ1n/+1lGhUH3VR56sfGOfqRnrvOkgrAswnI7kRbtFSLw+QYzmsMXC8ycHWdwRUI7RiezNFROHGC9a4ubpoGtwqFfQqZgqIojr/InIkQtduPPvooc5U5Lucvy4yWaBfXLlJ19ltQyFoWoxfXGMY12034PJ6qmJwqetxT9DhZ8kiEDKyhBOZwAlP0vTEUMVY9+zfg/H/aeMMxeOovwaN/Shq8CrEWZ35+C1y1bt6gfnOK0kKZup6kEdoAWRtgqxXKSFXe24biY1m2rLVxnTunklqmTzXqocYCTid9MmGHiGUTUetYahFHnaLOHA1jmUZ4Dd/cD9aUuoI5a2BMgnnDx5xSUBt7vApTw1jHfgC96wRFrcItc4prjRe5qV5lMuduTVT3RqIWSHXFkSVkP7Si0VGx8IWYlWAcaJYco9RMkrd7R/lSpJ+z6rZA0ICzzI80XuABc5IbsTGm9W2rkBzrPKRe4p6xAULHv0f6XdWXL1AT3mTLl4isThD1DrbGqCoKN0xD0i2FJcBiNI2THacrM8ZhvZvxr98k9YXXUPMHK2FaY2NEn3iC6OOPE3nwAUl5/6AjqK7infk9/AtfRF17Gz3YvYjgOYqsYRP+Z41WF9rIg4ROCS+te6TQjJJMkG/mpT2F+N3OFi5xfeH3WKpMyUXsim9QE8DUbvBw6QS/MP/HMdD5Zvwd/n7fv8G9TV1e0kwwZGYYbnWQKKZxClFcId28eWxUhyFziWHvBkPOBB3UCb0PFUQBvwqaRl5TKagbvaaxrqrMBN1MOGMs2KMUmodx3G1hlc3QjTUi4XmS1jK50Ao5dZqEt8rhGY+jVxJ03mxtlYg7fZ04f+AT6N/7SWLJ3BbACuvhu657/U7H/wJf/5OCr7rns9Cy21mqls10ucn15Soz5QZLvkcprMpC0zuFKeoBBB3QNOjBp8dp0VWvymxV59oKuaUFzOUl3NWNbNXaGkGrhWr4RHI2ka4W0c4WofRuNQFxtjULBpUVk+WKxUzTpBwLEenqpXPwCMOH7iPWPdAGWgJgdXTIOiIRvu9Sq12jVDpLuXyWUvks9fpurxMRUu4+SFMpt0GkYXiEw2IStkdiv56muniK0FyGE615crpCyz8lmy8mkjsfq7hcjUzxTvgKF6I3uBaawlFddF8n28ySa+TINXOk7BS6ptORTjGYVOkL1elUSyTdVSL1OdT8rdvL3N7xS4lD1wnoPoWXO8WKf4z5tTTzN6ss3Srh7hHnsBTo0BXSJkx3XuPXOv4TK8Z2Ybnma3Q2OuludNNd7ybiHVxDIlzpxbkuWjyaxLcTXPFCXEDjclRhJanLLOcAsxKIHfGvcSS4SlbwEveEWlUxbwaSphhZ9kn7LulRi1Akj7LLfFFpy/zKuq1noP8h0N9dLS1wXfx6fT+I2ZkNkkBnG9g0i4usNkss6hbLsQ7W0p24+m5Dz1ilQpcwgl9apnNlRdLs3k+oIQPVVOXqs99y5e/lAw1VJYiE8MImTsigGVJpmAFV06eiuxS1FgWtRX0DUAlw1TShbiltkCW32/tEIboIofq1ufIq2uZ2LtLuO6wkcS1A98oszVzhS89O8PXZOFd6j9PqSRBkTSzVk3Vbol4x7lQZd28w7EyRc9ekz4PjmriOheNaeG4YxzHx72ap+DahixX9DaAmmjh/RY3EJrDZpGyKJqh4AqCJWrpobJl0apFkaolUuE5q4UlSM89gNjdtFzxW4je4Yk4z1VSIRNPEYzGyjQaJulBGtdp2A1GHIHkLP3wZT5vcFp8Rv0lznETio6RSn0AL+nntv86ydLNNQxx7uIuP/pEj0sT+/YSgLQqa4qUbL3Nl4g2uVW4xYRZpHeBPK95SnxfnSPwwx4Yf5njvvVu0RXGcbNveRVEUlCcB7CpGhaJZpGgVZV8Ol2kdILyiKzqHk4fp13rw6w6L9SWuKWs0QyO41iiONYZrHiZQdy8giW/oSDTEg4koDyQjPBTROXzuX6O89I9gw1fNG/tBGtkfoDWbp3JtisJMntK6yLgJkJXdymbZ1u2zofK1VBsjtooZW5V92GiSanXSURkl1exC3ThfhMJmU4gw9MRopUyaqkKlYFNab7C8Wkdv3nnhQ3h1xTtCJDrCxDssQokmqjVLYFzFVt6i0TrbriXbE2FzkLh5lOjaEbRXutDcdiYh8D2a869zpXqTs4GJ4TqEvBaq0qCSqFDI1Mhnmyx2OCym3AMzL+GmAGQwvBxwaDlgeCmgb13Ul7Xvn4538YWRj/DNwdP0WxWO6KvElI33GAT0Lc8ywEXonmM+qTJlGEwbOlO6QUGIGm1GEJD1fMZsm3HH4WjL4bjtMeC05T8OiiDWR9BxhCB7BD92mNp0g+JXX6Nx5p3bH2RNk8bBRmcnWkcGxTDbtiWNhhQmC5pN2YvFMOGZGX3icSL33rs1v9gpmy/MihvnL9C4cJ7mhYvS9H0zjKhLVMxvelqy18w9n0FI94883ZbvP/R023xZnEdOkYnJf8r8/H+QNd1intLZ82Mkuv8wDWFMbVdYL0ygXikw/uYR1EBjNXaVq9lfIeSUiToNEo5DynNIez5pz9tVLSXOwhn6uMgRLjNGne3reZQaJ7guWyeLFDYAlOg3QVV7W6OgqlTMMFUtSl6NUnLDNGwDz7UIWl34rR58uxPfTgtZ5V0fXbAaR7tCPDiU4iOHO3lkKEtisUbtjWlaV5cJ7DLO7Ju0Jl7Hr2zPRaKjMTJ/4LNE7z0qxXykt5ygW8u+2e6dnbeb+/ef+CF4+i/xnY7/Bb7+JwFfecflz1+d2QJbeefuVjY6FIV+Q6XHd+hu1umulOksrJNbWSI7P0tc+MesreLl89v8ubsIYZK6BZxyOYxslHCsgKnMYdSvodZ3+04JaHbBMnkjHOL1UIiLoTAnex7k6f6neazzGAlfyEWfo1w6R7lyQarK7Q0hiaqqJp7XOPD+zRCDnVh5joRHiERGCEeGZS+aRpaVN1/DeP0fky09i0c/Lf8kBfcp3OAohrJ7BuOoHtfCk20wFrnJ1fCkBGOmZ26Bsc5mJ3EnvuFp1Q5xrnRnhF+YTY9eIROsE2suopemUITJoxhQdr9rGHwEnvjzMPrJLQ8WUTgvVvbF+SfbUgHnbAt3QSfvQN6V5ci7om6UWY5P0rDW0dQWSS8kJ0mCgrUJrjabeJ+iN9UIhfkWSzdLLNwssjZXlZTVnbEahrPdOhPdBuudJl6ofeFNBgXGuM4p+zynWufIRZZQN6/sm5/OD4hXXVJll6QdJZl7CuvQZ9sXrujt61febwh54Onpr3Pl6mvMzqywvp7BcXYXJAsBiuGhfkb7xxjKZUkoyg7q3h5QJwHdRjaquIZfXMevlNv3NW08YU5+hxorKU4Si6FJ6uOObFk0ihs2aFgKdVNMfD1Kuk1Ba7KuNlhRKixTZiEosq7W25Psu8icaIom6WnZSHY/sNrYJ1pHuANTu4M8+EbUXI9v5Ct8cbUovbiqOxUnbQ9tpYm63EDNtziWafLM2Dof6btBSJmh1VqSZrnBDvAtKFKua+E4llSFlL3ctvD8JL6fwHOFcXRIzslbFZeWqu3OUr7H0DRnQ96/rR5pGO2xxWp0kSznSLdiWBhYgYHfKuPMv0Xk6vNYrdtnXL1EQPO0T+M+H3u8bbC9Geq8inYhRH75KeZDPyjkDzHtPL0rv08oyItVDwIxMRRvREj1mwaK6Dda21fOAtvBL5Xw19fxFhehVpO1ecLQV8g+rycDVo9lWTwcYzbjMcEKa63tic/OyIQyEoSNpcdIW2n8wJfS/ZdWL3GteI2WVHnbHUIsJeNnOGyNcDp0gvuCowwtZ1EWRHp6+/feUJqcS17nrc5rvG6cY9Uv4hoDuNYYjjWKEj5OU22bAG9FENBfKfHQyhTjKwXSky6hooptZiTIcu8gOmLgE9EUrHAZLTUFnRcxkvMY0VX0cAmzkSW+/iip2hNE9TG0uIUaFeJBGoHr4+ab2ELevbl9XVUMleLRFD/fD+d9h7Ab8Fc6snxCC1HJN6mst1t5o2/W7syAMMMa0TRYsQpaeEmkvsC8SURR6Fp6nI61tp2MGNXrXWepln4X5dqCzI4ZRhf1H/rDvHb8Cd5abPD2TIFifcdrKg6qtUR3bo10YglPn2PFmcHZtdi1cbx8je5igu71GLlaDFNLSzChbdRvNwOd616Oa26OhllET76NnriIojh7ZG0VOlyfftsjV4KOZZXkvC6301VhiC5YBqBHfYyEhxHfaDEfVdinSDCxca2ThEZFiqA4qyrWhE10UYj1tMcYcV8+FGc5kqFiRrYYDO26u41+swYPhbhT5/j6FGHPlvWfoaNHJVgTvpL2xITMoO4/MAahQ4OEQotooQX8rEvj1MPUn/pz+PU1QtOvkZw/Q2b5Kvoe1dS8FWU9YqJGWlIhWPeh0oD1ekDE9ckIIOV7pDx/y+Cj6d3PmvP/kw51lvoWWeOXUDaB756oK8oWgCrqAkTp5FWdFa+bqnMI7CHUHSCprtWZj84zG5ulYBa2vjaxGBsKwpi2ScgxCbtida6bVquPstPDutdFaw/bRqhlng4t87A1y0PmJA9ot4j75d1AyXewKxqFm1GKExHJZhGh6j7JQ3XSozWsxAegunn/T8H3/1O+0/GhAV8vvfQS/+pf/Su5gvabv/mb0vTx3//7fy+L0p944gm+W+PDAr4arsfIS7s5yZbtkqq26CjXyBbL9NRKDDQLDJZX6FqaJTM5gV55D+aFqiozUJvUP1FXtRNg6dkcemf79h3T/uVFmHoJJl+AyReh2Pbz8VQox3XyCYPZpEkzrmMYB00kxb53P2WbzQh2K01X9z309z9AJNwGWaFQnyy2vVMEq9dpffUfYN34bSk4In4hC/ZHmW79CCoD5AxNZpV2hqt6EoC9E77KhciNLTAWU2L0+X1kKhkSpQRRN7oLjG2GaRh0RyMMGS26lQIZ4avTvEqkfmnrMQvxe3k78jQ3m2l5/omfbjSwOO71c8ztw9wYuheMFX4z/Q0uaIt0VQ4xXD5OR3UA1d+dtg/FdfrH0/QdydA3niLZGZaThYUbJRZvFVm8WdpVC7gZFS1gRvWY033mdZ+CFvDgcIaPdhs8tHiJ8sW3eQWdM+PHUXsVHq+c5enCW9xTvUo9plJMGJQSuvTbcsz9k+VQqF9SukRLJe8nGj0iOf3vN4rFKS5e/Aq3bl1naSlo00Z3hKYF9PYmGB+/l9HR49Io/rZ1W04TBEheuwarG23tenvfAVLl4twRpsp+bAQ/NoynROHGl6WZptPZz6VP/gWmoxHW6mtbtBTpVbWx7W4qPN5FCGrGJpDaBFObGaqdWat0KI36LUgQb0Zxw/T4C6tFns9XaO6YaHebBt+TS/KkauO//AJfeOMmF7V+ZsP90ipic5X0ydEsP3h/H5881omhlmk1FyUYa0pAtkSrKbbb+0QTsv63C+OqQuQLFiyFZM1aKxGi+ZEu3FPD+FqKVkvZECYRIiQtHEfbAnd3UzR/uzC9gJDdIlUu0VEo0LG2TqJYQHVcFMeRQEiEF2sDseb9Pi0BxHb8HJUli/zSkxSWnsAp5hi99Xv0zz//gbkZismor6p4Qs1QUIhiKjNdIiOuMJ3zZFuP+rvA4UEhqJg9boJhO0d2OcRQMct4q58cHUTUGIoA65reVi/VDPyYSXgkRehQitBwEqMniqKJeqKAq2tXeX7yG7w0+QIzhTXirQ7irX66WyP0NjpR7DRW3cC4Q2JJxSVMgCUynJqCpoJqBmg9NzF6X0dPvYmitbNmIlw/S817kor/NGrzEKGaR7TqEKu6JOoeyaZQid1+/oKhMGnBROBxo2kzE1M5d6/wBNPQbZ+TVyt0CDXZSBu0+aIGU4ALaUkhPNJ8rFaAaftSyTNki/PFl7YIQmRJ1PjeKcToF2g+i5rNqu5SUT0qmkdZ86kqHk3pAaOAZqIIFU5f1AsGeL4v8a94L+3YFArxUK0V1NAiangBNbQgDaIVbf8YJkSUInYKr9lLuTGC2+zHa/XQdvj9zoThOTw9f5bvm3iZ8eL2gu71VD//beRxXuy/V9aS3i403+NIYZr7Vm5w7+oNjhZm0KXNTjsK2RBLw3FmBsJM9KrcShSouGVZMZf0RdZJgCaPtNz2NgCUT4fr0u2K2x5iSe/9/H4bqkZVN2kE96JU/iIKFnboGsXufy/QjlxoFuwaU9Gx0IgGPlHPIeS2UGQmqJ0R8pw6JbuOyFnfUPqZCsbIuyMEO/JlLa3KXHSOydgcRb2O1xzEq4+0W2MAgj2Lb2oLIzRFMjRBr3mTEWOSbq9FzvO2WtZt90nPp7Fskr8eo7pgbR0NI+6TPu4RH1I3WEriezIlI0RUBmrCgqN7EEWI5wh/OT20o1kb+6zt20LOXvRC1fg2RvD/04Gv3/qt3+InfuIn+PEf/3EJuITBsihK/+f//J/zxS9+Ubbv1viwgK/FV97hP/6/v0K6UpKZq678GtFGu9j1TqFEIu0aKgGedoKpHbVVYp+WTqNoH6R6nC/pgsXSGYrLz1IunKEebK/AbL0/AS5qnjS2FCdpM6RRj7RNcTU9RSx6SGaxAjo5d3aRuXmXZiPO2NgJvvd7v1dmdL6lKEzBy/+E4J1fR9mYWK+6o7xZ+RFWnYfp0DVyhkLO0jD3ZINcxWXCmGSCa8y71yk1Jwk3HLrsCL12nExNI1Lx0St19Hpd1q3Ileo9YaUcMieqJPsbW0mNabeHF/WPkeABTjdH0TdmcTetGf5L9qucTV3nwa6H+PjQx/no0EdJhVJ4ji8FVuavF2RbulXGE27xO4+3uH4fMBrUwgo3fYcZrQ22KmpAPKTz0fEcT4Qb3H/zTdTnn8W+datNyehuyRbrdVC13TOLq5Fhns88zPPph3gteQ9prcinwlPcr9+k07mM37ixzz9FZB8SidNbYCyRuA/DuP1vznFsrl17iWvX3mBGUJJK4jK4c1YZ0NGhcOjQCMeOPcbg4Iikpe2KZgnWbsDq1W2AJfri9IYX3gEhLgZi8M8ewcuOkY91MGWGuKrY3KrMMl2elm2wMMc/WFmj0/PkquVfz2b4cuxgwYW91L9dmao9wErw478dyoo7Y6Xl8OW1El9cLfHNYkX66m3GUEgY8Kb4XC7JfYnIFm1LhOs43HzjFV5+9hs8P2dzLTbGitW1dX9IV/nkiW5+8N5enhzLYR5g3C0uVa5bagOzA0Ga6BfQzzZI/L6GWlawj/g0T4kWSIPyvaGqEUyzE9MYRa334894tKZrNOaL1FcLtFA2BEdMWpbV3o5Gsc0QzrvUs1maSW+mi/7efgZHBukf6pEUK0mDajZpCYGBykusN75JybsgKY2b0Sp3U5l7AH11jDEvj7K2hCtYCIWizLrK+r6g7YIoxsi2xHZ7SUeMIarrongumrt/Vt/SYboLJroUJrsVJrsUZnPbNNNdx0a+jMg6tCdIgW+2J9yBLopiyZR1Ogsq2ZJOR0kjXVYxHQPPjOAaURwrimuEcYwonpgwKSEpiqAFGoZo6JiYhBTzXc9bQR4vmVCMqKyndApJnUKsDSBFL2n0LQ+t1ORY6yKPmK/xUOoMcbMq1+k0O4lTGGV56QHs4hhJO8UgGj3vgjTFFUdwENojx+73NhVReKtD5+0wnJ8pUtqRFXs/YQSQ8BXZ+n2VR32dHl8IX0BDCOHcxQytRUBZDSipO3tf9qI1d3orHhARbMb1ZfqtSRqhNUkpLZhF8lYJT9u/4CHqeQO7A6/Zh9/slcDMa/WCG5EmuKIJhWBN5KWUAFUR+SlxLDd7ocQhLEosFFXUiSryHBCLXqJXNm9vPlY8pzA3F9RKIdjkt1B9W1LVh/JLPDZxgXvnb6BvLHTUTIu3B4/y6sBxSka8bbUiLFc8RWbIlqK7a5PCXpMj9k2GzSt0Z64wYsyR8TdBlSfBlgBd72fJypUS++3f0x7ix26fwAOi5Z9gzf4bBISx1PN0GH8L9SCPq7sM4dl4PRjhAke5yTDuDkZP2beY8DuY8jIUNwZLQ62TiM5iRaexw7doWrMowsrgLkL3IFUNpLqj6Dvj3fQdf5jeow/QGe2S168OJY11xaHx5grO4vYCia4sEImfIfqZp9HuO1hM58MaHwrwJQyVf/7nf56f/MmflBPhc+fOSfD1zjvv8NnPfpalpSW+W+PDAr5mF25S/fj33fXjg0gcvWeA0MgxQuPHsUbHMIaG0aImiqmhhnQUS/SaXKH8VkOcXrX6TSnTXSy+JWu22hSjA1b0g7a0qwBcHQWbniVhoLr7h15VFd4Kh3jNCpHvOYUZuofG5SaxRoyQFZLn1enTpz+wSahQy/IWrsMr/xzt+n+Vg7+IStDPO9Uf5GLto1I9Kqa266u6FEf2prE7Axh4Dl5hEm/tOt7aNbz8hEzH7w0/HMINR2gZhqw1SBQK6I6DGXfpOFYlNDhE1f8Rmv4jW38zyVVe6D9L5JFDPD34UU7nTqMfkCkSgisrU2UWBIXweoFFUSe25/jujIYSMKN5TBk+M7pPujPMx8ezPO4uM/bOizSef05KzEY6baLdTaI9NlZ8z/cazrQ9t/oekhLPhUtf4JtGPy+mH+SFzEPMhHp2PbxTa/K90Tke1G/S7V7Brp7F8/YX70ejY1vZsUT8PgoFlctXXmRy4hYrKwH+nixfLNakvz/N0aMPceTIR9rmsmLoq63uB1iif7e6vFBSAqwgO049NcBCJM5NXeOKV2OyMiWFD4QIgXvQOb75Ob2A/3O9yH21tsH16wP3cubeP0Am2v2+qX/frpht2nxptSgB1+ul2q68s/ByEhkuAbqOR0N39bvLL8xz4Rtf4aUXX+e80sP12BhFY7s+Jx0x+J5TPfzgfX08MJiWE7O7CdetUSy9yfra8+Tz36TemNz9AA/0hokfV/DdFtoCWMIqQXjXTaroK/tfR2AN77AJ42m04/2YJ8YJdQ0TsrrR1BytGyalt/Pk59dYVkusKEVW1DLengmKyB4IeemeaJa+TA89PX1Eu9L4KYtWqEGx+gKV4tdoVF5pm+pthF3OUbscp3oxRHU1jqsauJEYytAwSv8g9PbhZzraxvJugC3U/WTvU3dqlNwJit4kNaapKzPY6vKuGrTNCNwIQasXt9EnJ9SiBY6gAN79NSDkC9NjhZSvyj7tKVu3Y3eg3goD4KK60TR/e3sDTOwU9gssFT9lyhakDEYTkzwevMYT9Ztka1HMWo9seq0fQ2zv8d7bGc3ApeY78vktVKKKgXZAVlh8myuWQmdLuIntjrLXIB945DWToqJIKr14TGDXCApT+MWptseYBCUbJLitvg1ITD3CUP8j9HSf3sryF/ITzE2/SLW6gm/G8YwkvpnAMxJ4ZlI210ziG/u9A/cdX7VFxSxRtgpUzCIVq0DZLKApHt1OnM5Wcisb3sRhwa+z2NRJmD0Md3WQ6GkQJNZZ9me5VrrOarNtU7DvODntDJna6uXB9An+9498hMeMZZSJ5+DqF0DUPO8NMb713gf3/QTc84ekd1jFqcjM/3pjXbZNRsB6c33X/lp9lWG7ybjtcLzkcOSSSvRqCK++Of4HxHpbpMdqckGw5Ws0XJM1NclbyjG+6Z/kVf8EeXbP4bKUeEy9yOPqJR7XLtKvbKuLCvqjQEwHkSME0FrsCrHYbdEyNYTlWf9ik5EZofIb3JnFI74DcX2JdEC8G8IdtJoDrF1/hsAzMJMFsg9ekfO0rYyP8Bz0Fd66Ns/bk6vMNkxW/DhrQZwmJs3AlObaDQzhdkosZMpXrjVb9KslRrQ8/WoRfce4UCHCDSfNlJ+hHIQ41Zfkxx8Z5FMnM9S8QpudUV+VDI3NfqUwx9LyBOtukcp7sBbTBA0+3EFWy5Cqx0jmLTrsFGk3ScaN0aOXGXjkAXqefAZjTz32hzE+FOBLqFOJbNfw8PAu8CUKeI8fP06z+f4R/Hc6PizgS3jVXP0/fhHd7sDwBtG1nPwBB0JGtDyHXxJtFq88TyD8GQ4KsfKU6EVLDqAm+lCT/WiJfpRIHNXSUEJau7f0NigT4Ezu12UvmlBvaukL1JUpKu45avZVms48rifoje92iqlSKUxMpuPxoxv1WG2aoGlkUVYuS3piMPki/tRLaEIGdkcIxR1RL3YulMQa/QT3n/gRHu59RFKwDgRS5bIsnvWKxXa/se3K7eK+/Z6Qcd34iWiWR2a8Rnq8hiaWK8WFqmJyc+F+Lnifo5A6SrBx4Yyq0Kl69KhN0lYIXdsNxjxc5pjkun5d0hTPxifIRx0OZ4/wcPfDsh1KHeKthTc499aXyF5o8aT9DDlrM63uE1ZfJa7/Jn55kvUrMWrlHkInThE6dZKwUFk6fJy1isHizTaFcHm6jL8zVSFWtiwNrTPEvKhhK1RRWgF9nkqvq0oSwM4I0SC5doXOxgX6IxfozM4SzdnyerH9deow8MiGyfEz0HN6t+eW8Nm5/Hvw+i/D/FtMh3p4QQCxvk/zzdhxSsFu0DRoaXxPPM/Dxi16XKEO9w6NxtS+71bQx8rl3FazWxFynQaHDx3m5IlnyIpMyur1bbrgJtBqHix/LSPWDblx3I5R1mNZZqwwVzWfK811piszTJYnqdht8HRQCHNc4cUk2nBymOFEu/XGetvUP7FK+9z/Ad/8v9p/0PcA/MF/t1Wg/Z2Mm/WmBFufXy1yXhQo7Ih74xGZ3RKg63Dk/auLiWzYrbde49zXvsRbNxYlCLseHaWub2cBhUHo99/byw/e28eR7t2ZbN93KJfPkS+8IuXfS+V39i3qxCJHCK92EPzWFfR3qqgtRdZ5iLFAGKLujaA3jHtYpzls0xio4vburtPajJZrMlvtY6bcz2xlgHKzj46gg5yfoMM1ibtVDF+Yn5coqyVqe5RARUQCiy4/KZvpJ6gGEVYUjyC6SCZ9hc6us3ixRTyjKudpm7L2by/fy2RZnCM7fp9aTdLGRD2KGppv99bB473vxvElyOrF3wRarlB7PRggiRFNTHmE60YqUEiLLI2nEPN94l5A3FeJ+yKL9e7sCM93cPwaTtDAC1rYSgPdKNCXvEXaWMYUZsBBiGJrFLsVQ7dbaHaLqlplIlHkRkeZtVRAd5DhXj/DPUEHQ3YX0UY3Rr1TeiQd+LqCih1WWLB86l4Vs9kg2/IY8hOEdpxrmyFAk1woK82iWHFq2SF+8ckxXssZxJ2Av3y5yaeX3AOPlpxOiXoX3UTZsQAWuA28wg38yk1ozbZNhIUZfUT4z50GY3TLF1MxKhiZVbSkqH8KoVjCsH7DuH7DwN6xNOb8dWbcVabryyysF1hbq1DL2+i1CPFWZqtFnd1KvgeFoCEqlocatyhbJlNNm4l6i5LqS/Bb38icpSIG9w+mOd6vkE6v0mSGb159gcnGBDVj97V5M1Q3Rq82wkO9xxnLdZIrzdE5+U3Sy1fpa9Z2iUbMazpfjCb5fKiDG1qKQKx++NauXjZv934tCKP5ISFNiuJqfHT+Ip+bfJ2Rle0FfiPmkhmrER9otGuP1TaoFsDrHUYlCDsTjHMtGMDeU+OdcioMVxY5tD7P0fwMnY0i6YxJ53iW7lMDxE8Ms6CcZ7L+ddygPV7mog8x2vPTci6zix4nGBKblMj1WyCA6WYZhmBc7Ix4j6x/tpOfZPWFboKWjzEQx/6hQ5xdrfLKtXneuLnMdNnHPeD3F7N0yVJp2B7Fxv7F3kO5KA8PZ3hgIEbWXWdx6oa0lNhpxp4PIky4GQnEFDPKD93fxx95ZJCj3Ql5vtdfe438v/91qs89t02dGRkg+LHvp/XRB8lTa4Oz+souSv1KfUWarB+k0HrgeRQopNQEnYlucrHOLeZHZ7hTLliOJEc4lDzEdzo+FOBLAK1f+ZVf4ROf+MQu8PVrv/Zr/L2/9/ckMPtujQ8N+PIdnnv+2BbA0bUEltGNpXVjKl0EXpJq3aBcUais2ngTq1iLqyTzFeL5IqHCOqp38Aq9Es6gJvrRkv0SkCnpHtxsgBNdwYku0Yos0orNYUeXCIw7yHz7GrqbwGz1EGmNEXVPElGGsPReNMvcAHj6DqB3wG0xSq+cZ/6bv0Hj2pcZDBaw9lDUljSNt0JhltUsVqWH8ekYHYviYlqQwOu9iIfsFRIRkveCgqlnIiQ7ZokZF4QlYvvjWTmaIz/GbPT7mV00mLlRlTLSmyHA2EhXmF5RAyGKofcUYQs52auhSQnEzkevcyU8Ifc9XX6QH1n/JCOtPvk4T/Wpjgf0DgWEL/0K5uqzqBuFuPlyjpsz97FgH6eYGKUW7WmvpO2ISFwnOZRgNQRv1+s8t1iUGbbNSIYNPnokx1MphaHLV8hfXGCtGaUUH8Hfc0GKqmv0mZfoSy3Rd7yTxKlHUUaehNBd/h7m3mqDsEu/I4UBPFTOdT/Bi8f+GC9Ej/FW1d56b0Ipr7+4yqnKKn2lWySNBRKJVdni8TXUPT5ugoQVd2MkKz7JlXWShbqss9gfCqSHZBarlhpgKZJgwjC4iM21xpLMYgkj2NtdIMTr9ER7tsDVJtAaSYzQFRWKaXeRObj2Zfidn21feMNp+AP/GsY+wX/PEJeBS9UGX1gtyXa9vr0wJj7BI6mozG59JpuUBugfdBQWRTbsq5x//uvcsGMSiN2MHsKRP/p2HO2O8aP3OjzUcwuv+SbF4ht43u4Jn6jrTKceI94Ywryu4p65RePcOZyZdm3prlBVQseOEXv6acL3npbS0OI3vhmivqzVWmGxsMCF2WUuL1a5tuxxY91koRyVxft3E+L4DWBzSK3RqdaIqBUCpQ2o9tZT5YLEBiBL0eknCWHgqi3cyDJOdBk7vMJaeJ6rRpFLisWUr7DurVEPDgZacbJ0M0S3N0SfM0BfvZ9kLYbecOVKvIEigVW7KWiBqBMCIZwqtGJEHV/d9ai6LnWh0nmH89lsFQk31jCCArZZZNVaZC6xzFR2nanOOs5GHW+/4/IL61WebrRrj6uYfE15mEvqwwwPHWK8+xADsW6ijom72sBdreOsVAkatx+/m2qD5XARP6Whp9PktThn1IAXIyqXIxrengyq4QccLXscLzY4WlhnfHWWeHEat7kuxRcERXQikeb//N4fZSWdlX6Ff/LVM3x82SFq5YgZHRh71BoPjL0JDlWRMvpCuEOKeWyMS+ZAnMSnhrBGUzKDLMROlmvLcpFH0JXFWDRVnpLbC9WFO45JfW6MzivLdN4q06GksFIDVDtOUax1ojgRNM9C80Kod1G7JUrKBBArKtu0RtEqqo1iraIbq/T2qKR7PRZK08zXVmgoBTDXJe1wbwgKq293oDQ7OdLy+FxrgR91LxPfISpx3e/j895H+G/+R5gMdjMk7jb6Kit8/+TLfGLmLSKiHuqOn7Nt4NvuVdlvbQtzXzb6nftFL84ttQ3oBGVSMyPopoWm66iGLmnturHRTFHPbmCYBqapY5oGuhDTEc/RWEepzIEA/pU5ZB5VEay7AD80QrPzr6JoMQqtVf798jvkA3Fet9+HoObFwjq6FaJo+1TsoF3nKcRK1PbjhrNxjvanODmQ5sRAhkw8jCKUKVVBARVsJ42G43B9aorL168zMT3dXlDYiFU/yqSXYcGO8mO1CT5z7UWs+bZnoQgh+5/5iT9K9Mkn91mrHBSO75Bv5Hdl0DaBmewL86xUFikodfw72Jb8wOEf4O888Xf4TseHAnz93b/7d/n1X/91/s2/+Td88pOflDVeQr5WUBF/8Rd/kZ/7uZ/juzU+LODLttc5885P0WotyHqIO4XwpzGtLnwtSTWwWG25rK4WqU2vEZ116J6GgTokIgFeZ4C72boCPGH7cRdzScU3MJpZaVwcyR8junovhp161xowSdGwq+3Wqu6+vavVCFoVAqchJ9yhDptQt4060CITt9mroj+j61wOLMqrFonrBgPTGno0jpZuAynR66n0FrCSTW6n5AVcbieT++RoZbSq8Na/gVf+GdRWtrMlj/9Zgvt+ipUln+kLa0xdWGd1ZneGpCNpMNYXI2uoGIUmfnn3KrwAXlW1Tsprr/YHpkL80V7iT/ShJSw5IBaW6ixeXmTxzXdYmPOpuG1J7J0Rri+TLE1Qc4tcjoR5OTvMbHK71kbEoUyYZ05084RRYez8yzSffxa9cqUtpdvdIpRycQOTJXu87TPmnmbJHsPfk6WKpS16x1P0CRGP8RSJbPju6J9ChEUcR9E2srOOFuP6yB/hZeM0s4srqMXyrrmqpyisxVMEcRg3l3g6eJ2se5VyXAh66NjW/lXAkKMS99Loag8VvYcJLcVZv8Gt2ryc0DRv40UjIm7GJaDalcVKDjMYHyQkVjK/1RD1hf/lp2DxbHu2JjyMPvoLu7OGH3CIIvy3y3UpmCGyXDPN7XPQUBSeSMck4Pp0NkHO/O9D9/Bch5tvvs75r3+ZiYsXWM7mCIZ1cj3rHM3cIGnt/h2J2s9M9AGihW7MK+C9PkHz4iVZV7U3zNHDEmx5pTL111/fkvqPPvYRcj//89KTZ67Q4NJCmcuLZS4vlOT2Yung86IzbnG8N86RTo2+eAG3dZVm/RKuPYOueOiqi1Fzic3pJBYThCdstNlVDCGiokA1lSCfzVAYHGItmaS156ciJtdiPPPMJlWrwoq1xoy1wLpx8Djf42QYbQwz2hxktDnA4eYAyZ1mxRvhitVqH2peQM0XjY0+kDVG7wYqhaWE1cwTaaxKkBUzbRJZi9RAhuhIJ1+9cpE5AjI9PfyxP/bHCIVDzFZmt/zIJlcv8ND1F/mR9SX0IEoz6ONVa5TLxgidTg+DrW567NxWHetB4YTWsKMraFkDN5fkqlHn3NplaqtFxpqDHGkMM9TqRdtxsWqqcCWpcbHP4kLW4JwVsH4AeOmzDB5IRnkoESWmq/y1G/NUPJ9eQ+OXsiGy1RL5fL7d1vPY6zWSTpgOP05HECfrx4ly+/HgoBof34D8eIvJY0Xm1EUJsESbKc+85zFpM9O+OSaJDMbF3/1dXn/lFeZj2+dCjIDh433EBn0K5WnWl/M0yoKul8FvpFCaCbRWDMOOYjjWgSJRu76TjZqzfXVnmk0ltEQzMiuFPdoZ2UUUdX+dXODrJFpxTtkNPmYvyX7MdmRmbNLv4i1/nIv+CLZiSgPgKE1iSoOYoRBNZoilskQzvUSzA8S6RohmB4mFLUKGSqm0zNlf+ydov/cs2fn9NPbvlhCL4eHHfx7VisvMbOPl/1vOi75d0TJN5gb6mRkcZDWX26Umm11dZXBmhr7ZOSk6pMdjGGGR1WuDOKF8o2j6Vi/BmLD8OOg+TW0L9dzmvqBRpDZ5hUozJCXwG5pDXbepGzb1mEfdtOl/8En+wI//Tb7T8aEAX+Kpf+mXfkmCsE2zS+HA/Rf/4l/kb//tv813c3xYwJeYQA2/cJ6ErjFg2gxrBQa0PF3KGulglZi/guUug72IawuA8AFIeu6to1gGYzFKwjlEMnUf0a4TmJ1dUh7ZzxdxV/O46wXpn+HlN+l8RfyyKCAvE9RFYfT7PA31MIoVQzFjkpIR7mjh9qxippbo1kr7sOKEbjGn9YP7EL3eZ4mYPbsolJuUys3at836N5mF26yFE/TLLTqmDkET5ex/gG/+YyhvKC8Jzvajfxoe/hnJ4a6VWkxfXGfq/BqzV8VEbft70HSVkUMJhnJh0kGAP1/BK7UnwmrMIPZ4H+GHusivNqXc+yaNcK+EsVgdy5qz9Gjn6DUvk4ks8HL4Mf5e8WPMbYA4+Xq+x8n1SR5ZusTDS1foq4nj5RPrbEjAFcm1JHtwM8RkTBGc/NFn2r5b/Q/heipLEyXmrxelgMfyZBl/D6ddgDEBxDYBWSJ7+5ogMUlYnJ1m4rXPS1XCWTuJJwlP25HUiighj+l0L8/33s9ieDeI7Gyt81T5HE9689xrrtKKVFk1yjSUPBYlqa63M8TK/oytMmmrTLU0Zm2TjtjAVuZq56RGSHB/u8UspErVV/6/bRAqQsjt//CvfqCS+44f8FqxyhfWSrKOa9neznqHVYWPZRKSUviJjgRJ4/0rTL7v9+cUyBdelTTCtdUXaTm7ZZ9t1+Ba8TBX1o9wJT/OYqmbB5av8bHZd+T5bG2oQ6qJhMxkhe+9t93uOYW2Y5xuLq9w5pd/jXdePc+tWDe3kn1MZIeo7MnubsZwR4QTvUmO9yY4IVuSXHx3xkD6h83PU3r5a5RefRb77BWUpf2MALdXQ79nkOgDT5O+9/sxwj245SYXFy7xxvoZLjeuMc0sq+Y6rQPEDsQMPmdnONzq53jjMEeaQxxq9hPzI9vvQ5Qz7gRW3vb2nQQcVM8m3FwjvAGwRB8L+6R6E6QO9xAeO4w1Nop1+LC0Q9gZxWKRX/3VX5U2GMKU+Sd/4ifRher0Sg3n3Cu4V87itDpwgz58tk18D6pRakaWCGJL2JFF7Ogijcgis/hY2kOcMH6IbrcLd76GM18l2ONxKGJNL3AtPC09GW9EZrD64jwy/BGe6n+KsdQYMy2Ht0o13irXZX+52thnzyHi0WSU//fk8IELEJuWH1uALJ+nvFLAW25ilHyydoxckCTKwQBGQN260mTOWuFC5Dqvxs9xKzRLS22P7aJ2dyA2QH98kJ7IILlQPx1mP3GtB9WPU7Ndqi2PWsuVrbrR1+sNlPwUsco0ltc+B6Vqr5/gstfFvH97mum+82GHIEjygF7U8+0VJTlIwKRuQFkJWMOjbFaohFawwzMQv0IhOkVLaLDvCT0IOGw7HLVtjonWcjhi20TlfEFte0EKCxZBc+9/cJvOJ+ZodpnnZp7jq9Nf5ZWFV7aVY4UoTbAhgCEEPgOIuj7DtsthV2Fk8KMMjXyaodgAOasDxfOkibfwkRRt8coFvnntBa7VVWZL/dRaUbTAk/V8WuBL1cFso0imWSHm1OU+L5bEi8XwzBC2ouLYrmyu7aAIcZKNekDxPDu3ZS9tI3xCQYtOivRaIbqP/iyqkSSozxJc+ftyQdoLVKpBmHoQoaVGhOSnvILqiOff+dw+inhOX/ReW8BH0LDF5xT9DsrhzmiEQltAbC23vdArniu3AcT6Z+fetw/mtxrhBz/N8K//Y77T8aEAX5shDBsFj7Rarcpar9iOFZjv1viwgK+Vls3/81dfoRpWWYtrrCc01uMqawkNO9KgmyV6WKSbBXqYZ4g5cixi7jEcfi8hxN4ErVnNgzmvYK2paHnQ8gpaQTRQhLrQewwxYdrKOm1ko7xYlOuLSyyUy7hWmFRPPw8/+XFS3X2oRozAE9jHwxeKiC3Ru1u3qRdpVV6h1HqWmHOJAXd3fY8YYqb1OHnGidpPELcfJ9hhTHjXoSltMGb6RJRvELV/A91rTxp9NYbd9WM4gz8B8Vy7Pk5XWF5tMjtdYfZWiUpx9wQr3R3h8HiKzkyI1abHwmSZ5cn94hiaodI9kqBnNEXPaJJW0uClm4s4Z36DTxd/g2GlzXevBiH+q/JpJkZ/kvt7uzj11rPor3wVvXGNWFddZreMyO7nduoqjXoON3EPytFPEjr9KNbRo21foQPCsT2W7wTGMtZWVqx3LIWj1JmcmGDi+mUmpmdo7ZlAJahwiBnZRpghzrbsvR/p4GLPE3w1dT/PhQ5xXuvF2aiZ2Do+9hxm8yJG8yIx+wpDRoMR0+dIRGPAcLGU/XTbSOTwlqqi6CORQygfgCz7e4pz/xk+/+fElwDx3nYdmPB6e5/R9HxeLFQknfCrayUKO1Tw4prKp7Lt+q2PZuJEP0BV07sJ4c0nhHjyhZcl4KpUBRV953mjEfUHUW/o+C+vEz1XpaAneaHvPp4buI+JZJuOKyKCxzMd8IMPD/PUY8cxNsBj0/G4ulTh0kYmS7Sri2Vae9Q+Rei+y4ja5NTRQU6NdUuQdawnTjx0MCgLHIfKN56j8rWvUX/rLdy1AoqVRLUSKCHRUui9A5BN40dMfFVHcaKsYHPLWuBGaEZOtkWravuBmiqoiHaGdCtN2ImSbKVI2klp7q76JpoXIaHECfsGumuAHaNlh3GDdz9nBfsvqgRE/CZhu0y4uU64tkiUArGcgXWom9D4GNboKOboKNodrtlivHXX2vTA0tQaE2euEXMsUoiJ6e3fixo3MXJhyPhUzaus+s/TMM7ghvJSGEQMIQvVJK31cbLLjzBeHSft7b/eenqA1h8hNtSBNRBH64twybnGi3Mvyna9cH3X47siXTzZ/yRP9T3FIz2PEDEi0qvunUp9C5DdqDX5dDbJXz3cg3kXFCoxlRKCEJOlSW4VJrlZmGRybYLh6TTft/w4WbfN/mgpNp6o2Q1uD8jKuNzC4W3gspAKx6d4F7UxaaXOUW2FQ9o6xoboSyvQuOFlueZ1Ugk2s3KB9I3S1CaK2kJTbVSlhao1UVUbTXGJGpCxoMNwyBgN4kqFiFbBVBxMkdXVHFmnZygaYWUczT+M2+ylWIxTLqg4FRe9JeidQuDh3ecDLgEtrQnGGk1rjfVwnqXIMvnwKhVrnbpZJtiknQWQdkJ02lFydpSOVlyCXDMI0zAtJqMFJsLzzBuL+DuEb5JOkv56PyknRT1cp6KXKCorFPQankBgB0RIDTEQHmAoNsRIYpiOYBmz+jUSSglN88jlPo3S+FFefX2VV2crvO0nqAhlzx2RaZS4d+0m927I2idxuN5/jBcSh3gtO85aZLe3XSps0J0QvoZNipW6rFAQYhk7wfIAKv+CEBl0GqyRNP86neo2/U9EPojxin+CV/yTUlhkJui8LeDWVIVESCcRNkhYOglLJakHhJcWsG5cJry2RNSpE3MaZIYGSDxyP7VUmPmledaK+a3nEWii1DJoumEeHOzlmSNC1VBvgzoB7sS1x9/dC8PwLeDnHXDfzseI+zyXYOU6wcybuI0wrt+H62eIPThKz98V3mjf2fjQgC8hqnH+/HlWVlZ2FfGJ+P7v//73/Hwii/bbv/3bXL16VSqWPfbYY/z9v//3OXLkyK7X/At/4S/wn/7Tf6LVavHpT3+af/kv/6X079mMmZkZ/tSf+lM899xzEgz+1E/9lHzufZLTH3LwVS3W+I1f+m3M+HK7xTb6+DJ66PZiAOJbb7miWFVBVx1044CMmANqGWStuChOFQJxIoFyF3NRtbQBxiQg297WBUjzEoSyw4RHRwkdO07o9D3S6HDvxP7SpUt8/vOfl948QoL2ox/9KI8//jja+5wgOtUlps//BypXv0jH8mUGW7s9rMQRWIz24KYeJp34DIF1Gt8xCJqunFy0AZ67C+wdHB5h9SUS+n/BUNu1Jn4QouZ9DxX3h/DZHmzFz6/qw5KQgncC8t7ty09NS5Ngq++oyCalSfdFeWe+xNevLPP1qytMrG7Xvwh2+k+nz/Ez/A5djZvt1/IVmkVdZsislLtLvVV4bDjGCLVCgvKlGvXJ0v6BWtexxscInzi5JephjY2hCBPYvcfa9mRmbOFdwJinNnHMIrZZkr2hlxhhbgtwdVDAj2ZpaiarrTwzKlw1Td4Oh7kQiVLZIa4g3781hh06iRM6iWsO76p30wg4EVV4piPDJ3Md3BML0WxMUCqd2WrC/mBv6Lownb5vA5A9QCJxD5r23gC6+I49oULnOLKJcUtw/t81Vq7Af/4JWL/RFjH55N9qZ1LvMvtWdT2+ni9LOuGz62VqO0yPM4bGZyXgSklqofUtGBO/1/B9l0rlAvn8y1Ioo1QSIhm7F4JCTo7wbBztzQr660VUqZO94znCIQphk3VT41q6jzc7T3Ezc5LCDiPRiKnRnQzhegHzxYb0PNobUVPjWE87kzWuN+n+ym+Tffb3McWF3jBI/6E/RPZP/u/SbmNv2IvLFH7j89Rev4Fi9UixIkUALlFQvyM8PGasJW5KgDUj+wlrjsYBGS09UBn0kxw2ujgSG+VU50cYjD6A4+hUKjbF1Tork3nWl6o4TTGdfffvTTGqmOESsVCNqOIRr+skGhESapLQngnfvtAU9JSF1hFGz4TQO0JoaSH4oMksk1ds4azUN+qxGnild6uncaR0tKEtoQ8NoN/7OEZ3EjdRZq30FZZXvkC5fHbnixNvnSa++DCRiVNo7u7smqgOnQjNcS08xfXQlMxuzZnLcnIeIcJIbITTPac51X2Ko+mjMnst6kcECHtp7iVeX3qdhrsNdE3V5KHuhyQYe6znSaJaJ/mqzcLKOuV6g7rMJjk0RJOZphql1iplZ42Gu04zyEthFV8poyiuVLbUA41Haif4nsrjZL224EVBrfDlyDne0BcJvIisuzridXPa7eRQEKUDkam4/XdaFSqEVFlWayyrFVbUCmWljqp4kpoX2nBK2inRL0Ce6MWwIbJTQrRg899/txCZMU8sFIRQvZDsNd9CcUPyOJi+ccfMmY9HwyxTttYphtaohNapWHnZqlaBmlmUx12CrR1PFbfj9Nf6ZUs4B8/TxBESYiFlo0xZvMZGXzFEbebBV2PDM+Tzpf00WbJ0qp106V0kjTRrNbi+6nKzqjNNAneP8nBPfZ2T+Yk2IFu+jheKMH/PY8yfeoSZeCdnZvPMVfcTVHNRg4+M5nhoJMMjIxmGVY31f32x/dtLWzgfB231JcJzL5FaeQPD3V0Pu6Z3c9G8lze1e3jZO85MK0a54eAeMDa+l4gpTQ5pBQ7peVI7Fki9QJEZ1nKok6HBQU73xUhbAUnDJ2H4xHSPuO4T1T0iiiutA6Qxs7D1EebMu7ZtEErT0rhZ7Bd+Zo32dXL1ansRPncC9c+8yHc6PhTg68tf/rL0+VpfX9//wqKoUSDd9xif+cxn+LEf+zEeeughXNflr/yVv8LFixeleEd0gwIhQNUXvvAF/t2/+3fyIPyZP/Nn5OT95ZdflveL17333nvp7u7mH/7Df8ji4qKUw/+Zn/kZSZP8bgJf62s3+aEv/4AcstsDLFvb0k/DE2CnrS4s/DIUgVuMtsHpzr8RsrdaU0GvSeEs9CpodTFYi0myjqsbOJqJa+h44gIcktq86FaAYfpYhoOpSWFTeTHYfP72c+9/b6oToNfFayloVSEBrWMZaUKxHqxkPzNFl6lKHU/TyaQyPPnEk2QzWXneCGlSIWSw2W+2nbfltqoi/4nb6sZ94p/avn9p6TyzF/+jVBk6lJ9j0N2dCXEUjWLnONGxzxAZ/wz03i9VrDYjEPUR9mbGzcNvuu3t5sa+po02/zXM6V9Br19p/w0mjcjnqBl/CNfJbmXrNg2TbD9gxQ1YdnzKXkBCU8joCh26SlyFigKv4/KK4fOaZ1PZMXDqqsLDg2meOd7JE0qB7Gu/i3/5i0SjM4Qzzl7tDfzYAMqpH0QZ/TgMPgY75PGdlRVZO9O8eJHGpYs0L1zEy2+vcG2GqIWzjh3dAGSnCB8bxUz4KOvXaS1fY2Z6lom1JrdqSQrOiKz9M8XKvRPfN3kMG+tEU7O4uXVmOhZ4TZ9lsrly+8LyIKBXMRhOHWao636GUyOSIihUj3QjwyvFOi/mK7xQqEi59J2R0jUeT8d4Oh3nyVSUXl2lXl+hWDxDuXKWWu08zeYVgqB1oHyC74/gukPYrQFsO4rjuDLDvwmwNtvmvp3DrDiHOzo65PgjFoQ2eyFKtIva2KrA7/9ZuPTb7dvHvh9+4F/cVtCk4Lh8da3MF9fapsetHedGj2XwPRsZrkeSMXmu/PeITauJggRbr1IovLbPOsBoRQlNhdHfqGBe8tHKO96bqkqAv0UfPH0ac2QYz3V5/eXXePaFN7i8UGbVzLJkde1SStwZYUPjRF+CB4cyG7TBBMMd0X0y9kKcY+X//sdSwWvTCzHzkz9B5qf+N7yySvXFCzTOzxP4SRRjNwi3FYcZa5Gb0Tluxeal796ENoO9Qzp+M0KaxZH0UcZjI/Q1I8TLLZR8Gbscw6524lRzOPWMcNZ9t4MrJ4e+Koyc1/C1ArVYgKu38PSm4ANtHEJXitJsCtSk0w16uz5O1vw0Me84Xt7Dyzdx1xu4G/2B/Ls7hKBh67kIRqeFUX8HbfI/YPq30JRlZjKPMvC//WucsMXKypdYXv4CpdJb21lOYeRbOEp86WFiyw+g75go69kwZn9MqrwJUQqzJ8a6k+fNqTd5Y+oNLq9dZs6eo6yLlcKdh0cBX0z2E/RFjtIdHiVt9BNS0uTtBWYa77DsvIMbFGQ2cas1sySdOAISvOcI4JDfxf3uCKmgfS7WaXFWn+KqNv/uogEioxNEGfRzDHgdZIP4u9a+NbC5pM5yxZinpWycY7u9lN9zCAqZGFeFb5xqmmjC8sRxcF2nLYmvQkQo5PqbnluiJs1tW79pAb7ezkKKBT7hB7WzN1QTyxC/mywVP8tq3WKpYpOvOthuGNuNo/hhEr4q6YyidYjzwrszWPQUj5pZ2AJkQkpfZMwCI0+nsspQsM64meZo14N0LV7FWZukhUmr73Fap3+SlhKSC/WbTYzb9Wad+cYkc/XrrFKgqNUp6jWqeu22oEzYGghQlrDbLWonsJu9rLmdkvK5HuwV6hHWBYEU8tgbSRoMxzxOdkc42RWiL2kRCoVk6c5mM0Ux4+8sEpQdtLRF9mdOYWTCbQr77Gtw63mY/iYsvCNFrXZFepig8wRuxzi1SD+VyVnK589SX5zH1VV8XSOIhlBzHSixiBR32wRC4rtXfBvNszEQdXmiuVLKfkYZ4BbD5JVtWrGOyxiTnOSa7E1pyPABh1AL/plv8J2ODwX4Ghsb41Of+hR/7a/9tV1Zpw8yVldX6ezs5IUXXuCpp56SHziXy/Ef/+N/5Ed+5EfkY0SW7NixY7z66qs8+uijfOlLX5ImvAsLC1vv65d/+Zf5y3/5L8vnMw8SV/iQgq9KfpnH/tt/X2W0/5Fj06hUKNGKbIkEjBvb8j7VQNNDaEYYVbNQVe1dQWD7toraLKMWZ1FbJfncchUy0YvaMYpmJSQoFHMl1W+vTrbsgELVp0/pIdfsorye4kzB5IIj1tK3I4nCo+g87sGD6xdJ179OyLxINFfBjO9e3PCFyakVRWluLoYocOz74Mk/3/ZZeZeQBreLizQuXJSArHX5HfzZcxhaCSvhYCZcjITHSjTHpDrIBIPM0iPY5rueJ6eskDHnCUJrrPk58vXjBOXDdFQHpfnqzhCrnAuJmxTT85i9Lt3ZDvrCvfTUG+Sm3yEzd1GatsrhP9qHM/gkTu4kTqDuAkK247DkK1zRQ1wPxZmMJGiJgt4dkWjU6C+s0F9Ypa+4iuU6ctIQjRZIJFbaE9fkKpa1O1sqotWK7JK5r1XTBLeZNAtwdbshV1hziPFoJyDLCePzM/8WvvJX2r5wmcPwo78G3Se3TI+/tCYUCou8XKxKmtZmjITbpscCcAl5+J2mx9/OaDYXNuTfX5W9LWtNt0O1DaybOuY5G+uqirbaVmgTIajGW0Dr3tOETp4SB4bJtZoUwRDUwcsb1MF87WDqdMIpE/FqUsZ7lSg72YX39Cf5gXv7+L7TPXTGby+MUH3pFVb+4T+idX1DkdeIYI5/BnPkYwS6wbKRZ9qYZTa9yM2OVSbVORbtJVyxBLt3Qq1kOWXezyHlGF1OP9FGGr+kUV5tUt8jtLM3VN8j4pQI1VcIV+c36rDatVimXWRq+HPMDH6q/bm9de7vnsAdzLAaibLk2MwtLR1o6RIOlcjGKnSHTAaix8hqJzGqHbhrLbxC893dQe4QipCQZwFdWSKIOLztK1Syc3QMzeCHbmyBQvk+CmMScMWXH0K3U6hRow2wNlt/DDViyN9Mw/Hkd16oOazXWhTqNvmaQ6Fms15tMrtWYDZfIN+wabgqrpAe32OuHsUmrTbIKHUyal3S9BJK68CEshhpHdWRE235TwBdkUcSE28BThQdTW03sT3i5HioNkxmI1PXUFq8Fr3Kq9YlGjR3P4/i73tO8W9zv6EZRI0Iw34vY60Bhqqd9NTSMqO2F4AtqgXW1LK0LhjxOgkh/MZqrKsV1pXqRl/BUTw5xqTT6a2WyWTkQpBo2uIi+V/5F7Re/jxmzMZKuChdOkrKJ6lV0W5nritWdDMjkB2XzcuOUI5qlLUi5fo1aQEhjND3hqbFSCROEY6eZq15mPOr/bw84fP2VIHWjky9ONyirmzQUOizFtHdNbTAJWaniTczstcE+rtD5qxqFalaeVy9wJGgzPHB00TGH8WKWYSiBlZUJxQxCMUM0IpMTf0T5hf+s/xrVTUZGPjj9Pb9LJeXGzx/8zzvLF5mujxBxZ8nsFYIDFFvsf+1xSKA2RrArB3Frg1TafTgCBrRQYeSgKRSZ0AtMqQV5Xl6p3WyaGDxOft+EkGEKg2eNV/FUYtY2FstQp1eVuhijRx5YjsyVPI9tvPTUnFYgEEBqL5VPsQyHVxinIscIb+D7WMENoeYZlSZpjsQY6YqgbCNLq/lrcDY2ha9vC8Q/eZtQ9au3ave4n71BuENhczZ8FEG/vLrfKfjQwG+xAsLQ+XDhw9/u15C1pIJkHfhwgVOnjzJN77xDZ555hkKhQKpHbLBQ0ND/Lk/9+ek0qIAg7//+7/P2bPbVIfJyUkpg3/mzBlpDr03NldEdh7ggYGB7zj4yldWefgf/VfpG68oHh3hdbqjS3RHl+mMLpMNr6Orwkurn3D0MKHIEJo2QLNqUSk2qJZEa1ErN6lXW3hi8rDzgrB1kfAxIipWTMeKaphRDcIKbkilofnUXJeq51D3POqeS911aXiieXiiSEwupYpVNfFOXSy/jBVUMIM6RtDE8sVP3xdslzbFUWkPCOLsFH/hb22394tFfWlTGagEirbRpG3l1uOEGImQ6xVNfK679ZP4sIaU5211orldZPUBTujdPLM0zX3zr5BTbxDpEPz9nY9XaDYHaamP0VIewQmEB4aKFb5F3PhNQvZLW4/1ep4i+MifRzv+NIrwxJJPEEB1Zb831oYJsTiaa2Qk0BJtkn451O8MM6ihKAXWzBpzIZdAFZQcHc3XJNgStSuiN70QKbuXWDOH1cigt2L7MmOe1tiiKDpmCf8gMYK7CHE+rcZTzKU7mUvnWE5kpCTvZoiV3+56hcP1EmOtKofcFmGjLQ8swJdlzaLr06iaMPGd3Ti3d31qMMfxrRO0rJPUw6eo6xmqikpVyHu3GoRKBbSiEKBZp7G+Rq2QPxCUiSxtNpvlWKLGY3P/Cqu5ykx0iC8+8Q/4onWYN/eYHgujY0EnFKIZwgD52y4SIkUySjKjJQFX4WXq9d3mxqL+07ypYF5RsK4qGHOKpAgJVSzryLjMZkU2AJff28eN5dqO+qySrNeq296BdQqjuZjMYgkhjGNdMSIr15l48atMnX9Hnr+2ojOfPcFMzwNcqlpb4FRMaB4fzfL9p3v5zMluoqqKPVOmNVGiNVnGni3jOi7zxZeYKHyZmWiBuazCfE5jvgNsYXq1+fnE6rxY6RYZk2aOjNNNzukj0+oiVI8TtO5UfyUAVpVwfY1QdYFweZpwY2UDYJV2zef8cIDTG+D2BHh9BqHxY9jJz3H+uRGaNR/d0nj6x8Y5+pG2NLfnuKzdWmT1+gLV2QLeepNQQycVRLBkHcnBoVgqRmcUPRduZ7NE3xmRFESR7XfXm+0sWb7R3l5ax1sq4LltYR9PZAg6z1DpfoNa5lJb3WAjQqVDbcC18hCa0UMzbVLIWMymdeYVKDScNsiS4Gq7HVSjd7vQ8EkqDQmwskqNjFojpTQxbwMgGqpN0ahSN6vUwwor8TQrkQS6VyNRXyLaLMiJsa962EqThlajpdXlxeiB2nF+cvX7GG8OyecSKrW/1fEsv5d+bhe9VPdNYn6CmJckutHH/I1tP0U82NyfwFTEOCqu6RvfOx4lZ4GgtULGM+jx03T7KTENvaOa4mZUfZdi4FL0HeqBECCaJaZOkdGWyGiLdGiLxNXCbY+pi0XNGKZmDFHTh+R23RymbvSJE6b9oA3GzRbdZVNpX1tHMa8RmNfAvA6GoFMfoOTodaE4R2jURnlrcYznV6JMuWIOsvEbUuroyfMYsStYXoR05Tjp0giJVkpSL8XCZQiHsCLmGEIMBCxBd9wj3nTn8DG0OpZaxdQaaIqDRg2dCpZSIaxViChlImqJmFIiphXRtArzZosbhsFryiHO+UeZtUepNkfA25uRd1HD88RCE6T1dg3+mjNCqTWM3+qSGVsREZocU+cYU5c4pK6QUJpS7VFm7XY0PUjwPfZ9Mttao8kXzDOU1dvb/0SpM8zsFsU/TdvyYTMEwJmhn1l1gCV9kKqRwzQ0LEPFMnQsUzQTyxTXRNGHsERWLhRut3AEMxzFCsfQrTAzK0W++Mp55iZvYvrb37vwJot2DfGRB+6lf3CIqhNIKmS56VBuuLKv7Ng2KrN8PP9f+HjjK4Q2dAtu+T38svd9JB76cX7xB+/lOx0fCvD10z/907JG54//8T/+bXl+UUMm6saEwtI3v/lNuU9kvITE7U6gJOLhhx/mYx/7mKwP+9mf/Vkpef+Vr3xl636hxihoi0IO/7Of/ey+1/obf+Nv8Df/5n4Zy+80+JpemeDnf/03mCoPkm/uV48SDvYj6SrHulxO9oQ4PdDBsb4+opF+dH13EbXn+VTWmhSWahSW6xSX6hSX6+SXarRqt08TC6PeVFdECkW0+6jsU51hKQohpHqXWw7LtiP7JduVK/ZLG7fnaw2WWjauGNzsGQxnmrRzg+H6BEfKawy4Pp0EJEwfMwpeOiC4K80WjVCoR3r/iN6yejGtHkyrG8PqxjQ7UdSQBGabIG0TqO28LTwn3lx8nampb2Asnme82eBIyyYs6QJihVRwzaGpxymmjrISHWcuNMKakqZmO9Qdl8ZG33Qc6rZDzp3jieBtjmgzEgyIqeXlYIgX/RMsBhn6ghC9vkXDWqVkzVIJz9IUF/sDrqymHzDiOBxyHDptkxDDRJJPko59vC2B3GhhlpvYpQZ2oyVrJoSUvanM0qd+gU71ddQNGkUtyFBRcuhKi3iwgrVnhaxMdAtsTTBElb0XlW+R97IZvorhJCVFUVAV2zTFPc9p2miJFkayhWXMEa5fxrALwhlJrtwZ2RHM4Ucxuo5imGbbW8UQ3irb26KOLe8rvFZr8WqlwVvlGnPN3TQxIbsuaHtZU5eqomJSX/cDKq5H062Rda7T51/hcHCVMa4RZ7/07yI9XOcoNzjCBKPM0Y+3Q1lP8zwy9TId1RK99QrZWplEpYjutt9LIRJjNptjLZvkenx413OPaQGf6Yjzo0O9jMXeh2DMewzPa1IqvS3BlqjdqlQu7QagPhjTCtY1AbZUzAlFAjAtk9lFH3THj3K14GyJYAigdXOlemANgpCL3qzPkqqDPQlpuhwyDs4wllaWuPCNr3Hxua9SK7YnlHU1zPrY01yLjnKlsA3mLMXjPqPEEWuVkLXKvLkk6YPz+iqGFybiJCW4ijiCRtTejrXa2yE3QdiJy/Xid4uWXsZS6qRbNZKVAtH8LOH8TcKNZYwd9UeboYiJy+HDWEfHCY21RS+UwRQl7SqFwius51/Ccbbp/F65m9rbfwq91EdMVejqMEmHDLxCq71SdUCIxaiGbpMPKhSoU1LqFNWa7JtKk1Tao7+/j7GxRxgaGpUTin3PUVnD/uIvY1+8QpMh1js8it03qWcvEqjb1wyrPEhs6WESyw9jNkTh/8Gxhs8CPvMbTbhZbW4LwQlTU8lEza2WFr3hEQuqWE6FoFakWc7TrBYPzjAHCpoXBteipTkUjBLT6SpzWYtivAvHGsWTYGLP9xnY8tqk2xMY9iS6Pcl9xSg/tfp9nGi0F5ebisNL1lVe12dkPVNYnjNxeX6Ic8YQdU7vcVwUdbFC5bEZWdw+noGC1egkUu+lJ0gwaCnSRN6SflC7n18cA/Hvdn6DKiUMdQJDmcDc6G2/StHvoeD272pVv+PuCr7vEOL9iCycl1jGTy7ixVbwIgVaQjbcjVCyI8w1YqwIRoETxXdj+G5iA5C8+/FLUeFJ9QJPa+d5Sj1PpyLOA4W6n6LsdXLDHeUVo4dpU0fxY4TcCNlWiF47hOoK64MYTvCtjaEtfBqqOB9E9jOgqQQ4qoelF1GNFVqhBYrROVbDZVp6naZex9bq+DsWKEw3SqvZg9PqwWt1SUAmFl47W3UeLF/l/tJ17q/coqcvR+SRxwg/8DBuvJfi71TwCz5EFOxPxWnGVFqOu5U8qE5PU7l8mfryCq6u4RgGbiRCLAn96hz97gQjwbQEfTujSmTHIusAJe5+zivOyS2apGlie4EUEvHsJvqOxRCxgJ7JdXLyyCj9/f2yNnrz70LlCcw3fxn14m8iC7xECLbOE38eZ+yz0tNMLKilIh+8B+V3JfgSgOYP/sE/KGmAp06d2ldk/mf/7J/9lp5f1HYJCqEAXuLL+naCrw9r5st1K5w58+NylanQCHFjPc71tRiTpV4mS2IlYT9KsbQWg/FZDqeXGc/WOdGjMNSRJhIWIEW0Xtkbxra8dqNqSzC2CcpEL0Baea0pa58ODAUSHaE2IOuKkuoWfUT2kYQpKWFf+9rXePPNN+XDIx1ZHv3M96Blc6zYLksth4VmjaniLRYqNylWb2FXJ+hfnGJ0rcFo3affgU7Fx4wFeGnwMsKPLMATSc+70eVQkyh6D4rWhUOOpt9J1e2gZHeQb6bJN2JUmpsrMC6lhk2x3qJpOxxnmsfUS7I9qF4nIpVJtkOAqFf847zqn+BV7zjz7PfhOsEU/x/9GzxOHTcYxQ7GaPhHUA9QXQywcdUydbVMUa+yoleZM2rkjSolrUJRq1LSRV+hpFdpKi6+nZUDt9LKEraTHLI97vHKjLMk1RD7lBW6WZOr7wdFA1NSB65ziHm6qe0DW5vvLcAVsE71ZFM1H90wCFtRoqIFGtG6iyH8utbWBF8YzXbQXVcCD9HrnosZjxMZHiF6+BCxo0eJHT9OSAgeeCorUxWppjh3rcDqTFmqbu4MPWMS66gw4nyDe2q/RUxrT07nE6N86dAf5qu9n2ZdMai6PhVPGMf62B/A8GfZLeK1KslalXitwqg/yyFjku7wLKn4Ela8vP94eQp2NUWh1c2cP8wlY5w3oycphHcsoAQBhlB2ErniHUJAInv8kdI5vmf1RR5cu8QL9mMUSEkTzUo0QTOZxkt1oGc6CGezpCJR0oZGytBI6jppvb2dMtrbUeGn8i4ZsiDwKFcuSjVCAbaKpbf3iWToS7SB1jUV67qCautSRGeTPlgZO8H1ILpBHWz7aE2v76dwikhFjC2QtVmfNZKNyUzXew1RGzbxzptc/urXqU6sEiQT1BI6c5bLLbUq63EsITUtJ8hJok5iA2glpbrgnUQtNkMYpMYzFlbEAW8FijOEF2dIibY6hS5qJQ4IXzdpZNPUB7JwfIjMPfcxdPoJYn1D+yfSfiApgc5qA2elRmNhEXu5QJBXUFsH05hkGGDkIuidUYzsRgZLZLOE9YOhydrppaUlbtx8ncmJSywtNbDt/aqmmij4sTqxvDQ5O8a9DZeOwKCWu0JZZLiy5wi07YULp9rL9NIDvLx0H2/Us4jRMYdCHyq9qPSjMqzp9Ckq3b7CHsHVfREYKl5YpaY5lPw6626JVTdPIShRVVr76nAUX0d3YuhuFN2JorsxIZPKcspgNiuazmxOoxbaf6HIlFcZWJmglEiz0DGIrW1TVE8WPf7UjRaP5NtjpqvC7GgC454cvTGRZdlYgpLUjY33Ljba/9v9znFna397n+8HLK3NcfXWBeaWprYYG9FwjPHhkxwaOErI3Pi+xV1eA7M6iVaYQJvzCAppaWDcts7eHYqoyaYkM0ltqf/9n90NBNBtUdFc8jRZdarytuCObD6LfJ8SaKi4mokSChOYIVzDxNMMbFWnpajUvUCK/8jmtHuhKrlHe+k9xvb7EJ/iPuUmn1DP86R6nuPqra2FRBHNwOK6c5KJ1n14ToZD1lVMfZ23tF5+N13mVuY6wYbn2HA1wQ+vGzzolOgJqigiq+THaPoxWkFc9k0/3t4XxKgFaap+iroXxw3CKMG3NvH3dIemXqOuVTZAWU32mwBN9A0UGn6Iuhej7iVJFuH03DoPLE9w2l4l98DDaJ3fh19TpU1N7k+cQkuqlD7/eQq//h9oXbu29XrC3zD9R3+C2NNPtf21NsJ1bJzZMwQTz6NOvYS5+NZGbd921MM9rMZPshw5wrx5mIqr76qX2ztffr+RY41neJmjbIthTakjnIk+zWr0WDvbtgHQRkZGDmSt/U8JvoTfx5/8k39SFgkKTvHOi4nYnpjYry52tyFENH7v936PF198UR70zfh20Q4/rDVfB4X0eXHWaTTmmViZ5+xsnksLLa6u6NzMJ2i6+weJmFFlODHLcHKakcQMw8kZMmFnA4i1wVhYArPtZlmd+J5CabUhM2QCjG0BtOU6rfrts2W6peIoVTHE4+l1ho/08tQnHyXXl5TZsneLcsvhzdUp3li4xtW1KeaK89RXV4ittkhUwkRrFqGWRcQM0JIuelLw1xuEYw1i0SrJaIlMuEBY6ODeIRxPZ12AsI223shs3RbbRTsl1Rd1tcwxLvIR5SqPBTe5j3nMPZ5q1XAfK5FHudm4j6nyMGk3ylE00gdO8ETN0TJKYAgWOMG7GHfeLppKS4IwCca0KkW93Ze1MppSIhoUyPgFurx1etwShhujGKRZC9IUyFBU0hSV1DblY1MG2Sizbq1jGw0iikGv20Gf3Um/3U2/0kl3qg8zE0UTKmnpkKQqKWmLZtygSkDV86nW6jSuX8O/eAn16hVCV68QmZmWBd97Yz3bycTIYa4OHuby4DAXBkZw9DADaw5DKy5DKw69BeGJsvvvFKvMqPY2Q8ZZ+syL2JbLf+j5Xv5t7w+xENq9+h5WVQFf6G7UyTXrZOs1Mo0aqVoVITw1E45yI5nhaqYTZ48i6qG5GR64eoEHr1zgnhtXCDm7gYmgitkjAfYhH/tQgDMYcODiqg9uKcRiMMzZ2D1cMI8yzSGqSlzWIfbrKtlIiEjgc8/U1/lzV/8eCa9CnRC/rXyam5JWuj8qVpj1aIL1WLLdoknK4aik6YoQRr4SlAlAJoCZrtGvLDDknqWz8RaJ5hlUZTdQUottsLWV3TJy7Rqt0/eyNnaSW7EeLq+1DYtFW6se/FvrS4UlZVBksiTQ6kvSm3x/dEnX8aiXbAr5KpNT08xOzbO6WqBWa+E5mlRaE8AqLCbhdxnS6Dimks5EiKXCRJMm4YgKl9/Gee5LGMUlLCHVno4Q5NcJ9nz3WyEmqMkeWp1ZlroN3uks8GzHBMtJoc62+7NGvBAP6PdwWj3OqD9Ed7ODeCWEKrJ1G8I8B0YMvESRsr/E/Gwv5VaEauCgH/t9gsGrYD2Ab5zGVUepOak2ra8uKH6trdqpfN3Gdl3GY9M8nLhJl1ai2YjJOsbNrIegt2cy83TmpmWv6tuAq9ro4mblEea8J1GNw3TETNIRkaUy5Kr0rUtnuXHxHcKqxx/6oR9kpP+w/M5E7VtjrU5rpSEBpl9qQKOB6XpEfFWOM++WNRI0efFZa4FHXYhEGZZUxaynTK51GlyL+dzUHG4ZKs4edU+x8DM+M8GpW9c4ees6Jyauk65sL5j4isJsZw83Tn+U8fSDnGi1zx9Hgd/pN/i3h0xWQ+qWdcPpeIR7ExHZn46HGQiZd3U+i8nquXPneOONN1gTC1QbIeY3jzzyCON9Haj5m20a+NqNDQr4NSgK6vPu80LM6mz/CFX/+2gFD+D72z6PO0ONCz35EEuVMnP5Cp7kOqhUpNx9sNXLpgUUPQF8XaqKRlOqd73/MHWFkCnKG6o0gnVQhQJXHUVrkIlanMwd4v6uAfpCVXTvBtgXCOzzJN0CoRUdbSnCaGNF1kjtjDmjn5d5gN+tneRtf1zWDm1GRGQNfShoAQmjzOdGf5eVyEXerItKp3ZW0S09QHP1E3S4CkOKWKRcZkRZ5rCyTJ+yzIC6TFrZz27wAg1bGIf7Mep6N/mgm5VmkqKfkSDNVtNEhElyJCtF/Jp1l1bNedd50t2E9MTTGzRVBy9wCAk/NKWLmBHFCByU6W+grk+gO3VMzSPzzBN0/ZEfIXps/O5eQKgLzr0JE8+32/yZ7eyTDAV6Trd9KUUbfBSMsGSmiQX2nUImBzXbtplfK3FrbplmtUQYYXuw/eyCSXKc68SocZkxaZx0UDz44INSy+E7HR8K8CWKxkV26xd+4Rdk7cIHEeLt/tzP/Ry/8zu/w/PPPy/rvXbGpuDGb/zGb/DDP/zDct+1a9c4evToPsENoXIoxDpE/Mqv/Ap/6S/9JSmJL1D0dzP4ercQkssTq1XOTC/xzvQy5+fL3FjxcQRvbk+krQIjyRmGEzOMJKclOIsY2xQZRdGxLEHr690DzHolxS9ws5RXXAo7Mmb5xSr59QYtRaGlBLQUNvrtbaI6QUTDM1UcXTwOGr5P1XFl9kkYSX4QZ21Yb9CrL9FjrNAVWqUjVCARqxCPVkhEysSs6q5VtNuF3wpBI4xfD2FXNRpVg1bdIO2a9PoZckGKUNCL44/h07H/7wWlRhHSwVXqyjLDvMRx9QV0tT2Rq7phFu3D5IPTVLR70dU0IdMiYkBEdTADkTUKUByVwDU3LrTvfRWurjapqDWqaoOGYtPCwRHmmK5OHpU11WYp5ELIJaUb9GkdDPlpBuwIyWpApPnuS9fi3pWQwnxYZSG80UdU5je2a0GLw/PTHJme4Oj0BOPTEwyuLB74XDOdPdwcPsz0yCiLh0cp9R8iV9boXrZJzTcJr9qyUHtnxLwlcvZVsvVrJIIiuhZBihmWSnilEsGGEfy7ha0bXDg8ztvHTvHWsXu4Mbi98CPCcF3uWZnnkfVlHquXOS6yssK7Lplse9glE7jVCvXly9LTqhJMs5ys4GeqhM3d0sCb4VQtwpMe4ekAY8Ui4vQQSgwR6k2QMb8maboiVsb+MG+N/jTL6+sUVlapra3iVQ+2m3BVjXw0wZoEYwmceEBPZIr7nbc5pl4iau6eXIj5jchoWddUtOs6k+YIFw8d4eLQcWazQzSCMF7Zpl4UQGf/eSDmnv0dEUkdPN2X5N7+lARcgjZ2p3CEiWypRV3UpoqJesmWt8uFOvl8mWqxiV3xUey7r+vwRZ5WqeCLmkSvjubUUYMGSlc/87kRXs03WBG1q7J2BQ5HLD6bjvGpSJhuF/y6i1so0Dr7BexbXxfFVe0nVg3UeI8U0zH6hrGOjBF54ASRB8bQM+GtSbjIYlXXS8xNTZCfX6K1UpU2HKlahIyTvP2xUFwKkSbVmEIzHqIVi1MKhZkjYMXeBFAOlVKD+5d8RjaOyXXD48thm9uVn4k8yjgax9E4ttF3y9L7gFZslkLudZY7zuOZFcLhivQ42oxmM8rqyjCrq8OEw2P09Q6QS3eTiubQ/QiNirP93ZVbLM/nsWtisUQsLAWyltM1anh6FVev4RpVfG03iBVeYbEgRNKPkVBS0tsspkSI+QYJV9SRvvv3vWYqzEcU5iIqq5qLWl4ksXSLvqkLHLp1EdN1cDSNlXQHK5ksy5ks68kUA8uL3LdUJDfwcQyhpia/Ow979lXecm/y7MmjXB4ZYzHbST0UPtAKQiyWDCs+JyydB+IhHswkOZxJyUVpcT4IkS/B/hCLwWIiKsLUVU73mDwcWyJX2wBZ9f2q0ZvhhzJUMqcoJo9Sih+mGB6kaPZQUpKUmi75QpPqQoXEWpO+VsAoGkNtmaddz3MLjzO4nMHjLC63N6vZ8fl8n1jgETUVrLAQm2qhOk0Upy4pvRau9AMT26rSpBSeZzk2xUp0nmAH1a7H6uHp7qf53OjnuKf3nu05o5j8z7wGN58luPksysqGCM5GNFWd15UjfL75OC9697BMhlx4nU8cmuShgYBKuYeZ6W5KC2G6AlV6lJmDzzJy6EsYQhVUUP5n7+FlIlzivLytYRBtfpT1+SdoHpAFTgZVjrLCvcEKp4IVDinLpPQlktoSUeGt8y4RGFEUIVAi2yH85Ah2dISm1U9T6aDV8GnWHNkEONsEaZVKg3K5SqNm4zYClJYwFnj/9H7dVNtCI0JkJCpq+Q1CkY1+o22KkLT3te8z/CpMvdwGYpMvSKn3XaFZbV9KAcRGPto2wlbvAqh7LvVzv0XtG/8XxWqDSxzhSjBKa7OeUFzD43EOHzrE4OAgoUiIUr0kW6VeobenlyfueYLvdHwowJdQ0hGDygcpuPGn//SfltRCkfXa6e0lPqzgiG7SEQV9UEjNiw8vwJqIV155ZZfUfG9vL//gH/wDSbkQkvh/4k/8ie86qfkPIlqux7WlCufmSpyfLXJurihrLw5iE/bEyvTHF+gKz5ALr5A0KziBTt0J03DD1N1233BDcl/LT9Lw4nJfzTap2Zp0Yv8gwhBUH0MlbqkkDIWYFhBTfGLC0yZwiQYOEbeJ0swT1NeguioFJMziGplyhVy1SawVYDqiGHo3m1x89FbYoNYdotWpYedUvAz4aR8l4aLGhDFxc2sSojoRQuUhQuWRdiuNYDT3+wOJKjHXXMTQr2NqNwj514l6E6iBTb2WoFnKYrv9EO8m3NFCOfwUWudJguWbcqVTLd9E92fQlVmZvbqth5uWY80YZF5PM6UYrPthWkoKzU/Q4ablBC/pxYn7YWJeBOM9FyQLHrigp0ARn4IKRQOqmoKjKSiaQgSFzgD6bOhpQfgOtCJB36nGDeoJAztp4iUMFK9AaPEG1sx1jMlbKNNTUNxtlr0VIiMlJj9CGlkLUUweppgao5AaoxIf3FfHEa4vky7eIFW8IXvLLklpc23T7HsTMKXavboFoDb7FMVYglcVnZfqjjQynm/trhcTtL4n0nGeysR46v/P3n8HSZKm553gz7V7aJFalBatu6e7p0c2MANBkACoNbl74O1xb0E77vGMy3/W7G7PjsKMt2tLgxmX3CPtdrFL3hoIHgVA8DgAIQczGIxoraq7unRqGdLDtfvZ+0VkVWZWVnf1zBAzxMxb9tnn4RkZFenh4f497/u8z9OsctpzCCamx/92p8uv7vbpTkyPBRA+pt3kB4v3uRy/S6W4SeLcT1mU0MWeYWUsXjGlh8zXfEpBRrDjsHXrMfSpM1gLC+Tz8/SaDfZth70sZXswYHtnh6IYUW9s0Whs0GxsUir37qNFcsfBfReMW2VulJ/i66ee5o3WMit6lXiQoQ2T+wCu+l0diopFXrMoqjLbFFVTFDLuPkecKqYKnblUZzoqaEYFtbCgHOS4owxTFud+Sj5IyKOHF1pItVgZsobWkMKIyKW6bSd4rsnp1OPiZkp9V5roLXS7Al6NvNJAl6Z17R4QjCj4Kin/noSvkB4Ri38Sgz+Axecxkd/Mwz7FaA3n4lncp87jnmsqpT7dNcnjA/PhsQFxMpnlsfhlPSiGRsGamXJbzJjzmBtZwe3cRDTjHvpoFPBsbPC5QGQZNHwz5/W5bZLSbc5ScD7zOJ3WmI9aNMPWfRRLoWNFZ24yXPwaXftLZGKCe/eHGklq0evNsLe3TL8/Qxjcfx8U6p8l/U5JDUNonIVOZgWkB0BLvE0OLcAPR2CW6JRq7JWrjAwdM45p9HrMdPaY29+l3e3gu54CPptLlxjOnqNBiaUgZ2mUsyi09FFO/UMKC4leMKgYDJouYcMmaTjkTQfDM2m9ssvce/2xaAQFG/EK/nv/lpkbr6tq9OGQavJvPvdJvv7EM7y/fIa9xlEhn8NhZCmPbd3kyY2b1Ib3kj5yDXicqywWa4hxS7eo0KWs5n5RpmvP0bVn6epNelTpZQ7dRKMn9iYfcSUnKbqnMXkWg+cwOX+CeMde2WC/7TBsO6SegRPmlHsxpf2IUidCZJEOe4up0HPspRrGQoW4Bjtml98c/g6/0/0KV5OrSkXyICpx5YgP1wGYmLF8nvS2OZffZHb0HuYhkQahYWuLz8KFH6E4/8OEpSW626/x1soV/vX1gt9aP6vWIeo4aynPz77G55a/zAU9YPjeTzCMSmD5aEaMmZYY9C5yPS1xVc+4VrlFMvsFzNKt8f+VeUS7n6cZvUjT80iygvVBQnhM/EXgwXwmbRAGF5Kci9o2TWOThrFBbQLKZFSMXfQHqUYqYRIbrXkGmmNgdgDQ1Kgv32d1E44Stjo7XFu9xpvf+BrXNzcZWIaiEZazEp/of4xSIjTJnA2zg5bZOGnpQ3tUPyiEmXQYpDl2ipvt4Ia3cQbv4ibrSqjE1YY4+gDX03DPPo154bNo5z8H7QtHkhRJPCB6+WdxvvqPsXpSxYXUdHhp4eP8I+0CK7tyXC0WE6nkTc7RQp4T4zs9UjuijM2Z84/x03/4r/Kdju8K8CUUP6lCiRfXtyseVL7/2Z/9Wf7SX/pLR0yWpfp12GRZKnEHIT1fAtKkeia9XmKy/Hf/7t/9j85k+SN77sSZUo9RfUzBZBY1mTC9qyojs6hLbfQCdgYRvUAEI4Sn/e07VUS/0COkrIWUSaiIdCwpZaGZxCklEbQII7yRjxcMqIRDKtGQajSgGvWpSgn9uG/FwxwD9cU2CTyPoOSNZ3cyH9oXui75A8yc5QLQLqpM5VVmKDOd16g+gNYRS7N07RZh/SZh7SZh9Q7FcbqjKDLFGW6Y40Y5bpjhSALUnKY02McNR1hpjpkW91HrhlqLsFjAyM5AfoYkXyItlkXr6+5zeprP284t7hj7DISGcAxVi5/ZXn2GXqVCYoFbhLRDn1YU0YwzmomlgFo9rdBQ8wf7zzwopPk4MmSBnKGJumUS4AR9TL9LPuyQh13y0R6Fv0vu70As1Zdv7ZwTA2i9WqVoz9Gff4JO9Ty7SY1uXDlCp5Sol0csPr7A4pMLLF5qUq5/eAX8+PfrehDxxf0BX+oM+J3OUInNHI5pM6OfaUSHEhANA16sG3y+5fCpmkfFcrENWw09jxj5VxkM32bQe4t+93VGsVS67r+B62lB1U8p9zLSVx3ydyysjbHYRWEK7bEgulwQPQbJcnakd14JWw7bdDtzbHeWuNE9w05WYy8v0aFCL5eb/v3X3rJjcma2wsJ0makpj2rTQTcNwuG4QhX3ErJhIuVbTD/FHmUKYFXDHDv9aNQaAVW+1VOzGtZY8VIcmdp4zOY1lpNpzkYLtLP6N5UVlgV2nAXEeUiUBaQiNT7V4rV6k1/1Y76xf09dUrzSXjzb4o89vcCLp1uEWyP8jaEyHy72Qux+rIDkg96FdPOtknNnMm7f3c4+sOLgWtJPGZLrA2LEtV4Wkj6a6aMr2tb4cbvicbo5zzPBk5x/6TzNyKBpajQsXdldHI++mbBhR+w3rsHUq1Rar2Db996JHeeUNivcvPmn2Nr6DHZ1i+rSK1SXXsJtrqo+scFgmn5vms7eKfxArokf/hmoc892iXQTN05oDIfM7e4yt7dNe2+X2v4+uvQhVaq8de4Sb52/zNvnLvLe6XMkln1f5fmR7i5PRSOetTU+2aozNzWL4bXJI2OszLgXTHzNQrJ+9FCXGPfxNvUfPY01N+55zYZDhq+9Tufllxm9+irFG2+ij8aVa3m5wHTo2yXeXzzHG5cucWt2XlF+h3hYw5TaYKiYW5FIZ0vyUhc5bUMp1H4rIebidc9SQ3onG56tZrVv8rjmGty59i7vvvEybpHxRGWBH1p4Gu1mSOHf70l3N3SwFqu45+o45xuY8yWy/Yj41j7Dr7xNstpHK82iGff3monIk4jYXHdX6NZHzJxd5sLpx1QVU1pEejvr2Btfp73/KsvxVdoqrXcv/KLMWnGJveJxQp6hYi5QKzy82FT2LIcjIOPf2/v8YpFxNbnH757K4OnIYi7TWTdzVoyMNXMsjnHkzywK2qV3yWd/mdjbUvsqUZOP3/lxLu4+r87pLaNgxcxYMXP1GsLOORxmUTCfFyzmBUuZxmyq40gPYl5QN3bGYMzcoDYBZePtbQUWHxQizDVwmwxKUwzK0wwrM0R6Fee1Lbxfex+jNwaouWPh/8gLvPaDn+TNKOWPvTXH5aiNr0X8N0v/iHfK72FnjgJhblpWs4xSUmYmn2bGPUVDm6KUVbETjyLUiUZShUtVP+I3GzoJrj5QKpKJFTK0Qiyty4V0TwmluPqQ1PD5nXLGb1ccdMOlVrg0szJTaZOZpMW8iI4kLep5+b41yGvl2/zk/+0/4Tsd3xXgSyiH/+Sf/BOefvppnnrqqfsEN/7e3/t7/Mca3y3gK0wyfvG1tbtgqn9IHOIAXA2iMagSup7QDr8doQgp0ih8AhgWb5p22Gfe32NpsMX53gZLg01qSUg5DSglIV4aq9f4ViPXTDJDmnwdYstlVKowKpUIPBkuccklKTvEnkVs6YSy+P8or09CK7OZT6osJHWmaVM27wmRHI4g7HLVgK+YDu9oOZLjKxkJf8IZ8nl3QNXdJ/H2iLx9OpUtYncLx+pjPgS18e77yQqiDPxco1voDJWSk8ZI1tJpmVI8RSVcpjRcRvcrCmx3ikAS1UeksaeLGnM0KblSlQjxK7v4lU2i6gaN9hwLzedZnv4U05UzJOmAm72bXN+/yo33v8CNzZfZzC0CrTEBY5UxOMsqNNLJLJW1tDHenzsY30SmLU1GEPYh9iENKIR+kieK9nPQpa5JZlm3KMT0E58i7ZIPtki3blKM7l/CaqKc9MgjGI8/Q3/qAnudIZvbBbvRktiMH3muCMUsXmooILZwqXEEjEVZxF6wx26wq8ZeON4+2CfzTrDPRlZlYF0idh8ndSTjN/4/9HQXZ/QSdvASVnRVyVg/KEzdxBaDUsNR/j8lw2LBKpg1Y2aNiCk9oKENjyhHHYR8R7N0zPo4noRPOhrDVZv1rTbvd05x01pmtbxAz6yjXFMLk0K5p8q2QatImSdipkhoS3+c49JyK1haCS2zkJ7sYJCeSDt8UORGSmgPGJhd+vY+vj0BV5YArB7+BGRVCodT8RynovnxmGzL+fegSIoEPRxAPKJIRmR5zG69xtb8HCOpXFoalmUoBT0Zrqnjahr2KCBdWyPc3JLGGXX8Dc2iVKrjlOuqDzSNMox83Il5X+b/WEi/zG1ZfB4CWLcnqn4i3yyL41rJouJZlD2LkmviuAa2Y2K6BoZtoNkGhaWR2zqBJNEysfeQEdILVvBHtwjDO5SGW1zuajw5mOdycIbz8SK1/P4Gw6Qo2M9yeil0spygdh1n+RtUl1/C8u5VQa0kZ3o3orJZ5vWVn+JN7dMMXYPA0wlLBmkJDHNE3b7JsvcNFspvUSuNF6x5rjMctNjePkuns0AYngzGSr7P1O6uGu3dPRrdrrJ7WJldUBRfBbbOX2ZldiyffzhaFHy8ZPPCTJNPtOo8WfVwPkKLg1Qf085YNj/cGrB3Y5PRZh99kOPkFte1Hl8zV9g1Egyvhu5WKCyPzPRI9bHvUJDp+GFEr+erRGUvN8m+hX4odSWwJGEiaEdXQiPHZzkXju+T73i5SKkVIrMPLekRtUxmXJv5ssdipcyUAW/+yi+zcWdMVZa12Y//+I+rVguppCRrQ4J39gje3iPd/hAatqFhL1Vxzo/BWNxO+ZVf+hleuf4qUX2W08ky58MlzofLVPP7VXFN7Tal8hu41qtY4WtoxT3gJ9fiyHicUfYsSfQsSSEKr/oDEyZDhDIf0td8do0eW9YeW/qQzuAMK/Ey71misPiAP4OcWT1hWeihOZxKTCqZJL503m69wheX/h1De/ydmPLn+dTKH2G586gCQxIihL+jwNgY0K2aOeJ9fDiEFjuf6ixnOsuprrali/FwiOaxiESNq2QbR8GZsYmlf3CP+jBr0stm2Mun2CtktNihqfq4P6dfZFmrEBc5/yLZ5zqx6rXPLem575HZXZXIkmRTpiVkejreViMl08QmRmijYg9jYRUOZmZhZjZW6qohQO0AzImK5MEs+/RDPmy2BtIi6Wkani5Deq41xYY4eGw8ZM9vmCf4WsqImDtmjz/7d/4i3+n4rgBfoi74wP9Y05Q4xn+s8d0CvvqDEU/9nd/8SL9j5BmVNFQgqBKPFDWvLMAoCdRcSYKT903Akzw2C7nkwK5b52pzmfcby7wnc3MZ37pfeUte58Jwk8eiPS5nXR5jyIykvm3I7ZzMSsjsmEyyIoZPYvRJjCGFU5BZGpHlERpSK/OIZBQlYqEEJGXiqEEcuaRClXrI0HIDS3dx7TKVcoV6o0a71WDOq1NPbKxOIQ624xvQCWvJjrbPTnaHwXCN9yOPL5TOcb18T6nu6Z1r/MTNr/CpjbewrDLG1Cmc8xfxnnuSymefwb10gdS02AwDvrb+Zd7e+DW2uy+hJ+uqn2reEDpZiiE9XUaO9Pg/KOQb7A9bdLpzdDvzigKUy4L5UJRKPZr1XdrVLi0nxE3K6KGLHtoYgYEWaOi+jAI9LDBEujXM0EcRRTgiH/bJ+z0K1ZNQUF0K8Z7w2ZyB67bFddPk3dTlpmWxWdeOgHIBe+XcU8CsFdeZGs1TS+YopTOUJYslYK2wlfBIA00ZRwtN6lsJdVlLQ/KgQ+5vk/dWyDq3FP20iAYUAugmgEcrlTDPLtOrVtl0Zti1HmWY3n+zDyo9dpq3uFO+wvXyWwSHqgIfFuJrZucNcvsyZhpgx7dVv4PpmGiyqNal7zIhzmIF7D6qJ51Uki+7Oc94GeecnLZZjD3zTjw2sJdp3Il11iKDLb9K12+iRfUjcurHtz/MyPRwpEZEYo1IRKXLGqqK1cDusO/s0nF3icwhmRmq9+gWNk5u4xS2osPOxW1mk/GYTptMJQ1Kuadu/LolIEQnN3Xk6y5XIdFoEU8/kVAzowgrTlWPkHaIovMfPjIMbYtC22JfD7hjmbzj1flafZktr0bhmKSWqFZqxKZGaGoEBsQfQcHRSgoqYT4ZBU0/49IwV6qvpyNYkKz6Cd8bOTa9rKAjEs/peB4KcGzepqoA1zewy/f6VLRUZ253xOxORKVX8MbMOd5brKELjdN3IDlHHC0z6Lt0u8F9ku6OO2C6fYfZ9k1KjXuvq/wX36swen+GgdAVq1P06nWKY2BJqHris7VWb7NZa7NVaxJPKlyXSi4v1Mt8vF5W8xnv4cQsPug+Lj3hr7/9Lq/c3GE3c9kvSqri2ys8sdf9pl/bFCZHHqmkiDBuHdX3lFILfRZ2N1na2aCehrQXpihfvkj30UdYP32Kq5bJG4OA2+FJ4i0FpaLAkQRUnpMWBaFukJgP9mw7iOX9LX7o3ZfxRM1ON/jdC0+yM7tIQ9doC1CzLWZd5y5Qmx/lTN8Y4L7fo7gzOFohHPMwj0SsJVzxbvJG6T1eL1+ll6zwwlshn7qSc1Y/R/3H/iSlxgCz81Wc+GsY2tEetjSfJcyfJcyfI8qfkr/07s9yC/xqzK7dZV3b5k6ywX7eY0SiTNQ9qdxM6HRG6lEansIJZ+5Wv4dWjzcrfd41aowKk7M1eKRt8cxCmScWGzTrVSqVimJBHfTi9XcD3vvyOje+cYevln+ZL8/9OuHEt+1x/zx/eetPcCk8o0iUktM+mKVP+laR8Yae8aaZ846oxR77jsh1b8nTOOPCKRsWjAJTPtIA8lAjl+bMRKNIdKGKqDVIWevQ1NeoCSgzd6iZW9QVpXEDVz+5X/gggrxKXsyjMU+cz/NeOMtqPEcvmyPIhSXzzX+HpGddQJqANUPPcPUcERAVIFXSZBiUNZNaYVERZ7qHTEyEQqvMC4JC3n9BkI/3ySwJKGkxP7w025yK+Ft/+36l8u9J8PX7Ob5bwFfW7/Pn/8//6C5IOjI/AFQ5mVQJjoWmoTk2uueiu854VsND8zw1q1Euo3kl9JKHXiqjlytq1mQul8H1eHvP5+e/8ibvDQq2tZryU08Pl14mMVWxeXy+wsW2w5mqxqKXosU+g8FADTnGg0EX3z9Zpvmk0KWZ1h5h2wG2FakMilnUMbK2klvP/RbRfg1Si6oODaHhGONRM7QTMy6Fa2AuVPDO1HBO1bAWynx5a4v/+Yvv8ZXr0d2/rVIU/NhowE9sv8/S7lWCwQ3MQed+qqBXYmN2jr1Hn2D74mW2FpbYaLRYs13uJJm6wBwOl4jT5ohnSjEv1Ey16DCGXW7dWmflzi7r6yOiY/dpxwpp1Dept9ZoNDdwnAcbLn5gSC9cAHJt10eaEqTSAx09djALF9fRVEWvVPQm1EgI3Se5Pf/D3Gk0uKXtcSvd4uZolTvDlbGJ9wlh6y4NaxGvWIBoBvqzFJ0WVlCliaFA2QE4a6Kp+WBbOgWON45/+J+VEzBioPWVTP++PWTHGatBily/T4wRVSmNZqkOF6n6cuM6uhDrelt0WmtEsx2MxYhGs0JFq5B0E/qbfYZbQ5zUwc1cZSwtZski/BMEgVrwDYf3RC2kuVwUWaWPVUalXlFA7ACM3d3OI8JBj8H7VxjcfoN0+Caau4E7H2MdKgIJfSkeVRl06/h+k/1oATutUkk9jLhKGjRIwzqpVCOOgSpjsji4O6tt+W6I0XWoenYkS1oY8VhaXE/Q9AxNF8A3rh/ahaXAlFQPZHbzewBL5m+GtvqtRKpnpEZOahYkRk5iyCy9kWMqbKgXin40MjSGGgx06Gua6jUKZOWcxpzZuMqn9r7BRWuNGXcfTYS2NZHgjrnpTXEuuoN1wvl93VvileqjvFZ7VM3vVM4T6ZMqalFQjgrqYc5UrKnet3o0AVlBjhfkOKMMa5RRz4oxbdDQaBo6NalmnnC9EiNvAVi+pROVLWi6eHUHr2aReu/Rz34FPfs6dnnn7u9IW92ol/OJ9SGL+7G6Zv2GM8MvW89jx/NYQj8qTl7gO2GoqlWNTnc8d7vU+n1F30qbBeHHcsIXDOJTRy9SKee5qX+Wrw0eY2cnYWbQYba/jyOl2mNRarU4t7TI+aUFlhdnadflWy+rr2ysvDaZ80x8FH2G6ZBhPGSYDPGTEcPUZ6j2j7i1H3BlE1Z7Hr2wQRhPkaSi5viA0EMMPcAwAkw9xNLFn0hmAVYxhpYrgCXCEiIqUco12olLLfUwJwtN5bZVZMyEEWc6faZ2dqisrmIM7k/gWIuLeB/7GNGzz3Ht0Sd4p97idT/i9cGI9WM9pQdqpRc9hzOmzrQAviSBMGA3jtkT5kOWMrN2h7Pbq+r5u+Uav/rYx+mVHlw1Ph7NKOdHdxM+t51xed/nVe8t3ixdVYv2J4LzPDW6RDu9py6tQvmODbHy9yiZX6Gk/7q6Px9EUdiE+ZNE+XMKdKXaorpunXQlj7SYW846N9xVRV287qxy29tgtj7HhcYFLjQvsDA6R/jVCvtX7x0jsxERN9bpRGvKs/NhQtSLD4CYAmFqHTIg1mPerb/L9dr1u55cT0cf56dn/jOeap4h606orMp8PLzrryefvVS6XyPjVVI17x1DrvIZPt6u8IkzTT7xyAwvXGhTdcfft3Btla3/zz/F/5e/gNYf9wBnpsXW+ee5s/BZfO0UbuGrSlnd2DpSNWsYm5SMB/RITyIsLHZosZvNsp8u0EmW2csX6aXz+LncfcW0IEOI+hUxRdYFTFn3VaykmmU+dMWqUCMSsTBth5J+i4Z5hZr5Pga7CphrWkqQV7gdPctq9DQb8SP0c2kdOpasmUDHrJrw1/67H+M7Hd8HX98j4Cvxe/yL/92foRASv1EoVpMsgnTJpBg5llZgmzm2LtWTFMsoMAwxpMsxjFz5MalZz5Wyn9CfZAglUG5uclKLl8b9+w+2D++XU0lXfiDikD6kQmjUGWh13ssXeDdb4lo+w+2sxXZeVb9xPCpayJQ2Ykr31WhrPpYm+ceCipVRtXKqVkHVlgFlK6ZkhJTMIY4xwCj6pPmQREbaV6pmqrcnXkAPz6OH59CD8xjBmRN9OeJc5HTH4yBTHE6+IalRsFGXRvyEW4ck0S+h8yew+dGyi35a4/rsHr/p3eb1cI+VYYAeVDGYJranFVc78D5Y6looN7NxSM3QuWFYpJqOm0QsdnZZ7G6z1NmhFh6lhJhisLy1zezWFnNbm1T7g7tN4oWj7G0oyoWa1ZDtsnBTbIq6CCPoCDMk96QKmZJbMfkhCelvJvRCx7IaWO4MpllHM6rsZRbrcc56GLEaDlgZdVj1d0ke0L8ndLu50jINa0kpRhbxDINBm52uwcagQ6r1MIwhdT2iqac0tZyGVowBW27SKBwaWVn1qQkFspFVqGUPLzV+EHmRkeShomvJuR3kJrEIm+QQiayz7Ddj+lqHoSVjn8JMlKjPo48+qkCX9L/efb08Z319nXfffVcBMVE8Oxyzs7Pqdy5dvEg7CAjfeIPhGy/R2X+NfjkmnG+RlhoUQZMiqkNYo4hrZLIdS2OywBtN3dQPg6cjoOrQPqG+jvd9e8RwHvq4Iv0Sscqax3qijG8jmfWYQIsItJBQj9VzosPzZFvklY/vkzm8uy8h1mLyj0DtPRzVLOeHRyP+0HDEJ8LwLmSUb/8rtsO/9yp80SkT5DZTQZlnEosn8pxLhCzm2zTz3RP+ZpMup9hNz7ATnGIvOcUgayt55fG1NcfRdKqGQ1V3qRgeZb10YsZYaJtFKcCohVjNCHc6wq3kWLakCsbVET/bZiu5wlb+HqPikBJbZtFfe5r+yscp7Tr8wdrPUDV2WStm+YL2OVZZOPJ/iQ2EXFcOANbB8JIAvZJhVnLMaoFVM7HrLm7Nxa4ZGCV4353h5fI827WEqrvKafFjOrQAvcF53k6fIuhXWdzdYWawq3pNelINKe5Xf8y1kNjeYWjv0nH22HG69M0C/1AVTdHikxZZuEAeLoznSFR4T75fa2KC665juOvj2dlAs3poHyCQcO8/g3bU5nz/PIu+OJmN30dgBNyo3uBW9RbhRFlProum9P9kBnM9nUurBRfWU86tJyzsJfcl61LbZHR+kezxi/jPPcfaI09zQy9zNSh4axizm9wP+G1N47GKxyO2TnTlTcw7N2n6A5595mmee+45dkcBq/0h6/6IzVHIjgC1JKObC01WJ7BtAsshsB1CS24gITX/VSrDr5Emb4hN8t3/azGa4bP9j/HDvU+wnMi9TqSYZKl+lGqoMcLiGlqyRdQbsN5o8vbHKrxdvsNr0ZtshdtYucnpaJ5z0QFlcYmz0RIlZbB8/AMDc8ojrdqsbY+4vRnQF1p+Aa35jPbiPlq+g9/rMOx2FIASi5Nc0zEr1XHS2CmRizeZphFnOUn64U0JvulzpXGF25XbY4BZaJzvX+bT6Wc5u3CKqbkmlVKZqiZ9VBZOaGD64tEXkeyOFEBbzwSMpbw6UZXcOgbG5Ow5TcrjUZePb6/z5M77lPvr7Ojb/LunI37jaQ3fm6yfhFkS16iH09TCqUPzeFvUNg/oi6LIeN7ZVI9NbQND253IgFXJiqn7RsIMUS6K4JL6fDgmQU/36Yi1je7T1cW8PaCvhUSEGGmCGdlUo2lqeZNK0cbCuVs5dLVtpqw3mLbeYNZ+nZJ+VAhqlNdYi55kNX5KjX52T8fhyR9c5Af+/D0Rvu858PXX//pf52/9rb+lsgay/UHx/Z6vbz22N26w9/mfeOjnixGrUMKE2pEf39Y1dWE6vn34efc9R36uHd3+oJ8dbIvIQ19z6SIN/SX2tTI9zVNceZHBzjRdzQI8qkZES4CYOWTaGNA2fAUYtWNgcayDVFAuKjTyNo2iTS2fopa3MU+4cKTiMqZvM1Rjk6G2RciQlBJJXibNK6R5lbBosqY73BGnemEAFOMayLxhUndMIsekZxcMnYKR3ChtWx0v9Y7kuKls0Hg+2F9KR9TDLg2/T7PXo7XXY2qnR7vXpdXvYmaZcp/fnJtlZXGJsHL0RiY3i61ai9XmNGuNGbZrDZZ2N7iweZ3ze9c527vGcrSGV5/Cnj6HO/MIenOKolahqFco5PXKnmoEGmdl72XpDrbzIqHIpDHcpwgG6N0B2mCINhqghb5ooJNrPrnIRCupaNnuq8rIRylEieHmXqqxmehsqdlQ29upRvLAq1OhuOMHNRu5cAsYOgnQq78lLVOkFYq0ipZWqcVT1JIWU0WdWa3KPBXm8ZjJHVqpTjXKcJMcQyCK+dGEN8bHTiyxC2IrJy5nJNWEuJIQOxl1t8GymJhnJkWSqb6TYBAw3B8Q9scqeEZhYCJ9AfoYJMly7lugV30zIcqBku0V8BNoiQI0CggdAUMClKIJ4BkDHTfTOB3WOdsv0+hkpMMhfW1Ex0zoeTr71RK9ikeqCfSSxr17/6eeZapq0goj2obOdKXK9Ewbe3GOYn6atGTTe/Vl9n71CwQ7myQmSl1Tf/IR7B/4DMZjl8WKnDANiZKYMI2IkkhVDyPZTqWCKI8PVRXTkERENrKQJI/R0oDnh/t8ftjhE6PhkSvHm7bNFyolfqVcYvsDxJkkgXI6SXk+DPl0EPJYHDObZg/UFC1EBbCYIi4uEGYfV9SrsQnuoddEGtTfx9av3h0GOyepmzNydbamHDanHEa1Q/9rCvF6nf31JQK/zSNpxpX+n1b+RLY2pFL/ElfdEqYIX0yqWa3hPlPRPlP5Hp4XY5UzrFKmZqOcEbsFQ0PH1zWGus6+6fF65RHerj7O1crj3C4/QmgeTXjUiz0+nf4KL/C7XDDWj/j6rMYar49MXg8MtkWoIHNohS0Fbtphm2bcVN+PI+dqkbNiJaxrkjd3lY9ZEs9R5CeZTxd4xoBpw+eUkXJOKzirFcyiU7eGNOwudaerEpNZeYq0ModvNhlmLgPpnR4OGI4G+KGYdIf4eUqaGErZ8e7xt4ZslTfYLG8wMkNl4xEf0cw8OTyRgV8vuLQGl1cLLq0XlE5o9bkzBVeXNN5d1Hj37Ay7s5fJvAvE9hlG5iKpdv/fbRYpy3bMOTflkqvxeMXiQkkEfsqUrTKe7uEGJvlezGizR2dzl9/tfY0v51/jZfttlRw5iIV4mhf7z/ID/ad5JB7gGq/g6q9g69fHR1hY38VpRvnzdPkkWXYWuzgKoIT6ney/zzv2u/z85fd5vb2JYU9R9k7TLp9hoXSK08Ysp6gz3Umo7YR4+yn2QMcObez85OtykPp04k268RadaFvNw/SDqz9339PBhlqrSD+xUJ0tDMdFM01yXTrE5L5UsO/6vLZ8h436OKEhYPrS3mku7Z3Fwh7//t3XkTVPSmhFhFqoekzLhUOtKNGUrGfSZDttcTst8RawfgyMydfjPDrPYHJZz2g7Qwauz6oXseamrHo5KyWDUcmjZNeoOXXqTp2mVWHBN1jcTZiSY9hLKA8SFvwUT5l9j9tH9GNKwA8KnY6qShnaDjr7DLURu+TspinRoI+1t0XdWmHvfMytZq5aEq5ZFiuWpWjiJ0UtLXM6mWdeXyKpn2Wluszr9jwjvcwj/g1e7L7C57ov85nua7jZUQZPXj9NuvhZ4oXPwtkfoLK4xPcs+JI+L/HgEoPj7/d8/YeP7bXbbPzEn1BZSTWK8SzqUL/fQ9XlylPorTMYjdOYdRnL6Nb9Dea5LLSG60T+GoG/zijYJI73TwSbHwokHxZsHgO3H/Qz1SP1IYvrPO0S5lvs211ut2vsNM8wqJ4jKJ0jN+9VVe6GlPSTFczoBmZ8Ayu+gZGsfaC4w0cNEds4HS2obKXMZ5QgwpzKrLnOvyJ3v0rH0ejZBl2rRc+apy+ARHykzBGWmeCoqmxBySikz/ze31uIGICAMY2tRGcz1dlKxiAtOoHGKiF/W9MQelZBTS8oazp24WBkVcKkjh81GEYthuEMw3AaPykxSkrE0lRwAnDTDKmo9lnUYp6MMpbTAs8sUTFsJTMuIv1eblGNdbxCBDFczBPUvr6dIZdsyc8KMBWAJ0ui/JACn2fpeLaJ4RjoMmwDM3wda//X0IoQyk2K5/4y3VaNjWyLlXCVG/5N3h1e5eboFv1iqCpRxbFqkRzbKbNg3sqZtQrmTJlzZkz53EQKfJHS4Azu7jLu4DRO/xRGNl4E5q5B0nCJq6ZKcMSDLQaddfr+Pv3Up2/k9D1bKZGeFN5odK/a0ulSGUbYsUFSbpBV6mReiVzEY1xTdXNbToatjbC0EbYe4NzdHmFrgfqZ7Jf5gxTGDkKOrxznSNeI0VSvlsziVxhrGnuGwRXH4h3b5h3HfiAwOxtl/GS/xSdHHktpJiLiWNqqoi8ej6xokORtcUmi0EJyY4hhWUpNTpMeHyVyYpCPNDJfhHcS9ud8uudikvlD9K5co9OZZ2fnLHt7S1hZzuf4Ki/wmkpa7Sfz/FL3v2aYLavn15zXqMx9mX5LY6+h07PEwDjFzxNlZDzIIvqZgI6YsBA6Z4PUvkjiXCJxLpLap6WMdOwASi/eNazofSUwY0bX0Ytx9b6iFzzpZTxdSrno5Ed6FTu5x2Yxx75xBt1eoOxUsfMqu1sem5sma3s6G0OTvcw9sT9LWBtNLaCtBVxE49miwieLBnXpPWm5OKeq2Kdr6tzcG6Vs3+6zdbPP9p2BElU5CNszKdXs8ajbaG7M5vAaK7vXSNIxnVIohpeqp3nCOEVj4JKLyufhz5OMQI8IdAFj4zkspUS1nKiSErgJfWtEnyF9fAZCnYwGlNf3WbjT59RaoKpjc5377+19D64uarwngGxJ4+rSDEH5HKl99u4o9PsBmbQgnB4EPN7LeK5jc2EQsGq8yZdrr/JS5S1VOT4MuD7pP81nknnO6+vUsjdohm8fkYGX2DCm6eVP4MWfg0yMGcbpi21znw1rV6n+nQ0XcLSjYCzMRqzkO6zHq+wPrxOPTvZ41K2LmO4LeNasahWo63KtH1A3dKrCsDjhPprkEd14h260RS/dYzW4SpKF5Eq86VuLjVbIy4902G2MzwM30nn6Wp1Ld6oYh+5T6mqq7vVj9aPD4MxNUk5v7XFmcwcHm73WWd6Ye5RXZs7zdqnFxglV77PofAKDj2Mqb76anP/ykrapGFCyBCzibJzdfIjILF1d16S/ahjmDGPpXutjaztUjTVqxm3q5hpl1WMmlbMHGMof/L3SqxXbRDs2UTzNTn2Blfl53ltqca3c40Z6h7VQYOYD3p9RVyD8sdYFPtFe5oJX5mx/jfLKy9irb+LurKr17kH4Sxcp/+WX+J6mHf7Nv/k3+Rt/429QKt2/CP79Et8t4Kvf3+D2P/1/oeU2emYrH4eDWUsttFgnzgoCoc9lCXGeEhQhYREQFaEaYRGRSsa3ECnzHF0BufG2midg7mC/yKU6uT4ZGlauY+ea2m9NZiMX1cNc8dzFdT0vUlVJkbnIxSQ5U/M9vv6Yv38EQB68D/VeCjRXPJdOozdPYzTOoMu2cz9vvciSscBC97YSWci7t8gH4pDz3c2wlcpYfhfESXO+wfq0x9VleOPMiBvzscIHssQQlum4njau9aR6jcA5R+icZ+SeV9uZcT9dR89D3OgWXnQdN76BG9/CynbHUniagaabaqCb6ErlTUwzdQzdwNRMdWOTS4b0bskQHn2ai4rm+HFajLfzLCcrxB/po9kBeBi0dJuaYeBZBa5ZUDHEw01TAM2ToeUk0sScpnSSjN20YCeBrVRXyo8PiqaRMyegQcCDOd4W8CDc9bQwCLKxN90wKTOMKgziigJnB2OUjmf5+Ri0eXhJwlK0y9xoj1ZvwFxnj/mgx0yhYZeXCJvnSWqnyL02jiZN9+P3J9RcuSemE7qFbIuiFNYIBJiKmIfTV6IV27nF7bjKldEU+5mFrUkVeERbH3GhVGa5sUi7uoRtlcnTYnzsM5kL0jRlN99mkxX6+UvE/Aa3rZwblk34ALEHLdcVXaUVzNMczdIM5tRoBDOqAlepbtFqr1BprmA27pDV7pAdM2W+e04PZwg6y/idU0TdU4SdZbTIVSBIgSEtwJlsy8j1ACGojIqcgWHQsz18++T7iJGm1Hu9IxS4ereHnSToVn63OjOu1KT3tqVi40jl/MHnYYZFqLcJzCUic5bMKJOZFXKjQm6Wya2qGoVVobCrir7k1jzcqkep7jJyAq4Ft7izegd/dYCzpbE0mOF8tIR1X+9Uhm++RW6+hltcpZHdoW50jlSDJOROPRra+Ps20a5JsmOxU67Rf8EkezLEmrvXdF8UGt3uHDs7p9nbPUWaOuh5zHP5y3zOeIXyZOH0W67HfzvVYM1weeHOT/DMxg+r/TvlFX7t4v9KzztKh5WrTWYt3QVaMp+U+DGzLnb8PnrwLqaAreTOuEfrUJR1mPfqnK+f48m5T/PU7Cc4X5nj1rVf4Or7P0e9vs4o87jTX+LOYIn10SOsDM+y2hvTlI6H9KW0tBEtfURLviP6iLomoi7jJ1uayVx1ioW5RRrtefS0xv5qpMCW341ONKGV21I28XSSRWJidwhK68TO/t1cjZ66eKMF3EBqZxZexaJUcyhVLVzHUCqa8t23s1xZilhhijkQ+lX6wWIhQgOu2RhNF7PpKrCYVw38cJvhtTcJ332D/MoVnNt30Cdegeoa7rWgMs1wZoGgPUdRncKyWwy8NrdqHu/UDd6pGbxX04kE6eYhTvA6zuhr2OHraMW9RbVdVJlJKjwT7PFkssMnQ59zydFr+r6u8xXP5XdKHr/ruSoRceRzyU0luOTlLl7ujEdqszBqcSaY51y8xNn8DPYxdsowH7CW3uZOvsGtokOoLTOVPEopm8j9axlD4zZF9ArOYLwQNzWLuj1N056lYc/Qdhao2u0TKbsZOd8ovcm/rf8W77jXMGTtkmpYqUYpNKmEBl5oUop0vMjASQzsRFfDyqQ37TCwKrg9N+KVy1365fHxqfomH7va4OyGOF6e8DkXBc1RyOndPnPd4d3UQWCZ3J6qsTU1i+XW8Ywaht2icOYxrSlKZpWmbjGNjvsRKCbyHnNJARgZqZWSSYIqdnACZ2ymfjpVfbx6N0Yb5srEWS9cjIP+1CMhqY59TO0OjvEbuMY3cLSJ1YKsTT7kbQ21CjfLS7xbWmTFrnJHqmNGRsfoMcy3CNKT/S0l6nIfNwtO6SkvRBFP+BGn+jH+xU8w/8e/wPc0+JKGxY2NDWZmhCv6+zO+W8BXFPn8+s//Q0mCkrkGozDE9yOCUcZoVBBFhqK0PEzIQt4xE1xzrFjj6GPuuCUy15qLjYsjy+PMIU90iljHEBPNXIrstppVg31uf1OmvR8UcpqedKMS77E7ecz1LORWGnA789lKRupCXBEJWUsEIXRsTyNzNXyroD8BC2J0KfQ+M0ux+hHWfog5jDCLDDPPVB9MtWIwU7NoiomzYrGL2lSOLT1faUqRJOQiYx2PyOOQPAkpkog8SSjShELKE6JWlGpomSxsizHA/CaAYGaaDKdm6M3MM1y4yGjmERA6j/w/aUQhGdgsRhNBlSyj5xis1SqsNKrcUaNGaN3/uZTjhFP7PdVPNj3Yoi6KgMWAgRXQtwI1D60RvhUwMkcE5kejFYpKXiWrMJUmLGb7TGcJ7SxTvVhO/Emq8adppy2aaQ3vGDVF8q7DyfALlP+RzFJ1OOwqWpApb6NARDDcFfbdDfacHfbMfXbNPv4HZOnkYj5rTkDZBJxJVaf8EJoQQkEVEOYLIJuAs7tgLfHIfTD6KXYfygMbJ9IF12JWUsr1AdVmF6c2wKn3sWt94sLmaucC7+5d5E7nFP6oqhaQdQIqWoRmhoreqSiehiRM7i0CpO8s0UQ4IiawfIZOF18khPWTs7tWUTAT22jBGarSJxC1qQfTVKOW+szUKxfSUD0WeBCZ6pY5brbWJz876G1J7XXixrtk1etk1TXSSp/MOZlm5UQZlWFGdZiOh58qj7sHnVJiNrvFFFtMsykjn2Zba5NqJ1cYy8PhfX1J5aE/ef0Cr51QOxtROxVgHjIby8wa8fwniR/9POmFJ1RfUZqNyI4MfzynIl0/mTNfrTbK2iOUho/hdBbRtkqk6zFFeP+xD+2EO5VN3jKv8przLlfd2/TMo+B1qZvxue2Y54YRl4qYGS/B9e5/rUyHQcWkXzXpVSzWmONW7yLre9PsaiF7Xpeu0+Ncsc5f627wiIgxiAiIZfL/bDX5qlfCw8bTHEpGiVP9x3jkyo9hJS65mRJ9ZoOdSw5beYuVtMqN2CHIj95P5NFjFZeP1ytKhVDGkmOp63WSJdzq3+Jq5ypXtr/Bu3uvcr2/xm58FOyo/qy0ThEuUs4vYPkX6ferDI9dDw6iZg+Zt3uUs5BamtPSAqpaRLMocTqfZsmZwZj12LaHrPrbbO5vkWbHzkdpk05LygBaxnRrnqWzc8ydrTN7tkZzTgQXoN/zefnrr/Dam6/QH9yjrzW9OVrmGaygSTBICPrxRzI61g0Nr2zhuQauNQZoTiGe4GOA5ogipaap+/CJQgZClvAkWSYVjpAsTNA0B+1DKGRR5rOj9fhy8x2+1LzCLfcauXbv2GTGNIvGJT4d6fxg730+1XsNL793/UzReNco87Lu8pJR4qrpkIiQjRq56o2W+dhp8oFhFoayRnjKv6TEOx4LzinRnsMxyguVaNvIIn638RVebv+akk0SsGRlQk81qRQVatRo0KSmN8gNjVCLcAqLalamldb52OhRpbB7OPaNAb/YfpVfmb5Ox9OxzSquVaVk1akIjc+u0XSbtJwGU16DabtKLQxxBl30Xgd6HdLuPsP9Hb6cvs6XGlcZTa4t7Z7Nc+82WNgbVx4lqbw8yDg7MqhqFXSvieY1SeuzZI1ZTLcxXmc95BqqW0jPmMa2lrMtsvfkbIlJThbTLETWxuDjRoXFB1wvHyakKjlKe/iTkRZ7LJZe4kL5DbyJ+XqQObw2eJzf6T7NhtPEsENML2La6nJK3+KMtsUpbYtp7cHASiI1NHxXZ9+22TRcbhsuV3WLN0ydq0Z2or2RxB+yfpD/9i/8D3xPgy9R7trc3Pw++Po9iEGvyz/6P/6najEvH2TueGRumdwrk7lCxfFUaVvUeyqVMuWyTalk4HkFrhPjiCqgOcSSZmNtnyzdJ4k6pEmXLO2rathBW4ZKIKrtQm0fsJLkObItT42VAIFJWlTI8jJZ5pFlFnlmkqU6uRi+SnY+zSjSXFXIRCa2ni5QzRYoZ3O42Sxmcf8xlbyNpnXRlBrOLom5pxaXQzOlq2cMipRQsv5pgp6I3LT0gomQiPTujIeq+Ethx5IqRI4pzcpxqqpu6jmagDaYRmSbx+Ii44qZmKWK9J+MibiINm6OHx8I2VZis5P8sMyTip5sSzXvUOZXfetkd6GN6QG5VJRU+evu4yzWGW63GO40iPZCsYI/ejw0g6B2mn7jAp3aefZq54gPUS6VYawRMLIGE++kATvVIbvVIT1vqPYleh8t76Fn/fsy0x8UsvD2kqoaYynyqpIj9+LJrB6Pf2Zn3l2AIFWbJ0tf4OnyL+Hp44vwMGtxZfRH2Ih/TC0Aa7pGVapdUox7wIVWlJKkuXqQCZiWGbX9oDrbyOyzW15jr7ROp7RB19um7+4SWg+W6HVE9l7XaOlijgwzVs6CndI2MzxTkhMFWaKRDC3igQx7MltEkzkLP1oSIhUam1UQODHDUkLg5IR2psbdbUce50SWnHt85JDv61/sDfivOl11e3/Dsfkb01NsnADMv5Uo6wWLVs6SnbNk5SzaOdOmiP3c/1yRD96MdTYinc1AYzvS2Is0NKm82o7qudANqbxK75tAP6HqeJTCCl5YwgtKuGEJOzkpS1swn23ysfRNHjVuUJXkwSTSSGOw4tG/4zHasVXz+l1QU4NBQydolYmaFdJqlbxcRXMrGLIwy6qU0vGoR1PU4/Z9/3NCzG62Qi+4Rdy7ibZ7k/r2Ds3JaTd04easxo05uDkns8Zm6wQvLL3g83rBD+ch52Of+kCMelOsEyhFQ8NmxW2zZrfpZi6P+Ss8lo29t4Lc4De6S7zWmcdMDZXtP5yVH5RrbM9cZC7+OO3R+Fry+hmbLzxbJplwgt0845Ei5hlb5/mqxwtTDWbbbWyv9FCy7+I1+dbaOr/yzld46c4aN3dN9oczZJOKxvFokjBr+pyubLHcfofLC6/S8u6BoDioYfSeYdn9g0zNfpZuATvbAdu3+mzfHpBEUmcuVMIisfokdp/MHZCKjOvx41wqsby8rMbM9DRX33+f119/nWQCWsUP65lnnuHjH/+4Ui89HGJAGw4TRv2YUT+azJPRO7Tdj5Rh7YNCMItU48si1a1rqkJYMWXWEI2FD1OUU0s6YZbEQ4pgh6x7i3TrKkG0xcvze/zupYxXLsh15t7rzPsmP9p3+FSU8Cg7tIujQgdrzjS/2XyB32x9gi81n6VvVqmEI6b7+8x0d5nd22K+t00pj9CtgsIuiN0Y340YuDF9M1KJvJ7m4xMxFdZZjuaVrYT01QoF84CSOdSGJFnKM3uf5dnhx5jXXZrS13ns796wdni9dJXXy++puWN98KL+7vEtdF4YPsEf3f88z4zuCTQMdJ9Ne5d82WH5mUssP3EJzzmpZ/CDj30hfYG7PX71zS/w2vWXqEVlZZmxHM6yNKhTMipoxsOJV4SZzygdEGQDEjNBchFa1cRsuDjTVYxWRbwd8Ifw5nrMW9sBbw9ybsoa69jNYRZN9Yw9g6HG0jiF9lDvIxAKbNolSu8w7f46p0qvYE0oqf3E4Rt7S7zVnVUMErWvXOf6mctcO/UIK81T0E3R9yL0/Yhq6nNa21ZATADZJdZ5lA0W9V2qxiFBoBMiLyxG2hR7eoVV0+aanfGGG/KOG/OZ4sf5v/7n/z18r4Ovra2tI+pev9/iuwZ8ba2S/v1PoBdjcHBAQxMxCqVAKDQ1XRuPiZqWAgLfoZ6wvPCI8wskxQXi/KJqMM9EXvz+Z2Jqa9ja+1i6NJm/j6XdPLE34vdTCBgb335lZHeVtuRbGnZdBtunGOyUiXaHY9GLw78rMsJtj+tLNm8vF7xyOmKn9tE47ZpeJTHq5Hqd3Dg62lmJc7HBo6OUp3pdLvc2KMV9jGSErmCPnH/pPaA6JtRNQOmhoRXkWoZldGnpu8r7RkK05L5uzvAbzhTrpokvVMeigVdUqRUV6kWFshg2F5q6UY8FICIlAjGeI0ZmRGjGhGZyTxGviJSQwjcTRqZRCQwqI5NKMBkjk6qMwFRUlA89plaOUcmwqhlOVd6Lg++XSX0Tzc/R42Qs6f8R35so+I2B2QFAk1462ZbMM0Q2JGIuahdgxeimCDbIDXOcqHkuDPmv9rtUi4K+rvN3mvN81WuMexGVFIZ8TgdbknGURWyu/t9xSkEEZD7ae7a1goVjgGzeEsPV+58riZyNRGc10VmLNVYSnY1YzrQH/6d2ZlOP62pcCg1+MNrh49ltprl3U5fq4g0ucyN/mt38DF6iUY2hnhjUYgupBel2Dd2pgCWA4uFS+NK8Ho02KPZvoe3dJOvcJO+vj6nVJ0TgGKwvNtmbm6bfmiIoNQh1l0jL6Dpdhu4O01NrnKvvc6Ekkub3flc82l71dZKgzif1Nh9Lck4P9qh1bqnK94nROkf+1J8jnP4YA3uR/iDgrd6AV4OEN3OD96wS+/Z4sSkV+hffCXnxnUCp7/WdhBu1t2juXWF6f/vEnmLpRys3mpTqjbuzXWuyazZYy0rcDkyu9XLe3w0ITjDiFnrgYjlgQe9xJoWl2KNSjNgydwgOVa4tPWW2vUFt+jqN1rryFjqIzG+RrD9Huv487J9VfVi2pVFrOFRrNuWKjecZylLED3w2httshHtsRntsZz113p8UZafGx5ce5+lHn6A8X1cqe9JP+c2ELLmSboS/NiTc8Il3ArJOCP0IfZRiJA+uAh+E9OX4ecFILAKye9syy18g8t+O3PONiBuNK7zVeJl36u8QizXEJAHz6d2YP7ke8GwW0qrHqoJ29zjmGmujOu+lc7xjLPN26zLb7QV26rPsVafplmoncstqgc/0oMP0oKtGe7hPpg2UUa+cv1JlreclprI6s3GDmg95HGCID5TdxjOnuBHn3IiEWj5+TZOIUnyNKatDe/ocDWcOb2SrBODhGNUSdmYGrE3tc6uxxU6xRy/oKcsBPxYQMyLIAvzcJ52k6USp8Se6L/Kj3U9RmZiRyz3ji7WX+KXmF9kpdZmtzjFXm2fOm2PJmmcxn2Mma9GMqpTFK3OQk/Uisl6sZhFMephIkogRhqJYJ0aC1SywWhlh0aPn79DpbtDf32bU6eDpFcpmnbLVoGzWxtvmeNsz72+98Cl4YyJrL+NdaRM49nE1ioQL+oglJ2DZSfj0aJblZJxQ2NHEGr5QgiCu8ubaoGr8K8rGr6FNqqRJfopB+qfoZp9SJsdXagVfmXH42kyFG7WjVPHFIObFvSGf2u/j9n1ezwxeSS3eTm2SQ72aDjEfN1f4jHODp607nNHWKOf7OOkQJws+kDG0Wltg6a9fge918CX/+Ydlwfb39/mPNb5bwBdJAH/nntzmtz00g0LUBydGpuq2IDe0SSZ6XEoyJs+TL9F4QSbN+mL0GyeLJMlpkvgUSXKKPL3fp0EiM7aJ7TVG1gZDZ4eO3SM2pEVZGlTFUFWEk2U21OJQtmURbxJjabFa/OvEqrFbUYGSIWEuUtUpkZ6T5wZxcJokOEeWVRVjWV5D1asKVR+7u0/dyq0ehtPDsX1qdkTTgHpWppyUKMU25dShlNmUcxFcsPGEfnn375IDMJ6VzP24S4tCE9tNAQ6FatwPNMaDgpGuEeq5anAeK8pFJNqQqeIqp7PbPJpsMnOosVm+tSuhzZVuGX/HprlhMHdC0mizAe8vmawtVNiba1NUWjSzuqL4NdKamlvZeFtMkEWgfGTAuzXpC9B5W/oD6gZrJ7g8G3nB+WGumrUf7+U81s84N8wxH+KKIjc/AWC53FiN36Jt/CKONs7OZ4XDJp9nhR8mpEym5ZMh54PcRHJStT3Zd2T7QT87+P0xlBgXcQuKLEePUvQ0xUwzVUkw44wiiMhHIVr44QplkZkxLGWM3AxKFqVGk/nF05x3Yy5vfImznav3zvgLPwqLz0E0gKhPGHa5Fe3zXthTstG34oL9Qs5kDScZ9xq4sQwdNzLwZDvScdKPvvCT71FhmlimTsWyqRt1yppHy/gGNUv8YGJi+zlG5c/gzWRY0xlaK0drQm4WZPq4f1Nod2nmk2cBSeqrbaHnjUdAmoWT4zsZ4+L53Zrw0cfjxagcHwFgB0OS8idVyFR1vRCxC5MEuXFX0NIZrLiNnTaojWCxc4v5/hUqydrd38uVPcBzhOnnCPPnKXAf/rip/jyhE/sUYV8MsSDoUURDingwnsMuWe+OlNLu/l5WK5HPVMjaNqO2Sadm0nUM+maDYdZgFMp94xiFT0+YmlphfmGFanUNTbjKBz9zT9M1L/BW6PLy/h1u9m/e510kVNKPZzqf7/d4OvA5laSUT7jFp5rBu+VzvFx9lFdrj/Jq9VHeL51C0w0er3h8vFbmuYrLwu0eb//LVcLh2Mdt+fIQr7JB0O/idzuMeuM5DkbEmsWOM8WOPR67zjT7VlMJDR0P8UNbKiIu6BqP6g6Pag7nMksp0PpaRKSlE5VPA6swZPmneuUOlD+FpiaVEPGb86feYDDzDYbTr1OY946/GTapbD1Pdet5vK7IbRx9H0MT3q4ZvNkweKtu8HatwI76zPX3ld9Ye9hjr1LnrYVzrDemENbnE72Mp7oZT8vIDFpNTwGxu2PaU/1ZcqKmnUj5PWV7gZoPhgCtQjILHxCaraPVLIoyxKaInQQMohH7owGdwZDRKCMaCVVT6J0lNL0sbvGqUpzoEXca73C9/Rp3mu+QGmPg2sgyfqhv8iN9k4+l21T0ox5jg7DJbneRzm6b4Y6HGY4wi5i4YbM943BzweStxZi1UodNa4TuXMK0HyVxzuJbCwzt+3uM5Tg0RkOmhwLGOswoQNbDOix0kWWId7EXLOMmZ8QgQ+0WIaazbs45y8bSDPKgS7r5OkZ1ROsv/Djm/KNEN3pqJOvD+1q6zdkS7vkGzvk6ztk6emlMu5PrTS/qsTXaUmPT32Snu037XZvLN+eZDu/5lfX0AV1TLGwMptIGrvi2PEQkci8oQrTddfKdVfp0+PLZLl853WPX6tCzff7gwh/m+a0fY+ulACPOVJVTbrOz0x4zLVdVOXMBdf34Q9vVJbk4TDv4+R5+vs+o6BIUPUJk7hNqGevWAivmIqvmKWUncVx7tWoNeNKI+US4qCpj5VO/Ql7/LSUCMj3YuZsQGOnLdLUfZ5i8yGu1Nr89bfKlaZMtKddOQsD9k92cF3dDPj3YYF5bJfW2iUvbJDK8LRJ3n7gwuNY9y5X9S1zZu8yt/vJkzTR5HWkdqe2w0N5mtrnLkrtHLY6phwGtYMhUOGAu2Gcu3OO3pj/JH/wr/xa+18HXz/zMz6g38EHxUz/1U/zHGt8t4MtPEv7cv/1nY7GGsckXbctm0XE4U/ZYJqPZ3UVbucnu7dvsrKyQJMJNn0ihF+N6mVdvMnP2AjPnLjJ3/hFmL1zGq9WVB9Ev/dIvKUNYAdMvvvgiP/ADP4B5TM1LKISJZPFWB8SrQ5K1AcmW3B3uf887ns7bNZ03azpX6gbvVg36oht+LEz54hlDZrQOM2wzVWzQzm4zVawyzTZVhCp3+E2M6Y9GJoIfhZpXBov8ysaLfHn3BaKJV4io9DxSe4ML1W+A2WMtnGczWqQbLeJHS+TZCeaTWjLxgFnF8FbUrNliBHjvDxRREFPGZHvssyWfy7jmk9yVnf8moig4m6S8GAR8dhSqqsVh4oKAutW4Qne3TrbtYG/lGN3oLjX07su4Nvl0m3S6TTI9Q9JokeuW8h4SYmZxaMgnMJ51hobDzWqF67UyNyolblQ9us79NDUnK7jUTxUQE0D2RC9nefQgAfjDkeHpX6Zq/n+x9VuTP9nGz36UQfonyXgwhXkMpiQxcP+c5gmjbKB46tLEO5KR9AnTPkE6IM6O+qV9WMhrBk7GqKLRr+vstVJ2aiP2nL6qEp0U4ukzFTdpT4b46Q3tLuvONptW74EeVOWsYD6Ahm/g9T3cTonG0KYUyhL0oNtf5L5jtFJM4WYYtrixlDAShyIyiEc5sZ/zABu1DwzdzrC8FNPLMN3JfPD40H7LEwELWZzf9wqYZhlDPKrMkmoPpzMiW92l6AZIEVuPwGrNU37sY5QuPYmh1zDiMlrokox6BNEqYXKHUXaTQL9Jdsz3RcKKc+bXLWZ3YmrBvQyE9LpG+ccYZT9AkH/yru+QSg7Z0qcm6oUpgQh85BGDdMQg9QmIEWHwA3n9SLwCtUJRzlzXxTItnCzDG/i4nS5Ot4sRxWSNBmm7RdRsEVdLxEVOEAZjg9b45MqrbefUal1a7Xep1zdxHP++goKmCVX8PLXaE1TKFylPRqbXeL/7Pm/vvc2VvSu8s/UKNwYr92W4xavs8dTgscThiSjm6dEuM+n9AimpWaZoPwmtZ8jrT5JVniQ3pggGKb/zlQ3WNsffleVpj7NnSlwPYq76Ee+GEe/FCWvZyRU+sUO+iM4lDC5iKF/E5YmFwrcjJKEiCZfIGtKdep1w+hXy9rto5qE+pdhje3iBq/nzvOU+xx2vxboxtgQ5HHIbetJzebZS4smKx0o/5Ou9Ia8kMYPj8t/FOPmkwFhnDMgWg4e51k0o4XaiemiHRZ9B2qEf7TEIdhkMd0lG95svf1gC6ObCiDtzIVvNgEz8PItCfd6fG6b8gJ9yIesdSWgkuctq/KQysV2JnznimXQ89CzGTgbYcR+nSHF0aV0o45WbuHYJR6p5lsaNpqESd2/WciXqsVu+H6wI66Y57CkgtrQ/5PKqQ7PXuguOU3PIqLxC5O7cpQN4ha2k2SuFS1lGalCOh0w9forFn/ysaqtIbvWJrgsY65JMztV7/ylY82Wc8w3Mtnu3SvXNVKyGRsC+02fT2GHL3GPH7LBrddk1O0pB+dk39/n8qwmNyVuIDfjqEyYvf6pBZ95jO+6xn49/aGPwuWyBn8yXlVpjocu1JiGX2UgodFGdTSn0WPW3ictkIb6FeixKLxTSA2UWanwUi8Y4s7jRO8N7nfNc3b/A9d4Zkty+73v7nLbGZ4xf55P6FaZb61w91eLX65/mZe0F3uQZwkOWBk6e8Pxwl0/v+Ty3a+EETXzTYGhpDEwZMLA0hrJtjR9LRb1nZ2p7KM/NdeJugSZG7/sR+jF6rsgX5E2HvOWQtx2KmqUqsHJO/cGtV/jZP/+X4XsdfH2/5+v3Jm4PAj71b16lKJsUFROMk7+BQiPxopx6WjAXjVjo77K0c5vm6lWy3bVJb9LRECPCQLdU71h1boE/8uf+ImfOn1fVArm4DVf69O70SdeGODsBE9P3I7HjaOoi/E5d50rN4EpNpyMdxBNh7wWzYNnMOaUnnNJCThUjThUDlrMuc0kPIxE/qRFFPILIJw6GjIZd/KBHqBZKQj8To9WUSMQGNFHSMXmFM3yDc6xpdTS5UGkxVb3PkrFOU98j1eW5UoWSCpQ05epqO5HMfFonC5bIwyU1Z+GyaGXf/8fpwQSMraJPZs0UU86H//zkqUKjk943K8mxE2m4Bjcu8OLxtpVKQ7IAOg2jVEGv17ArHucZ8Ii/wxODbWaTo3K/m4bF17UqdzoV8m2Ls2siZZtgHVsfBbbGrTmHG4seN+c9VmddckO/K+Qg/PgD4YWjs05mVvFLiwxKc/RK83RLcyQneGI5ScjscJt5GYMdFgc71CKhEBz8PwfVwXFV5AyrPM9rzGvb6velMvkOl/gqz7FL6y65VnXRiXhJlqDHEXoSoyXjWU8iNJnTD69cjWX+9Qkd9/7Loaq8lipkqo9y3E9ZiNT3oRC60tAaMrAG9O0+fauvtmXkJ30xDkUlKymp/lNKrn88n4rnVHO4HB2h+0m/Sqh32EtW2As22R/t0R30yaQkfSzcRkp5ZkRpfkh5LsCpx6rXMg1N0sAgDe6fk0DujDrFSCdIBYx/RLNlUWZzHEzXxXCFkiXUPQ/DdtETDWMYYY3E7L2EZ9ZwzAquV8exyqqx3s4MJUEtFY0PWrCmToeodpu4fI0yL9Ec3aTRF9rrvejUTbamHTZrLUbhPNFwCt9v0x/U6UUukaK+frQ/70HvSBfjeitW9OAwFHD3kOJGWkazuc70zC3a7TWMCR1MIstciryqxEyMPMCYKNnqmTNRs5VZ6LdVHG2BUlFheveLVDtfJtLgbafM/3v20/xGZQpNrCaS1Qn19+BtF8xmGc8F8OLI4okoYjHbwzrBhyotWvTzi9zKL/Dl9Dz/Pj3DDcNl9IA/cwZtArLugS3pNTlIFowJrDkhCSMtUiORb0+uK6YEuUUu518uVbCxIqgaCiwkpOkeSbZNkmyQJOukeeeIVLVhZxilgqDdJL7gUZ/e40zpOqVD/V09arzEJ/kan+RqJlXTFZbMAY+XDT7RnObR1gUuNC7Q1Opk3XH1KtgZ8HZnyCtxxOtazhslg9UT2ADtKFdATADZM92My/0c69AlRRRg/aRHP9lTY5h0GCT7akT5MQ8juZ+pXs/xCFXP53j4bsLISRmUUrVfMfA0mElT5Sv3YhDxqTCkegwQp9OPUFz4YfTzf4Cw9RyjIXf70ETx0d8J8PdCRt2I0SAmCBPShxTsOghJycktXj5B30lZbRTcbmistB1Wp6r0HZt2P+MzVwKeuB0r5V6J3bbBzmMmXj2gPdjH3d1m2OsqxdYPC7lLVb0K9VaDRrtJ060xFVWo9EysrQQ6D5d9yp2QvOSTeQMye4QxKmF2ZjGSMciQlJ7ffovu8m/gT71JQEY31chvaMz9ts7iGxr6RHG3U4NffVbjV57WGZQ+/IIjqc7WxCalbo4tUxpGTl3NBQ1TlH4/XElw/EY1tNxS3nPHZ13NR/eJGup1f5ob3TLvDuq8ll0kPC74YUB2AHyaY+BjklESYC4JLV169yyOXo2/+bCzgtIwxtwbUeyHRL2MVMTLDoWTx5wKd7g0XOUZ9xb/+T/8J3yn4/tqh98j4KvX8fnH/+VPk2kmkW6xX22y3Wyz22iz32jSbzTwa3Vy8+RFjZbl2IOAUq9Po7vH7P46p3dusdRZmVRvNKpWm5YzR9OZp1FaomWKafH9r9exNN6pa1yp5lypZrxbjcj0XWajTaajTZrxFo14j2raoZT0cHPxE5qAoAn4UUBIzfqh7Xs/F0GC34uQ2o8z6VFRy+xkijRYJgyXCIJl/NGiav48Hq7Vp1nZoFHdol7ZoVLdA0My6TBKMvzQp5+MGE76nIzcoJJUKKfl8UjK6nE1aijhClnUSfbrgxeLBW06XOQWF7ilwItcFA9CqJS3WeRadoqdvSbmTsz07i5TO7tYx25sYsK9126zOz3FzvQ0u1NTpNbDqSTJhaTrVdiRc7DaYLvWVLSdTGipx6IUBSrzOaaijOkozhGgVKi/4zPF15lOtuklLr3Y4UYyz+1kljDRxwArFbj8EHQ725YL0/g4CmBLU7RJE/194ThobgncMoVbpTCFpmqTFRaF+szHK517PQcH20cBqqklmNqIwOowcHfpOvsqYxrj4CZ1qomYPdeoJXWWknlOJfPUhWGvadhKafSDm+uFciYLuN1wjb1oTc1CPTkelu7Q8NpUvCYlr6EAfKhLr0FGINUZRf6U5EVClU3+FL9ELe8zTG1+O3uOd9JzaFmqjpnIu5tpjp6lkInKZkyRfzjAPR66ZuDqJRyjjGvIPB6yzzI8dKlKmJaiSWa6qSjIsTakpb3Bsv4Ki7yHeYiSt2W2uVOdYXPKRWsMcd3ukar0QaSpzWgwjd+fZtSfYdSfVWbD8lzLjDCtEMuKMKxQbY9HgCELMXuEKcMKMK3xc/VDwFoARBDUCPwGkd8mG06jDeex/VnKeVl9pmbrGvrcmzD9Dlj3EiZ60MLbfI7q5gt4g7PYH6J0Jpnit+s5ZfOX+MHB/0Zpkkn/lzM/wt8+91+wIWp/ecHlQc6TnZD5wR3s+Bab5h2uuXe46ayT6ve++3KtPxtnfGxY5rnQ4tGky5liU4kQHY9r+QKv5Re4lp1nWH6c+YvPcqlSVRWtSicm2fRJdw9RpCnY1nrctne5bewxjHPMuIqVVDFtKnIzAAEAAElEQVRFaTAV0Hq83Aet+TJTyyXq0zrlWoKu9/jdL32RzZUVjDxjeW6GPArp9TvcNjzWppdYn11mY2aZbv2eQbVRJDzBm3wy/x2e5xuU9EO9spGHtnUJe/tJnP1HcCgrTV8X966S54Ni2yp4qRbzar3grZbDjUaF7Fjy00ozLnVGCow93zd5pldQf8DXRXpWhW6/7XZYcbe5at3mhrHCur1NILy8w6HkvDXMIufZMOIzQcAPhinnomMiIm4Dzv8QXPgRNefuDJnQH4UKuTeZ1eOQrBveMww8pigc5RAUcq8e0u+vEQx7pFGuPOhiq0ps19TIP0RIQr6RqXwPDvW43p42+eITnpoPIwu5/152bB6xDc7pCQvBkMqgi98f0NvaZbC3TyAWJ2aI6w1V1dh1fTU77hBXzT62HWKEDUqdRyjtP4oRV0ndDqm7T+rsk6jtPVKnq6pN90WuU9l9hsadH6K8/8Td3bG3iV/8FsXXv4J14975Hp/TGH3WInlClCctosxUAK2b5nTSlG6S0slT9ouUdb2gK3m/h1zWuLlJK68ylVVppzWm0jpTSZPppMlU0mY6buNmNYampSpMqurkGYzKJn7JZOjpDBydoaOratTQgBEpH9/6Vf7czX/KRf+G+n+GuPwz679hd3BZ9Yy9rGXKWPrIZ2lo5A17AsZsirp9hCtuJTFuElPKU6UULSJadcuk4di0XIe6nlNNI0qDAaVeQKmfUe3p1PslapGLc+xclOvILXJeIuWlIuJVckaH6BafGtzh5/7BX+E7Hd+vfH2PgK9wv8M/+Cv/6Qc+R6huvWqTveYMO60pdppN9ht1erWK6uPQighNVIrymOWg4NG+xaNDl0cGHpf8Km5x/0LA10bcsFbUjfyqt8K7pdts2XsUH5Ll/3aFVIG8wsLV5GLv4Kc2fmJR5LZaILuGx4WpFo/Nt2mXqnimh2u4eJantj1jMluT/ea9x/Iz6fxKkn2SZEwlkFkex5N9o7DD9d2C93Zc3t9rcKM7y9pwjvyEzH3b3WO5tMm8s8e0MaBZhGSjBok/BVENPXMxcldltA3Zzly0Q6+jSHRCRTCEjiBjTDuQ7UKP1HZhRIqaUOghth6pRtULrHCuuE2zOKoC1dcq3DaXuGks0h1UqG4OaG7u01jv4ARHbz5yUxhOleguVugvluktlknLojonypMi6DKWvbz7WIm83PuZgOUte4ZVe5EVa5kVe4lNc1YBIvU5ZhlVv0d90GGxd4el3grtwQ6VQR/TT8hGk5TuB4Rm5tiVFLuaYFUTnGqM4YrMv0Hsm4x2SoS7rlKPvO93DRfdmkHTltGNBTRzVtG8PvDc02Nsp4ttS09gF0dmq4ttyXYHy+gp9VBD6qhjUUwlWjD2zlNMQbpJg+vRBW6k54gPCKTi+5LmVMM6RXCGMG+p5Z896YGyD4EyTyq5xpCyEeHqqfqZdMaIsvlOOGA32mQvXGM/3lTZ9iN/MxoNe5Ypd5FpZ4m2s4hnVg716Ir31hvoCpbVSQpRsWqjYd3XNyORFxlRFhDlIyVJHGUy+5NZhvRf+sR5qJ4nnoIfNSwDSkao/l7PSCiZshj3GOSn2cseY1QsoukldMNCd8TXq4fbvI3bWMGpr2OV9zAFkMkHcCwexpfmg6LIxdFUzqUHiNvkmvqOizWA+vAnIQvC2tYLCnC5vfMPVB5Lipw7Hrze1Hmjaar+pAvxS/w/bvwDzger6jmvVy7zd879NIE1y2PxBo/Gq5zPVnF0/+71ISFjFDv4scP2yOPNoMm1qMpuVmOQNkmk2f7QtV5qhE9oN3naeJ9nzXd5RrvFYnE/uM8KkyQ/T1JcIs4vqTksZtmqj7hpdljr+eSBg6nAVhX9hPtJuW4ze6bG3Kkq0/NlWtOuqoiIVH8epnfnJIj5nStXeVPLuNmocGeqxhUXZQ57PM4OM57o5jzZy3iyO+5H1bWUUfNdBrMvMZh5mTgfEQ8tEt8k7ZdgZ5G80yL2DXX+SuJxylnANSpKcU4U3/ykq2ZRoRvLWwid2yStlthZPMPG3BnWW4uslKfxTwAjp7SCi+GI890Bj26NeGTPYjq+12d0PIT6+tv1l/li5SXeKl9jNhvx2SDk06OAT4Yx7iHWiiI9zj9HNvsiae1T6vNI9+Mx0NoPyAcfkigxdUXLM1uSxLDoF7vsDlbZ2b3D9sp1dtflfBs3ZMo1XD6j+ihivtBpBRHu3pA0t++CsYMxaJxjUFkmMdyTk3ZlnY2mwXrLZENG0yCSi92xsNOc+f6QpVGHM9E6Z7nBgnULy+1juj1Mt49u3S/KlaYmUVQmCivEYqOQG2oUUnEtxtuSPJHvstouDralIjveZ+QWjbjFxeFpFtPlu/5XkoBK1r5BL1hlOHcOw6thd7exggArSZSdjfiMacoPsILm1NC88bZulJWh/S82f5N/PvUritIo0UrbLKSXicwKe3ZKx8oYWSaFXiLXSxSaR6GXx9uTMd5fEr44DxNeFvLnN/8df2Xl51mOxAcVBkaJ/3Xhj/KPF/80206b/+RmzP/lqiJe87PLFl+eMgl3Q3a2hsTH+hZNLWfBGLIQbjC//z7z0SrlaoBdS3BqMXZ9fG+Wx3Y1VlIBJ4a0jsRV3PAi5fQSXnIWO5hBH1bRuhZFML6Gy3t6l4yXBByS8ge8nL/yf/9xvqfB1/dCfLeAr872Kj/9P/3XyqA102MSPVYNtpLVFAWdxJQ5IzHSMY/4oC+lgNmkzaXwNBeD01wMT6khZojHY6SHCmS9r8Ztrnp3lMzrB62J5b8xMw0z0yfz8W0xNDz6s7FnmImZmdgTvzAlZlFYlDWXaqVGuVGjKpQSq8qXehV+c89hR7g2k3hu1uKPX6ry4tkarudiCRXKtjFtZ7xt2WhKGOTbF0KL6HT2ubN6i5dfucIb60NujEqs5zW62v03G1mQt3ONuVRnLhuPaTkGxw6obo3IE/n9j/Z+5fcMZ6BuRIbdo+ncYUm7ykJ2i+l4B+PQzVoYEr2axW7LYq9pEw5NnOs69nUN+5qOuXv/h5zOFEQXcpILGslFnXx6bMysaQfDULOYNBe5SSILmb5O1JcZ/J5Or2sqGoHmj829PyhyqViVXGpOzjzbzOm71K1QjcAr8ZbzBCujMxh9ndwPiMMuaXKS7LCBZsyim/N3B9ph0JFTmCGFEaqFcqolZDIUvz6hpOs0NJtm4dGiQrMoUyvuyegLSO4b/qQHoMOatcO6tcW2tcu+uU9XKIlGqJ7t5TolGfKdEEprXoasgi0eeUrERadtBExhUA0XcQenYDRDnFQJc7npn/zlc/WYlrVLWyqwRkhJKqeZULZs4swjx8PSPRxVdfJUBerbE7Lsi0RORjoE0RiooesDNDuSi4FKIMh1KspSBmmKH6cMwgI/0hHMH4nrg1Calen6iDwPicWD/SPTWApMN7vXm6a27+9Zk342Q/XJnWC4LGAqc1WFxgxbWKMZnOECVtRGTyoYSRljMmu5g+4ZpJUOcW2dqLyGX36b0LtBKj1qH3CddPRFStZZSs55yt55yqULGN453ol0vj4M+IYf8GoQsjcRKLgwus3fvPY/8EOdr6vHXbPJF6r/B1aCH6S2H5KEPmZlA7u2gVvfwKluKVn1zazMynBRmRWLafHWSMw0Tljc6hEVaw/T2iFzV4kr1ynczbuqq80sU31EzwQpnx1UOZft4+r3949FeZlOdoFecplBdolhdpGCxl0xFdvMsA2hoQqzQMMQerNQoI4drFBH0dXfbOh3hTF23Pvfdz0ulBiGgCyZH+um2NEeQbZLmO0TCFjKBkoy209D/DRilArh8eFC5JgadshcJWZ+rk51bony/HlKy49TOvssZmP+yPNHyYhrnWv8zu4dvtrtcyXQ2MzbxOb9CWkt6+NE1zgdD3ghtfls1GQ+rnEtusnv5N/gTfcNnoz7fHYU8Jkg5NRxtgJNQu15Qp5jmDxBlHoKaIvBfToRIEqlL+5ArEik4Eu68r2US4m0+qRmQZD4+P6AwB8SjkYkcTwWS5H7paaNBbUeuGKeRFFQ7Q+Y2t2lvbuLHTfZmf4Buo2Lk5/nzG6/zPzGb5FUPEbts6TtWfJGXQE/oZgmhU6YGWyK+W65rrwq11sGm02T5ARZVFd6Pvcz5vdTFjopi52AZjzCMnJ5SSXYYgox0coxmzqZl+EHffxY1A99giIk/VB2CbR397h09X2WVu6g6xbW0icwL/wwZnXhaC+fptG1YGDrk/4mqVTf63U6vG9wsE8eGwHp6Au4w1++a3QdeR/Db/xZMmvxg9/cfZ+DUOgDtHykhj6ZZTSSPX5q5+v8pZ1XaaXjivmO1eQfL/0pBbzERqBmaPxIu86PTdf59Hs+0f/vpnpe5TML1H/ynEpYXdns87vXVvjd61u8fCegKz0fh8LUE87Vb3O5eY1LzWucb9zEOVRZzDONuG8R9S3ivk3UtyePx5Ythfh9KIq2TrnRoNqeptJuU6/PUC/NKMVHT3qGhzHaIKX0SIXKH36R73R8H3x9j4Cv3W6Hz//iD3zwkwqUUs8lBbIOgNZpaif4qgj16Ja9xy1rj1tOhxten1vlBN91GXglMsOl0JxDQ0rNDnpu0Oz7NLtdpvc7tDs7TO/vUB+IL9dYJVGokVKFkzNOpIqFGidy+A8b8sx1d543q49zvXxuLDAiF98s5NHhuzzRf4dGen9D/vEQAGYKEHMcrAkwO7rtYsksjyfgTXqC4jQlSlJGUczIz/D7OcGgIE1s9KKEnnvohZhQi+/8+L2FWsGmkd8bZsHghOy73FNOl+FiK+byTJ/L8xss1oT2Y5BGHkkgo0QcOCSBiBE4xCObeGQRjSxV4Yl8ATsffAcR2d4F+y1OO69yynmFhrlx5OehMUW3+gLD6U+RLH0aRxQlb74HV6+QvPkG8fvXj5gby+IlnZ8le+wRkqVFokYdP0/p727T295iuLenJLg/KAzLxBUfpVqZfqXCRrnF9dIs65U5+rUGI1eUvMZ/l5nlfPL2Ff70tX/NTPc6W0GFrbBy11/kcGh6A01AljEP1jSFXVUNyrkekRlSOYwUyFJVRGNcQTzpBizgTIC8XtJJvZTYiQmtkJEuzfID+mmPbtplv+gQn9A3882G9NvJd7SeVqhnVVpJnZlU6CV1mnmFalqmmpeppBXKWRmvcFTvw0eNg4qUZPrVyKWK5auexrbd5WLpCo4h/Xc+3ewzhMUn0YoSGiUoxBXvwV444361Ebk1JLN8skOz2mcPyM0emdXDS3aY6u0x1RniTJrf5VQbmCYr1TIb5RI9zZn0rh3qWwsP+tYMslDOg492DOTztS0LywGzlKBXfIxycEhU5EB4JKXkTVF1L1PxHqFae5xa6yl0b5Y0zhmFb7DX+WU14nis3ClhmjXqtY/hekvkIgAzuoHvv086uVYNqfA+l7jKI2rc4AKxdrR3sp32+dvr/xt/5Na/wCikAd9Ce/4/gxf+CxDV2SymSAJW90e8sR7w5kbE27sZ7/UK5Zl2UtStAadqdzhd2eB0aZdzXodZI1bXLz0tYUR1jNE0u7HJLUJuGh1uWJtcd1eVIqtE05/jz1z/CS5lXWasqzRKr7KobSJdg8cjLWZUJeZgiN3IYdVJuaqslDTeqOu83tB5q2FwvWKSH6tqCfX18u4Wl3bWOLW/wUxvA0+U8NJIXaPjNCdKJlWghwjXjvHcBLuUYNQLtFqGUY0wnYzBWpnu9RqJf4967VgJZ5t7PFHaZsbrc9M2uV5pca02zTXb5joxa5Pkj1CQRSlPDamQajWc6sfQKo8zsJfZ1hukB9SpPMYOX8fzv8bj/a/x6dEYcAmt8HAtQyo1o+IR4uw5kvx5kuLsiee8gK2+Jt5aIzX6Muvjbem5+1b7HqW9xJLvzbFhyPnYrxKvVkgFZUxU66b0K5xa/xVq12+iHRO3KPSCZKkgPjceybmcbMIcLTKx5FgmG5xiNbzEde0UN50pblRqrFYdZcp7PEphzsJ+qkCZADIBZtXwg9caai2iFcRWTujAyJaeO5G8j0CUXEXUxdYJbOnhcuhVSww9l/OhwY/vaLy4W9zt79u3NX5h0eJfLVtsHlIBfJjQpS2j969x/S+q4ybnsWG9QMX+A1T0OmWRDhLz7ThH76fQjXFjFBCRymB9Oqe6nGO0I7p5j32hpsc7JMMVPr+3xh/vrFKdsA/uuHP8w6U/xz+b+3HivIsTvIwdvIoVXVUKo02jRcus8SN7z/KTKz+ofuf63KvcvPgL1KwdqsZIMQ3lOr3uz/He/gWudsciHr34qOCeeKieKUVcLOlcdGqcMxqYcUwkydKwSxz2SaMuSdgjjWW7R5oMHmjVcTymWov81P/4j/hOx/fB1/cI+Hpna5U/+8t/6Mg+kQyXatalA6AVnKKV1U+8OO8Yffa1EZ0spZcYBLGrSr4n8d1lXd8p6+zUDXZrhpp3RNGoZpCdcAGUMNNEAbH2/hZTnW3anW2m9rcVKLPNGpY9S25PEdotBladoaUzLGKCLGGUxkR5onjdA7NC124QGvcWerWkz1KwwmKwjltI426KVaSYeYqptrPJY3GS+Ch0SOlqLaPpNTRDBDtqx0ZVVXY+KBTgKET+doimjzCMEEN8lpyE0NHYskqsazVWsjK3Ew9fbszHwtKhZGq4po5j6XiWgWubeDIcC882cE0DR/ZbunqeVM9E5VHPCrSkoPAjsu19sr0+WS8Y03d0D80so+sOU8YG5+1XOOO8ypL9llKCPIisMFiPL3N99BS3g3P00xKm3sGKNijiPaUUGOlCifzgu7huaDh1DaeaY5UTlYHEFOBepyimyJIpsrBJFtbI4ypFXKEoDAaexkajoG/tUKSblPw1ZndWqfr3V7UcPWHOG1LzCracy6zpT5BZugJV48qVZAI1HEycwsLBwp3MttBOtIzADPFNn4E5VEMqVV0xZ56oWcUTU8nxBzxu8j4iSjLJ4IskfDWyaURl2mmd6bTFXD7LYrHAXC6SBCLwIlVqlYue3F6l8ily2iauqvi6eFJR+SaalweywDIH9AwZQ3qGj6+HjEiU9UJQZMTKEF0jVUIXUA8iPD/A8kPMYKBoyAfhGgl/aOE9zlXGlLO3o4t80X2OvB1g2xGWGeNYKY4Z4xoZpuqdCkCGfVQI5kgUBRU/Y3YnYnY7UoJAByEZ7u0ph+1WjYE7hZFVMOIKuqo4HR5SfZL941mLPdJYqmgCKP1DNEgBlbI9JMr6k30hyUPe2A+HbmVHFSClndApsCoiwJOpRbsuWfzeGcLtJwn3HgdFCRK7CYNORedO22RlGm63LTZLAmCPRqXoc4n3uFRc4Y9ufpFP37pxF5DuVCq8V77ATf8Ca4NlVqIZbucNbmllJTZ0UiwWKReRAZdEFEOzaKmuGrES/2jnmJyx0ockTIjr7grX7XXaW89yaecF9fOt6rvcPv0/caHY46kw4ZkwYzmTiujRyAuNLX2JK95Fvl5/lN+afpq3q2fwghHVYZ+K31eU5FZvj3Zvj9qwhxcMMWOpsH54KDVf1Tdok1uTHkLLxiiVsUoV7GoNq6SjGz5FsUue72CYMYYRU/L6eKWBElMxzZBg26Z7rUb3Rk2B/4MwBJQvpiRTLr7RYOi3KFJPgS0zF1H8D64UxXrB+60+PfcWT8bv8JnRQFW3RBDlcKzYs7xcepz3OMdu1KYUCGNg4ptVlJR3lprV8KgWJSXJ/8D/t4jp5wN6orSY9ehnPQZ5l8jbw6wN8RoxpWaIXUkwzUwJyxhGqnocx9viQTkxdJ4YR0glo3/nOXav/Bhxf1wJ1IyIxrnfpnX5V7FKE8pqBtaqhn1jPKzrOmb3BPBY1wlPuYTLJcKlKuFMSanvSmLAiRrYcQ0tbrBpznDDk4RdjfcqDterBtkJNNSpfszTN9aY7g4YOg79kjsZDr2Ki+9ahEJ1/CY5yCK28sdWE/74asLcBOjJp/iNhs6vtnSuOgVOKiNX32UnydRwkww7zVTPs5VGOKmsZWJ67javzX6F1dpY/VfA+4XuZS53H1HVPEUv+pC3KhY9SRU+y0v86OArOMX4HnalfJa/v/QX+JL3FI3+Ler+W2jFDQJ7wNAY4cs64Nhr/4Hup/hrG39R3ZO+0Pgyf3/u51RFvK5r1ApbCUdV4jqlcApvOE88OMt+NMO25rB6QtJZE/ZVprGcGiynOouZjnus+W1sGD6iyAcU+ZA861BkOxQyq/WV3F/G3xW3PMX/6X/+X/hOx/fB1/cI+Io2h/zvf+NNnuoEY58lX6chGsrHQhZ5t5x1rnq379IHbzsbagF4PGQx2SrqtGjS1ts09RZ1mtRoUcobSizACuvgC00txfcTtvX8Lhg7AGcfBMqkGbMloEzA2ASQyfZMblNpnaIxe4ZB6wxfiWx+a71LmI4XHrah8dxsjSebIm8LHT9hP4jZD1M6cUonFfWhTBy/joRIkZpFhlmI4l9GPS+oy6KzgGpuqFHGpCz9YtgfuugdgysfLe+DXBiKAXneI0s75Fl3DLwesqonz5JS/5Yzw7Yzo+Yde5pEfzihi28l5LiUs5ECstPpPo+lN1nONqhkA+JEY5A4H0r7EuggcuqlOKU+GlCKxBQzxYsTvFjAr07YOEPQPE/aukjeOIttjhva73Z0Cd5VlyLxNAnvDq0QQY2xxNPBP3lyZtoktkvkeOSWyXTaZzbeVw3oQqdKNJuOUSMQU+YsxRTj2cl/NlY3VCWPI6871l28p+Yo29JbKHvV/uLg5wfv/D98HKjD+VrIUAsViBrqAnpjRSFOpUnf6LFj7rNtiv/OHn1rxMgIGZmh6un8Jv5TZSI923GY7zpMdxyqA5NPtlf59PRtlencDsv8m83LdIQtNBPgzQRUZwMqnlgGSJbz6EuamqeGVXiUhhrtrR7N3R3ccHjEi8s3nsTPP02UfhJdBGe+TcpZ98JXtEhdG6DTpyi6JHmHUM73fECYBYRZRJglhFlKkMEo1RllNqPMUr0gHz0KRamKXI+eV2Pg1QjdEoFTInA9tV0mYzkJOB+FPBL6LNhdXHeF+eBV9DjkvWKZV8xzfLV8nvfj06wOFoiPSUNLiEDGKSvgggaPZB6XszIX5br2Ec5X8XMMZZASClAX+efMJFWUMKlgFeNZj9Gr65jN2+it99n1q4Tv/lGlxBgbI750/p/zfvtV9ZrlPOeJIOY5P1W0xUfTmKn7rtJCj9VVJXszqLIxGYP0JJqt9B2VwCiDIX0uHoXpoolJtOWC7SiwNTYJT1Tfo+rP0u65/N3dVsIskkQaO88d3jf+bwsl4FApdyiXd1VyItnI6d8ukYuO+CSkj6Vxvo+3lJFYFXy/xXDYxh82SWJXSagoarlWsOVuYzpXOMctPhX6PBnFR2BajMWb3pP82tQL/MLci9z0Fo+AAi8LOButci5a5Wy4xpl4EzdPJ1QuSLo5+qCEPapRKhpUrSZVq0XVbFEyRcjpweeDmAyP9ICRFhKYQyKrS+jtkJa30Cd9VYrSLsMKyTOT3s3PsP/ej5H403ep780Lv0nz4q+hmwmJmF+P2iR+ezK3SGUetUmDOk7Yo96/Qb13Q82Vwep9CdNMt+hXT9Orn6NXO0e/dpbEvj9xYYsGobFLnm1jhFuU+5vM7K6zsLuFkT84CStiU6HtqBHZNrFpkZoWiWkRmy6p5ZEaDpmIAek2uSZWBcKKke+huHxa6IWFWVhMWS6LrkdLxJtEAdiwGRYmt5KClfiegfTDxGblJl89/W/YrI3FMJy0xLOrP8rjmy8qkK/OY01o3aJ6meE7BdfnHGit88dGv8CP7/32XeGcr1ef4J/X/iibo9Oc2d/DS6LJ7977fRmSjAwN+fwDQmtIaPmEZshTwSV+avcPqzvgr9a+ys8s/NMHWqUchDy3rrWoZGcogguMBrPsD5sMVEvFMesGdnmSdZ4q1jifraHFAf1Yo5+aqjjgCzddGDpWxl49ZleNiL1GzGfzp/jv/8r3uNrh90J8t4Avf29E5797+cg++ZrdKWvcrmkKDA1qJlQcqvLlGu3SWb9OHu5AsU9oSZa8z57eZd/s0THFr+jhTgknt2jnTdpakymjxZQxpcrUjaxJZVDG7pTohhXulBxuVXRWqwbrNZ3t6geAsjCicmuXfDMhiu6Bj1Yy4ulgxONpGc9o4VUs3LKFW7HwKraanZIpdXfiYkhn6LPXDeh3Y4JBRhpoGKmJk1lKMeikxfOYsKSW7hRFSChmn0SMnX5C8iImV6BAso4xZh5h5RFmHmPkY9GSIhcPtW+deiY0Tan2JZpFqonim6Hme0OqNTa6VLCMivCl0GQBorsUhkOqWYp7HklmPxthpD3MdICT9HHTAaW0TyUdUEmHJyqaHQ45JjUrom4Hk16riLIZsWlN84r+CF/UnuVtzqhn6nnG+d46T+zd4Im9mzy+d5N67N+XjbtWX+RK+yxX2+e40T5L7FRUDv4gF398HsuqaIqkdPzn9gf83uH5eE/d71Vkk+XegamwT0F37NiioIUj5prKHC6mmw8YxT5pJKa+sTIgtaWaplsEDmx6AevW2FNMMtHCoT9llNRoGBqZ7ZObQuvzyewhgdmnbwzomwOGeoCvR4wKGOUafq4xysfbxx+LkfHhsBKN6a7D57oJf1VfoapL75bBL29c4tpg6u7zOpWYnWZEp57i1zW1GK7lVc7EJp8Jd/hYvMJCvnP3+eIt1ymews9eJE8/o2DCQWieiV62wDPJbUNVMhNd+sIgkd6wKCOOMpIwlSYjtEgUGXNl2yCiJPYhkRJRkPxmYyyqIBlWn7ToEuf7JPk+sVTQii5DvUeQJ+Qjl2Fu0SlMgrxQjfimfIbf7P8r31/dYWiUCHSXUIZxb04Nk1o5oNnYZaa+w2J7g8XWJrZxSMUwNfH8Mp7vUPJNSiONsp/hiTUDgbqWdbM22+lZNpMLbCfn6aSLE3+/w5FTMneoWCtUnTvU3RWq3gamE6G7IzIB/LFNtz/D9Vs/RjDyVGY6crbU8XGCGG+ycDqIihkx5w6Y8ybDHeKcIFripyW24kW24nNsJZfYSR8nYlr1ghw5XmopI/2Ccv2OJ4kc2RYOYnxk/337ZJ48X/aPf56AmBebDTBrFFYVrBqaU0W3LGy3i11cJeutMtoZHWFHOY2I5vk+jQt93EasxFWyXoWi36E83OFRP6RxDAh08jn2ohe4Ez3L7ehRRmJGJ/I3lsbqlMnKlMlq22StbZIe633S85zZ3W0WNm6wuHWbxc3bVERHfhKaXkcz5tDNOUxznoo1Q9U0qTkhFSulouuUCgf7JEuVg+MrMNbbJS5tkpS3CJxd1jszbK18jDQae2NqZohTX8Wwh6RBU4GrPD5BzfLgfQmGNjTl9KHrGtJJILOYO1d6t6l2blDdu0Z57wbmsfuIRFxuklWaqropXmTmYB/T7z7wb4gtG9+tYEmVKZNKU6LuWb83IfK1Uia3yQ2LyBAVV4PEsMgMk0yAnW4rcJer2SHXnbvzdnWPt2dfZbfaRZZGZlHl8s4Pc6r7PHv1EleWy1xdsFiy3uKvrv7c3b5Qia87L/C70Z+k2Ll0n93MSaFcMo8BsoPtZdPmE1ZDmZzfyPv8pnZTAbXIFGXfEYHhM7KG+PYA3xwQm5FK1CSGzJIEHa8v86RONjpHMThHPjpLkt27jxxEO95jLlqjqt/BKN+gX++y1xYGVoPMmicz59ScmnM86XT4wg/9NN/p+D74+h4BX6vDHq/9/W+w6eq8U3d4p+HwXtUgOKEx9aQw8xFOPqJURNTyVDVU15OUchriJiOspE8hKn/ZLn0G7Gtd9o0eA+P+C+GDopJ5tJMG7aJBW2vRNqfQS4uE3gJdp8W2UWItgL2VIfZmoMrysqCWW4+oLhnTLp5T0B70aQ0HNIc+jSClHmmURSHQKGNoDoaIPEhyM5P2YpHAjsiLSDXuZxPglOUxaR6RFhFpHk7miOTQOOwb862EqVlK5vvwsHX3Qx6PZ1OyatIPkvbw067ygznYFrUtMQs+UNp6UCiPqIf4W6S6YBklTEOAXAnNKFMYZTKjrIxXE90j0zLmzWuctd7mkvkOM8a9nhaJTl7jlewJvp4+ydeyx9nXqsQKxmpMD3e4uH+LRyZgbPaQEe5BiEz4W+1zvNU+y9vts2yX7klFf7tCOYNpQpsp1M1ek5u94s5Is78MOXeOdooc3j5+JLVjY+xUNu72ExUweWwVQieUvKgMkZ+HO+Ts3U1wFNhGTNkcMWX6nDICFoyAWWNE0xphKYnzIYbtYzojTNdHt4fopo/2zVS1Dt57Zo8pe3EZI62gywIpdskjizzWCVNNGV4OsRgVFqPcIcxMoqLA1nf5M8mvci4bg6gvhot8aesU3ugo5c0zYs42drjY3OacKdWm8X6BBl/1XH65XOLXyyWGhwRw7NzBy8rYaQk79nDSssr0ukI1mgw3ObxvvC3Z5uMfjiRjVIJGRsmk7ApdVx9TdA39roqkyF7rWY4utL4oJR+NRxQkjPKckakxMjTkzwsMDd/QEFu0g30yb7karzcMbleOghZR85wb+Dy5M+SR/SFnuz4zA58sC+jlAbvFiH4u1baALBVPrxA3iz40IfLgKLDsHMNN0YUS6WRjKqR7dNswxR+vShItEIenSIaLRP35ceWi0PH0DrPW+8xaV2kZV/FYI8pyhqnDILEns6PsCPqJSyQeXQ8RmYjnOBqGHZJbPh03ZbVcsFvOlW/VlBnwSB7yRByratDlOD5RdL+feOxEVbaiGuujKltBmSiT693vTSRWjdSZIrXb5FaLQm9gJ0KLfA9tdFsJHkjlZqHU58LUDmerHVqHqLzqNeS8KTXY4BK3g4+z332CZDitlHbld8ULTm1r2bj3RwvIsj2ifJ+1as7tVoW12UXW5k4zLN+/BmmNAi6EPo/g86i2zZJ+C8PcAXObQno49V25EBz5HT2Ratks1mhOWSTYal7CHs0orzmJOC+4Gedcj+5VcEQD5ULD4eypCnbLRW+7GHLvbnsYFRvDFHqnNgFY2nhbWAcn0AMPRyE9fHdWiK5fJ3j5JYLX3yC+dYus11OWIQ8KvVzGOrWM98STOJcv45w7i33+PKYocIvViGGQ7u7S+fmfZ//nfo58d2/8i6ZJ5bOfpfIjP4K9ME8eBORBSB6MKE7cFip/QD6SxwF5GFDIdjj52WhE8SBLk/8AIUKipiG05wLNKIgsl8CqkJie6tdPkXu5R6p5Y3VEWzwrXVLNIS5MZR1wFwAasj2u9I0BoRgaj6/V85bG8yVDfYZrcc7LI0kxfsh7E1ZK3iHLNsnyLfJsF9J9JQYiIUmmFW+Om7V5NuwFRtr9YKwoQdYq3fUaw713vf1UeoN//aN/gu90fB98fY+Ar85wn6/97idIC43dyGEtLnM7a7KWN9go6ozMKolZITNr5Hqd3DgYDUlTfSR6mpOO8NIRpSSgGie04oxWlKu5GWc0IgFuCe4h41fpYxHlQullETNVNefW3W0rt5XLu4ScimkRE98FQqGa7z0e7zv8WMQCDgOn47La32woQHIAhA6GIcPFNGy1bViuohRoQnMxHTJLmn8dIsMhFApVrtOLpZcuo58VjNAU0SYe51jVLJdlgXuTPK3U3NR895ZYFJSEFpgOqKV9NVdlOxlQT3tUUv+bWqgFms3AqtI163TNBr5ZVtn0SBfYK4vgGFsomjLnCXYRY8k8eTxrdHjCXeVRb5NzpR3sw35HBWwENW4Om9z0W4qidgBjpKInZtJNP6Llh0wJkA7v7wvqeGVuTi1yq32GO83z7HhLqn4lzebyCYvXohCJEu3e9niWx8W953xnil332GYKdBWYYsZrpBhmolSgTEPmGNuIsLQEQxc5Yhn5oe0MQ88PbR+eJ/v1MQkyTwuKbAwkxZem7NWYbs8x3ZrHxcNMXKzQwRgY6F0Do1+gBwlGVGCmhaoKjsf4Pcs38oOplSl183+hav6CehRkj3HN/y9Zj/YoG7/F8v+fvf+AliS7zjPRL3xE+szrb7ku2x6NhiMAAgRBb0SKIDUSqBmJQ+pJbzQjjfw8SetJs6RZaySNZrQkPY0oiTKk1hNlKD5Q9KIBSBAgPNoB3V3e3LrepI0MH/HWPpHXVd3qruoG0CSADew+JyLzZmVGRkacf5v/d1/ghLd+UPaF2+MGLw9meCadYalusNmKWG+H9KsRsegYvY7vytYcalaDmt2gajeoOA1cq4Fl1jHNGoZRR9NrFEaNXK+SalUSrUqggGXOKMvxlWeMFcjIiV/jrfFsrvMWTN5qOrzNdTjlWiwnGS8NAl7ujnlx2+fF9SHb/tFZsWPFBk9Wejxy9hxn5mc5VskVLXQ4GhGOBgTDIUG/z3irR9DrE46GhJItfQ0U/rumGXm5YFN9GZpgB8U2JufU/R6G2LQZ1poMqw0FBhqJyanNOiYN0ES35xNo8UXS3FCljNJTKiYBorGbsd2IVSmR8kZM4GbYecEjCohFCoxJyeKpI0R3s0JjM2qxFnZYDabYjNoMs5rqm5WAh6EXahT2O0MrEDkucyIMO7DqdJ0W284Um84UfbupFpq5LnTjY4xkC2e8RrO/wlRvg0o4vvdxbGo057Z5k77Kk0UP9w7plbWkynKtTnQqIZyVf//wSR8VHlucZDtbZDucZtNvsDlqEMc6RVpgCG25KqPOlJSFZ4e0qjFau0qvMcO6M81trcN60b4r2+QWAae5yjkucYGXlde0CNdZwHUX8dxjuHu+OBkXVG+w3JtHqz7P/upNXvz8JumkDaBq6Zy3NU6YJZg68pg4BuaMhzntYU2X467rbrkGyX2f6PoN4mtXia5dI756jej6NeKbt+Be4EXXMaen0SoeRRQrMHXXcw0D9+GH8Z5+WvnN4w+zurbD6Y/8HNkv/+Le882ZGdp/9Idp/eE/jDk1xZfSBEAqMDYeK0AWXd1k/NnbhJc2IQkphCxHPI3IGZJpXYqKD05AXvjk4yGpH5IGBVHikcWW1P7iRj5OHON8aZY892emSWEaivlS020M6YmXtY8EpTRhjSwzdVKxI/IPsrZJFA2VBMDHZEZBpmmkuq6CD6tTJjcX2tyen2V9ZoZhbYbUmlcZLRIDvRujdyP0nQh9dPcHdSoxM+0dzrRvci6+zd/8sX/NG21fB19fI+Bre/UyP/PX/+qewOue1P2EaUkVNhUH9k8EYMWEeTCfLFDlByFrNxFAlFHNJ/vEH8QkNiICn0YhukaFmgu7oZRGKBca1DxDU5GrXF3Y9+rwv0Sm8hd7N4Myw7FPY3x3vmK34LBccu7ue43/9m6/wF5fQfmOZH9ZflbmrNRcvpJSHUuBlpJ6oew1Ekp4Jxup6OcrmQCa0KgpoB1Kn5NRJxENEpV1ybCykEo2oJL18aT8cEJje8/Xk14Kq4lutjCtFo7VxDHbeFYLW5fend1FejnquU81+12a+qeZtS7Ssg8zTvqpxY1RmytBm8/rNW62UtamAjabMbGdUx8XPHy74NGlgkeWCs6slZmjgzZyNG7NGKy1PHZqLUJnBi9t4qV1pFtP06TssjIhSqlMtj11Xu2DtTuA2QSwJQfmKruhXLIbGpFRAjxhwjLlfFYCwbr63oS0QjJBSS706DlpXpAJV/IbVN74pTZZh6uFq3zXan4QnJVzG5+adpt36V/kvfoLvEW/jD0REBe7lD7Eb8dv4aPRY+yk0hMRldcC6SQsJtcCKaG1KsSuQ+TphJ7QY0vHUaJYKTM9JTdTJYAqBCqFFpFrAZo0YgutvUTvlbac/E5U+lK5okdX+3YfP0LjC2HTk0xvbaKbc+co51MdS6viaDVspDy2ipN7eFLemIIpvY1Rxvkk5WEyYj1jJc9Vb8f1Yc61QaZ6x+46vgK03JRH8+s8llzigrbKGTsiOfEdjFpvKq8gAoLkYwSgDUEbaegjTaTYJtesyXWkKBgXKX0tYJSNGUdjkqhHnvcgH1DkQiohZYZRWU6nfgWv73qrOjRth8DxCB05hi5mbuMmFtVYAmsiMuuVrKOG/Ebl9yhXDbn67RIbHLwG64d6kXyrz1b1Npu1JbaqS2xWb+M7PZpZxuNRzJsUGIsUIJs6IgsS5RU2VCnledYnPs7vnVGXUkMBW9LLW+QBRSHhMumpcdB00WkziTyD0NOJ5bzEx0g2OJ0+y0POF3jY3uL4HX1sXU3ncthipTvNyqBNMMkeybHbmZtheKFJ/fSYU94tTnJTJIvvel+iA7jESW5whuuc5iZn1Lb0tX4pTGXohdHV0KgIg6Ew6elSrqthazpNP+PM80PmX/YVmZNYOG0zeEeH9EJdPa8zzukMElqDhGY/oSbei3EGUvuwe3wLimhIPlolH66RD1fJg3U1L0aTzNMRpnke9umHcM6cxTl7Bvv0GTVap06hCxPx7usnCeHFSwTPPKN8/OwzpCurxLrJR489xS+efjcXO6fKz5xnPNK9xTv0Ad/yzU/xrh/4Vizv3iWXXyoTttMwXCEIbhFs3SZ5LsV4aRY9qJSfgZzRzLP0Tv4m486LbGttnuHtfI638SJPqnYCMQnG/Nj6z/Pf3f5PfMhO+FmvhpYLAyI8rZ/jHcXbsPo5Qa9H2O9jJAlGmmFmaTmm6aG5gHpLiD+yFDtPVaBV/kZL0gOR4C+/RZZ1qO8us3Ulh6DbmZIrkc5IP/fYSlts5x0CU4LdNqFhUVRNfuJf/U3eaPs6+PoaAV/j7SE//j/+8Bv273/dvhImq//6HtuiLvX7e8yLTdBFq+puIoA7Sw4VxJXUfxFSFBNikLxHkUkJRx9kVOxB97ZCetBMKUOQ10vRswQ7Efi3b3Uz5KH6Dseb25xxB7i72nKTjN4Ljs3HPI9PuDVuWS3cokKlkMWsi1e4NGKLkyshx5cGzK10mdroKYr5g5YYOtvtCpvtKptNj+2GZMUmQsbC9pjKKEQgQv9fQVfikxVajkajYRK6DbbNDl2zxbYhLJtVAkcntDQCRyO0pPlaI7C1yagTS43aq5liZ1KheJUClDJG9cZkzHIFhGUxLUILwuhYIu6iLIMv0Xf5Ggqd37H/jscVQcl97JdjonwyP/iYWhC9hjuARcp79Bf4PuMTfIf+WWra/nlzJV/k57N38wv5u7heHNY/eiNNjku5xC9B9H64Zd/LctEDYZhJ+ajKBCrCld3nSHBE29ufaBqbesGWUShgfqcJeJ3ONGYznblMV6No+0kp6oPaPgPYaI8FTAh/ZFTkP5P9979qktWNgCOXxHIYOzZj12Ts6Kq3RBaqTixslGPccIwXBthCKfmaTV7ULYGZ6CAqgDYZtXL/7uOSMZMgiurs1DQCq8tW7TabAsZqt9mqLDO0uyym2aHs2GNxjHfEid3PGqwk86zE06yETTaCCmkSYaRjRe7z6lbQsQNO1ndYbO1wxh7gHLjOCqR9wXa5kkzR21kk2u4QJfI9RK/4mqaFYoR1T2Y4CylmM8B2xnjmSGW6OYIpcocOGyywmh9nOXuI2+lpxkWTdCLiolwXh0TXFQhJDGHcvD/SmKlBxrtfCnjyprDvlftuTxl87DGPywtSgnb0uSsB1vntTU6uLXNmZZlHlpc5tbbC3OYKTnjvdgURIdbr88qpLdCfXmB5/hhL8zOs1yw26yabDZNh1cCSsmFd3wOJJWCUfeVvVEoju4OQm1/YYO3GAMEQ6r0VOXUp+TUPy+zUXZN3n53ivedneO/5aU5N3S3Dc7+WpiOCYKkEWMHNyXiLcXCLKFqhuJNhNTeobT5N69a3UO0+trd7pZLz0yc9fnHRYjS59zyejPir3V/mfdf/PXa4U/65N8P1x/4o/8wa8V/Xf0Pd54Vl83vmvo8PLvxxmnqLsR8wHI4Y9fuMdtbxJXPuB/hRTigniCIS2g/OSzWFMPdKd7yRayUoiwOcZIgRDzGSAXo8Qk/H1PUax7zTDPWM9aLHaraJ8NBIdkteT4LwbhzhRpHK1HlxqEbZlv3Sf/qlsCun38H3/cpP8Ubb18HX1wj4ytKUiz/1G+QiqpjFpFnMxvYGK6u3Va+TLkKrVR1dj4hjYbCT3oKMLC3IJaiRaeSJtido93ot0WVhWyd0a8oDp8rI9Ri6HoHjEjsOiWOT2tZ93wT22ArzAD0X4cARZEO10NDyoepbM4TKWC1CBupY6LSxs3l0ZsjMaUJ3ilFVykmOpv41soR6OKAVB0zHGccDODU2OB4IS5VkQEICY0wobo5Vc6nMhdUrErfGRFpEmplY42nMYAoramPEDcykUmrn5BL5v39RW3UbUaBLwNX+30l0TAgXdsHVLgPgLuX5azUR6PbNDUJtlUTbJM931IXWDeViKcDhlV9biFpCO2NQSenWMyLXRjM8HtZT3lL0eSpfYVHbOvQ3ft7gevIEV5PHuZI9jK95E8HwlHwyFkVEp7/B7PYK89urLAhLXnx48ScX+ltzM1w8dYyXTp3kC6dP0280SUx7wlDlkhlS9+7s1a2/VrPSAjspsLICM5PMrmKyV1lebS+jqalks2SSRYcmNSSqpykKdfmbWpgpr0Q5nmi0pAVeUmCnWplNERApIE3X1I0sMjVVphGb5XY5lq+XmOzP1XNKqvY79ZFe0e4B5A4CQFmEv3P4PB/Y+Qjf0/8dOvIbnNhtc5Zfrn4jv1F5Gzf0BchNYqGCzvQSDEsmXPrp1PwAIFTANATph1HAVOaTxYlk7tXbkEyjlIGVuoJqLpqBk8yxdBvszmWUv76DL+QrakLdL+BqNtP2gJaIqsuvU4IfuvQdyqea9PJI1irNy/5MtTDLfVVuhAJRkrGSeTmWmRlJe91vqbE9ATKu0mMUMFNuOyR2hbWpaZZn2yzPNLg93SB07mZYbfohJ3d6nBpsczpY42S2gmMPKGxfkVPkcj9JCvKkIEty8ljGgizQySJNuZTF5ncEUB7E1JXOKNCtQolim06OYUvvWkqiFWzrBuuGzpaTsl0JGdZCjhchTyT7JYtnJfp/5+sWsB1VWAtLZsW1qEG/6GA4ntJ3lPurFvssWuuc8DYV6OoYh689q4bBp+wKV4MWg7UZ6l3RwLufE3D3anGU5Xh6qkiO2u0xtVkfbzoWlWuCuk4iLDJHHCRzbGEM6tBvkfXmiLvHiOIpwry+54Fwa+o6/WrO2kzK8rTB7Y7HWr2qrlvz3ZRvfCnk0aV471MMjrtk755h6mxDlToLuEnDEHtpCffmDapLN6ktLdG8fYvmym3Me5QKyutvTc+yvHCM1dlFRs15ssYCtjfHfF7hxDjnlJ9Te4W4QayJHpzOrarOLTVq3JzMdwTXb8cYSz76Zilor8wx8OYq1JqOojM3ogyzF2MPInQ/VxeNg0GYqpYzbWd0rIyOmasKgP3qofLEKfJs0sdUukTVylH+wUnF0W410h64KYHOJGyjRtF5uzHlcmnBIWyafPdmxvcuJ3vHQITGLzoRC9Ev8aT501h6GegaZXNcDj7AUvQtaJo0b2iseyv86rGf58XmF9Vz7Mzm2ze/le/c/FaquavKwFVPsqrmkLM0I5Pe9zyYuPTBi+ajtHfIGKgxFhbYPKBn66zXq2w3O+y0ppV3m1P0mtNlP+c9rFL4LLDMAivMsyIhAzXOs4rta7DTRO+2MPtt7KF4BzuoKQCoOHjyjFyVZ0aqPHO3VFPpG2YRWhaTPfIW3vzPpQrsjbWvg6+vEfAV9Lr8+J/6715Va+lBav/l5qab0hhrokmaW6tCUSfP6+RpDXIp65IbuXPHWNKuShZm1Uh41k24KAvByXtzdHj3MYfvf2uLC2cr9LOcjThlI07YSjK2k4KdpKCbQS/V6Gc6/UwEJe+/N01MAJqR9THyPlrWR896aFkPO/SpBhauEA1IhNCaoteaY6c5pUpKjnytLMSKV/CSDdphj/mxzwk/5lRgKQ0nRSQiY9qikrvqZl6Wo0nZS6FGuVGp7aIQUjZGWsS2tcOmvUnP3WFsDQhNn9gQavAUK7OpJE2c1CtZg5wuY2vI2O4TmENS8x4RWiVeLRGrku7WKEyM3FTjPuhTPEaKSlY8VQAnUWxEOSmdoc1M11bMdtM9h+b47sVYpuUETkFm6OrfsxPtruzX3WZg6A2ajsnpWo9TlWWOuTew9f2ol7zCkn6BF+y387nK23nJu4DvmIxtjbGl4U/GwCg4sb7Cm668vOczvTIKeNCuLZ7ghXMP8/y5R5Rvtfdr+QXcOFmBlQub4BiLQIEnuRimuklsmsS6dFVYKInTA+D3K2kC8mphTjXMqYWH59Ugn2wX1KKS5e+gCVYWEKbA2B5Q04gErKn9pSvwZup7oG0X5MmYGgUPxxf5zsGH+Y7ebzOX7JcHbVgdfn72m/nQzLfyucbjr1kf59VMelxEmkI0A600xkpKljIrzZUrsJqVAqNWmqrHzCQtPU3R07JPxkik7CYpyTXSDD2XzG25WNoFbAoOSbn1pDiuLM1WVC17o5ShJhLZNcpRtkWsu6hskDRu4ze2KAwhSMj3AyWihzOGt4xTHh1nHPM1rKHLjd4MfnZYUPm+TRZcmvSlurjipktmW4xcna4LiW2pnhcxEwvTO45fW2C52eTFpsmNO8hBxJy84LEA3hRpPJXovCkzWNANNEufeDnXZfEv54fVJ9RvEnCTIL9JkF1nnFwjye7+Papr46DB7c/8EaKNE6oM0q+9zHLnU8ShyMKZeJGOGxu4qYmdiabTBPy/Rst2RXPtjNBJ8YyUphEzR8wJQua0GM9IcI10byyMgquVChdtm3NxwmPjkeSp915TNKs/67p8vtKgVz/DDAtMjyDsRUSjgjQySCOLLDbIYwGeco5FKoikyj5fd3l9gV1NqM4E1GbGVKYj7KkIo3o0uDWDAm+QUxtmNP2Elh+jxx7RAUAWFnWWsoe5nL6DItwvzby4aPHsQyl2tM7JtRVOrS3z8PIyJzdWaXU3J/Igd5tm29inT++VCVpnTqMdP04xP6+ycFEU7Xkcx4RhxDiK2I4itvyQ0E8wRwVeoNEIDdqxRSeRe66tsjH3sqAo2CxyunnBMC8lE4RHJ43K+8uDmvzFPliZjArASI/bEY8JnDq0XY4Hny+jb2l8ds7kU3MWn5kxDxGkuWnB+zYS/sithMcHkbqf75qtfRFX/wxxfoaw+MbJuzvQLy/6hnnA894l/tOxj3C9VhJjVWOH9948w5PLM6UY+QRQJUe0IAjFvgCqroCr5rQaZVuAlki73MtEW/WkD6f8goVsi9rsLzBv3FBAq5r55EpeYIZk3CYedwj8FmHQUlqTSvFSK1UvZS7rEtWmcaiF45W/q7e+9a183/d9H2+0fR18fY2Ar6jb5Z/8D3+s3FD9Vfu9FKqfolxClJ1Gk0WAaTapumfxnIcwzXlCTZaestg0SQVATOrSX8l0y8fwehS2gIERI3PEuubzhbzGreQkYTy//1xnDav1SazmM0p08UFNaI9zo6FIQvZJQ1r75CH67narjO4+gOlCvx4PcKJILXRFyDA1HQK3gu9Jg/7Ri25NAF6yjJksT8bbVKJNZsN8D4yVYwnQOmmT6bQchWxk1wItpGeKEK4I+g7pTUYRyRWx3LJMyigjQOIH5rulAep19FhR4wei7ySCunqIbwRKMFF0oYI7j3sBldBQIKt0m6m+rTS57jLHwam0aTvzHLNOMW8dF2liBbwk4+SbBl0rZ1Mbs6mLR+yYCT0jo2/BUEr4HJdQ9YbIWFGjNL6/o/8C7+9+mm/Z+RSP+aWOya5tW00+0n4HH+58A7/VeTs7Vuvwd1cUiqlOegqlzOXxqy/z+NWLPH7tZU6sr971MVamZvfA2AvnHmFpbuGBAIMsNKpZRjXVqKYor6QFVcmE5RAaMDI1dmyNTUdjOIlOG9IcneRUwoJmkKn3LPTRUsaoAI6uqTLO3VEEQlUi+kHBzCRLpc4ZyTTJsclyVbJppVLTn+IojxXNsmR7BYwYWTzxCD2P1Hgquc03Zl/gncUlZtmnrB5j8wXjFM+b57hpH6ewXHIRzNYtCkWXbFMtRjwePqd6sITB6gX3EW6a8wrMRgJoNYNYM1SWfM8NU4Fe0bbLDIvUEMD7pdb4OtqMNMVOE9wooDno0xr1aA0HNEZDGuOhouwWr4ZjakFAVTwM8aKYSiRlNIk6F0PLwHeEytpi5FlqHjgWoSWaQY7qw5Q+Btkn5V97HagHbsGqi0tQoCrB81S57F4PoyrPq6JL75TKRopowYjMTMisnNQrFAhETwlNm22vyo5TYbvWZKs1TWbdfV1vjQccG3Y54fc4OR5wPAuxDQPTtDAsC9OyMEwLS42mygaZpjl5zMayTAzTRjN1ummPjXCL1WiNjfF1gtENCLappynNVKcW61iRQTI2SYbvIo/fpxaPUvacjH4Fsl0G1V3AVRw6Pgd/DfJbEbr1VIm8l8/d7S+WSLmZHezxfTCTrKQCY2ZCzYypC1W2k7BSzXmprvFsQ6dbyUmllnRSxlo3CppGQUMvaMhoFNQ18GKDuvSl9mco+gsk4znq9mnMrEkwSIhGManIEShW3gByKQdfp1DgNXmATBmK4bIyHeJNXOZO8+hAXTI2iLdt0h2LtHcMv/8+8u4pKuN1quNV2sEXaY5voAUp5gHx8zttUKmyPLdAt9UmqNUobE8JWcs8N0UORXQKM/LXQ4Ql5b7Caiyeu9SzKs28SgNPdWA2NJO6yHWoNsJ7f+dhXhBINYIwLU7K8CXrLCagaa+XVRElSXfiblXJl8ZuVjQ+Omvy0RmT59pybPZfezrMee9myvs2Ut6yneDlX6Rp/iw143MkPMYo/V6CfB9sCchaGl/m+ugFhsk2iVQN3JENl893c37M5y/0GNTK41/3TZ6+1OLUWo1BvV0CLAWudoHWlCLNeaUKpCm29rJXZTarzGJNs4Wx/TBnnv1zmJlLz13jd9ofpTd2iKJ7yw2Iyfcm15VdN9Q1qBx1IfeYHKtduFKWyx+eP/HEE7zvfXJNeWPt6+DrawR8LW8u84M/+70kZk4yuRnYmUc96lAPp2hEHTrBHAvj42qfnnpwH6VvuSbN7iGZ6VNUttHqa9iNFWqtFaZaq1S9/drtNX+G37r9Hj6+/A7GaVkrrWsp8+3nqXU+Qd++xVhS/XJjnDTsqwvdLuuUBFEnLFTqMYkQaXdvywVRjRM3hFY5EzZFYVC0sQth6qsT6XNExjShOUVgTjE2WozMBt3CZSc36WkWQ7VgfICyxzxX0XPJMIomxz0XxnmgAFkJym7vgTM929m79IgSvACxg1mzTrIPzmRsKiIJnVhL2Db7bFo7bJk9tqTnweyxaXWVb5ld+ub+4vheZqYa870qp7YbCmzVB2BISHBPXFLEXj0yt4lVW0SvzkNlmtxr4dsWA0tTPrREEHp3LuWYr+/mJKVssuh1w4ATo2XeN/gs7xk9z9vCl6kc6JOQd3pZP8an3cf4aOMtfLr9JnXjiO2jm6Rbgz5PXr3Ik1de5qkrL3F26aaqPT9oo7rH7bMzrJ1rs3mmSW9mFi+wONNd41xwkabooGVjwqzNp4zv4TPue1UmTjIe+4wpE8AjwGdSpudEwkRVkswMPY1uTWOzabLakYX33b+9EiCVBBd75Ytq3OfKUSV1E4FoVaIv23pJmFNWtzxgiWGSoCchWjpGT6V2f8C58ArfP/gs3zt6iXOi1DqxsabxWxWPX65V+LgnNMWv/m/V8py/s93nm0dlaeKnZ07xi499O5XqDE2nSctp0Xbae3PlbgvP9EjzlBc2v8BHV36Xj698ji92r04ETcuyw1Y+z5n4DPbAJIlSskIIOnQlhiogTm7Ujmoez3EScJNMASUvCqhEYyqBZMBHNMY+tbFPwx9RH/tUQ8Vi8RUxAWS9eoNuo8lOvanGnoyTeXfymGwPK9UHulZ9TZg0TpaTI0chdHKiUDETyvXFk141GdW83N7dL9eech4oEoL7tciGkQsjL2HsRIy9hJGb4nsZYxndrKw223vP0LJbzNfnmfamma3MqnFKn6UZTeMGdcxRhbxvMNyK2Fm6Sn/zedLwIhQHrvGai24+hG6eBL0xIQjpUShiFSnHH++Rq+h2hDe1C8YivFZALU6wNsBc00pfL12Xer57WGCZjFyLbrXKrbkFLj10nucuPMmVU+fuqhqRDPVcd5P57gYL3U3m+ts4UqInz5P+LNHxmhD66BLsK1yKvEpWVMiLKkVRwSgcTGFJTnR0SS3fhyVGTqees1DPaFi5Ugx0Uh07tDHi109OIsx90QG24l2W4kgIZm0d0zWpehYVQ1eljcJO+ayd8LF6zqc6JqvVw+9hfqfHhVtLnLt5lblNaRMZc9xb5R2da5yq7WuVXR12+PT2CXbiRc7Wn+JM/SkqZqmrlhc5K+PLXB48w0Z4U5UxWsLK7NjorsXAcVixPS7ObbPceZlcL69xqfUQo/YHSdzHj/ystWKgSgTvLhNcQ8sTBlGF8WiavHcco3eB+bnHOf/00zz0xEnSFZ/Nf/UFijDFOlGn9cceZhCN6PV6h7zb7apxPL43e+iueZ5Hq9Wi3W6r8U63DxCvvNH2dfD1NQK+Xrh2i5/5p7+uStQEdInmzcE09b0s12IyIyIXcTw7JHLGhJWIuBGTtjPMlkGl7lKv2LR0k4pExdVCLYBkpLS/Lm1W+cTyBS52RWC3tGl3m/ed+DjvWfwkDWf/hiFn2auu2VLQR+LC6AXGUDu0vTfu7vdLRseDJl03kd2gX5+lW59jWJ1h7E2T2G0QWlTpodIEtsHY0VSJju+Wo5p7mmLWC5xc7R96popkv16TC5aeddHTdYxkCSu+jhldxsi2j4wHSSSuWlQYavenp2bjMGUcp2ksUNNmqMQNCB1FUeynBQMpe3Q8AtdTo4AtNXcratH6hplkk8YjWoNt2v1tWoMdWv0dpnsbPBFf54K7zkO1LrPu4eMQpCY3/DbXxtNcTeYZ1erQ0bDbMfVOj0Znh6o7xtNCFZkWtjj7uoZ12cC66OAsScnZ4UtfYLtcOnWeZy48wrUzx/km83P8yMYv0MjKf/uGu8g/OfHD/Kf576KaGMwnOceygpPAKUvnjGtyru4y1fJwmx6G66A5jirB0YS2uihYCmOiLMP1Rzjb2zgbG+SbG6QbG4w3t7kWRFzC4IpX5WprihuLJ1iZmbvn4ZMSPFUqab36dyjrwDtj2PPRJn9w48P8wOaHeXr48t7+SLNUxvHnZt7PR5tvxjID2uaImpbgaSmOJgWZCVohPaRdomiVUbRJL+wyjIdlP2JR8McHQ/7CTk8ttq5aJn9hdobrUg53D3MMRwGyttVkLq8xk1VohDp5f0DY3SLY2cD0I2qBlGBCc2wwHbg0xhp2EGHHyV0g+0EttExCyVApJj+Hseviux4jz2NUqTL0qgxrNfrVGv2alJ41FTtXw/dp+z5NGUd9Wn5PAbvWaEh96NMYjVQ27UEbzCW73KvV6TXqdGtN+gLM6i12GuIlWJO5jP1avQwOTbKgssDdHbmDdXW3pOfu/rhJlPkOxbsS5H91g0ARxRZikRKg+dT8AfVRn8aor8bduZO8+neo7jGezcgzGVUKRq6AMgFqY8auz9gNCCRlfsQNoG7WaVktGnqDTjzL9KpLdWmEuble1tDtmlbFsB9WLiLKuxkCMxmrLFZlvErVX6Lir1AJNqmEgwlH5hHv1yhIZwvSeUjnC9K5gmgahrbLeOAx3nQIxbsOxYRVRu4dazOiNXZS+crcSUK3ZO47aFM76xxbuzXxm3SGA9WnVOySqijilYNtDIe3Dc1Q+lUDw+Gm0WDHMBhoBb6ecdJb5m1Tz3Fq6iZG1cdwBpju8JAOop66WOM5bH8BI5FghjCnxuiWg+11sCvi09juHJeTOX63V+GzO1IOmWEmOVaY0SFRxEL90Gdn1CcdDbF8XzEPukqfL1TkKNuLC6ycOMWt42cJvH3iDj1LObFyg3M3XuLszYs0R7sAq+BcfZt3TN1mwSuDVdIKe2kww/O9RcWQWXE0KjWXSrOF25qjaT+B1z+O1RcRdliq6lxyQ541N7nqJqonS7JYh4KTeUhl+Kt4g19CnxBr6e455ptv5iE7nYCsZdWHVWdEP9Po5x7YczSq51nsvJWH576Jdu0skZ9z6TPrvPyJVTZv7ff+Vpo2D3/DPBfONYl/7orSS7SO1Zj+sScwqkdf9+M4vguQHfQgeIWgmFR4SfAkjTj18KP8kT/1p3mj7evg62sEfK2vbPGf//bzd+3PdQFXoQJYhR5h6hmmlABoOr7SYHCJNY/I8sr+FukFsSZ9IJLRMA/O5SJbYJs+ej4k2Ezor1kk8oCyglPTy7zjxOd46/RzNLV+uTCTJL5E5u9k99k1WRR0wZLo25KGdVPD3nKoLTxF5amn8Z58Auf06TKtLDoZYaREDMf9iGEvYdhPGY0K+oOMnl8wTiySXOjGXyWzV0gJVh8v3Kbqb+AF27jhttp2wx3s+PANSo6PRKnLxU6L7WaLrWabtU6HzVZb7ZMFkejbZA8KZKRUJpMyMB8jlR61TYrsNnq2qijc5eaka1XcrIGdV9Gl905vkBpVEsMjtqSEzyOyX2PfyJfQpC+nEoYqg1ARj2OqSUQlSaimMdU0UWV7tTyjKl4U1LScaiF8a1LIaKIpraGY2MzpFTq9VGfkZzjBFqf1FzltXuektYGj70eo5Qq2Htb2dMXWgrpaOOpWjlkxyN0qY2+KLe84N5sPcXtujs1mldOrN1RmTHrGnrh6iVpwOAKXmCZrp89QX9C4YD9Pq7GDYRfk9QX0d/9ZeOt/D/ZrZ8W6H30Y0a7pr6/z8maXlwY+l5Kcy7rFlUqNrUrtnlla+Sx2IjTtElyokByIDHaSHn9g87f5gY0P887+cwqciglNzcfrb+ZDU+/nl2a+mUHlwa5rtvQMmhkzlkbHMmmZFo085NGbH+MPfvIf4w1HRKnNZ6aeYEkY7AZD9OEYYxTijGJq4xJQSQ+bF5Vg8bWarA2ltC82RE/GIDFlPDg31HZ8x3ZilqQUe4tARVKxuwiUUlkL13CoGh41w6VmujQNF89wGRcuqf4Fznv/kinjunof3fQYHxv8KLfit6pt3U2oT/WYae4wXe1Tt4aYIsTaEw9ItocEW12y7gijH+AFD05S4VfK0sZIlTt6pUi67irxVLmuR2ah6PBDM1YVDg90XCX6b0vPZ0ZgZypIJS49VWp0ChLpN/N0ctfEshwFqA29Qqo1iPQmI61BnyaxViEXOQjN5cmlGt/8RSl71ghc2H6zxlwz4GTUpZ6ukgoBkLEF5g6F1QV7uK9JduhqrZGlOvHIIerZhOJDi6jvEI3sfSYW7fDflLsnoHMylyyzXFvHboXAaxNUZgkqHcYVIfIRFjgfIx5gh1KaehigiZu7xDGvYJluKEA/qriMKibDiobv5fheiu+EjB2fVB+hFwO0IlIZ8fltl9OrVU6tujQDqEUx1TChkqRUY6gHwiB37wVrarj4lXnGlTn8yiwjr87YM0mnu7hTt3E7a7jTXbz2CMO6+/zLM41wxybYcgm2G4y3W0TdFnnqUmgGO602y3Nz3J6bVWO3ebhcXKwyHrG4XgIxAWTChHg/x0t9P4ocysLRM9qGT9WI1P3AMVKl62boBoZmKh0qzbIxLNET8zArVcxag8JpEcZ1/MDFH4Pvp/jjhDCISCLJGgrpj0gNhIrcphzvDbblvn/11MNceegRbh07WwY/JuZEAadvXeLE7evM7KyRmSaZbeMaCW3d513aRd6uX6E+IdGQczltnMK48O3oJ98B0+fJmg+xsdXj+VtLfHFji8tDn1spbHg1es0ZhtWj7wO7ZYIzbBwqE2xlt7k0WOLzo0T1toqdsTMeqTi0vZNMeQ9zovYWzlUfpaq5ZGmiiGfyNFVjOS/3iQ+2fNau7rBxq0caSTAwU2usuVaDd1aexpKOatPnSvuLqh/t4N/me6+ZkGf7r3/w30jznNy0VTmryrIq1t4MPYnQ43CvLNmcmuPP/dN/xRttXwdfXyPgy18d8pd+6iOqSVI3Mxwtp03ObKLTCQwc3yAYWgwfVKzrgEmc9JaZ84ydcsWSvrFyv+jcPJqZnMfCMY0JWCub+VNpynZ0LEvDLfq0tMs0i0s0rZvU6pt4zS6G0LwdYbrmYFtnMIrT5OFJov5xRpvzDNddBluhYmp8xferxHGGmHZMtWnRnq8zd3qOhfPHaM5UqbYc9AkTnAJ2kj1Q4C6kiKI9oFdEB8eQQsBfNBlFyf7gc8KQ0dhnOY64russOTarXoVBpaYER4XUY9Bo43t11e/0iqWLX0GThbolhAZZipsm1HOJ8BVM6xp1TaOmo+rpq4ZO3TComaU3bIuabVF3HOquTc1x8DyXwrSIU50oyAhHCcEoUc3TMlfbfkIwCEva22FMMi7IwwP9GVqOVd3EaS7ve2MFu76ONhEsFYa85jClvZkwtZPQDA8vIIPM5OaopYCY6IuNj+hhlIhtr9EhbE9jTM1Sn5nloRxOb20xe+MaxrPPkolo56GDBU4HKp0RlZmYyokK5rf8aXjHnwRPRE1f2fI8Yjh6mSJPsKyWctNsoesPRiiTDQb0f+7nuPGh/6LA2PXFE8pvnrvAtblFhkcEAESM+3s3fov/ZvXXeefoeUVGvWufbjzBh2a/jV+YeR9b9qTZPsmprfdobvbJIim1MXDzmHoRUE9DaumYWhLSTEfUw7HK8DRHg70SPlXGFwQ46YMt7u806e3ZA0oHQdKdIMosR8lm+45ktQtiu1AgXggXZCwmZUheaFALTOpjad0//BuUfrvNdoWddotRbYbMnmMqrjPf92gNJABydJCjpm/xrvpPccH72J7G1Gf9P8Ql9zGczhLu1DXM9jUyb5uo0AgL6UHRyrn0C+Yao9RkOT7HsvYI2/YZRt4JUmNaZc7awwHtQZ/2sE970GN2+zazO2tM97u0ByNqQaL6Ch8UsCbCXGeaSl9H+tIkmBPbVWKrRmw3SewWsSMR9GmCisWoEjD0dui7W3SdTfrOlpoHloiQla+pRKzt0yT2GdKJ5+YRv488xkxuYEXXMOPrzHUHfNdL3087mFP9K5dPf4zlC5+mYjlUTFd51fSoWBWqAnr1lLo2xiuG2FkPPd0mT9YlanHkZ5VityhsMOh7ROM2M+5b8Nem2bpuE4/l2pKq3qTCDDhWv8Jp7yqpbtNlih3abBcN+rqn+pgOsoAovTPTIrAcAssmVKNFKtlH0VCKQ8wkwpbSx8CnMh5S9wfU/OE9s1AHLZYeKleyNQZ2mlIPAqYGAxa2e7TGEW6SHpntHXg6qx2ToHISS3uC2DupQJdosiVmhp1Kmf4r3IPkWly5gdO6jNu6jttZwe1sY4qY1B0mmbB4OE/YO0nYPUnUPanmeVLBtxNudVJuTxsszbisTUl29nCA1EgT5jdXJmDsJosbS+SpRiABjyLDyWOcPMK8VyD3K2SGoVGYOuuzx7l46lGuHj/PRutwZcKUv830+jr9dYNR31HU/7v3+sLVcToFHzQ+wv+z/yGOxZtqf9+o8e8Wv5/fbH8LZpiiRSF+YdC1K2zV2vTrLfX938saRe/IMsE51pQsyCDTWE80NlKdjUSjGxgEXYdNraBXL38vkih85FadN11p4gqT0JfAGtYU3zz/QTyzRj/e4rfW/gPhpJLkS2cSxdfptGr86I//e95o+zr4+hoBX+s7q/zD/7tE+xuNDmvizQ4b9fZeOZlZJDTp0cwHNPORKqNqpCHNrKCZ6bRSk2Zq00gcrKRCnnnkicMwsviUn/OJMGZrr8Yejqc6b44MzieGomF/bVZgOEOc5m2c1u5C+zZOYxXdPBqUpVGNqH+MqCc+S9jrEHZr5EmCU4H2fI35s7OcevI0i+fPYjlvbB2w/LT6G+usXbnI2tXLytevXyGNRCRWIpx1VmaPsT5zjO7MIsP2DAOvim/cW0OlfOFcsbhJpknciUOqoj/jWEzXaky3Wsy2mtRMk7ppqHr73uoqW0s32bx1E13IRbJEAa5mpcKjjzzCo48+ykMPPaQaXHctzwvicUowikvwJMBpAqLu2t4FWOPkPsm8Ckyvh9NYLr//xjJWa0V9/8YdVM67FhdVutoptozTrJmnuM4Jnk8WMcKIb+l+SpF2fFP3s7TSwz1w17V5rgSz3O7V2e5baJmI7t77nemGSXNmlplqnekgobbdxbq1RLG2Swiwb1YtpTJXUHnnu6n8oT+P9cjTe6U/aTqk3/88vd5n6fU/y2DwnAJgd5pp1rHM9h4gs6y2EreW8eC+/NYO41/8CP7PfwQGQqOsoVerNP/gH6T1wT+Ce+GCOufW45SX/YBLvR7mlV/j/LVf4u0bH8NJY7KkpP++ZJ7iE+5TvGCfRw8KTmyuM9Xdpj7s40WhovKXzJmMXiSMW6/9NqEi1QcA0142SmWa9sGUACiRpfA9T/U5SURXiG+kpFKycpKJkCinXogwqIiHp7QaFTqzLWYWOzQXW9wyh3xu81mu9y6xEyyXWfdCWMqFqKYkAEj1EhAamaZIZuZ3bOZ7DlNdF+eIRUfuxhT1MVbNp1IP6FRSGrlOJdMU4Yqwk53Lt3EUVxd8pLHIv5tbYMPO8LOYII0J8mSvuX/3oBh5DYzzZPY5Ivcsfu00mXl3yVZ91GNxfYmF9SU1zm6tqs9+94EuVJ+bnUgwRWLaUoKZKNFUL8uopBlV6X8Lc+ygXGw9iGWaSWLXicWtRjmqeZ3IrdJveqy129yammdlus5O3aJb0+krJgTRV1qinlzDS67hxFfRk2WiPNsDoEIPZWY233j9h3h0853q31ypX+E3z/9bfOewcPu9TMBMxyiYt3LmrYJFNebMSOXGPZCplHiFkU19y2JhM+LkaAfvHkL0ch72aLBDiy5NdmjQ06v0jCp9YZuUdbacX0aCtuuSpTd296XkekJsmIyl+iR1SUObfGxhDnSq2wmVfowdSE+m9Bjf33cj1wLJ2vqVOjutKVZnFuk22iSWTWIKxXhd9UaP7GX6zkUl0VINCqb8KjODBjOjOh2/STWuY6eOYsg9KG+ybwVWZRunfQu3JcBMQNkS1oEe8IM2zF22igrbWYWtxKMr5AuBw4g5+tZxuu5xdmqniOy7szean6B3Y+ydMReWX+Rty5+mk97rPJB+cKGEFwmHfXBcZkU18lwnzVUB+h6pimTKhFilYiSKXOVO1suDo7A/f37qCX59+t38+tS7WXbnDmWX3jb4It+x/XG+Y/t3uTCW3qtSi+2F7BQfzd/Ex/Inuc4iP2x8mB90P8Z2pc017wRf9M7wCecJbnoLDKvCwHzvNYtThHeBq90ywSpjJCYtwEoCDDOD85zpPoXrH8MezzPKUj6jfYZL/rM4PalOEWqR8lhsNyI++0iP1eky+2alGm+6WefJlYqo66EZUo0y8clcP2KfnC67+/YeNwoq+RRv6v9pnLxFYG7wxbl/Qmr3Dr2u1EVnsU7cc1S2OthxiQ6Utx442LidiOpsQHUuoDIX4DRjtVzqrD/C0z/8S7zR9nXw9TUCvpY3N/mJH/+/72rmkC+1W2uw0phSgGy92WEoCOVVsi1N06Dpp2Q3R3RvDcknfTGObfCex2f5obef4G0LTeqKvjUnuL3G4PMvMPriRfyL14j6UsveYlCbZVyRiGmD3G1QGK6ERNFk3ftKZ9yhzMftveyHXduYlJncbfFoSmXHFDCbeDycwxC6W8fEcQ0sKYFxDOzduWtgO+Vc7VOPTfar7d15OZpCs/wlyFTlWcb28hJrVy+xfvUyq1cusXXrhtq/a6luKDKJ8dQcSG9QEmKrxbCMEXXXZfHcBRbOP8LC+YeZP3sBt3b45jUcDnn55Zd56aWXuHHjBrnoj0ysXmtwYv4M8+2TVI0O0Tjdy1JFBwHVfQOpuy2YiBULTXxSHSuA5dVWqNdu03JvM+XcxpOmvSMs02yG5kNs6qe4UZzghfQY1/ITdOkcef4K58cJ1+a053DWMXjH6GWeWPsYx5Z+G2f9cEluXtj4WzW2bsJar06v8Bg7FmPbYlxxGVvGPRWUhCmwM46YS6HtB7jd/l0YzqibFI9NM76QMTixSroozBn7j1tWB9OskSQ90nSf1OKBLAZjoGEFFey8gxl5aCMHbaCjDXLs7R52t4s+GqlqGbmpiefJ6+vXyTRtApgmYOmODJSU8I0tB99yGFme8oFd9khlFQejApVGxMPGdb5183kahBSWxi+efDufrDzJNjP0tTZDq6EWkb5XvacExJEmYp55RC2T7JxPMx3QibeYjlaZjZaZj9ZZiLY5FnVZjIdU8xyXVBH3TP6cXuKyMm6wGjRYCepsHsHSZWmZ6s1Y9AYsVgZqLou0z9oO/1dziiVc3MhQTHdqjAycxCRyjjNonKbbfoiN6RN0WzNH9hzNby7vg62N29TGZU9Fahj4XoXQq5BXHKho5E5MaPn09B022CKUckCnzPTdK8CgazqLlVne5J3hbeZDnM/bzAkhwU6fbLtLvtMjE9/uke/0ybsDivEri6/f9VWgTbJnDQXYsmqFoumhtVz0aQtzVseaL7CP5ehtjdwoiPOYsQDVLGHr0nG6n3gvpDbYY8K3/QLj2UsEWUqQZwRZRpjne6Mw2e373b2NsgRv6gUP6Q4P6TaLhsa0HXMy7bK44zO7JQKwB0rNLY2NaYHTKLBaCUSTLzvQO3e0jSydvmPQnfi2bbDtmGzZJiNhNi007AHUNzUaG9DchNYGtDc1RYJ01G8usE3Ve7zRsdmuuwxdl9iwKQq91F8UKvtXMQn2jSp1xWQ3qLXUWM7LUTyUjGKM0h6sRIUancl9RxhShdmzNoZGYNIKPbx4P29suD3c1hJu+yZuewmndRO7ti9LcdCGicGtBG4nGrcTnVuhzbr/PiLeR1abJ2/ZFLW7s/eGZNkH12j3rjG9fYOZ7dtUQxEAN3BjHSeR4yFBlge9TwtwE3CeYemZGsUj1+XiiUd54fiTfGHhcUJrn2bdTUPevvkc37TxKb5569PMp9tYeo4tUjF6Tm4Y3PAWueqd4EXnIa5XTrBUXeRa5fh+dcERJoGlWdYPlQkKuBLQ1aYk7ernJhsJbCYcymbtZJpi3T2RFpzI4Fzc5C3Db2Jx/B7srGQwlJLArnuLbrFNkEl5ZhUbF6054MXFz/BftBe4LboPai1o8oG5Od7bbmNM7rv7FSp7XK17730/i3rgOUVBHGZEKy7Hn/0AdtIgtLtcPPUfVQli5usEWxn9lYT4iOWAaabU6wG1mnio3Kw66LU6Rr1G6jZZH81xe63Bk0+e5H3v+x94o+3r4OtrBHzJ1/fcc38a32+y0/XY3jLZ3MzxRTjwDjMrHtbMDOn0LIPOPOu1JhtpoXS2NsYx2cq4FCcc7JcK5XWL7ESVbMEraQkn2kOdUcqxbZ+57RFTg5TmOKcSC/Oggya6SK9irqjUOwlFMcQPtlSjfmAWqmxorESYK2UU2KmjOTXJKtOw1mhaS0w7S2oB36zcxnX2WYEOWp6ZqgxiD5D1SnCWBlL+8hr0PjQOATXlu9sKyN0buNlHPNeQ4vSJpXHM5s3rCoitX72kMmQ7K7fVY7phMHPqtAJZCmydu0BzdkFd0PbK+iQz5Sdsbe2wtHqNtZ2bDKLDNz4jqeBE0zjhNEZafSAK3UT0tYScxJKxLO0a23oJrhQ5ia5AVurEtNwVZsxbnNUvcxLR+Fijxn5D7kET7su+cYxlTnIpP86N4iRLnGCDOdU7cNCE3fKk6/CQZ3OmIqOjwNYZz+G4aysGrSNtuA5XfxMu/zpc/bCI8Rz+bNoc/maV3hdGBOsGhUThLRPfsQiqHtHiPGGzwUiH4WhAEu1nrswso+2HdPyQ9iikGYSiA3vIMkcjP9fBe9uTTH3T99B823dgOBLRy0kHPeKd24QbSwTrSwQba8SbGyTdLunmDvlWl2IUoIsEQiwui/NcMSK+HhOdnYM9UAKiUlU2bBAKuYQjCzwhl5Cy2Tp9IZeoNRhVZTHtUqnWmG3UaYi4aG+HeGuTWiQ6adClwobeYEv3SLICLw4Va2VFm4yEVPWQh1jhB/WPM62X58ZGWKEXu1h6oRZApllgmDm+U6XnNOmbdXasNht2R/mm3WZzd2512LJb5A+gxWbmKdNJl9l4h+l4f2zHAtiGtGKfdjSmnmZEY5Pt0GNjBFujnFRSJXeYodtkolo/WfbLQndl9jircydYmTuhstuShbjT2r0tjm2tcmrU5XEt4dGqS7XRJPBqrJkZ1/IB17MtbmdrbCe3SeNl9PzeWaBCr2E7x2lXT7JYO8Xx6hRNIydPNrnZu8gXd75IP7r7723d5kL7Ao9PP87jU4/z2NRj6PZxXvBjnhn4fHGzx+3VVSpS+rjrUgI5HLAwHHBiUJZFuoMe5gQsPohlTpW81kJvdTCnOthzs+jtDpcvRuz4tsq0nX3/Bd7+waexGmWgSQSdN5eGrF8fsHa9z/q1AcH6Dk60jRlvUuQbRM6AsJoR1mwCr05mORxjnSe4yGNconlARiHQDK41XG7PWMQzJhXrCHAvouhBmT30wmwyl1HKtw+UIuaQ+AbRwCQeWGqMhqYai/jeQZCsURJdKMKLiSdzBfk9bluyessiWcBOs33pbfgrJ8mzMVkxpNB3oOhCJMQ4r/4dCFPoqCqArMGwJgHUuwHaQQIHTcoBszGe9PVGKVXpPwsMamOLWmhRzQLa1iod7xad6lU6zUu49TW0I95MFHls9I9zc3icZK1g6sYagdbg6omHuXLyYa4dO3vX70ckIk6vXOeRG5d45MZFHr1+SclElIzEuiptlICFkrEwSibUxBApC73M0sjFWk8VMYf0GYsGyEZrmi+cejNfOPEU12cuUBwgmKnHfc4Pr3AuuMlivgOOR9dpsm1W2DKqrBcVenmNsdkicpqvSE7TyndY0O4uExTgJSXhSa6zFXl004K1LGM1K1hPNTYTnWACLoULOrVPqrLexD49Ke+d52S0ziP+dR4e3+BhGUe3ODFcJEq/k7h4cu89mNotqsYvo5kfZmxEitV2pGt8pFLhZ+s1ts3yetrJMr4xyjhZyD1C+khtJceTSqbX8kgtGSulzIBZxRASE8NW1xQZTc3EGmW4VyLeffkpakLnkfT4yNq/Z3wwAFmAles4uYmb2XhaHbd9DGdqHqM9i9meQau3FdvuVn+F9eENuuHqHsX+bOsY/+Of/5O80fZ18PU1Ar7S1Oe3P/qmIy9mg8EMg8Esw8EMo1FHRcoOmq6nJJWUS9k8zw9OKVYdMUPPuTAX8nBHoxnZZDsG2tDECgwqkY53H20cvqPRq+r0qwajqo4w0KdWTJoPiUdrOEFPsdxVlY7OUM2nTJ3FM+eYO3uB+bPnlVdbh3sFZOHTTVO24tK3gy2Go4skwWW08DJefI1megOboxuOw6zCTnycbnCC/vg4I/8448Ex9KiClRY4SYGbCT11obbN5DDn15fKdEM7BNoOZuUErEm5Shr30I020jt9sMxPslPyq5VevMwcEzlbxO4WqXU4dGTGdQW47HAaMyujdpqjk3sGiWso4DS0oWvCtll+Z7tAqgRWOoGtKUrzvfddZEyxyXkucYYrHGeJWbZpaQPsQnoYjrauNsvN4iS3OMESJ7nNSVY4Rioi3hOzNI1Tnq2AlYAqAVoKYFUcjjk25r0A1v2alGqtfL4EYld+HVaeOfSw3Dhi8yz+usfO57YJNwLVEC8kNaINEx7TGT81xWheymZComFOMjRIhwb50ECLCprjSIGxziigPQ6VmPOht6DrKipqJkJI89pNbjeZqSkCENdNsN1MEYwYTk5uaXSNKpt2na7rkHkZeTWhqBdIytpwCww7xTJzbDPFNjJsaVYnx1QEMIUKXoyyJn7WIMxrisgmzSyMLFPsXrKwEq9M5pU8oJYGeHnJLvnltEizlStNMM1UPRUJJl1rim1rlk17nk17hi1rii1LFkd1tq0KXculZzuMHpCWWI7Hnph1kFMNhEmuR9XfpDJawRuX5ahrs8dZmT2hANegfjfJgBOnLG75HNuKOLadcaxr0LYcrIpObidE1hjfGNBli81sjYHeVeLrkTneHy2fRI9oerM0vBMYzjFCY5EdbY61YoZMqMfvkaGXvs2HXJsFo0stvUUWXqU/usyt/sv4yd1yFYVmkVqnJv1bsrh7iKp3gqcaNZ6uV3hzo8Kb6xUWHOtQVYD00KY7XbKdbZLNLfylNcZLG4SrGyQbm2Q7OzDooo/7WNFQlYA9iEnzfSaSBCpnOSlFzaXRP8Gve3TbbTZnZlifm1PZC/mhzbOpANfjxSXa2v6CL8lMlkZzrHtP8cQf+st4p89gtFrq80h2OsvG5HlMUaTk8m8UUj6aMBz2+J2PfoSd1WVq/TEPmRVOZhrFjVWypS2ydR/uYFPdM63AqmY4jRS7keI0EsxmRjRtMWw5jByn1IizLULRhJP7gZ5jaKnQiSgZl13QkPjT7Lz8nfRvvlMuYOrl3c41ph77JWoLLyigI/eKdGwy3nLoX68zWqkSy4V//xu774BkbNsKhPWrbQYig1C9G6AdJJw4/LELKnFKLYmpFT4NrUvL3KBlr9HQejTpU2dAgwF1+lTTiNTvkAVV0thiQ+tw2T7BlepJLnoX6B3RRzgXrPFo/yKP9l7mws5lpoIt1TcmeoNDXbxKULhkkUERGGShwUZtnlvzD3Hz5Cl6ncOvOb+2zsNXrrC4saZKfbenplifnmV1dpaNqSmSV2CZ9YrxHri6M5PlUWaXsriBHzfZSW1upilX0yHL+VhlsQ6uPGTtpseztPJZTupNzjoOujXNlt1g3aqzZrVZsaYZmUcTQMl9+0S4ynu21vjuZYfzg0WMYvccCNG1z1LwSXK6pIXovlr8ajvkP0/3GIoOCnDWr/GDq2c57c+QFTZZId1kNulkLvtkO8o1wmRElHRJkx3SdEMRl6hjYtR5/8IPU7fa+OmQ3978uALZurGAZs6hHVgPHDRZ66TWgNDbIHI3KQ4Qbklg2Q3ncI47/M9/4YO80fZ18PU1Ar6yLGJl9T+RpT5ZNiLNfDVX466nPlEc0O1a9LpVtrszfKH7MC8mi6zl+++9roU8onV5LM2oJzWsuImReUdmSXTGUBmQNH3CesKwmtOvmWy6FVbsOttmQ0WrE/P+Fzryr3RMjRnbZNaxmXNsZm2LWdm2rXL/ZFvKI+9VBiiEG2G4wsi/SG/wMt3Ry4xHl0jDG8IldOTf9JjmFie5iWRfxE+V4AATK5VG/QJbPCnK+YGxlWs0C416BtUM1QviTMCbaGnpcU4R52RRRha/vrRFeREaEjnbxN4WmXEQZGqYzhR5bZFBc5EN12XFyFjXi30g9Qq6XNIb2GH7gG+xyG11A5mSnkEpyyChyAXkHf05hMWsBFcCsk4pkLXMcQKt7GWR2/KsZPP00qc0nQ7iGvVCI8shk6xQXpBlhRrTI7YzGbOiHHef8wrbMiZZ+Xe7++rpDm/LnuFd+TO8s3iW1oEouNiL+Sl+K3+K38qe4vPF+bJx+hW+GemlnE53eDK9zLuy52kmPaKuhdGD2jBRpYrOHSVCJaHEftleJOQHwj4qkVrbIa/X0dodjEZFRfynqmNOGFeYi76Im+xnNwvTQZu+AO0z4LUgEUmIsdTkQjymUONIjVo8RksfrIzstVigOQS6SyTFfXKjzkyyzCAuHCQPNqaugiEz2iaP25/H1KSkrMonx9/HZnqOQgTfc4+0cEgKj0TYBAtHZUxfj4lOmgQafFcnqBnK/YpB38no2Tl9O2dkG4puPrEejAzl4EJzahiy2A1Y3Ik4sZOxEJpouUEuwavX0dAugRunauEqNyejhVkRinxNCZtvmQVresEtMq6Rcq1IlaD3kSbgJd0o5S/2/Ab6ZMF00FzT5dHOo3vZMRlPCTvbK5AB3Mtk6REOY3rXNxhcX8a/ucr4xgrx+iZZT8DZCCsZKvZZOx5iJ0MMdfAe4N8Q9ju3wHYSDDfHFK8YGCcfxnzk3fTnHucXPv5JBobOyccf54N/9I8e6nuVe4kAr2R7i/jqZaJr11n//DOsP/8Fqt0+jaH/ilpuIuR+a36Rlbl5xm0Xu1YwY/ucjtY5Fa5wPFlhJl+THMYrfg4/a9PP5hhk8/TTebby02xk5xnHAvLL79XrdJl95GVap65hC2W5liqwKO+/9GxvDAcF25csdi47jDcP3KO1QumCSR+NBHMEtMUji2RkkcX39x0nnkVQqzKqSta8zU5tiu3arApICFjzK7X70q2TXqoqI4QbswRkA+rFkEoIXqBjJjoj3WPFnuJ2pcO6JWXph1+3Uvic4yIXeJkLXOQsl9XxeoGn+Dxv4xneylBrHgIpnWILNw/U9b6vtwn0ezPaGkXKbLE2yWIdLhOUPnv5ZrJcZyOYYTmZYz2tqmqjnSRiGPWItQAjt5SmmZGbk9GiPm7iBtNo0RxxNkuUdTALs9Q9lQxRoal1RyPXqOcaQgMk6zThVRMdSiEO2m2XkkSjBAGlEtmY3H7kdU7YOqcdIdLavy5spTnXo5zVpJSjiI2QZxd/k+cXfot00ot9aucJvuHWH6ATLJSEZaIxl66SZ6vk6SpFJiQid8IKyTbOopsLVOwTvLd9nrphEuYZz463iIoxphZjaEk5SjhNi4mNmE07Y9PWiA6cM2aRKv6Cmhxj3SeW3ryTp/hj//0/5I22r4OvrxHwJV/fZ3/5BqZtYInQnyOK4BppnBMFiernCYYJ40HMSnfM7wxGfK6I8PX9RcIpQh42Npi3Nu4KmuqpRk0i+npKuxPSvtDHmd6kMLoEg4Dhaoa/YTHedJVn4eHFiqSox40K6bxDPOMSTXkE9SojVy6pLXrK2wxoPlDZkLTgTxkRHSNh2syYNlEU17OOwawtwM1lzvWYc2tUrTqGNJyQ4Y+v448uMhL3y1E0io48thgqre9bZ9g2TrOqnVSlcdezKbaSnJ0kfeWYrfy0JPMhT1KjiEIXTAnYEMAmF05p2s8KnAwF7sxMFkIFRZojzLuyRgv1QokU+kGPdDygSKWxXyuzH+gMHY+hW2VkeypLsyv+W4r5lKMI/lpFqhYvcsMQBim50QhzoJwDNXPIfGWDueoW89VN5qtrLFTXcY2jKXYDXAWsdrNYu+NAbmRZgTZO7/AMfZxK6vHLkkl8vSbR96e0q3yz8Rzv05/lTdr1snF7Yn7h8mxxnueKC7zEWQI8nCLF1VJsLVN+529nhi3ew2d4koukmU43drnWm6ebt9Qi0HIyPDulosdUjAhXS7Ap9bPsYjJKlPvLbIVZITerFIZHZpT035kumQVxizi3iXOTMLOUvloQm4SRSRQLYYBHklZIijoZVQWQSpBkPxBIahtLfFf7/6Bj3iYvdD4x/OM8O/7+QxH5WKQyDMmgl7IYUvoqDIixWTKsiie7chmKUl3DMMdUzR5Vs0vT3KRpbjBlrjNjLtMxVkqmz66NPa7jZFN4xiKee4rt+jTP2D0+kVzjc/0ljLhDMz2Jmy+g6W1Cy2XsuIwnennSmzbd3SjFZXubzIiwrJETNRNW3XWuaTdI9JRML8iVeraBVXiYuUeVJsfth1gwTzCjz9FkimrWwEpcYl/KHndJbYSC+bUHbyRTKgGYkWS2d30SlBHfDdCUDqGxScZ13PQmlfQGWSi9o3cDsopZ4dGpR/fAmPjJxknVW3boPBPa6PV1kpUV4uVlNSq/vUy0tEy6voZ2gB0z0y0Cb5rAnWFUnWWnfRq/+hBWKn1IQywBZAqUCTgbKZDmJVtUUik7DCgekGhTqrmSqkHcsAjrJlHVxOoX1NcCPP9oAg4x0WC7NbfI0twiG50FdpoLjGrzYNSYCSPa45T6GNyxhZ7eDeYlm1XVd2iY6zSNNZrmCi17iaa5Sl3bwjkgNr8aP8zn/D/Ezehte/tOuZ/hsan/H7X2NQLXIHB1xp6MBpFbEtW8kkV9i+7VBr0rTaXhtWu6mdM4NaR9bkD9hAg5i8SntQfG4pFZjv7+diGRjVcxuUclnofWtMkaFXzRyqs22a622PCm2KjN0PWmXhMbsJvFmBJk0wxCw6S4o1pCSiUVGcaBtYaAPJVleoV/r1Nsq+zVogJX+2WCQuO+2yVsxZIVz/D8DN9f5Fr/naz3HyMaTaPlNnpuYOa2IjP5vWC7QK1j6ZyzdE5q0g9aHgOfgi9qcEkr9QI1rccLU7/Cc1OfoNBypa96bmWON1+qqM98p+l6Bdts4VkNqrZH3TZw1D3Ox9ZGOLpGU/8DGNo0BUN07Z/h6DextIBEK7ikn+CL2nmWWdh7TYuYx7jCm3iJ0yzdVWFxufEdnP+LP8MbbV+14OujH/0of//v/30+97nPsbq6yoc+9CF+4Ad+YO9x+Tj/6//6v/ITP/ETSqDtG7/xG/nxH/9xzp8/v/ecnZ0d/uyf/bP8wi/8Arqu80M/9EP8o3/0j6jdQVrw+wF8+b2In/yrH7/n4xK/uGHmPOukXBW65cn1pZLDmyKDp8OCltDhCp90MSa1+iS1mKiWMTZD8jvghfy5XZjo41jp9BiBCC/L35ZRNl3PaR+bYvrUNO0THVrHa9SmpTk4mGTjRpPM3HiSlSuzdXE6ppdq7OQO3aJ6CJj1aav57r6xdv/fk5ibj6kJu1M+plqEVPKYSp7g5TmVQnSnQjraNi19h4qxTdXYomauY03U4O+0OHfZiY6xGRxjZXyMZX+RW8NFemFFZVfStMyw/F78ZblGwLHaGsdqKxyrr3Ksscax6ioNoYo+wqScSzKAZRbrxB7Q2sqn0Ma5AlRGkCkRSlsatCNpAJe+HU3R8lq6jiFzXcM0NExdV+WDu9uGrpfPvef2gb9Rf3/gOQe3955bbpu7fy89v2lCkUTkaUgWR2RxSDjeJhitEI43iULp5UrJMos8tchSmwoBZ7nJeW6osXpHGes601zmIa7wELdYJJ8ADUsa1OMYJ4r2fKqS8KbpG5wwXkY/QPH+mpkDMfHxGFElwiGW21JhqXlUuMpj8VyyRR6plAzmwmJaJReGvbRGkddJ5TFVevKlFc6Va04uhJ22hmYLu2BOUOSqn8C3LUZKg0rY2faBkrihh/yV7f+Tbxl+VL3Op51H+QfGf8Pl0SLbkZTTHf53GvaAY61Vppp9qk0NrVphmLfpJTV6sUOUaCUjaJqo0U6FHTRRi3flSUQtGVJNfSqJj5OG6jnSS6InEvnQyBPtrnLtL5fpuqF6PCXzsjeKZtFkrsnjol2ke3u6Y+BSSI9tIeQQDr5eYax5KrtYyGIv0fBiYYh87e9LyDsiR/S9ErrVdbart+i6t+nbtxkYa+RHBAicXOdY4LEwdJjrOsxtuNS6tgJUqSm9N+bh0ZT+HFNlGqOaSVoxpQ5ZlCKxE9S1RX39BVRGp6j4JycMfxHh9E2eMD7H09lnOEapryaWpCZLgye5OXwTW6MTGLGIcA9VNk1PhxiZzEd40QAvGL1quexaZ4a16QW2WgsM6nOklQ6508HNq3hjHSu8vyoP0w1waj5OfYxbD3DqAW49Kr0R41Qz9b1rolWlGehRwuCyy9VnzrC5Oa9eQ0JvZ6qf4q3uf2TGunnPf0uyH6GA6gkYCzydsRrL7TsrIYIdh97VNr2rDaL+PkAxXYPjT5zgzNuf5NSTb8b15rHtacwDZW6y9gqGA7bXN/jwZy/yyReu0t/apJ6OqKUjOoxxJQN/gDn5XpZoJiOzxtCpKx87FWLXJW8Y5B2LsFNnXK8ztioMNZEErh/qz3otJqXT88UKi/rSoTJBoWuX/L36jJK5H82SDGepBTGnkkvMRNuKkIXE4sXxt/Oc//0M89n7+0e1FN2QElJhw0wp9IxCfk9aTq5lZFLpIkEBIbBBI8IgKgxCGXODIDcZ55bqgxIwpVS2NIm95uSKll9KVSX7KQHb8n/SI6Ue0wpy1yKpOyR1l4ZX4bsCj+/fgum4/D676Ta/a69zqVjB7i9jRcs8c6HLzYXxHmPsIzfqnL8+L7UfxFaVTLQvbVfpygpLrhQHS/BXfl+6ZNnJ1ehi8m2Vb6RtNAnzkN+IPs6WG5G69j4QLgqlg2gP+lijvgoYT4S+1HlUwpdCMcnOHT/Nf/t//n94o+2rFnz9yq/8Ch//+Md561vfyg/+4A/eBb7+3t/7e/ydv/N3+Kmf+ilOnz7N3/gbf4MXXniBF198EVfqv4Hv/u7vVsDtn//zf06SJPzoj/4ob3/72/npn/7p33/gaxDxk//L3eAr0Au+YGc8a6f0Dtx5TyQ6b45Lmni5sb2SyY80NX0Se1CCMnugVOHvND1zsGKJ1DYw4wZmWkPTNTRTRxOmBENXjAmFMXF9khY3JtEXXS4Raq1DqhVE5MRFTlQIDXFGIGxWsp3njPOMcSGsUsKyZpAIO51tUDgGODLqFGpbV9s8SJ+QZIviHC3K0OKMTrLFCW5ywljihLnEcWeZBW8N60C98UHrRQ2WhwvcHi2yPFrk9nCBVX9eZQ7EzCxVOiXG5EKkGo/lOOlKpkJlWmS/JVTaTC5WuxKiAiIUG1pCJQiohmNq4Zh64OMmscpkGbn07EhGSyh3U6VH5c6O0Y+lWLMBtfaAmjc6+qOjKY6lElyVJYMb8Tzals7c1jbHtjY5tr3J8Z0tTnQ3mRsNMKVx2TTRZGEopTqTOVISarzCXBp5J/sOzu/93HIu51Gk6apaPpRFhVBEF7KwLwgzYTwrWdCCNCVMU4IkIRQNN16bOY5DpVKh6rkc17c4GV9kwf8iLf/qIX2ezKwSzr+bePH9+PaTDG/0CS9fI7x2g2h9U0VZhUDEcmIWp69iuhmp4ZGIyGxrgby1SGK1yNKMRvwy09nzdLi615gulMW3sgu8lD7Ny/kjBLpFoceKslr8YP37g5iWSxmchSClQlzq9nOp3TdJcsm7mUSFADuTQPZpAh1TKGIKyfRZGnbVotqsMjvXZnGqymzTpV13aFdtWp5Fq2LT9CxMvSAY+Vx8+WU+/8ILvLDdZafSYEei3jURKW+xbUlJIfzI6n/hb1/5JzhFwnX3GP+Px/8WS+kM9fUuRjckGukM0spdiy25Mc9EW4rVcD5aYyFco/4l1JRRt3khHdLlGlYoBjnRyUIrdXwEhEiZmp7nGFLmk5X9c/puuc9uMnrCPvd6dP7kNUSnTvrMVmePsTZznPXphSOpqiv+gJOrKxxb32Bha4fp3kDpnWm6OxGUFjDnigK0mpf73PLxVzFZzPW8dTZrS2xWb6lxq7pMNqHzP2hW6tAK5qgHs9TiGZx4Bl2ypUaGYaocK16RUMky7CNITQSgS1YnrOi0I4vjVx+lSKtylvLexr/kUe831HF9uXWC59rnuGw/ShG0cEYe1qiK6VcxRjX0aJ+1btdEtFWAmLidbuPpqzjFFpHVZGQ/xMg4Qz4pnX4lMy2NWtulPuVS67jUOy61tlPO2+VcqlSOsiwLieOtiW8SRVssfTHiyu+2GKyXawzp82qe/jSdC7+IXd9AzwvcCeGHEH9UDhCAyP47CYDu+jeFhKS5AO1T0DmHMf0o+tQj0D7N+nqPlz/xO1z8xO8w2t7XPPTqDc5/w7t5+F3vZeHCwwogynl/a2vMv/vMEj/7zCr9sLwmCbY703FZqBnEUcR6P8DvdamkvvptSuCjmvlKh7CSjdXcO0KO4ygTNmPD8dBVoCdSfa15MyedMQg7HoNqi4HRZEhTdZINpYCxKHvfF7UlFrTdLJbkV1ZVWePuLzINq0SjGRIBWv6M8tSfQos8zlm/zVPmb1Kf9A6OsPk142F+yT5LT3qE9VTJWQjrYUfXmdYLZq2cBSuhbkXoto9uj9GtAMOJ9vQrX48lsUGYuAzTGoOszjipME49/KRCKBJCQULFH9Mc9an6vup5Uyy40vt2V7ZSw7Lq5FlIlt+93hO2zJcfqnF18RqRWVYNyW/DMN7HzOAcMzvbqgpgameDun83I/CuFfIdVqd5//QHmNY7hCT8iv0M2/pQ6arGo5CR6IVKH5nuEOqOGiNjf65Go5yf1Tb59b/7J3ij7asWfB006fk5CL7koywuLvKX/tJf4i//5b+s9skBmJub4yd/8if54Ac/qKi3H3vsMT7zmc/wtreVqftf/dVf5Xu+53u4ffu2+vvfT+BrOF7lX/5f/57UCkmsMbexeDGY55a/QF6UF3mpJz+WrHN8uEnFz9DqHWhPo9WaFG6FJExJAskIpKo3SUrRdAScyX91zGIySoxVS3DMAaY1xDAHYPp39+rmOlZSx0qaCowJKNO/DKl21WI9ifao6NBu5Ed5XrqWl6VKZlmupMqSbHGDyBN2NxGhtAgqooci/TcShZdyv6P7o6RkT2q6H0qvcSa5ysniBouqTKR79JsU2uOuSbLjEg5rjEZNBn6LQdiYlCVmSndKbv7i7M6zVAkgS5TIIFOgStsFYyp6VJYDCGDT2hrpMYdiTsNuR1RrA/V+dIkOHWE7dCZ9bSdZTxcIulX0DZ3pjR0WttdZ2FxnYWudzqCvIleltlIpbqwAoZQqymJXjfvbD7KUlItOZNtEjkvoVlRjfOR4hK5DbDtEjkPs2MS2NfEyKv5aF6yiaWSkmorUGZmOLjS7uYGemegTEKIXAkRsdMkGFQ5yV881KV0pwdOuudqAE86znHI+zwn7GSrGYY7ofjDFVvcYvc226qewwwFO3FdR913QJjTcgTdDVGvTXBwxN7vEXP36oe9sNX6Ey+F7uRq+i3HexrT1faZNIWpRLJoGpq0JsifSInwtZpiF+HmIn0WEaUCcRmRpiJFJ9qfUeHstR1Eq3tJcm/TNyakrDXrlOStkJnqaoGcJRhJjpiFWLsGGRJW73imOKqQJSWuKpDVNYZWlTqmu0zekz8Jg2uryV4L/yEK2Q6Db/PVzf55/v/C9+y8gpa39iOpaD2snIBnrpEdcY2QxN5dsMZ9sMZftMJ33VTxGek6EPS2wXQJHtMWkFLrC0KspYXRheSw1kuzJKCxp5es7uZQ3QSsM6YjgbRBSi0uQL4QqQg+eC7GK5Cd0XUWlhbQmF/C6OyqwNoFik9+YqhBWAvFlKZT8/o08Q89L/bBUBMwtm0h+H7aj3o9UMuwCuvI8T0sSo1Gf5qhLc7hNNRhhSrlxnqrXEqZHeZ4xudZI4EbLBDSmk1G+3Bw9k/fgTLJsjsq4Fch2Cc4KBdYEwMk+T4E3AW65btDz1tisLrFRKwHZdmWZ/AhhMSepMOOfZGZ0ghn/BNOj41SEnUkXIBtRSJlj5qMlI+HR5HzlRS7ULnHM22CcNfmN/p9jKX5avVateJbR8COEE6ILWdQqLaEDekKl/pAFehtNk34pcSFZaFMIpWAh/T/3+nXkaPoQTe+jGf1ynDgyan0oQiWnIo2FmkiriBKmXDv0g66V31ueKc/zXTIP0aSTTKtGkT1Okb1bOmQn/3YC2jOgfUru+pMvXF6/zJChQqn6ZJRrlcw1qnpEwwho6D4NY0TDkNGnYY5xjwDIBy3ODfqJSzfyWPKbLAVNNT8o5S1vY2DUea75JM/VnyyjiNI/ngx4Yvgijw9fUiQ8b4wVqnTSrGSqh81tRUqX0a4l2PUEs5IQJTrdyGBZVQiVjIJC3T6eNEyZiTA4WnSSnA8kPT4Q9WlOZFvWDYN/26zzn+s1ksxguu8o3cBp8Z5zSEvrld6jJsyudobpCGFSVrp9eG7eY7/4azoyIuzecxive4zWPDVG/bsF5CUA2HCbzNnnmXFOMuUsYpoOz9ur/MJ0zKdndkjyX8dMl9XzM2MKv/GDRNa7JKmHNY6od/t4/RHOKMQMRAokYsaKmHMDPKH1L0y+K34zs0WTMRl/nR6f5d5EJveyRlXn+b/x3bzR9jUJvq5du8bZs2d55plnePOb37z3vPe9731qW0oL//W//tcKnHW7+4vlNE1VVuxnfuZn+MAHPnDkvxVFkfKDB/jEiRNvOPjqd7s89fck8/WV7aSRRaRV5HgkzGg+M/qQKX1MywiU0OGdlsbCLGRJFzJaaGImpgJ1ItKsQJ5WAjzpE5CZ3LBKN6X4XEWXCxnlxvkVskLS8pKVMyCRvhJDJ7Ckx0S7C6gVdkS9ukzTW6KtdKyWmLaXqBpHl/OlmUHfb+D3KoTbNtmGTrqukwXGkd9lbJoEc1VYMHBmUqpNn7bXZdbaUID4KBsVNZaLE6wni/T9KYJ+nWTTw+vF1P0hjdFIieiqm7UCFyKGeuDmrfYp5cTJvnJejuVzZL9kIKS2XrD+bmYzN3KVHShE80TPKYxMLb4kS6MyBJKtuWdIrFyM7rosBg/O9UxT4MnMDPRcn4Cp0mVu5Jrar7ZzTS2GpMxBLWiVZ0fO9TsfU4vVEkiYWYChxkiV6Bp5hJ7FGFLC2hxTX4yoLoR4HRFV3f8oWaIxXncYrZaejE21AKwthDROBtQWQ3V671rYNeku1dhc76g287FXYVz1CGoVxg0Rs3UVSYfiPdMEg5TBBim7KBfuRQneJ4t3mcuCW3ppBBxpaQpCiS7fmSzeTWsymuVoWEfOXxPolQCCuJTxCeATtsk0VaAt3yM+0chtV4lFW1VZzJf/jno8HPOHzV/nKbssJfsl853877M/xnZrGr9RJzkoop7nWOMMrxtT7cXUuhF2IKUtZTO6jJLHkZzHjJkxa0ifaEbbyEQuSwUXTDlvCk1lC+T8ESpnTYkzmxgSUMpFsPwrU4L41WRl6/7BsZxL70hxx/7dLVm0asLwxpCa/glqxsdw9S+gHbi3BNljDNP38sL4/VyMyqypp8FTHjRUrET9K3sAV8DT3rbM9srfDr6D3TBSWXeQFAlxNibKRsTZUGkTxVlwxBiSPWiT2V1mYNiPYbhvRzdKtkwBoFn4LGn0+T3GuC+VOXpC0w5pWSEtGe2A5mReN6Mjf/KSkFwat3i5P8Ol4bTqBd01CVR03baK0kyHm0cWM09gcbmx9/oHtw8w/B18A3uZ4nI8QOo/+X95fyivf5Pr930cA8mUShBDSl4Dp6BbD9ipj5X3qxENK+JHBgN+aOjjTZbKN0yTf1tv8zv6LO1hm+PdDu3AKyuJVDDk8Hvd+yy7n2fyWLlv8pn3nl8+N7AcbnXmuDG9SK/aKIM5uq7ufx1/QGc8VBUvmVSIOALcUiwrxbYSHCvG1kMcxggvr5P6uKMh9laAvpNRbEO6bR6p+2g3YiVe7M6EGFMFSd1knFWJwyYLm2/hwvZTtBXJS2mXrT7/1driw9XnCVqfBLNc72ThPNHGd5H5D6vP5RFzxtjmrLFN50A7R1wYXM86rKbT/C/M8iZMRkXB30y2WY771OKAahJQSUPcNMJLY9XXJwFE5RLgk3scGdMLNj/yb36C3y/g6/dG59+XyNbW1tQoma6DJtu7j8k4O3u4Htc0TTqdzt5zjjIpZfxbf+tv8XvNXnrxKn/qCz/PwKrSF0FTp0LFzehUAzwnU9kDFWWbcD9ICLWQfqRslwSijHTaaYqdJLhxosQUbemTkDK5NMUSlxNd9snz0kSV0EmZm0RP9QOjXPj8WlVRsm5PT7M1Pc2oXseUyI6dQaOMgqlemMGAjj9mKgxoSZTVccCyKRyH3LYpbIvCssntySLQspV2R2Y6exTT0kOQFUbpkiMq9FLNPoM0EX2bgiwt1HozFyKLTCWbVL1jIQspQQolapj8HCRSWV6UpKnUkIxJJjTRskfFtl/h2zg2cSGkgCWhHHAHe0LRe8LRjVVMM2aq0VXOyf1XSGLJji0qD4MWnr1FrbJK3b1R9qCVLBsovBWhgEWe2MTDaZLBDOlgmnQwRTbqkEcenSJn6hDQiCdzF62w7wIgcoA0UeZVy3tVDFqOhcxLqmNtd66eu+tlXfcugccuYDq074jHy/ndf/P7yaKurXzri3UViazOl0CsthApYo368VC5eu7AxPQyDGt/+RANDAa3PAY3PeJhGVywCJme0BEfNAnISoZIaXKJ22bZR+WW1NRCQCHMiZJheWXAJFmrFCaCmodMrT2Fnap0pe8n5WxC0W6aUls1AWWGCohIpkOyOZJpV7dBFTWWcuMJcLNkKS2BFgEy5WhOts1if65JNhITW4RoJQRjGWzwNFfSX+Os8XN8b/pJ3nt7lS9c/3+RFgaO0L5oJahyCskx7Fr5O76nlaev+v28Hsu1hMKIVSm26tM4JDQ6OZDqa9YOP6Iab3d7GuQXVV678t1i42JfvHbvmYUszSf9EpKBmWS/955RHCF0ekBstsyKvDG2+57uygJMKjPufv4Iz/gkFf2jOPqzhwBXlD9MkL2XIHuPLLfUvoc9IVvK+ayfERTwqTE87uqccfR7MuI+mN1NaX6UJeQIR16gJUTERJp4RKLFJITkWkBGQK6NyUVg3hxRmHIS6kTDh4l6T1JkZQ+VZgS4redx2l9EV0BIvmDJQJbZiEl6FF13MEwP0/AwTBfDrGCalXI0KuiG+nWojGWcWfTGGt0xdIOE1SDlpTCnH+UMExilGkmqqUu93F3njR0W9Q0WtU2O6RscMzY5JvPKJqeqPb6tuMKNUZuLgxmuDKckwsrsqJRdEAC3WB/TrGvETp1tvcOWMcOmNcO26GCZNoFpEBgWkSHXLWF6ldFSo3LL3NsWyY+90/l+vtNChLHH1Ed9Gn6fKX9A2+/TGPVVVrgy2Mb1R2UPklrXJHgRdAYaZ5HvoPwe5LcWWDE/b4UUTspL9YIvNDSGaYbvBWzMD7l4fJWp2jlm6w8zV73AvHeWhtbCkMBTkshNHdRYzoskJo9jcjUmZIlUHcVK71PGRHqGi4Lzgx3lO5U6l2ePcXn2BAPXo9/ocINCSV6c21zm3MayAmP7n71AF93F8QgtGGMEkjmWc3ASQN09XzWTDWeGTXeGTW+aDXdW9YwKI226asLRPGS8BZ8fxOY9mJxPmso/OD7Nh7a+jf/S/m3S6Q9juGtUTv4kbrDAY90neChq7P36lUSOF2I0elRqfd5spbzVKLiqGSxe/ADTw+P8H26Dzz/8G/j6LRw/wRgX5IFOHohEgEU+lmi4RHknQBaNVvv19VN/pe2rCnx9Oe2v/bW/xl/8i3/xrszXG23H2x4fuPI793xc0YVKeZdblqocGp07vO4QOg18WWB9mU3+vZWZGVZmjnrTEsHajeLnaoGohWUpjCrN2x1V2YxE1w+U6u26/N0k21GizgfR65KsjyyCpZhcsm0OGA6FGu1JCY5dZuE02RadG0vt09VolaPo5EjMZ3CBYe8RdaHQ0hwnHlI1bir3tGXcfBMn6WGFAcYgRB9cxxhcRw/ufMev1NAtNfn7dflfrZZPIoOll5HL4uA+edLkLq3AndouG31LYFeCvQdZikqJmMo2iRixlKsJNbyAHF36DkuwI6OweO29l3WUt6sBx9oDFpsDZmojpe0j5kcWtzdarK7VGfdt7DRXDJ6i1+WkGZb07x3R9yIZGjvLldeiRGipjj5OuzT2alFjEFsGqauRy5qikaO1M/QpCYYYaFUbrWaieSamYWFgo2XCzmWVY7a77exv5wf2Z87hbQH1si9z0FUZ5+u9nvwJtvK30bH+Pg39Ju+0/zI7yZ8nzN99N6PxJL8RURDqEOkQmBpjSycUBkSl9ZcTpTlRIp6V++T5Co/JWKieJc0Y4zhDau42ntvDtEbYzhDXHuC5I6pOQN2OqNpxmbGX379e/u5l1DV7/1ogfXqaza18lsvZIpfSWV5OZ7iethRgvdMWzZDH3YDH3YjHvYTHnATPD8i7A/LtPslWF3+zS7zdJ9zpE/V8lUUU7bNUt0h00UGzibVJ6aS4bZG6LontklrSw7evJbS3MFLkrBJHLssmJZglvxYpet7bLwGuA/ukJ7GEhPt/kx94vtLiknI0AfMSFJDSS/U8Tf2tvG+rCPmG4jm+Wfskb9deUOyhu3bZqvBRt83veFOsi6QCzysvkhp5PEcRzZNHc5jmHE/7dY4lBl8Ic34jTXimkrIb4N8FsvtgsCzvrEcjWkFv4l01NpX3MSUjLkQnEgzQJxUamoGp6ZiT0VYVGnLP0LF1A1tlNCZVAhPffXzP9Qqp3uKmd5wb3gn1vYmZeUQzXMGOt0h2NCLtScYCzjRdES7Eko2bfG/qnFOVIjIeqCDZqyKR9yHXpBx0AX+x+tDSgyp3kllNV3IfJZg5nH1U971MmObEC7UOXwE+o4BfTk3r0jS2aOkbdDqbfOvUJqOgy8rI4KbfVILpvW0XtmHGGfFI4zm+rbFJM4uIC5t+Pk3vkE/RK6YZFG0ViJicjZMvTlFEkOrxhDE0UfN00ltVbiekRkqq7W6Xj5WPpyRayko94VYzxbeGyqUKthqa1AKTamBQH1ucHtoc96X3W2eU2OocHiSuclHXkVipFIMeNMk+ZVZOZl8hs24wti18x8Fwq5huFduqYMn3qxJzxSu6IpiQoNZkWyoAqv6Yx6Sq69ot1X8rROzRgfFaWmEtTGiGO7TCbaaibVXqfad1rRarzhxr4u68Eq2/i6TkjrhnqeSV4WgpjrD6kjLUMv4DCR/WCt5XNPmGfJrpwuZPFnX+xM73cDE8z093fpXPVy8Teqt83ltl3T/G28PjnO1sMD1zHc8rCTvutJ23v4D37J+nuvMYb7v5AZaf/oeMz4o8wN0mIFHIsdLUJpGRc/x+sq8q8DU/XzIBra+vs7CwT1Ep27tliPKcjY2NQ38nZYfCgLj79/dqvhf/vWbCBPfco48r8gUnjnGjEDuKcCNhdip7O2S/uKoVvw/LZEHpmsSepTJnApRCcdslsl1C6dOxPbUdS2+OZKnuQ7fjvm335nUgOPq6a2MnJQO7y439KPTkIqceP1gGsF8eIKOwYkppqiJgqFYVM2a9XlderYhAc4o+GGAMBuj9Plp/Ha3fg26Xotsj39kh394ml3LXSc343fbqx1AdZ7lhGlLqJzfWyah6ScpFjQAUldyblDuUj5WUw1K6cOe+o+ZCpCENzaZqanawRORYjS6m62C5Lra455Xueur5QpChCDPuIMvYm++Sckz23TkXGu5xeI2B/zx9/zl6w2dI8rJEWILFRl9D72lUwgW8cBZHGuj7GvlWX9FYKwHX+zTNdTFmZtCnptCnOmidNlmrRdxsETbq+LUafqWKL2UgcUneEUgEMeqTRz3yuA/JAF2xp40w0rEqT7QzEVeOVHZ1M7fYyKf5fDaDFabMa2MizWBT99BPFOinc3RLehM0dEtXJYi6oWMYwthoYMcmdmxhRRamCDmvGRhbchwyjHGCHoXoSrcruutMEtICO06pxhLKdtGLKTRtBl2fRWceI1tE82fQ7NqXKEPw6pYreCOZIvGkdFWKKmWpudLgSvX9Hs6YQkXk/dRBz/4nvtH4d8xpN5i2/3eeKd7Fx4v3I1e2VBMqkJxUdXqWBDWHLJGkrgBlm1DkL1wRWy49ygzCWCcKdKIRRJnoz5iKIZKkCqN5VU6rDmpa9lreaXXXpGIbVGwT1zLwbAOtYhBWTEaeTs/WlJC5fK47raHrnLdMLmQZ50ZDzm5tUJdzeXONbGOTcGuH57o9tfCOBViJJpwaTRK9Qqw3Sl04wyJtNMnqTbJanaRSJ/UqpI5HYjlK9iPOCuJsAjzTTIHQUABonKn96ZerCUFFAw7vEga5b9Gf4Q8Yn1TjwR6ki/lxfjF7J7+Yv4tbUQcrXcXIb6O7y+Asg70JjlSqXDn0mreTJo+vfRvvWv5G5lOD941yLrtL2PEKs+Me0yoL0qftD8osiD9SFRyvxx403h5bdZaOv5/bx96uxKLFvGCTU7d+jfm1T6MfsXD+ctpub6K6f+zeJ3aDWbv3i73t/dI4VTana8SazqaAOt2irsGjxoCRY9BzLPqWw2ZUY3Ozxu9snmYq9TmV9jmZ96hzg4Z2Q1GcSz/ebrLWN3QGhkHP1OmaGjumzrapKZKt0NDUNULd4yajFLAc2jdxuW8rnDvh/JK5MfGKIdfHKrW8TTVt8Y4i4rvti5yZWoapcr3xYnGOj6VPs5E0VeZIT2K0JJqMk+0ix0gz5QRH97ZJz7D8/lL1G/SITI/AquKbVcXo6OsesWYdAlS7o2SrDoaNhVRoOt4uSYVCIRZap5ke7jkWExH6dWeWNXeOTWeGrjut7r0CoARITWkZi2ztbStgpYmOpxB2SVXLJNg9CWjurp4krCJtJSKBYmkZN7jFrQIeyud4ND3FfN7m0fHD/G/jh7ls3eZfzX6I5+ovsVxdZqWywvlgkadefjcVqXiwEiwzxjQTTCtWrI9SxXr9+K/zltRjZnCaxc//RZ5/7D8wmnoRxwhx9ABHD9XSzBCiHj3ElT5OE7pf4d/N67WvSsINIduQvq7dDJWUGd5JuPHZz35WMSaK/dqv/Rrf9V3f9fuScGNj4POdf+eX91jx9pKwqo8hpxaPqcc+9TigLvNI5mMakU8jHu3tq0bCoBdgSRP9A5qcQKFlEahSAqucS2O4GqV8YHduEwuVsCPN4mVJSMn6J4E2A8My0SwLXYgVZKF+14KwwJQIn0QgDUMBTymP2pO1kv4XJa4rTcy7NKSv3fQsww3DfQ/K0QuDQ9vippAOPMDxUoDW84g8j7haJa5WSKo10nqNtFYnb9RJq1XGcYwfhowj4YC8z5KLO8y2bQUa73TP8+65X0pxvxKWpj692x+jd+13Gd16hnDlKlo3LkFWX1MCxUZfRx+U7LL3Y1ISl05PE01NE3am8NsdBp0puq0O3VabzWaLtUabHdtlJGWyWY6fJBjpkE7WVaQMU8oHtIoh7XxIq/Bp5j71PFC9QULQoRWlaK5iDVTzA16YikEwyavERZU0r5IXHhQeVmHj5iaOcgNrt1dN0X5KXeF+9u5+rUgjcn+TfLSuxsLfIB9tkPsbFGHvlf/YqqBXppVrXhvNbSrXLRFDlc+XUGQxZPHRo9T0qkD/hAJYlqNyI8ylxCYiF82l0Ccfi9DzvfWSXs16zSY3zpziobNrvMt8Vu0Tmv//6n8zlbHw5mkkngSIbHUNCnVDsWIKG6b0xj2opeiK5VGonP3CwcciKMRtQuHYk5IpTOJCSi11sCduSj/dJL8i5A8Tjb+yarlQAE5ty2GaBH0O9cL8nrGSJloKItWo7RIA7e7LMYRO+uA+rZzvPU87/HxHS3gzF3mn9gJPaReVtt2ubRQdnuMRXuBhNpnae92jLnmpluJrW2TJBnq8gx0MqfkBU0OYGkAtWWTj2I8RVOZVFcXpG7/MQzd/9RBL6f6nhMDzCCoe40rlkAsoObJsWpVpH+hNvXP/ZPvgvkyrMaq+Bb/6BMUk02XFW0x1P01zcEmJ5ypq7lz6DoWmW7alF1XYMkt67vK1dmm2y7EM5ikGnEkwUcb8bn8DTPqk15tVVlo1tmveIQrxjh+y0Bsx3xvhvIZ1x5fchLzKKFSpdGZVyAyHXLJQhqVKqzNdgh2SMTaJNVNlIwPDINA1QqNQWfbUEJIv6W2WnG+CfiCDey9LMfCNEogNzDp9q8HAajA0a+oxoepfVAyu60zHm3eRF6n1hNEhsueJ7XlyZw7d6eCYBU2roOVq1KVox8zQjQTdTNEMyezLXNgWYzQjRNNDMGR7rDwlZGlU5VJvhpd7x7g+nFdAe/f21NB83la9xjEk8FiuFzp5jceLec6mi5hCYAVc9a7xr4/9NJ+3Vvbqd77davCd+jSV3FWVE6p6YjKqeepS23oKK+oo1u1hbYtISk8zFyOzsTIDN9Owd5Wkgc+eWOcH/qc/xBttX7WEG6PRiCtXymjX008/zT/4B/+A97///apn6+TJk4pq/u/+3b97iGr++eefv4tqXrJh/+yf/bM9qnlhPvz9SDW/MQ55x9/+zS/Ni4n2QhbTjEY0Y5+mAmgyjmjEY5rxSIE29dhkfz15bU3AoWExsMs+tYEjY63cVvMqvjThe4bIReBJ34yTCPv4IZP1Sx+P7aLGdl5lK/MYyyJ4EqQuweiEmW8yF+2JVhYzlYzpJD7tyKclnykc0QgG1MYDqn45etHRqfF7WWQ5ijVNuVtR7Gm+6zESNjXHxZfR9VTmUFHMq0VK+T4Va+GB97z3fieRJjXK4sWx7xtEfaWB1J2WjXzSjQ2SjXXStQ2VmRqv3CJYvkK6sUKx3UfvR4oV6X5MIq1RtUlYaxPIWG0TVVoklRZZpUXuNckqbTS7qvqGzByktcoREF1kOLkwKxUqI2TlUuIjixodvTCU/1400WtJi4QsT1RDfy4LYEf6xhzsegWnWceqOGiWjmYZaPZkVNs6mtBaZxHZ9hrp5grJ+m3SVRG4XSJZXlLfySuZ9GHq9Tq6iKJOzqMiSciDgKzfVwQar9X0SgW9VkPzPHTXRVOZU0n9SZh6kpWWiLL8e9IjMR6T+z5JEJA8avHYwzcUaYDw4P0s381yNMepGzc5fe0a1aHP2HQZmw5jy2HoVEsWQ1e0gyqMxG2PseUxFsZD0yUwhNLYIpLFVWGoqHMyIV74Spt2EODcAWL0ve3DoEftP7Tv7tfQXxE47T+2J3PxOvGgVAzINfesdovH8pe5kF8+JBrc11tcsZ/kivsU29ZiWZ6o5I9SnNEIZzjEGe6OQ7XPlf2jkarweCVLDYdL5/8wa/PvVNtGdIkg/im2GgNGnkPqNNCsFrY2RTtq4SjSnl2GybIXWpE4HGUHdh8F6A4+Lyka5DyCzfG93jufEcussJkPSAQ8qUV6UtKyCiCVbcUUJOA0VeyPhpHj2jk106JhmjQMh5pmUcHGE89t7NzBUe7i5B5eUaFCVQV81JtRAG0fkO0BOAXiVCO4AnMH90fFeM/DwidW84CkCNS+uAhJlAckeaJISnJpBRDCFGF8zCejBCl0l9SwlSj5/jEqsBKhxk9pxTE1kRuQgn0twSVRAN1RwhfyvsqVf9mVUPY6Klw5KWHNC7N0OavVNb3soZTH1XepmDylITwW1qsve8BDSr+lFzewTAJ74mpuqbn0tT2oCbB3U6jEOvXYpBE5mJpNrglINO/wXcbeA9syagfmulmWS0sFil66btjohuw3S91MyTSpKHnCjr3Jhr1O3+zvvSe90JnPpnkoneM4LRzVxysFsYo6Rz3nBe8y/2ruQ1z0bqjtZlrjh7e+m+/pvhfrdRbhSbXDxcWcb/+fv5k32r5qwddv/dZvKbB1p/3Ij/yIym7tiiz/i3/xL5TI8nve8x7+6T/9p1y4cGHvuVJi+Gf+zJ85JLL8j//xP/59KbI8Ggz5W3/lbxLJ4kQuRKr0rCwhUPHHSX24KieY1OFLqZqUGuw+965RGuhNS3kmpSwSCdJNUhX1MUg1Q/W7SNBByBca6ZhmNKQ1cQXKwoBaKNm0kJp4FCivh2NsdZF/MJP368tiSUodXZvMsZSrXrUDfWwj02VU2MSJgTUIaPV6dMIB7XBIOxIfqWji/Zp83q5bp+vUD407amywM9nXc+qqifjLba6l41lleZOUNnmmTtU2qFsGNdOgaupUDJmXo2fIqOFK2aSu4em6Upd3NE31JihXtd0lYYFc2AvhFM+EnKQgSzKSNFcuBCaiRZWGIfmgSzHYUc6oizbaAb+LNu7BuIcW9EQs5b4/lwAmzW0p1yej5jUPbzt1JTT7lTDVJyHiTLJWMjU0yWSI6K2UTVrWpExyomUnJWmKnKLcFnGb/flkvzxnohEVx2MG3Q26m6vsrC+zvXqLIBwqgJUWqQJZMtddm7mzZ5g7e4H5s+eV16dmvqQlggKi4qUlklu3iG/eJL45GW/dIl29R8f1xAQ4WcePY83PY0xNYTQbirUQyyoBWr9P1utNfH+eDwav/f2aJpFXI/RqFA14/NGXaFWHauH16+tP8/P9d7Nj1xmoYE5F+dCSDMbrP28cYdmSWLSAFKEt1wpVeiPyxlKyU4KWSbZmAl4kSi2ssFaRYEsAQAIpuoZtalimgWUZOLaJ7VilT4TDLSkrnly7ZZFZ5jakT6rUfZOxdOmZEoK5giRHlRMGYU4YZYSRlBYWE34RTVHeS9Kt7BkqqGoxdS2koYU0hR3tiNSyn1vsFBW28wrbRYVB4U6gWbnYLe8pZUetuqdMtuU4vFN/iT9ofJLv1D9NU9tvTtzJ23wheZql6BxZ4FEP+tRHPSqjPo4/wBYNweA+9dlMswTujnMIvJfvSYiWMm5XLnBl/vvIDQczGXLh0r9ldvPFLzukHlaPcfPUd7Ax85YJSyy0uhd56OZ/pd27+JXNc8qiWsqL5Tp713hgbpUjaruyRz71oFbkIlvjU8SjI0c/GbBi9Fl2YvoHbpvSjzszHKuM2OzAP9T3qls5VjXDVnTxKVa9nFu1DMvLDrHM3v1+IBkbJCMDu5lieSWQS0ON7sUa3avVI9n/Xq0XeLfPWOmwK3AyKRIsGwr3Aqp7PeeTjyP/dqib7Hgu29UK/YrL2BH9xjtJaaSa4NXPFCdJ8eIUT41CnHZ42zqif/i+TbU2CCOkTqrKPnVFfCZZYUO3cXQPV6+UAG7CSK0ZIudQzvXGAkbnLHptXh2X360/y7+Z+TmWnU318rPjBj947Z28dfMRCk3IVUxSIVQTMjXNZLFapy2ka0XBNU2jbxkUu8FFS0e1FBsaxx5t89ZvO8MbbV+14Ov3iv1eAV9pGPOpv/xv9rblYqlLCZ9ELaRvR0bDwJCblCwe9/aX8/J5k/1qn6EA6Z7dcXbcdbJMaF7lt53kBbH0DiTiOXGaKeFIihE6I0x9rAQxjTxAC32KICIfJxTjmCKMKKRmOhRyjQBN+lgiX7n+GrNrqfRGSImjJZkBYX/ycI2aGnO7Su7WSJ06iVsndRukXoPMskuwaZgqOieLtr1MmkTPDsTKdo/SHgnTHuPY7hX2wGEqmSBKLpG947ZbdrTXkra/PdmpSk4KRf2hAJKM5oG5+D432uszpTsTDSjCPnnYowh6qmRNzcN+ORdQlTyAcK30Gu2CJyll81rgNqDSJPNapNU2abVDrpgsSxHukuU+Q9dTVRKhazG6LoxfASgfURQjcsaqX0i5oq+Xco+yh0hEgA8+Jue86dQxnQa228J0WtheB9vtYHszOJVpbHcW0/ZK8PQg4tz3sPGgz87tJbZu32L79k22by+xffsWY+kFvMMMy2L2oTPMT4DW3NnzdBaO7WUC3ggToctkaUkBsfhGCcjiWwLQbpKuru2ftEdY5lWIZhfxp+cZTM2z05pjqznLan2aLd0l6w9h0Ecb9jFHAyx/SDWclETHB8ZExnJuHRG0kTKh+bf0aZ0ts9SjFYeVT7aVgOhBkwxXIKCtUiOWXqhanazWoKjWQDJwnofhuZiOjWWauIaOW8S4oY/nS8alT+GPyIcj8tGIbDQkH/nkw6HKym1XGtyaO8flY4/wUusEL5odhgeYxXZtvuHy9MnWxNs8sdhUQZTXYnGYsnFzyPr1PmtXemxe3SIahCogJppdyosUz4XmrI5R8UmLHuPhDv2dLUjSQ1keEYb2XA/HcrFMB0OkBtKcXEqeJ9lHRM9MJAtU30spXaD03dIEI0toNwdMzw6otQKKVCeVBe/YIB4aRD2LVNjKyvppvpI29mb5wmM/xqhekmSdvPUbnLn+86qHRqzUURPZkIm0yGQ8UNVU2oHtg2f/wSrhoHKanZnvxG88ubevOnie9uav4QbX7/l6qrdYrubSqzQh0yhL84WoZJJF2NtfEmocet4uAcfe43dvq89aFPSClK1RRBBne++9YplM121aFaeU61IZppIJWEqoSxKqXcIpqWPz0Kzq3QBOSKkewIbJDrdGL3PLf5FBsr23X7hP5+05Fq1ppkRHLgtUmXqajkmSEUniE6djsjRWqp6uneG6MZU9j6h6EVU3wlQq5wfOh9Dm2s05VpZb6KnI5pSU5RIsUSzOisFZfhcF2kQo/ZVNgmtO+dkNB83cHct9mWnTtzW6dk7XTNjRQ8IjSj5sbKaMDh1zio45TVtvk2gZQTbCTweMc59xPmKcB4yLgDGh6nJ9NZNKDy/T8CRjlha4SYYnHifKhemaLClLyMVff5f93UfIa2M99E1Yp95L7lb5tdbv8v+d/iW6VhmMO71a8N/+Vs6bbtzxb2s67lt+FOvEN6g1iv/MvyVaff6ubJ729vfx9h//f/NG29fB19cI+MrjjJW/+bt8NZsCBYlPEUnUbDgZRxTRcBJJK+d5PCKLB0jXvGgbPbhpZf+LIzeRWplpOTiquYz7+4X98CtFVnC/FmlltLsUnZayF8lajSjGAqq6EPTQwr7KThlhHzMQpsU+VrQvAvxqJlnQodtk6DUZuA36boOe21KZwB23ybbXYNupM7SkcEQi74UaBdTYtoZnFThGim0kOEaEpYfY+hhLG2JqQ2wjVvttvXx893mOEavHysdjXLOg7rWoVVo0Kx0q3iyOPYvjzGE7Mp/DcWYxzfvPan85QZaYLKSmj59UGa2FcxcU0Jo+cUoFSL4SFqc5fpQyutPDcpTHhmE5Hnx8b3+cEvkB9Z0NpgebLPpbHBttsTAZZ4LehAb9aJNSwNXqFCvVaZZr06zKWJ1mpTatssgVx6TqmNQnY01G21CaXO0soJ0ENBK/7FUNfSqhz0L8KU7aH1E9FknscvO5E4xvp1ivUpr2Sqb6T1tNzFYLo9nCaLcwWne7Vq2iu56i5ZeMXyGU0WHE+s6I66s9bq31WN4csNUdKfppEZ8uXcpgM2ZdnTlPZ8bV6dgaFclATV5H+QT4pOOIVAJWofTRSdlUsgey7vd3+6U3EYjN0W1Fk0gaykL91a+HJQO/9MYZKsiVmaZiZBRSAinfDiyX0LCJdemtkd49ISDQCdDVXPpuSsIRo6zIEFKD3eqMybx0Qz1P9ksU/Ummebioq/ewrif8ihuwLT16ZoD+/2fvPeDkOMr0/6fThM0rrXK0LUsOspxxNs4J22RMBhOOHI874AJwd/yBu+M4jsOHCT+ODOZINo4444izLQc5SLJyXK02Tezw/7xvdfd0T9ik3Z2d3fdrl6q6urqnZ3amu55633orvR1GSgX1MNJboVsltyp2cXNp9D2hcpcWYlc5LdmRNlI4KLscR+08Cl29XeE6kd5BGcw6xcHsxSmkzTRSZjwPUpKsctEF/yaATd2D+NmDm/DrR7aiN6vm2iVMHZeuWYB3nLwMxyzpGPWzzBkYQHH7draSFylt34HC9l2wd/fA3tcPtz8PzWyqtLhZ5ZY3Fdp9f2EPtgw8h82Dz2HALt0/LS2BRc0rsbT5cMxLL4MeWfSePBP0Jgt6WgdSHq+A7iULcJM5OIkMXLMPnrcNWuFl6LmtsE0Hg7MPhYZWGG4ahtsEjeceUUpAt1VUV82xoNkWtIIGt78Ab6AIZGiAkn6fNBBrKYGlk8AqrT9KXepBuxfd+e2c9ua2YX9hN89dikJn6EjMxezkIsxOLURXciGazaH/BrQYt+sW4Lp5uE4Brp1H3u5H1u5D1u5HzhlE1s0g42SQ8XLIgJY6GF6c0eALWcrIQlakKRMdHSimabFzcj00kM7lsXjbdizetg2tAwOj+o5UvpgJc+FxsA46C3bXEvx+1h34v9m3ImsoT5mjNwJvvdPGQTGPeA2p494Fa+mp7BKbe+zHsLc8ED/twSux6sZrUW9EfM0Q8eU5LgYeKHMPKvvtVvyUy3/cQ91vKw+Ov77nIZfLYqC3F9m9e5Hr6UGB3Iv6+uFSJKlCAVZRjaxQbhaLPHHYP7rsXBXvTkVAohDZaRdu2qN4BXA5UdmDm6IYBh5vh4ui04nIB2fQRq4PyPRqyPcBRbpnDBZ5jbFoUpEhxxYIwKEAI81tyDW3ItPUisGmVgxQSrfwdpj8/Rlyl4pYM8rjKsRGU4Nw4bpa0oLchSgmA29rGooaRbPLorWvB219Pejo70Fn73509fZgbm8P5vT2YHZvD7te0rpsI4FcTu1Zs+DM6oLdOVuljtnIt81Cf3Mz9lo57NIzPFehP9vHkeIKTgJ5TkkU3SQKbjPnOcdC3jY4jSbQ/4GQIIuFpZfcMtlF06hSpv1+Oz86He+LlClqHe0PymSF7du+1RdZpVRLZBFtc+Zi9uKlYSLRNXvJUo4aORqK9DvPlQkhPy+vV2UHA7kiBvNOqZ2fSHyNN/S5taSUUGo3PSzO7sPCwW7M69+Drt7d6Ni3C63dO5Dat1cFDKgBzf9KLFuGxNKlKl+mcmvpMphzh3G53LkW+PU7gX0b2N3Fu+ir2Nx1Lp66/35seOopGIOD/DuntQwXtbdhUVsb2sgFO+YeqRKJnkYm6hoUJHLLM1MpWM1NSDa3wKJ5fMkEdHLXs8iyocNzHCX4yNqVzapEc+0GyOrXP+LPxWhvgblgEaz5C2AumA9r0SJOiYULYVJd12wV6XQMUPjtbNFBpuCw5SZTtLmcozJvU72q43q/rWpvw9yRw5KXsjBpOqEOPDJXw4uJ0vnIhZPRM+zOMDvdioUdLVjU3oSFHWks7EhhEedpLGhLoX9DHx67ZTN2v6xG8HVDw6qT5+O4C5ahY14T6gktZH7X87vxkwc24e4XlJtXsETN209ehjedsASzmifOXd6zbTXv1xdmJNSKOyjtgE3lbdv5+8Xh+K2mUJB5NBc8ncS2tIPtZobXTgtI6CksaSEhdgS6kktCi169sN0C9uV3hmKLEomgclKJFsydtQxz5xyEufMOwtwFy9VvkCKjJnTofs7b/nxdSnpYZ6CAAtbtW4en9jyFtXvW4qm9T2HbwLaK12oym7C6azXWzFmDI5tXYZnbBX1/Dr17dqFvz270792DfZS698IZpEHXoaH7bnO6GS3pJjSnKKXQbFGy0GxaaCbPKZqqQINGtu3nRc5pXnAmk0dP7yB6+3MYHMzyGpM0+NTcNAddi04EFq/ANfNuxw2df+bItcTpL6Tw5tsGMDccA9GQPOZtSCw/kwVY/omfobjp3vAaWy++CIv/8z9Rb0R8zRDxNZHwjbN7Hwq7d6F/82b0bd6MzPbtKOzaBXtvN7SeHhgD/Rz1j0KtjwatvR3WnDkw53TB7JoDs4vyLn+7CwaX58Bob2dLQbHYh8HBF9DXuw779z2HgYHnkSu+BK9G+HwafeXRSer2G4VQbxaLCfT3zUGfn/r7u+ByhDqXO2Ysxgo5zDIL6EoAnYaFds1CMg+4+zNw9vfA2Udp35g6aDyvrrUVNo0stbUj396BbGsbMm3tGGhtQ39LG3pbW9HT3IKBZAqzB/owe38POvf3oL1nH1p7upHe141Udzes7r3QcyOfV0Xzcsy5c2HNnct5Kc1R9fPmwejsDDtFOXqgdN+Dnn33o7fvceRyW6qcNXjw1e5QKy1ssUArOEm4+jx4xlx42lzY2my42izYXjtstMH2WlBwm7htruiqDhF3ppx4uWgjW3BVJ6voDOUBN66Qm5JJVguvCNPzLRheESmae5dKoLU5hdbWZnS2t6Gjsx2tTUmkfYEXiDibIiyG1iUHA/miEks1xFT/BAomtjClyMJksHAKElucSEwlVB6zRKXi7cgqZVL85hFAFpvi1m0obHo5Ps9s82YUt20bYhkGX5iRKKO0nARZINCW8b2CXTRzvcC1HwGe+6M6aPUbgMv+CznPxNNPP43HHnsM27erqFsE3bspcBMtRdLZqRbT5TV2KKBImSCzy7ajc9ncbMaP1JqAlrCgca5EDedVyxYGXR07MzY29xSxpb+InQUX+ajVxi83wUQ7jf63t+Cg+WnM7nSR0TPY3dON3T37eP6J6wsuusckkkksXryY16KktIh+2729ykKxcyeKOyiRxWInb1M9B1AZAbRur5kqwmpyOJmtBqxD18A65gKYx18Ma8Fifo9Tmb7uLG79f89i5wb1no88YyFOf+OhMBMG/9boPkOirT1t8W+2HNdx8dKju/HozZuwb7vqaJuWjiNOX4hjzl+K1lmjG1wZb/YNFnDNw1vw879swtaektv+WavmsJXrrFVzYYyDa/WBwsu89PcrcbZNCTM7JtR2oLh7N3qaktjR0YId7c0cMTkgWXSwYMDGopyB2a3zYc1ZBHPWPOits6E3tQNkVdPTHHzDy3twMza70rK40Vx4FMmVo7EOwOnvhVfIAHYenpPnoBycOwVoKQvm3Nkw51MU3WbsM4vYO9iNXVvXY+/2zfDK7ls0jWPuQQdj4aGHYcGhq7Bw5eFo7Rrf+boBe7N7QyFG+dq9a5GxK4OFLWpZhDVda3DUnKNwZOeR0PfoeGbtM3jphRcAP4Q+LQI9YFrIkhtrsYAOCj42sF+tmzoMTe0dPNjY1jXXz+fEtpNNyrppOy6e2taLe1/ci3te3IPHN+9HyvVwERI4w+rDbXNuwF3tD3NbwzNwXsercObyN8PdWET/hn04ZL+GpUV1f3m8M4sNyzy4zQZWLVmAS445EvVGxNcE06jii292fX2w9+yBvXcv7D17OacbXHbHduR3krDaC+zfD31gYFT2CpoM75BY6uxkYZVaMB9NCxfBos49CSwSViS4Zs0al4czvZdCYQ8LMRJmA5SoPPAiXK+6KHFtC3auFZ6j1myjOUJ528RApkMJst45KBTUTSJKMjmI1rZuzOrIY+68JObNm40mfS6S+TZYmWaY2QT0AR3u/gE4PT1wevbBpnwfuV/sY7F2IIEGhqQpDZBo6uzk9apo3apAvJrz5sGa5wuudBMM0+KQ/hRVy7a7kcvvQiG/i3P6DOmzy+W3oVjcD2+U62ZYVie7/LG7X3Je6P5Hbn8JzuchYXVxBKXxhL4HtF4RibLoiLgq27H66Cj4wGAWPft70ds3iP7BLAazeW6fp8VqacIvuTbRorUULarOo6sBZNWLCaQglQuksvqWUGSV3PhGKpgmCxrMKGwjYbbJF2al4B8szIZYzoHWbFPWMhJny2A5G5DY8jskmvMwl6yAdsVPgbmHcdudO3fi8ccfx5NPPolcZPDi4IMPxnHHHYfDDjtsQiOEDvTksHNDH3a93MfztWjelkOWeg7S72G34WGH4WJvi4bthos9xcrfoQkHXfog5miDmKv14YhEHoe3pjHfMtHpuBwR0CGRxWkHnL3dQ87RC6CAKWypmr8A1oL5MFstWPZmWL2PwSy+DKvJhU4x0Wl9qpUXAqtfBxx6AWCp9aoaCcdx8dAfN+KxWzaxm8HsRc244H2rMWtBc+1jii7WPbgDj/1pM/r2KFGTSBlYfdZiHH3OEjS11U900n3wiS378dMHNuH6tTvCARsSkG86YTFbupbNrv3epipkPSnu2sVijO4PW557Ghs2voTNfftQjPiKpPNFLOgdwMIeCvhFS1LHoaAsFq3/Sv2GzZur/x4sC8lDDkFq1SokV62CftBy9CQM7N65HTteeh7bX3weuf7K53jL7C4sXLEKC1aS2DoM8w46hNfIrAeO62B97/pQkJGVbP3+9fz9mJObg6UDS7FocBEsPxQ80TS7CWvWrMGpx56KFzwDv9nVg+t292Bf0WHh1Zzpx6rCIE728jiYpifs34dM9x7kuvegsK8bXmF4925abzDf1olMWycGWzvQ19qB/a0d6G5qx56mNvSmabEQ4BXdDs7cvh5Pmr/F4y3P8bEpN4Xl3kV4dsEl6E6m8Onn83jrJmUN/fphSfxqWQJvdhP45rlHoN6I+Joh4ov+fD+6/2W0aA46s/1ozfSiebAXqf79sPr2wdzfw4v7KqGlBBfNJxgpFC0xWJPKaWvjDj516lPz56F50WK0LVuKNhp9njsPejNFR5oKo2kustktGBx8HgMDJMpInL2ITGaDWgMl1pZcNzU4+XYU+7qQH2hGZiCNTKEJWXadSyFPi62WxcbSKMKZ14uk2wur2AeTVmelyem2Cc+2eEFXmr/KnaogEAeF1LUdJBwXCcr9ZJVtJxzKXZiOi4JpIGcZHJI2zM1I2VKuRQeyromuq5wTRXEzXF7jWtMpp22N5yNRMAor0QQz0cS5lWxBItGKRKodiWQ7zETKF3dK4HE52KYgL37ZJAuBvx2Wqd5UZQr6Mp7fo2x/H7q3bEb3ts3Yu2X07oKzFi1B+8IlSM1ZAMdIlIk75coUE32h61NZ2XePokh2taxI8XqyRllslWr186kmmCZTmHHnK2IpU8JsE1vShhRmhotEqwfr0NVIrDmFxRmJNG3RIqzftw+PPfEENm4sBUKg5RmOPvpoFmK0RuSBUCw42LO5H7tIbFFgjI19GNxf2VFJNpmYd1AbOhcngZYBDBT3YfvObdi+bRuKA3kUBj14gzaMTAHpTBazsn2Ym+lBV3Y/Zuf6YI4ggiu7HQbCav78UnnhApjz53PH1GhtBfZtBJ75HfD074Fda0snoGACK85XgmvlRUBy4uZRDgV3WWwXXtFlK0bVfKh9JEoi2zt2Z/DgC71saad4P8fMT2B5UxGaQ4uAU7ALk9db2tgPvLDPRtZfiZoiU66cl8aK+U1IUtCUIFAPLSPCgYOG3w7roseWbw9zbNb1cMOLu/GLp7bj6V0lT5DVC1rxjhOX4rI1C5FO0oCbH8xoCjyjxwPHLmLTU09g3X1346WHH0QxXxpIaUumscRKY+FAFqntO+Hs2VtxPA1Qssg6bBXniUNXYjCVwM6X12P7i+uw48XnsXcLCfN4F5meVfMOWuFbtJTYap2t5vlNBjzYSK63rouc6yLreH7uch3luWC/46Jv7x7sf+FZ5Ne/CCMy2NSfMLC+qxkvdXVif1MLPC0BXU/DNJoACooEA4WRrDfpeUjnMmgb2I+2fj8N7Ee7n9N2Oj980LSiYaK/hURZO+desh0LCr3YmnoYL3VsRybpoMNtwwmZS9HddTbO6jNw/kb1fu7rMmCuno3XX7QK9UbE1wwRXwM7d2PdeReieRRhvQleiDSVKqU0pSa4nbOgzZ6D5IKFaF+2FHOXL8PChfPR2dEej4I4SfDCyeT+mM/zzZUTRWEr5DnkeZHqC3lV5+9XbYP2KrfzORRyWRRyAyhkB1RdwfbF0dA3GHoAO+lmOOkWOE0tXEZ0nRIfPZ+FkRmAkVVJK+TjwawMNWpsWBqMhAmLIqslU0ikmpFItSDZ1IZkuhOJVBOsVAomRQC0bX7IOMUiCvl+FPN9KBYG/ZSBXcjBKeZhFwvcjkIruw65XGosKim5YblBOu6axkLMqCLMKsos7NQ2Qd8D+jvnM4PIDQzwKGX0oVxOuq0d7XPmon3eAnQuWIjOhYswm4RWS0tcONK10Doo06TjMp3gkXESZtGojOTWSCJt69ahhVkyicTSJcD8BehOJLAxn8Ne08JAK83ZbMLiJUvYLXH16tVIJpNDX4fnoXd3NhRZuzb2oXvrAM9Rir2mrrGVZd7yNqRb++H0vYzBbRvQv3Ej3N170JTJhCmdpblJwwcPcjQde1Nt2JvuwB4/9TR3IrV4IeavWIYVqw/BUUcsw5JZNQbI9m8Bnvm9El3bHy/Vk5X6kHOAI18HHHYJkGqPvV9ekqKq+HGGFEYsnihyYjEH5PMqL1JO7l55dn/inJLvAkbuX1x2i7ScdpgorJCmBdt22ba/n4Lta2XbfjsKB5R1mnB73wextXA0v7dDU3fjrLarecGAtZlL8OTgpch56r036904pulaHNF0B0yOWKdWSuOVxv2V1Cj2eFCncrr3quWnEa5BFWkf3Re0J1e5sn3R9pvcVvzaW45rvcXo8yc8J+DiQm0P3qjtxmrQfS9Ypy5yLhpZo6UXNBJkZrxMLuf+NolOWnCYc4r6y2H81ZpRtCYUPQN56Q9aTmMYAVkhIocSm8MdW3EunSMebnz6Mbzw8L3Y8OSjcOj74zNn2UFYedJpOOSQlWjKUUQ/B8mVK2GnU9j5orJm7SCx9dLzyA8OVh2II4G18FBl2aKotPRsKP/tFzyvJHw4jwgi12MhVBJMpXbVBFOsLjhXRGgN12lvzmexYvdWHLprC7oGS5a6vGlh/ZxFeGHeEuxsmzWiMPbhrYAW+IYHWj46OI62O1HEQi+LRcighSLEeg7S5IrvFZF2C1xO5rOwBvpg9PdDG8jAG8jAGcyhMFhAPlNELjf84JGreSzABtI2vKSFhe4h6HBWYI05C01mG7BwL474zPtQb0R8zRDxtXVXN3pfeQbfYm1dZwtVTFD55Swt7ptO8QKje5Lt2K8385otfV4S/a7Ks3wDr/5jTJr++lKRuSHkxtSastCW0NBhAW2WizbTQ4vhIk3r32hkHaI5MRTSuAgnEEmUs4jKww7LtYVTuT/1RKEbOoyEBt0ki08Rmkkhyt1Ssjy1j1YcTMxDAXORsTswaFOQicrzGZqDpsQgWlu60TF7G9q7tsA0h3flM+0UknYTLCcJ28ijYOZQMCjE+sh+qhoMWGiBiYRat8vNwnb61arUHFOZBBn5vLcgnT4MTenD0dS8CsnUMnieCYfW93JomQDXTw6LP04RMRgrV+zzt4NyscZxfhte1LMBCIRYNYtebF+VdjoFNTATMEwDCV9Ecn3UAhgeF1gO4+eMtQ3bkbVQhGGteavFrVtQuP7fUbj/9ygMmCgWZ6HgzOGobEMtEk1BKwZaWliIDba1w1pwEFqXH4X0gsNgt8yCQxbzogubokfuL2DXy73ID5bOpzs0h7QH7eYA5jbn0Gr0Qh/cCbd7B9w9e2Du389BiEaCMaerqsWK11ibPx/ZdBue2taPxzf14PGtvXhiey96/Ih2UWYnDRzdYWFNi44Tkt04OnsPmnrvg5F5sfSZkV3fOghF6zDY+kFKKLDwKUCj3C1AcwtkjoTm0YLA9ijFUCB8Rv6bp1uXQ4v0cqJ1znTYbqQc3cf15ftUubyNHamndv3aKzFgnEMjZdDc/fC0FPm08jVobg9M+34Yzlr2fKC5daXPzM+pToVyLF17UK7WPtKuNAZYvY4jLlK0UCQxgDSyvEKj//2AixYth2Ytx2usRc8df43ygFmVdcExfFwstH7wPsrblV2z7yUSq9eiK14Fb6n8+rSyz7HsWv1zRD+/6Hb4Pnj+dh4WdfiL8QHQIq1dSvdguwDLLlYdxBikeddtnejp6EJ3RxcyzW0o0nqnpqUibJp+mXJ/m9KQC41NEBTVmd6HaReRzmewbN9uLOvZjTmRIBr0behJptBjJZDRNRVx1VHH0LHB8ZTT58IRVGk5CW5jw/RzEl/RSI28kiFHnVRijDx71LqGNowyD6Ph8MLVefzviP+3j6yQNuzyFB1NS/GeH15V9+egiK8ZIr729A3iE//7Mwymm1CkzhitWs9eFToKFInOpWhzOoqOhiJ3ul3/B6V+gCqpGxHlCUpFyvNI0D4WT0UOMsABBjxasI/Kah/V041/MqBRNprQbtDN00rCpGRasKwkLIvW51F5wkwiYSVgcvheA6ZGK7RrMGlRVBZFHkzQ2h4uTE2t7WGBwjaTn2AeGo+u0qLVvXD0vXCMbnj6frhGH6AP+OvoqLU/DD8n98Qd9gLscBZiqzsfOzAXTtmq7ToczNd2Y561E12pXeho2Q29OYd80kA2qSOfMnitq5p4FN3QRTJIeXrIuEgVXCTypXqrOL6xBdXZIiOwWmQEleqVjyI8Gj3lclnSqS2JA4NHUOOjrWo/LU1LoaBdzVCdIejI2Br2DRTRM2Bj/0ARvZkC+gYKyBdr39iTSQuppiRSqSSSqSQHHrASFneACrSwpafxnC4aEy54OsgBLO9pKEAHzRJQicoUXVLnoAe05hsletDaNK/RMGAbtAB5UFb7KPKlTe1Nf9uozKORLjVXrSmjR9ZaogdcaTu6BpPDUQKpPfngq/WZ4scZnhrbpu+2v+6kn9N3nyIyq4W0LXoAU1AGWiRTUwv6WkGuG36uwzIMJAzl6kg5J9NEwvRzw0KCFgk2LSQtS9VZlCwk2U01UbIa6tWDFqiFu10lYorxPBA2quxwfbgvWPS7ELShAYLS+exCpI2f5rsP4czE15HS+5FzW3Bbz8ewq+9gNGX3IJ3djXR2jypn9iCd2xuuAVUNRzcwmOrCQNMc9KXnIJ9oRlOuF82F/Whz9iOd7WHr90goJhPwWpphtTWjqS2NZEsSRrMFs8mEkTZgpmgNJ7o3RYUPjeAXoXmqHBM/ZI3xitiKdjzhLsMT3kF43D0Uz3jL0IYMLjYewqX6gzhJf44Xiua/hQfsRbua+4Re7kyVC5mSwFGiJe+ZoGWnCzCQ1yg3UdCozkJeozsqRWezeP4k1Re5zi+DgolY7M5H22pRVXLvUwFGbM2IJVpvkQOKUE4RKg1d5X4957wdpHi9E6uLtImdQ6WF+zy8+i9ZtGfVZ7O7zcB9R6TwzJJE5SK4wpQmlcvg0I3P4rCX1mLp9g2RSMuKnrZZ2D5vCXbMW4Jt85Zi76x5cMcYhTN6X1eCxg6FDScnLnS4H2YXVF8sVh8vc+6LJXXOQiiK6N7vNLej2D4LdmuHep76GJl+mL3dsPp6+JqmC7buYs+sIvrTRTTlDDRnDbRnEpjduQLv+Z9v1vvyRHzNFPHVOziIVQ+VRi3HFRInjgPdURHejKDskCVLbXOK7NP9ziGJE45c5tLNyK+jRYO57LFIpNER6kRS11tFjVMiiKxlSTJVewWkNTJbF5BGHq1eDm1eFm1eDs2wkUYRKa2IFApI0nGUNH680yOck15lIcPJgDoVOzAHW7CQ02YsxCAqJzu3YxALsR/z0YcubwDNWhbZBJBLeHASLoy8AaNgwsprMAu04CbJHweGNQgzMQCTcmuQ3yf1o1RfylM5rRpqWxQPHyBLl0fHO9A0BxpcLusayRKa6zBxnxNdCnXO8nqQrEg5wRbYvW4b9jot2Oe0oMdrRo/bjKyW9MWOGmEMhA6JHNJt5L5JUavAq9xTh8xEwbBQ0C3kIucvaAnkjKFdxoTxp7q4LNWp33/kPsDiUtXTgqdBPQ2OBIn3BQui0vAGDQSBRltVPQ8WUeeHuvG8TfuKsLiNjU6nD2dnHsVsV7nivJRYjJcTC0DynM8bjNzSPW7AhtVrA/0uMn0pZPtTMPodNA8M8nWPhKJpYrCpCYPNzRhobkaxOQGzxUa6JY/WlgxamwfhUIQ9vmtZLETyfs7ChcWKpe5otI+FiyoHiT4BJWDU+lcsWPx5SrSd8vI4MbcOp2eexNG5l2KDZU+mVuDmtlPwp7aTsTs5W0VjHVKcBOLnwDqoU51U3sWJL+Wxq8PACwvJqtHYoivm/j5EXbVjqh5LhbKuY7ydN/K68DTx/aEdjFecCfZ5VY6N1PnXFLWxBXXp7ACWbXyB8/5ZXRic3QU9YSLl2Eg6RaScIpK2jaRdRNJxkKLcdZByHLXtUju/7Li8L2m7XE7REmOGhrRJMTssaEkLesqClkpyWUsn1HYyoaIJc9ApPRx8DAYpVa5X2aaBSmpPQ2sadu/rw7qNm/H8xs3IRuZxdbS34/CVK7BqxQq0+/1S9g52aZUxT3XJaCoHbXt+csmqTDltl/aX2qjlHaiN49rYmdmGTQMvYcvAemwZeAl78tvgVThCGrCthbCtpWhJLsfK1AqsScxHC/U6wtf1yGu5dA2uq16b/qzhNfjl6DE8jd5Dxu3Fev1P2G48yGuoHYoj8Lt3X4N6I+Jrhoivnu4evPtHP+ERQeqcBqPxDi0qaUTqwm01Ak8P0bCt3972wxQ3ApZbRIKS5+e1ypE6EngqVLjq1JFApA6f6hh6LAo5p18Eb9PcKXY25nlTfONyNXZ3sV2yKurIk4XRNXhZseZEPzqbejjNbt6HOS170NHcw8YhR6PQ7a0c5r6XQtz3zcHgIIW2LnOtoJty8yzozV1IJDthaAZ08p82tiFlbOKUNDdD1+KT9snmUSwuQDG/DMX8Utj5xfCcpvCGSuNefPP0aAFm8tsmgViqV2UXtka3MVfVax4c3SPdxtfv0vvwy46uwTU0vww45HdPo+U6jUz5ZUOD7Y86TzVKYeJtJPycvlMqt/l7w/tpbN8PJx9vT6Hmya1W1VNuRo8ra8ff12CfRqnAucafsRrtd7jzHIz2B6P/pc602leyCigLQrxzTmVlTSCLg78YbVBGWQc92lHnss453Uuonq6HLXlULssDKwRbIqpYtaYyCbeAL63/H7xn++95+56O4/Chw/8RexOzRnQ83S/m9HRjya7tWLR7J5bs3oHWwUHs7ejE7s7Z2DVrDnbN7uIyeSTUo+PeYg/iou578erdd+KVPQ/zgFbAky2rcO3cs3HdnLOxNTV/XF9XDby53OENc88Lc7oT6B4lZU9Xs5FoUEhZaFWuLLKc62S11dn6auqUDM4TusbWWpMWbScrLeca52S1pTKt+ZfQNHaZp+0UW3B1JHWdl4bgNQFp20+hdZiswPz6ZHGMvLeynMvh3Jeh25U89rRhzgcOznPDU9vx84c245ltpfk6Ry1sx1tPWopLjppfCn/P+sPjDnaoh7SIS2GwdEJUK0WOCfb71X6d2hE9prx9tNfoDXM+3hvRWLH2wetFX6vsfKh4D6XXD6+57D2Wv4fgfEQgOqgTHwgMWttNdex9kUHrvWVtFLM27JwNJ2ejyLkDO09z0B04eVrQ3VHn8t37gkRRS0vleD0PFJoaXB4w1OCRFdd/nno6jZNqHOiMzxEIJ3LpszOYXdiFucWdaPZKASxosGab1oVNXhf2ec0sXoJjVI6JRc/x4uRGegv09GaVm5VWf1dvhY3lcDJL4e5dAHdwMTwvpVw0+HNQOYnMsBytD8r+nED63CjXtV1osv+Ag1ovxy2vfh3qjYivGSK+Mru24Zd//V7u/BfJfYp6yqN0OqNRorRRRNosImk6SCQ8mpoCy3Q5p8XbOdFK8hY9oXS2LnCijp9vyeDOX40yWyA4JVEwkn6utskSQi4ovJ+P891XqIPJrivUGZx6Hfiq0M8pmFvlCzoD1Im3kdDySOtZNBuDnCftAtr7smjry6Glr4imfpsiwAuNRNiDoA4mD9OxxZhHW0mtR+rD/YSmKSsDi1MleMjlkhIFeOF5Ip4KnMIPT5cEsqpnEURimB/wdA5/Kl/ZHI3wEqPzMSLzKOILepcNAtQ6vsoLqPkW5HZaek/8MuH0F3VUMLBDETXD+SRqCF3to3acR/bxx+DPIdFL23R+fj3dL5NVxn/9wHrDnyN3ZHQ+tlSn0oV9D+BL27+HJjeP3WYnPnHw3+Iv7UepADtRa0+V4DrB3z5VyLPwStPcVB58UMdEy6E7XPBpRTrHPEPGFyqBVTAmYELx4nsO+MKF88AhmMtA2s3j5OyTOGvwIZyQeQoJHlJRbLQW48/pk3FP08nYbsyD69H3Sg0uOX6ZggDaroa8AxRsoECLEFPAnuAPSddBbyLoFbux3m30rTUApRlR/MSkBetJ3BnKLZdTxAW8JCyqdf69eCe/9BJxMVJFyMSEkG9lkA7ZOMO/F+WWzX9R/nspgaP8PgJ7mn/fis1li85Ni857m5xvOv2Glxv7cIjRjXl6SdCQ2+9mtxPrndnY7raF8+zGgopZQiJG45wHHWibApqYOhvpNBI6ps7z3Wk9RY/yUBj5YoljxKgBWQ/dcN31cJ0NcJ310Iub/PmfKJtbuhDFxCGwk4dw7liL/WkNo+eClmb85MRDUW9EfM0Q8WXnM/ivX3wutO6YbhG6Q5GlPI4u5doe3CJFDOQpArAdAzTPtFjQYBc8DiLFLjZkDfLzcr/ocuhH2USLxzal0dRCqQXp1hY0tbZx9DhabK+pYxaa2mejqbMLVks7NFoHplYnZgTQKE7BVRGF8q6LYlj2UHRd3pf3qKz2F8IytXNRcDwM2g76CzYGig6nQQoBbrvI2BRhyOEoQ9Sez0nHcIeXbnT+yBZ38tRNhu9YdAMeao5WOUFnKnDB8ufuGJFyR6YfcwZ60ZXpQ2suE+lIjsPNPvJ3jcXuiLpuVNTHeg2xfdw5JNdFdh3zO48efX9UxzF40PHUbf81lHhQVp5oqpjMHXTcuVw2/Ox3RvnxSAKXzkrb1GHlMnUSg/3+Pj+Fx3L7aNk/LmjnltXHtuPHTzb8ieokMnwxEYgFPw+tVpTYKY86/moeHf/FPLXN7altpMxCgcrknMZ1tE2OaqVyRXveF2kfOZbrI9Gx1MCmslgED3kqq1xZOmKprA3PY9N19sBRbchKEpS1sBNRaq/B5NHR+DlV7DcXc7Lrcfn6L2B2bhNf52PNr8F672RYmQEkBvthZgZgZQdhZgZhZAZhZjMwKOWysHLZ8LcRiL7gdxrb5u9y7W3uBAbzknyhxosms4VR7Svfz8ewG5KG1o4sOjoH0daW5aUhgmAJmXwK+/rb0DPYgbxNI8wGd6Ko88SdKYM+R7VNwYbCPKj3c7rvZYouBosuBvylE6jzx6KXRbQqk3UqSW5cNA8wYcAiJeMPDCjNpjq0arskQHhfMG4ViJOYpvPiwsYXO6pYMueUbmPxafphV9p3X5NpW0I1ooMeUcttrEzeMf69P3zOhc+RwHNGPS8Ci2/p2RKcq7J91DpMX2kK8rO/swO97W2lecKeh7a+PszZ3Y2u7h5Ytu2fJ3ieB4N/gaul+t6zVTTyO6HrDcu8M3DSDH7TkcAX4QBZZLgi+N3zh1atLto2GiCF5qjm/UA9eR4uLD3jgyuie1oSmp6CbqRgGCnousX37zD5EWN1TfeDYdK9XkfyrLOw+sMfRL0R8TVDxFfGdnDwPZG1WMYBugHwpH1fGJAo0ynaTTEPo1jgCF30w0/wxE8/Yo5Dk0eDiaOlMs0HS7oekoaBJpPcPSykTRNpi1ICactCU4LyBJqSCaQTCSTJtcQfqbJoHhnNPeP5IOomyKGjXQce+Q34uUfqknKaWBrU2064HeZl7Tjnduq8pXaUR+tVriL1OSqku23DdVTZ89vwcTx6rRYnDDrrgUAZK1kzgcFkGgPJJpVbaQwm0hhIpDFopdS2nwbMFAb9NGCkOSdXtPKR6TAoV2TdGHJ7CMrBdtLJoSu3F3Ny3ZiV7cbsTDc6cz1oKtZeuyNL15RuRSbZjGyqCdlkE/KJpNKrvtsnfTfMaNkJ5hHGy7Ftml84SdEvxwILDnbz9YUllf2AAUFOH3ogVIN5TbTNo7OR74sSklHxGbVslTrxqq58RDayP1pfbX/ZOcvPFXuNEe2v0tbv5HPitZNKiaOEcVnjfRw5i+tpm86jRyxifiIHLr/Tz5G3gteizgv9Dn2rI+e8TZ0QlZO5R237iR75Jq1zpyLTeSROaWmGiEAKOxFT0IVWEARhrGjDCM4K4VlWDubxV9tXIVZH0U7zRlFHYfhPOB3n/fe/NYw2GLspQpgSULj2//x//1Xq6AduK44TCgA9VleKjsZR0/wgGSrKWmCBUSkWSY06vpOo09nixP7MUxfWKeN4PrZw6aVRbvq0zWKBR7+hOUgXM0g6WXRm1eLXaiTcdzvz/cTViHhluUDBKBIWChwW1+CQubah8/ysYCSdx8xJBEQEQHR0bDSkizlOwJ4RHxNOtw5H2wJLge/DRtdoJtitIdim5CqfWHg00h6sP8M5jfT7yVRr0qgRfsoNf9Rf5aEFgMVBMKoW7eyHnlXxUXm/A+8GOUWfIkFOk4fLUlAfHFP+3nme3ag+5WmMW/7JjAMk7NijZRi3lmAy0jhRPrenXKeSeIzWxdupfSwFXZsjsWpu3rcs+4nfVxIwUoCeCL+gocubP5rO3zsWn2qCe/g99OeHUK7a0PeVNKoSqYEVm+8D4Qi+P2/LrwtcI0v7gpF/P+fOkmpPg2gcuZOjcpIF0p9763esVK7m5IbbTnBPUs8weh6VWyrCYAuR7dDiNUSbobYnAxb8UZfVwNoZtXT63g9BXbxNlTKniFWVzs+DCIHVOur2rBxYw9cLXPL8emXx9l1p/XMEFlu2pkdeU7VXbZSFldzTDBhsUaVc3XtpuQ3KTb9MuWmZME1lMaUItRRJlaKnWpaJZJCSlrKu0jqZlqnOxxbb4NxkPVcWXtpmyzgtxUEupfSaNAhGwRps8nohLxgb2SD5dTmb1n1zkLMd5Is28uwVY6NgOyo5DgrkfUPLWDguJ9tVZZt+OzQAG85xjH/3+dONuBMn7CJa8xm0UFj8yOCsrWnImQlO9HcIrWP+8zj4TerR14la0AJvk+A3UNVhXMGu2365kZ8/ZqoL56FxEPHV4NAD+ZhHHsRUgG7iMdeY2HbwgIjc5CPuM8GDR03iV1G17DAYSGn+RXQeRfQYtT8QJGR9MKq0q7wubld2vdF2wfFhO3+0XbkblVzQqHOg5vqUXAj1MEWsOFyOWngCS2E8tCylwOtcWagiAiQiCAJxELj/VIiWSLtwnZSKYxCxJKiHZ+y1ooIoclxgleA5OSwQlQVDRWcKyn59ucWlbM5E1JVowvDn4ZWkfWNAn3OQAnFYnmrV8980sPD4KYgqVS4OA2vRWKDOArs8+r/fIKfXU4Fs/AEc+r4XbSTYel7kZS1Urr77lBvkThP97vulSLyAcG8ohoO5Y0FngiKKkWudP6DBVlz/txu4sCnBTJ0lSna4nmAsilrMJTfqthuLXlDRdiI920rvf+xMpP2u5kBCoCz16uIjnDfIzworZnWMdvhjbSPz/4L7XugKya8Zuc/F7qXBBanflzL4++6pZJn23ZmUKPHdJYM5Qn6uXEMjdZFycB8N6pBxgAEHXlHNb+R2SQNORxJuS0J9P8PrLeWl91X6AINymJcfV2aRjuVRV7BYPsT5qh0X7B/J9YzwOsLnVuC2G7RnT5YCvHwRGMzWfI1a1xF7rXKPgGrf0arf6eDLSxOgaGAvGMOp/Uun66CAL2kK8ELBXXQNaUNHs13ErF1b0bFjC5L795UOoDUclyxD80GHoG3+Ql5XVR1rhMfyNpcNFV2RhGe1gdEqdS4tyZEpwO7Lwx4oojiQgzNQgE0pW4Qz6JczBbgUSERTXgHsIuwHEPEq6lTuUSyApAEk/TyhwUtQJGJNBVumACM0H0z3A5o4Lj+HOJFHEYlZx8WeQgF7ikX027S0CglI6jNlkXJzMN08XCfP92waktI9NWyleTr4v1ndaCREfDU4NOLz2zPOCi0c1NGgkSp6gIUjXYEwCR5gEdFTyv2RLRYZpZE0Xj2e1mmiRNF5gjK14w6OEjmqo22EHXheeb6G21AtVyL2gKMOWD4Lk+ZU0GhQLgMzl4NRzEHP56EX8tCKeejFArRika0+FFWPo+3xZxDMt6CHnMspiDAVuCf5j1r+8RoOYAWT3n3/bOUGRp1FNY8p7DgGFsPIA6hS7Pjzs6qJpah44c82yQt4RttTOe+n6DkamtJM9bF1G3kUlb5TNHIZ5KpOj1iuaNSTchr5VHlk9NMfEeWoacFoaSQZB1A32rZRoTQSMTWZFItFZDIZToODgyrv78fA/v0Y7O3lMtVReONMsYicL1hMclXmkM0jELUWzdMykKQlJWxya3VViOccLUqcDxOZXygKZM6ykEmm0d/cgv6mFvQ1N6u8pQV9TS1cr3JVX0iUFp8NSOoamg0dLYaBFspNo7RtUq6jWdPQpHnozO/H6X/+B8zdei/Pj9u74lLsP/lTSBpJJD2XE7k1kwsyuR47jsrZUhS4PbK4JVdkta06FzRqTqPjjhoxdx3YtO04PGJOc1fpXFbfdjTtexGtPeth+K69FBCjaKTQ3bIEe5qXYH9ilh8JjaypJSHNVit2q1QCO3DBVBYuNQ8ymA/J800i2+zCU74/ZtEqjdoHI+r6UMeO8LfO4+5+oJqGH34fLSN3DBBmAuse5a9/j59mGmkAS8d6sLEIjYSIrwYnaRpYubiDH/Q8/4hGcXkelD8fieqLamRXbat5SeG+YD5ThJqWlmpWFl/gVbW4VLO21BQtQ5y7/DqsFJBIV2lbJljGseMa2EumCpUR5vw8HIWlkdqoG0q5K0sgxiNWyfA8kTI0Fp3KIqfm8Vm+5YKsFslCAUnK83mO/JbK5zjyWzoXpCzM0M210o98uDxwrygaBgbSzRhoagpzCuM90NSMfspj+5IqT9G2apdLJGPfBw7c4IeTVmGlaZBOhZkOEu/jaLZ+WGu6YboaLDdoQwt1+2W+mVLuqTWpaN0oCnfhj6Qr9yoNJkemo3OTa4g/yk6uWMHggx96OzAQlG+XUjCEoCZbq2BTkXZ+BDz161Hrw9HcRi2TgZbJws1kgMFBIJOBN5iBk1V17mCGc4fbZOFSPQ1+aDpaAbTGRq3Vd47WYiuaCdgWLUJt8bZtWeza6lgWbIv2qUWp6e8YtQ3lE5Qiv9NINMbQMuH/S66jhmGi0zDRQctluAacjAEnZ8DuoVD6tCh2DnmtgIKmcQRYEijhuHoYBMa3IfjagOQNrae7N1L3oPfXWOG+EasGN0F7Auh7+g483nI4sgZ1D9Tfhf/evJC1mqtGlvqiDhR1cvMFCjqtbwfkDaqjJRg02AZg82BRZFsHDipsxiv7/oKz9z+AhYWdMGi9wo4CBqwkbuo6FdfNPRv3dR7LwWrGA90XpJQ4yh9UhD8aqQ+CmgSh11XY98A65H/P/LhD4V8qtCiWhlfUMhfKdVGJRCVClQuuC9tzkc8WUNiXQ6EnC2d/HlpfgefcsoDzgwbQd9dL6kCzAbSa8JoNaE1G3AXLd1ksuUBGRWNkrsgQ+yqP9edcRgIGqe9PPJhQEMgjdG/M2ND7CtCz6onBDt30odK1t9B6TdoQ5wteLxC4Za8/0usIfmVetXyo8wWupdTWHfL8leeOth/i+srcOsPXjH0GlcGayq9VHVv+udVuU3qtyt8DT62NlKMMYduquacU0Kp0TVVahbe7WACseIuaryHEObjjYDQSEnCjwQNu5PMF/OGnt8bdh6q5FPkjsexu5Au0kuuREmC0JoTnOWPwPKp55xi2/chfapQXVetuFoYD988ZhAqPPPCCduFNMzLSG7QtnSf+UCuFHY/MEIrMjYi+XjQvvXYQkjxqLfIf1sETgh9igRtESWxW1EX2Va33/fw9ijbIft/KskmWJoq0xlY83+9fCT0VBl2J7ZLbjwrRqwIpqIAE/uKR/hwAw5/nYTheqcxzEFUQlfi2WnMtCK6iRtCj7kR+gO3Q1UQJ/2hdqU2prbLw+qHcQ0tvNIUza/yeZGlFHuXaMLGuZIIwFLR+Hrvt+InX3Itss5jzyyToSACSqCv6ZRKCLAZJBEbaUxvOQzFYJgz9NhzldTKge+GADb23AG1/QeUDvIhCHArc02rCbDOQaNWRbNGQsNTARxgkynft1v2AUeTyqtlFLpPXhE6u3XZBzXt2HLbc8uLfHPnXdxmP3ftri488EthkLMIGcxmyelP4XhY4u3BIYSPn8Qiw1cVF6RkTDBaUv270uVRLBAXzASlMuG/xJ4u7P/eJt/05V+wlEM67MtVcWGrHc6iqbPNxfj0nf96V34a3o214wMTf9uurbvPx/utEzkVt6HVVoJ7JuwPToHQ+n8NgLoeBXF5Z+gt5ZPIFZAsFZApFZIsq5SjZNEfM5qjJBRrU6t0Pq6+Pv38BecPE3uZW7Eq3oC+RVNFoI2uv2uG6q9W3R0zNDpw3qq7SyMSlGojidfUo4iAF6ArW2dOCOg1JKlPyAMvxYNl+KjiwCi7MooNE3oWZc2BkVV3CdlV7F7DIs8IFtqZ1PNxp4JEuA4M0OuSzfNDFm6wmfPzSNag3EnBjhlDI5LD3ETUaW69AEcIwBFqnkRmPiSbV4I5d/ayK4Z8m+t7G4X36nvC+hTIIxateLPpRhpOhQytffHQ+DFoQCYoTjjBHR4zDcnR+UnQE2F9ri/z0fffiYH4ju+wa/nwtCsJC5UgqmqUF2KPvIXwv/nc7PlelVBeWI3l8vki0rvKzUm5stDh6sDRDaaF0WtjadCq3ad2myr9I5A9cGl4Pa8lNm9+vb6HjnCb/o4gLBu/F4uJOvpb72o7B9XNeyZPhw7XLePFlcr0ETEd1FDjnRBZTlXcUBrEg242ufB+7W9qeBYdWAfSSGNSaUUAKrmfCcuK3DMOl5MEq1meslN27/QXX3SCnBdl1158bQrlaWpYW5nA92x/IK6ol3T1K/lxLl+az2tBo7RNP5ZR0TgVoboFz3VY5veU9iVnYlZqHnUmVckYaTp/NiYIy9QNosjOYl9+F+X6am98dW1x6pBiWBcO0lHCoKUQsDiCxVevEA84CPGnP5uATRLNm49SWPryyrR/z0x50Ywl0Y3lciAwlTKJCpkLYVBEmHFzCrDgPRwwVxgR9xummZk5dI+xwr127Fk899RR27doV1qdSKRx55JFYs2YNli5dGhOQJPDsQh7FvEp2PocibefyYb1dGEQxl0M+m0eOhF+xwAKQg4IEoo9yEou8VI7LAUJ4iR1aMofv9er+XSHq9EqRp8pqu1RvlY6neb2mGYv6Sn5TRQCD4WTPUUQjNgMVEvRKxyZJXuoArnE9fByNg4ivBoe6VDbUREPV7YlEM6hsHDefROqD+VfhPBPfkhKMOKkUhH8OwkL75egxsXbxemVRqHFp0WsuaxPbrPHeYtWxU9U+b+3ja1jmap0q0t4NQ1z7oa3Dsh9ZzJ/kr/rDpS74EFcWS8FaPDS6SA9igyI+UWeBggvw5+wHaOBNf1uP1qsyyveFf8/K44K26vXVd0fNafavP4zZUZIVoSCIpjDCWyBL/GVnaQIuRwxU1leyyDpU5uAQZJ0Ntum+Th1WtRgpzZPhNp4Hm9v4a7KF2+p4L1gmoaDmDJp+soo5WMU8rEIeyWIeiWJOuU7aOaQ4WmMW6UKWR9NL7jqRCJD+tioHefWp3A40fymAFJ+vtZBByqFH1thQ50uF5xxMpJAxk8hYKtHSBFkrgaxpIWdayJsWsqaJvGWpWCjKzqncEXntI9d3KXOhOWoxXZ1W3fXn5Gi8ui59D6hc+vtH4rWEEdjUwsdBcJpIpLYwoELJ9TW6VlnYhudDluaWcu7PKVV1vrWSg2nQnNNE+JqBpZaEKi9/4TgqkA3NpeLlMYpIkPtsscBldpkl60fE9TqYS5+iqoKq+4vViRbrWZyGR3GEfRsu3PFzXINLsdeahayVRN5M8GdLn3PBos+a3C+Vq+V8Zy9OG/gLzu59EIdgs5rYkCaXRAsPdpyAuzpOxSOtx6IIi8WG4WSh2w5MDkJCuQOjaMMoBmUXhu1A59xlocfCz9V8kaYmowe5ThPS/cTWED9XTq9koaZuAM3XVXlpuxQVkt1YKcwoz8eKyUKMG0OMCnbCwyrdg0bzcHMkANWATQ4au4wO0gLRtCSt1gE7uRLZFLCBhFBKw6wmA3NaLcxvt9DVnoRlGdCSGnRLg2bRHETyOaYhdsAzSVDacAwbjk5uwwYSehKWbiFhJJHQE3BtHfe80IM/PrEH63dn+dMgl9zDF7Ti1Ucvwlkru5Cg7224uLK/KLMfcZLr2LlBWa+CZ0F0AedoHd8facFrWrMzR8+TSJRVur/BhuuS2A2iVap1McMc0WPotf3FnCPtwyA0/irP6pjStXA0TJ42GLyXSATNyALRgVdJcH5VDt5nZDscE4qfJ2gX7itrF/sMq3x2JeeTYBAq3j54cKu5w36wlWBBYc7VPjU3l245ZClUdfSMVblqT/f8vv092Ne9B729+9WJtSSa9WXo6pqNBQsXYM6cOWxl7N0APL1xW+XzNnhOc56GrjepbYpd0awh1RptUzqWX99fMzH+vI+3pz+uTRGTnSLsQgFOMQ+nWIAd5IU8l8OcBV8fioMlAVjM57iehCHlhXwOhQIJPht5iv5Iz91Q2FW33EVFXWxbryHyWPzR/TO6HZzDgmP659FNFXMAwMJiLxoJcTtscLfDgWwOl3/rNfDID8Uz4Xkm4Fk8eupxbsHzEnC8BFzQKGtSlb0kbFCiNdSTgEchiumBQU8gda7RmGuCBUzNstyILJ5aWky1vK260UXbRRdNVdu6yg1/DkJQb5QWbOU2tH+Yc4z2GqJtar5+eA0jC5JAPzu6qWV6e5Ht60Wmbz+XM320vR+Zvj4Usll0zF+A2YuXoGvxMsxatATJJt+lpQb0cFST+j0UbRVBqBgp8z7HqyirNMqyrRa45jpbhdktVCmrELweCpEy1QfXSQJpyuJ5SDpFNBezaClm0VzM+blKLcUc5602JdqXQ7Ot6poLWTQVMmy1US5cKhGqk0w3YAop3ITBRBMGEs3ot5rQZzWh10yjP5FGH9Ulmrie1nQLyiS4lMvn1IHChdPCxUHioDXkBkY2Ab/Mbl2c25G1ztQS0Hw8r3dWKqvzRNoF5ej5gzXS+BzR85Tq2Go4xOgLCzca0aUlC3jZAitSNuFy2cJKawteY96BlFbAINL4DS7BxipTxDvQiyPxAqeF2B3Wk3XkJSzDM95KPO8uR8Ex/BDyjkourUPoLxPCdWQZUmsORtuxW3SV90GDM8pyQ1YSZcFhawkP1ETrlRWF6nSy8rDVxAJoSQYzoZZq0FXyKCQRRR/kSG9qeWrPM+B6QVmD51JkSw0u5by0mkYxSeA6NBDicqQ1KvNyC1S2XXgkJnhNRRpVKYVI1ByNxb9GCksQhMnFH4BlTeOL01Aw1hKPoTeBPwBJ1vDItAqOlegPXNJaqh4PQKs62nbJSs7BgSinwQQ/5/gENLAQLMUeROQtJeXNASw5ciHe+Jm31/vTE7fDmYING3vmbxvTsaHFtxb0cHWVGFOizoTnqrwk9OJ1jp/yVdqhGJRL54Mb2V/jnI3mJBkTbVpcoAXirppY1fVmmHoLDH0xzJQOLQ3YWQ/28y4Kz+5F0d7d2EJmhNDNPWHosDhpfl67bBgeTMOBqbvQDZfLuu7CMMj9zIFhuNB0m8u6Tr07m3p2vA2tyGuoUaIRZPpFueQ2xY4UDhza9opwObdhU9nPe70i9tH6Lm4RtlvkvEBuUg6VqR4oOuRSNpwYJ8epPEyt35+fYXI0QE40JwOUm5x3agZma1RHIYYpTEIYtsMPzWFw6N0wXAeVPeoYqzI7NnJHWdVT2eVc1aukweGkc247GhxKfjlI1FadMzi3DtszYPPr+dfAr0OKkwaB/O2g3g8HMllQB4ECqKhAEhTkxF9MnsphToFSVB4mX8SRgHvBW4VH7KPxMeMnWKbvwDu83+JG+0zc5pyMTn0QRxvrcIy+Dsu1bTEr5QYsw9NYhXU4BHka7CpFV4FH22PxeNBoAW8TRZo7ohko6BryFHSEgnxoQEHz+FtM364CPBQ0F0Ua9GFHQPUdV4GZcvA09ftQvw363dhlZbWPfkeskPRh2tNvzzpAZ2K23pkwPBOma6ky5VW2qazqgm0LZrCfz1G2Tfv9dnrsWLWf2po0EBl1IY5Y9tm90u9ssn2GnTp8S74fjjsolyz9dI7AG6N0PuViG3FU5nLklYN2vqdBeHx4flpOInDr9csx34NSuPhwXmuV3LdxVy2r36y/bIhyBPYHGX1/m6hrfSzXKuvC5U8iTaJ1kXK0nfKeiVZENiNLqpRWR1ENYt4bvmOQihAaSY6/9ARHJ1Xr4LGVz08JN49WbxCtXoajIwc+0QXPQq+XQp+X5ntf0FMJAiFFZ1gHAZGi9cEdMFofBEmKHRtY7MpnbfsBnMpnc1P7ijr/vMP/7nxrK3+d2Wl4HAhueKOTHZoKqD0i1he3opEQ8dXgpBMWPnHcJ1BwCsg7ec45uWqbOoJc7/r1fjvqKMba+8fEoAcpxWKv85Ql6nTqmgVTs6DDgsG5CV1LqBwWx5PTyG2HxZoFzVPboRWPLXolYUcij6yFlFPUNKqj3KXcUbntUEfUhOMYKlHn078pk9CpBQkgSmWfZl3g4JCG7osZjUOtR8tqnxIyZDUM2gZlioBG4oUTCRqTRIvNIoa24Zc1KtM8DhY31AHz53povpDR/HkgLG78xKLGYSHjBKKGRIxXhE2LWoZCpiRqKB/kBS39OpeO813GGm8Jrxg2vX9y5XDqtLT4CCeDDjtoM6qXVO5tesQNriQmVV1URHKnMBB3lPvikbZJNKpciUYWh9xpNGLHkji0A8FYIRKDc6tyKF5j19CK670P4f36TbjYeASHJh5Am7cOs7T9HJxit6ZhC9J42luCv2AlnvCWY5D823yxousDHM0wTHoRulZUgwOUs7ApCZ5wcMBPrkbzq1w4/NtS5dHeoOsy75fdHVVMUBVfsXTvVvdztW34uU6DEIa633PSTZi0X1fPAmpncjnh5xa7B1qGqqMy1ScMi90F6RnSm3Gxq9fG9h4bW/YVsLffrvgk6P53UFcaiztSeHJbL/YOqDs5fcTHLu3ARUfOxeolLXz/sj26BxU4VwMweRS5rOqKjo2iR8/bQiwVnDy3KXAbv86l57Fqq57X/vPZLeV1uzdUgT5TWn4hYZTylJmKbSf1JJJmUi3TUN7WKLWN11lV6uKvRYNSE01PTw/P4aLU3a2mdtA3wWxqwlFHHcXzuObNX8B9AU7hwCjNwyogR30vm+ZnUU79rQLyNrnqFZCn7wrvL6LgP+sot/m5pp5v9Gyj74/6XtGAn9pf9Gw4vE3rY9Gz0y97aoCwlNP9Qc29VM9csjhRfy6wQqnFoP1V7JT4o2d3KJOUy3KQq8BTal2tYH0tHvyLrLelypU5uT/T75+PDPfpZceVn9tfw4tHFYLcv5Yq7ZymZjQS4nbY4G6H4wm7wlFHt5ZA8wVdVLAF7ULxNwKRV+vc0f2TsOTuqKEbBj/c9QR3BiinDoMBXwSSKPRKQlB1+iKiL0hVxJ4TE34GbIfaGKEFR+XUcVMiSDdI/Kh6Ej9B4g6cb/EJRqG500Y3Yd96wzdk2KFVh27WTpgrqw7d1OlmTx2BRkP9jajTlYiVyaIUrY/upw5breNqHV+zPMS56BwEPRhtlx6iTqkcqaPPPrqvYttvP9a2sdfzRtnW3xfdppx+00E5djx3BkYxCVs4IKgzYtB/PBJv+PO9NOiuDs2NzwFTbUrbsfpg8dLyOo8GaBJIWSmkE2mkrTTSyTSakk2cmpPNaEo1oSXVgpa0Sq3NrWhON3MAgkQiwXNqpgI9gwU8sXU/nti8H49vobwHfbn46E1nk4U3nbgEbz9pGZbMGtrte7Ke0fSMDJ6fQbli247X0TM2Z+dibaicc0p15ecrr5sqv2O6j5YLumoCL1of1JFHgR16LJRyuk/li3ns278PPX09GMwOsmWRBjcoT6QTSKQSHEGy/Ljg3hkbDJxmKG8M0/fMsMJtHizxy2oAhQbLjcggi1E1gftHanAt8MxQXlGUaGBN5cpTg/pFVDYiXhrUTyoNtl142CH4x4tPbRhtIOJrjExH8TWRkCUomGek5gGp7XwwL8lPBVuNHNGIUNbOI2PnkSvmkbP9RA8LO3iw0AhSHkUaPQpGFP2cRotoZJGsKI4/KsnWFZRydrthIVJktxt2O/PdZ9i9hl1xpueNdKzQvI7gpklCUuObp8qDUe3YyDaLU0vlnKwwp5FqupHT6HQwSm3RiLVh8qgqiRgetTYSfrKQpNFWk5KFlJFEispWAmkziRTt532Gb7kLLHq+m6I/JzB0V5ys8NnTHDVBPohup/zz49skxgos6MkyQKO3NuX0G3Uiv1XffZPaqs5MsM/v5PgDAjzqyyI16PjQ+QPRGYwIR0Wt629TgBbH30/lINE+L5KrBYqDchDUhevInusHDeDcD2bAa0QFLsT++lhB4u8dlw22JKvcgKXRHFS/TMKe6g0l8KkuEVhudDMi4oPfgrIqKKGvrAsJtjpQcIgENJ1+c2RVtDiQhqabKoeBQsHzk4NCwUU+T2G1i8gXHORzRV6+JJcr+HkeuVwuTIXCgQ/GkItYMplkIUYpnU6H5WqpfL9FQWMmKOw4eTVs2DuIxzf3YN3OfhyxoA2vWrMAKWvklpYgYAQv5eLnpe1SsItgX3x/KajFcO2D4ETDni8MzoHRtY/U0TZbdvg3VbLO8e+Zf9fBc5eeuf5vm567ZB1kK2De93Ao8POX9gVlB37u7+dnNAq+Z0T0Wd3Aq2+HVt9yERKvqyZWonWBqFHP2EgbX/SE7dmbwK/zy/F2yrU9KMdEEz+zlaBSwzh+OQjCVvLw9IMw+dsx79Hy+tLvVXmPapFypD4SPbbkserPMxvm+DWLO3DeEfNQb0R8zRDx5ThFbNt5o4r85ru7OTzJWZVDkzjvp/ZBPZnISy5yQbuw7MSPKZ3DC49T56C2fhS64Bxsfqc1XYNzUmem8mFZ7YtXPVZcWZuKcw1/7qrn9YZvQxNHTYOiHpGFicIGk/WJov5RZ4vmEtH8BxUNTrncKVc8lXNXLZxHxCGYOQVudzTzQm1zx49d8MgiVbIW6NRB4xsqjSzxv2o0WtWEJZ3FT+AeQPN+qOy7b7GZXtXxzNSgTCNNvK3ctyjRtipr/DfzeGRJK9WH3uhD/YFG8DcctgV/+sOfxzuA83AnWc3R4/Vv/IApHJDFn2jMka7ob0ARrvzFZsNyMFePzkFijo7xA7+o45RrJy+AHHbMg6cFfVfIz0OF5laRHznKgH9xNPpO+8jNM4jhq7bZosntg+PITuzXVezz57bx8ttBVANeijvcplyVVR0nPk/pXBwFMVIXtKFAGZVh3YUpQfhnicb6j3SCeN5KZGJNrL3/K/f8uX8cYMNE0TFg20kU7BSKdgK2nUCxmIBjW7AdCw4nctUmK76pLPpsxVcj1SO5N4zkballD4I1+jj8Cq/hRzP21Ew9k6/X0VTwKQoq5Xg0/5LcP2mf765UOeUodofjWIIcD6QUTVVFVPUjq6JUZ0fq/F8zH1f69UbrR/+uwwWK/Wij6vcembXF9UHAA3UMB5nx26iopuEiD37HVd1vwgimfrCEcJZYpKx+5+WvVSoHQRfUuaMzzYLX8mqfI3JM/D351+e7xnm+N4fru+PSNj1v1Rw88vJQ9eSOy/Xkokt/hcBtlz95UpM6DNdAytaRcnSYvusbPU1t6MjoJrsK2xRoxp+eoNydS/PWebqCv83BZ/zpDOzBEgQ843IQoIZcpOkTV67S9Z3M0ZjoZSlwkaT8dScswT+9Qdb5EiaJgXwOLz7/6RG314absxGZCC4IgjBWVAhrrSTkeU5WZe5Go+VVtA/mclFdEFGvNLdLcxIctIeSxsGBLGjhNllk/fmfXI7k7EKnyipXAxhU5tFgT7ntaVH3PS6rQQ817KFyOqI06T0YueUJFGHnHlVzv3VFfIHI6G/ZJPmwHC60Xt5CuUdPVWhoqQAbBY3W56K8yNt5zfbzYtX9wTa5f6lAA76k4Tkso1uywXB1JGEi4ZlIwEKScxNJz+I8EW6rv+yIiWvbYVEDKdFgHCrYRjCgwvu0eJtgwCYWsMPfDpf64AGdyPGMOo+q84VM+fki21WvKTxfEBQkui/yfoI24fn8aHfBtfLnVGrjVW1Tei/8WUXfc8VnFAizyDGR4Cjxz1zDwMAs7N51MPL5lrA+kRjE3LkbMXfuy2hu6vUHLdXApBqkUNtBMCMevAwHNv39/twjbofyuspjOGiJL+Q4WFIQXCzcVqJNXYvyNlGeJuVzWf0gS77bnnrt0jzXYPBV/ZFK81ij11N+/eF7rPoZBJFL/HL4fuNlFUAk0o5/t2qQR4tshyvW+O3DP2NQF9QP83vavb+xBgFFfDU6dg6ZgVSsKlxoNVpXsWCVVrYmU7AZWeMquhZp0LDK99sbqiJ6gsj5av9MarzIUOetdcJoz2bYCx3iFEMydIvwIy1rFpjsw4hN0X/Loz0N8/ojft6PqGGdbmBTwnoy/PdhbGcsN7FqMWES3WYLI6/Fo+rCfdwuUh8eX1YOREv0fNX2c38lEEFl5w/Pq4eChQUJCReSGtQpdX33F1+sKCur79rCbYzIPAA94taix+so991dVHu/XK2urJ6OnXQmYrB6Knz1xwEVDjqIwBfW+v+WnjVJaCx0WjmuZLLy2ROLKohYZ5/Fm+YLOF+UKWHmoMjijZ3Y/H2qTE5sar+6PkdzkUEBGa3x5rOGEbYnjGhUutESt7qFVq7YdsmCpqxqJetauF2l7fDb8dcMrX1l1rTgNVpbu9HR0oM5XisWuB3o8OZC758P9J+C6YPyiFB21rGvKVkxajSqK4jnE4nu9AE4Go2CiK8Gh+YNNLXk6n0ZQt3xx939UMAlX+zKeq7lnf5oVyROcOx4fmIFolAt4ly5L3p8fDvWho+PXE+4T48IzvJrRZVzlNf7o3KjfA+hTUKLlyuve/j3EFoaYsdEA/tWfw/+RItwzpASPP66KC4tdExCKGjDE4wq2ik/Jn9xVD8F+9Ux8dcgfymuD5ZMCc9DKXg9v62KgV3xLaugpitZZJXiakcO44JWctcqb1ftXIFXnQrnHTRTZTde54+gs0gIQ3SXwnqrjgKN/vshxYO9UYsAzYVRK8yEIcK14WX3SN965ZH8nak2kj/S44e+Jjr9UB2kyJBc5cc/zHsZfZ9tdEfQ/YbXSgZQOxyGWtxejVXQf/SbUds0n4+7qbxgMf9Egp9NmaCIuMBFhuuj7ncldVQSBDFx4B8TLQcugSWBEH+NmDApP7aGwKkQNDXESzVxUvN8QwmbKTF4Njao97Sz3hchHDBefjaAf0KjIOKr0XEszNp4ScQFoHIEMTp6GNw8QzeGSDtuFX2ARB4eqh8WnD8IbVxa4yQ2xhGsRxK6PJRepxTFMDjOrXE95ecvvWawJ/5+S+ugxEdOKz+X8Loq2pXOFfvsyl4n/t4r33f8uOj5JtLCE3QKSwFCZDbnDCIYsBZ34bqgjVO9IMwU2NrO65IFoySRMrvlVSsjXh+6wAUjGpH6yHapTK/sW/6j11J5dUPsGw1j+aWPw93Bd3ke6UvEWmtDD3RUOvJUP7biNDWC5GjaKM8V7UOF8xmB5mRjWS1FfDU4hpZE07o3Vv0iR7+w1b72tfaX/0aGGuistW+iolFNF6qJ30ox5+dlC3zGBHRstDUqcMuPL5VjIlEbwXVEz1t2HSU/+8j5a4j4WF34esq6EBe+kc9nJAK7lsiv+pm5Ix9wqCqsS4urRvdV/8yU1WQ48R0Oj/i7In+F6CddGlP3qtTFhxHK2lU7X1kbeg+BsSs+5BK2rebOXMMUVq2y7DhvTMeNfIS9rN0Yj6t+G6t17XLPm26UOvdqvmG0M09zduLiQK8iGsrblOYCxUVHMNcmqB+6TUlgBO3Gfi212oz0Wmq1Kf/MVDnyHsZAyT8isu1vVNQH5WikvOjftlY5ct8s31etPirkqp2z1nmE8WXloe3ARWgYRHw1OGaS5j7E6yp+7H6nqtaPv6K+7OYzomMq9lcfWqp9DdX3VHe2qX0ttW6u1a6h2u2/1mtptfZrY7i2is8icEmrfWytB0C0onab+Kc75Hmr/9mGrPNGWh7mITWya/NGd0ykMKHHVPl+l//myoVP6EkYOjTVOnslw1o1WWjW+HLWqA7kA586dK8cRlQM465WtX30BxiePvpLj7/uaH7/tdvUeh+l1yRXwuBSan2+0bPEmtQ4/Yi7mIFbYeSkBzqANfLX9vPYa5dVqfghE38tE3T8sOer9vfzRtCxj5W90hiLX6eH2+pvW1U8+IXSPrW3XDzUfP2hrjF2XlUxovNqQ79e+bTk4LrLXzN6fHRfRX2N1wy3qZOjayrXgjJ9wJH6MFcn47Lhu8H79eHxkZEmdrWO3Zz9Z0zFCFa0I6UGq8IHW9nxXo1j+H9O5PMdVKl6t/zc3CRyTKQcPVfwUkM9v7wRPp/jdbWftUO9xnDHjFSs1r6uII/2cIC5HQk0EiK+Ghzd0jH33KVqo/zhUba2QvWNsrtcUNCqP+mswgABAABJREFUNa/cFzuu4vWju4c5tsY+rcZ5h7yW6FoRo7ze8BE1yuusZnKseO9DXUvZvui0oNgdaIQ394qHRvRh4pXXR++gZa9TXu+XY9cVGp6qtC9/UIX1pesLV7uI7qeimnRR0b7mdflzNsrPU/26Kh9kI7quavXVrqvs2r1an23ZZxL7G0WvSxCE6Q3d8yNCIiowQgERERPVxAeLDlrbgqedRs9T3g5+u/LXi4iUMsHDr1d2TCh2osIodo1l+8vfH71no1xU1RBSQlVqiccRP6uqHF/rORoKoWp9hiGe2RNxHeXv3ZwVDzw31RHx1eBoho72C5fX+zIEQZggwgdMmTCLCTV/YdbqIjkeRCMuBP26IAhH+QMzcnzpAVomRP3Xj+6LHRsdEIjVxber141ApFatG8trjOY1q7wvbYhBnHITBcoGiqoNjtUY2AlPX21Qp+xYbQTnU1Vlg1HDta3xPmImjvLrLR/wKh8sG/J88eO1muerrAs/g6HaRhZuLTfzBCKlpjgJBIcvamoKjTIREhNVHIdHBIYweip+u5EN+UZNXUR8CYIgNMzDVR6sgiAIgtDITN3VGAVBEARBEARBEKYRM1p8XXXVVVi+fDlSqRROOukkPPTQQ/W+JEEQBEEQBEEQpikzVnxdc801+PSnP40vfvGLeOyxx3D00UfjwgsvxO7du+t9aYIgCIIgCIIgTENmrPj6xje+gfe///248sorccQRR+Dqq69GU1MTfvjDH9b70gRBEARBEARBmIbMSPFVKBTw6KOP4rzzzgvrdF3n7QceeKDqMfl8Hn19fbEkCIIgCIIgCIIwUmak+Nq7dy8cx8G8efNi9bS9c+fOqsd89atfRXt7e5iWLFkySVcrCIIgCIIgCMJ0YEaKr7Hw+c9/Hr29vWHasmVLvS9JEARBEARBEIQGYkau89XV1QXDMLBr165YPW3Pnz+/6jHJZJJTQLDgqLgfCoIgCIIgCMLMps/XBIFGqMWMFF+JRALHH388br/9drzmNa/hOtd1efujH/3oiM7R39/PubgfCoIgCIIgCIIQaASaolSLGSm+CAoz/653vQsnnHACXvGKV+Cb3/wmBgcHOfrhSFi4cCG7Hra2tkLTNNRbaZMIpOtpa2ur67UI44P8Tacn8nedfsjfdHoif9fph/xNpyd9U+jvShYvEl6kEYZixoqvK664Anv27MEXvvAFDrJxzDHH4Oabb64IwlELio64ePFiTCXoS1fvL54wvsjfdHoif9fph/xNpyfyd51+yN90etI2Rf6uQ1m8MNPFF0EuhiN1MxQEQRAEQRAEQTgQJNqhIAiCIAiCIAjCJCDiaxpAURi/+MUvxqIxCo2N/E2nJ/J3nX7I33R6In/X6Yf8TacnyQb8u2recPEQBUEQBEEQBEEQhANGLF+CIAiCIAiCIAiTgIgvQRAEQRAEQRCESUDElyAIgiAIgiAIwiQg4ksQBEEQBEEQBGESEPE1DbjqqquwfPlypFIpnHTSSXjooYfqfUnCAfDnP/8Zl112Ga+Qrmka/vCHP9T7koQD4Ktf/SpOPPFEtLa2Yu7cuXjNa16D559/vt6XJRwg3/nOd7BmzZpwYc9TTjkFN910U70vSxhHvva1r/E9+JOf/GS9L0U4AL70pS/x3zGaDjvssHpflnCAbNu2DW9/+9sxe/ZspNNpHHXUUXjkkUfQCIj4anCuueYafPrTn+Ywm4899hiOPvpoXHjhhdi9e3e9L00YI4ODg/x3JFEtND533303PvKRj+DBBx/ErbfeimKxiAsuuID/zkLjsnjxYu6cP/roo/zAP+ecc/DqV78azzzzTL0vTRgHHn74YXz3u99lgS00PkceeSR27NgRpnvvvbfelyQcAD09PTjttNNgWRYPej377LP4j//4D3R2dqIRkFDzDQ5ZumhU/dvf/jZvu66LJUuW4GMf+xg+97nP1fvyhAOERuh+//vfs7VEmB7s2bOHLWAkys4888x6X44wjsyaNQv//u//jve+9731vhThABgYGMBxxx2H//mf/8GXv/xlHHPMMfjmN79Z78sSDsDyRR4kTzzxRL0vRRgnPve5z+G+++7DPffcg0ZELF8NTKFQ4FHX8847L6zTdZ23H3jggbpemyAI1ent7Q076sL0wHEc/OpXv2JrJrkfCo0NWapf9apXxZ6tQmPz4osvsiv/wQcfjLe97W3YvHlzvS9JOACuu+46nHDCCXjjG9/Ig5nHHnssvv/976NREPHVwOzdu5cf+vPmzYvV0/bOnTvrdl2CIFSHLNM0f4TcJVavXl3vyxEOkLVr16KlpQXJZBIf/OAH2Up9xBFH1PuyhAOARDS58NNcTWF6QB5CP/rRj3DzzTfzXM2NGzfijDPOQH9/f70vTRgjGzZs4L/loYceiltuuQUf+tCH8PGPfxw//vGP0QiY9b4AQRCEmTSi/vTTT8t8g2nCqlWr2JWJrJm/+c1v8K53vYvdSUWANSZbtmzBJz7xCZ6bSQGshOnBxRdfHJZpDh+JsWXLluHXv/61uAg38EDmCSecgK985Su8TZYverZeffXVfB+e6ojlq4Hp6uqCYRjYtWtXrJ6258+fX7frEgShko9+9KO4/vrrceedd3KwBqHxSSQSWLFiBY4//ni2lFCgnP/6r/+q92UJY4Tc+ClYFc33Mk2TE4npb33rW1wmTxOh8eno6MDKlSvx0ksv1ftShDGyYMGCikGuww8/vGHcSUV8NfiDnx76t99+e2w0gLZl3oEgTA0ophEJL3JJu+OOO3DQQQfV+5KECYLuv/l8vt6XIYyRc889l11JyZoZJBpdpzlCVKbBTmF6BFRZv349d+CFxuS0006rWLLlhRdeYItmIyBuhw0OhZknEys9IF7xildwRCaa9H3llVfW+9KEA3gwREfkyD+dHvwUoGHp0qV1vTZhbK6Gv/jFL3DttdfyWl/BfMz29nZem0RoTD7/+c+zOxP9JmnuCP2N77rrLp5/IDQm9Pssn4vZ3NzM6wjJHM3G5TOf+QyvnUkd8+3bt/PSPCSk3/KWt9T70oQx8qlPfQqnnnoqux2+6U1v4vVtv/e973FqBER8NThXXHEFh67+whe+wJ06ColLk0rLg3AIjQOtGXT22WfHBDZBIpsmDQuNBU0KJs4666xY/f/+7//i3e9+d52uSjhQyD3tne98J68ZREKa5pKQ8Dr//PPrfWmCIETYunUrC63u7m7MmTMHp59+Oq+7SGWhMTnxxBPZm4QGwf75n/+ZPUrI+EBW6kZA1vkSBEEQBEEQBEGYBGTOlyAIgiAIgiAIwiQg4ksQBEEQBEEQBGESEPElCIIgCIIgCIIwCYj4EgRBEARBEARBmAREfAmCIAiCIAiCIEwCIr4EQRAEQRAEQRAmARFfgiAIgiAIgiAIk4CIL0EQBEEQBEEQhElAxJcgCIIgCIIgCMIkIOJLEARBmNG8+93vhqZpnCzLwrx583D++efjhz/8IVzXrfflCYIgCNMIEV+CIAjCjOeiiy7Cjh078PLLL+Omm27C2WefjU984hO49NJLYdt2vS9PEARBmCaI+BIEQRBmPMlkEvPnz8eiRYtw3HHH4e/+7u9w7bXXshD70Y9+xG2+8Y1v4KijjkJzczOWLFmCD3/4wxgYGOB9g4ODaGtrw29+85vYef/whz9w+/7+fhQKBXz0ox/FggULkEqlsGzZMnz1q1+ty/sVBEEQ6oOIL0EQBEGowjnnnIOjjz4av/vd73hb13V861vfwjPPPIMf//jHuOOOO/C3f/u3vI8E1pvf/Gb87//+b+wctP2GN7wBra2tfOx1112HX//613j++efx85//HMuXL6/LexMEQRDqg1mn1xUEQRCEKc9hhx2Gp556isuf/OQnw3oSTV/+8pfxwQ9+EP/zP//Dde973/tw6qmnsvsiWbd2796NG2+8Ebfddhvv37x5Mw499FCcfvrpPL+MLF+CIAjCzEIsX4IgCIJQA8/zWCgRJKLOPfdcdk0kS9Y73vEOdHd3I5PJ8P5XvOIVOPLII9kqRvzsZz9jgXXmmWeGgT2eeOIJrFq1Ch//+Mfxpz/9qY7vTBAEQagHIr4EQRAEoQbPPfccDjroIA7EQcE31qxZg9/+9rd49NFHcdVVV3EbmssVQNavYI4YuRxeeeWVoXijuWQbN27Ev/zLvyCbzeJNb3oTuyQKgiAIMwcRX4IgCIJQBZrTtXbtWrz+9a9nsUVh5//jP/4DJ598MlauXInt27dXHPP2t78dmzZt4vldzz77LN71rnfF9lNQjiuuuALf//73cc0117CQ27dv3yS+K0EQBKGeyJwvQRAEYcaTz+exc+dOOI6DXbt24eabb+ZIhGTteuc734mnn34axWIR//3f/43LLrsM9913H66++uqK83R2duJ1r3sd/uZv/gYXXHABFi9eHO6jaIk0F+zYY4/l4B3/93//xxEWOzo6JvndCoIgCPVCLF+CIAjCjIfEFgkjCqRBa37deeedbL2icPOGYXDUQxJP//qv/4rVq1dzpMJaYeLf+973sivie97znlg9zRP7t3/7N5xwwgk48cQT2ZWRAnKQEBMEQRBmBppHs4kFQRAEQRgXfvrTn+JTn/oUuyUmEol6X44gCIIwhRC3Q0EQBEEYByjqIYWZ/9rXvoYPfOADIrwEQRCECsTXQRAEQRDGAXIppHXBaB7X5z//+XpfjiAIgjAFEbdDQRAEQRAEQRCESUAsX4IgCIIgCIIgCJOAiC9BEARBEARBEIRJQMSXIAiCIAiCIAjCJCDiSxAEQRAEQRAEYRIQ8SUIgiAIgiAIgjAJiPgSBEEQBEEQBEGYBER8CYIgCIIgCIIgTAIivgRBEARBEARBECYBEV+CIAiCIAiCIAiTgIgvQRAEQRAEQRCESUDElyAIgiAIgiAIwiQg4ksQBEEQBEEQBGESEPElCIIgCIIgCIIwCYj4EgRBEARBEARBmAREfAmCIAiCIAiCIEwCIr4EQRAEQRAEQRAmARFfgiAIgiAIgiAIk4CIL0EQBEEQBEEQhElAxJcgCIIgCIIgCMIkIOJLEARBEARBEARhEhDxJQiCIAiCIAiCMAmI+BIEQRAEQRAEQZgERHwJgiAIgiAIgiBMAiK+BEEQBEEQBEEQJgERX4IgCIIgCIIgCJOAiC9BEARBEARBEIRJQMSXIAiCIAiCIAjCJCDiSxAEQRAEQRAEYRIQ8SUIgiAIgiAIgjAJiPgSBEEQBEEQBEGYBER8CYIgCIIgCIIgTAIivgRBEARBEARBECYBEV+CIAiCIAiCIAiTgIgvQRAEQRAEQRCESUDElyAIgiAIgiAIwiQg4ksQBEEQBEEQBGESEPElCIIgCIIgCIIwCYj4EgRBEARBEARBmAREfAmCIAiCIAiCIEwCIr4EQRAEQRAEQRAmARFfgiAIgiAIgiAIk4CIL0EQBEEQBEEQhElAxJcgCIIgCIIgCMIkIOJLEARBEARBEARhEhDxJQiCINSVtWvX4g1veAOWLVuGVCqFRYsW4fzzz8d///d/x9otX74cmqaFae7cuTjjjDPw+9//vup5X/GKV3C773znO1X3/+hHPwrPde+991bs9zwPS5Ys4f2XXnrpsO/jrLPOil1fNB122GEj/jwEQRCE6YtZ7wsQBEEQZi73338/zj77bCxduhTvf//7MX/+fGzZsgUPPvgg/uu//gsf+9jHYu2POeYY/PVf/zWXt2/fju9+97t43etexwLrgx/8YNjuxRdfxMMPP8yC7ec//zk+9KEP1bwGEny/+MUvcPrpp8fq7777bmzduhXJZHLE72fx4sX46le/WlHf3t4+4nMIgiAI0xcRX4IgCELd+P/+v/+PhQkJpY6Ojti+3bt3V7Qnq9jb3/72cPud73wnVqxYgf/8z/+Mia+f/exnbBn7j//4D7aqvfzyyyzEqnHJJZfg//7v//Ctb30Lpll6LJIgO/7447F3794Rvx96L9HrGymDg4Nobm6uan3L5XJIp9MYK3R8IpGArouziyAIQr2RO7EgCIJQN9avX48jjzyyQngRJJ6Ggyxlhx9+ODZu3BirJ+FEoovcBUkQ0XYt3vKWt6C7uxu33nprWFcoFPCb3/wGb33rWzHefOlLX2JXxGeffZbP39nZGVrdSCDSNd9yyy044YQTWHSRdY/YsGED3vjGN2LWrFloamrCySefjBtuuCF27rvuuovP/atf/Qr/8A//wGKV2vb19aFYLOKf/umfcOihh7K1b/bs2fy60fctCIIgTCwivgRBEIS6QfO8Hn30UTz99NNjOp4EBbkpkpAI+Mtf/oKXXnqJRRVZfMgtkVwPa0GC55RTTsEvf/nLsO6mm25Cb28v3vzmN4/qehzHYUtZeSLLVjkkpDKZDL7yla+wy2XA888/z9dO897I9ZJcLXft2oVTTz2VRdmHP/xhthiSRevyyy+vOuftX/7lX1iYfeYzn+Hz0+dAoo/EF7l5fvvb38bf//3fs7vnY489Nqr3KAiCIIwdcTsUBEEQ6gaJg4svvpgFBgXIoAAa5557LgsEy7Kqiq3ADZDmfNH8KhIm0blh5HJIgTJOO+003iYB9cMf/hBPPPEEv041yAL1+c9/Htlslq1NJNZe+cpXYuHChaN6P+vWrcOcOXMq6j/wgQ/g6quvjtUdffTRVS1yJBxvvvlmXHjhhWHdpz71KX6f99xzT2glI8G2Zs0afPrTn8arX/3qmFshCbNHHnkk5q5IYoxcLL/3ve+N6j0JgiAI44dYvgRBEIS6QdadBx54gC04Tz75JP7t3/6NRQe5y1133XUV7f/0pz+xuKFE4oXmar3jHe/Av/7rv/J+27ZxzTXX4IorrmD3O+Kcc85hF8ahrF9vetObWHhdf/316O/v53wsLodkRSM3vvL0yU9+sqJtdI5alIMOOigmvIgbb7yRxWk0KEhLSwv+6q/+iuezkQtjlHe9610V88TItfOZZ57hYCSCIAhCfRDLlyAIglBXTjzxRPzud7/jeVYkwMiNjgJo0JwtslYdccQRYduTTjoJX/7yl1lY0Vwmmu8VnS9G4mzPnj0sVMiCFECWNHIrJJFWLfAEibnzzjuPLVHkCkjug/T6o4WCZtB5RgKJrJHWb9q0id97OfT+g/2rV68e8hz//M//zBaylStXctuLLrqIhStZzwRBEITJQcSXIAiCMCWgeUkkxCiRQLjyyivZsvXFL34xbNPV1TWkuAmsW2TJqgaFjychVg2ydJEr386dO9kVsloQkPGkVgTDA4lsONQ5zjzzTA5wcu2117JI/cEPfsAil9wh3/e+9x3wawqCIAjDI26HgiAIwpSDIv0RO3bsGPExFNSChAW5HJJoK08LFiwY0vXwta99LVvFaI2xiYhyeKCBSSgQR7U5ZsH+kUCREknUkhWQApWQ1YsCcQiCIAiTg1i+BEEQhLpx55134qyzzgrnZ0XnOBGrVq0a8bnIXZEE2Ec+8hEO3FEOWXtIhF111VVVF06mOVS0WDPNobrsssswlaBAGd/85jd5fhxFZiTovVLwDJpnFnXNrAWF049GhaT3S2ukkQgTBEEQJgcRX4IgCELdoCiFNMeKrE6HHXYYz/u6//77OWgGiQqy0owUsmqRuKCQ7NWgoB7f//73OeofhZ+vBgWqOBAoPD1FW6zGWBZfDvjc5z7H1ipyh/z4xz/OFqwf//jHvL7Zb3/72xEtoEwCjYQuLRxNx1M0RFrL7KMf/eiYr0sQBEEYHSK+BEEQhLrx9a9/na1RZOkiKw6JL1p7itayokWCRzrvavfu3bjtttt4fSzDMKq2oRD2FKSDxFEt8XWgbN26lYNYjLf4mjdvHovSz372s/jv//5vDiVPLoN//OMf8apXvWpE5yDRRhEkyQKYz+fZVZGCl/zN3/zNmK9LEARBGB2a53neKI8RBEEQBEEQBEEQRokE3BAEQRAEQRAEQZgERHwJgiAIgiAIgiBMAiK+BEEQBEEQBEEQJgERX4IgCIIgCIIgCJOAiC9BEARBEARBEIRJQMSXIAiCIAiCIAjCJCDrfI0R13Wxfft2tLa2QtO0el+OIAiCIAiCIAh1glbv6u/vx8KFC4dc+F7E1xgh4bVkyZJ6X4YgCIIgCIIgCFOELVu2YPHixTX3i/gaI2TxCj7gtra2el+OIAiCIAiCIAh1oq+vjw0zgUaohYivMRK4GpLwEvElCIIgCIIgCII2zHQkCbghCIIgCIIgCIIwCYj4EgRBEARBEARBmAREfAmCIAiCIAiCIEwCIr4EQRAEQRAEQRAmARFfgiAIgiAIgiAIk4CIL0EQBEEQBGHahwHfvXt3vS9DEER8CYIgCIIgCNOXbDaL733ve/jOd76DTZs21ftyhBmOiC9BEARBEARh2nLrrbdiYGAAnufhuuuuQ7FYrPclCTMYEV+CIAiCIAjCtOTll1/GY489xuVUKoXu7m7cc8899b4sYQYj4ksQBEEQBEGYdpCF649//COXjz/+eFx22WVcvvfee7Fz5846X50wUxHxJQiCIAiCIEw7yMJFlq6Wlhacd955OOKII3DYYYfBdV12P6RcECYbEV+CIAiCIAjCtIIiG5KFi7j44ouRTqehaRouueQSJJNJbN++HQ8++GC9L1OYgYj4EgRBEARBEKYNUcvWqlWr2OIV0NbWhgsuuIDLd9xxB/bt21fHKxVmIiK+BEEQBEEQhGnDo48+iq1btyKRSLCliyxeUY477jgsX74ctm3j+uuv5yiIgjBZiPgSBEEQBEEQps1iyhRanjj33HPR3t5e0YbEGAXfME0TGzZswBNPPFGHKxVmKiK+BEEQBEEQhGnBjTfeiEKhgEWLFuHEE0+s2W727Nk466yzuHzLLbegv79/Eq9SmMmI+BIEQRAEQRAanueeew7r1q2Druu4/PLLOR+KU045BfPnz0cul8NNN900adcpzGxEfAmCIAiCIAgNDQkosnoRp512GubNmzfsMYZh4NWvfjW7IT777LMs3gRhohHxJQiCIAiCIDQ0t912G7sOzpo1C2eeeeaIj1uwYAFOPfVULpN4IxEnCBOJiC9BEARBEAShYdm8eTMeeeQRLlMgDcuyRnU8zf0i0UbiLQjWIQgThYgvQRAEQRAEoSGhcPF//OMfuXzMMcfgoIMOGvU5SKzRHLEgTP3LL7887tcpCAEivgRBEARBEISG5L777sOePXvQ1NQULp48Fmjdr+OPP57LtEBzsVgcx6sUhBIivgRBEARBEISGY+/evfjzn//M5YsvvpgF2IFw/vnno7W1Ffv27cNdd901TlcpCHFEfAmCIAiCIAgNheu67G7oOA5WrFiB1atXH/A5U6kUXvWqV3H5/vvvx/bt28fhSgUhjogvQRAEQRAEoaF4/PHHsWnTJp6vRYKJwsWPB4cddhiOOOIIeJ7H7ock7gRhPBHxJQiCIAiCIDQM0aiEZ599Njo7O8f1/OTCSFawnTt34oEHHhjXcwuCiC9BEARBEAShYbj55pt5PS5ao+ukk04a9/PTvK8LL7yQyzT3q7u7e9xfQ5i5iPgSBEEQBEEQGoLnn38ezzzzDLsZUnh4wzAm5HUobP3BBx8chrInN0RBGA9EfAmCIAiCIAhTnnw+jxtuuIHLp5xyClu+JgoSd5deeinPKaN1vx577LEJey1hZiHiSxAEQRAEQZjy3HHHHejr60NHRwfOOuusCX+9WbNm8Zwy4k9/+hO/tiAcKCK+BEEQBEEQhCnN1q1b8Ze//IXLZJFKJBKT8ronn3wyFi5cyFa3G2+8UdwPhQNGxJcgCIIgCIIwZaFw7zTvilizZg2v6zVZ6LrOc8soX7duHZ599tlJe21heiLiSxAEQRAEQZiy0ILHu3btQjqdDqMQTibz58/H6aefzmWyfmUymUm/BmH6IOJLEARBEARBmJJQmPe7776byyS8mpub63IdZ555Jrq6ujA4OMjzvwRhrIj4EgRBEARBEKYcNL/q+uuv53DvFPb96KOPrtu1mKbJ7ofEE088gfXr19ftWoTGpiHE11VXXYXly5fzauO0mN5DDz1Us+33v/99nHHGGbzaOaXzzjuvoj39mL/whS9wiFIyYVObF198cRLeiSAIgiAIgjASnnzySWzcuJGFDwXZoPDv9WTp0qU48cQTuUxz0AqFQl2vR2hMprz4uuaaa/DpT38aX/ziF3mNBRr1ILPz7t27q7anlcjf8pa34M4778QDDzyAJUuW4IILLsC2bdvCNv/2b/+Gb33rW7j66qs5cg6ZsOmctFq6IAiCIAiCUF8GBgZwyy23cJnCylPY96nAueeei7a2Nuzfv5/7moIwWjRvisfMJEsXjTJ8+9vf5m3XdVlQfexjH8PnPve5EUXIIQsYHf/Od76TrV4UMvSv//qv8ZnPfIbb9Pb2Yt68efjRj36EN7/5zVXPQyFGKQXQWg90HXQs/QgFQRAEQRCE8eG3v/0t1q5dy/2zv/qrv4JhGJgqvPDCC/jFL37Blrj3ve99WLRoUb0vSZgCkDZob28fVhtMacsXmXMfffRRdgsMoFCftE1WrZFAEWmKxWI4YkLm6507d8bOSR8UibyhzvnVr36V2wWJhJcgCIIgCIIwvrz00kssvEjc0DyrqSS8iJUrV2L16tU8oH/dddfxQL8gjJQpLb727t3LX2ga9YhC2ySgRsJnP/tZtnQFYis4brTn/PznP89KNkhbtmwZwzsSBEEQBEEQhhp4pyAbBA2MT1Wr0sUXX8xxAygE/n333VfvyxEaiCktvg6Ur33ta/jVr36F3//+9xys40BIJpNsQowmQRAEQRAEYfygufs0n4q8jM4++2xMVShewEUXXcRlCoW/Z8+eel+S0CBMafFF6ymQqZlGFaLQNi14NxRf//rXWXzRWgy0GnpAcNxYzikIgiAIgiBMDNu3bw+ngLzqVa/ige+pDPUvV6xYwV5aFP2Q4hIIQkOLr0QigeOPPx633357WEdfbNo+5ZRTah5H0Qz/5V/+BTfffDNOOOGE2L6DDjqIRVb0nDRBjqIeDnVOQRAEQRAEYWIIBAzNozryyCN5XtVUh+akUQh8y7KwefNmPPLII/W+JKEBmNLii6Aw87R2149//GM899xz+NCHPsSri1955ZW8nyIY0nysgH/913/FP/7jP+KHP/whrw1G87goUcjS4IfyyU9+El/+8pd5kiRN6KRz0Lyw17zmNXV7n4IgCIIgCDMVGgTfsWMHTxMJ3PkagY6OjjCuwG233cZxAQRhKExMca644gr2o6VFkUlEHXPMMWzRCgJm0EgDRUAM+M53vsOTNd/whjfEzkPrhH3pS1/i8t/+7d+ygKPQpeRXfPrpp/M5D3RemCAIgiAIgjA6enp6wjWzaG3W1tZWNBK0JBIN5m/dupWDhbz1rW+t+4LQwtRlyq/z1eix/AVBEARBEITqUDf0Zz/7GdavX49ly5bh3e9+d0MKl927d+Pqq6/m6TGvf/3rcdRRR9X7koRJZlqs8yUIgiAIgiBMX8hiRMKLAqxddtllDSm8iLlz5+LMM8/k8k033cTrzApCNUR8CYIgCIIgCJMOCRSa9kGQcKEo140MTWOZM2dO7H0JQjkivgRBEARBEIRJh5YDIqFCguW0005Do2OaJi6//HIuP/XUU3jxxRfrfUnCFETElyAIgiAIgjCpbNiwAU888QSXSbCQcJkOLFmyBCeddBKXKfhGPp+v9yUJUwwRX4IgCIIgCMKkUSwWeU2vIFIgCZbpxDnnnBMGXrjjjjvqfTnCFEPElyAIgiAIgjBp3H333RxenkLKn3vuuZhuJJNJDh4SrF+2ZcuWel+SMIUQ8SUIgiAIgiBMCrRm6/3338/lSy65ZNqusbpixQocffTRXL7uuutg23a9L0mYIoj4EgRBEARBECYcWgOL3A0pP/zwwzlNZy688EI0NTVhz549uOeee+p9OcIUQcSXIAiCIAiCMOE89NBD2LZtG7vlXXzxxZjukPAK3ieJL1qIWRBEfAmCIAiCIAgTyv79+3H77bdz+bzzzkNbWxtmAqtXr8bKlSvZ2kfuh5QLMxsRX4IgCIIgCMKE4XkebrzxRo5ySJENjz/+eMwUNE3Dq171KiQSCWzdupWtf8LMRsSXIAiCIAiCMGE8++yzeOGFF6DrOkcBpHwmQWHnzz//fC6T9Y+sgMLMZWZ9+wVBEARBEIRJI5vNstWLOOOMMzB37lzMRMjat3Tp0nCNM7IGCjMTEV+CIAiCIAjChHDrrbdicHAQs2fPZvE1UyFr3+WXXw7DMLB+/Xo89dRT9b4koU6I+BIEQRAEQRDGnZdffhmPPfYYl8nd0DRNzGS6urrwyle+kss333wzBgYG6n1JQh0Q8SUIgiAIgiCMK4F7HXHcccdh+fLl9b6kKcFpp52GefPmsTsmCTBh5iHiSxAEQRAEQRhX7r33XnR3d6OlpSUMNiGA3Q7J/ZCiID799NN4/vnn631JwiQj4ksQBEEQBEEYN2gxYVpUmKBFhtPpdL0vaUqxaNEinHzyyVy+4YYbkMvl6n1JwiQi4ksQBEEQBEEYF2gRYXI3pJwWFz7iiCPqfUlTkrPPPhudnZ3o6+sLF58WZgYivgRBEARBEIRx4dFHH8WWLVt4UWFaXJjc64RK6POhICTEww8/jE2bNtX7koRJQsSXIAiCIAiCcMCQFee2227j8rnnnsuLCwu1Ofjgg3Hsscdy+brrruMgJcL0R8SXIAiCIAiCcMDcdNNNyOfzPKfpxBNPrPflNAQXXHABByWh4CR//vOf6305wiQg4ksQBEEQBEE4IJ577jlOtJgwudNRLgwPBSO55JJLuHzfffdh586d9b4kYYKRX4YgCIIgCIIwZiha34033sjlU089FfPnz6/3JTUUFJTksMMO4yAl5H7oOE69L0mYQER8CYIgCIIgCGOGovX19/dj1qxZeOUrX1nvy2lIyPqVTCaxfft2/OUvf6n35QgTiIgvQRAEQRAEYUxs3ryZo/URl156KSzLqvclNSRtbW08/4u44447sG/fvnpfkjBBiPgSBEEQBEEQRo1t27ymF3HMMcdw9D5h7Bx33HFYvnx5+Ll6nlfvSxImABFfgiAIgiAIwqihABF79uxBU1NTaLURxg6tiUbBSkzTxMaNG/HEE0/U+5KERhJf69evxz/8wz/gLW95C3bv3h2GIH3mmWcm6iUFQRAEQRCESWDv3r1haPSLLrqIBZhw4MyePRtnnXUWl2+55RaeSydMLyZEfN1999046qijeMLg7373OwwMDHD9k08+iS9+8YsT8ZKCIAiCIAjCJEBR+cgtjqLyrVixgvt8wvhxyimnYMGCBRxFkgwXwvRiQsTX5z73OXz5y1/GrbfeikQiEdafc845ePDBByfiJQVBEARBEIRJgNzhNm3axME1XvWqV7G7nDB+GIaByy+/nD/XZ599ltdPE6YPEyK+1q5di9e+9rUV9XPnzmUz9Wi56qqreAJiKpXCSSedhIceeqhmW3JrfP3rX8/t6Uv7zW9+s6LNl770Jd4XTbS+giAIgiAIglAbcoP705/+xOWzzz4bnZ2d9b6kaQlZvk477TQu33DDDchms/W+JGEqi6+Ojg7s2LGjov7xxx/HokWLRnWua665Bp/+9KfZXfGxxx7D0UcfjQsvvDCcR1ZOJpPhaDtf+9rXhlzk78gjj+RrDNK99947qusSBEEQBEGYadx8883sDkfigAbEhYmD1kyjtdNo+g55kwnTgwkRX29+85vx2c9+Fjt37mSrEvkGU0Scz3zmM3jnO985qnN94xvfwPvf/35ceeWVvAL41VdfzZM6f/jDH1Ztf+KJJ+Lf//3f+RposbpaUCQZEmdB6urqGvX7FARBEARBmCm88MIL7GEUROUj9zhh4iC3TnI/JMgAQREQhcZnQsTXV77yFXbjW7JkCat1Ek1nnnkmTj31VI6AOFIKhQIeffRRnHfeeaUL1nXefuCBBw7oGl988UUsXLiQrWRve9vbeJHAocjn8+jr64slQagFrc3huR5cSo4Lh5Ltwim6sIsO7IKDYpDyDgo5W6WsjTylTJET7adzCIIgCEI9oX7Q9ddfHwaEoD6UMPHQNJrjjz+eyxTkpFgs1vuShAPExARAQTa+//3v4wtf+ALP/yIBduyxx+LQQw8d1XlofhhF0pk3b16snrbXrVs35usjM/mPfvQjrFq1il0O/+mf/glnnHEGnn76abS2tlY95qtf/Sq3m2pQZ/7On62DBw/8f9BPp84/50Gdvz+sUw1j6/cNeYwXto3tixwfLgYYOT52TNlrRhcPVPtHeEzkmsqPDz6H6PGRy42fL7zcERwT/eyCjdLbjR0zEWi6BsOkpJeSpcfq9HBftJ0q69y2yv6y+tI5arUr1dE1CYIgCDODO++8kweeaWpJEApdmBzOP/98tjru27cPd911F28LjcuEiK9//ud/ZhdDsnxRCqDJguQSSKKsnlx88cVhec2aNSzGli1bhl//+td473vfW/WYz3/+8zz3LIBuQNH3Vi9cz8Pzf9lZ78sQJhiyotkFSi6mCrqu+aLOF2TGMIIwJvQiwnAYQVher5eLUBGEgiAIE8q2bdt4+SDi0ksvjUWyFiYeCjhHUSV/9atf4f777+e4BWJ5bFwmRHyRheiDH/xgxYJ7FAyD9o1UfNE8LPIn3rVrV6yetocKpjFaaBRn5cqVeOmll2q2ofljQ80hqxeGoeHU16/gchDpNQz5qgV1WmRfqWE0MiwfE20THBNUR48JTx89Jr4/vk/9E75c9Jjw/JFjIq8Z7IteV2x/cNLI8RWfQ43PYszHxD6/0ucU+yxH8llU+3tFrotsaa7j+e6Kfh5Jbo16x/bibWi7WLsN7Qtfx9+uaMPnipv12KUy78DOY+oKwnKRFhF2ulFbEI7WIlghCINrMEQQCoLQ2JAH0nXXXceeJ7SeF63rJUw+NJ2HpvFQ6Hn6e1A8BJlz15hMiPiiH2i1NR9okWWK2jJSaGSF/Fxvv/12vOY1r+E6Ct5B2x/96EfH7XrJLXL9+vV4xzvegUZDN3Qce/7Sel+GMFFYmDLQ75oEWFUBNxJB6Is6ngM3AkEYrVMiskEFoRG1BGq8zUtckFDUlXinsqpTYjyoo/0kxrldRdv4tk4zeDmP1Ncq++2rnSe8hvLXr3Ge0jX7rx09b5XXiJ8zfjyXI/WlNtVfX6cCtQsGcmStIUEYd2iOPQ16p9NpXHTRRfW+nBnNJZdcgg0bNnBAO/q7nH766fW+JKHe4ovWeggeomRJij4IaeSERA5ZxEYDufq9613vwgknnIBXvOIVvG7X4OAgRz8kKHoiha+nOVlBkA4aFQjKZCqnxQBbWlrC0RpyiaQoPeRquH37dg5jT6MHb3nLW8bx0xCE6QX9ng1LWYumriAMxJsXBjkZiSCMirlxF4QOWTBVYBVhYqkQhdUEZQ2hWS7uqgveaPvSNltcjWjSY2WjRn1QNobYF5TZAqvXaqOsrCzsxdIqjCPBHCOClvlpbm6u9yXNaKgvS3+Ha6+9lv8uhx9+OGbPnl3vyxLqKb5IGFFn6D3veQ+7F7a3t8esWBSxhSLkjIYrrrgCe/bsYVdFUvrHHHMMrzERBOGgKIX0QAogMUXBPQK+/vWvc6K1EoIbyNatW1lodXd3Y86cOTxy8OCDD3JZEITGoeEEIYkz1/OjYZaiYnKiOC5+hEwK3BLNuV1wTJXj3di5/HKkTfycfvvoecJy5DrKr9GvD3P/mIpzRdtHrk+9bvnxNd5Pef0II37y+ZxINJwZCAvHMmFWEnCRer2GyBtOAEbrY2KwXFCO4BqGEp2+wBXqB/3+KLqebds46KCDeJ1Vof5QP5iC2ZEFjNwPyUAR7QcLUx/Ni4acGyfuvvtuDitP6xNMVyjgBonL3t5etLW11ftyBEEQpjUV4rBcnIbiLdo2LkDjgrfKOangqkBGVcVhRKTGBC8d5qi5kyrR8hJKhJfX8/zK6DYvR1HeLtLWrl7PaZovQxG3KEaEGQf4qW4BDLf1EQhKnpdZWwDWjN7rf+6x6MKRiLlBVF61HY+wW4pMXB55N37+aPTceJTfyqi/YVv//OE5q0Tu5dwdJqKv/356enp4sFrXdByy4hAkLJr3Xjp/RdTkWMTkoaMl0+8p+vmUf85DRUgOoxIPFRk5fI/xz5ms1WbCgGnpKk8YsBI6jITatiyqo3Jpf6mtXrFtJQwe/KOcvk+TNWBAf5v/+Z//4bDzFACFvMOExtEGEyK+otAq6OT+F2U6iBURX4IgCEI9CURlVWEWKccFXFzMkXturX00x3r4c1QRlFXFpS9KKwSp2p7YnoggTDyku0KRFoq46qItFH1+O6vqccExQXtDiUQ/si/N+brllls4GNxHPvKRqd8XdR2gmFXJ9vNiplQXpgxg50a4j/IccNQbgLM+1zDaYEICblBUw7/927/l0O3k2lcOzf8SBEEQBOEA3W7ZsoOGh62JFWKw3NIXWANrC83Q6jicIB1KdPrbTHn037JowRVRe2PblZFvw+i4kai3YVv/+Kr1Nc5fqq9+neXRgWNRg6tGSFbtnlq7Fju2b0drawt7MpFFMDh/tajJ4XUNEy05Hlm5emTgmlGB/X1DRUau+HzKohvTgIVdcHjZlmLB4bm8tF0sqNz2t+1htsP2BTdmCaW5vWp+78QuhByIsjmFU2G7Bfzinx/AnLmzS6KNhZxedbtC6JkeTL0AUyvC1PIwkffzLHSnmggKBFA18eRvVxNIzgRGwurbjkZiQsTX3/zN3/BifN/5znc4guBVV13FgS+++93v4mtf+9pEvKQgCIIgCA0KLySvT635mzMVWnZnw70PA83A297zPixevLjelzSlISEfirNCLfEWlJXoi27H90fPUUok9shwFMBti+S7afJ/zgCwc6Bv3N+bDheWpsHUDBhaApbmweRtC6aWgIm0EmoaiTdftEW2LX/b0AqwEGlnBQLSgJkk180kYKX91ARYKT9PA2akXL6P96eBtsZa82xCxBdN0PzJT37CK6BTVMIzzjiDIw1SdMGf//zneNvb3jYRLysIgiAIgiCMEZomcv3113P5pJNOEuFVDk32LLP6GHYWRjGLZDU3ubG419ExTgYwPCANlXiqng7bS8D2kiqHyh/yjseT3lFIewWc790Pw9NR9JJwvATn3N5vGzs+2Bfd5nalNW1dkkwepZYJH3wxy9wzyS1zuDl3wfbsRS1YOPKVrKan+KLQpAcffDCXyeeRtgmKKvihD31oIl5y5kJDId85LTJiEB0l8PPYqMFwbaKjDWma8VzvdygIglBXggAfHLTDD3QR1vnuaRVR/iiqn4R9FxoMigq9f/9+7rudc845aEicIpDvr5L6gMKAcoGLusTVdKGrIpCoPNnoFvfJdCuNBCXqm4VWoDQuMnJ4+eXd2Ju3sH7OarxmlVnZ5wvaV+sPBvuMBLtq0r2N5niO1EpX1X2zltsm5cXSdjRASjHncBoLR5yxEAsP7cCMFl8kvDZu3IilS5fyitw094vW6CKLWEdH43w4DQHdEPY8N3HnN5LDCLShRJwv4MpNxdWOoSRCT5guI6NOAXCLqhNAZU5+OfQdiUQYKIULq1IXqa9WFyuO9fjS6weRzpTIUG8nDClPdRxhMBAk6lASIJ4fJVDt86MQ+sfH2kWP52iC6iMJ6sO2fLzm10WPi7bhwG3wosd7VdpEymqfVtneq9yn8gMQUBz2PS7IjCFDr9Ni3FXWBiuv848xqp4nEu49bBcRhBUi0S+XvUawlpkwc9ixYwcHcSAogh4FcphI4vcaD24hBy/TBzfXDy83AJfTILzcoCrnM/DyGbj5LNxCFl4+xzkd5xby8Ip5uMUCPMdmK5EHA65nwIUBjxzoPDU50tCK0GFzbmg2DFDup7C+GKm3oftlfy37SP8oPTahU9WVLnKusO+UBgxr2I785Zs344c//CGe2GPgqIvejkMOOeQArVBqrlgK1oQvy1L0xVuxijir3A7KLopc78ApuJi7tBWNxIREO/zP//xPXrT44x//OG677TZe0JhehkJifuMb38AnPvEJNDpTJtohdeg23V9m3i43dw9RF47oREZ2JnJS5FDwzSg9jIgrv3lFrXkjbGMmI3dPoSGg21Q1IRMrD7d/bGXPLviLJztwbVstrkwT8jn6m59s5fevrCMaXJhwPZMf+g4//K2wE8DJo84AlXV4nh6pCzoMeqzDUNGW9vH59LCNqvOPD4/Ta7f1X6fUJnjNaRC9YZLQ4ID+2gR9djRDYjpRLsiGEnjVxWFt0RdfMywQndHw8IEgjK4ZVlkXE69BuQGEY4XwiFhVg+AiQRCS0mBGaYkBL4hEGbapXY5uV5zHz23HwcPrb0F/bh/mtCzB4XNPrWrxLb9ObkP3RYfujw48yil6Jd8P3cggTjCAosH1aICD8ga812hQQW5oIMWi7y19J1VO32GVl/YFdVXbhG0r63hQhM5hDN8m+L7fcMMNePjhh9nI8eEPf5jX1xVmaKh5YtOmTXj00Ud53teaNWswHZgy4mvCfJqzJUFWLtCq+ijXahMx55fXsV9zfBmCyUEbQrBVqavlkln1mLI2NGI1VTsByhQxzoKlUri4RRsOi5aSeFG546+FFAgYlVMw1DD6GBmRaJse3iRoWDCYcFjcBELGCsULtXF80ROIn3h7lZdvB2LJCY8rnV/ESFxs6JoLTWN5B03zeFsnEUK55rEY4Tpq55e1cNtv79eV2gbHeaW2dO7Y8f6+sK7UVh2vzq3pwbUh8lr+Pj6ejguuIzhHZZ3a9o+jc+uRawtfy/9t0xe0OMgj8y6PxquReMfRanwH49vx72x8O2wXfC9Z0PtlLQVXS8LRk5y7WgIOEnA1+k1YkXPROXQ4ri/sXRLhWum35VsEpxOjszRGxWNJ4AVru0XdTCuExzCiJqivtm8qkWnaisG2DdBcE7P2ngDdrW/HPbinhL9D/v0pBxlKWmSRb80woFMyVa6ZJjTDDF1/6XcaiHIexwueNTSQVlQudvGknktkjaHtKb8MAglCCj9vutjd+hc4eh7t7jLMM44YUhCWC0D1exmBIIzVa0MKwplG31QSX1F+85vf4A1veAManakivujGcPP3ng5DuMbCzEbD4tLNqjwMbVkeRo+tcR6u4oHdGvujYWb9AeDK/aXrUVfkQiP3LLfIuSoXVO6oXHNVZ17jbXLbonLer8v7+/PQ7Lyq5/05gLdzfBwJPc3zR6n5klRPg1+fSyqnmz25XlHnKsjDst/Bq7rf31exn963lYJGkXxMP7eS0FicUU51aVWfSEPzLXVaIsUuDUqwFFnAkGBxilER48DhshMTMKUQykGOiJDxLTOBO1goRGoJm5IQKe881mofbTddhYtuqA5AqWNXrYNHnQE95koWtOPOgJ+0Kts6fW/ChWLVQyx6XLQzoepKnRH6bUXbVJw7PKbKuSPb5dcS/f0KI8QuqDkmhcFSKkbK4b5MvF3NNv521GV0nGBLBAk7Em9mGxyrDa7VAtdshWtS3gzHULlrNMHR03D1JriUGyk4JASjIpByFoAWHLb8auo+ZEfDwQfrfkXvV/G6MGy8v85YeH+Lrjs2xYTMgRL9rcZ+w1ymQQU1sKCEiaMGG2hgBLbKPeVWp1HuFaDTc9TLQ3epTM/MnMr9gZOc5uGBlja4mobVuf1YZg+UBlT4vDToop6f0fpgMEbVedATCehWCnoixc8wPUnlNLQkpWboySZOWqoZOqdWTlq6BXq6lZNG24Y+pe41wXcyKtDc6LYv0srbUH2wRl6pXRWx5wtAfn5XO88oBGE+sQ99s57mW0RH97Gw7Na6CsIDtQjq5XVlFsFA7LV2ptAxrwkzdp0v27axbt06NneuXLkyrL/22mvxhS98gfdNB/E1VaCHzstP7cX0hG6+NPompvPpBI1gqtFLTwkVKrMoQZURaD/5QsagkU3TH9Esd3Wq6s4UdWOKz5+pmPsylPtTRGDJXBhhxJgJwJwFNI1jGC5eTCg7ciHHAQYyw7bTKGIbzXcB+dBmAGcnkBvnoAGJZiDRAiSaSuU05c3KY4D3NVdvF9RbwX4/6UZogaq2EHS0rly4BQIwLghLAjA2uBEd8AisLlGRVEMwhcLJK0JzBqHbKmnFAegkcooD0Ip90Av90It90AplASLKg0bQ33I4RnF78jQTP9deC9dtxzKrB69b8gILIiQptft5m5+3AqlIOVpPf79pel9UzwIaNzUaQhDefLuNFzesg3bIZlx87hWAq1UVgHyOEQvCau2GEIQ8U0DtmwyOOH0hzn77YWgUxlV8Pf300zxJc8uWLbz96le/mtf6etOb3sT73v/+97NPqjB+0E3+rLetUl/6wIecfwDKT5xLYT39E6+LHRPU+ZFn1Gmq7PfPxfv9dvH9/jGx/aqi6mv6r+e/3DD7S+epKJedp/Qeh9tfvV3l+4rsj71+5DqoQK4qHLhg/AeoeZSRxUtEwLB4Aa+RUxIw1eZKBOIlEDMGdIuEjAGDBU2wr5aLTtn5hhA6teZ7zFRXBEEYN+j3w6KERnnnjN95yUQ+ApEWF3NlVr1Y8tsFruXkzZDbr9J4YpKnQBOMRDOMoURaNTGXKm/ntwmERCB0hxJDVevK6/vG38WevCRCEVQmhCrqq+1TdU8/9wJe+t3veJ7+ZR/4R+hdXeN7ncKkC8LXvP4yfPvbm9DT240tPc/hla98ZX0EoS/KQuvfEBZCZyjL4ggEYUvnxAaHmdLi67Of/SzP6/r2t7+NX/7yl5yee+45vPe978XNN9+MdNpfrEAYN6iDe+QZi+p9GcIQREWaEqRxcRbbH9SpfyoEjQgXQRAmBOrJBR3z8Xa9jFnoqljrWMyNQMhFt4PRRZo7TCnTPc7zgpvUPGUKpTmekNirKZLKrUs1RBOJRLKqHiCZTAY33Xwzl88880x0ifCaFjQ3N+Piiy/G7373O/z5z3/GEUccgTlzxnGgpoEshFOVcZ3zNXfuXPzpT3/CMcccw/6OnZ2d+PGPf4x3vOMdmG5MlTlfgiAIgjCjoG4LCaMxWeWigq+sXU2XPq2GYBrKulRFOJFoMiZkhZ8x8Yc//AFPPPEEd8w/8IEPwDSnzrUJBwZ17X/+85/jpZdewpIlS3DllVeyq6wwDed87d27FwsXLuQyvTip75NPPnk8X0IQBEEQhJkMeQAEkWabZ4/feTlqpS/KyFoXuPaRtWqadVw3bNjAwoug5YBEeE0vyEuGpgFdddVVPBXokUce4fV2hamBPt5/7P7+flZ+pPpoO5vN8nY0CYIgCIIgTClIYCVbgNZ5wKyDgbaFSnxNM+FFa65ef/31XD7xxBOxdOnSel+SMAHQel/nnXcel2nNXeqXC1MDc7zNnNEIh7R97LHHxrZJkDm0uIggCEKDQPeuAiXXT54blouehzzlrsttVNlD3nM5V+0pV8cYmoakriGl6yo3KNeR0rVYHrSJbsucP0EQDhSaB7Rv3z60trbi3HPPrfflCBMIieu1a9di69atLLjf+ta3ynNkuomvO++8czxPJwjCDML1lJCpJnBKwseNiB0lcAphWQkg3jfkefxjap6nXDSptlOBqGgLhFp0m/K0L+ZqtRnrMfLAFqbCgAf9ZoPfMP3mVZ3/G+ffdOn3TuXgXpCvdg6v7Hxl94uErqHJ0Dk1G4Yq68G2ymP7/X0V+3Wdf2O0Zl692blzJ+677z4uX3LJJUilUvW+JGECoXlel19+Ob773e/ixRdf5MjjRx11VL0va8YzruJrssJZCoIwdoETEx5lwoQ7K+VCJTgm7OyUW31KHZyRn6dcNE0dgTMSDIr2rencOeOk+bmuh2WLLVw6LBYwapvK9DZzvkisyB2V0+eX8+ujn4raVx/PASXEAjFWaaGrZs2rZr2rus3t45a/aJup0Gmd7kJnSJESETrR33Jc9FQXMVGhExVF1c8Rvx9NN9IRcRYKtKqCTQm5ZjO+P5b0kiAc6eCI67r44x//yPlhhx2Gww8/fFLet1BfKBjeGWecgbvuugs33XQTDj74YI7JINQPmWEpCHUSQYOOiwHHwYDtot9xMGir7X6qtx3eX9NaUyZwqllrgs5PVDTZDdSfMWlOvaY631aZwEn6QkYJn7gIUmJHj5WVEFICKBRJNURTKJ78jn9wTHAeKpPr4GR2igOBRoKM/u4swhwX2aDs5xXbETFXOkelwIu1cYYSflQzOYtmRqG/ScooF3Bl1ruybRJ0Q1n8YttGbUE53sKvXOgEv+ValpvhhE5wf1B1Iz9HVEipv+vUvx/Efqf0t4r89oPffFiu0kbtV3/X6L0kLJfdE+jzyjiRFNkedJxYPd2/o/tVG/UbDaAype7i+H42NCMtXVPQ+RY7Si8+C23bNmhWAr3HnoRf7uge1qJHn4dYvRuf008/Hc8++yx2796NW265Ba973evqfUkzGhFfgjBCbNdTYslx0e+LIxZLvmga9OsHIuKp3xdXgcgKjqd9U7FDE1hr4iJEdWBKYmcY8VJm9aF6JaDKRVP5eUplaxIFzlSGOj3qswFaMblrp5BIILFeTbCR8AuEYE0rXpk1L17vVheU1Dl1KoUfiwW7fsIvFGxGuYBTZVPTYhbd4aw/XoNYdksipVLolAYlSr//sMztawsdq5pwCu8V/vFl94VGvR/QQFu2XMRFxFlJsKlnRkzQVT3GCc9Hvx1+DYDbUtpT4zqacxlc8fjDoNXB7l5+OJ7d3gtQGsF3QQm6koiLCjQa9IgLNqO0XUPQqXqDvwfC5EDRLMn98Ac/+AGeeuopdj089NBD631ZMxYRX8K0hjpzUdFDoihqWQrFUhXxFNQHxwcPuvGEHmyt/sOqxTTQSjltm+qBRZ27mJiJCJxK0VRWV9XqE7fmiDuXMJTws8j6qBt1E34sxsotcyTYnNrWvLFaAENLoOPGJB4LJsfj+wbG2WJRy4W1luWmltCJWXyGsf6UCx0lhOLWn0YVOlMRuseSdYnSeON4XoVlbtB2yix0qq771hvhODbcrrlYfeyxONitPDZ6TOAG7nhAn+2ibwIGPug7GBV06Zhgi1jsqlj0KtpERCCdR77DlSxevJiXf3rwwQc5+MaHP/xhJJPJel/WjETElzCloE5XlixMEfFTsjKN3LIUHD8R84ioc8JiyTDQavpiydDRahpo8etbzLLcF1ctfrvgeAlkIAhDC7+WSRZ+RLFClA1tzbM9r2IQJDlCNzfpJApjhb479DyhNBTPPPMM/m/rJg6+8OE3vYHnAI3kN0DWuNoWuNpWu2wVN8zQTdN1WdCVLNoO9tvjP4+Vnq0lYaZEGj13uxImjmxJY3VLGke1pjEnYWEmcc4552DdunXYv38/br/9dg66IkwD8UXrR6TTaV68b/Xq1eN9eqHB5i/FXfQC8VRuZQraquMnwrGIXCMCoUQCiCxLZHEKBFFJKAXloD4ilvw66jQJgjB9IWuQEn6C0NjQWqsUZCGY9zMS4RX8Btp1E+0TFMUyFHBlLpXl8+bKLXOxuXYVIrDkrqzmrTrYVyRhFzdZX7t7f1iemzBZiHFqbeJ8eToxbb1CEokEL778s5/9DA899BC7Hy5ZsqTelzXjGHfxZVkWL9gna3k15vylUDxVc9GLWJei4mki5i/RbS9qLWoehZWJXPeay6xMMrosCIIgzDRocd2BgQHMnj2bI95NBat20nd57bQmxnMmsMaxKCuzwG3NFfDMQBZPD2TxUiaP3QUbd+zr5xRAfYbAOqZEWRqrmlN8zdOBFStW4Oijj8aTTz6J6667Dh/4wAd4TpgweUzIp/33f//3+Lu/+zv89Kc/xaxZsybiJQQfcom5aU/vqII/qAm73oQEb6gmjkpWpqFd8FqCdr7P9nQdeRIEQRCEiWbTpk149NFHuXzZZZfx4Ph0hoRdk6HcDUfSvaW+0LqBHAsxTv1ZPDeY5f7SQ72DnKL9m5VNKRzZmsZRLelQnLVbjSlaLrzwQl73a8+ePbjnnntw9tln1/uSZhSaR0MF48yxxx6Ll156iV0Qly1bVrGewGOPPYZGp6+vD+3t7ejt7UVbW1vdroOE1Yp71h7w/KWoOBrKshQNDiHzlwRBEARh6mHbNq6++mrs3bsXxx13HEe6E0bmFbQ+m8fT/ZmYKOupMS9tSSoRijGaQ0b5wqTVEH0hWnD5N7/5Dc8F/OAHPzhil1ThwLXBhEj217zmNRNxWqEKNMJzakdLzJoUm7/EdSWhpFz4ZP6SIAiCIExXyJpBwosGv88///x6X07DYOoauxhSer1fRzaK7fkiuyuu7c+qfCCLLblCmG7cWwrbP8syytwWm3BIOsnnnkoceeSRHHb+hRdewLXXXov3vve9LMSEBrV8zQSmiuVLEARBEAQhgFzJvvOd78B1XbzhDW+Q4GcTxP6iHc4fCyxkL2RyYTTHKOQZdHizmj8WiLLDW9K+i2T9oD7sVVddhUKhgIsuuohD0QsNavkKIF/j5557LlTY5I4oCIIgCIIgjD8kuCiIAuUrV67kvpcwMXRYJk7rbOUUQMtQPJ/J4Zl+ZR17xk80j+zx/gynAJJdhzQlWYgpt8Umzikc/mRBQoEsozfccAOHnl+1ahU6Ozsn7fVnKhPyF969ezfe/OY346677kJHRwfX0ZoCNKHvV7/6FebMmTMRLysIgiAIgjBjoTn1W7Zs4ZDitIZTI8w9mk6kDB1HtzZxii7H83K2gLUDmZgoo0iLL2bynH4fCX+/IGkpMRaZS7Y0lZiwv+Xxxx/P878oQAstvvz2t79dvjeN6HZ4xRVXYMOGDfjJT36Cww8/nOueffZZvOtd7+IQl7/85S/R6IjboSAIgiAIU6lfQi5k+XxeXMgagN35Ysll0Xdb3JDNV21LS+jwPLKI2+LK5tS4zdun+YHkqkrLRFHchmOOOWZczjvT6BuhNpgQ8UUvTGtLnHjiibF6WtDtggsuYCtYoyPiSxAEQRCEqcI111zDUz0WLVokwRMaFIpg/WxUkA1kORw+LUxdjqWp4CDBWmSB+yIFVxsLf/7zn3HHHXcgnU7jIx/5CFpaZJn5idIGE/LLJF/jautJUB3tGy00krN8+XKkUimcdNJJLOJq8cwzz+D1r389tyez6Te/+c0DPqcgCIIgCMJUZd26dSy8SHDRml4ivBoTikT9io4WvGfxHHzjsKX40wmrsP7MNbjzxFX41uFL8YHFczjCdbtpoOh5LM5+tXMf/uHFbXjN4y/h0HvW4uQHn8V7n96Ib768E7fu7cXOfJEjNg7Haaedhnnz5iGbzeLmm2+elPc7U5mQOV/nnHMOPvGJT7B74cKFC7lu27Zt+NSnPoVzzz13VOeikZxPf/rTvF4FiSQSU7Q43PPPP191TYJMJoODDz4Yb3zjG/n1xuOcgiAIgiAIU5FcLscBE4hTTz0V8+fPr/clCeOIRZES/eiI8P+0JKYoxH0Q9p6jLvZnsS1f5PlllG7YUwp/P9sy1RyyyCLRBzclYUTmdhmGwevB/eAHP+A5YEcddRQH4BDGnwlxO6TJnvQHJCvUkiVLwjoKd0pReBYvXjzic5E4IvfFb3/727xNljM658c+9jF87nOfG/JYsmx98pOf5DRe5wwQt0NBEARBEOoNCa+HH36Yo9R9+MMfrup5JMwM9lH4+/642+KLgzlU8zlL6zqOaIm6LTbhsOYU7rn9Ntx///3ct6XvE3mICQ0Qap6EDEXcoXlfZAonKPDGeeedN6rz0LoDFK7+85//fFhHpnQ6zwMPPDCmaxvrOWkCK6XoBywIgiAIglAvaGCbhBdB7oYivGY2sywTZ8xq5RSQdVysG8zh6YEMW8dIkD07kEPWdfFoX4ZTgKEBK1sX4vSWVu7n/vSGm/D6yy/j8wrjx7h/msVikSfrPfHEE7x2wIGsrE7RVyjyCvmgRqHtQNRN1jm/+tWv4p/+6Z/G9JqCIAiCIAjjiW3b+OMf/8jlo48+mqdczHTImctxBlAs9qJo74fNea+f98F1ctA0I5LMMW1DM6CXbVOuB9swoOlqvx5sV5xvcsK5pw0dx7Y1cQpwPA8bMvmS26IfAr+7aOO5nI3eg9fg8qfuw7a1T+IcvRXGvAVsHYuGwF8ygeHvpzvjLr5o1GXp0qUscKYTZCmjeWIBNCIQuFQKgtCYKK9rF57nVE9w4LlUtrmdS7lH7e0q7anO38fnLG3HjoFHL0yvrsqli+E6LnIetEPNtqW60jn9rdh22DZ2vrDlyM5X9Xqix5fOGrSNbXNd9NXjx4/kfJWfT/iBVBwf247VRa+nyutUPb78/ZR9vpoO02yFabbBMtvCshmW/W2rDabRCsNokk6LcMCQaxitq9rU1MTz1qfTfdl1cyXRVOyFbUfz/SykgrJt94U5JbrPNgZ6FXE3BmEYEXrh9giOJ1G4RjOxRjegdxhAh4FuN43nC814cXYz9vcsQPuWHTjrhSfwf22dPJ/slr0lr682w8WqlIsj0h4Oa9JwRLOGQ9IGErHXolwf4baOmcKE2BH//u//Hn/3d3+Hn/70p5g1a9aYz9PV1cUTAHft2hWrp+2xTigd6zmTySSnqYbj5PHgg+fxw1+j4JX8JaZEIy2a/0OkelVWP/aytrGyEZZrt6Vz+uereS7VBmXnpP187mhbriudK/rasTr/vXCnJSxXa0vnolc3arxW/PMJ32f0PXNZjUyV2lBnSZ2zUW4SleIiIhDK8kBc8IOrqhipJi5K+6sfM7woqfmaqHWuKq9Z0Xbo9xk9jyBMNnRPqRBmMbFGIi6or7a/pWHuQcLEQF48d999N5dpTS8SYFMN1y36AolE0X5fPPWVxBPngVWqtI+Ocd3CAb22rqdgme0wrfZYrhsp/9lReg5UDqpFnxtOzWde7UG46HZxqE8InlcojWlNEeb5qbg4gUd3XYaOLPDxl7+HvQclsAkHYROWYyuWoM+x8PCgjocHS8daXgGLsRnL8DKWYaOfNiGF3AheWRuBWKsiPDUDc+ZciOXLP4gZLb4okMVLL73EkQ6XLVuG5ubm2H6aDzYSaIV2Wnn79ttv50XfguAYtP3Rj350TNc2EeesLw5y+e31vogZii8yWZSVCd6a4tUXhhFBVymKg3Nq/ACoLjDKHw7xRN8LERcHSvmDQK+44QeuJvEHQln7cHQz+NuW1mBRAxSBBUT9zbmON6P10baRba6Lnq20P2gbni9SV+18YdvI+SqvZ5jzlb2fYE/148vfT7Qucr7I8SM7H4a4nvLPp3SVQdvYduTzqH49we+UOpkD/sh7vz8q3x+OxKuk9ge/2WKxh9PY0FiAkRWNrWmhMGsJy3ELXLnIa4Wuy9ygRoUG1a6//nr2MDrkkEM4Kt3EvZbrf3eVeIqJpbI8tD75244T6ZWPeZCiHZbVDtPsgGXR97rD/85TfYf6nnNObdq4jvYZRuVgOUXdpsiQFIyNBuEnC/WsriXOamyHz3BbeV9Et/2BR7dsOxCS8cHMsQjGktgkC+RDf2lCbut8XHLIFrS1PQzPewAFF9jszMIGZy42uPOxwV2Al90FyGhpbMQKTuHfES7mYReW42Us9TZgOTawOOtA+Zq/Ht9LlRitvth0LVrbVqORmBDxFYia8YBc/d71rnfhhBNOwCte8QoOCz84OIgrr7yS97/zne/kBQVpTlYQUOPZZ58Ny/Rjo/lntFjcihUrRnTORkLXkzjxhN/7LkX0o6EfDMW18X/skXr1xaY6v41/Q4jWs2tNWHb9tvRDpjMFQoDKwciRW6rn1yq9rjp3/LXCNnx82fVE28Ze17/+8LWC/WVt+TWDcvn+eBslTsquMTzPSAnOi4ZF06wKsUBCkPzWlWAcyiWi3L3BP76aWKl5XOn4wIceI3iNimsuF0Z69dcov6bwNSteIxDQgjDe7lTZiECjjmog1KJiLbK/bJ/rUqfEC+sxxsE3XU/7HdehLHCtVdwoA/GWFNfJOvH444/j5Zdf5mkel1566bB/h+B7V7I4BfOh+iLzokque4H1SVmq+vlZdyCo70x7RDxFrVER8eTXKwHVBsNoPuDvGL339evX45577sGmTZu4jqJC0ppWNE9uMgKUlLxlGm/AY81RQF/vrzgmwgvPn4T3ve99oXA9tayt64e/X9ufjYXA35EvYicWcHpQOyVsPzdh4IjmJI5sTuDIZhNHNBtYmqQw7EEfrbqYdKuIy2RKLWs1Y0PN0wTQr3zlK3jPe94zqpDyw1nS/v3f/x07d+7EMcccg29961scLp4466yzeBTjRz/6EW/TDemggw6qOMcrX/lK3HXXXSM650iQUPPTl7irHpXLRW1EBEbFa9X9gdAsO76GQI6KS1J1FeIiYkUZzlc8OCYuLqJJxIUgNBokvgLhFbOwFSvFWqUFboCDEYwHmpZQ4syfx2bWEHK1LHDj0bGeaZAr3v79O/G97/0MuVwBp512MFavbg2FUtyFLz5Xamj3txGK9TIXvniuLFJR8aTy1pi1f7IgjyYSDCS6duzY4b8HnaeP0CLCBA3Kn3LKKTwQPxWnlUwVqL971VVXccTvCy64gNeSGw17CkWOrri2P8ORFp8ZyOKlTD46gzak2dA5mEcY2KM1zeHvkw2yaPhItcGErPPV2tqKtWvXsiiaroj4EgRBEBoN17VZgFVY2Ir9sJ3+iIir5kLZPy6WEIUeCrJQoAVCzqpmfSu3wLXUpVN/oNCAG3+uEWFUzQpll8+LsvvgOBmse+507NlzEJpbunHssTdB07xReToEoihucSq581Gu3PxKLnwksMnS2QiQKyb1P++9916eF0eQdYumm5DQomjcNPWFgpUESwbROlbkBUUD8OXTZAQFLdFEkTVN0+S1vw4kngMx6DhYR4LMF2NkLVs3mEXOrfw+mxpwaFPKX4tMJRJnHVMw/H1dxderX/1qvO51r2PXvumKiC9BEARhpkEWeprLE7e+DW2BK293oFaYAMNoqS7OrGrBSspdKMl1MnEA4cwHI8EjesvEU7X5UEpsKfE6tm7Xvn0L8czT5/LxJ5/8GGbNNkqWJxZMKo9bpXyhxfOgpm+UTVrmiNwx77vvPu6XDSeqyEuLRBq1D0QaCYtApHV0dNTlfUxV6Dv/4x//OPQuoyk/4/1dsl0PL2VzYdh7EmW0LlmPXX06CIW6D8TYKR0tOLWzBTNafF199dW8Jtbb3vY2/iKXf+kvv/xyNDoivgRBEGY2e7N78d0nv4tn9z2LdxzxDly47MJp27kdL9T8I3KdrGFhI/HmRIScb40rRoQczV8aDygiXlycRZcEaIFD8/N8UaUi9pWCSqjIqWOH3C6VdSkqmkrWp/h8qDZ4XjP+3//7A/r6+lkcTKfQ8gcCBdCgRaYffPBBnrtPUJ8zcCckATYS90SylG3fvj10T1yzZg3PC5szZ86kvI9GoLu7G9/5zndYuFI//rjjjpuU+8X2fJHdFYMFoinR3LIob5rfiW8dvgwzWnzRF7fmC2ratFgDTMSXIAjCzKS/0I//ffp/8bPnfoasXRICa7rW4K9P+GscN2/iOyUzfe5TuStkhQWuqiulajee894qLU7VgkrELVMkukZrdbv55ptZYJBFhty+KHLzTIaEFn0eDz30EM9FIqhPdvrpp/M8/tEG0qCu8IYNG1iEbdy4Maw//PDD+ZwU2E0AWwpvvfVWFrUf+chHeJpRPdhftJVlzE/nzGrDa+d1YkaLr5mAiC9BEITGxvE85F0PBdflPOfn0e2C64XljJ3Dg5t/jwc2/hy5opovMrv1cHS1rcb6HX+E7aq1bA7uOg2vPPSvML91GZKaBosm+usaLE1DQteQ4FxX5fJtvw21FSvaRM67GqiyHEB0jls/DLKM+RYpDixSNh+KLGeT8TeiqM0/+MEPWCCQR9Ghhx6KmQr1uWi+Fs1BIgtMsH7rGWecgdWrVw8ZQp5+75QMjRd1qfm327p1K4swsogFHHzwwSzCyOVuJv8uyXhC30UKYkLC9Iorrqj3JU0pRHxNMCK+BEEQxgY9dmwPLHJognWeRI4XFT+qLtw3AmEUHEPb+WjZC+rKz+OhONLHn+ciOXgvmnt/B8Pp5irbXIjBjjeikD5erfnl7Of9qYG7aOUveNCRazkbg+2vhWe0j+lzCoSYEmU6LF0rE3ERAVe2TW1UWyX8SnXqPDHRF3uduDAMj/HPJ8Jw8ju73/ve97Br1y5ez+v1r3993a6F57p54N8NJ/83FCu7LooeYHvqtxr8znmbfvdVjynV0X5ux8eXcq+vF+l1a9G8ab0fihzItM/CtkOPxJ65i0BOaHbNa1J5+a+dBBgJMUMDdMr9bV1TecdAP1a+vA6Ldm6G7t8rettnYcshR6Bn/iIYGi3Loo4PBJ0R246U/bz8NWhRlLBMeWy72jnLrrnqOYPtauesfA1eaTT6Gn6bWp/Nvl278Ksf/j/+PrzmjW/C4Ycf5revLWhnCn31EF+XXHIJfvnLX/ILE1/72tfwwQ9+MJy4SP6iNDoRrMPVyIj4EgShEaFbPnVuYgKlmmhxa4uWoFwpmiqFUa3XGI94eeMJdWpIZKRY4ASWKkDLPIbB3b9AMb+F21nWbCxd9DYsnnMumgyLxQh1SoIO3r7Bl/Hi5v9FT+/D6rx6Gh1zXoum2ZfCRoI/r4L/+aiynyiQRQMNhZJIi4vBchEXF4ZREUfbUWHIYjA8Vh9SGAZWxGrCMDjPVO4ABgMPNYUKLWocCBDXw4sP/wUv3ncPzGQKR731nUA6HX7XygVKNK8ljmJthhAqsXNGRNFkM3ugF8dufgEH79nGv1FiW3sXHl+6Els758QWVp8oWnIZHL3lRRy+cxNMV9259jW14oklh+KluYvhNkgY9PHkFRuexXFbXsBgIoVrTjwHBVO5wdJfo5r4ZBGHkQs8wxemJZFZRURGXuPMzla8deHsmSm+yNxLpsi5c+fyNr0wLXBM5lqCRm4WLlwoc77GEboh/n8btvOX0Ix8oYMy5cGXNyhHv/CqXaTsf/njx/s/CupkhKMuZcejVKawoEb0eBkREQRegJKi6Aad75G4uSmRNDphFFiMKl4jtABNvR5+0HkORE9Qpg52KuhsazpShl/vt6d9qiOvl21X7qvM/bLfgTfphhXh8d2P4z8f/U/OibZEG9531PvwlsPegpQ59CR+4uGdD+Prj3wdz3arwca5TXPx0WM+issPuRyGXt01ilyiAosc/R2pIxwI5WK5YKsq4tzI8aXzVDtnVPSVyvFzUlt1HiUMGoW4wIuLOBaMEStiNWEY1FE3u1hNzIxQqNQSOCOlLTuINz5yByzXwZ2rjsXz8+sfUKAcerbTZ0vPesv/3KhMn2Es1+g3pv42JLotLqu/g6rT+DcYnMvcuwvO04/D2bo5fK300uWYfcwJaFu4MDzGCs8fOT5ynuj5qUx9HMf/rdGtkF0R/fuzE25H9nn+PrK0DQ7g5ccfw5Ynn4BdUPPMkq1tWHDM8Zh1+BGAafnn8aq+htouvQ4vJVx12z8OkX3hdrVzRq7Vb1PtNdT7qHbOyPuv8hrl18x/d9fBGx+5Ex3ZATw3fxnuXnUs6sk7F87Gv61aghkpvijQBi1aHIgvmoj35JNPiviaQDKOi4P//BQagZKwQ5ngiwu5ULyhipCrIiDjuS8cI68VmN/NoY6LjMiEIrZchJYJz6jJvtox0WuJXfsIRXAgVgNXD156mXO/Ix/J6aZIv+SgLrhh8nLRkZtmUFfa9s9fdk7VpvJ1Srn/mmXXQLm6jtINPagb6hrC9xY9Py9eXdpXer3I+cM2pYdJUBc8QGPnL8+j5694PV4+e/SvV+NvNlUhS09MpPjWiOFES0wY1RI/fqe2/DXKhRF996cKL/a8iG899i3ctfUu3k4ZKbzt8LfhPUe9hwXYaHA9FzdtvInPt31QRVJb2bkSnz7+0zht0WloJGoJw1D0xQRdNRFHVp5ggGCk5/GFI7uPls7TyMKwGvRsigsJHabm4eRH70FX9y7snz0X604+l61+QwocP7eqiI7yuqhAGfL4svNEhQ7Vjedvl+6b69ev54WRN23axHX0HDzyyCN5vtX8+fMxVSIsPvLII3jggQfCCItNTU04+eSTceKJJ/JaYtM6Wql/P3j55U34xU9+zPWve9vbsXDZ8hEIvKFEY0R8jlKYHt6Swumd9Qn+EUXE1wwRX/Qg+tqGHeGX0fa/4DTiFv0iB1/24MtsR774XPb3B/V25AcRlsvPG/uxxEdFhAOHBJh8ntOXcje32hab6kKIOkAl8VMqVxNGtV5jqrtoTSbbB7bjqieuwh/X/5FFt6EZeO2hr8WHjv4QW60OhLyTxy+f+yW+t/Z7HCmROGXBKRwZcdWsVeP0DmYu9OypLeJK5ZKIC6zBlcIwKh5pcKxc6NQSJdXKgUAZTghV+w1S3+n3v/89rz31oQ99CLNn19+laqIIwr2T6CLvqaA/SVELKdz7VH3vtLYYeXdRBMD9+/dzHUWhJAFGQqxekQAnE1p4mYKf0KLL9D0dbZTJ6Ubd3A5JfAXrItAX76mnnuLoMISIr+lPMOLPIs0f9a8Qb2VCMCYQXZWrY6LHK2FXISojQpCEZ2DWL5VL11L9uFJdXJCWzPfBueLXWzLPB3Xh8ahyrsh7j4pYb5w782RxI4sh574FLVZXnpfVsXtouK2sdFqkTeBzTV2FwFIX7FO+3jVex29fXsfnD/bFXq/kA66HbUoTjoO60nb0/Uby6GtXfb3o+UuvpyyP8derdc7o51zt/QTnCt53ooabm1AfenI9+P7a7+NX636FoqsW/z3//2fvPeA0qaq8/1+FJ3Tu6emenp4Mw5CHHCSJoGACF0XFtCBmF1R0XVf9u6u767um131dFePuimtYlbCgqJjABIgiOUicYYYJ3dM5PqHC/3POvbeqntA9PTPd/XQ4X7hzQ90Kz9P1VN1zz7nnrD8P7zr+XTioRb27ZorB3CALYP/zl/+BF3j8W7tw44V8rpUN82NWX6g9pE354he/iImJCTz/+c/ntfKLERoLUqBj8ixoAh3T4N0EOjb+AxbC53j44Yf5c/T09ETj4eOPP56Fx2XLau8CfTa1gFdffTVGRkb4s5533nlYygzXSvP14he/GJlMJpKIzz333CjIMsVioFgVInwJgqLEzjwhDJJwRr/MEsGnbCBfLviIBkMQps94cRzfeuRbuObhazBaVHGfTll5Cq464Sps7tg8q+fePrKdTRFv2XoL1zNOhoM0v/noN6Mx3Tir5xbmPzfccANPXJMV0dvf/vYp3acvREhjdO+997LGiMZQBMWNOuWUU3DqqadGY8aFqMF74oknWINH7uqh38vkAp/MJjs7O7EYIa3l9773Pf6sb33rW1nJslQZroXwdfnll0+r3ze+8Q0sdET4EgRBWHiQduuGx2/AVx74Cnon1Gz74W2Hs9B1+qrT53QS44E9D+Czd38W9/Tcw/VlmWV4x7HvwKsOexVS9tI231mq0Jqnb33rW1x+y1vegjVr1mCxUG2tFAlapOU66aSTWABbDNCwmtaskRBGf0/DoYceylrMtWtr7xhiprn22mtZ+0fr8kgAW2wTBtNF4nwtIeGruHsM7vI6WOQ+SBAEQajq/OLnW3+OL9z7BWwbUR7U1jSuYZO/Fx30IthWbZ6f9Aq+bftt7Flx6/BWblvfvB7vPeG9OHfduaLRXkIUCgV8+ctfxsDAAGuAyJJoMUCC1l133cWJLKAIGj+RmRqZ5i3mdUI7d+5kc8RkiKX169ezELZx48ZF8/seHR1lU1kSsBezqezeEOFriQhfoR9i5z/didAPkF7diPSGZmTWNyO9vhlOo4q7IAiCsJS5Y+cd+NyfP4dH+x/lelu2jTVMr9z0SqSc1LzSyH3p/i+hP9fPbcevOJ6dchzbcWytL0+YA37xi1+wKR6NKa644opoCcdChcZHpOUihwxkaki0t7ezCR4FjF5K2hFa00Z/W3KkQuaJBGmJ6Ls48sgjednOQoecj9x44438dyXnG/S3XmoMi/C1NIQvrz+Hni/dh2BUPdiSuMuzLIQZgcztqIcli/wFQVgiPNz7MP7fPf8Pd+26i+sNqQa88ag34tIjL0V9qh7zkdHCKP7rof/i9Wg5Pxc5ACGzyHXN62p9ecIsQV7+vva1r7Em9LWvfS0OO2zhesHs6+tjQYMG40bQ6OrqYm3I4YcfvigEjZkUSMlTIAlhxxxzDHu3XKjQvfvtb3+bTS1Ju3fZZZctub/1sAhfS0P4ijwM9ueQ3zqMwjPDyD8zDK97vKKfXe8ivU5pxVg7trYRVmrpzDwJgrA0eGb4GXZo8fNnfs51Wj91yWGX4K3HvJW1XguB7rFudn1/45M3sut713bxmsNeg7cf83a0ZltrfXnCDEICyte//nUWwEgL8upXvxoLEfJ2TSZ2tPbHDC03bNjAgsViMrGbCcbHxyNTTDLVMx7CTz/9dJxwwgkLVutJJrNf+tKXWLC84IILeC3fUmJYhK+lI3xVIxgvIr9tRAljW4dRfHYEYbEsapRjIb2qUQljG7SpYpOYKgqCsDDZM74HX7n/K7j+ievhhz67cr/g4AtwxfFXYHXjaixEHut/jNeD3b7zdq43pZrwlmPewoGfyUuisPAhTcjPfvYzHnBfeeWVCy4+1LZt29i5BHn6SzqXIKFr3TrR1k4FrYEjLRjdA+SunaAgzbTmj7w/UvDmhXw/k/nsfBwjzxYifC1x4ascWhNW3DmmtWNDrB0LRsRUURCEhQ8FLv7GQ9/Atx/9Nia8CW577prn4t3Hv3vRBDGmdWv/dve/4bGBx7je1dDFzkJeevBLa+YsRJhZTcGFF17IMa4WAjR0JPMyErrIsx9Bmq2jjjqKhS5azyRMH8/zeD0YmWv296s1n+SIhDRH5A1yIYwzk5rc//zP/8SOHTvYfPY1r3nNktF6DovwNbssNOFrUlPFZ7Sp4tZheD3jFVF/rTpXO/Bo0qaKTWKqKAjCvCDv5zk4MgVJHsqreEHknOK9J74XJ3YujEHsvuAHPm5++mb22Ng93s1tR7QdwU45Tu06FUsZeqflvYBTgXNf56pe8APki5T7Olf1POd+YnvcD1FgdRX8PQpAb+tg8CboOm9PBFifpA/KthO77/kFxvfsQF1bJzY856Xczn3s+LjRObSAEwWVt03dxHvUeeIcph5fW+K69DnUpSSOa/rYlee2EGLLk4/jT3+4Az3du9Vnsm0cfcyxeM5pp6F9eXviO5H4k/sjuJBnRDLfJDNO8/0ed9xx7B1y+fLlWAh0d3fjq1/9Kn+eV73qVSyULwWGRfiaXRa68FWNYMJDftswCnrtWGF7FVNF20JqdWPkUZHMFcVUURCEuRZCfvjUD9kz4O4xNUDZ2LIR7z7h3Thn7TmLfsCX83Ks5fuPB/8DY0UVL+ms1WfhfSe+D4csO2ROr8UPQiXcaIGnmgBUItiUCUWmf7X2gj5eaXu1furYC42D7D6cnX4afmjhh4WjMBTWYb5iIcDBdj82u7vQaqs1SsXQxuN+Bx72VmIc1ccBRtAzwl+l0KoF1Wh7UgjUgiQJmQnhMD5GXE4et/QYRghMCIR8tnlOGKI+34tlw0+hrjCgmsghT91KDDQdjEK6BfOdtqHH0TbyFDw7jW0rz0Jgz95Y8ZzDV+DNZx6EWiPC1xISvgo7R5Ei88AZjvMVmSomtGPBSKGin9OWVcKYMVVcIaaKB0IQhBgv+hjPe/DDkJ7BCHRuylzXs71BeXuiTn1UW2U/2hhU6af6JM+p9i09Z2k/dSx9DphzxddetQ5dD+LzUzs1Vl5X2XVMdl1BfP7K6yr/jJNdV/lnNNdV+t2bfZPfRemxzHXF10w5DQBcmlG2Ve4kUmndjuuWBcdJbKdjOGYfWx0zqqsBB/d19L7m2I6t9jXncxJ9S+r2pNvj49k8KIr6ln0mk8+kIGRiYpEzjaeGVPDSzvpOXHHcFXjZxpfBsZeOVt7zA3SP9eJrD3wNNz19Ha9xs2Hj7FUvxUUbLke90zaFwDJdAai6oJNs9/jHMf/IuDbSro2M63A5rsft6Sna047NA/Xk79w8Q5P10udE6bOgWh+Twytg2TO3wvELGF52KIZbNyX2qzxu+TOovG/V509iW7XnZuVnq9zHDgMcZPXgSGcXGi31/i+EDh71V+ARrxN5zI9QDdPD3KsLa3zSYY3gGHcX1jpKu08867fgQa8L3WHjvP08NgK8LP0wC+tPeO243Zs94eh1p67Dv758M2qNCF9LRPgKvQC7PvlHfto3nLISDc/pgtuanT1TxYG8Esa2DrFAVuyuYqqYJVPFpkgzllrTBDu9uAdF9N3kigFG855KOZ1zKmI07+u2IsbyPkaSZe6vymYfQVgs0DyMEdCqC5nlgptdVZCbsJ/ELud6jFlP8nFdNGCdcyHWu+ch7WSqCqhRnQVGOxJQIwGyat0uE1i1sDuJgKoETCSEmL0LLPssAFWYxAWscTJY6T3IdNyCVPPDXA+DFAp9z+WEcO6cctD3wEKMYyOTcnQe1zMldZ1XCEGTC0WZafZLOTMr9M8GN910E+699150dHTg7W9/+7xzMU4e+O6++252nkBBkomGhgY85znPwUknnYxUOl0pWJoJrqC6kBhNjNGEWxVBMpqECyYRPhP7VwqfpZNwJcf1tsGe+DGc/C2wwnF46XNQzLwEgbtZ3bQLhImhfnQ/cR8Gnn06EiQb2jrRuelYNK9cNy/v+dG+bjzxux9yeePpL0bzijWzcp71yxtw3Nrae4EV4WuJCF/FnnH0/tdD8AdV1HiaAKk7ajkaT1+F9EEts/5jJFPFwjbl3p7NFReYqSINbkjYGct7WiDSZS1AlZdHdX2sQsDySgZDMzVoNZqDkrUEym6iwsSivJ9VxSSjmglIyZqC5PESawTU+crNOEr7xecrP5Y5X+W1Vx6r8torPsskn3GyflN9xul/F9XPV/G3mPT6478P3SV0r3i+GjCQ5sAPgrK62k6aTy5TGw24Q9pXaRv8su00IIn2DWhwnuibSJX1YC/by8tBlfPN3mvEzuxmwcJt+kssWPSfgULf2UAwf8205hK6x1gYaXwGWHYzgsxW1R40o61wATpwFjJuah+0QNMTgMr7koA6HweA840tW7bgm9/8Jpff9KY3zSuPgCRokfvzP/7xj5ELdBrr0Hqj448/np1AzHe8QgH9u5/Grp03YXD0F/DsLVX72UE7mrLnYtWqi9Gx6mi4mYzW/sWJKG/b27aZap9sG40/H330UXZ4YuKo0d+IYqitXbuWf4Pz6XPQ/d7T04N0Os1rv2zbnvFzkHdNmhioNSJ8LRHhiwiDELlH+zB6+07kn47V0qmV9Wg4fRXqj1sxZ5onNlXcZbwqKqEsGJ7CVFELY/tiqkiDPSMksTCUEIzKBaKKtrLtJHzNJDTuaEy7aMy6aMi4aMy4aKKybqM6p2S5vK7LNJiRgYywUCgX/mhMYAQ7bz+Evz0Tu/GTZ6/BPf2/5BlzMqk7ftkLccby16LOaVOCqp8QPqO6FjhJMPUT50vU90nonYYgS6/RyYUWp6SeKe9Xph2aqp7l3KnQHrlObHJO1/KLZ36Bz93zOWwf2c5tB7cczE5Izl5ztjxTagx5Nfzyl7/MHu3Ik92LX/xira0JooFksjzVtumUp9uPhC5yFU+eC33tbIQ0XRSfq7OzMxrQz9b593Uf3/PgFYvwvSK8ogff9xD4RTQ27UbHyqfRvmI7HEd9jjC00N/Xhe7dB6NYzKJz5RZ0rHgGjuPF2/tXoXv3IejvX40wXNyWOouRE088kb2F1hoRvpaI8EV/vttuu41noyhlxx00Ph0gs7UAy1ivZWw4m1uRPXkFMh0N3I9MHObiJRyZKm4dwvgW5eI+7JmosFAuuhb6W9PY3ehgW52NLSlgwFOmemMFJTQZjdN4QT1QZ5K6lFMhEDVowalauXGSbXQc48VKEIR9ZyA3wOuYvv/Y91EMVDiM89efz27VN7RsqPXlLRiKfhE/ePwHHPdsMD/IbSd1noT3n/R+HNW+757HzMDXDH6TebW2fe1f6+NOda6ZFITIpbgws2QyY1jR+RQ6O59CXd1o1D4+3swCVXfPQSgWSuNl2XYRHR3PoHPlU2hp6Ynai4UMunsORvfujRgfXzbta1DWDpVpsm372j7VNrrHSHgeHR2NNGGkXaIxKo1PHceZkfMcyDVTSIWHHnooEpSam5tn9PzkBXLNmtkxadwXRPhaIsIXBej7xCc+UdGeDl0c6nfhSH8NmkP10KHZ42fsXjzibMdOewBuymUhjBL9OMvLNi1ed1x4cFGEE6VCYKMQUrKQ8y0UAgv5QJVzXoicD0x4oUpFch4Rcru50RoAHAUHm+FiMxwcCQf1ZeKYhxBPIMCD8PAgfDwAH31li8tcC8g6QNYNkaFcp4xN9ZDLaTtEVtepzNvsAGlL55xoYWh1dfx0En+34ezsTw/Qaon+RpNtm0460P1n6xqSD1NhaTFeHMd/P/LfuObhayIPfqeuPBVXnXgVjm4/ep+ONRez89XK81GYmAgm8Cf7T7jfuR++pSauNhY34uSJk9HgN0z7uEJtSA4yk8/IZHmqbZOVSQikccz4+Hh0rmw2i7a2NjQ2NkZ9Z/Kc5fv7hQJyoyPIjQxzPjE8jInhQYwPD3GdF4/ptV70P7m5Z8IQlhOgc1MOyzf2INvaHS3dspBFY8Pz0Nb+V2io31wieND5CZOb9lx+G7p3XYe+/pvhB8qzIFEcbkP/U8ux+34XQUGfwFyP+ftQsm00d6xAa2cXWlZ0cs7lzpVoXdmFdHZuzKMLhQKvI7zjjjt4bEpQoGMK1kwmeaTJrCXXX389HnzwQaxYsQJve9vb5t06x5lAhK8lInzRbMdnPvOZkrYgtFAECUcO/NDB2nA5TvRX4pAwdk26C3ncag3id9YYRmFz/2IYC1hULrBIMrMeFOnhmYKvkkV5gAx8HAIHRyCNQ5HBIWEWy6p4UBpEDrvsYXTbg9hjDWDIHp2vTn6EA2ChC5B7S8RsCgILbX8v9PCQ9RD+lPoTJiwVIHm5vxyn5E7BquIqNqve13MKlYw743h42cPY1rSN63ZoY+PwRhw+eDjSwYGvwTWD2/JBezI/kLZaHGNfBIm99aP78rrrruPYTWTK9/KXv3zagtVMsn37dg6M/Pjjj0dtmzZtwllnnTXja8/INHCkdw8Gu3dhqGc3Brt3x3n3bhQmYsGvGm46jcb2FWhc3o6GtnbUty1Hpm0UyN6HvH8nwlBN0hCp1GZkMucg5Z6CIEhFkwdkQpks0/dpJpqNFZApk3OdYvFejIz8DCOjv6dPwMe2rAxaGs9CJjgN+f5lGO7pUZ9Jfw6vWLm0Ikl9S2sklLWwYLZSCWgru3jbTP+N6XOSkEOxwnp7e9V36bo44YQTcPrpp6O1tbVm49UvfvGLmJiYwDnnnIOzzz4biw0RvpaI8NU/MoHzPnEzx9tgrRQJXJMITOtg42Kk8WKkIk3TCEL8BAXcgAJ2lLstTECCUhqByi3KA6TgsfCkhCgfbkh1T2+LBaw0bdP7Owj27lwoBBqRRWfQgs6glfO2sLEiNkceRfTYQ9jjjKA3NYK+1Birw2hAXC0ltXpJTV817V/ywUztxGQq8r2l/d2XMC+NfU3JF86BpJk4zmTHEJY2pAt/tuFZFgjG6LdLWvFiA44aOAprxtbMeiyemZjJn2qwPl+FjS1jW3DNlmtw/+D9/D00uo14/cGvx8vWvwxZN7vf1yFMDTmw+MlPfsJOB6688sp9GjdM9jydbpnSrl27OHjvnj17ouN2dXXhoIMOYk3X/h7by+XgjY2gODoMb2wUwfgY/PFRhBPjCPMTe/9sbgpBKoMwneE80DnVQ8flhdSp1ARWrNiCzpVPoqEhXteeyzWgu3sjeroPRi7XhJmEztmxYgtWVpyzEX29h2No6EiE4TI1bgh8WIUcJ/rcvv4OvNER+HnltGQynFQaDcvb0dTegeaOTtaWLVu5Cm1dqzhPZzL7LZzR3+ixxx5jYXvnzp3cRr/VzZs348wzz2RPm3PNAw88gBtuuIG/t3e84x01uYbZRISvJSJ8kRviwz5yS9VttDi7MZNCY6Z0PVOb6+CE4QDH9OTRPBHEwftW1WNs83I4BzejqY72S6Eh47CziH1Zx0QPZFJ/08Liaml/tgU5Dy0TWbTl69HuNWFF0IwUSlXWAQL0WaPotodYO0b5uKW9QB4A9JAwa+qSiV6i+9I+1bb5Mng5EPPJybQQU/UxL3IzQJhMYNtb3ZzPtJeXy02oyk2sqpmCTfU5Stqj8wQlWhoWL4yVjPmCo5eoDihmmss288Ce/tPRQTkIKbVFA14bNk8qUN3RZWVeMx3t20zM5O/v/pQeHHkQ//Ps/2DLuPJC1ppqxavXvhrnd52PtJOe8XNWKy9l6P78/Y7f49/+/G94clC57l/duBpXnXAVXrjhhQv6+yl/rkyWT6fP/gg81drovUaOLOh7J61DfX39Ph27psM0MvErFmAX87ALeVg6j+rB1GuwQ8tGkE4jTFUKV5RzsMAylFUB0Na2Ex0rHkdr63ZYlhqrBIGDkZFNGBnejFxuAxzHrWqJMFmZIJNLGlck88nbCmhs6sPKzifRsWIrXFetQ6U/yeBAF3Z3H4K+3rWTO+nwPdj0/RVyFd8ffa9T/dL4LZJKI0xnYdXVw66rh1PfBLehEammZqTr6qtq8MrbKA0ODuLJJ59kr4OGDRs28Pqr1atXV+wzW88Aupe/853v8LWQZ8bLL7983ox/ZgIRvpaI8EX8+rEe5VHPOILQQlPSA1Y1aKCYf2IAo3fsRO6x2M7Zba9D42ldqD+xE3Z2/tnksqlSwUNuxxDyW4ZQ2DaKYMc4MFq5kNmrA3LLgLFmHyNNBYymCyh6Uwt6pn0uoZdDuWBm7KFnS/iplpY8QQDL92B5RZUny54H2y9yrrYVYc0jDV5Ipk20TpPc+CbykF5sOqe6RYMVerm6KVi0tpPMbdw07HSaZ2GddBpuOqNyrfk1g5fJtMrJNFW/p8efxree+RYeGHyAr7neqcclGy/Bqze+Gk2ZpqrHWsiCwHzHD3zc9NRN+OK9X8SeCaURObLtSLxr87twTNsx+y2wzITQs7/HWCqYiYRqQgZtI8GBzLvMd0JtpOFatmwZv2tK+tN3Spqa8VH4Y0pjUxgdZm1WgdZe7eU5l25sQv2y5WhoU6lxeQeayFywvYPN6tQa8ukJSOMTT2L3ruuxa/eNKBb7onM0Nx+Lrq5XonPFBUil5m7MZSbyaEyQz4+gt+8X6O29CWNjf476WFYjstmzkU6dgzBcsxdhLm4r0lhjVGkN6XsPJsYQ5saBvBbUaM3bFASOq4TadKVQG7qpA4phNpkwtzdBbzptExMT+P73v8/fwXnnncdr0ubKCdxsI8LXEhK+ZoJi7wTG7tyJsbu7Eeb1wzrtoP7EFRwzLNVR6iloPuIN5jjWmIk5Vtw9VhkAOuMgva5Jubnf0MLlam74WcAzD8gD1NpNtW2hM5nJZLlGohaJZ9No5riQR1DIIcjnEeRzXCdTECr7uRz8/ESUB/vxNyFtlFtXjxSl+gak6uuRrmtAqqEBmfrGKE/TbGW9+h0VJiZQyE2gyCnHcWm8fA5ePg+vkOfcLxZ4QTrlAaciAq+I0POinGZVI/XaLECz1pWCnCnvXdAz7SOZcTzU/hfsaNSmL6GNg4cP5vVGmWDqIMAzJfjtb9/p9KP7bTaFjtkWWIoo4omWJ/BYy2PwbfX87xrrwuaBzWgqzqw5Vy1IDu6r5VNt2xetSrUyrfH685//zPcIDTRJ+DnQYye1OEnonXLffffh9ttvZ01H7HDhZBy1aRMKw0N6zZVer6TXX7FziylwUim0dHTyGqWWFSs5ta5UObWnstkD+vsUi8Po7rkZu3Zdh+FhZQ5LpFLL0bXyIha6GhsPxXxiYmIbdpKQuOt65PO7ovamxqPQteqVWNn5MqRS+7e2yow/hvt60b/zWQzs1n+vPd0Y2dOD0b49KIzFXh2rQZNsqcYmOA2NcOobYZHTj2wdglQWQSqNfKHAzlbm0zjEnWQt3nTaVq1axWsXa40IX7PMYhO+DAG5cr+nB6N37oTXE9trZza1shCWPaxt2vG4ag2ZKlLQZxNzrLBtBGG5m3obSHXpANAbmjl3WqYeDM4U5gE7ldB2IILHXAg4cw0JKeQJa0InKo+Xl4eGMD6i2kjI2VfIhK++uQV1OlHZ1OtbdN4UlzP1DTWdsaNYN0aIowXsBcq1YEefPz8xjvz4OG+jshL8qO8Einkt/OVzKJLgRwJgoTBjAt14xsN9m4bwxJpRhDRWDIGNOxtx/JYONBaykcDGicyTODJ1pfA2lXAHa/GYrNSanJ3DI8sewdamrQitEFZoYePYRhw7fiwa0DAtQWU6+Vweo5baU5rhv/rqq9kF+HOf+1yce+65s3IeCoZ81x/uxB/+cBcmdGDklG1hueXD7evGSE83Apqo2ZtTCHIEQQJVpxawKO9cicZWeu/P7O+MzLQHBu7Ezl3XYc+enyEI1BIBy3KxfPnzsKrrlZzb9vwO6hyGPvr778DOXddiz55fIAyV8w3LSqOj4zys6noV2tpOh2XNbOwweqYbQXqQhLPIkckuDPfuQTiFtpLM1pva2/nvm2nrQF/o4Nn+QY5lSJDHSzJHJMcwNEEzXU3evrQVZ3gCWuJ8zQL08CKPfjSDdOyxx+ILX/gCqykn49prr8U//MM/YOvWrSwJf+pTn8JLXvKSaPsb3/jGKLq84YUvfCFuuaX62qmlJHwZ6LbIPzmoTBL/0h9pkJzlWTQ+ZxUaTuqEXTf/TBKnIvRD1oaZ4M+kHfOHKteEOa2ZKPgz5amVDQtG4FxoTEuYSuT7K0wZIapCmKK8RQlTRriqtTA1P8x680o404KcEua0wMb1nM7L2vOqPFIYxp2tT+O+FbvhOerhsaa7Dic+3oplIwfuWS8Jr3fTZpKcp9LKlDJFZpUp2Nq8khMt4KfEYTRUUoKfw2abgRYAk9qicu1ReSpnpoWMWggsW0e24t/v+Xf8+tlf82eqd+vxpqPfhEuPuhR17ty4zV4M/OhHP2KtF8UgIucCNFO/v9BgeqS/jwfZpAWhgXbv7l3Y1jeAActVkxE0qCvmke7bjdRgLw3wov0d12WHDkagYq2V9rpHnvjmyh36xMR21hbt2n0DcrkdUXtDwyYWVFau/Cuk0+1YiBSLg9jd/UPs3HktRkcfidozmS50dV2MVV0Xo65uZj1KTuZlcri3B0O7d2Gwp1trOnfxPUNlsqyoZsJYbFuBwrIV6hlJ5qSWhQ0dbTjy0E1oX7UGLStnVhDfs2cPvvKVr/Bz9KUvfSmP1/dXmFu/fj3LB7Vm0QhfZBd66aWX8h/o1FNPxec+9zkWrsiDC8UKKIfiG9AME8W+uuCCC/Dd736Xha977rkHRx99dCR8dXd34xvf+Ea0H6nmyRxguix24SuJ1zeB0T/swtifuhHm1OyZlbZRf7w2SeysbeyIA8EbzKPwzFCkHSvu2oupIqV1zbApsJhQVQvDwtLQoMpHhuMyxZQZVuUJXT4gYaqpGXUtrVqQakZ9sy6LMFUzcl4O//OX/8F/PPgfGC4Mc9txHcfhPSe8B5ubjywV2LRmrnpbLNxFgh5r6mJBjwYYs4WbyfBglBKZVKXrKK9DOpNFqi7Rnq3jvrRGLp3NIpXJsntsSskybTc5DYIXCn/a/Sd89u7P4uG+h7m+om4Frjz+Srxs48vg6MG+UB1ysGHGGDTmIOcGe4Pu96Ge7oRJoDI3owH0cM/u6J4nD4GFtk4Ul3WoiQR6LuYn0DA2hM6meixjgYqEq06tyepCY1ubit1ZA3x/Aj09t7BZ4cDgH6J2121CZ+fLWMvV1LR5UT2nR0YeZm3Y7t0/hOfF3hKXtT4HXatehRUdL4TjzP1EBg356Z1stGSDCaGM7rWxkWEUWleguLxTrRujMZBXRKq/B+mBHqQcB83sNl8L7uwyX5VJuHf3cYLhN7/5DW677TZ2QnPFFVfUPBbZgbJohC8SuE4++WSODUDQDCR5SHnXu96FD37wgxX9L7nkEo4lcPPNN0dtFFzuuOOOYwHOPAjJHvrGG2/c7+taSsKXISj4GL+3h7VhXnccnyOzsQWNp69G9oiFY5I4ldklmSdG2jEyVdRr4CIsMlVsSGjHWuC2zo2pYs2EKTbnG2RhyghXJExNjBhBa4aEKRaYWkuFKV02mqpMgwhT8w0v8PCjp36Eq++7Gt3j3dy2sWUjC13PW/u8Wfl7+V4xEsyM9i0pxBmBrUJLR3m19okJNoWabXiNYCSMKYEsVVLXbSzUJYS3lC5XFerS+hhlx81k+Pd1IARhgFu23ILP3/t57BhVmopNyzbhfSe+D2esOkN+i1WgmXgab1CMJYqt9LKXvSzSXo0O9mvhqjseAGttFj1LpyLM1iFctQHjmXqE2k9eW3MTTj7xBBx/0inIzqOBKw0th4fvZbPC7u4fw/fNGiULbcvOYE1QR8f5cJwDWy823/H9PHp7f8HfQ38/xQ5TQ27HacTKzguxatWr55XgyRMA3bvRt/NZPPToX/DEjl3I+/q56PtKABvoge1VMRm0LDS1KXPGKJ6ZXidIwlm2sbHqb+VrX/sae2EkF/gXX3wxFjKLQviitTAkDVNgwosuuihqv+yyy1h4uummmyr2oSCB73vf+3DVVVdFbR/96EdZ0Lr//vsj4Yvq5PGHtF1kh/3xj3+cTQMmI5/Pc0p+wSQE1lr4CgIfT9x1J2xyOU1ezMhsJEpu1XZyzWoZF9XshYhyMjdR2/f2EGCTxKeHMHbHTkw80hebJC7LKJPEkzth189vO+3pQh4hI1NFrR3zB6uYKrZkojVjlM9XU8V9E6aG9hoEc5+Eqaa4rAQpJVyJMLVwoWfBrdtvxefv+TyeHnqa21Y2rMQVx12BCw++cEFpR9jcslioNK+kdXK0Ji6xpq6aySU7TeGUV8cxde1EpVbQ77FEUCMhTmvrJhcAE0Kd7hu6Fn42+ntcu+fHGPPVc+HEtuNw5ZHvxJHtR0b7Haiwt9Ah7ewvbrkFf7z3PmRSLk7oXI7xPgrKuxvDPd17DcibbWwqWW9F5SBTh0ef2Y7Hn3wy8kpLZlYUGJnW5cyn52c+34Pdu/+XnVGMjz8Vtddl16Gr6xUsdGWzq7AUyeV2ssklfTe53PaovaHh0ITJ5eTj0FpAJoEPP/wwB2w2burJNHldx3KszKZQ6O+NtGf0HJyKbENjFaFsJcZDC/9z3fXc5/Wvf/28cJyxpIUvCgpH8QfIlPC0006L2j/wgQ+wqvKuu+6q2IcEKlrP9drXvjZq+9KXvoR/+qd/YlND4nvf+x4LdRRc8KmnnsKHP/xhdsF65513ss17NT72sY/xMcqptfBVLOTx+b++eMZnZpWApj15leSl7fV2I1ZZG9EVbEAKai2HDx/9mR70NuxCIVtQi57LhTzjeta070Vg3Nd2I2BWfg6XP9+BvKy8oTyvFzPaseLO0WmYKjbBzrizK0yVr5caGlLClHFCcSDCVFNzqROKMtO+eC1VqwhTS4S7d9+Nz93zOdy/R01qtWRa8NbNb8VrDn8NMs4i0wT7RaA4AXi50pzLEwDFOnJSgJMG6LO7pXnopOGHFjyfXID7ykkKCWmcEkIbOd9hYS2u02DdeMCs6Bvtn9ym6rNFPuXjgY1DeHT9CAJHO1HZ0YATHm9FQ46ew26FEBdr8aoLgKky08zJBMBqwuFcm9JFZltlTg7INJDqw2PjGD/oSHYGk332KaRG4jAuBL1/mjtWxAJWJGiptVc0QDVs376dA+Q+/vjjURsNTEnooonm+UIQFNDbexub2fX3/5adUBC2XYcVK17EZoWtraewowdBOxsZvAu7dl6Hnj0/LXE20t7+fO2k4yzY9vwxUab7nu5DEsLoviToPX/UUUdxwObOzk4eexhBTK1LNOVdGBss/R2UU+haj3xrB1IWcPKaTrR3rdKCmvpd0G99MQlf8+cvO4e85jWvicqk5jzmmGN49ujXv/41nv/851fd50Mf+hBr1Mo1X7XGK5tpYMGDFptzLB/SYpE3MIvNHQJaOO57CPxA59VjolBfLygANN6YxjU8gwfgWC7WNRyBTc0nYlmmEx35Lk49E9vwxPA92DH+OIWcxXyBhbQqwmA1ATMpuFUIerYDtzONBq8ZjV4z6vNNqMvXw8kD+ScGORH02Yv1HorNRXgtAbxlPtDgTClIEjlaMzWZR7/9FKbo5V/icGIyj34iTAlVeKz/MXbG8Lsdv+N61snir4/8a1x+9OVoSs+RW3Ly3EZCTzFXlk9UEZLGy/qVCU5Vj1HWFhzY+jJLv2zVC9eiRWVVhDSdqm3LZIBlpp7R/ZrIA1JinzhnYQ8O/NBWQl+gkw94fsh50aPko+CRxs9jgbBcGIwEQSMA6m2dA3mcODiMO9Y8iydXDOOpNWPY2jWOI7c2YfNTLUhPePv1bNofaA1dpWCWmZ4AWM28M7E2jwaMPHgscc/ePalwS2+4/PrDWPCq9ws4/MgjeYa/NeGevWl5x5TaQRrkPv300yx0kdMwgxnkdnV1Yb4wMvIor+MiJxPFYn/U3tJygo7J9RJe1yWUQuOytmWncTq0+FHlZn/ntRgeeYA9P1JKp1ewppAE1/r6g2p9yTwGOOyww3DooYfyekYSwihQ8kMPPcSJ2un+XHfoEVh16BEV+5OVwGByHWN3vOZseE8PUru3o1DfjGI6gz/d/wCyP/9x8uRobFseeeIkgSxac7ayC3WNC+8em9fCV3t7Ow98jcbKQPWVK1dW3Yfa96U/cfDBB/O56EaaTPgihxyU5htBfgIbG/vQm6/HULFOC1gUtyXGQohldR46Gjy0N/hobwzR0RSiqV4JFdCeksjjF7ltDkFl7QEMdmkZNgI+IpUtBIl6EI7BD3+HYGw5wv5NCIdXYUXdOk6hO45iyxYUmrfBtwscs5HMiIOQcgoKbHFObVHuB/BpuxdwGw0YyBWqT4MF6uP5XFfCpPrc0xUwTd9pSZf7iAULLel2tGfWoD27mvOGVAvS4ylO2K36jXvD6M1tR2/+WfTmdmCw0LNfAmqpMEUmfa3VPfrpepYcUCyiiPLC3EDrfSgY74+f/jHfp47l4OJNF+Mdx74DHdk2JbSM9VbXDE1LSJqqrUxwCmoYm8bNqpSqB1JUrlPewUg75uUBv6DzPLnzVHnJWrJQfQ5Ks6SgKhX2prlHVYEwAzSVC30ZreVrBtzj0Fccw929D+DZiT0otA3AWVGHY9tPxGHNRwFWCj4JffSR6RlPmj/yOhuQ4BcmUoBCMUSx6KNQpPAbAYokECYEvqSm0E+4qCYnFJTy42OTfjobDlw7BcdKce5acTluc+FYad3mch8/9FAM8igGBfhhHpkgjTa7E16miHRLA+o7WtG4oh3N5Nhi5UrsGBzBb+/6I3s1fNtVV6G1dfpxnmg9OzkSI6GLrH74um2bPbidccYZPEaZTx79SOgipxIGFhZWvpyFroaGg2t6jQsJChi9ZvXrOI2OPsZrw3bvvhGFQg+eeeYrnFpaTmJt2IoVL4brNtRcCCPnMZR27drFQhiZJZJWjBJpZEkze8ghh5RM2pKDoo51GziVQ2Oxkb49ePiBB/Dz39+BYlsnDlqzGl5fDwtoZNo92tfL6dlHH6rYnyaIj37eeXjepW/BQmFemx0ahxvkVp7cy5sHFP1xr7zyykkdblDgOHLxajj99NNZu2UcbpTz7LPP8jFpHZhZGLtgHG7kR4BPrOFiwXdYCOvNN2BPvoHz3lw9ckH19Vdp20N7ZgztmXHOO7JUHkPWqS6w7Ct+uByj3osx5r8IAcxLqIB65zdodG5G2o7twQ8IEhpJPW8bN9JxUrGAXO7DwiW1sZCZEDBZO6jrRvAkYZLyUMcfigRMnVOdZpV5ptLidpXI7NJm4ZLK1MZrVb0s7NwKuIV2pIodSHnLYFGQseT3hSLGrT0YRQ+Gw26MYA+cugyyJDA1qVTXpBxPUBt5+qtrXoZsQ5Nyoc2fQ2k6hX2AHoE0cI4Gy4XSgTMPpgtTbDeD7uT2xAA8OSinxANdPaDlwWxC61EyuM2Ulik3A+CSvqZs2jPKAxoN+Pmz5acp9JQJSbqtkB/GjoEnMTC6C+kgQDYMsczJosXJwKVF19SfP1eNoM+bqlOJhaJEXq2tXHCK8mkeY39+X6Sp4/sjKZwl75PyPHF/RXlyn7K2vR2j4h4tFwhrhxqBuAiRRYAMwjDLZU52E0K7AYHdgNCiRI4m6hBYdap/SP3TCAKTpxCGKYT0zgtchKHLOQd0nE1sIJcu4ge4AwV4OKPhSBy/7GDYGZs9A3OesWGnKbdK6mEKeGT7VtzxwIPY068sJVzXwYmbD8fpJ25GS1Olk4KaxLIavhs7+36KPYO/RxgWYzO5ljOwqv0laGs+CbY1P+bzeVjrhwg9nRJluuEs14LlWDTvrMr6FlG/bD0kLh8aR/WwtDxT26J6iCD00DvyZ+wcvg19Y/fGTjqsDFY0nYZVTc9DS3ZT/CjS+01+zP25Nkx7v76RHG5/dCfu29qHgAY+pAhprcOZR6zEkatbORzjVJ83ue1/734W9z8ziPamDN5x7kFwbIu16GT5MzY0oNak8xp1tT69oCddVpz0Qpzwto+j1iyKNV/G1Tw52PjqV7/KQhi5mv/BD36Av/zlL2xjSm7oaV0YuZYnaH3Y2WefjU9+8pMcN4DWd/3rv/5r5Gqegh3S2i3yqELaMFrzRWvIRkZG8OCDD05buzVvhK/CBPCvk2v16K876qWVQJZrQG+hkct9eXpxVX8hNaVJQ6ZSB2vKfLTVh/wjKBnccz7ZQCT+AYeBjfGxzRgdOQ3FQmyqmU4/jcaG36AufQ8seGrdRKhTkExFZfYzTwYLM0EQZlEIDkUhPBL54AgUgsMRYrIZrSIsFGBxToGX6cVXTNQLpXVu81Q/y9dlSj75jOXc1C07UG12oNo4DwCb+gSwnCDRRmWlHY1SVDf3RrXtZput75dqD3D+Vkpz/nvzDRQLEpzTPUKSLYm+JOXqNr5/qG7uIU9v0/cW1xNlGhDztn0zKaNLK1hAzrJQsCzO85aNvAXOo3Y7uT1OBOmR3RBwQxL3AScMWUvhVKmzBiPRTnnJfnQcmjYI4+Mmj1F+jlkXzSkoKgmFUUpqikiYqQfSVDapbprCkW5P9qMkWtz9YzKBsGzSICRhrZBHmC+wN9iwUETIua9SMUBQCHhgGxQD9I8OYXBsCFbgwA0zyFJ4ZqcFDglKJBAFKQQsGKWjpO7YuYCej3lOtpXT5RysqJxsLyBECgHqEIYNCFCPMKzXdcobEKI+OvKvUg9ii9ODjqAZFxZOgr2XX5oHH487u/CA8wxGbRUYOR06OMpfgc1+Exr4mT8O25qAjTFY1jhsjFfm3Ke0jT/nDPzQx7M2dq3MYldnBvlEeJXGUQ+rdufQ2ZNHmgSaBDxhiRRCXgOuhWGkozaqczu3UdL3gu6j9kls4330sRLHmOoc1Lbv0JQqfef6fWrRPWDuF/WeNds5JeoV/a3EfmV9k/Xo3W5NPgzPp23+/nd2ZjFRH/8N6sfpb5DHyp4cMoX5MYwfRgPuxIm4G5tR1D4A2jCAM3A3jsWjcHm6emrGkcHVuAxjaMDZ+APOwZ3TOnf+qNci86rqCpa5ZNEIXwS5mTdBlsll/Oc//3nWiBHPe97zWP15zTXXRP0pDthHPvKRKMjypz/96SjIMkWcJ8+J9957L3tMXLVqFc4//3z8y7/8Cwtz02XeCF/5UeDLp+kXKc1okqBCs/iFKQeVtA5gIF8Xa8jy9SycjXjV3b6SrqctM6E0ZKQt01qyJpceHNO7VLrTCuFhGPUuxERwRvSAtNGHRvenaHBugWNVcbVLA3qa0WfNll7UXqHpSqmBvtF0sbZLCwOmbBvhICEMlAgGfLIqQqURGEI9/rf1IMIGfFpP56g2Kvu6zHXdZsqmnftTopeOzgMXAb3QgwY980uTAPPRa5gR8JQwCKtc6CPBMCEolgiLup2EvOjFE/fh40Uvo3iberGVnZfPTQMYEni0gGOXCjnJlBSIJusT9Z1EaIrSAh/s22UCHOclglsI+gvRZw31zyAVhmjzAzQFAW+n/klhkY5h9lXbSo9ZKRyaugXXIpMwB45NZl8uXEflVGcTMCelyo42CXPScEjrRmVXl2mbQ+t0MqruZlXZzcJ1VNkmgS3SIhpN4RQaxQXiqTGa5S/4LPwooYgEIjLbK8LPF+EVffhULngqL3p6uwpYGhQ9buP2og/PU33YtNvz+BwB/WfRv6ocWiF83UZmqMltgUUtIXxuK9tu2sz+tNXS+9khQidEwDdHyPXADhDaQZSHlo/A8qOcy/B0Tkf0WHMQhh78sJgoUx4gCMklVMBln+qsaTDXnkiJAbF5I5i3gtmk6jYac6uwrv9M3nNL+29RSJFbdTtKVujossOm+rRSe4yvQkGCWmOYRUOY1UIbGa/TeeL3ELWoVnNeU4v7xdvVogCTSLAwCwXoPWYlEtdD/eqj95vlo5gdR6FuFH46tou1AwfpiRZkJtrgFhqiz6Ou3gb4MzqTXKm6xviKTa9EjbfH20pzuszSo1Yey4rm9UxP9S81+mqRhJ7AVcsp4ustEx/1v3FrPE2Y6JkYIpj2yZYNxNsr29SXr/5GdJ2gSU8q02RpMifbmMwIJhr6kK8bQkgTovqvmcnVoz7XgHQ+q/6uvI32VXezOp5OdN5oe/l10k1Qfp2Vn6x0Grzy0xdDG71eI/q8Rnha6+xaHjrcESxzRmHrH5AeVfHfVoVOUBZFzvgq1A2cxNc+2nE7gtQYWxipe07fuaw80PuFNk5oWInLL/0Oas2iEr7mI/NG+KJZ/G9frGeDy2aKqY0HGwmBhc3TjMBiNBNaW0FzgPkiensG0dvTh97uPuzp7kfvnkEUCtUFuUzaQXtrGh2tabS3OGhvstDeFCJj0wKtYqVAyG00CMhibOw0jI6fiSA0JolF1Nu/R6P7I6Tt2LtTNdRdm5g9i2bKKJEJCm3LVN+uyyipU1+9D9fN/jSrFh9n/2bUFj80jChYBRRsj/O8XUTRKiJvFVGwiyhQuUpboWR7AQXLK22zCijaHuel+6jzJAdItcAOgUxIBlAqz+h6lALKA12mnF6ENnzHhW878GwXnu1w2bdteJYNP5GKFsn3lko8W87yPq+F9NSQE36oh5x6MKmSD097HBOqC53OJEJoLChS2YJjWSwkOrDhWiQs2nA4OXBpNRE57NGmyfS3UUM9PZyydE7mx7Qd2jw5atPmyWafqEwTPfR3RYlAwO2JnEQGPym8JAScxYwaJKYRWhmEnOtkp8nVbEkbom1xe8W+drItFW3jv1RIUxGkTfdgkaac65SrNjv00T7RinRgYcwdwnCqP+4Lsw/95b2ydr9Kv8nOpfuX9DO5DOGERUYInNZzGlaNr0Jfpg+/7vr1Xk02XhQej8+88b9Ra0T4WiLC19hoH4753a9g64cz5SpRuQjHlOHBCTyuqzYPri4nk2vaAx+uFcANA7gIkJ0IUTcaIDMSIj0cIjUSwh7VM2ZVsBtsOC1pDj6caq3nxcmp5kbYVpqGMLDCDOwwBdtPo22gDZ3dy9A4HmvdxjN59DUNYyQ7yqNN22PrOFi+BcuzYFMb/UezJDyzaOoqL22jfy04If1r6bpqS86e7Suh5SFwCgjtIqeAcq4XdFm1UxvXo7Lex9H7UN1J7qPK/KFDB1ZA3xflpqzWMai2RM6aNBt+YKOgPZwVQ60nogXuPCMV8uJ3MqgpItS5z+0FK0ARlHwUaPYTPvIWlT3dZoSepHBE5QLPcNeaTJBCKkxxng7TSAcu0iGVU0gHKWSoLXSRDtKJtuQ+cd/k9uT+qcBVeejy4nvXaCd5Uk6tI1C5TQb6al2BKbt2tF3tY2ZlrVh7XHI7qlnIEkr6JTaW9aPjqcE5DdR9/vuQQOZbatbfj/4jPYGPvnw/nhl9BmPeKO9D10zxulrrWuGzhsL0Vv1Njf71SmqBTl7F9ui8rHnQW0hIND2i9sQVhlWOEX0Gk2g7tev6PLgXFwo0ecB6C35WUh7pMXRZ6zdY60BCqdrGT9aS56uq02SVZeu1W3aWhZjAyqBopzHiAnl27KQEpGzYiBSv48rAt1MILBc+ObmwKVGZJihSPEFBZS+arHARLBCt5Fxg8aRBAIe00iVlk/xE3S/Zzm+H1DCC1CBsm8wbSU/hwfUcpHPNyORauEwaI7OfGk/Ex1BjjQBWoI9NbTTNkDgnCZSO7Stlskm8FFs9G+FaCOn56DpcRopyh5+ZvJ33oWcnOeIqYLAwgN7xXvTl+tA3MYDBwkhVrVODm0Frqg7NqQya3RQaXZdN0yf8AoaKeQwXCxjyChj16M1XSYaCBrs2mhwbzQ7QaCtDSHqyKvN4pbWilslGEtXa+ddm3uc62aEuhw5sWqeYfL+bFJgyjW+UtjFwcyjWd6PYsJvHDuakbq4VmdE1SI+uhs3r/pV2ERVaw+R1Vb6ISrYnNLGV+yRfT6VHpe+n1xrBbnsAObaEAY/HyES3K1gGepPG+5FmTq0YGJpQ2rD6TIg6mtnkpS/61UjvWX5AqXfqppWH4PyXvQ61RlzNLxEmLBtj2UNrcm7H97B8YA/a+3ejo68bHf3daO/bjabxEQRjAYKxHIo7c5jAEPf3HAf9La3obWtDb2sL9ixrQd+yFMbXTMBaO4bDh128amcrnr+HXLZnUJ/vQF+qBT/s3IWbup5Ff3oiMVPoV5lRTM4MmrJfNptY2q5eHPrlYOlBhxlL60Esm2uo577KOVmcaAac3MZSTrPfLOSZMrWTIJgos2DIM+l69pxn1HU9pH3TvE6CBzsUzBohchYJQYHK7RzyoY986CEf+Chw7qEQ+FwvzgNtBz1UUrBUsmyQuJ0KdQ5H1yl3lDAUuizMmHI6IQxlwiwyYQbpIINMoMoZr47b034dMn4GGT/LApV6MdkAmYPSSyI4MOF6crSNTnkTmX6RSoqrtf87TO39zrQoOrEaR2J16U57sCCJhE4ySdPCmMcCmxJCSSsUCaS6TNtVuxbuKsqmvxEA6TiJMuu9zMRO6USQyUsmfsq28VazbyKnffi5kZg4oueFVe34VSeeVF+PzG5tBwXHQtF2kHeobiPnWMg7YFPbHOVUJ+cRztT1CV0nU1/TnqOBcQ0Cy2f8EFlONAGDqJwlrbNfWufcJy10ZXumrJ4O1HDesy3WQBdtnSwLni732uO4K72FtdZHBhvQiAYU7RCeFWDELmCHO4o9To6/F9Jkp60UWqwGZKyMPq6NIudWlNPfSp1LnVedK74G2q/kfud9SDCdQYGUrN5nwameE4RIBWTGDKR02TVlzgGX5MGC7sfb4zIJcpHGEUUsRxFtYYGnDGmNVdrOI+tMoMHNo8mZQMaZYKHSccZh2+NwrDE4TgEZO4O0kwKFjiLrBNezMFoA+gsp9BcyGCymMVpUE3P0bkrxxJ56N7VaDtosl/MWy0UDTcEFKS1ExTkJPBa/l0w7tanybECTwWPtD2Jo1e8w2nE/0LIH6NwDy38YTd0noWXHc1E3cNgU70QSivT4iJ9nNM4KS00ho/XZWtjU/yiTQDNqUo7L4tyNcjrKVrsH97vPoM8e4bPmQgtr/JU41t+AljBeQ0k86jyL21OPoZBzcOHwc9AU1k36+RtqaYG2H4jma4FrvsZHc/j8t25hLQZrMvTDmh/slOsHuUft/NJQpjFFNnFSL0syaaJtVObcmDrpbfTSIM9/9IKJypYT5fF6KUU2N46Ovt2RMMZ5fzfS5Bmt2mfI1mNPWyd621Ziz/JOFJq6cNbEMrx8R4DOvLo96Vp/1eni++vSeKB1ljz6VTP7iIS8yQW4SkFQ10uEv2T7PhyfZ9f0Q0/7XFR1bbuf2GbaTR8yj0o7LrJuBhknzfGYKPgtJ26rTFk3i7SdQpoHCpZKNJ9tKSNNXuYc+rwMPR1Q7qt6WFSJHVoUEHo5hBQrzs8j9PMqiKRf4LbQLyKkMte1SWpQRBh4sCiZNQg00cW5qqt1A2qmV+XlZW0BrstmPx6AkiDG7SafJEXnNuekXK+N4Jy+36SHNrPwOwVE5qo6RQvLE4vBuc2I99rGvWRFSWlSd/8k26IXnvktmONiL8esvo96WSbOF1bbz+xj+qny3j9D5T6Vn6H084Ql+0z2GUpW4kzzM5R/fn0t2Nu1TP75SzG/ydJk1teYVRQkICrBh4QXlZKCjirbiW02cq4RlGzeL8/b1TpF1VcLVFqwmnBsFpBogD7XJp7ZQAlDdSEJNEA2tNgsl4ZPoZ9DvjAMy88jFfhotFysrevAcreBTXhpnzo257WU30O9H5nvqnbKSTMR/8Un/65Lt6ttlX+j0n3089WxYaUcWKkUkHJhpSmluB6kHPznLbdgz+Agjjn8cFx0wQWwMhl09/VF7rfN8Gr9+vXsfpviiU4nZmLoBQhyHoKcj5BzD8GEKvsTHvJ5D4Uc5T7ntCwgX/BVO8VuK5KG2ghxykHQRMMeDLc9jpFlW1B0aLvLyRpfCWf0YNjjq+GBNI0kYMaCZtEJuX/BCTgn4bJo65z7aaGRxw8kEtHYwiFbGwS8rmrh4GqBnQSyNN2/WlCnlJ5smxbUVVlvi+pT7EfHpLJlIUXxPVnzpywkwFYSaoWI5ZLjK7oXyUsjaQtNnRxgmTqVQ+Ucy6Fx4CB67bvQHd6BCRPbhsZm1nKsdE/CytQJqLMbKFYRrCAHBDlY5GgnGdKD6/lKT6tcV8tH6F2vcu24Kiprp1Y8/tFr06J3ZppGD3gqXI/braOxzVqhLi4McRh68ZxgFzrJmDrkUQa+76zDs1YW64MCLvJzieUipUtD6pqfRtOH/i9qjZgdLhHhqzAxgJ5/qox7EFgJszcyidPmcKpcLCtXM4czZd0vWU6a2jkF+LYP303Bd8hchBa118EjV8BWFj6ZoFCODPJDFop9Pop78ijuycHfM4ZwYKx0daeGm1pasKrtaBzmHolOry3atqupiD+uncD9K3KYoAd/SOZ0yh0Dv3Bgo6ANKIpwULAcFHXyLBdFSuSgYwlBQhov9Q5DbTaizVNYYKSyMhkhcxIyIVHrY3Qb7UMO9LVJS1Qm0xPWHCrn+8Y0pbysjjFJWZ/bTpZpqBtdT5Xt+nymTNfBny/6jPr8iesgoSk2iSm9hsmuufR70d9b4nuxqnxHPEynukWfg4bnylNktNjdyAP8RzGisoWAnFuQKG6rnoRqo4XhtF0NDzn8gSnTvto5DPfldWHKYYwWwbktSjQ3HNrY49voJwcweh1Zi2OhPRXCtY1oaY6n9y+5xris+ulhq97G12fOaxZG68/JAg6f0y757Op64+PQt2XazD6mbL4TdX7zncRls785b/IaSq4z8T0ZATHuS3cAodZxRQu8aV8dasKUafSjwk44KJIHU0qglEEeGRSsFPJWBnkrhZyVRt5OIadToQbPoFRQRDbIo87Poy7Icc51Xa7jMm3PcVs2KOiyaivZt+w4lKfhIUtDeLZZdBHSGmMnhdBNIWQvmGldTyOwU9iaG8Rjo7sxFtKaUQsdjZ04qes4tDWs5HXLVioLi9cv18NKqURlO9Wg6uzx0sQnM6EZyFnK7DvF+e1vf4tbb70V9fX1uOKKK9Df388xuijWkWHjxoNw2mnHYfXqZfD9HIIgBz/II4jKOQQ8OWXKiT66zu16H1XOwdf7qLLabty/MzRx5KdheVmepyTTNHp/G2wvi8zwBtT1H4bMRCfsYj1srw6OR3lcjg5WLpRGDiLMLycxCZiYKGRXKCQMuEUU0kV4qQDFtI9iOkQhE6LHHcGzziB22kPYjWH0BuOghQ5G8qAyubNvSi/Dskw7mlPL0ZBqRdpuYLP6gh+iEKi4ceTwj3M2r9dun2hcQAIijwmUprHAExIOT1CQNpg0wbWG3i2ZoMi/qUxQQNZXOaWoLZHT77K0TafEfmabn/Uw3AaMtFIIAxKvC0iHeXQOjGP97mGs6CuoSc4asR1d+B1OxuPYGLVtxFaciT9hA55FH1rxZfw1fLi4CLfgODxa/UArjgT+ZnqeEWcTEb6WivBVGMQffvaSCuFp0sVY84zAs5AbyGCiL4NcfwYT/VnOvYlSi9jW9Apsaj4R6xqOYC9nRNEaRe/y32Po4F/Cau2d3gmNNoRMSnwHgfY8GGivg1T3yY46TJRJXR64up1m9MhhKnkndOCFquwjbjczilQmQS8p8CkhkAZeqp0GYDRLSIO0ou2ysFg3lkfHnj40DY9iZ0cnnulajVwmq7SOOv4YDTjpnNTGccqEBQEJbMRcayKE+Q3NgLN2iGbCI9M347gl4AhYPIUV+khblBeQtUi8m0CG8nAMGWsc2XAUGYwijVFkwmFkMYx0OIJsOIyMP4oUzXT7PmxauxPQRIF6FlLEiZI8rNJWrf88fc3Q8zGgiQzHRmjbvEYsdByO+8i54yaSckoVOmklLCZi8LH4wFp5k8hjYhGDuTR+8MxZ/E44tuVR7Mktw868CfkS4qDsUzi+4R4sT/XGnhETudKka11qST6N9sgCQG0sPz71G2xJYU97GgOtJPTqdT5BiOUDHlbuKWLZEE0kmeObYyfzOC12hx6koVOTIhnk7TRyDk2SZJCjsk1lyjMl5WRbzqHJFb3NqdxP5aX7mb7zgVRIU0Ue6kIfWZOgUoZWb1LdMs+gABlLR+SzSJMWcorLQNa2kKGkyyrZrOHLOlR2kHFs9mqrvFGr1D2Uw+8ffAYPbdkdaYzXdLbhzBOOQs/AMG79w/2oy2ZwxRtfjcaGxoTTOH0c41yuxojwtUSEr2JxHLf9+tzylfiqVD7Aq7JYkpv3YaloNZMJdQeVt09+zHKbY96/7LjFcSA/YCPXT8KZhVy/zXkqrMPBTcfikObj0eC2cF9yE7wz/zi223djvHUbsm0hp8wy9bvc+3UlzZBKr6uqfXTVjzbF90IaEh7s6IXPQQjH8+H0FZDq8eB0F2Hv8WH3+kCvD0uFfCllmQVrhQOscGF1poCuFNCW5uDKNMgIeBG6WrROZfrgZhE7DS5oITsc0tPQICOl+lNfGmCQCSl5w6ScFruzN0zalwYplJP3L+XKX5mZKpNT1vsEStNATj4CKoc2PA4wTW0WhzQweeCrgNM0Yxn4oWon6wTKdbguysl7HwVqDCgnj298PCprz29R2azvUd7/zNJnpY1JzMGq5V9RG5e5zSrdrnMeuCX6kRlu5LlOa6HiuvZOV4O1LpOhzCTjwZhy861jo7Hehn5HZp1RuXGgmcku1wehuotqk2s7UaUz0u28TiCeMac+CZ1cleMqTSF/hujYpWV7ku3xNZfrsqpvn+r8yjRVO68uK7MJq068Disq0zIZDxmLBi0eC0hZq6jqls8B7SnP2B4yTqDKjoesTdtCuLTwhe4htuLWC0tLbinzx0y6v650CF1C1b4Jp9k6fh77TWQT52Su1nqoXLllp+Pxr03vwzlpzn1ytuDB5h8q5crZAj3v1DY/KpPQp7YFuh5GbfliiG25ACNFindFpoYhupwAK2xy/KQEPrq3S4RBKvPzFXMGfSPfxMXYinWs3yQRmCDN97F4hGMataNKyJRZZqzOwa6VGexakUUhE0/INY0UOR5U5548UmUxuWYD9WtSGnqlFY4dqUdGn5FmnKBVjLxaWiWOE2bciZeaCyd/qSo3fe2qfePtOoRMFH9SvSNtGvg7KX6P2pS7adiuMit1UmnY6TScdBp2NgMnk4FNiQb4CaEhGvzzMekdaaF7ohfPjD6Lp0e2YcvwNmwb24kiOQ+i94n2ikpWOh2N67B22WFYs+xwrGndhI6mDeyVMxc6JgIdr4kii4VcCK7nAyAXhMgHAec5zgPkTZtv2kwftc205XxaoVp7XAvI2DYLZkpgUzlZkuRHRpAbHoLte3CDAA3pFGzPg5+bwMq2Nhx31JFKoNOCHe13cH0WxzeXrhmrBSJ8LRHhq1AocBDpJQENKgt5OPlxOPkc1gUrcHjmSHRm48DNA/luPDl8D54ZewRe6CFIZxFk6lTK1sHP1CNMkaAxN4Nlx/PQNDKCpuERNA8PR4naSBCrBr2YxhoaMFFXx/3qctWkMeXAZIjuwdYWDLa2cpnywjQDhc84CWE1MQWQmBYo94FU2rc8bku8Lf436hPNAlfGhknuUX7EyqNOfq6KIyQnLxL3D/eJzNpIYNNmaZRo9t206bU3NNDg4UagPcWxnGLDDpQXT16fRjk5V6A2dsaiPXsaRwrU33j71PFOjMClhs0+/rT8j/hZ108wlFYDwZW5Lly45yIcmz8WrmvDSTlwUpTbcNI2HNeBS3nKhkvb0jbXVTnexhMwPJZJCAh8cl0x26q0U1kZI6lVqgEKCELld5N9bbLplC5zXZlTUT+VFxHSAnu9yD4q0zG4Ta0rVPsWENAaw/I+tF23sTaDF5YLc4FSptjwfW1NwNYHlLsIfBd+4GJ3PoX7RsnpQQpu4KIODg5OWVhBvyXqG+i+fAyHczoOfPpd6Fhy2h6BbBBoyim2RfDL6mZ7eX+V+JoT4jmZzD6EQ/FswjkNCV2rsRtrsRMZvq/LpymqJTVVUTqNsbf+pW08jeD4yHb0on5lN9LNyoEB4RdSGO1ZiZHdq1AYb5rGNezfdRjT4aRQtNhRjhctsKNGEhZ0ch0LDiVXl10bLpVT5MmRAhCPo88fwR5vCLsLg+grjignP+zUx+flGxTHrqu+GRualuHg5jYc0roc6xpbOMSFmfBQUylKK6mmHk1ZR80Ky9uSEyeqjT0eBxYmQhtj+X6M5LoxUhhCURklokhrqVIdQKoTvt2EAlwUSCAMHRRCW9fJq7Kr2mh5R6j6qG1u3BbV1a9utrgw9SS+fuYrUWtE+FpCwtcnPvGJafefzmLf+dJ3uv1ai1kckevEQcEquKS1ocC7/gSeHrkfT47ci3FvuKQ/m55k64G6eoR1DQizDQjraC1Bar+vITWRQ+PQEBoHh9A0NMjlpqEh1I+OTvo68h0HI03NGG5u4XxE52NNzWz+wvtZIdK5HJqHBtA8OICWoUE0Dw6ieXgIDqmNqjCRrWOBbKilFYM6H25ugk9ufIVFCYtklppBpsAAY84oihwCgBy62GjML0djflnCRbHyyReVdT0qs0CXKJt2csSSTiOdSSGTTatU7yKdpeQgVeciU6fqqazDOdVTdarMqc5BKuPs0zNjNiHtDQtkiRQJabz2JpEbgS7ZXwtx1awPSoX0ylLpJFDllMHkfTF53yrHLO079TFprbznBZyKxbjs+SGKxQC+p9qo7Hlhom9cnqyN9pmrEYdNA2IeANtIpZySciqVbHcS7aaf6kPPXw7G7HnYsWMYTz89iFxOPXfJ4mntunqsXZNFijwSaU0h9Y9SpDWk4M7xIDikL9n45dR9lSYxqVWMt5ttRjvpuANIp7uRTvfpgLpKsC0UWjAx0YZCvlGvTzQazqR79KQhYbl1TNxW8WfSk08TgYUBz8KAb2HQtzHkWzyYV3vGxyMztFaHEjhvcULWdsQOcSpPUmKFMx1LnWj/Ktdctn+k5yYrDFpqoIX3yqQnBWiZgV6aUN2xztzBlgs2uev34No+Uo4Pm1z3257Ko7pqY7f+yeTofnaVfoljuW6By7P1aFbBDVIoIq2FPCPslZaT2/J+PQaG1mBwrAs5aqO1ybaNxjoPTtZjSx3qd9rIM/joq9+NWiPC1xIRvoSYYLyIsbu7MfqHXfD7c9HzN78sj53209i65yH0P7uNX6TVyDa2omHZatS3diHbuBKZuk446XZ4RQvFvIfiRBFh/x64vduRGtiJzOBO1I/uQv14N9LF0Umvq+g2YKy+E+MNKzn3GlbCaepCtmE5ml0HTbaFJrJ+2IcnHr+kR3vgD+9AMPwsguEd8IeeRTg+ydo3confvBJW61pYy9bAalsLq30t0LRcx1Kh9REq7govIeO4VBQshOrkfYnWP2tvTCZmVcrSHprictJ0KqR+PG6ndt1mjs1aocQ72KyF4sFLXN6XbfNt/yDwERSLKObzCIoFePk8vAIN4nWgXDKtZLNZVeZgyVwOuO75PjzPq5qKNCqeL7DpXalAlxTYkkJcsu44LlzXRSpFObl9dpFKU55Swl1dBplMGum6FOrqMsjWZ5BtSKOuPoP6xizqGjMszM2EEBcEwQEnug6TiGR9Otumu4/v+5zMfZC8J0w9WaYJOtNGZbNtsjJ9lrnAtkngSXEigb5a2U252Dq6Fff134fRYJQnE1a3rMZLDnkJDmo7aOp9KabTAZLP53H33XfjzjvvxOho/IxvamrCO9/5Tna2MVdMTOzArt3XY9euG5DLbY/a6+sPwaqui7Fy5cuRyXRMeYzYjJTWrimz0SjpdiXo0XYfo4Ux/GXgMTzU9zgeHngCj/Y/iT25gYrj1jkZHNa6Doe3rMHhLWtxWEsX2jNNVY9v6uQJT12DvhYWXktT8lri/eNcHYO2lR2r5Bxmu0n0W9XPIe04SFkvaAuCqE05/iFNLQtsvo3As+FzshB4DjzPQuhZ8Mw2Mq33bGVizykuB4HeHsYm+myOT+3aPN+jnNp0aInaEKKhsR9dnU+gY8VWuKn4XTPStxyDO1ZhrLudw5Cq8ABkxq5SOnTghjYnMm13QHUKRs8l/tcJU3AsCk9PdRe2lYJlpVRuZ2BRcHMKem6rnByuGCje4w3puzBsT+BwbzXO9A6PL7txF9Z85NWoNSJ8LRHhKywW0fv1r3NZmQFp1b8pW9NsVweItlFeud9k7WXmRlX2m6w9Ol7y/NqMi72RUXBgmlnzLBQ9oKhzmn1VdVr3RjOxKi/STGwBqM+H6MhbaE3Mfg37IZ7OediW64fn9yDwexH6vZwjGJ7kCwayno2mXBGtYyNoHp9Ac66AbJEstkvJ1bUh17QShZZVKC7rAtpWIb1sFeqzzaijRfTFAKk8rX+Y5I9JpgrLM3A7skh31sFdnoVFGgIOMkmCjM5JQDKBe6M2JSyF4+PIP/008k88ifyTTyD/5FPIP/EEguHqn89ubETmkEOQ2bQJ6U2UH4rMIRvhNDXFf5NyJhnsVm3dh/2n3Xey8x/gIJyE8mIuh0JuHIWJCVWemEAhP4Ei5VwfRzGv2ou5CbU9F/ct5k1bjvsd0DQ/mba4LifbceHQOgSqc1m1UcqFBeyc2I3B4jALtJbtYFXDKqxqWo0Ur+kr/e0pL4klwQui9XQsEOpA3CQ80vo9IxB6JqfOvMYt8duvFWUz38YEtET3U21y3/zLZpryCpyKEuFvCgHR0rEPabImmZOQFZUdpyTniR2OkxqXKS8XPD2/iG3DW7F9ZBvfl/SH66xbgY0tByNLAzat3WGMpodeHvwHNrlOAWmejDZJ1bk/1alM28MAXqGIPb29LOjSbyiVSqNILx7LxnNOOw2ty9rYVFf9Dh3Y9Du1KXf4N8ptlJfUq7S5pdscx4m+C/JkuGfPz7Bz13UYGCBPbnqVlNOIzs4LsKrrlWhuPm5GJiC8wMNTg0/hgd4H8FDvQ3hgzwN4euhppbFLQDErN7VuwuaOzTim/Rgc3X40Dm45GM488Bo4m/C9QpMT+TxCnYJ8AWHBlClP1guqXyHeFuSo7iHIBwgpFUOQpXXIi8CUoAfWtJGnRwc528aOhjE82dSPrQ392FbXi91pEn5pbRQJMg6cQOWtXjM6C8vRXlyGZcVmtHhNHPNPDYFMfC71cOS7i0zY6TdmAqzzijslIrEApUQj1KWKyDoex+y0/YzyohmkVXkOBcSQ3k92iJ1OP37u3M9tZxQOQ2vYwHH1iqtcPPfKC1BrRPhaIsKXNzqKmy69FK7nwS16SBWLXI5yr8jtVJ5sjdFMQk4cPDcD38nCdyjPwKOyq+qeU7bNLetH5URfdk98ADTawMEZG2tp7Yp+QRXDENvyHnYM98MZ2oaGsV3ITOxGGPSiYI1jIhVgJJvGSF2a11VVw/UDNOaKnJrzPlqdZrRmVyPTvAZ282rYTatgZxqr7kses4KR3aytCkZ2Ka3V8E6ttZKf43Qwi7Y9x1ax6yhuHZcpHp1q5zYqU5vexn2jMrXr7dqUYXYullzWJyNqGTfwahG6+TyTWNnMa9iUh51DqEXvUZkXtpuyWeherazqey+XHjNZn6y9ZGJoVj585NGE/6JqUf8UkwtzhREyTGy6pOCBvdQTMe2ieuJYk9arnW+/zo+KtsXubW8y2EGNie9EMZyoThoRL8WJ1nwarQ0NnHki09TJ0iGh0Ynb6d5Uwm3e9TCYzmEgO47+zDgGsmMoOj7HqA+sMMrr/Qw68s3oyLWgM9+KFbkWZELSSCjnFdHQ3Tbn04I1n4fen/oaojWiCfPXRJiIeBxf2i8yM0z8rtTa2bI+yWOX7F96Lha0SaCm5CkTUFWnmSaVq0SeLlWZbHE5N+cvOZ++Q6PrMw4+kp+1dB/HJo29zQK3Qzl9ZzRBQcI71XmywoGTaON+PJFhwUt78NIBQvI0Q4YploNMkEYmTOk8jblEhaRXq3lVosD2FEvOh8dlH0Wo5JFhvEVlyqnmJ/qqgPe8T1k7HX8qWa8x04r3f+gqLBTZYPZWvwlzApkyPXjMMdPqS16ojFDGgpoWzDhPtFEfp+gj5VO7D7fowyWvM8UAru/D8T04Hr0MlJtzjqvDgx5a6mwGTWohrx14SIVjcL1x1a7j6CTLyu22GWgl+xivSfSSofNpT26RZzW95DcKfqu8rVFwYoq1pHIftl/ACAXr7DwO6XWnI9WwAhuzKRyc6YAf7Eah52n4ex6p+L7IXC6XTrEQNpLN6DyLUVcN7gcbMpxidqLeHkRz7lm0eG1ocVrQ4rmoy43DGutDMNqNYLQH4VhPFHhwqUB/MRZ4HC0oJYQkVVbbqpd137JtszXI5fhdgfGVZYY3WmigRf+0ZpDdWJOXSBeB66oYdymVvDRNGlBf1c8IG3v/kkpn7tUANFlW9ZJykBjkmpl++l1Es/46EHfJYLr6tsrzVSmXf1d0br4GKvscP3x+YTR0RiAzzxP9t1T2sLGQFzlISQpzaqo4zpOCpfnbWlWFDfYmqV1tmsDhcR4HEy/dz4QkSGhtEovtoyDrUbD1ZHne/QFmh2iNUZnPl0lXuJXVEwPlaBCvB8bJQX3D+BgaRkfR09GBwWWtcAtFrHn2WTUPb+LTWaVx8UzZxN5LxsJTHlQry1U/Iq3N8tgtXhl01vyk38m0oYBY40ArrZumIPFcmoohjGEIW7C/6N+d/s0p1xVqHWlpu96mBbvptXO0YXW8kuNbVdrVeCLWBsUeQFlk1Ob1ZDRns8DLxiXGp6J6TOhnH881kQdOKw7Drqeh1L6JOq3NU+MXOlMhPr12oEG/XBJc1Fq/uD26VP0c4G0540xDC5OJPaJca/XJ+ZJvkYdDKhnvpsqVrzIF12sD9bGS30ecl5XdCfjpYfipUfZqq67Ngl1ogFNsgl2kUOmln89cT7KVUJ+39D/6nlI6JHP8X/Kzkgdf82xUv5/GlUdgISHC1wKHvC8dvGMnBwqkQWrRceC5bpR7rgNf274HjoM8pZk4cRiWaNgqhLkpcrNfKrH/XGjlvMGd8B77KZwVRyF98DlwV26Gu/JYTv7IbhSfvg3F7XeoaO70kw4C1OXynFYg9iRFL83RTDrSjnGezSCXdjEejHPaXdwZ9SeXyA12AU2pAprqCmi2MmiaKCDjcUjceatVKhWOlMBTolFKlI02qUJw0v1mTaukv1+Hg0ebl58xjdUDbBpc6xg/JCiRwMRBwUlQYoEpze20nVfRz1DcNLp/6B53vXyJRppccqvJARKSVFDmZM4v82Qbu9eu7F+1X8J1vF7VoAcCqkyzpjyzaky/bD37SjmbP6WV2ZOjZlopp5lXi9soFIHy4Mg5TbyQxpBzNXTxtXBS4o6fp0bCRG7CA+hBhw4poJJa76Zy5aSA1iBxmZOvyrTuicq+r8q+x3nleqWkEKP/Lsm/EZYGJi4Uz8nrMrloV/eMjn2og4XHbfp+Ku8XHSO5b2kfvv/K+iSPmzxOxb6J83IbB6E1+8X3eDX4nuP7NKUmRsj1N+f6d0/hNigMh6PrertKcdnT+6IpxFAb0L2+lQW2rqe7UZ9dpyYgKWwIfWc+hQ8xsdN00HVd5u+Uw4uYsprw0AaWbCacOwzIHRsid4jWjlAA9IKF9BMO0o84cHbFQcNjgS4RPJ3bjWv3agJg/EyPy7HmPXIJz/21fzzdLy4nQ3iQ4KnDevD+xqdenFf/A5kJglJBcSrbq6Wp81wA0BBpjArlnpXp7zuk0+xildWbnDosJET4WuAEFIPipDZkYCPNWiNaHKoCS6rXm82LN5WHI/0oIxW69qCkEgkBAUKOy0M5zcEoJTLNl/AiVTJDIAcCFRqb2CiEo8nTwmCes6BUVxlDrGyNhmmigaFrk2mgMg9M0XoXyklFb1kU514dteAjMzCKVN8oXEoDlMbgDI9rLUAl9KLw6jIo1FNKI5/tR278h7B234kVjSdgef1mOE0r4Rz7WqQ3vwKF4ftRGLgbQZHWgsVaBsppoEjxOujbydLgOvTREkzAGhtDMBYiZ7uYcHRyXYyTRsS2MVJHmrPSBxVpEZuKBTQW82gqFNBUzKOxUOCYNiUTTRzfJhG4qqyNulNsERpIJE3pYnO72CwvEqIS26qVZy0IsNYqcbDWhGbJaCVYm6oDoQYmkaBE2iWXtEppeCkVDDUWlmboWqMJhVzFhELShJcFKt+0Ka2wyklbrHJKPGBk4S/W8NIASAlRcQwkWmPC9y7dYwmNVXJUYs2lhlKX98elx2ys+uDnllFDKj8eJXl5G93j5DwmiMpkUqomE8jdvwrAm8h12Y8ESZt/TyqmmwpormK/6b9hmYtt/tOxkBvPfnM5IWRw0GJjeqrjgimhQsVao5y3R3HDVMgB1Z4IR6DrFIKAZq15G7lY5/+UqRebnFF/ErK5HjsPUL8VKqNUI5hwNMC/Kd4cOyFQAo1K/Nuk78VMaHA8QPqelKaX+/HvWE98mLysbLTXxhLCaCBLzEbNdRuzOh0fqsRRAn26cAyOP6z/MvRfBrCbASsdfdcUSs0YY6nv2vwNYmFRrYXRtxX/XQP81P0TYI3iYL8T5656PrBqZu9vXl1Ly3j+VGXjRp0WAOp3SmMFH4GljMwoDywyIqMwEeSZTuXKmEyFeeA67UN5qMuU8/hDjTko5/FIaOp6XEIl7UmSNCiqnSZpjKdI5UxHPU7VBE88CWPWhyZ1o6UaUbNcSuUJP42RAoee5brBBKg21gjqwVB1zWHJ891IxGZNltbmcNgQ3qa1O8Y23WjOtXY06meOFQ3RtDaeM7120glQdH02Ly26Hgou/a2UME1QmR8BvoO05yDju8gUXGQ8l589ZlygNPPqUqJvr6UId8MYnPUTsLNaA09zCT1p+Fsa4D9bz+va+JKsxM48wUKTgqRppHWTLnuttu0Ulx0rBYfLKrl2Gradhuukce+ycezOFrDMy2AVVmAhIcLXAqeQH0bXpmuxqKCX3wjg7rbi1K3zgcmHoUE6hNcZwltZlsgBlEs2FrH2Sr2En8AA7sCgl0XLzjPQuv35yIytQrb1FE6jyx/A4LpfYqz9wThi7eSXzGR1Wmbayf3vSAq5/gwm+jPI9WU5zw+p9WQDTh0GsqUzNunmArJteWRbyIU14BcdBEULQdGBX9BelXQe6HzWoPUGLiVUzWn8RE5EMhM+MqM+ssMe6geLXKd1cSRoUZBExw/g0gvRBfIdKYyvSHOaaM9gfEUGXp2bcH5gPFnRy1lN0Sq3ybqsX0QcL4i9Q1HuwOOcXK078AIbeSuNfJhCwSKXtYkcLoo2xR4ht7Xk9taFZ7mcm4EjDSRVHiel6dF5JFDpAWNSyDLrnnSwXGWuFpuv8cuHPVwoTRYLX9psMNZq6dlzFGBjAk5YgEMBbEMy/XXhBhZcMukNikgHBaRCyovI+HlkwgKyfhH1fg6ZsIg6L4+6MId6L48sOfAlV9UWhdv29bLugGMa0boSNpUhExo7sc6Ex+BxXVnvmLJ6WVuxZFFlZBu38Uue8kS7aqPZBBKeVHBmdvGtouqqfWcBy8TswfwmoS+YHB6UkdDlwKIF+6ZMUmhJuwpiq9oT5aCsnVWZan/luVJvD9yKY9sV50zsy8dxK45dcl2mHKh+Fef1k9dLn5NCKpS/Bxp0qvhiDui7f8DZjn57FJnQxWnFQw/oWKwRIiw9wcmCiofQprUtNC5VDgUCI8DYRpDRuUPChofQKiK0PSW4mDL3CfhY8bH1b8gOom3UbgSkuOyxG3NT599fVCcHI/RsKN1m6b6mnerWpJ6kFvfvc96i5tD5z8a3Hv2pTJ6wWFZlJbSVtCf3K+mr+k/WFzsBvy2E1xXCbwewdgw4cYBn81I7LKS2WnB6lTk2H0Pvq+f8q18P3YYJy2pTPn5PBr8//UzkUmkMbHkSwDuwUBCHGwvc4Ubv7u246/eX6MkQbYNePpliqCJARHqrCpmmyqx7xf4JvVZU0LM2CRv82BRM/8jMuvQAcAcCpLoDpPYESPUEcHt8zu2JyW/LoNGCt8KGv8KB32nrsoWgxRhjl7gxSCwQSNbLbJj1DEzdwOFo3nku6vuO0QZbQKGuG8OrbsVI1+8RuBNR/3jup/T7iSZ0Kr/sCHJVmxvUAtmAEsgoeRMH4mAkhJ0O4LgB57YbwNF5SXsqgJNSebKcbDP1cgs89Xx1EiFKVaKYHKZM7daIhbodPup2FNG4s4CmHXk075qAU6z+dx1tyaJndSt6VrVh9+rl2LWqA7tWtiOfysBLHLs0Vbb7+lpIeBIqoTWQSgT1dE4iZxxlpbSuyib6iioXK/aP0+THNftSne6eGdXkseCgHQnomDwlru5LcjPoN4N48iqQjHHmJNZCmL5apVbhLl/1peOop4gKkqoGwmowTANVjtFTMjhODr7VyMUMjE29pMzb9EiEt8krO2QbOG3lof82PEETlU27cRUeb+cU7a/cepduVznNi3CcPJsmKPT6HpvclFMigSPQkxDqb8LxttQiP1XXfTk2k1vkCY9a+2KZawJ2oe5EE2ScgmRu2u24vawP/X28qF33T/Rll+0BTb6pSQTy/ucGDlK+ixRpcAIbad9F2ienFC4Hqvds5RCiSIIvlW0KcqwCHpPQawRePyGs0t/Zpr+npSavHCvgOq0zd6JtKtm6zivi+f7RfXmCy2xXZb5HONfb+L4x28KKOk+S2epYQszA1vV45ZtuRa0RhxtLhCE/jX8Pr1TCjPYMpWzpE3HnzSL8SKVtVOelbwJWYRuvXdE2/UKLxDR9VK2BMOtVydjDbDWyjCm7xSJWDPSis7cXK/ri1D7Qx2Za1SDxabClFXvalkept60NfW3LMZHNRusWaPjEacSGM6zKbkg5L5flBy0niirBZXKhSmaMyoRHuVTV2zhXpo+NrRZW54DOPJCe6ET7U6/Fsqdei/60hd6MhQKt+40+Z7Wyx+Fu7bDIjgi4jCIPfsk4k7Yv40GoKjutRVitHoreOMZywxjNj2G8kGO34XBcwEkBer1SyGuU1Folj8wa02kUXUopXquQtx0UbRsFyi0bRdviNYEF21Z1bqNE261ErrZ5tJ2u1tN12t+Kt03LJLFZp8QaWNLkrO7ZjYN2bsfGHdtw0I5tOHjHNqzu7UHjEAWp3o2DH9kd9SdTye2dXXhq9TpsWbUOT62hfCW629r32dTQCUks8+GGSTHNK6uzDixKJCRovRicaFtZbhV0Xr49meI2h81nrISIktZnSYguYRrFMJMQV6geB6I0+xjRx7OSIpCLopXU5qXgJwRREkrzqJuZdZ8HQJq1dj5SQYA0CYS6rHJTDvj5QGtr0gFpTpX2lJ3+0Do/1qYq7So5EzJrcFiDqLWtpFVU62/0GhzqR5o/HeTWTFaxpi0xSaMsmI1tr17ozmbZ+qnHi+nZEFHFr5vUJ1/lfTr5kGn6fZVGUg/y9aCMBv2qPdmmtJjxNrNP3MeaYlvy+JW5ETAmPy6fP7Ft6uMlchZqaJA6ybfKA096li4uByMU/0kJfpSUYMgCDJeNlt+GR8JGkNIpDT9wddwoiiGlhJkgIayo4yghhctBHGuKtAzk6twIpZzI9TlbQqvtZpGY+tk4rH1gUzfaj69Z9Yn2D1SMKzaJi0YOmsiMVE1TGuc2at0Zrc9VJvTGvJXW4lF7SpugpWh5AhykHOUKnVygkyULLU8gk1s2Mdbr23K2hXG6zJSFIK1Nj0tDLuvfcmxKx9uiOVmlkVet6j9e60ZaSm2mZ+pKKWPKyXaVm1+zGZvF34lxIpb0naivIXmdsR/FqGz2Vc4/9CiMhT7t4EOPB8l6gHPdl4W5sjqfWx/D7KusfZN9lCWEGfHFv2l9faZurlM7KqF2CuicsgscJBqR7SYptbSQbVZC8vPCfEh9DSXrCM1gK6E4sIAGiuezgBDha4FDg+TH6ka0By96SJE9mFLWh2U5tauyW6VNR3E/gKm55tERrOveiXW7dmD97h26vBMr+/fwmodqFNwUD7CfWbka21auwraVq/HMylV4dkUXCum5dZdajawX4iW7irhkWxEbRwN0FEJOd7U5+MH6FH7X4bIJ2lKGzAtpnZqrHV+wqWGYMDnkNlV33Dr0r9mEwdUbcZ8eLGcnxrFqzy50de9EV88udFLq7UZdbgIbdu3gBFCMG0U+nUZfWzsL4v3L2jCwrBUDra0ouqQBoWvQg2++Bm3KNwOfM/K6pF+0yhQxg4BNGjPI63WTRrtB3pgicyC2q1drFRRqVl5pVfRkhwlSzFdbgBWSCEUrKLWdvPlPT4xEXhin2EZ1vlZyOMADE8rJ6YBav8MOByztmICTcpRiyspxCvWxq26P1hnyOqm4LdmPtrPzggQFMvu0a/v6IWHMLU8kpLNg6CXa4zrlqTBR1okEusgpRHLgxGMRff9FDi+Sgz3joMKsfdDtUTk5oZXwoJjoH609Me1stqo/pF5zZjyN0f3AHi71mpLkqo3EkCmypKiYdIsOm+w7i2bPZtBXIqCVC5nVhLipBLuphL+yY4chTg4fQEOYi5yBsEIzcg6iy0bpVeKkRFvTljglMcox0y8+ZnLFUTV4Cs9yeFKFcpo0o99c0XKjPLnds01dpWS/kv4pF1467hcdn/vRJF358Uv7xddj+u3tuibb/8DCyghLm7OLd2EhIWaHC9zssKdvD55/87kzd0AeAMauWZNl1jOFFtpHgK4+H6t7fazu89DVV8TqvgKaxyefeRjNutjVXo+dyxuwq70RO5c3YWd7C/paG3hAyMvVOZK5Pg8lLSiqa6C4GHGbsQZXgwA2CtGO5lU5FkRjr1D88fSMmJpx0+6jIyVgvC1e36q3hxaOHQYu2gmc3hfboe/KADeutvDjlRZGUibeUfJYehCj3Q2ba1DtcTm6NrOvbmMvfmWChMlVe1jiYasyj/efMjfHLj+u9qSXPHfkzct4R5vsVlLLoZWwogUPU1bes+K8pI01Dz7qJ3JY3j+G9oFRtA+Mo6N/DMuHcnxN1RhoSmF3Wxq72jPYvTyNHe0p7FnmwuN1FNrFLp/fCEKluRKq6FrMdavrUm1q+2KGB+ZVhTljRlwm3OkF4qac6JXYpo4bDS3Zz7KZ5InDTCTLyXAV6nekf+dRCAvK6fdt2rSDhyipWdSoPbGOz7iaVucw15U8f9LJQ+RQeoq+if4lr9Jyo++wIo+Fn2nkUd+kOXi1c0zev7xshDqFDmFQMuOeEA61m/tITEvG8SprS9bjc5htCUFyknNFfaPjlZ4rikOWFA8jbULlNcTnTT5dq3yWpOipz9WIMdRb+fj+M7qE6D5U7x+ltVH3pLr3TAgW/U4ya0fNfUk6FbOuFCbXEyT6XiXBhQ3XtBBkwrCUG/TH96YRiJPthBGOTcBdU9/LMUru8cT2KsdVh1N5xTWWHaf0t1fZN36j6ADc9A3z5J56z6XoPUTfFmmwqZ0mTGgtLE+e0FQYvaN8tbY1TEyq6ETvreSvoPSajetzc5fEvxlTL/0VqVqkQU/8zuLFD2ZPOn68f/JYpa7Y4yUN8Rgi/h2p42qtYaSVj3/z0b6Jc5afMe6T/GTJPrRfkNi/0pm9cvteejR2lkIOVvRaRXYHb7YnHUiF2tEQjXFsHw6vFzSfJdSaU0pOlTNHs0/6J63au0ZX4JZ3fha1RswOlwjByBjaJ9rjQasZ4CYGjFUHvZMNJLkPmeyE6OoHVveFWN0HrO6lPMSqfiA7hRu0Pc3AjuUWdiwHdrRbUXm4nn4w4yqwCPbEOyjrkVlDeQ5z4sQewxJ1vWC8vM0yfXlth/IwRlrG6ywbt7U24ozckXhOfhO68lm88+kQb366iPvSW/CHzOPocYaVt7LkerdomKZMO1Urvb5NhHnlucyUzUC4RCBJ/G33VaApETT0A46Xb5fsq/u4lcKJuoZEW3RuXY40PaXCzAFzcGnV8W2s6gPW7QmxvifEuj3Aup6QJwSWjRQ5HfEM+8Blig7wbDuwrcPCthUWtnUAz6ywMEhr82dIY2kH1e6lsvuK1iXRX5ZztZZkLDOI8dRIZD6R8jKoKzbBoTVE+u9szOGUO2etQSurY7Lt0drH6WH2ndkFWbNEuVyRgC5fVvzNT8pFy1kneS/vx31NYe9n4p7cV4wjT2FyPJ2qmlDr92fpBFE8cWSekZFolHiOCguTrobNWEiI8LXAcQsTuNA7iGcPXNuD43i8wFclHzZ5JrL1Ak+u00JNWjyqPBxZ5Kluj4f0Hh/pPSEyPSEy3SEy/To4aBVoTfvYcmB0BTDSAQyvsDDUAQwtB/IZvVA5tJAt1mNjsRGHFBphTZCb0TqEXgY+DfxpMSvFOudFrWqha9EuoOiMo+Dk4Dl5FO08PFvFNteOcODTsbW8RmXjLMc42ymHhQxLxUifSe7Eb5AOUnje8El4Wf/zsDG/FqcUNnF6oP5x3LTs1/hD0wN65kcwGKd4rLvkNXsmp/V4VNZxqXgNnq7zWj3TZsFqsDBeb+Hx9Rae1Gv66iZCdPQHaO8L0N7rY3m/h7Z+H+liiIO6gYO6S0dGdJ8Ot7kYaU1htC2NsdY0xlvSCF3tnpvPp513m3J0DeoFrgRnOlpS41lqh25QL3hlRzbm5DBm54AxcoO9HOnARZPfgAx7jEuYu+tAzyq4uLaxjwy+tBdCPQ8arfUs62PmX1UIiXgWlmc1lY2aur5ozZP+FGVroIxQFg9azGcys7N6VjdxjtIZSxM8NNZakEfLKMAml00Modh7pHIHH5ejALXRmgrzadS1BCVxiIxjLhN/iITeuFy+LdqH2/Q5TCyk2AFXVFbzRvpbNjF6o79f8t/4ljDaQPMFJr32RduTWsMSvWOsfTRz06X7JweXZfdeyRx3qf6s9Aqju08XExqBxBmMDklp/BIWA0lTxUiLUXmO2BDYWAXE2hiz9qeyru9qE7Q40jzp+8Ic29wnZl2yvka+Hh0WgKoTVoAcmxaq/dKhcshgJsTUra/XvmjNoG2T4wwPlk1reMNoO8WmKwYUOpcXQ+k1MXFOFgLmmZdiTY42B43utFLNXfwXKnceZX5RyjSymk4jetaESc1JvI1Let/qZzPfU7z0u7RffI7kUyPub7QzseMvo60xQwqznfPE70odYwZmf6JJqAM/1L6fWsem03XlBDb+JcTBmLWZarJf4hdk/GmU9i/TFSYmee0Sa4PEpG+yXHKMeLvaM/FMSfRR20p/yaV59V+52Vv5EtDPLPObDGPvxeoBkLiihJCsHyTxEZPb9LFNjeI8LiRE+FrgdPu9OL/nz4kfR2JwZhZb0g+fvAeOhAhHgHCYkoVg2EKYq/Z00je4G8Jt9uE2B0g1e0g1+yo1khAXW/1z3hvC2pM8v4rwPpdoj6TwyHkEue6l5diWpdz4kpMDi9bI6TrF9UmUaT9yOmH2V9tLjxX3VWV2nWH9DL9f/nM8WjgUR4ydj4MmTsIx44dyGnb6cHfTrbi76bcYccb0cRPHqnJMcy3m/DTQT7FphYr2zmurOGaNeoFTW4pybo/78TYTJb58fxZ29H5mn+T+iXNEeUk5sV/imOZanCrHpM+gVxXOHuTjf7VOauyB4piD/GAK+SEXOcoHXRRGXWTyATp25TnFhHxvZ1uLyFBq8bhMbZOOB6z9mAWf7jxA8suajUFE+QhdEASGJg+VsGmcGdT6ipYW8RRRqWBnRNpoe+TVOZ5qigVDMzUV9zPCpQlCnxRmosmrRFjBaHxjImkkkjG5LxeqhNpwo38mFhIifC1wevsmcLa9lctkylwYdVAYTiE/7KIw7Kp8xEVQnHzY62Z9pJs9Hmymm4rINHtcd+to8fE+XMwsPnnI05N6eMbzhaUp3mZiQRnXBaTJUEt5y4KjJhaNq+PHM7qxniHy66PPa84fz/eocjdCfBvDqZuR9s9ENjgdzf5ynDv4KpwzeBFy9j2YsH+Por1zkkXrcR7NKpe0Y9/qesao1MzHmuZxKv+Q9NIq6LSgqNOpK26y/BCp4SJSQ0WkByn3kB4qwskHKI66nEaejWOvBY6FYouLYktK5y68FhdBhoJCo4prgriN69qTlELpLOI58Sr3oh4amFU2UT92nlG5jypTf30vhsl63I/bomOo7eq+j/sm7/+K39VUv5+y36XRbHA5+m2V/66q/ZbLPvMkv/GS9ujZsA/7lH3GKfdh1+PauUK0PlJ7XOR1kWrtI3lZU+tIVaw37UOlJO6bCvWmjm5WqZq6WT3E7drbmKkbz4px3axwjT2aKZfXyXrp8SevV57PnLPa+RIrn/T54n30aqbK48/gZ2QX3dP5THq/+HzTn2FwEt7p9gWOO6jPTrkJyG1+jyY3fcxvsHx7nCd/y3RMc8+rQOB8jGj/uB/n5NUwcbyw5BjJvtq9Pl+X/m3w+jL1LDFrkdV6teRv3C77vSR/V0aUUY5C1LPDrvI7V885dvc/yfMl6hNPJyeeI/GzRvv4K302JbWkiSe1Eq6N5lr3Z6ua0ve7eV6aZ7Fqi9/T0efXjo3ic8crRjlF74n4XVH6bSS1S/EVl44QSrersU1s+VC6PeGlcJJ9k9+0WTVf8Tzha4+fB+YvGQurxkdh5XUS5s2RHDEZrb0ibjPwqn49WasmidVzJuVOIJ0eQzo1Adfy4VpK+xwWMvDzddgStmIhIcLXAic9Poyv7Pw2HN+D7RXhBEXYlJwi7JYi7KYibJ9cnHuA4yFI+wgyAfxsSFaAKNSHCFI0YFCLI9VandJ1POxWldcGkakgrRWih7N62LH7Wn4g6fgb/CBXLwaq08Ob4kIpV7kmNoerXeCSg28dp4OP40audLkvvSj4oaxeMPFLJX7AldejtmimKzkww5zNTaUR4lxM4JVI43CkUBecyukBeLgOBfwGZAq5sCh/KZjXa1S3Ktunta3kAZ84XsX+pdtKcu2Ottr2im0UJLothN2GknPX5caxfHAQy4YGsGxwEK1Dg2gZHmbX5pn+IqckFPJgsLUVQ62tHBZhqLUFw83NCJxZWnE0S7euWQcdDyhKBxel25JDBT04K9snnpCYZB/K2SOfDizNSTl4iYQbKiedvuht1EYCj3Elb9zIO1HuVzqSoQX31JfbtEv6KE+WlQt6DmSdaKMyn38Gvms2fdQOQJSHSOUlMojKSe+SyvmCV6V/VDZeJqv0j3PVXiDHDeTBUjt1YE+UJceK+5eeR3u+tOLj+onjqvOQFzs9aDcmg3rAWXKvmUKVbRX3pbZ8rNYrbksO54yDgbiPao2Pwr9/bfaqni9qAKvMv+LBpXn2cIwpfkdZKIbkJTgWnpICjxmyLmrYk6N2xqSdXUROmrTzJeP1M27Tv0H+3sscO02yT2lutpc6mzLbJjtW5XHMdvW3jsxVtZkjl3VuvBerXJuxcj+7Sv+4HvXXQp8ah5i+SVNec//rZ6spJ/2glJhf0qSNumYjVHEr13XcL/NutON3npqkIMFFvT/Vdv0eN+/EeIYwElSV5Z+5tunmWgTb5/2s+Py67pHFCjlX43eLQx9EhzewYY3bsJwAbmYEqbpBOKk87KzyYNowiSOu+YoIXwsc16d7soHuSzImnz50nxr/F2VMJ7I8z+tZFN9IxbMiO3hOKM2pD8VIIlHKsZS9vIOcLlN7Ufeh/gW9nXJqV4FauYwC19koTxtDm9UacU3NPak1KeQPWLcnXvRqfZgSGFWuE5v50VUqodFjYdCO2zg3QiJfbVQOEseLZjYptgosPBQ62IEVOCQ8EmvDDTgGLqcxjONR6yk8bG3BGHkH0kKqmaVUM3BWYrbZBPpU9bhN9YxnrOOAjuVtZh9+CZkAkInZ5njQYWbCSudT6WEfD6TV8IWItRyl83Pxt16qCSmd50uuJzDGG8k1CMm5vGS/6tpC00YxbHjejAZMnCujR8pNnR/sLNxTrvs1tWOsaQVGVzvYrre7+RCpfMDJLYRwiirubWCltCc9B3XjKWRyDtp7XHhuGh7FYXNSyrW7rUM7cLw8ug5So6hpDJXrQLsmUkzknp7ubR1sl9doqT5Jz4y0n1qjpFdFhh6s0IMdUE7rT+KyEmaU90tVNgKKFoKMF0vW6CTjY5lBT7xPUuCZvM2cs3q/hQ4LOzoRyqW4+rzK5XjlYIAHSRwsiR7cWJTw75HX6emk1+xFbawV1Hm0TSdWDZbWjfYwyvVjIm5jG3eEdqjbKNfrfhJ1i/5ONnnMtWFTbhsPuqTBduDYKuft2ttuOGQh2BMiKGgnVoFageyHIfzQR0AaT2qhMq0sDrXDIVrDaAbnyVUqlooyaYUpWFwm+wzl0VetMTUBvLUOJSxL/Bsvb/fU755+57zmzOffe+W+esJD35umHnmt1WUloJq2WHCJtRiCUFvCKgLc9o3rgHdhwSDC1wJnyHNw1vB9CLOtQH0HgkyTHvwnHVSooKE8PDMOK/Ti9mS/qD+9WHTfpJOL5KOXh/IspGTm/DOzYKgniuJcO8W3UJLzrI/pZ6k1SJQrQUaptakPCR8pK0Q2UY9zZepi1PhlluhJ44Mp2icQ4nH4WIYAbWhAPU4KN+Ok8CjYGIBrdcO2RqNvN7ZY55VpAC3yVqvMuIeO3qQCEvJKNt1PrR5L9E+0Ua7bS9q43Y+OE2+rvtZJOUYg4ZOc+rpag5lCQHnoctwrlVM9Vbpdt5WUzTGicoq3qeMlyvq4pq9fZT9218yPtZmbhWZzS17wBoC8JO4HFRZPNPjiHHMDC1ZaK05xqUyZtOK6rNpIWCuU5E6iT2nf+Hi2b/Yx2qd4WyyuT40JjMpONvSA3JRL6yqQauyQw2zTjjl0v9AhzYxupzZOqo0G/RxwVWtvAtOXhAHTryRRm9JQqfPF7ufNy5/dsetBLOeBHgDr74RjO7G2jspKQIvaqa4FXx7wct2067ZIwKsWOyqZ6xhSPLg2sacS2yNnLmUxqUycssS+ybboGNGgvboylreTgKnNLfnJzGE/UrAoYDzFcyLBh8uqToJQ1TYKLk/tervqkyrZh4LQq3Kib9l+vM8+/2YSweL3g9AvkhcAhIEHUCIhyfcQ0ncT6G26jbZxO+0T+gh1G+2nyrSPFx+Lj6uOQS9q0iTzH4fKfB9aul3tV7nPzDqgUq7z1e+PtKLKtb5xp69/s8YVv24z7veNkB6V6TfIvysVJsKUSXCOw0so1/smqe26jkSZhWl9H7KQbZzE6EdvIrAyOf+he1e5RDcd6LtU242joJKydreuQjGY45VuV45Zyn87+neYEIKV9jX5O41jAppYleXtcTiH0utLhoJI5tZ02yqOhar91TusrF/J/nE8w5JQEQeI+eyMzov5heXjVoSvBU7/kI3xDas4WKuFHCwaKrJ3mdhdeeS3RkeUp5p5FZX1mLIcP7CMyZFZE6F/pmbtRWK7sY82gpwyEYwFu2p5ueBXPkduhEcm+h1P5wcda2QOxP0vCXKlwp+l2kqEvrg92ifabqHBCdBkW0jTYA7LUQiXwwtCVrkbe3HWthvBNyEAq+9GCdSR17by7zG5vVq/KtvZBj76O5UK4KZ/bG+/sOBBDs8Ka+0SDWb0LLG+4yZp8/QLON6mvpn4WGYb7Z/2iqgr5FCXz6Ehn0dDbgIN+ZwWSJQGin8PrBVLJjIJS2tNmcqV5qx68vU+XHeSbYnkJIKUs4CR4TT3X75Xmvi7UoI/5er7VAPMkHPzt6Jt1fdT21SfqJ7YT3k1mc4zIYz/hmaeZAqMVcC+slclFw0IyVz1AMYPpK2xSYNjUe4mypS7JTn1Nf1Mn2r9om1wK/rH+7l0tJJzOzUOoL03SAgJjTBDwk0k2BS1gKQFFj3ot1iwcxICXVzmOg3yywKJs8Dn6EmWeYjyMBqUeB5VFiSVMZxKKYshxoJN0temHjFYFuzIa6U5pzpeyTRl9F4prSe3K3+QlftE+5XtY95VkTfGsu3JkYC2uEtYBMZjH2MpaE2yj+6yl+Mljmms7Mq3T3K88mso2T5DoVJqQVgREzHaUNqvfHsVzJ13UHEHFhLz+wkp7JUc8vi/6z9Z0Z78WYYB2Wa4CMMU+4nnnMywAnZ6C4tyquvtahvZ3KqyReXAheVbHPvKpfhXAZXJzbfNsZccH3C5DXA94xqc2ow9t16LoQMgsrGX2cbrKbQNOcpsxtlenPZR8a94UEBiDc188gBCBVxWJhz0IqRZVjW4CGmm1QRlphco6b10oGYVgFnNyLJJmN4WJtpUOX55GAGGBKTS58FkD4e9D/5aHQsHZ2ysSllwLUpALgixpRDgmXyA3Lyx9LCmJZiyrXky57JVpS2Zx9t5cKvzku2T7lu2PdGPzfi0GVBszmklTEd1rDMtaKqhPg/3o1REqDxksjdMqqtE+hxqL1jlOcWDUx4yzTFpILdsZA86hneic3g3XL+IIq2b0YnWzBR5PY4qx8nldTamXrLdouDRap1OMdFW5Dbylklal3EdhJTWMClPlBSgVHnK1J4pjVfK0NJeMKluQgDQLyZ2/69+JfRtqwkEFYKdf43KPT/9x39rmmFODLr4t+ZOeRsd6DCi6v6sdSoqEyzWwinNMQvBOrG2LjQaP7U2VvVVZdNXCc7UV/Vj0y7dhwbpak2iGoDTgNMMyqlOzyY7UbaSJm82bdN1PahXfRL9uK/SCsWD/bhs7a9mZw5hDQwP8EnrQkK0mtBQuUpx2bRrs3JTpj4sGOhhuq7zGp4STYRe16POHBmoR7P0/HxRgbyjYL9Rbpzo2NGDKIoHTE8P/nsrE2J6L6r+SS2LKlvJgN5RgGR1OUnn8fEVJr8s/Y8xq9Ibo7G2/jDJe75kKVGZADDZYF1pguZ2nZq5BmcaP35l0mkmAMsmbfVflZ6xNM7w9URlMtgx3wlVJh6Twlf1ZNZElbllT36npr3s2ss/SvzdVwpp5n2X/NuZtuS+9ExdbFglnylR3sv3ORW21YiFhAhfC5xUMIZLbl1zAEcIJgtTOPeUv0Emn6eOqBrvuUIrdiCBnPWwkwePahDEQhsSgl1CADT9+EUcbdODpcj8Ju7f77noL7jIWC42ZOtxUDaDOtvG4VmHk3Ag7G3l4hxBt1BLC9ByyNyed/G9s/eDGmj65gFm4OpXtSzQq2UTVgax1ntyjXpiZWHCksHUzYrbSi186fwRR/Qraak6gK1ipFBtYDtlveL+t/b5GLG2hAS8hNYlsuqo1LgQpZ/bfJu1Z18nyVghWzZJZtqU584DnySL20q/fXM9kz/H5AFHqFiJldrC6bdNrlWcXltif2MdVabJ5OtM/i7Cst/Wfp239LdpTezCUVg4iPC1wFmXdbCrysJuJtyXx1W497Yqb6pIXoqm51SKZhpRvR4de7IXbNU+egazmpBWUlb7VXn3VpStvXxZJbtU+/Kmc8xqByk7FnmTfNIHnhqz0ZU6Age5p2K5u77KCQVBmH1TmCn6TdJeVdjRgok/qWl1PLtf3scIQuUCT4WQVGaiPT+G+fvAnGn3540ZAROwPt041tHOdVjjpR3r6G2JuyTWBMbG4LFwpzWFsUm0XjdsNIqsJTR9tQMOdnwTJNYrGgcd6ljGeYxpMx5KTd045Yi2m/OZ69GeCo2TENYY6+PyeU0eeaONzVdpcpNNaVmbrCYrk6auSpNM2nZlIqs0zHFf1kKzNlpvN1pl9lhJA3b6nunsSoOqhBgT+F238e9M/y209pUdHbG2NW5XVhZ6vZ3+GxmTTqPVDZOa2+hcKtF3Y44TsPMWPov2PK0nNsJAeZ2mXtqZGGv8qGjWvuoA4yrAufbYmPBEGG03a1UTnhuVlpY2aSf5yjWi9oCYdMWoTU2N5xue1NC5bmPNVrXtJdHS7CgYUGm9cr/SKGtl/fW19zWJ2aEwhzS3Z3HM5X+ZegpvH7bNsRWCUJUt6MVP0O9llQnoDMPPfjMjFVvrlMzqJteyJtfwVc486fgmSbv9aMY7Xg8Ym4poE5JyO//yAWtSy8lue5Pb6ZzxTLtaW25ckKhBKV+7sTIy4wC9Jp3WHfIYx3zO+MKi3CqN7Kn66pycFST7qPGRPmZQmvjylVPCeB8q2uw5l3ejdwmHbqAXq/beFpg2m0I8JDy7cT+zzcSRivcP9XGjGFP6xW2ml5WzCeWSm2aW1bpQqiXz5HpPbXIT5VRSH0wZIJpXIO1Nhp3JvuX7J7QakSkPDd6S60WMiQ5dm/FdSddqri1hhqXNV5LDgtJzKg+d8bb4M8b7633ZjEibmZUdR93JVfZNnpf//HoOV5fV3WjuRD0WiWapjbeKkh6xW3S+4dTRlfdWXU9MRKmKMa+LqoltpX35syrXr4lr13Xt1z2uR3+hiu8n6p/QJKl6qW4p+V2pj1tlH1OJ1ifr71MfL74KM8pMHLnsvkl+tnjwlzhGyRdVWk8er/SvXPqDpsEwP330j1r9HeOnFf8Fbf10YqGKriXSNUaeePny+GdkogRHo+ioHl2ffv6WW3SY51nUpn8YUZt+EFol/Uh4Mt+xfo7xMyrhEIKfU9rJimnn/cgBjOrDOTkUZtNeS2/T7Up+VA5Zyp+HvHCZbLLNQ93i5Qz8AGPnr/rzU26emZ7ZRtejPheVKV6jSuR9VpXJjHo6zPRbVQk6SsBRzkRUOJ7IKVDCmVDcJ+FcqKy/qVfrHx9z8vNNdWwW1BLHil3aV6dSGxyqdxe/m6hM7y3d5piyem9FZU70birdJzBtUfu+7cPCqN7n0IH99IxTI0T4WuC0rt2Inu1YslRMVicG3lO2ldX5OHrwknxHR0KI2aafRGZsXnGcSc6R1MWVX/Pk++cjW/E4zlWprbgpm/Z9YSr5XBAEQRD2hVprXUsmBhPl2Mwt4emw7G2rlwOWHTAulsx7JLCqtZW91MsnIuJy+QSFNums6DfF8RMDnJLrT0jse4svHm+2pmkPVaVfNPkTa+NMJdpeso816bmibWXjrsn2JfIDE1hILAjh6+qrr8ZnPvMZ7N69G8ceeyy+8IUv4JRTTpm0/7XXXot/+Id/wNatW7Fp0yZ86lOfwkte8pJoO6l3P/rRj+LrX/86BgcHccYZZ+DLX/4y911otK85Ej+//QqlstbT+ypTdzyZvESzrXqb6mNS3Ddqi/qaYybrqqwm1ciZQLWrqi5klBjklf+aptx3KtVd7WETAYtMAvwoRXXbn3xbWZn7wrSrbckZ43gOOJ7vTc44q9lhdqGihLIwtsXnQMJay2GENBV40TwjjSt91c7bE8EYowXD2t0+H8/W59FJlclVvzo5h9VRF6fOycEezTFUHp2rJI+vxeTGx6Ly2Jl0+2/c16opXJ6hNX2i7XGK6/pz6c8fBXjW6w+ib7xs/YPpV03wTbbXiqTN/eTiddKetlxjEXs2LdVixC/3aI/kjzQ5sx7VkxejympbYkpir/sl38pqFr4a1drNdVY8Syouu1Sroi6nbGBQUjeaCN03sV/FKGOKc0ebK/apGGnoLNaOlE7mxH+V+O6MkzIbistGh6d+uAkTHjZBMt7rtOMIc4cnnVMY86TEL8WYMhmTJBUh2ZzT7Gt+PeahU/JLi0yltO1TSbzAuJ74ThLareTfMeoXbYvc9Gotkl6wpdeK6C8x+i7JHI1NRX1tTsYBbuP4esY80NSVmZmKL5mMNclmZ6z9UqqnWBuqz61UTPoKy1XuSmsdu5ZIqq/ifvyNJdVfsco+MWo3/cydkyhHz1FTT9xFkfm+zhP16L2QfH9Ez8z4L1Xy7kg8U82+yedm+XN1f56lSa+B1YcJ01SNzTiTnTecB0fbnyOU9psPI7TmZW1YSFjhdA3da8T3v/99XHrppfjKV76CU089FZ/73OdYuHrsscewYsWKiv533HEHnvvc5+ITn/gELrjgAnz3u99l4euee+7B0UcfzX2oTtu/+c1v4qCDDmJB7cEHH8QjjzyCbDY7resaHh5GS0sLhoaG0Ny8sNSdM0lSSKuWgiDYr21LfTttI2ybAoLacByHkynvbz6Tx1gMkD0/BUj1Awqc6peUvYCCXwdc90Kvah9TNtupvxcU4Qce/FDnVA/jnFYipJwM0nYaaSfF5ZSdQtpJI2VnOFfJlGm7anPJ5TUL06UD04XsdvhA4N+K7yPwPPicilzncrGAoJjnPPQKUT2g3NO5X0TgFRBymy5zTi7H6ViqjdyP0zngFzjuErWbGEwqDlMAy6U4VinYbhq2m4KdynAb5aotDSedievpLJwUtWVhp7JwMhkuO6ks3EwGdroOLqVsHe/HHg2X6N9ZWFyoCVy1ssk8Y82zmMOcsACcfDZTaACqF3UbPZt14u1UVu0kPHOdj6naqcxrpshbqQVk3TrUUXLqUJ+qQ9bNcnKNV1Z1ldP/PJP1rTq8nm7bJMed9jHn67Xuw3VNun9leyazAvX1B6HWTFc2mPfCFwlcJ598Mr74xS9ynQala9euxbve9S588IMfrOh/ySWXYGxsDDfffHPU9pznPAfHHXccC3D0cVetWoW//du/xfvf/37eTl9SZ2cnrrnmGrzmNa+Z1nWJ8CUIgiAIgiAIwr7IBvN6+rpQKODPf/4zXvCCF0RtNONO9TvvvLPqPtSe7E+88IUvjPpv2bKFzReTfeiLIiFvsmMS+Xyev9RkEgRBEARBEARBmC7zWvjq7e2F7/uslUpCdRKgqkHtU/U3+b4ckyAzRRLSTCLtmyAIgiAIgiAIwqIQvuYTH/rQh1iNaNL27UvYxaAgCIIgCIIgCItL+Gpvb+fF/d3d3SXtVF+5cmXVfah9qv4m35djEplMhu03k0kQBEEQBEEQBGFRCF/pdBonnngifvWrX0Vt5HCD6qeddlrVfag92Z/4xS9+EfUn74YkZCX70Pqtu+66a9JjCoIgCIIgCIIgLPo4X+973/tw2WWX4aSTTuLYXuRqnrwZXn755byd3NCvXr2a12QR73nPe3D22Wfjs5/9LF760pfie9/7Hu6++2587Wtf4+3kkvmqq67Cxz/+cY7rZVzNkwfEiy66qKafVRAEQRAEQRCExcu8F77IdfyePXvwj//4j+wQg1zG33LLLZHDjG3btpXEHDr99NM5ttdHPvIRfPjDH2YB68Ybb4xifBEf+MAHWIB729vexkGWzzzzTD7mdGN8CYIgCIIgCIIg7CvzPs7XfIWcbrS2trLjDVn/JQiCIAiCIAhLl+HhYfaGTood8oy+YDVf85WRkRHOxeW8IAiCIAiCIAhGRphK+BLN135Cjj927tyJpqYmXkc2HyRt0cItHuRvujiRv+viQ/6mixP5uy4+5G+6OBmeR39XEqlI8CI/EsklUeWI5ms/oS91zZo1mE+IC/zFh/xNFyfyd118yN90cSJ/18WH/E0XJ83z5O86lcZrQbiaFwRBEARBEARBWCyI8CUIgiAIgiAIgjAHiPC1CMhkMvjoRz/KubA4kL/p4kT+rosP+ZsuTuTvuviQv+niJLMA/67icEMQBEEQBEEQBGEOEM2XIAiCIAiCIAjCHCDClyAIgiAIgiAIwhwgwpcgCIIgCIIgCMIcIMKXIAiCIAiCIAjCHCDC1yLg6quvxoYNG5DNZnHqqafij3/8Y60vSTgAfvvb3+LCCy/kCOmWZeHGG2+s9SUJB8AnPvEJnHzyyWhqasKKFStw0UUX4bHHHqv1ZQkHyJe//GUcc8wxUWDP0047DT/96U9rfVnCDPLJT36Sn8FXXXVVrS9FOAA+9rGP8d8xmQ4//PBaX5ZwgOzYsQNveMMbsHz5ctTV1WHz5s24++67sRAQ4WuB8/3vfx/ve9/72M3mPffcg2OPPRYvfOEL0dPTU+tLE/aTsbEx/juSUC0sfH7zm9/giiuuwB/+8Af84he/QLFYxPnnn89/Z2HhsmbNGh6c//nPf+YX/rnnnou/+qu/wsMPP1zrSxNmgD/96U/46le/ygK2sPA56qijsGvXrij9/ve/r/UlCQfAwMAAzjjjDKRSKZ70euSRR/DZz34Wy5Ytw0JAXM0vcEjTRbPqX/ziF7keBAHWrl2Ld73rXfjgBz9Y68sTDhCaofvf//1f1pYIi4M9e/awBoyEsuc+97m1vhxhBmlra8NnPvMZvPnNb671pQgHwOjoKE444QR86Utfwsc//nEcd9xx+NznPlfryxIOQPNFFiT33XdfrS9FmCE++MEP4vbbb8fvfvc7LERE87WAKRQKPOv6ghe8IGqzbZvrd955Z02vTRCE6gwNDUUDdWFx4Ps+vve977E2k8wPhYUNaapf+tKXlrxbhYXNE088wab8Bx98MF7/+tdj27Zttb4k4QD44Q9/iJNOOgmvetWreDLz+OOPx9e//nUsFET4WsD09vbyS7+zs7Okneq7d++u2XUJglAd0kzT+hEylzj66KNrfTnCAfLggw+isbERmUwG73jHO1hLfeSRR9b6soQDgIRoMuGntZrC4rEQuuaaa3DLLbfwWs0tW7bgrLPOwsjISK0vTdhPnn76af5bbtq0CT/72c/wzne+E+9+97vxzW9+EwsBt9YXIAiCsJRm1B966CFZb7BIOOyww9iUibSZ1113HS677DI2JxUBbGGyfft2vOc97+G1meTASlgcvPjFL47KtIaPhLH169fjBz/4gZgIL+CJzJNOOgn/+q//ynXSfNG79Stf+Qo/h+c7ovlawLS3t8NxHHR3d5e0U33lypU1uy5BECq58sorcfPNN+O2225jZw3CwiedTuOQQw7BiSeeyJoScpTz7//+77W+LGE/ITN+clZF671c1+VEwvTnP/95LpOlibDwaW1txaGHHoonn3yy1pci7CddXV0Vk1xHHHHEgjEnFeFrgb/46aX/q1/9qmQ2gOqy7kAQ5gfk04gELzJJu/XWW3HQQQfV+pKEWYKev/l8vtaXIewnz3/+89mUlLSZJtHsOq0RojJNdgqLw6HKU089xQN4YWFyxhlnVIRsefzxx1mjuRAQs8MFDrmZJxUrvSBOOeUU9shEi74vv/zyWl+acAAvhuSMHNmn04ufHDSsW7euptcm7J+p4Xe/+13cdNNNHOvLrMdsaWnh2CTCwuRDH/oQmzPRb5LWjtDf+Ne//jWvPxAWJvT7LF+L2dDQwHGEZI3mwuX9738/x86kgfnOnTs5NA8J0q997WtrfWnCfvLe974Xp59+OpsdvvrVr+b4tl/72tc4LQRE+FrgXHLJJey6+h//8R95UEcucWlRabkTDmHhQDGDzjnnnBIBmyAhmxYNCwsLWhRMPO95zytp/8Y3voE3vvGNNboq4UAh87RLL72UYwaRIE1rSUjwOu+882p9aYIgJHj22WdZ0Orr60NHRwfOPPNMjrtIZWFhcvLJJ7M1CU2C/fM//zNblJDygbTUCwGJ8yUIgiAIgiAIgjAHyJovQRAEQRAEQRCEOUCEL0EQBEEQBEEQhDlAhC9BEARBEARBEIQ5QIQvQRAEQRAEQRCEOUCEL0EQBEEQBEEQhDlAhC9BEARBEARBEIQ5QIQvQRAEQRAEQRCEOUCEL0EQBEEQBEEQhDlAhC9BEARBEARBEIQ5QIQvQRAEYUnzxje+EZZlcUqlUujs7MR5552H//qv/0IQBLW+PEEQBGERIcKXIAiCsOR50YtehF27dmHr1q346U9/inPOOQfvec97cMEFF8DzvFpfniAIgrBIEOFLEARBWPJkMhmsXLkSq1evxgknnIAPf/jDuOmmm1gQu+aaa7jPv/3bv2Hz5s1oaGjA2rVr8Td/8zcYHR3lbWNjY2hubsZ1111Xctwbb7yR+4+MjKBQKODKK69EV1cXstks1q9fj0984hM1+byCIAhCbRDhSxAEQRCqcO655+LYY4/FDTfcwHXbtvH5z38eDz/8ML75zW/i1ltvxQc+8AHeRgLWa17zGnzjG98oOQbVX/nKV6KpqYn3/eEPf4gf/OAHeOyxx/Cd73wHGzZsqMlnEwRBEGqDW6PzCoIgCMK85/DDD8cDDzzA5auuuipqJ6Hp4x//ON7xjnfgS1/6Ere95S1vwemnn87mi6Td6unpwU9+8hP88pe/5O3btm3Dpk2bcOaZZ/L6MtJ8CYIgCEsL0XwJgiAIwiSEYciCEkFC1POf/3w2TSRN1l//9V+jr68P4+PjvP2UU07BUUcdxVox4tvf/jYLWM997nMjxx733XcfDjvsMLz73e/Gz3/+8xp+MkEQBKEWiPAlCIIgCJPw6KOP4qCDDmJHHOR845hjjsH111+PP//5z7j66qu5D63lMpD2y6wRI5PDyy+/PBLeaC3Zli1b8C//8i+YmJjAq1/9ajZJFARBEJYOInwJgiAIQhVoTdeDDz6Iiy++mIUtcjv/2c9+Fs95znNw6KGHYufOnRX7vOENb8AzzzzD67seeeQRXHbZZSXbySnHJZdcgq9//ev4/ve/z4Jcf3//HH4qQRAEoZbImi9BEARhyZPP57F79274vo/u7m7ccsst7ImQtF2XXnopHnroIRSLRXzhC1/AhRdeiNtvvx1f+cpXKo6zbNkyvOIVr8Df/d3f4fzzz8eaNWuibeQtkdaCHX/88ey849prr2UPi62trXP8aQVBEIRaIZovQRAEYclDwhYJRuRIg2J+3Xbbbay9InfzjuOw10MSnj71qU/h6KOPZk+Fk7mJf/Ob38ymiG9605tK2mmd2Kc//WmcdNJJOPnkk9mUkRxykCAmCIIgLA2skFYTC4IgCIIwI3zrW9/Ce9/7XjZLTKfTtb4cQRAEYR4hZoeCIAiCMAOQ10NyM//JT34Sb3/720XwEgRBECoQWwdBEARBmAHIpJDigtE6rg996EO1vhxBEARhHiJmh4IgCIIgCIIgCHOAaL4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEIQZZXR0FB/96Efxohe9CG1tbbAsC9dcc82k/cMwxLe+9S0897nPRWtrK+rr67F582Z8/OMfx/j4+D6d+4EHHsDll1+Ogw46CNlsFo2NjTjuuOPwgQ98AE8//fR+fZ6f/OQn+NjHPoaZho5J381kaffu3TN+TkEQBKG2uDU+vyAIgrDI6O3txT//8z9j3bp1OPbYY/HrX/960r6+7+N1r3sdfvCDH+Css85igYSEr9/97ncswFH7L3/5S6xYsWKv5/3617+Od77znWhvb8frX/96HH744fA8Dw899BD++7//G5/73OcwMTEBx3H2Wfi6+uqrZ0UAI7785S+zkFgOCaKCIAjC4kKEL0EQBGFG6erqwq5du7By5UrcfffdOPnkkyft++lPf5oFrPe///34zGc+E7W/7W1vw6tf/WpcdNFFrMn68Y9/POU577jjDha8zjjjDNx8881oamoq2f7Zz34W/+f//B/MR175yleywLgv5HI5pNNp2HalAcvY2BgaGhr2+3qCIEChUGDNoSAIgjCziNmhIAiCMKNkMhkWvPYGaaFI4Dr00EPxiU98omL7hRdeiMsuu4w1T3/84x+nPNY//dM/saned77znQrBiyBB4l/+5V9KtF6kXXvVq17FGjq65rVr1+K9730vX5fhjW98I2u9iKRJYFJQIY3aUUcdxefo7OzE29/+dgwMDGCmIM0hnfN73/sePvKRj2D16tWsHRweHubrI63ZU089hZe85CX82UnrZ4Swv/3bv+XPRZ/vsMMOw//9v/+XzTyT0LGvvPJK/u7oc1DfW265hbfROU888UQ+bnNzM5uD/vu///uMfTZBEISlhmi+BEEQhJrw+9//noWU97znPXDd6q+jSy+9FN/4xjfwox/9CKecckrVPrQu7NZbb8Xznvc8rFmzZtrnv/baa3lf0pgtX76cBbwvfOELePbZZ3kbQYLUzp078Ytf/ILXpZVD22k9G2nn3v3ud2PLli344he/iHvvvRe33347UqnUXq+jv7+/oo2+j3KzQxIeSdtFWsJ8Ps9lgkwrX/jCF+LMM89k4YoEMxKwXvayl+G2227Dm9/8Zl739rOf/Qx/93d/hx07duD//b//V3Js+v5IA0lCGGnhNmzYwJ/5ta99LZ7//OfjU5/6FPd79NFH+XPR30wQBEHYd0T4EgRBEGrCI488wjmtC5sMs830rcaTTz7JAsjRRx9dVbAh7ZSBtDdGaCGBoq6ursTU8ZBDDsGHP/xhbNu2jTVip512GmvmSBB5wxveUCE8/sd//AdrjGjdmuGcc85hZyMkwCXbJ4M0UtXa/vKXv1SYGpIZZ/KaCRLESIOX1B7edNNNLFCR05L/7//7/7jtis5cAyYAAQAASURBVCuu4H6kuSIha+PGjVH/xx57DA8++CCOPPLIqO2qq67i74uEtn1dJycIgiBUR8wOBUEQhJowMjLCeTUzQYPZZvpWg8zviGpOKw4++GB0dHRE6Yc//GG0LSnEkIkeOQo5/fTTWWtEmqu9QcJVS0sLzjvvPN7XJDLTo2shrdN0uP7661m4SybS9pVDJpjlgpeBtHdJyFSTBCbSxiUhM0T6fD/96U9L2s8+++wSwYsgzRt9L3Q9giAIwswgmi9BEAShJkxHsDLbpvJ2aI5DLu7LIQ1QsVjE/fffz+Z6SUi79Y//+I8skJWv0RoaGtrr9T/xxBPcb7Jr6+npwXQgF/vTcbhB7vOrQSaK5eaWzzzzDFatWlUh2B5xxBHR9r0d+2/+5m/YFPHFL34xrzM7//zz2QkKafUEQRCE/UOEL0EQBKEmGE0LxeYir4bVoG1GgzUZZCpIAgi5lC+HNDpE+ZoycnFPGisyS/z7v/97dktPHgJpPRQ5sUiaKk4G9SHBi8wOq0GatplkMq0XOcio5vXwQI9Nn+2+++5js0PSlFEijRytw/vmN795QOcTBEFYqojwJQiCINQEcgtPpm3f/e53eV1StXVFFJ+LoLVKk0FCEznb+M1vfsPCE2lp9gatb3r88cdZiCBhwlDNxC7p3TAJrZmiGGT0OSYTjGrF+vXr+dpIc5jUfpl1ZLR9OtD6OPI6SYmETdKGffWrX8U//MM/sNArCIIg7Buy5ksQBEGoCeSV7wMf+AA7ezBOIZJQbC/yJEgDf3JxPhVkPkjaLHKKUc38sNy9uhH0ku1UruZG3cTMGhwcLGknEzw6J3khLIccgJT3n0vI7TxdG3leTEJeDkmYJFPCvdHX11dSJ+3aMcccEzn5EARBEPYd0XwJgiAIMw4N+kn4IDftBLmKJxfuxLve9S52VEGQ8EWmbeR58M4778TFF1/MWiTyJPjtb3+b406RALY3zjrrLD4nHXvTpk0c64pMCSlYMGm4yDSQtDgm/hhtI80VrQMjbRl59SPHF9Xic5EDDYKcV5BLdxLcXvOa17BJI7maJy+D9BloTRS5lqe1YOSMgwQ5CqC8N6677rqqzkLILJLihu0PJLCS10USardu3cpeI3/+85/zGjjyYpj0dDgZb3nLW9gs89xzz+U1ZbROjFzxk9t6s3ZMEARB2EdCQRAEQZhh1q9fTyqlqmnLli0lfYMgCK+55prwjDPOCJuamqJ+L3jBC8J8Pr9P57333nvDSy+9NFy3bl2YTqfDhoaG8Jhjjgn/9m//NnzyySdL+j7yyCN8jsbGxrC9vT1861vfGt5///187m984xtRP8/zwne9611hR0dHaFkWb0/yta99LTzxxBPDuro6vv7NmzeHH/jAB8KdO3dOea0f/ehHJ/2OKN12223cj3KqX3vttRXHuOyyy/gzVmNkZCR873vfG65atSpMpVLhpk2bws985jP8fSehY19xxRUV+1933XXh+eefH65YsYK/S/pO3/72t4e7du2a8nMJgiAIk2PRP/sqsAmCIAjCbEHeCUlz86tf/Yo1ZuJdTxAEQVgsiPAlCIIgzDsovhQ50SAHEeRI44QTTqj1JQmCIAjCASPClyAIgiAIgiAIwhwg3g4FQRAEQRAEQRDmABG+BEEQBEEQBEEQ5gARvgRBEARBEARBEOYAEb4EQRAEQRAEQRDmAAmyvJ8EQcDBQ5uammBZVq0vRxAEQRAEQRCEGkE+DEdGRrBq1SrY9uT6LRG+9hMSvNauXVvryxAEQRAEQRAEYZ6wfft2rFmzZtLtInztJ6TxMl9wc3NzrS9HEARBEARBEIQaMTw8zIoZIyNMhghf+4kxNSTBS4QvQRAEQRAEQRCsvSxHEocbgiAIgiAIgiAIc4AIX4IgCIIgCIIgCHOACF+CIAiCIAiCIAhzgAhfgiAIgiAIgiAIc4AIX4IgCIIgCIIgCHOACF+CIAiCsJ+MjY1h27ZtHFxTEARBEPaGuJoXBEEQhH3E93386U9/wm233YZ8Po8jjjgCF154Ierr62t9aYIgCMI8RoQvQRAEQdgHnnnmGfzkJz9Bd3d31Pboo49ix44deMUrXoENGzbU9PoEQRCE+YsIX4IgCIIwDUZHR/GLX/wC999/P9fr6urw/Oc/H11dXbj++uvR39+Pb37zmzjrrLNw9tlnw3GcWl+yIAiCMM+wQjFU3y+Gh4fR0tKCoaEhNDc31/pyBEEQhDkyMSROOOEEFrwaGhq4Tu0//elPcd9993F97dq1rAVbtmxZTa9dEARBmF+ygQhf+4kIX4IgCEvPxHDVqlV4yUtegjVr1lTt/+CDD+Lmm29mYSyTyfA6sKOPPnqOr1oQBEGYa0T4mmVE+BIEQVh6Joak8bLtqR0FDwwMsBnis88+y/Xjjz8eL37xi5FOp+fk2gVBEIS5R4SvWUaEL0EQhKVpYjjd4/zmN7/Bb3/7W64vX74cF198MWvOBEEQhMWHCF+zjAhfgiAIS9vEcDps2bIFN9xwA0ZGRlhjdt555+HUU0/dq/ZMEARBWFiI8DXLiPAlCIKwODgQE8PpMD4+jh/+8If4y1/+wvVDDjkEF110ERobGw/42IIgCML8QISvWUaEL0EQhIXNTJkYTgd61d5999342c9+Bs/z+Pgvf/nLWRATBEEQFj4ifM0yInwJgiAsXGbDxHA69PT04LrrruOcOO2001jYc10JuykIgrCQEeFrlhHhSxAEYeEx2yaG06FYLOLnP/85a90ICtJMzjja29vn5PyCIAjCzCPC1ywjwpcgCMLCYS5NDKcLrQG76aabMDExgVQqxZq34447DpZl1eR6BEEQhP1HhK9ZRoQvQRCEhUGtTAyn+y4hb4hbt27lOgVkvuCCC5DNZmt9aYIgCMI+IMLXLCPClyAIwvxmPpgYTocgCHD77bfj1ltvZcccra2tbIa4du3aWl+aIAiCME1E+JplRPgSBEGYn8xHE8PpsH37dlx//fUYHBxk08NzzjkHZ5555rwSFAVBEITqiPA1y4jwJQiCMP+YzyaG0yGXy+HHP/4xHnzwQa5v2LCBXdLT+0YQBEGYv4jwNcuI8CUIgjB/WCgmhtOBXsv0OUiILBQK/Fle9rKX4Ygjjqj1pQmCIAiTIMLXLCPClyAIQu1ZqCaG06Gvr49jgu3atYvrJ510El74wheyZ0RBEARhfiHC1ywjwpcgCEJtWegmhtPB8zx2xHHHHXdwvaOjA6985SvR2dlZ60sTBEEQEojwNcuI8CUIglAbFpOJ4XR56qmn8L//+7/82R3HYQ3YySefLDHBBEEQ5gkifM0yInwJgiDMLYvZxHA6kOBFQZmfeOIJrh922GG8FmwpfHZBEIT5jghfs4wIX4IgCHPHUjAxnA70yr7rrrtY80fCaFNTE17xilfgoIMOqvWlCYIgLGmGRfiaXUT4EgRBmH2WoonhdCAnHOSMg5xyEBQPjOKCkUmiIAiCMPeI8LWEhK97772XBySHH354Ta9DEARhpljqJobTgdzQ33LLLbjnnnu4vnr1alx88cVoa2ur9aUJgiAsOYZF+FoawteOHTvwn//5nwiCAOeffz5OO+00WYAtCMKCRkwM942HH34YP/rRjzhAczqdxgUXXIBjjjmm1pclCIKwpBgW4WtpCF80O/zTn/4Ud999N9dPPPFEHqSI6YkgCAsNMTHcfwYHB3HDDTdg27ZtXD/22GP5XZDJZGp9aYIgCEuC4WnKBjV/m1199dXYsGEDstksTj31VPzxj3+csv+1117L5nXUf/PmzTw7moRePqQBWr58OWuA7rvvvopjPO95z+NtyfSOd7wDCxESsl760pey22Hiz3/+M7773e/yDKggCMJCgCaR/vCHP+ALX/hCJHiRwHXllVdyYGERvPZOa2srLrvsMpx99tn8TqPv8atf/SpbRwiCIAjzh5q+0b7//e/jfe97Hz760Y+yzTrN1JEQ0dPTU7U/BZl87Wtfize/+c28zumiiy7i9NBDD0V9xsbGeOHxpz71qSnP/da3vpUXLJv06U9/GgsVetGSueFrXvMapFIpjgfzX//1XzwTKgiCMN9NDL/2ta/x2iVa20Umhm95y1vEhfp+TsaR0403vvGNPOva39/PZum33347m6YLgiAItaemZoek6aIgkV/84he5Ti+HtWvX4l3vehc++MEPVvS/5JJLWLi6+eabo7bnPOc5OO644/CVr3ylpO/WrVvZ9S4JabS9XPNFbZ/73OcWvNlhOTt37mTNF5nv0MCFhFVZJyEIwnxDTAxnl4mJCV4H9sgjj3D94IMPxstf/nJ2TS8IgiAsQbND8tJEJnIveMEL4ouxba7feeedVfeh9mR/gjRlk/Wfiu985ztob2/H0UcfjQ996EMYHx+fsj/NyNKXmkzzEZo1Jq1eZ2cnC6rXXHNN9PIVBEGoNWJiODeQMPuqV70KF154IVzXxdNPP40vf/nLePzxx2t9aYIgCEsat1Yn7u3t5ZcwCQlJqP6Xv/yl6j67d++u2p/a94XXve51WL9+PQsqDzzwAP7+7/8ejz32GK8Xm4xPfOIT+Kd/+icsBEjqftOb3sQxYJ544gn84Ac/YKH1jDPOEE+IgiDUDPFiOLfQ856cMK1bt47fB/S9k2UEWZ2cd955LJQJgiAIc8uSfPK+7W1vi8rktKOrq4vNXWit1MaNG6vuQ9oxWp9mIM0XmUjOV8jDFa0B+9nPfsZOTH75y1+y/T855xBPiIIgzCViYlhbOjo6eB0dvQfuuusuTmSa/8pXvpK3CYIgCEtA+CKTPxICzAyogeorV66sug+170v/6UKzgMSTTz45qfBFwsxCc9lL3y/NKlPATRLCyKnJwMAAXv3qV/PgRxAEYTaRQMnzB3LG9OIXv5jfcTfeeCO/O8nRyYte9CL+m4hVhCAIwtxQsylHCgRJ5hC/+tWvojZyuEF18txXDWpP9idoNnWy/tPFuKMnDdhihJySGE+IW7ZsYe9XJIQJgiDMFuLFcH5y6KGH4p3vfCc74CgWi+yUg0K4kIMOQRAEYZF7OyRX8xSXhGKRnHLKKex9kNYn0ZovWst16aWXYvXq1bzeyriapxgmn/zkJ9l87nvf+x7+9V//lTU65DiDINM6CjJJXv9Mn8MOO4y1Y5TItJBs3kkjRLHAaM3Xe9/7Xl5z8Jvf/GbBezucCnKpT599ZGQE9fX17AlxPptOCoKw8BATw4UBTXbSO/XWW2/lMr3PXvGKV/B6aEEQBGHfma5sUFPhiyA385/5zGfYaQa5f//85z8fmQGSS3gKwEwe+ww0Q/eRj3yE7dU3bdrE8blIkDJQ38svv7ziPBRL7GMf+xi2b9+ON7zhDRwbjLwBkvBB7nfpmPsiRC1E4ctcNwlg9H2TWSJ9diO4CoIg7C9iYrgwoSDM5IyDrCHI9JAmOM866yxZGywIgrBYha+FykIVvggaGF1//fWRy+Fzzz2XX7Zi8y8Iwv4gXgwXNvROoL+f0VaSd0TSgrW2ttb60gRBEBYMInzNMgtZ+CLIzOTnP/85x9shSOt4wQUXiOthQRCmjZgYLi7IDP/mm2/mOJzZbJZjhB111FG1vixBEIQFgQhfs8xCF74MZCZEM550G5CJJ3lCpPVggiAIkyEmhosXWjdNlhFkjmj+ruQRkZxkCYIgCJMjwtcss1iEL4ICMdNaOprtJCckr3/969k9vSAIQjliYrg0hGsSrH//+99HoWEoJtiBhnURBEFYzIjwNcssJuGLIAcc5IiDPheZDpFrevF6JQiCQUwMlx5PP/00brjhBv7bkwOO8847jx1iyfpgQRCESkT4mmUWm/BFkAv6//mf/2E3/fSi/au/+iscc8wxtb4sQRBqiJgYLm3IK/BNN90UOWgiL8MXXXSR/O0FQRDKEOFrllmMwhdBpoc000mx1oy7f3I9LDOdgrD0EBNDgaBhwh//+Ed20kTCeGNjI4cp2bhxY60vTRAEYd4gwtcss1iFL+MJ8Ze//CUH4CRI+/Wyl71MPCEKwhJBTAyFyczTyRnHnj17uH766adzqBJ5NwiCIECEr9lmMQtfhrvvvhs//vGPedaT4r7QOjDxhCgIixcxMRSmYx1BGjB6Pxht6MUXX8zOmgRBEJYywyJ8zS5LQfginnrqKfzgBz/ggRh5QHzd617Hnq8EQVhciImhsC888sgj+OEPf4hcLsdu6F/60pfi2GOPrfVlCYIg1AwRvmaZpSJ8ET09PfjOd77Dn5XMjy655BKOCSYIwsJHTAyF/YXeCbRGmAR3YvPmzSyEUYBmQRCEpcawCF+zy1ISvswAjTwhUuBNGpDRGrDjjjuu1pclCMJ+IiaGwkytEf7d736HX//612yivmzZMjZDFI2pIAhLjWERvmaXpSZ8EcViEf/7v/8/e+cBHkd1ffGzfdUlq7oXwB0XbNN77zWhhMSEJCT0HkILptc/hBBaIAmQBELvvffqhrFxAeNu9V63/7/7ZmY1u5JsSZY02t3z8zeeuqu3bWbOu/ee94JKNxH23ntv7LfffnRCJCTBYIoh6WvWr1+vzDjkmigddHJt2GOPPRg9JYSkDA0UX/1LKoovo5fz/fffx6effqrWp06dqsYDc7lcVjeNELIVmGJI+pPW1la8+uqrWLZsmVofO3assqRPpWskISR1aaD46l9SVXwZLFy4UF1kRYyNHDlSOSEyVYmQwQlTDMlAIbcUixYtwhtvvKGyJUTgy6DMEyZMsLpphBDSr1B89TOpLr6En376STkhituV5PmLE2JhYaHVzSKEmGCKIbGCqqoqPPvss2psMGHOnDk4+OCDmSVBCElaKL76GYovDRlsU5wQ6+rqlMPViSeeiHHjxlndLEJSHqYYEqsJBoN477338MUXX6j1oqIi/OxnP1NzQghJNii++hmKr3aam5uVE+LGjRvVTd1RRx2FmTNnWt0sQlISphiSwcYPP/yAF198UV0rnE4nDjnkEMyePZtmTYSQpILiq5+h+IpFcvtfeuklLF26VK3vueee2H///dnDTsgAwhRDMpgjseKWu3r1arU+ceJENWRJenq61U0jhJA+geKrn6H46oiYb8hYLx9//LFanzx5snK6Yo4/If0LUwxJolwjvvzyS7z77rtqWa6dxx9/PMaMGWN10wghZJuh+OpnKL66ZvHixXj55ZfVxVV63MUJMTMz0+pmEZJ0MMWQJCKbN29WZhw1NTXRMSP32WcfOBwOq5tGCCG9huKrn6H42jJr167Fk08+qZwQc3NzlRMii6wJ6TuYYkgSGeksEDt66awTZMgSiYKJcy4hhCQiFF/9DMVX96yGn3jiCdW76fF4lBPidtttZ3WzCElomGJIkonvvvtOjRkpYkyuE2LYNHXqVKubRQghPYbiq5+h+Ooe4m711FNPYf369crZ6sgjj8SsWbOsbhYhCQdTDEmyUltbi+eee0455gozZszAYYcdpsQYIYQkChRf/QzFV8/GepEasCVLlqj13XffHQceeCB76QnpJkwxJKnQufDRRx9FDZvy8/NxwgknqO86IYQkAhRf/QzFV8+Qr5lcWMUNUZg0aZJyQnS73VY3jZBBC1MMSaqxZs0aPP/882hsbFTfcemo23XXXfl9J4QMeii++hmKr94h0S8ZD0x6OaVH85RTTkFWVpbVzSJkUMEUQ5LKtLS0qGyJFStWqPXtt98exx57LF1zCSGDGoqvfobia9tSqMQJsbW1Vb2H4oRYXFxsdbMIGRQwxZAQLVti/vz5eOutt1TqunQ6SLaECDFCCBmMUHz1MxRf20Z1dbVyQpS5pB7+/Oc/xw477GB1swixDKYYEtKRiooKNSaYzIXddttN/S6cTqfVTSOEkBgovvoZiq++SS15+umn1Zhg4oQovftz5syxulmEDChMMSRkywQCAbz99tvqdyIMHTpUmXEUFBRY3TRCCIlC8dXPUHz1DZJO8sorr0R7+6Ww+uCDD2ZPP0kJmGJISPeRGjCpGZaUdZfLpX4rYksvnXeEEJLU4mv16tV45JFH1Pyvf/0rioqK1Ej1o0aNwpQpU5AKUHz1HfIV/OSTT/D++++r9QkTJqheTTohkmSFKYaE9P7aK26IkjEhyD2HjB8pvyFCCElK8SV24TL44R577KHG41i+fDnGjRuHW2+9VRXHSm52KkDx1fcsXboUL7zwgkrDkrQScULke0uSCaYYErLthMNhfPbZZ6rDTm5h5FosHXbSAUwIIUknvqTYVcwRLr74YmURLj23Ir6+/vprHH/88dER6pMdiq/+Yf369coJUerB5H0VJ8SSkhKrm0XINouu77//XkV4DeMAphgSsm1s2LABzz33HOrq6lTq4b777ou99tqL0WNCSHKJLxln47vvvsPYsWNjxJekAEycOBFtbW1IBSi++o+amhrlhFhVVaVSD3/2s59h/PjxVjeLkF4ZBSxevBiff/45amtr1TamGBLSd8g9x2uvvabuS4TRo0erjmC5PhNCyGDUBj2+8ufm5qK0tLTD9kWLFmH48OE9bykhcQwZMgS//e1vlcD3+/343//+h6+++srqZhHSbcQQQKJcd999t7oxFOElokt65s877zzMnj2bwouQPsDr9SqxJYMwS2edmNg88MADqiSCEEIGIz2OfF166aXqRviZZ55R0YiFCxcqp665c+eqad68eUgFGPkamFStV199VQl7Yeedd8ahhx7Km1YyqM8LX375pap/lY4DQc4Tu+++O2bOnEkTGUL6ERk3UurOjQ5i6eQQ91z+7gghCZ12KDcU55xzDh599FF1cywDHcpcanNkm8PhQCpA8TUwyNdTCqvfffddtS4DMUsaosfjsbpphESRFFlJLZQ0bDkfCuICK8ZEU6dOTZnzIiGDYfgSMeKQ36NQWFiorhnFxcVWN40QkuQ09Pc4X1LoKjnWYpksPbpyU5xKUHwNLMuWLVNOiHJhlYuoiH3m9BOr2bRpEz799NOYFCdxXNtzzz3VOZHjDxFiDTIUjlwz5B5FOj8OOeQQzJkzh79JQkjiia/rr79epR6mp6d3qHG44447cM011yAVoPgaeMRJU+q/mpubldmLWNGLYxwhA4mcMn/66SclutasWRPdLmnYIrpod03I4ECElwzK/MMPP0THkDz66KM5rAMhJLHEl/QgST61pNTE51rLNiPlJtmh+LIGMS4QJ8TKykq4XC41tou4bBIyEGMLiV28pMEaNSVSf7jjjjuq9ML4cyIhxHrkFkfq1GVQc7k/kY674447Trk0E0JIQogvudkQgw3JozYjOdYnnXSSuilOBSi+rLUWfvrpp1X0QZB0kl133ZXpJKTf7OKllktqSGQYBEGEv1jFy7iH4gBLCBncSIeJmHFIR7EgUer99tuP9ZiEkMErvvLy8tTNrfGE5htd6U2S8P6ZZ56J++67D6kAxZe1yHfu9ddfx4IFC6KuVocddhgvpKRPRb64Fn7xxRcq1VUQu/hddtlFOW/Gp14TQgY3Yhj25ptvKpdmQYbHkewJGd6EEEIGnfh67LHHVPj+N7/5jRq7xmx2IDauY8aMUb3AqQLFl/XI91FujN9++221vv322ytXKxn3hZDe0tjYGLWL9/l8apv8xsUuXqJdtK0mJPENnF555RXVwSK/5yOPPBLTpk2zulmEkASn39IOP/roI3UTImk3qQzF1+BBnOaee+455YQodTfihMhUMNJTJB1JUgsXL14crV2V9Gqp55K6LkZVCUke6urq8Pzzz2P9+vVqffr06Tj88MM5jAkhZPBazQvSa2QMJGqQKkKE4mvwWX6LE6Kkv2ZmZionREkpIWRrbN68WZloiJmGcTocOXJk1C6eg3oTkpxIJ8vHH3+sJvntS/qhpCHy2kEIGVTiq6WlBZdddpkyPDAKV83Q7ZBY2ZMpTogVFRVq8G+5iE6aNMnqZpFBiJz2xCZe7OIN4xZBxJaIrtGjR1vaPkLIwLFu3TqVPSHXdelsOeCAA1QZBTteCCH9oQ16fGb54x//qJwNH3jgARWe/8c//oHrrrtOjbf073//u6dPpww6pF5M6nSkkP3rr7/e4vHPPPOMshaX4yUVSEwXzEgawcEHH4z8/HxlCiIpRJ1F7M455xx1jERJ5CZdHBxJYiOphlKTKLVfkoL41FNPqYjGNgR3SZLaxT/88MPqfCXCS84TUu9x1lln4dRTT6XwIiTFkN+8/P4nT56szhFiS//f//5XuTfz+kEI6Wt6HPmSAUTlpmXfffdVqk5cg+Rm9z//+Y9K+4oXQ1tCbo7nzp2LBx98UAkvMfIQcbVy5cpOx8yReoy9994bt9xyiyqQlSjHbbfdptowdepUdYy0Q3q0RQyeccYZWLRoEWbMmBHzPHKSfe211/Doo48qhXruueeqHi65Ue8ujHwNXiT6Ko5W33zzjVqfNWuWyuVnzU7qImJc7OLlN27YxUt01LCLFzdXQkhqI7dDcj/xxhtvqHOGIOcGiYjLJB3FqV7vTgixIO1QIkXScywibMSIESrSJLbLIngkEiU1N91FBNecOXNw7733qnXpcZJai/POOw+XX355h+NlHDGxfH711Vej22R8JxFXIuDMrF27FmPHju0gvuQNkSJ6EW7ijCesWLFCpaeJc548X3eg+EqMgTVFhAkyoOaJJ55IJ8QUQ6LcMhyB/LaNc5N8B+ScJeefjIwMq5tICBlkSMTrrbfeUvc15lIK6bCRa4khxmjsRAjpjTZwoofIiUdOSCK+JP1Par/kRkZsW3tyIhKjDrkpuuKKK6LbJPp04IEHqhulzpDtF198ccw2GWD3xRdf7Pbflb8pg6bK3zGQ1yGvZ0viSyynDdtp4w0mgxdJJZPPUnotZWBNSS/75z//qZwQGeVIfkRoiV28RD/NdvES5ZJoFx3NCCFdIR20v/zlL9W5Q+53fvjhBzXJdX/VqlVqMo4bP368EmLScczsCkJId+ix+Dr99NNV+s4+++yjolNHHXWUilyJoLnrrru6/TxVVVWqR6m4uDhmu6xLJKozysrKOj1etncXOVbG9YgXilt7Hkl1lNo2klhMmDBB1YFJpFN6M6VGUZwQJWpLkg9JKZT0ZIl4Gz3WBQUFUbt46bkmhJDuIJ000jkrk2RTSG24IcQ2bNigrikySTqzHLvddtspMSalGJIlRAghndHjO5GLLroouizRIxFKEk2Sk00yD1IoETpz1E16wKSniwx+hg4dit/97neqJlEEttT6HXfccZgyZYrVTSN9RGlpqboBksFTjUxqsYvea6+91M0QXcsIIduaTVFSUqImOa+I8/Pq1auVEPvxxx/VupRkyCRI3blExOT8I9cgnoMIIQbOvnAJMtzBJL3LqKPaGtIbLSH6eJdBWZeTW2fI9p4c39VzSMqj2JKbo19bex7p1WKqUuIiObgStRU7YUkZEWMXiZKIrbhcVEniISJLajvFLl5uggykI8iwi+dnSwjpD9LT01U0XSapV5exJo2omHQGyfiBMn300UeqtlTOSyLGJDqWlpZmdfMJSQpCoZD6vcnvUcbpS0rxJe4/EumStD3pzTF46aWXcM0116h93RVf8hziQvfee+/h2GOPVdvkBCbr4j7YGVKvIfsvvPDC6DaxhJXt3UX+prgVyfOIxbwg7ooyyn1PnockHiKeTz75ZFVILWYc8h0QAXbEEUcwHS2BkPOEnGsk0iU3PIKILIlkiujqSWcMIYRsKxLVkkwYmfbff380NjaqaJh09EnHkBiFSbmGTHKukhpzw7RDnJ3ZSURI95DgycaNG9U9u4zPJ8tS9iSlBQcddBAShW7fcS5dulTZu0ues3DMMceosb7EQU72ia272Lf3BEnjO+200zB79mxl2iFW83KSkgiFIDb0kjok9VbCBRdcoGrN7rzzTnXD/OSTT2L+/Pl46KGHos8pN9PyoUiPkyGsBCNdQCIgv/3tb9XfFpUsRfjirijCq7tOhySxL5KHHXaYGuNN7ISlNkiioPI9Zm/k4EY6f5YsWaJElzHAu4jmmTNnqt9vIvV6EUKSl6ysLHVekknOW3LfJEJMomJS7y43jTK9++676p7EEGLi0Cwd04QQDUnnld+P/F6Me3vpgDWTiC7W3baaF7Ejzj8SdZLaGZnEzECEjAxY3NsbVzHruOOOO1QtjljC33PPPcoCWpCxxGRcDanRMZB0sauvvlqlG8nJ6vbbb1djOBnIsYZ4MzNv3jxce+21UfvpSy65RL0GeU3imHj//ff3qMd8MFnNf/Pyc8gdOgyjp06HOy3d0rYkEnIxlFRZ6UmRNFhxQuQN/OBDfqOGXbz0KBsnWxmmQs4VLGwnhCQKtbW1SoTJ9UfuY4zxxAQpxRABZogxXo9IqlFfXx+Nasm8oqKi084NKSuQCLLMxXV0sNRU9vk4XxIaf/vtt5VAkicVu+7HHnsMv/rVr5CKDBbx1dbchPt/9wtEwmHYHU4MnzgZY2fMUlP+SNa8bA0R/eKEKJ+n5AxLWqL8oMngsIuX9FCxi5cOE+OkK1EuSR9mDSYhJJGRjj8RYIYYk/sJM5KhYZh2yHWJ6fEkmYhEIioSbBZbkokUj/wOzGJL/BoG671tn4svUZVyoyoizLgJkpHg5cSQigwW8dVcV4svn38KaxcvQF15acy+rPxCjJmxkxJio6bOgCedUbGuPkuJgkrRpvQ8Sg2iFFET63qGDbt4o1dYTr6S0y2OqrwBIYQkG3IrJrb1hmmH3Iia06skHdE8wLPVGTeE9MYcQ8zt1ulCS+aSVtiZq6ghtGSeSNktfS6+5KZUemYkvCcPkcJScRmTtEAzqXJCGCziy0xt6SasWbxATRuXfYdgwB/dZ3c4MHzCZIzRo2IFo8YM2p4Dq3ogxQnRqBHcb7/9sPfee/M9GkCkc0fquaSG1GwXLyYakuI8WNIKCCGkv5Fov2FlL5PUw5uRG1QjKibnSZ4fyWBDjDDEFMsQW1K7Jfda8dpCxl01xJYsJ2INV79Gvsw3ovKwztaNgU2TncEovswE/D4lwNZ8u0BFxWpLNQMSg8wh+RgzfRbGzpyF0TtKVCwDqY70Mop7ptQWCdOnT1eDiDPS0n/IeUNOzNKRI+5gBmLHLKJLOncogAkhqX5tkswMQ4gZLq8GUnNvWNnLXFLoCRloWltbO5hjxGsCKRcYNWpUVGzJeHjJdI/V5+JLxqroDuJGmAoMdvEVT11ZKdYsnq+iYhskKub3xUTFho2fFI2KFY4em9I3vFJj9PrrrythICeHk046iRezfriZkEi6iC6xijXbxUt6oQxKSgghpPN6WOmsEiEm0TGjJtY4j0r0wEhPlAhZKl/PSf8hBljmFML4cXgFSRk0hJZMUrpkT+IobZ+LL5LY4is+Krbp+6XRFEVJVzSTkTcEY6ZLrdhsjJ42A96MxMm37Svkwvb000+rELk4Tp166qmq7ohsG1LD9d1336n0Qim0NdIOxJJ59913p7sXIYT0AIksSAeWYWUf7w4n9fmGEJOaMRoVkd4gUkGGcjKLLanPjkeu4eZ6LVlPJfHfQPHVvySy+IqnrrxMpSZKZGz9siUI+tqjYja7HcPGT1RCTCJjRWPGpcwPSXpxxAlRPmNJ6xAnRDmhkN7ZxYtBj6R0ym9HkJsAsYuX8fUSqaCWEEIGK+IWZ0TFfvrpJ1V3YyARB0nlNsSYdCimyvWc9Dw7xWyOIZNEXOOJN8cQsZ/KNFB89S/JJL7MBGX08BXLsFZSFBctQM1mLSXMICM3z1QrNhPeJL9plrC6OCFK7rJEaI4++mhVC0a6hxSJi138119/HU2NEaFl2MUncmEtIYQMZkR4yc2zYWUfH6mQIYMM0w65eXa5XJa1lViflSK1hEZUS2q3pNPUjNwDSY2WIbTEeK+3Y/wmKxRf/Uyyiq946ivK9fTE+diwdAkCPnNuuR1Dd5igjSs2c7YWFUvCXF5JPXzhhRewfPnyaF2jDADOHsMt976KXbxEuwy7eEk/MOzieZEnhJCBRVK9DdMOGV/MbGUv52TzAM8ylhJJXqQzVASWIbZEeMWbY8jwBiKwDLElrpq8dm8Ziq9+JlXEl5mg2IauWKbEmKQpVm9cH7M/PSdXrxWbhdHTd0JaZvKEn+Ui9d5776laJUHGAZMoGE9EsUiagrxHUtdlnFrEPEOcCydNmpTUhbaEEJIoSFRD0hINMSZZHmbEGMEQYnIDLlEPkrhIymC8OUb87b8YixnGGCK2iouL+bkPBvElIWwJMS5evBhTp05FKpOK4iuehsqKqGnH+qXfItDWGhMVK9lhvBYVmzEbxWO3S4qo2IIFC/Daa68pMSYXJKkDy8igTb9hFy8XcQMp7hbRJb2pjBISQsjgRG4D5WbcMO0QAw/zraGkh8vwH4aVPWt0Bzfy2UmKqSG0ZF5dXd3hOIluGkJL5qwBHMSRL7mhkhSsVK97ofiKJRSUqNj3mhhbNL/zqNi0mcq0Q6JjaVmJ+56Jta84IUrPoeTMixNiQUEBUg0RoHKhFtEl6QsGkydPVqJLcsMJIYQkFi0tLeo6J2JMzDtk/CYzkn5mRMUks4EZDdZfi8Xl0iy24iOZRjTTLLZ475pA4uuf//wnnn/+efznP/9JaVtoiq8t01BVgbWLF6pasXXfxUbFYLNh6HbjtXHFZs5CybgdEi4qJic6cUKU2ibpFZSxwCTCkwpIXrhhF19ZWam2SWrCjBkzlF08LfkJISR5buylHsiIipWVlcXsl8wPQ4hJdIwmSv2P1FGLCZjZHMM81psggthsjiETzTESWHzJeDzSEyIpiPKhxqdcSYF9KkDx1bOo2OaVy6MpilXr18bslyiYuVYsPTsHiZJD/eSTT6oUDTnRSQ2YCJBkRYxH5PctRhpmu/jZs2cru/hUt5glhJBkR8795gGe5bpgINdBSccX90QRY4WFhUxj6wMky0buM4yoliwbRlYGUn8u770R1ZLopBhmkCQRX9ddd90W98+bNw+pAMVX72msroqadqz7bjH8rS3tO202lGy3g2ZnP2MWSrbfAXb74C34lE6IF198EcuWLVPre+21F/bbb7+kSsOQFBSxihfLeCP9RDpdxC5ehBd7OgkhJPUQASBiwLCyj68rknskw8pexhejGOj+EC3mFMLS0tIO5hgSxTKnEMp4WzTHsB66HfYzFF99Q0jC56uW64M8L0DlujUx+70SFZs2UwkxiY5J7dhgTMv44IMP8Mknn6j1KVOm4Nhjj014J0RJqZRBkSXaZQzUKTVuYhcvNZ+J/voIIYT0HTU1NVH3xDVr1sRYlzudzugAzyLG5FpC2q+1IrQMsSVDAsQj95uG0JJJ6swZVUxB8SWub8a4R3KzKemIqQTFV//QVFONNd8uwNpFWlTM19LcvtNmQ/HY7VWdmBYVGz+oomKLFi3CK6+8osTYiBEjlBNiIrpCST2bYRdvjAMjRdUiusRMI5mieoQQQvoeSUcUAWaIMblXMiPiwagVE1Eh4iwVkGuqiCuz7buRxm9GUjYNsSVzjruW4uJLbszkpvLDDz+MfhlEtUuqldS/yBcmFaD4GpioWOkPK6K1YpVrf4rZ783IxGiJis2craJiGbnW96TJxeapp55Sxa/y+xAnxET5TciFQJwLJX3EQExExLlQXE7Zy0YIIaSnyG2mmDMZph1yrTHfeko6omFlL1My1Q9L9E/SBg2xJVO8e6RcW8UcwxBbUrvFIWwSk34TX+LqJgPz/fvf/1aDpgrff/89TjvtNDX+w//+9z+kAhRfA09TbQ3WfisOiguwbslC+JqbY21Ux26nxhSTqNjQHSbAblH+s/RqPf7442qcDTGkkN+MiJfBiPz8Dbt4uSgYyG9bRJcU7RJCCCF9hYgPuY80rOylxsmMZFoYQkyuQYmUbSERPzHEMKJasmyk7RtIlE+yY4yolizLvQJJfPpNfMmTvvvuu5gzZ07MdinIP/jgg1UULBWg+LKWsPQm/bBSj4rNR8Wa1TH7PRkZGD1Nc1CUaaCjYnIxkUiwWMDKhePII4/ETjvthMHUG7d06VKVXijRbEHaadjFp+K4ZYQQQgY+DU8iQ4Zph1iom0lPT1cd+4aVvawPNkMqI6IlYktei5GubyCmVOYUQhGXqZJmmWo09Jf4knCwGAvEW2pLvcs+++zTae5qMkLxNbhorqvVomKL5mPdkkVoa26K2V84ZlxUiA0bP2lAomLS2/Xyyy+r2ilBaqYOOOAAS3vxpFdOfqtiF2/k4EvKh2EXz+8yIYQQK4dwMazsZS426+b0PIkSGVb2xcXFA54OL9dNc72WMdZl/H2yYYwhYktKDxIpekcGofg65phjVHRL0gslR1WQAfiktkXca1544QWkAhRfgzwq9uMqrF08X0XGyn/6MWa/Jz0Do3ecgTFi3DF9FjKH9N+gwPLzkvrIjz76KJrOd9xxxw245a70zn3zzTfKLl6WBckp32WXXVQUm4MvEkIIGUxIhoZkjxhRsXihIyLHcE+U+uS+Tt2T67eUERhCS6Z44xAhPz8/xvZd6r1ZI52aNPSX+JIfggwmK+MaSVGgsW3q1Kmql196JVIBiq/EoaW+LlorJvO2psaY/YWjx+pRsdkYOn4iHP2QDvDtt9/ipZdeUukIksMupjUDUVQs30+xixd3UrNdvKQWSvSadvGEEEISAen4N9wTpWbMPNCwjHElwscQYyKIeiP2ysrKYsbYMjorDURUyZhahtiSKRFdjUkCWs3LQ6Tua8WKFdHe/AMPPBCpBMVXYhIOh1D24w/6IM/zUSZRMbPrUlq6FhUTMTZzFrKG9F3t09q1a5UTohQby3fnF7/4hUqb6A+kh1DquZYsWRLNP5e/JSYaYhfPwRgJIYQkKtKZKNdUQ4yJwZWZIUOGRE07ZHyxzmqs5DnizTEkNd+MXCvN5hgSdKA5BhlQ8SVfVElPWrx4sYp0pTIUX8lBS0N9tFZsrdSKNcbWLBaMGtNeKzZh8jZHxaqrq5UTogxGKSfwn//856qYuK+QKLQ4F65cuTK6TS48IrqkWJmpEIQQQpIJuY2Va6thZS9Cymx6IRke4jhs2NgbBhlSMhNvjiHXZSOiJYJLymtojkEsj3zJF1jquqZPn45UhuIrOaNiUh+2ZpFExRagdPWquKhYGkZNnaEiYmOmz0J2Qe/G75I0BomAyQVCxNARRxyhDC96i/yEpTBZRJc8p8HEiROV6EqVVGBCCCFExtmUMTcNMSYmHl0hKYPmeq2ioiKaY5DBJ77++c9/4vnnn8d//vMfFdZNVSi+UiMqJs6JRq1Ya0NsoW3+iFFqgGeJig2fKFGx7tdPSa661EhKWqCw22674aCDDurRSV/y06X2UtILy8vL1TZ5vHSMSE1XogzuTAghhPQHcosrdVxGeqKk/ZvTCOU+lhkhZNCLr5kzZ6pedklBlC9v/CjcCxcuRCpA8ZVaRMJhLSom44p9uwBlP6xCJGJKa/BKVGy6lqI4U6JiRVt/zkgEH3/8MT744INopOr444/fqhOi/PYMu3hjXD15zKxZs5RdvHwvCSGEEEJIEoiv6667bov7582bh1SA4iu1aW1siImKiaNifFRMmXZMn4Xhk6bAuQVXQRkH7MUXX1SRLBl88ZRTTun0OyU9dmIX/+WXX0YdmGTAScMufrANPkkIIYQQkio09If4klSpm2++Gb/5zW9Svo6E4ouYo2IVa39Sph0ixkp/WBkbFfN4MXLqNGVlL5GxnKKODodS/Pvkk08qUSXfJ3FCFDtb47tm2MUbTkwyjohhFz/QY4YRQgghhJABinyJU4z01IuDWipD8UW6orWpUUXF1i5eiLXfLkBzXZwF7rARmmnHjNkYMWlqNComDojihCiuTSKoDj30UOVeKGOEGY5MUgwsJhpTpkyhXTwhhBBCSLKLr2OOOUbVpZx22mlIZSi+SLejYuvWKPfENYvnY/OqFWqbgVNsbafoUbGZs+DOylFOiDJ+iRmpr9xjjz2UVS6LgwkhhBBCUkR8Pfjgg6ru69RTT1UF/vGGG0cffTRSAYov0hvampuwbsliJcSkVqy5tiZmf97Q4Rg9fSYqfCGs3FyOCZMmqUiXDOxICCGEEEJSTHxtyQpbeuTFNCAVoPgi24r89CrXrYnWim1etTwmKmZ3OOBJz1BOim6vF640mWuTy+tV445p+/T9MlfbvPox2rrxGImyMWpGCCGEEJJA4otoUHyRvsbX0ox13y3WB3mej6a4qNg2Y7Mp8w9NkOlizRBs+vao0DPvU2IuPSr4zOKvJ2ObEUIIIYQkKxRf/QzFF+lP5GfZWF0Ff2sLAm1t8Le2wu9rjS4H2lrhb9Pnsq+tLbpN229ab2uTJ+yXdtodzphomxJmurgzom+xok3fl6aLuZhtmgi09WCgaUIIIYSQRNIGzu4+4eGHH47//e9/0QFcb731Vpx55pnK8loQh7a99toL33//fV+0n5CURtIDswsK+0zIBX0+XYgZAk4XdWpq6UTU6YLPWDYJPVkPBjTL+3AoiLamRjX1FSLAtp5WGRuJM6Jx7amW7dE7h8vFdEtCCCGEDAq6HfkSW+vS0lJldS2Iolu8eDHGjRun1svLyzFs2DDWfBGSAoSCQQR8bZ1G2QKtLbqoixVsseKvo9Azj43Wl0jtXIxgixN2mohLN6VkmmrqPCbBZ9pnt9PmnxBCCCH9GPmK12jMViQkdXE4nXA4M+HNyOy76FzAj4A5hdIk0PydCTaZq3TMzsVc0O9Tzx0OheBrblZTX+F0e9pFmUTq0tJjUi07GJ+YBF/RmHHIyM3rs7YQQgghJHHotvgihJD+QtICXW6PmtK1zOZtJhwOIdDm6zStUovQdZ5WqYk/PXoXF9kTISeIsFPirr53wnXSXvtjztHHqwG3CSGEEJI6OHtycxRfN8E6CkLIYEVSAz3p6Wrqq+icpFu218t1L63SnIrZ2lCPms0bsfSDt7H0w3ew/exdMOfoEzBs/KQ+aSMhhBBCBjc9Sjv89a9/DY/Ho9bb2tqU4YYxyLLPp6X4EEJIMiKdTU6XC05XDpDd+/DcphXf45tXnsfq+V/ix2+0afjEyUqEjZs5h26PhBBCSBLTbcON008/vVtP+MgjjyAVoOEGIWRbqN60AfNfeQHLP3lfRdSEIcNHYs5Rx2PinvsqoUcIIYSQxIDjfPUzFF+EkL6gqaYaC998Bd++/bpKaRQy84Zgp8OPwbQDD4UnXcsuIIQQQsjgheKrn6H4IoT0Jb6WFix5700sfO1FNNXWqG1igT/9oMOw02FHI3NIvtVNJIQQQkgXUHz1MxRfhJD+IBQMYPmnH+Gbl59DzaYNapvd4cTkvffD7COPR/6IkVY3kRBCCCFxUHz1MxRfhJD+JBIO46dF85UI27RiWXT7duKQeNQJyqSDEEIIIYmlDQaFrdZ9992HMWPGwOv1YpdddsHXX3+9xeOfeeYZTJw4UR2/44474vXXX4/ZL3rymmuuwdChQ5GWloYDDzwQP/zwQ8wx8vcM+3xjuvXWW/vl9RFCSE8R18PtZu2Mk6+7DafccAe2n7ObWC5i9fyv8OS8y/C/P/8RP87/Sok0QgghhCQGlouvp556ChdffDHmzZuHhQsXYvr06TjkkENQUVHR6fGff/45TjnlFPz2t7/FokWLcOyxx6pp6dKl0WNuv/123HPPPXjwwQfx1VdfKTt8eU6xxzdz/fXXo7S0NDqdd955/f56CSGkp8g4YMdcehVOv+sB7Lj/wWqg5s2rluOlO27Ao5ecje8+eBvBQMDqZhJCCCFkK1iediiRrjlz5uDee+9V6+FwGCNHjlRC6PLLL+9w/EknnYTm5ma8+uqr0W277rorZsyYocSWvJxhw4bhkksuwaWXXqr2S/ivuLgYjz76KE4++eRo5OvCCy9UU29g2iEhxCrEkGPRGy/j23fegK+lWW3LEIfEw45WBh10SCSEEEIGloRIO/T7/ViwYIFKC4w2yG5X61988UWnj5Ht5uMFiWoZx69ZswZlZWUxx8gbISIv/jklzTA/Px8zZ87EHXfcgaA+1k5nyCDS8qaaJ0IIsQKxot/rF7/GGfc9gn1++RvlhNhcW4NPnngUD539a3z033+hsabK6mYSQgghJA4nLKSqqgqhUEhFpczI+ooVKzp9jAirzo6X7cZ+Y1tXxwjnn38+dtppJwwZMkSlMl5xxRUq9fCuu+7q9O/ecsstuO6663r5SgkhpO/xpKdj9lHHY+ZhR2HFZx8rc47qjesx/5XnsfD1lzFpr33VoM35I0ZZ3VRCCCGEWC2+rETqzAymTZsGt9uNP/zhD0pkeTyeDseLODM/RiJfkh5JCCFW43C6MGWfAzB5r/2wZvECJcI2Ll+KZR++q6Zxs3bGnKNPwPAJk5W5ECGEEEJSUHwVFBTA4XCgvLw8Zrusl5SUdPoY2b6l4425bBO3Q/MxUhfWFZKWKGmHa9euxYQJEzrsF0HWmSgjhJDB5JA4bqc5atq8aoWKgP3wzRf4acHXaho6fqISYdvP2kUdSwghhJCBxdKrr0SbZs2ahffeey+6TQw3ZH233Xbr9DGy3Xy88M4770SPHzt2rBJg5mMkSiWuh109p7B48WJVb1ZUVNQHr4wQQqxl2PiJOPqSK3H6XQ9i2gGHwuFyoXTVCrz8fzfhkUvOxpL33qJDIiGEEJJqbodiNX/aaafh73//O3beeWfcfffdePrpp1XNl9RpzZ07F8OHD1fpgILUZ+2zzz7KLOOII47Ak08+iZtvvlnZ1E+dOlUdc9ttt6n9jz32mBJjf/7zn7FkyRJ8//33amwwMd4QMbbffvshKytLrV900UU47LDD1GO6A90OCSGJRHNdLRa9+QoWv/0afM26Q2JuHmbqDonejEyrm0gIIYQkLN3VBpaLL0Fs5sVtUAwxJDVQxuiSNEBh3333VbbwYhNvHmT56quvVimCO+ywgxrX6/DDD4/ul5ck44Y99NBDqKurw5577on7778f48ePV/tFqJ199tlK4ImLoQi0X/3qV6qmq7uphRRfhJBExN/agu/efxvzX3sRTdWaI6I7LQ07HnAoZh1+DLLyC6xuIiGEEJJwJJT4SkQovgghiUwoGIhxSBTsDgcm7bmvclAsGDna6iYSQgghCQPFVz9D8UUISQbkErBm8XzNIfH7pdHtYtqhHBInTqFDIiGEELIVKL76GYovQkiyUfrDSnzzynP44esvRJWpbUN3mKBE2Hazd4Hd7rC6iYQQQsighOKrn6H4IoQkK7WlmzD/1Rew7KP3ENIdEfOGDsfso47D5L32h9PttrqJhPQrvnAYbpuNUV9CSLeh+OpnKL4IIanhkPgqFr/9atQhMT0nFzsph8TD4c2kQyJJbJqDIaxsacPK5jasatbmMm3yBVDsdmLX3EzslpuJXXMzMCHdSzFGCOkSiq9+huKLEJJaDonvYMFrL6KxulJtc3nTMO2AQ7DT4ccgu6DQ6iYS0muR1V2GuBzYNUcTYiLIJmemwUExRgjRofjqZyi+CCGpRigYxMovPlHmHFXr10YdEifusQ/miEPiqDFWN5GkOL0RWUVuJ8anezEhQ5vGZ3gxLs2DH1t8+LK+CV/UNWF+fTNaw7G3S9lOO3YWMZaTgd1zM7FjVjpcdooxQlKVBoqv/oXiixDSXVoCLfh448f4YMMHSHOmYeeSnbHz0J1RkJaYY2rJZWPttwvxzUvPYsP330W3j505W5lzjJg0lelZJCFElszzXM6t/j1/OIwlja1KiMn0dX0zmkLhmGPSHXbMyc5QkTFJV5yZlQ6vw94nr5cQMvih+OpnKL4IIVuiLdiGTzd9ijfXvqmEV2uwtcMx2+duj12H7opdhu6CWcWzkOXOQqJR+uNKzH/5eaz6+vOoQ2LJ9uOVCNt+zq50SCQJJbK6SygSwbImTYx9WdeML+uaUBsMxRzjsduUAJMURZlm5aQjw8HfAyHJCsVXP0PxRQiJJxAK4PPNn+ONtW/gg/UfoCXYEt03InMEDhlzCILhIL4q+woralbEPNZhc2BK/hQlxGSaUTQDHocHiUJt2WYsEIfED99DMOBX2/KGDsPsI4/H5L3pkEi6J7LMAmtVSxs2tlkrsrpLOBJRbVZirL5ZzSv9wZhjnDZgelZ61MRj55wMZDspxghJFii++hmKL0KIEAgH8HXp1yrC9d7699Dob4zuK8kowSGjD8FhYw/D5PzJMal4tW21+Lrsa3xV+pWar2tYF/O8brsbM4tmRsWYPN5pH/ibyp7SUl+HRW++gsVvvYa25ia1jQ6JpK9FlkxDLBBZ3UVurX5q9eELPSomYiw+WicJiVMz03QxlqHqx/Ldg/c1EUK2DMVXP0PxRUjqEgqHML98vhJc7657F3W+uui+wrRCHDzmYBw65lBMK5wGu617NR+lTaUqIiZiTKbKVs1V0CDTlYnZxbOjYkxSFgdzXZW/rRVL338b88UhsUp3SPR4Me1AwyGxyOomkn4kVURWT1jf6otGxUSQrWnVIsRm5HUra/sczVGx2OOypK2EkJ5D8dXPUHwRklqEI2EsrlisBNc7695BVWtVdN8Q7xAcNPoglVa4U9FOcGxjnZOcltc0rIkKMYmMmSNqQr43X5l27FKiibERWSMwWB0SV+kOiZVmh8Td98bso09AIR0SExqKrN5T6vPjq7pmfK7Xjcn7Fo+4LkpUTKJjMo30Mn2XkMEKxVc/Q/FFSPIjp8fvqr5TguvttW+jvKU8ui/bnY0DRx+oBJe4F/ZnSqBE2lbUroiKsYXlC9EWir1RG545XJl3DFYnRXkv14lD4ivPYf3SJdHtY2fM0hwSJ+84qCN5qQ5FVv9T5Q/iq3otKibpimLoEX+DNsLrUmON7a6LsbFpbv5uCBkkUHz1MxRfhCQfwXAELaEQvqtejnfXvYWP1r+NypbS6H6PMwPjC/fE9sX7oihnJvxwoDUURms4rM8j2jxmWxgt0fUI0hw2dfOZb0xup77uUOtq2e2MLqd1YlXtD/nxbeW30Zqx7yq/QzAS7NRJUcTY7JLZg8pJsWz1D/jmlefxw5efIRLR7LpLtttBc0jceTc6JFossla1+LCyuVV3GPRhZUvrFkVWoduJCemxphcUWdtOfUDEmNSMNavxxr5tbEEo7o6t2O2MRsUkQiZi104xRoglUHz1MxRfhAwccppqE2FjEjTtIicSs60l/hh9f7sA6nzua10PR/OX8LR8BWewrP1v2zzwpc2EL30X+NOmAbaBTfuRsYM0IeaIEWsFuniT5XSbD2X132FV1Xx8Wz5fRckSwUmxrqxU1YQt++CdqENibvFQzD7qOEze5wC43Na3MVmhyErMz2x+Q0u0ZmxhQwv8cbdwcp6QyJiMNSY1Y5Mz0+CgGCNkQKD46mcovghpjxa1hTtGeDpGfToTPh0FVUsX+/vjROUIlMLToguuwKbo9ojNhVDaDNizdoc3ayekO9NUBCrdblfzNLsd6bYI8sNNGBJoQF6gHjky+euQ6a9Hhr8e6b46pLXVwuOrg7utFq62WjhaaxF2uOFPL0CrNx+N3iGodw9BlTsPFa5cbHblYoM9B2vt2fjRnoMqRyYi3TTsMNtZ59lbkOlfCUfrUvial6LNtzn2GLsbE/OnYeeSXbDP8N0wrXCKpU6KLQ31WPTmq1j81qtoa9Jq29Kyc7DToUdh+iFHIC1z8ETtEg2KrORFzouLDDFW34T59c2qs8lMttOuXBQNA49pWelw2SnGCOkPKL76GYovMtiRn7avs2hRV5GiOMHTXdEU3/Pa37httqgAkhQ+bW6sxy3bzcfaVRSpra0MK8s/wLel72NTww/qOZ2RCAojduw5ZEfsnz8ds7JHI83fArTWAC36FF2u1pZbxeGwf197xO5EKC0f/vR8tHgLlFirc+ehyj0E5SLWnJpYW2PPxk/2LNREOr85tger4Gr7Hu627+HyLYMjVBf7d2xpcKZPRnbWNJTkzcSwrHEocLs6pEEaqZHyPvZHnUmgrQ3fffAOFrz2AhoqK6IOiTvufzBmHXEssgvpkNgVFFnEHw5jSaM28LNMX9c3oymkpfUayLlwTo428LOkKsog0N5OUpuTERnoXlK2czw5VjeFJCkUX/0MxRfpr4E6y/0BrG31Y1ObPy5VLlY0RQXSFkTTQP645Va8M1Fkjhb1VDSlG1Emfe612+HcUq+tnM4CLZpAMgmmhrq1+Kl0Psqqvke4uRI54TByQ2HkhsPIjwDeUGy9VI+QC3l6HpA2BEiXKb99OS1PW1fL+nrIDzRXalNTBdBcpa/ry2pbJdAWK5C6Q8STjVBGIXxpsWKtUhdrm5y5WG/LwupgCza1/oBAyzIlyuyR9sGghbA9G37vZAS8U9Q87IwVPV67rV2YRVMhY9MijZo2Wc5zOXpUhxIOhbDyy0/xzUvPonLdGrXNZrdrDolHHY+iMeOQqlBkkZ5kJSxrblUpiqpurK4JtcFQzDEeu00JMBFjMs3KSUeGIzlqLqtbq5VD7cKKhVhUsQjLq5er2tgx2WPUGIoy7VS8E0ZljaJpCekTKL76GYovsi29k3KjtLbVhzWtPqxr9atlEVzr23yqtqmvcaloUUfBk96daJG9i/1xgkpuyPv0AhYOA776jpEnNY8VVzHLIV/v/p6k94k4UsLJJJhEWJkFVby4cvTTODxBP9CiC7OmSpNAq4wVacYU7qGAdLgRyShEML0AtU4vNiOINcFGrAnUoNIeQbXdjmqHAzUOBxq9xfCnT0OzeyJaPJMRcfSs51j61fPi6tY6Go5okTUj0uax2zWHxCWLlE39+qXfRp9vzPSdlDnHyCnTkvamySyylMASh0GKLLKNnXvyPdLSFLXxxir9wQ5py9Oz0nUDj0zsnJOBbOfgF2NyrljfuF45wYrQkmltgza0xdaQoUJkiBBDjE0YMgEuO8dXIz2H4qufofgiW6IpGIoKKpmva2sXWBLRik0EicVhA0Z43Go8lyynIzYS1M0UOyNSJOsSLbI8xz8UAFpruyeejP1yvO6E11MCNjtq7DbU2+2oc9hRb3fAkVmE4oJJGF08E5k5o0ziSp8kgmVP0PQbOY1LpCxGpHUi0AwhFzdmWHeotdtR49AEWZs3B/bM4bDnbA/kTEJ92lCUuXKwyZGL9c4cbA65UB0MoSYQQn1cT3t3yXTYYyJrJZWbUPDFe3AtXaC9XgBZo8dh0hHHYcfd9kCOy5WQQowii1iF3P791OpTtvaavX0TNvliv3dyRpySmaanKWZgl5xM9Xu0mkA4gJU1K6NiS6JbNW01HY4T11clrIpnqnmGKyMmGra0aql6LjNpzjTsWLCjJsaKdsK0wmnIdGcO4KsjiQrFVz9D8ZXayM+mKhCMiqu1cREs2bclRBiNTnNjbJpHzcekeTBGnw/3uK0XS1si0Bpb+9SViIrur9UiWL1FLnpmkRQXgWpxebGwaS0+rFqCz2qXo9oOtMpNuM2mLpqHjjkUB48+GMUZxX35LiQ28hmqdMetiLTmSkRaqmDrqQh2pgGZhUBGIcJ6GmSzNx8NniGo1mvWypw52OjMxUZ7JqoCEVQHgqjRp+AWrko5DTWY/e1n2HHlQriC2k1TXXYeFk7fC6U7zkFuWlqH1McYO3896pbndG45hbWPocgiicD6Vl80KiaCbE2r5kJqRr6HSozpJh7Fnv6PErUEWtTwGobQWlK5RNVwmZFoVVQ0Fe+E6YXTt1rf5Qv58H319zERswZ/Q8wxdpsdE/ImRFMVZeL1hHQGxVc/Q/GVGvnym3z+GFG1rs0XXW6OK2SOR1KsNFHlwWhvrMCSwUct76WXn76vwSSYamMFVVfiKu6C131sQFquKWXPnN6X10lKnz53drQbb/I34YMNH6jBjz/f/DmCppS7SUMm4bCxh6nBj4dlDtuGN4gowiEtCqnXqDXVrcWGsoWoqlqOltq1cPsaMCQUQr6awkjr6SVF0j3lc88oAjIKEMks0mvW8lHvGYIaqVlz5aFMnCAdOaiIOFHtD6Khvg6F8z/Bdt9+jrQ2rWat2ZuBRTvuikVTdkGbN32rfzrP2Z7m2Jmdf/yyRJT7U2QZAosiiwwWSn1+fFXXjM/1ujEZWDuecWmeqLW9pCtK1sa2UtlSGRVDIrYkyhWKxEbRZezCaO1W0U6YUjBlm4fQCEfC+Knup2hkTKZNTe1OuOZB7c1/e1zuOCXSSGrTQPHVv1B8JQdiSiGCyiywjEjWhjb/FnvgRToN87hiRNVo0/KA5smrG+S6TgTTVtL7elonZCC25DERKF08daiXMptN5ALbMHiu9Hx+tPEjvLnmTXy66VP4w+09sjvk7aAiXDKNyh7V679Bek5pUym+KvtKDfYsU1NzuRJhhiAbChd2TCvG9q4cDLe5kRlohc2Itsl3sKfIYNEZBUCmiLVCBDxD8O36CBYsLUdTg9YxYHO54Jm9Kxr2PAw1WXlKrBmRNZnXBkK9MqORiLUSaOaaNZd0pAA/9MD4giKLJCpVfhn4WYuKSbrisqbWDr+lEV6XGmtsd12MjU1zb7GzUW5D1zSswaJyTWiJ4NnQuKHDccMyhkXTB0X0bJe73YAInvLmciyqXKTaJ21bWbtSiTQz2e5sNX5iXwpBknhQfPUzFF+JQ62eHrhOF1XtAsuPMn/XN0qGrbmkBZpFlRHFGpXmVqYA/VO/Uw80bNbFkzka1YXxhBzfW29DSRFTIqkLx77ossmMwpOl0vr6m7ZgGz7Z9IkSXB9v/BhtofZe17E5Y6OCS3odifUYN1GGEPu67Gs0xtWXSXH7LiXaYM+7FM/CCLu3Q6pjNB0yxhGyQnOK7ALxqVnVUIhvqkegwqfVZ9gQwYT8JswZCxSVSHRNUiELEM4oQrM4QXrzUePSImulzmxUhu2aQIuKtZCay3pPhlSgyCKpQn1AxJjUjGmpikuaWhCK+6kUu51KhGkmHhkY63FgRe3yqNiSGqxaX23MY2ywYXze+GgKocxLMkowGJDMiyVVS7TIWPkitdxZCuTUgqnR6NiMwhnI9eZa1mYyMFB89TMUX4PLwanMp9mzr9WjWGtMdVhbK/iXQSjHeGOjVkYd1lCPC46+FhkSpWosA+o3APUbgbr12jy6vqFXhggxtucd0ve2IK5caRhMyDgskkooKYUfrP8ALcF2G/QRmSOiKYVyYbY8dZNskVA4hBU1K6KRMamrMAtoI31HCbGSXbDz0J1RkFaw5TTZqEDrzFikCpGmCqwva8U3ZXlY15wXffjojFrMyd+IUel1XfcbeHP09MfCaM2aTOIK2ZampUFWu7QBscttaagOhpVYC4Qj2C7dQ5FFUh5Ju51vDPxc14SFDS0IhFrg9P0Al28VXL6VcPlXwxaJ7fiUKFF8vZakFSYC3TX/2C5nu5jInZz7eA1LLii++hmKr4G3Z5c0QBFYmj17ewRLtm/Nnl163syiSk0SwUr3qLqPPj0ByuC8DZt0UWUSVEpgrdciWt1J9xNxJOlVnZlNdEjv62fb8wG4eMnNuUS43l//PhoD7eJzaMZQJbYkwjU5fzIvVgmMCGspmjeiYt9VfqfG3Yl3JzPE2OyS2b2/AQv6UL58Ib557WWsWrxUReWEovw0zBnvxfi8RthbRbjpQi6unmSrSEpRVKQVAVnFQGaJNs8a2r6cWZywv0tCep2mp4uQBeUL8UPtD4jEefyG7ZkIeMaryZU2EbMKp2KPIbmqbmxaVvrgNp3aCnKukbRJI4VSRFlntvdFaUUqVdGI7EmHolNS+knCQvHVz1B89T2N8fbsRnpgmw+b2wJbtWeXIl8tghVrbiERre4UyncL+blImp+IqKio0idDYMnYTFtDTrDZw4CckdqUK/MR7euy7N66YUCiR0Xml89XEa53172LOl/7oMKFaYVKcMkkjoUsZE5OmgPNWFC+ICrGJEpmRj73qflTlRiTqJjcoPSmjqK+ogzzX30RSz94B0G/Ng5cTlExZh15HKbueyBcLrdm1R9Nc9THUjPGVYuJtolVf1PPGpBeAGSVaEJMhFlUqJWYtpd0ai5DyGCmuwYVkrUgImPHwhlIz5iEtcF8fFnfgq/rm9EUZ14ltZVzctrHGpNBoL19dQ23CImEyXtj2Nx/X/V9h46ndGe6ivhJdEzOddMKpiHdldz3AckGxVc/Q/HVc+SrJgM6aoIqTmC1+lVtxZaQE3J8WqBYtcs2sWfvE9toGY9KIlPRNEBDVBmRq41AoD0Nbov26PGiKndU+7LcaG2D+UQiX6jlAiQRrnfWvYPqtuqYWqCDRh+kBJekZThS8P1JdWrbapUIM2rGZNBUM267W92UGGJsSv6UHvUUtzTUY/Fbr2HRW6+irVGzk07LysbMQ4/CjEOOUMvdjm5HBZqIs3JtaiwFGmW5rH3eE1MbiV7HRM+KTQLNiKSVJH3HDBnc0WuxZpdOk61ZsxsRHZmK0ou6dBVe1twaHWdMnBVr40oFPHabEmAixGSalZOODEdiXx+kRkzGGDMihN9WfIumQGynjsPmwMQhE2Pq3rpMy06REpNyv15i0urDetP94zFFuThzVOffsYGE4qufofjasj17p+NftfnR0kN79rHpenpgmkcVsW9zypmvsWPEyrwuN0/dGdNIboqMCJUSWHERLCmsZXqcQk4x31V9hzfWvIG3172NipaK6D4Zg+XAUQcqwTWnZA5TLsgWnRQrWytj9me6MjG7eLaWpjh0F5Wy2J1zRMDXhqUfvosFr76A+opytc3p8WDqvgdh9pHHIqeojwr7w2HNGEdqPGVSosy8XN6+vAUzkU5rO42Uxk4jabpQE2McQrYBEVYSrTHS50QwmJ1mjUGJJUpjRGwkeiODGff2BluGZ1A1Y/p4Y9Jpa8ZpA6ZnaZExGWtsl9zMgXUX7qdMkB/rfoyKMXmvy1u0c5OZUVmjomJMUhbHZo9NqlR8n6nEJF5grW/zqRITb8iHvGA9hgTqkRdoUPPth0/AZXseYXXzKb76m1QWXyKg1sWJqnXbYM9uXs7alhOo3OhIL3R8GqB5XVKLtobDrUeoZNKjVTFpgSOYHrQV5LSyvGa5Sil8e+3bMWkocsO8/6j9VQ3XrsN2Va5QqUIwEIDdYYedUb3eOSnWr4mKsa06KQ7dBSOyRmzxOcOhEFZ99Rm+efk5VKxZrbbZbHaM321PzDn6BBSP3Q4DglyGZSy1DtEzQ6iZtvdknD2JwHcWPYuPqnmy2VlEoh0e0VqlioX4sfZHROKcdOV3Zh7jamL+xH47j8vv/qdWn7K1N6Jjm3yxZh2SkDglM00fZywDu+RkquEgUuGzyPPkaXVjYuJRPBOTh0yGa5DXmNaLA7UIrJY2lDbUoqq+HA0NlWhpqkKkpQZ5AV1YBRuiAssQW0MCDUgL+zo+58zfIOeYv8BqKL76mWQWX/KVkLB/fFqgEckqj+uFikdSBEZ5NXt2Gd9jtElgSV1Wr+3Zgz7dyKKTiJWaNgGhjj/KDkhUqkOdlQgsEVojtSL6/rCQTwGksFoiXG+tfSsmZUx6Rvcdua8SXHsM3yPpxj8JBYNorqtFc20Nmmqr0VRboy3XaOva9hq0NTXC4XQiu7AYOcUlKsKSW9S+LJMnnSllA+2kKOe89Uu/VSJs3ZJF0e2jp83EnKNOwKgdpw+O3mXD8VFFzEp1URYfSdO396QuTYab6LQOLS6qJmmRg+F9IH2WBi7nbOPmXuZlzWUdjhudPToqtGQu61b+Hta3+qJRMRFka1o7Ro3FeVSiYkaqYrFncAuS7kYhJT3R+LwkCumLu+eRa6tY3Buf1fSi6WoMsn5HXJxlyJuWaoRbqlFbX4mahgo0NlairakKoeYa2Fur4W6rQ5ZfE1K5wQa44+reej3W6ITDgd3PhdVQfKWQ+CorK0NaWhrS09Phcrm6HdovVfbs7QJLTmAqgtXmQ0MwvHV7drNrYJw9u72nJ2ZjbKsYUWU2tdio3VBsbSwrMWaQG4aYlEA9giXL2cMBb3KJZauRaIREuN5a8xZW12vRA+MisPeIvZXg2mvEXkqAJRrhcAitDQ1oqjEJKl1cmbdJLZH6DvcBUncUFWbFJUqo5errWfkFsCd4rcNgd1KsWPuTEmErv/gEEYmmiyvZmO0w5+jjMX7XPRPn/ZcUa3MErdOoWjngkzECu4l0mihRpkfNOnN3lGW5KWIH1qBDbtSjdUbl2vhaZmdZo85o0pBJUUt0iaoM9jqjUp9f1Yp9rsRYM1a1xHbCCOPSPCoqNjM7HcM8bnWfUuJx9b3bsQX1d4aJh3yuZuMqY7y0HfJ2iBHPQzOHbvmJg34tEh8zxqhpubUWoeYq+JtFaNXA0VoDt68e9l6ONRpweBHwyhA5eXBmFMCVkQ9bB0fnuOFyBmm0nuIrRcRXKBTCDTfcEF0X8ZWRkaGEmFd60D1e+NweNDpdqLW7UG5zYjPsWBexo97hgt/p6vILLPbsYmjR2fhXPT5hRce26sLIortjW8kNfLwzoHldHAQHecg9GRAbXYluiXHGytqV0e2SeiKRrcPGHKYiXYPVqUlOe62NDdGIlIpOqSiVNjUb0au62ugN+NaQm/KM3CHIzBuCjLwhyBySr5ZlrtZle24eAm1tqCsvUw58MkWXy8tUm7b2N7ILinRxVhwVaCpqVlwCb4Y2uDCJdVKUyfw9NTspSkRMBJkMgup1eqP7pRZswesv4rv330bQp/UuixCWmjCpDXN5249NaMQ8xGwQ0lVUTW7GetIrbaQ1mo1C4qNqMpQGU3D7jbq2Oiyu1G/MyxdhWfUyNaxHVw57cnMuY20N1vN2d6nyy8DPWlRM0hWXNbV2KQu8dhuK3a6oGJNJSiNKTAJN7oXcCdCZYAxybwxeLWJMrtXxlDgzMdNdgJ3sGZgZsmP7tlY42kRs6eKqt+OMyr2xIwO1rmzUunLQ6slF0JsHW/oQuDMLkJFZgJzsQuTnFCMts6BdWCWRgRDFV4qIr/qmZjzwwP1oa23Vap56SNhmQ9jthd3rVWItMyMDQzIzUJSdhdzMTCXijMkQdY7Oen5jxraKGzC4J2NbSe9GVFCZbNeNddk/CHs7UgHJP1eCa+2b6iJu4LQ5Ve2WRLj2G7XfwKQ4dIGczvytLR3S/bQoVXukSiZJFewOUgOUnpvbLqrUpAuqIdqybJOIlW0bL9C+lpaoKBMxVldRHl1uqCzfaptFfEVTGIslpbF9WaJmkvKYqnTXSdEQY4aTogjixW+/hkVvvBIVx15xSDzkCMw45EikZ+cgJQi0mVwdu4qklXVvqA1zpoIaIy3Ocj8+qibHOFL3u9vdc5/U1kZTCMsXxWQiGEgUy4iCiGlDKowtJTVGX+lpiquafSjz+1XmT02g+2P7FbicSowZgqzD3O1Sph/9FkUzUo4NgdRZRCq6XKvPq1EV8WORx4OFXg8WeT1Y4XYjFNfGrFAY030+zGzzYabPhx19frgjQK1TRFQ2alw5mqByynIuapS4ykaLJw+ejHxkZRUgN7sIxTmFGJWZpTrp+8yBOsGg+EoR8VXpD2DHz5apH6Y7FIA34Eea3w9vwKeWs0N+FIWDyAsHkRnwwx3wwe7zIdjWiqC/u+5aEaSjDTloQA4aUeBoQb6zFbn2RmSH65EZqoUn2I2eEo5tpYfz9ZNkzNTJNl+T5lYmdRbRKTduPU+rYTOWXX3bG1/ZUqkcCiXCJT2o5qiBuBNKhOuAUQcgV9rQz0jEKJryJwJKT/uLTwc0ohTdIS07RxdTIqzydTGlL+vb03NyB0WqmaRAiqg0xFhM1KyiHC31WzaTEWGYXVAYFWOdRc0SNf1mIJwUR6eNwPcff4D5rz6v3n/B6fZg6n4HKhEm76VDMglSHTnHyfhoUVFW2nlUTQ1s3d0OQ5sWJesykmYSb043UqXmcVXtqvbxtcoXoaK13UnWYGzO2Gi6mczFhCaVfucGcqsr0XCpC5WUeK/Di2DEjopAUAmxMl+gfe7X5pt9fpT7ggh08zZZhsPRomaxkTQl2tzacpHbBaeMWtpaZ7oXqOlkOU5kybwnw1aYX7vdBb83F83uXJS6s/GNy4VFjjBW2dtQZmtEELEiNAIHgu4xCHgmqDHZRuVNxbisQjWOqnn81HzXwKRsRiIRBNpa0drYqOqmpROstalRDRdibBs+cTIm7LYXrIbiK0XEl3x8u3y5XNmwm9MCu2PPHggE0NLSgpbGevir1yFYvQaR2vWwNW6Cs6kU7tYKpPkqkRGshQuxqQqd4YMbdchCPbJRr+ZZqEM2Gu25CKSXIJJRhLSMzGgELT6iZixL/Zo9AUL8KpXSyIvujpiSbdJz1Z9IWmYHkZbbUaTF7zPlT1e3VqtBjyXCJWlbhruS5I5LT6lEuA4cfWCf1QGIA6BmTmFEpsyiqlqPYtWoiFZ38WRktEenOhFXsi89Nw/ObtZIJgL+tlYlwtTUSVpjKLDl37AnPUMXYh3TGUW0JbOw6I6TYp47DzPzZmFa5kwM2+hC6ZefobZ0bcwxLk+aioqlZ2cjPSdHRcVkPc2YZHtWjprLuhK8iXCu6w9CQX2MtE5SHWOiauVApPsRCpXGFGMU0vmYaRGnR33uMoXD4U6XJctDUvk7zfawaFwoqdVSg/VWLlZiwoxkIUwumBwVW1KvJc6EVqPe11AIoWBARe/lXKQmWQ6altU8fln2d7IeDMAf9KmatbpQExpCjWiINKMRLWiMtKDJ1oomuw/N9jY0O/xocfoRssfe7toiNrjggNvmUpFvQ5R5XV6kudKR5klXc9jdCMGFIFzwRVxoizjREnGiOexAcwBw+NrgCfiQF/IjV9mgtyEv2Iq8YAtyQy0YEmhCXlCmRmU0kRNs6nV9FKQ9UaOJIYikDYHPm4taVy4qHVnYZM/EOlsWfkA6VoTTsSKSgWZHWpcZQ06EMMxWitzgaqBtJeobv0eLv338TYMx2WOiY43J92tk1sheCS/5HrQpAdWI1qYGtMXMYwWViCzj2LCcL2KIwGkLw+sIqmnkdlOx/7y/wWoovlJEfHULiaBE0wDXx6UEbgQaN3erBzKSWYxw1nAEMkrg8xaixZWPJkceGmw5qI1koL4NaGltVYKuublZzYPdTO2KRwRYvCjb0nJ3jUa6RFI2pfC8U9HUhaCSnqvenEAl1SZaQKoXlEaXTZMnUyuaVz1ktbFTW/y2up7doMRR73DivZx8vJnhxdeOCEKmc+p0dwEOzZ2Agwp2QnHO6LiIW26XNXZyAZVoTDTdL1pTZUoHFAfArdQ5mXF5vNFaKnNdVWw6YJ46jrQjdWtSv1ZnipqZ0xrl89ha6mVWQUHUkVEZgRQVa2mNxSVaymU/9oCGQmGEAmEE/WEEAyFtOWBsC6ll2RcKtq+3HxPSH2dsMx2vr0efOxhGyB+CPxBEhXcDNuX8gE05q1CWtRpBR6x4zWodgkmbRmPsxgi8LXWw9epcYIMnLTMq0NJzdFGWlQVvZhY8MmVkwJOhrbvTM+Bwu6PiYEvCobPlnhw70I/ran8kFIQn1ARvsB5pwXqkh+qRFmpAukzhBmSEGpEeaURGuAmOuB78LdEKDxqRgSZkmOaZMduCcCICmxoaQjpqnE6XNne5Y5Zdbjdcsk1NruiyXJckOipzt8zVpG2Pn+IFXk1bTTSiJXMxVog3kJHorLjZGWJrypDJcMO1FZHTLl5ihI+x3Jko6iCQtrC/CzHVXUOigCOMNncIbe4wWj0yD6HVo2/zhNCq5mE197nD2vg1fUEkgrRIBDnhMPJCYeSGQ8hR8zBy9fVc03pOOKSOy9iGW+gGuwN1DidqHW7UqcmLGqcXdc40NDjT0eLOgs+bhaA3RxlStLpzEHRlqu9la9iFupAdFUEbmsNOwOZGJDpJLb+2DJsDmQ57bOe8zL3a+rC49MDupq/me/Mxo2A6JmdNwATPWAxHAYJNLbqA6kRQ6dt9LbEdBnaE4dEFlJrsxnJAzT3mbfYA0vTjZLvDJKbL/ZNRfPMXsBqKr1QRX/LxNVXodVZmd8BtHNsq3oa9F2NbqVCxRDV0IWYWZV0tt7V1dCnqDmajESXKvG6ku6SYOIx0ewAZaEN6pFlduCWS5/VVwRaT/lfTe/EiIqSDeOpCUMl2Ob6ve7tVPnhj90SaLuYa22rxAVrwZpoLX6R5ETTdPE/2+XBocwsOaW7BsOCW35eQIw0Bexr88KAt5ERrwI6mtoiafEEH2sIutIacal9byAWfHBNyqpQP48rpcLk6pPu1i6v2iJU7LcnTUi1CBh1uqKyIMf9oF2rlCPq3nMrpFFGcV4SM3EKk5RQgLasA3sx8eNLz4U7LQzhs18SPLoLMAkoTQbogMgkqTUhp2yLhvr1MRRBGxCZTSM2h5tpyp3NHEC3eOjS769DqbEDA3gZH2AFHxAFnxAl72AZ7MAJnKAxHMAJHQOYh2EMh2IMh2EJBtWwLyjyo1m0SOe9N2202RBxObXLK3NW+Ht0WO6WG86CWHp+JZmSpqanTZZm7eiDS+qelMmnnPpmHYcMGp0PV5CzyurHY48G6TsapKgyGML0toKZprQFs5w+pMa7k9G/cyannNn4usl26BfQN6m9G4v+++lLp2wQ53nh4e5dCRD9mS88jxyDueWRZIk5Be+w85LQhKOsOqHnAHtbWbXquhQ3qfTHapSa1rf1vGl3GDolc2dwqeuW2e+CxaxEsj0SxnGnwOtOVsYhEsRzhEEKNFQi3VMHWVgtHoAHOYDM8oVYVBeoN8qhGmwP1Njvq7XbUOURU2VHrcKDWaUOtw446tV3m2r4Guz3mmttfiHulGAkZUT2P03hvPO3LdjecEQccYRscIUD6mWzBCOz+kFhkwudrRVWgBlWROlQ5GlHrbkE47pTiDNlQXO/EqHoHxjXYsX1zBDkItYsqRwCeqLAKKiElc7ej514FZiJhG8IhN9o8uyPjmhdhNRRfqSK+JC3jzvG9G9vKbGoxSMa2EvdGQ5BFhVlDHVrqq9DSWIfmpka0tLSipc2HZl8QLYEIwvoJvyfYEUIa2pCBVqSjRV20M2RuDyPdY9fEm9j3S5qkShPKg1O588SJKYkAJVAheEugBR9t/EiNxfXZps/gD7fX/Y3PGoUDc6Zib1sxihtbEKivQKipEpHmGth8dXD4G+AMtcAVaYueQLcFyUOPeHKUIBU3JFtXKZHmFEr5Hntz6JCmd24YAsUc0TFHiNqjQZ0LHHOESAkeY9kUMQrIcb5GBNpqEPTXIhKqRzhcj4hMIbHY78Z4UrZM2Bw5sNtzYbPnaJNDm8OW3iFqtiVxZHOGYXdGAGcENrlwOyKw2eWOwXiM/jiEEJYpIvMgQuGgqpsLhoOIdLvWqB+JhJUYU0LMPAU7Ww9oy728XEdsdk2EORz6XESZQ61LLa4s20zbbOr3ZTPd5mqoW2G9CfGRPnPbYm/Bzcd0tj2ib+/4vPGvN3af/ti4v9nVc8micSvvhh8Z9jak21qQbmvVJnv7PE3N21SvfKxM6hu/JzlzrnS7sNDrVYJroceDamfHc9r2fr9mgtDmw04+n+oIS71qrQFGOqLThiAiKX3eXIRcWQg4MuG3pcEXcasOxJagE80+oLE1gsaWEBqa/NH0uM7S48M2EZkRTXzq85AjbFoWURqBz+1Am8eJNrdTLftcDvhdNgRcdgSdQNgB/dgwgrYwwjb5JgVhk7KQiB+2sB82matvmMVEIpA7I284jMxIBLnBELIiEXgiEXjN87C27A7b4Ak54A454Q654ZIpnAZ3OB3uSBZcyIHblgc3hsAVzoYrnANXOBeuSDbscCB9VjGG/Lwb98L9DMVXioiviPSe3jxcuzHNHQVbZ2NbybIYNwwGZKDkruqhutoWbO3y6SJ6rVkz0tCipvT2ZXs2mh05aLFnausRD5qDDvjDvbt8eTyerdarmZfdbvegKGxuDbTigzXv4o3Vr+OLyq/hi7QLrsJgNibWF2H05jR4Slu67QAodyCZOTnIzctAXk4acrI9yE53IcNrQ7pby8P22AJwhpphi4m+6ctxdsc9w6YJsO6akJiP62H0tjfIKVXES6AtBH9bUM0DviD8MpfJJHLMKXHR9bhoUNfiyDoBoYmjkLpPgSsAG5oQRgMikSaEZAq3IBRqRTAkFs8R1bETkc4dmz6XdZtDW1Y3/i5NCNjkdteoMhyIFxJRKcc2EWPhEGySfiyiSOayXe0LRZdj9pkeI2JObogCrpBKm/I7wwg4ZR7SJpe2zaeWtcknkzOMsENer/7P1nFZoekg1bvsCdjh9Tvg9dvh9TmQ0epAepsD6T6n2uYJOOAK2OAK2dXx8q+Xb06PH6HEyRYe19XpsLspmx0eblJaW3qV8dK+y31xTxKK2BAKy3cyvmPSJPpsgMvpgsPlhN3phNPlhNPpVM6iDn056IqgNL0ZG9IbsM5dqya/LTbK4ozYMRoF2M5WjO1QgnG2IqTbvKbIj6RhhtUYnVpqZkilZ2pTSKupCgVVB6aa6+l+kvovsTXt5bXHsrTldkHa+XLnjzFiYp09Rp0bIMJAjxwjhIg9pK6FDoekcNrhctjhdDjhdhqTG16JwrgkYyUNbocTDocdDrsddrsNDjXZ1fihsqxlyenhPtWRYizr75Zsi182jtPFVTQ7Ra4N5rGk3Bm9VtihSAQbmlrwU3UN1lXXYHNtLarq6lBXX4+mxgbYW5qR1tYCr69FzdPaWuGVua8V9m3pEJJSBvWWyDc1DLcjAJcrCLsrAIcrCIczCJusO0OIOENyIoGEuCLq/KNNIuoCdqDNZoPPZtfmdlm26dva52rZtE8michbhcvmwvGjj8XV+1yDRNEGidNlTzolEgrjs6njYQ97YA+5YAvXwVHdBnvVetixEHZ4Ybd54bClaXO7mFmkweHQJ6dMGdrclQ6nMwN2sZ53OWFz2eMmR+y6cuzpoeFEb8ePsLs0t6u4lD5bej68+pRvTvXbwtgRkgrZ2tra7XRIOVYudj6fT001NVuujzGQHP7u1KvJlJaeBpvLhtZQK1qCLSpCJQNiyngrGa4MZDgzkOHOUONodXAArDPqqdqNKuprK7GkbSW+da3BT9k1CEikQCer2YmxpekYU5qBvEaX1muGQDThQmpOVNqfMU5VfDrgkCHIyMnrvQOgXAj9zVtIjTSJNPOyHOeXKIsMyF2nTbVrelGsbBZpmogLqx7OXAQd2QjasxCwZ8MXydKnTLQFPAj4wjEiSgkrX0hfNy37Qn2eJrc15LrncEtdih0Ol01NYi5qd4dhk8iQU5tH7DLXI0n22OhSOBJUUaKQMZcbuHAQwVAAwZB2ExcIBhAMBtQN35bx6lNeD19J3PsWkUhGCPZwGPZISF8OwREJqnWHrCMERzio6n2cEamGCKr9xrKUyat5JKDmbgS00nlZFwFpi8AuETX5p9/Yyb2MzWmDTe3TbzrtEVUkL9EYOd4uhgwyyd9XN5zazbs8xrgPUY+T5RCgAngB+e5q++wiriJAq82GBodNpStJKlK9w45Gu6zLdn2b2m5Tyw0uO+q8dlR352YnAriVWNNEmZr77Ujz2ZHttyPTZ0e6Idj8DtgDDthCxu+6ZzdTDlsYaY5A++QMxq1r9RqynK7qOQJwxhkgDFYku0K+Ydo3yImAzam+RQGbCwG7C0GbGwExZXC4EbJ7UOu0Y5UnjB/cAfzgaMF6WzPi+/wy7R5MSRuFaVnbY3reREzJn4wMGWQ2LQtObwZsyuhBanl6d1MbCAVU3ZgYKFW1VKGqqQo1zTWoba5FXWsdGloa0NTWhKa2ZrT6WmGTlDNTKq2x7Ig4Y9aNZZm7xK4i7IA9LPcDce1sz2Nszw/clj430xMrAed0weV0wCmmKA4HnHa7ql0SkSYCTdZFkjhsNjWX36LMlZhzudXkdIfhdDfC4WqD010Nl2e92u7yeLSaPY9HiWgRjvI3ZfJFgDJ/UE2bZAoEsdEXwEZfEJvk+qCnBsswPhFbJiK5mXBkFcPT1oY0fyuGh/xqSg/6kB9oQ5a/DWltzbA3NSBQX6fqo6Quyh5q1VL11G8mGJuyZ9RDxWzTaqPckjfYvbdR+zziPhM5tftDLrSF3WiJeNFkT0OdIxM1nlxUe3LR5MpFmyMPAWc+IvYhSAtlIi2QgUyfB9kBIMffBlc4gHJnNVamr8UP3nX4ybsRZa5q1aFkxg03Cu35yHfkIdeVi3R3BkKOEPy2AHw2P3zwq/ug1mAbfME2tAV9at0X9iEYaW94IBJAkzifJBCMfCV45MvXVItPv57dt08q6RkhqWNwwBGywa4myeoJq3xpZ0hudgJwhgOwhyKS7QNHKAJbWJYjalndmMgNh3R6q2O0bbJsizgQdmYh4s5G2JWNsDsLYXcOIp4slYYW9mYhrCzW5ZgMhD2ZsLk8qiYrLFarkZAqwlaD30oKkUyhcPt21VMtN8D6PGJeDscsx+xXPWTSi631dKtj1b4wQqEAwlI8LDeiUkgc8qubUnWjGg4ipKc2yePD8jxyAyZXHHVzYdyMyRur9yLq6yGb9BFKzruWNy4nbMl599vlls6u3nNnGHDJ+xoJax1WKtNKz81Wf8qmbuzk6t7e2de+LNvlwmoLycXVDnvEoSa5JNkictGUHkV93ebULqLynHKpUvtlXetZs6lEb9kuL0CeS5tr+23R51TbtGfWjxVLWnl+dXcanczrcuOrLRvHaT16xjHRKKKcdEMBRIJ+NSFkzH1q2RbS0y/CfthlimiTvEntvdXaZxDtudUvClpaUXsvr3aM9ObaEYpIpYgHgYgHoYgbQXgQjGhTAF6EIl61T5YDkTQEI16EnemqI8Ppdmi94G477Kr3V34YYdjkg7PLd0IijrIuN+TyTdDS5tSy+t5r31uZRCRFlADSeru1KYiwmFKE5Dcgj9NStow0MfNcpXLFbevOPu1t0t+b6HOL+JDftry3Iki0SQmU6Lq2bGxr36f9Ruymv208Z7Qt+s/GUDbR77S+3ahXUTPT9pjjY47VP2XzurFtwNHPA/r3UH0Ho8vGdtMx0S+vdj5R9S+mScbuEaMcNdmh6kmCxtwGBOzaXLZpN4emOhr9+YzlkJx/7HaEJDop8lIilSIxbSLMRJlqc7VNvgFGz3sPcUVC8NiC8EKbPDKXdWObLaD2p+nb3LYgHPr7YQhd46NT74/x2zWOMf3WY7ar83Dc86Dz9zl2e8fPQ24o17udWJTuwaI0DxanubHe3dGEaGggqMZQ2klPI9w+EOgQT4tHflVa14HWjdBod6HK6UKNw4VapwvVUkPkdKDWIXVFQL09glp7GHX2IJriImvdIdOZgSHuPAzx5GOIZ4hySsz3anNxtx2SNgRD0vLVPN2epl37AgE1bI2/rQ1trS3wtbXC19qq1o253+9THYUBmctQNwG/eoyaAv4Yg45wUM5lck2XuX4+C2nXZBV51qNYvU3B7SnqmyLfdz1ar/XMSPRe/96rSY/i6+sRSduVeYdj5Tom+9uvfUrYqeufVvkhnTLq/KiuV9q5UJ03TXObujswOo3U1UnfLpEvvYpQ7wCUS4wW4JcSDZmHVUQ3KFNY5pK46EAopC2H5F7BXGmnn3Ql0i/nBb/ThYDLDZ/LDb/LA5/LA7/bA58xubwIebxwu9OR7vUgw+1CljMCL1rR7F+NWv9qVPrXoDy0CaG4Gju5n8gJZyE7nIHMkBfpIUmtkL8bRMgWQFDNQwjaggiquTGFMdM9EXed8jishpGvFKGlpR7LV4+Hwx5Sk/wY1bIxt2s/4ug+W0jd+NllXfUgafvVzWBMcr58yYNQnaDGt8R00VMbt3lgxmZ9Ku18t2QbdpJxqN3mOdQJI6z3O7cvS8msPWYuSRd+mwMBhwP+iA0Bux1y2QvoJ6BAxK5OOrJNnYjUdk0Mqbma5FZYbsDd+uRVoimsp4GoSf6JWFNpISLGdCFniDh5RrWuCcTu5WVvPbokN0DazZD0kmrLxjab2q7vVwLLvKxdGLRtInIMAaZPccv2mG2asGpflgtI7LK6oMiz27RxweTEKssiFpU8i96kayd2tSw33irKoS+rSb9Rl21qnx32sBu2iAv2SJq6yMQ8Lqw/TtbN+2S7rOvHRZdFwKq0Mq2TwDhW/+CVcDWyVuR+JhK2a+JG3Q/I3arstCEi9XOSThlu0rarbXqhut8m+RxqWf6mOxxQ4/K5Q0G17FKObn5tHvTDEw7CFfKr/dEbNLO4iNtm68YxHZbjju/wHOb1rh4X04beCRhzob81ImjbaJf0Ws969zHUT2wVlIqhRc+1mhoz+jjMb0/0I9C3GbvVWdkccTD6TuTmVU9fjHnOzp5PdUaZH2v628ZjTUImZJxjZVIRIgf8+jwQMc1NkzzYOL4Jni1/30w7nfYQXA5tcuqTy6mvy9wZgtOlTS5nEHZVE2h6LYYY0ye9X6l9m9FfJG+9vG7pRFQXA+nAkrpkYL3Dhh9ddvzotWO1145GZ+z3Vs4/I1oi2KEugvG1YYyvCSO/WR7vAsIutIQysUTOLVqPm/b+an0u6iZZmQhoWgPSryg3y9Ixp4lseUgImfYIvHYbhtq1fSGZG8uSvivXAdMNvraovbBoR5bR+aWrSfkuh2ytCNk2IWzbrF3zbDZlJFED/blFAgxgiln3zgoR/bIW0T5vh9SFhk3Lkeg+IRwUkwYbIkG7modlHrQjIttlbgpTah1CQe17gP5HJQ6KWyEk4ubUr+vaZDOWjX2m/do+V3RZpXXL98AutWJhhJ0hhF1BhJwyBfTlAMJiwe/QJ7sxD+hTUM1F7MiyCB25L9TmIQSly9km3c7GFGmf5BRi3OJ197XbIqh1NKgJPTSwrmzsaI8/mKH4SnDWLV+MfcqXqhOiiAxtrvWPyNxY106iIi5cpm0iWmRZEzByAdWTeNrnNkcn25xxy5r4CageVnHwsSNol2Q27SKg9cBKe7QLhPTKar208vfC+npYiwKpC6N2tVMxIXV10orto1dBm/ZKtbJlWe5s3qrPtVc12Oj8JC6iRBNA2j8jg17PodfnHR+lizpJeIi/Gd8Cnd2nDxQRdfOli0R1GtKXI7IscswJRIxt2jHyGJs+aZE7SXfRlrVJxJikxkjE0KmnzOipMmGn7uYk6TJ2uGQdMnfBJdvgUHM1RRxwR5xwRxzwyFz22RxwO2xwyqS3WL/cxaw7trDPWA5Hsz3kIqU+NXWxMjJApEqqMX67RC70uURG1bpd26acwvSIRkC5hWnbVNRDzdsno7Bb6pO0PH8p4A5py2qb1ERIZ4x02Ghz6ZhRnTd6R43c/KreWem0iemRNXpd29ej0a6YXlv9cXqEq317J8vGMSq9UEvxU2Jc36/Es/58dnMETpYlUh9th97ZpB/T3t72fVoUTvZLZFkeb1NRXIkyKzdDfVm2SZqVCl5KZFilXMm9q6RyBmGzB9Vdu9S5qPOY1FaofSFA7dNSPpXqVzf+mjiQG2UjoqW2mouKEgKtM0lekkipLVVXSudFyG9HqM2BYKsTQZnrU6jNiWBr+3qwzamOC/k1wRYMO9XU2s0sI5s9DJcnBJcnqCa3W+Yhbe7W1mXyGHOpkZFotB7plUzR6qAN1UE7qgI21AXkht2uDAKmtNgwtUkyCmzItgHZNhuyYIMkvEtnk/qGZTsRyrSjQrmy2bUaMrnhl3lYaspM25XosqsohJob69FjZW69MVYsES11V84TNjlXhNvnapt2LomdtI5hZ/Q4eYwxb98m4zhp27XnUev6+chYl+cwUn1jzVY66SQygukmka1NkiJoBMG1O4ZQRDpo5Z7JoUeJ7AiGHdpctqvPRdals1b7jGS/moe0zyqgJodaV/v07fJ5q89fBKDcAMV8pqK6xV3W18Gdv7fXai33RJviNY0y/FBGIOE4QxB9Wb9mmOchh9zTORBy2DsxEtGfK+5xYaPeV67bEf0aHr12O2CTa7m6+9HPxfI9UOdih37uNa7lkurqhiPsgTPk0tNi7Qj785FIUHwlON9vrsNeaFE3X36b9D5qokc62tvzjkUUSS+j3NDJXJvUsXGPM9aN/cYx2nMa27VjgsYx+uO2teDSSFbrb5ziwhMxzWFaj9vvMu13q/UIXOKWpS/L3C3jVETXteNkkm2ai48cI84+YXjC0Fx+wlIRY4NbBnlUk8gu7fbQOE3KKUgL/6sYm5FDqARrQN2Aq8S06LLclBufs5rrn4u2Ta/sMn0XZH90Wbaj82Vt3bSsP7bTZf3vRpf1KR65UdWEcfyOnn2WysZYn/ob3WBPfV7a98ZY1tdlro6L36Yty427rKtETjU3UjT1ZVPaplqWm3z9cqSlb+rCXE8V9aiKTrmAyd72VFKV4inHRJywhewq3VR7jN7rraeLtv89fTm6Tf/e6VFJLeXEKKrXvodayqCe3qIEjxZS0dIJ24v1jRqpaFAhuqxdZI2Au6ZBpJ16qrLaposfdbRbD7JoF2jtAq5JWrt61+U1Sf2iJoG1T0KP8OpCX4sOa5Mmk82TfvaJpsFKQ3TrDxX+0AwwtLs7La1Y6xzSt6vXqYVRjHRdlbqrp+Cqedx+9S6rcZuMjiT5haqzqVo25kqGK1cz8X8OmWS7kt6ISGeU6qzSlrXOKq0jSltWic2xHVlG51ZMx5Z2ntGO1/arz1vtl9cpnTzG+6GlyGrhYMOQQYsBRo0Y9O9BNK3UqLtRn6+2bnyPtEh4+zYVqcgE7Bnad1A+IYn+qKEsgjJ0hTa1yDwoQ1e0b9P2a9vkxlki1f5Wmbrfle7Wa2kEdTMtxhv6jXhXN03SYdLLiuZtwqhbVGJEFzGGaJGautj1uP260Om4v6vn67hfnSPag7jR7kEjpdX4dsRnBbfvF+Is5WMzhGOfQ752EoGUa1tc5Lzz5459DnmcT+5nwu3mEX7TXH6BPrnHsXe+X/b5pDTAFtbnscdoy4CvJ+7Rqs/LpgxypNTDWFbryv5dlu3ty50cqy3bO3lcJ89nEnvaug1u6a0bAIw6WrteW6syZPS0S3Npghp8Prqup3vq4k25HRupz+o4Bxo8FUgkKL4SnE1NjTh85DAMNuxR4WISLVFhEo4KFBElxnbtGH273OSaRE77Y/Vj9Me1Hx/3PDA9j+kYac+g7Ec2NcqIUsq8PdMn1nFKE3nmWhkN7WbZOmLqbkzbDPEWNcY1RJq6xdRrUWSb9DCahKFKB1U9ke3bOyyrjCC5OdLFpoq46imjupDUbm/1ZUndUX9H7+GMVlzpz6MfZyohiWYqRWujosvmY7Ttkq2ooludPLZHdPOL2t2n7cWIDL17rvjUONvAvlajE8i4mVfL5s/JSA80av707fHZZ6IxROqplFlN3qqoqhaf1iaJeqm5CC2TU6FR3xkVcLqTmVZirYs5Xbwa0WzN/l5rjOYYZ6oB6yS11FSm1GUYO/5tjat3j800j3t8Zx+J9vjYbrIO0YYtPGdnx3THc6Ojrb2RY6k7t8Efk7qq3mb5HkjKsPhzh7WedTWXcG90m6RdaHNtvyaK/WGnmraG4UopL0K5U6pOyEjspHS3ltWhrcuyvk1/XNjYZjeWZQ7TshZVkOeXubYujoK6ztfbY2TLmcWH8Rs00lnVt0z/AWjHaRkG2nGa5I5GX9XrM567ffyt6LfcFKU1p63G7Is7X7RHmLZtn/a3BuBqHunqfG9MWoeYQzq65EwRtiEt4kC66kzSzxmSbRExMisk48JYl7l0yjlUJNWlsjE0cSRzOVY67KKdfGF90teNDj21LNF5vftJBgUXUSOmWFpHnJFloip0EZZIkZpr1ztVE6Yby6gseuNzU8vaGUpz29TeCO2cFolbNo7SR2dTHTNGbZ5EJg3nVb2Dz0iBNbZE1/XjVOeV6TEdHq99GYzHNje1uzgnAoNCfN1333244447UFZWhunTp+Nvf/sbdt555y6Pf+aZZ/DnP/8Za9euxQ477IDbbrsNhx9+eHS/XODmzZuHhx9+GHV1ddhjjz3wwAMPqGMNxLHuvPPOwyuvvKIcbE444QT89a9/RWZmJhKJtlWr8EDzOaa6Ae3rry+0bzPfDJt3mPZpvyvj4i9ffpPls/kh+g/SOAtGOtkWvYkw/WH1E9aPU52m0b+k/agNO1ztoPbC+/bWaULDeJw8n1+M88z9XqrIxvS8+k2xuU9O9dHrz62dKPSi1Gj1Rmw1vhEFMF3i9BOxub3mGwSp8TKPUdPeI9wezdr6/Wa7ma+xQesR0gbNNDa25+4bA1xGPz9Ttry5ZzD2L0djG6ZtemuN+hC1rH+u6u8bbensOU2Pjdseu01vV8zFs/PHagXP2hYtZiEXKSAt7rHm4zp7bPT59fcr9rHmbd1/bPR9Mz02+n6YHhv7/sb/5ZgfoOlI08ip8TegUWER6eI7GPt4JeNVhEKv/ZBj1cXMqAPR9mn3ZMYFTdumReaM0pDopdH0WNNzGc+vv37jwth+bPt+7Rhjn/m5TRdi03NE180XYaOXNNoGY91U67KVbe3PYa6PGTypXdo5tP3Gx7wt/hhtOf448zXBfE5vP5eaj40eHz2nm8/v5r9nembTfuM6Evdspq9558fFP097O4wzeayOU7W15nXT64/7Fejvn/Y9j/49LYCqCxERGdpgxNr4cFoFS/s3td1EqH29Cxt/45QQvyH2MhT9fXSXmGO7eFiHv7nFY7oWL93a2vVL7+GWDo3ayruin3O69cRxO83ncyOqojCZPOkdC9FzQNTsSW9XtEYu9hsQncd1mLQv27ayv/PHdzjOZPYSv38wDG1jFesaViCRsFx8PfXUU7j44ovx4IMPYpdddsHdd9+NQw45BCtXrkRRUVGH4z///HOccsopuOWWW3DkkUfiiSeewLHHHouFCxdi6tSp6pjbb78d99xzDx577DGMHTtWCTV5zu+//x5er9gfA6eeeipKS0vxzjvvKOvx008/Hb///e/V8yUSGYEwxmRPsboZpJuYbxg666zuXCx38jxdPX93t0X69jm7+zdib63662/04H3oxjEx2qgP/oYQ45+gX2zNfgqavbkxGSIkbur2ttS9IPf379g8dexGMC1vw2dgFp6EENJfHTvtUSyj46PzbdpjYucxy+px4a1vg2bFqP1r78yOPjc6mRttMiKw+rF13kokEpZbzYvgmjNnDu699161LjaYI0eOVFGpyy+/vMPxJ510khp76dVXX41u23XXXTFjxgwl4OTlDBs2DJdccgkuvfRStV8sH4uLi/Hoo4/i5JNPxvLlyzF58mR88803mD1bs2l/8803VfRs48aN6vGJYjX/j9vmYdyGnU29Ih17hcw3eMYWcy+K+cj4HpfYx8X23sT+nc63G60x2tBhW0wbO/n7W/ybpn3mbdHjungvTO3p2MPU+WN4A0uSFTVoq1z6jIuaflE2BjvWhEb8snbBjk200y6Epkux/pym9fakvOhztVczxl5mzaku2oDDkhITv18ipdox0eeJqT9pT+6L2adHR5SDnH6MSv9SURZtn6pI1NOw1HMbKVtGzFE/XxnrRoRd0h3V1qhbZ/v2qAmJ4d6pP85w/dRq5HQnUKNeKmo6osdQow6hev2USu1pr7Fqj6gYkUvj3GqWiO0990aENzay0v64Dv3rppOn+Yyt/lrMid44b5u6EYwAuvm51Aa90tD0nOa2x7yiuLa2n5tNZ+/o3zbvaRew0YhF9JrT/r85ittZxM3AeDe32OHSye1Vx06crm/BtnRzFv+4LR8bu9S+3v4+dtYZ2H6M8bjY62Bnx7a/9q23q0cdae2pGF2g7e/yeTrp0DTOGSZpERugjJ7jYr8L5kn+k/OKOpNEc3C1dNNodoxRG6rePq1eUvut6jWTUVv5UHS7ZiOvDUeiaqSj68pvUhkgKeMeVZep12ZGa1LF3EeeVzf6Uc8pA11r4zoaz6UdE1ZjGBrPow3NYLRLWzaGRFHfALUvfr/xmPbXE32c+Tnijrd1+FvGY41l83HaWItdUff9WJxw7ruwmoSwmvf7/ViwYAGuuOKK6DZJATzwwAPxxRdfdPoY2S6RMjMS1XrxxRfV8po1a1T6ojyHgbwRIvLksSK+ZJ6bmxsVXoIcL3/7q6++wnHHHdfh7xoD7Jrf4MHASWf/EXf8/UG9YFEPk8sYEvIvZiwJVdmop9S0T+j0YmUUnet7zRdRdfXU8nf1YebbL3pGd71xqYt/TrPYMy56XYrF+PaY98e1I+aeobOLsumSYDYR0F3RlLmrfhbV3M+03hhx2zFSH2W7FPPINuW2pqzJjWN1l7RQ+xhHykHNbHGu7g41BzbNVr3dat2wU7dFLdK1Nqm6dvWZaeXp7e+52RhAxhIxbixk2Xi/2lMmjFcsj1cvU9ItlBOXESqJPVa7hmnPqd1gylzs1eX9bq+MMdJF9be1/WPQ38voDZZ6ne23fmquNSR62ZP3X0sF1W42tWO1G9HoH1AGDO1X9PbPVF9WjzPMIoybIyOl1bhatt9Sac9tfBe0lCP93rW9rdHXYnpt5jZF8+PNy1o7lTu32qb/xWjvoqDd8uuSwCRPzBJFO04TDFpNiCxptRft4iT6aFVfpAsKZZKgCQtlRqJEhGbQor8NKrlKG5/JsM7QP67oLYPdlMHfnjqp1YTo34+ouGlf196a9hRNbYth1WG8jfpz6m9s+82N/NK0ufEt0IaraT9Wcz2X25R2MaF9x7VHtqfctp9QtK+P8deNx3R9txdNodbfO9NXsJNbWb1V2t2W8SaahI3pvKhl0emuhtrz61lvUVESLwaMufr9R4ekM52zTVJEc0jVZYqqT2v/vbVLIONdispG/RxnizXQMKW2GgJQe/naq9aqhIzfpWZ5Y5xbtcGx4/9O++9Uez49zVt/E43nMJ5b++22G3Von3L77119u03vh3z/o2JY//GatsSkEbffx+uDY0e/r9pf0er3zAJMP6+o34vxjdbOpTFSUH2uUiGoHWsWK1pKr3xHtO+3cZ2T74GR7mt8l9XrUyYD+ndFtUn/hE3W8PJMMqCwena7IYClTlFrhyFcxZRA+87pxi/GtVul3ml1X9p7qF1TZdxHzQFQH6dKvdGG4NaeX7a3D4sgTnQiBtq/8/Fi3jgfGGnO5vsH43omj42+w9FLU/t3KSoa1OejfV+0U50RDTFSXLWzgXJPVqYysk38gtUZNGrWoBk5GFWgYq3VbsxjnNuMFOpoV4Z5fEHV6WPs0a6rUZEnnTUmc2Lj+mJEe7TGGCUW7eUS7YrOOMMb2/WyDblP0H9H6tyoxi7Vr3PR59CfT++g0f62rT3SZBR2Gc1R4xoYv13j56NfU43roj6MjlHeoT52YzwEvZMoYnKsNV5XdGxI5UCrv6sR/Z4n+veM12+Y8bRLXs3PqL1dSqAaIlO/YshBOcXFSCQsFV9VVVVqUFCJSpmR9RUrOs/fFGHV2fGy3dhvbNvSMfEpjU6nE0OGDIkeE4+kOV533XUYbGRlZeJ6PcJHCCGEEEIIGbwMnqriQY5E5ySMaEwbNmywukmEEEIIIYSQBMJS8VVQUACHw4Hy8vKY7bJeUlLS6WNk+5aON+ZbO6aiInZMgGAwqBwQu/q7Ho9H5W+aJ0IIIYQQQghJCPHldrsxa9YsvPfee9FtYrgh67vttlunj5Ht5uMFcSw0jhd3QxFQ5mOkPktquYxjZC4W9FJvZvD++++rvy21YYQQQgghhBCSdFbzYp5x2mmnKfMLGdtLrObFzVCs34W5c+di+PDhquZKuOCCC7DPPvvgzjvvxBFHHIEnn3wS8+fPx0MPPaT2SzHnhRdeiBtvvFGN62VYzYuDoVjSC5MmTcKhhx6KM844QzkkitX8ueeeq8w4uuN0SAghhBBCCCEJJ77EOr6yshLXXHONMrsQy3ixfTcMM9avX69cCA123313NRbX1VdfjSuvvFIJLHE6NMb4Ei677DIl4GTcLolw7bnnnuo5jTG+hMcff1wJrgMOOCA6yLKMDUYIIYQQQgghSTnOV6IyWMb5IoQQQgghhCSGNqDbISGEEEIIIYSkQtphomIEDAfLYMuEEEIIIYQQazA0wdaSCim+ekljY6Oajxw50uqmEEIIIYQQQgaJRpD0w65gzVcvEVv6zZs3IysrSzksWq20RQTKwM+sP0sO+JkmJ/xckw9+pskJP9fkg59pctIwiD5XkVQivMQ53WwWGA8jX71E3tQRI0ZgMMHBn5MPfqbJCT/X5IOfaXLCzzX54GeanGQPks91SxEvAxpuEEIIIYQQQsgAQPFFCCGEEEIIIQMAxVcS4PF4MG/ePDUnyQE/0+SEn2vywc80OeHnmnzwM01OPAn4udJwgxBCCCGEEEIGAEa+CCGEEEIIIWQAoPgihBBCCCGEkAGA4osQQgghhBBCBgCKL0IIIYQQQggZACi+koD77rsPY8aMgdfrxS677IKvv/7a6iaRbeDjjz/GUUcdpUZIt9lsePHFF61uEtkGbrnlFsyZMwdZWVkoKirCsccei5UrV1rdLLKNPPDAA5g2bVp0YM/ddtsNb7zxhtXNIn3Irbfeqs7BF154odVNIdvAtddeqz5H8zRx4kSrm0W2kU2bNuGXv/wl8vPzkZaWhh133BHz589HIkDxleA89dRTuPjii5XN5sKFCzF9+nQccsghqKiosLpppJc0Nzerz1FENUl8PvroI5xzzjn48ssv8c477yAQCODggw9WnzNJXEaMGKFuzhcsWKAu+Pvvvz+OOeYYLFu2zOqmkT7gm2++wd///nclsEniM2XKFJSWlkanTz/91OomkW2gtrYWe+yxB1wul+r0+v7773HnnXciLy8PiQCt5hMciXRJr/q9996r1sPhMEaOHInzzjsPl19+udXNI9uI9NC98MILKlpCkoPKykoVARNRtvfee1vdHNKHDBkyBHfccQd++9vfWt0Usg00NTVhp512wv33348bb7wRM2bMwN133211s8g2RL4kg2Tx4sVWN4X0EZdffjk+++wzfPLJJ0hEGPlKYPx+v+p1PfDAA6Pb7Ha7Wv/iiy8sbRshpHPq6+ujN+okOQiFQnjyySdVNFPSD0liI5HqI444IubaShKbH374QaXyjxs3DqeeeirWr19vdZPINvDyyy9j9uzZ+PnPf646M2fOnImHH34YiQLFVwJTVVWlLvrFxcUx22W9rKzMsnYRQjpHItNSPyLpElOnTrW6OWQb+e6775CZmQmPx4MzzzxTRaknT55sdbPINiAiWlL4pVaTJE+G0KOPPoo333xT1WquWbMGe+21FxobG61uGuklP/30k/osd9hhB7z11ls466yzcP755+Oxxx5DIuC0ugGEEJJKPepLly5lvUGSMGHCBJXKJNHMZ599FqeddppKJ6UAS0w2bNiACy64QNVmioEVSQ4OO+yw6LLU8IkYGz16NJ5++mmmCCdwR+bs2bNx8803q3WJfMm19cEHH1Tn4cEOI18JTEFBARwOB8rLy2O2y3pJSYll7SKEdOTcc8/Fq6++ig8++ECZNZDEx+12Y/vtt8esWbNUpESMcv76179a3SzSSySNX8yqpN7L6XSqScT0Pffco5Yl04QkPrm5uRg/fjx+/PFHq5tCesnQoUM7dHJNmjQpYdJJKb4S/MIvF/333nsvpjdA1ll3QMjgQDyNRHhJStr777+PsWPHWt0k0k/I+dfn81ndDNJLDjjgAJVKKtFMY5LedakRkmXp7CTJYaiyevVqdQNPEpM99tijw5Atq1atUhHNRIBphwmO2MxLiFUuEDvvvLNyZJKi79NPP93qppFtuDCYe+QkP10u/GLQMGrUKEvbRnqXavjEE0/gpZdeUmN9GfWYOTk5amwSkphcccUVKp1JfpNSOyKf8YcffqjqD0hiIr/P+FrMjIwMNY4QazQTl0svvVSNnSk35ps3b1ZD84iQPuWUU6xuGuklF110EXbffXeVdnjiiSeq8W0feughNSUCFF8JzkknnaSsq6+55hp1UyeWuFJUGm/CQRIHGTNov/32ixHYgohsKRomiYUUBQv77rtvzPZHHnkEv/71ry1qFdlWJD1t7ty5aswgEdJSSyLC66CDDrK6aYQQExs3blRCq7q6GoWFhdhzzz3VuIuyTBKTOXPmqGwS6QS7/vrrVUaJBB8kSp0IcJwvQgghhBBCCBkAWPNFCCGEEEIIIQMAxRchhBBCCCGEDAAUX4QQQgghhBAyAFB8EUIIIYQQQsgAQPFFCCGEEEIIIQMAxRchhBBCCCGEDAAUX4QQQgghhBAyAFB8EUIIIYQQQsgAQPFFCCGEEEIIIQMAxRchhJCU5te//jVsNpuaXC4XiouLcdBBB+Ff//oXwuGw1c0jhBCSRFB8EUIISXkOPfRQlJaWYu3atXjjjTew33774YILLsCRRx6JYDBodfMIIYQkCRRfhBBCUh6Px4OSkhIMHz4cO+20E6688kq89NJLSog9+uij6pi77roLO+64IzIyMjBy5EicffbZaGpqUvuam5uRnZ2NZ599NuZ5X3zxRXV8Y2Mj/H4/zj33XAwdOhRerxejR4/GLbfcYsnrJYQQYg0UX4QQQkgn7L///pg+fTqef/55tW6323HPPfdg2bJleOyxx/D+++/jsssuU/tEYJ188sl45JFHYp5D1n/2s58hKytLPfbll1/G008/jZUrV+Lxxx/HmDFjLHlthBBCrMFp0d8lhBBCBj0TJ07EkiVL1PKFF14Y3S6i6cYbb8SZZ56J+++/X2373e9+h913312lL0p0q6KiAq+//jreffddtX/9+vXYYYcdsOeee6r6Mol8EUIISS0Y+SKEEEK6IBKJKKEkiIg64IADVGqiRLJ+9atfobq6Gi0tLWr/zjvvjClTpqiomPDf//5XCay99947auyxePFiTJgwAeeffz7efvttC18ZIYQQK6D4IoQQQrpg+fLlGDt2rDLiEPONadOm4bnnnsOCBQtw3333qWOklstAol9GjZikHJ5++ulR8Sa1ZGvWrMENN9yA1tZWnHjiiSolkRBCSOpA8UUIIYR0gtR0fffddzjhhBOU2BLb+TvvvBO77rorxo8fj82bN3d4zC9/+UusW7dO1Xd9//33OO2002L2iynHSSedhIcffhhPPfWUEnI1NTUD+KoIIYRYCWu+CCGEpDw+nw9lZWUIhUIoLy/Hm2++qZwIJdo1d+5cLF26FIFAAH/7299w1FFH4bPPPsODDz7Y4Xny8vJw/PHH449//CMOPvhgjBgxIrpP3BKlFmzmzJnKvOOZZ55RDou5ubkD/GoJIYRYBSNfhBBCUh4RWyKMxEhDxvz64IMPVPRK7OYdDodyPRTxdNttt2Hq1KnKqbArm/jf/va3KhXxN7/5Tcx2qRO7/fbbMXv2bMyZM0elMoohhwgxQgghqYEtItXEhBBCCOkT/vOf/+Ciiy5SaYlut9vq5hBCCBlEMO2QEEII6QPE9VBs5m+99Vb84Q9/oPAihBDSAeY6EEIIIX2ApBTKuGBSx3XFFVdY3RxCCCGDEKYdEkIIIYQQQsgAwMgXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYQQQgghhAwAFF+EEEIIIYQQMgBQfBFCCCGEEELIAEDxRQghhBBCCCEDAMUXIYSQPuWbb77BueeeiylTpiAjIwOjRo3CiSeeiFWrVnV6fCQSwX/+8x/svffeyM3NRXp6OnbccUfceOONaGlp6dHfXrJkCU4//XSMHTsWXq8XmZmZmDFjBi677DL89NNPvXo9r7/+Oq699lr0NfKcNputy6msrKzP/yYhhBBrsUXkqkcIIYT0ET/72c/w2Wef4ec//zmmTZumRMS9996LpqYmfPnll5g6dWr02FAohF/84hd4+umnsddee+H4449X4uuTTz7BE088oQTcu+++i6Kioq3+3YcffhhnnXUWCgoKcOqpp2LixIkIBoNYunQpnnvuOdTU1KC1tRUOh6NHr0eE5H333adEYl+Lr+uuuw4PPPCAEomdvY8iIAkhhCQPTqsbQAghJLm4+OKLlXByu93RbSeddJKKZt16663473//G91+++23K+F16aWX4o477ohu//3vf6+iZccee6yKZL322mtb/Juff/65El577LEHXn31VWRlZcXsv/POO3HTTTdhMCIiSwRjT2hra1Pvr93eMYGlublZRRx7Szgcht/vp/AjhJB+gGmHhBBC+pTdd989RngJO+ywg4piLV++PLpNolAiuMaPH49bbrmlw/McddRROO2001Ta39dff73FvykRJEnVe/zxxzsIL0GExA033BAT9ZLomkTnJC3S4/Fg5MiRuOiii1S7DH7961+rqJdgTgk0C5W7775bvTb5G8XFxfjDH/6A2tpa9BUffvih+ptPPvkkrr76agwfPlxFBxsaGlT7JGq2evVqHH744eq1S9TPEGGXXHKJel3y+iZMmID/+7//6xDBk+eW6J68d/I65Ng333xT7ZO/OWvWLPW82dnZSkD/9a9/7bPXRgghqQYjX4QQQvodueEvLy9XN/cGn376qRIpF1xwAZzOzi9Hc+fOxSOPPIJXXnkFO++8c6fHSF3Y+++/j3333RcjRozodpueeeYZ9ViJmOXn5yuB97e//Q0bN25U+wQRUps3b8Y777yj6tLikf2PPvqois6df/75WLNmjUqxXLRokUq9dLlcW22HpEPGI++H1L+ZEfEoolaihD6fLypwJbXykEMOwZ577qnElQgzeb+PPvpofPDBB/jtb3+r6t7eeust/PGPf8SmTZvwl7/8Jea55f2TCKSIMInCjRkzRr3mU045BQcccABuu+02dZyIZ3ld8pkRQgjpBVLzRQghhPQn//nPfyTcEvnnP/8Z3Xb33XerbS+88EKXj6upqVHHHH/88V0e8+2336pjLrzwwg77qqurI5WVldHJ5/NF97W0tHQ4/pZbbonYbLbIunXrotvOOecc9fzxfPLJJ2r7448/HrP9zTff7HR7PPPmzVPHdTZNmDAhetwHH3ygto0bN65Dm0877TS17/LLL4/Z/uKLL6rtN954Y8z2n/3sZ+r1/fjjj9Ftcpzdbo8sW7Ys5tgLLrggkp2dHQkGg1t8HYQQQroP0w4JIYT0KytWrMA555yD3XbbTaURGjQ2Nqp5Z2mCBsY+49jOkPQ7oTPTinHjxqGwsDA6vfzyy9F9aWlp0WVJ0auqqlIpk6JHJHK1NSQ6lpOTg4MOOkg91pgkTU/aIlGn7iBmIBJlMk8S7YtH3jtzm81I9M6MpGpKiqVE48xIGqK8vjfeeCNm+z777IPJkyfHbJPIm7wv0h5CCCF9A9MOCSGE9BvidHjEEUcokfLss8/G1Fx1R1gZ+7bkdmg8j7gpxvPSSy8hEAjg22+/Vel6ZtavX49rrrlGCbL4Gq36+vqtvrYffvhBHddV2yoqKtAdxGK/O4YbYp/fGZKiGJ9uuW7dOgwbNqyDsJ00aVJ0/9ae++yzz1apiIcddpiqMzv44IOVCcqhhx7arddFCCGkIxRfhBBC+gURJnLjXldXp8wtRAyYMSItMjaXuBp2huwzIlhdsf322ysBIpby8UhER4ivKROLe4lYSb3Vn/70J2VLLw6BUg8lJhZipLE15BgRXmJU0RkSaetLuop6iUFGZ66H2/rc8toWL16sasUkUiaTROSkDu+xxx7bpr9HCCGpCsUXIYSQPkes0MWtUAZWlnG64lPaBLGFl9Q2saW/6qqrOh1/69///reaiythV4hoErONjz76SIknidJsje+++061TUSEiAmDzlLszO6GZrbbbjv12uR1dCWMrGL06NGqbRI5NEe/JAXU2N8dxNRDPkeZRGxKNOzvf/87/vznPyvRSwghpGew5osQQkifIlElGdfriy++UHVRUuvVGeLKd9lll2HlypVKfMUjY3uJk6Dc+IvF+ZaQ9EH5u7/85S87TT+Mt1c3hJ55uyx3ZqNujJklETwzkoInf1NcCOMRB8L44wcSsZ2XtonzohlxORQxKRHJrVFdXR2zLtE1GTRbELdFQgghPYeRL0IIIX2KmDpIHZWIJknrMw+qLIhAMhDxJaltYmUuYu2EE05QUSSxoZfHiTW9CLCtsddeeymhcd5556kxxWSsK0kllMGCJcIlqYESxSkpKVHHyz6JXEkdmETLZAwrMb7obHwuMdAQxLxCLN1FuJ188skqpVGs5mWMMnkNUhMl1vJSCyaiU4ScDKC8NaQWrjOzEEmLlHHDeoO89/vtt58StWvXrsX06dPx9ttvqxq4Cy+8UL32rfG73/1OfX7777+/qimTOjGx4hfbeqN2jBBCSA/pgTMiIYQQslX22WefLi3UO7vshMPhyKOPPhrZY489IllZWdHjDjzwwBhr+O6waNGiyNy5cyOjRo2KuN3uSEZGRmTatGmRSy65JMZeXfj+++/V38jMzIwUFBREzjjjjKht/SOPPBI9TqzWzzvvvEhhYaGyaY9/DQ899FBk1qxZkbS0NNX+HXfcMXLZZZdFNm/e3GureZnEYt5sNf/MM890eA6xmpfX2BmNjY2Riy66KDJs2LCIy+WK7LDDDpE77rhDvd9m5LnFTj+eZ599NnLwwQdHioqK1Hsp7+kf/vCHSGlp6RZfFyGEkK6xyX89FWyEEEJIfyHuhBK5ee+999TgynTXI4QQkixQfBFCCBl0yPhSYqIhBhFipLHTTjtZ3SRCCCFkm6H4IoQQQgghhJABgG6HhBBCCCGEEDIAUHwRQgghhBBCyABA8UUIIYQQQgghAwDFFyGEEEIIIYQMABxkuZeEw2Fs3rwZWVlZsNlsVjeHEEIIIYQQYhHiYdjY2Ihhw4bBbu86vkXx1UtEeI0cOdLqZhBCCCGEEEIGCRs2bMCIESO63E/x1Usk4mW8wdnZ2VY3hxBCCCGEEGIRDQ0NKjBjaISuoPjqJUaqoQgvii9CCCGEEEKIbSvlSDTcIIQQQgghhJABgOKLEEIIIYQQQgYAii9CCCGEEEIIGQCSQnx9/PHHOOqoo5S1o+RZvvjii1t9zIcffoiddtoJHo8H22+/PR599NEBaSshhBBCCCEkNUkK8dXc3Izp06fjvvvu69bxa9aswRFHHIH99tsPixcvxoUXXojf/e53eOutt/q9rYQQQgghhJDUJCncDg877DA1dZcHH3wQY8eOxZ133qnWJ02ahE8//RR/+ctfcMghh/RjSwkhqUzrsmUIbNpkdTMIIVvBZrcjfc4cOHJyrG4KISTJSArx1VO++OILHHjggTHbRHRJBKwrfD6fmsxe/oQQ0l1avvkG6+aeBkQiVjeFENIN0mbOxOjH/6uEGCGE9BUpKb7KyspQXFwcs03WRVC1trYiLS2tw2NuueUWXHfddQPYSkJIshDx+1Eq549IBO7Ro+HIz7e6SYSQLdD2/fdoXbQI9S+9jNzjjrW6OYSQJCIlxVdvuOKKK3DxxRd3GMWaEEK2RvWjj8H/42o48vIw5qkn4cjNtbpJhJAtUP2Pf6Di/+5ExZ13IuvAA+DIyrK6SYSQJCElY+klJSUoLy+P2Sbr2dnZnUa9BHFFlP3miRBCtoZ/4yZU3X+/Wi667DIKL0ISgCFz58I9ZgxCVVWourd7Zl6EENIdUlJ87bbbbnjvvfditr3zzjtqOyGE9BWRSATlN96ISFsb0mfPRs6xx1jdJEJIN7C53Si+6iq1XPP44/D9+KPVTSKEJAlJIb6ampqUZbxMhpW8LK9fvz6aMjh37tzo8WeeeSZ++uknXHbZZVixYgXuv/9+PP3007jooossew2EkOSj6b330PThh4DTiZJr56lxCAkhiUHmXnsi84ADgGAQZTfdpDpTCCFkW0kK8TV//nzMnDlTTYLUZsnyNddco9ZLS0ujQkwQm/nXXntNRbtkfDCxnP/HP/5Bm3lCSJ8Rbm5G2U03q+X800+HZ/vtrW4SIaSHFF9xuYqCtXzxJRrfetvq5hBCkgBbhF05vUIMN3JyclBfX8/6L0JIB8rvuAM1//wXXMOHY9yrr8DeRT0pIWRwU3nP31TdpnPYUGz32mv8LRNCtkkbJEXkixBCBhNtK1eh5tHH1HLx1VfxZo2QBCb/jN8p4RXcXIrqhx+2ujmEkASH4osQQvqQSDiMMhnTKxRC5oEHIGu//axuEiFkG5DOk+LLL1fL1f/4J/wbNljdJEJIAkPxRQghfUj9Cy+gdeFC2NLTUXLllVY3hxDSB2QddBAydt9NDZhefsutVjeHEJLAUHwRQkgfEaytRcXtd6jlwnPOgWvYMKubRAjpA8SpVFnPO51oev99NH38sdVNIoQkKBRfhBDSR1T83/8hVF8Pzw47YMjcX1ndHEJIH+LZbjsM+ZX2uy6/6WaE/X6rm0QISUAovgghpA9oWbAA9c89r5ZLrrsWNpfL6iYRQvqYgnPOhqOwAP5161DzmGaqQwghPYHiixBCtpFIIICya69Tyzk/OwHpO+1kdZMIIf2AIzMTRZdcoparHngQgfJyq5tECEkwKL4IIWQbqfn3v+H74Qc4cnOjN2aEkOQk5+ijkTZzJiItLdEaT0II6S4UX4QQsg0ENm9G5b33qeWiP/4Rzrw8q5tECOlHbHa7Gr8PNhsaXnsNLd98Y3WTCCEJBMUXIYRsA2U33YxIayvSZs1CznHHWt0cQsgAkDZlCnJPOlEtl914EyLBoNVNIoQkCBRfhBDSSxrFcvq995T9dMm8a1SPOCEkNSi84AI4cnLgW7kStU8+ZXVzCCEJAu8UCCGkF4RbWlB2441qOf/Xp8E7frzVTSKEDCCSYlx44QVqufKeexCsqbG6SYSQBIDiixBCekHVAw8guLkUzmFDUXD22VY3hxBiAbknngjPpEkINzSg8i93W90cQkgCQPFFCCE9RJwNqx95VC2XXH017OnpVjeJEGIBNocDJX++Wi3XPfssWr9banWTCCGDHIovQgjpAZFIBKXXXQcEg8jcf39k7b+/1U0ihFiIjOuXffRRcnJA2Y03IBIOW90kQsgghuKLEEJ6QP0LL6J1/gLY0tJQctWVVjeHEDIIKLr0UhUBb/t2CepffMnq5hBCBjEUX4QQ0k2CtbWouEMbVLXwnLPhGj7c6iYRQgYBrqIiFJxzjlquuPNOhBobrW4SIWSQQvFFCCHdpPKuuxCqrYVnh+0x5LTTrG4OIWQQMeRXv4R77FiEqqtRde+9VjeHEDJIofgihJBu0LJwEeqeeVYtl8ybB5vLZXWTCCGDCJvbjeKrrlLLNf99HG2rVlndJELIIITiixBCtkIkEEDZtdeq5Zzjj0f67NlWN4kQMgjJ3HMPZB10IBAKofymm5VBDyGEmKH4IoSQrVDzn//Ct2oVHDk5KPrjpVY3hxAyiCn60+WweTxo+eorNL71ltXNIYQMMii+CCFkCwRKS1Gp128UXnoJnHl5VjeJEDKIcY8YjvwzzlDL5bfehnBLi9VNIoQMIii+CCFkC5TffAsiLS1ImzkTuSecYHVzCCEJQP7vfqvcUINlZah66CGrm0MIGURQfBFCSBc0fvghGt95B3A4UHLtPNjsPGUSQraO3etF0eV/Uss1//wX/OvWWd0kQsgggXcShBDSCeHWVpTfcKNaFlt574QJVjeJEJJAZB14IDL22EMZ9pTfcqvVzSGEDBIovgghpBOqHngQgU2b4Bw6VA2oTAghPcFms6H4qisBpxNNH36Ipo8+srpJhJBBAMUXIYTE4Vu9GtWPPKKWi6+8AvaMDKubRAhJQDzjxmHI3LlquezmmxH2+61uEiHEYii+CCHEhIzLU3btdUAggMx991WpQ4QQ0lsKzj4LzsJCBNatR80jj1rdHEKIxVB8EUKIifqXXkLLN9/A5vWi+OqrVeoQIYT0FkdmZnR8wKoHH0SgrMzqJhFCLCRpxNd9992HMWPGwOv1YpdddsHXX3+9xePvvvtuTJgwAWlpaRg5ciQuuugitLW1DVh7CSGDj1BdHSpuv0MtF5x1lhqvhxBCtpXso45C2k47IdLaGj3HEEJSk6QQX0899RQuvvhizJs3DwsXLsT06dNxyCGHoKKiotPjn3jiCVx++eXq+OXLl+Of//yneo4rr7xywNtOCBk8VPzlboRqauDebjvkn/5rq5tDCEkSJIJe8uerAbsdDa+/juavttxBTAhJXpJCfN11110444wzcPrpp2Py5Ml48MEHkZ6ejn/961+dHv/5559jjz32wC9+8QsVLTv44INxyimnbDVaRghJXloXL0bdU0+p5ZJ518DmdlvdJEJIEuGdNAm5J52olstvugmRYNDqJhFCLCDhxZff78eCBQtwoKko3m63q/Uvvvii08fsvvvu6jGG2Prpp5/w+uuv4/DDD+/y7/h8PjQ0NMRMhJDkQG6CSsVkA0DOMccgY+edrW4SISQJKTz/fDhycuBbtQq1/3vS6uYQQiwg4cVXVVUVQqEQiouLY7bLelkXRa0S8br++uux5557wuVyYbvttsO+++67xbTDW265BTk5OdFJ6sQIIclB7eOPw7diBew5OSi67I9WN4cQkqQ48/JQeNFFarnynnsQrK62ukmEkAEm4cVXb/jwww9x88034/7771c1Ys8//zxee+013HDDDV0+5oorrkB9fX102rBhw4C2mRDSP4jzWOVf71HLRRdfDGd+vtVNIoQkMbk//xm8kycj3NiIir/8xermEEIGGCcSnIKCAjgcDpSXl8dsl/WSkpJOH/PnP/8Zv/rVr/C73/1Ore+4445obm7G73//e1x11VUqbTEej8ejJkJIclF+y60It7Qgbfp0dVNECCH9ic3hUMNYrPvFL1D/3PPIO/FEpE2bZnWzCCEDRMJHvtxuN2bNmoX33nsvui0cDqv13XbbrdPHtLS0dBBYIuCMAVYJIalB08cfo/Gtt+QEgJLrroWtk44XQgjpa9J3mqnqSyGDut9wIyLhsNVNIoQMEElxpyE28w8//DAee+wxZR1/1llnqUiWuB8Kc+fOVWmDBkcddRQeeOABPPnkk1izZg3eeecdFQ2T7YYII4QkN+G2NnXTIwz55S/hnTjR6iYRQlKIoksvgT0jA23ffYf6F16wujmEkAEi4dMOhZNOOgmVlZW45pprlMnGjBkz8Oabb0ZNONavXx8T6br66qvVmBsy37RpEwoLC5Xwuummmyx8FYSQgaTq739HYMMGOIuLUXDeeVY3hxCSYjgLC1Fw7rmouO02VNx5F7IOOgiO7Gyrm0UI6WdsEebZ9QqxmhfXQzHfyObJkpCEwvfTT/jpmGOBQADD//pXZB9ysNVNIoSkIJFAAD8dexz8q1cj71e/QslVXbsuE0KSQxskRdohIYR0F+lvKrvueiW8MvbeC1kHH2R1kwghKYrN5YoKrtonnkDbqlVWN4kQ0s9QfBFCUoqGV19Fy1dfwebxoOTPf1YpyIQQYhUZu++OrIMPBkIhlN94E42/CElyKL4IISlDqL4e5bfeppYLzjoTbg6WTggZBBT/6TLYvF60fP01Gt94w+rmEEL6EYovQkjKUHH33QhVV8M9diyG/OY3VjeHEEIUruHDkX+GNvZo+e13INzcbHWTCCH9BMUXISQlaF2yBHVPPqWWS+bNg93ttrpJhBASJf+3v4VrxAgEy8pQ9feHrG4OIaSfoPgihCQ9kWAQpddeqwY0zT76KGTsuovVTSKEkBjsXi+Kr7hcLdc88gj869ZZ3SRCSD9A8UUISXpqn/gffN8vhz07G8WXXWZ1cwghpFMy998fGXvuqSzoy2++xermEEL6AYovQkhSEyivQOVf/6qWiy6+CM6CAqubRAghnSLuq8VXXgm4XGj66CM0fvCB1U0ihPQxFF+EkKSm/NZbVPG6d9o05J54otXNIYSQLeIZNxb5p81Vy+W33Iqwz2d1kwghfQjFFyEkaWn69DM0vvEmYLdj6LXzYLPzlEcIGfzkn3kWnIWFCKxfj5pHHrW6OYSQPoR3IoSQpCTc1oay669Xy3m/PBXeyZOtbhIhhHQLR2YGivT61Kq//x2B0lKrm0QI6SMovgghSUn1Qw+rXmNnUREKzz/f6uYQQkiPyD7yCKTNnoVIayvKb7/d6uYQQvoIii9CSNLhW7MG1Q8/rJaLr7wCjsxMq5tECCE9Nt8oufpqlTYt6dPNX35ldZMIIX0AxRchJKmIRCIov+EGZdUsls1ZhxxidZMIIaRXeCdORN7JJ6vl8ptuVOc1QkhiQ/FFCEkqGl57Hc2ffwGb242SP1+teo8JISRRKTz/PDhyc+H74UfU/u9/VjeHELKNUHwRQpKGUGMjym+7VS3nn/kHuEePtrpJhBCyTYjwKrzoIrVcec/fEKyqsrpJhJBtgOKLEJI0VN79V4Qqq+AeMwb5v/ud1c0hhJA+IfdnJ8A7ZQrCTU2ouOsvVjeHELINUHwRQpKC1u+WovaJJ9RyybxrYHe7rW4SIYT0CTaHQ6VRC/XPP4/Wb7+1ukmEkF5C8UUISXgioRDKrr1W3DaQfeSRyNhtN6ubRAghfUrajBnIOfZYtVx2w42IhMNWN4kQ0gsovgghCU/t/55E27JlsGdlofhP2sCkhBCSbBRdcjHsmZloW7oUdc89Z3VzCCGJJL5Wr16Nq6++GqeccgoqKirUtjfeeAPLli2zqkmEkAQkUFGByrvvVsuFF14AZ2Gh1U0ihJB+Qc5vBeeeo5Yr7/oLQvX1VjeJEJII4uujjz7CjjvuiK+++grPP/88mpqa1PZvv/0W8+bNs6JJhJAEpeK221URunfq1Oh4OIQQkqwMOfVUuLffDqHaWlT+7V6rm0MISQTxdfnll+PGG2/EO++8A7epKH7//ffHl19+aUWTCCEJSNNnn6HhtdcAux0l116ritIJISSZsblcKLnqKrUsJkNtK1da3SRCyGAXX9999x2OO+64DtuLiopQxfErCCHdIOzzofz6G9Ry3imnIG3qFKubRAghA4KYCmUdcggQDqNczDciEaubRAgZzOIrNzcXpaWlHbYvWrQIw4cPt6JJhJAEo/of/4B/3To4CgtUrRchhKQSYi5k83rRMn8+Gl5/3ermEEIGs/g6+eST8ac//QllZWWw2WwIh8P47LPPcOmll2Lu3LlWNIkQkkCI6Kr++0Nqufjyy+HIyrK6SYQQMqC4hg1DwR9+31772txsdZMIIYNVfN18882YOHEiRo4cqcw2Jk+ejL333hu77767ckAkhJCukPSasutvQMTvR8buuyP78MOtbhIhhFjCkN/8Bq6RIxGsqEDVg3+3ujmEkG5gi1iYKLxhwwZV/yUCbObMmdhhhx2QKDQ0NCAnJwf19fXIzs62ujmEpAwNb7yBTRddDJvbjXEvvwT3mDFWN4kQQiyj8f0PsPHsswGXS50TPWPHWt0kQlKShm5qA0siX9dffz1aWlpU5Ovwww/HiSeeqIRXa2ur2kcIIZ0RampC+c23qOX8M86g8CKEpDyZ++2LjL33AgIBlN9yC803CBnkWCK+rrvuuujYXmZEkMm+3nDfffdhzJgx8Hq92GWXXfD1119v8fi6ujqcc845GDp0KDweD8aPH4/XWbBKyKCm8q/3IFhZCdfoUcj//RlWN4cQQixHaueLr7hCRb6aP/4ETR98aHWTCCGDTXxJr4ycLOKRQZaHDBnS4+d76qmncPHFF6sBmhcuXIjp06fjkEMOQUVFRafH+/1+HHTQQVi7di2effZZrFy5Eg8//DCdFgkZxLQuW4baxx9XyyXXXAO7x2N1kwghZFAgqYb5v/61WpbolwzFQQgZnDgH8o/l5eUp0SWTRJrMAiwUCqlo2Jlnntnj573rrrtwxhln4PTTT1frDz74IF577TX861//UgM6xyPba2pq8Pnnn8PlcqltEjUjhAxOIqEQyq69To1pk334YcjcYw+rm0QIIYOKgjP/gPqXX0ZgwwbU/OtfKDjrLKubRAix2nDjscceU1Gv3/zmN7j77rtVUZqB2+1WAmi33Xbr0XNKFCs9PV1FsI499tjo9tNOO02lFr700ksdHiN1ZhJhk8fJ/sLCQvziF79Q9vcOh6PTv+Pz+dRkLqqTmjUabhDS/9T+738ou+562DMzMe611+AqLrK6SYQQMuiof/U1bL70UjX+13avv6bs6Akhg8twY0AjXyKIhLFjxypbeSPqtC1UVVWpqFlxcXHMdllfsWJFp4/56aef8P777+PUU09VdV4//vgjzj77bAQCAZW62Bm33HJLr+vRCCG9J1hVhYq7/qKWCy+4gMKLEEK6IPuIw1H35JNq4OXy227HiL/ebXWTCCGDoeZrn332iQqvtrY2pRTNU38jgzoXFRXhoYcewqxZs3DSSSfhqquuUumKXXHFFVcoJWtMYpNPCOl/5AYi3NgI7+TJyPvFKVY3hxBCBrf5xp+vBux2NL71Fpq/+MLqJhFCBoP4ElfDc889VwmgjIwMVQtmnnpCQUGBShUsLy+P2S7rJSUlnT5GHA6l5sycYjhp0iSUlZWpNMbOEEdECSGaJ0JI/9L85ZdoeOUVuaNAyXXXwtZFWjAhhBAN74QJyDtF66gqu+kmRAIBq5tECLFafP3xj39UaX8PPPCAEjX/+Mc/VErfsGHD8O9//7tHzyW1YhK9eu+992IiW7LeVf3YHnvsoVIN5TiDVatWKVEmz0cIsZ6w36+ZbIhZzyknI23HHa1uEiGEJASF558HR14e/D+uRo3uEksISWHx9corr+D+++/HCSecAKfTib322gtXX301br75Zjzei5OE2MyLVbwYeixfvhxnnXUWmpubo+6Hc+fOVWmDBrJf3A4vuOACJbrEGVH+toz7RQgZHNT885/wr10LR0EBCi+80OrmEEJIwuDIyUHhxRep5ap771O1s4SQFBZfInzGjRunliV9T9aFPffcEx9//HGPn09qtv7v//4P11xzDWbMmIHFixfjzTffjJpwrF+/HqWlpdHjxaXwrbfewjfffINp06bh/PPPV0KsM1t6QsjA41+/HlUP/l0tF4sLKdN8CSGkR+SecAK8U6ci3NSEijvvsro5hBArrOYNRPD87W9/U8YbBx54oBJMIp7uuece3H777di4cSOSxU6SENIz5JS04fd/QPMnnyB9t10x6l//6nRQdkIIIVum9dtvsfakk9Xy6P89gfSZM61uEiFJS3e1gSWRL0kH/Pbbb9WyRJvuu+8+eL1eXHTRRaoejBCSujS+9bYSXjaXCyV/vobCixBCekna9OnIOf54tVx+401qwHpCSApGvuJZt24dFixYgO23315FxRIBRr4I6XtCTc346fDDEayoQMHZZ6Hw/POtbhIhhCQ0Uu+1+tDDVPphyfXXIe/EE61uEiFJyaCOfMUzevRoHH/88Up4Pfvss1Y3hxBiEVV/u0cJL9fIkcj//e+tbg4hhCQ8TjEtOv88tVx5118QqquzukmEpDQDLr6CwSCWLl2qXAbNvPTSS5g+fTpOPfXUgW4SIWQQ0LZ8OWr+81+1XHLNn2H3eq1uEiGEJAUy7pdnh+2V8Kq8529WN4eQlGZAxZeILkktFJElgxpLtEsGQxbjjd/85jc47LDDsHr16oFsEiFkEBAJh1F67bUySB+yDj0UmXvtZXWTCCEkaZAa2uKrrlbLtU8+ibYVK6xuEiEpy4CKrz/96U9KfEmU6+STT8aLL76IfffdF0cddZRyOLz11lsxYsSIgWwSIWQQUPf0M2j7dgns6ekovoJDPhBCSF+TsesuyDrsUNXJVXbjjcpZlhCS5IYbRUVFePvtt5W1vBSj5eXlqYGRf/WrXyHRoOEGIX1DsLoaqw87HOGGBhRfeQWGzJ1rdZMIISQpCZSWYvXhRyDS2ophd9yBnKOOtLpJhCQNg9Jwo6qqCsOGDVPL0riMjAzsuuuuA9kEQsggo+L2O5Tw8kyahLxf/MLq5hBCSNLiGjoUBX/QzIwq7rhDOcwSQgaWARVfMl5PY2OjUoaiCmW9tbVVrZsnQkhq0PzV16h/6SU5OWDotfNgczqtbhIhhCQ1Q04/Ha5Ro5SzbPWDD1jdHEJSjgEVX5LhOH78eJVuOGTIEDQ1NWHmzJlqXabc3Fw1J4QkPxG/H2XXX6+Wc086UQ0GSgghpH+xezzR2trqx/4N309rrG4SISnFgHYzf/DBBwP55wghg5jqRx6Ff/VqOPLzUXTRRVY3hxBCUoas/fZD5j77oOmjj1B+880Y+fBDKhuJEJJk4kss5QkhxL9xI6ruv18tF//pMjhycqxuEiGEpBRicNT8+edo/vRTNL3/PrIOOMDqJhGSEgz4IMuEkNRG0o/LbrgBEZ8P6bvsguyjjrK6SYQQknK4R49W9V9C+S23ItzWZnWTCEkJKL4IIQNK4zvvoPmjjwGXCyXzrmGqCyGEWETBmX+As6QEgY0bUf2vf1ndHEJSAoovQsiAEW5uRvnNt6jl/N/+Bp5x46xuEiGEpCxqYPvL/qiWq//+EAKbNlndJEKSHoovQsiAUXnvfQiWlcE1YgQKzjzT6uYQQkjKk3XYYUjfeWeVCl5+2+1WN4eQpGfAxVcgEIDT6cTSpUsH+k8TQiykbeVK1Pz732q55M9Xw+71Wt0kQghJeST1u/iqqwCHA41vv61MOAghSSS+XC4XRo0ahVAoNNB/mhBiEZFwGGXzrgVCIWQdfLCyOCaEEDI48E4Yj7xf/EItl910MyKBgNVNIiRpsSTt8KqrrsKVV16JmpoaK/48IWSAqXvuObQuXqzVF1x5hdXNIYQQEkfheefCMWSIGn+x5r+PW90cQpIWW0R8nweYmTNn4scff1QpiKNHj0ZGRkbM/oULF2Kw09DQgJycHNTX1yM7O9vq5hAyaAnW1GD1YYcjXF+Poj/9Cfmn/9rqJhFCCOmio6z0qqthz8jAuDdeh6uoyOomEZIwdFcbDOggywbHHnusFX+WEGIBFXf8nxJenokTMeRXv7S6OYQQQrog57jjUPvU02hbsgSVd96FYbfdanWTCEk6LIl8JQOMfBGydVq++QbrfjVXLY/+3xNInznT6iYRQgjZAq1LlmDtiSep5dFPPIH0nXjeJqQvtYGlVvMLFizAf//7XzUtWrTIyqYQQvqYiN+P0uuuU8u5P/85hRchhCQAadOmIednJ6jlshtvQIQGaYT0KZakHVZUVODkk0/Ghx9+iNzcXLWtrq4O++23H5588kkUFhZa0SxCSB9S/dhj8P+4Go68PBRdcrHVzSGEENJNii66CI1vvQ3f98tR98yzyDtZi4QRQrYdSyJf5513HhobG7Fs2TLleCiTjPsl4brzzz/fiiYRQvoQ/8ZNqLrvfrVcdNllcOidLIQQQgY/zvx8FJ53nlqu/MtfEKyttbpJhCQNloivN998E/fffz8mTZoU3TZ58mTcd999eOONN6xoEiGkDym/6SZE2tqQPmcOco49xurmEEII6SF5vzgFnh12QKi+HpX33GN1cwhJGiwRX+FwWA22HI9sk32EkMSl8b330PTBB4DTiZJ518Bms1ndJEIIIT3E5nSi+M9Xq+U6cUBcvtzqJhGSFFgivvbff39ccMEF2Lx5c3Tbpk2bcNFFF+GAAw6wokmEkD4g3NyMshtvUsv5p58Oz/bbW90kQgghvSRj552Rffhh0muOshtuBA2yCUlQ8XXvvfeq+q4xY8Zgu+22U9PYsWPVtr/97W9WNIkQ0gdU3n8/gqWlcA0fjoKzz7K6OYQQQrYRqdu1paWhdeFCNLzyitXNISThsUR8jRw5EgsXLsRrr72GCy+8UE2vv/76/7d3JnByFGX//3XPufeZ7OZOSAiBhAS5L1FuETlUDhXkEPUFRVBe4CWA3KeALyogioKoIJcKeAAK3oh/XoNAgCSQG5Lsvdlzdq7u/+ep6p7pmZ3Nne2d3d8XKnV0dU/Ndk93/6qeekqVTZ48eZuOKfPFRMxFo1EccMABePXVV7doP/GuKGZRXPiZkO1jYNm76Hj4pyrdcPVVMEtK/G4SIYSQ7STU2Ij6889X6ZY77kS6t8/vJhFS1Ay7+EomkwgGg8rT4dFHH608H0o46qijtvmYjz/+OC655BJce+21SsAtWLAAxx57rHJpvylWr16NSy+9FB/+8Ie3+bMJIYAtJimyplcqhfKjjkTF4Yf73SRCCCE7iNpzz0Fo2lSkWlvR9n3tyZYQUiTiS5xqTJ06FekduGjft7/9bXzpS1/Cueeeq7wm3n///SgtLcWDDz445D7y+WeccQauv/567LLLLjusLYSMRbp+/WtlkmKUlqLxyiv9bg4hhJAdiBkOZ+7tYuEQX7nS7yYRUrT4YnZ41VVX4corr1Tre20viUQCixYtyhk5M01T5V955ZUh97vhhhswfvx4nHfeeVv0OfF4XM1J8wZCCNT6L2KKIoz76lcRmjjR7yYRQgjZwZR/5CMo/+hHlYVD88230PkGIdtIED453Fi+fDkmTpyIadOmoaysLGe7mA5uKW1tbWoUq6GhIadc8kuXLi24zz/+8Q/8+Mc/xuuvv77Fn3PrrbeqUTJCSC4td96J9MaNaj2Y2rM+73dzCCGE7CQaFl6BvpdfVqH3pZdQsR1TRggZq/givvx0btHT04PPf/7zeOCBB1BfX7/F+y1cuFDNK3ORkS9xHELIWKb/tdfQ9ctfqXTj9dfBKLB+HyGEkNFBeNo01J73BbTf/wM033obyg49FGY06nezCCkqhl18pVIp5V3wC1/4wjZ7NvQiAioQCKC5uTmnXPKNjY2D6q9YsUI52jjhhBMyZe7CzuIIZNmyZcr1fT6RSEQFQojGTibRdO11Kl11yqdRuvfefjeJEELITqb+y19G1zPPIrluHdp/9GOMu/CrfjeJkKJi2Od8icC54447lAjbEYTDYeyzzz546aWXcsSU5A866KBB9efMmYPFixcrk0M3nHjiiTj88MNVmqNZhGwZHT/9KeLvvYdAdTXG//d/+90cQgghw4BZWoqGyy9T6fYHHkDig3V+N4mQosIXhxtHHHEE/vrXv+6w44k5oJgRPvzww1iyZAkuuOAC9PX1Ke+HwllnnaXMBgVZB2zevHk5obq6GhUVFSotYo4QsmmS69ej9Z57VXr8ZZchWFPjd5MIIYQMExUf+xhKDzgAdjyOlttv87s5hBQVvsz5Ou6443DFFVeoESgZtcp3uCEjUVvD6aefjtbWVlxzzTVoamrCXnvtheeffz7jhGPt2rXKAyIhZMfQdMstsGMxlOyzD6o+yQXKCSFkLCHTRxqvvgorT/4kev74Inpffhnlhxzid7MIKQoM2wdfoZsSQvKD3pFrgO0sxOFGVVUVurq6UFlZ6XdzCBk2ev70Z3zwla+IDTFm/OqXiM6e7XeTCCGE+EDzrbeqdb/Cu+yCXZ7+NQxaD5ExTPcWagNfhoNkTtZQoRiEFyFjFau/H8033aTSdeecTeFFCCFjmPoLL0Sgrg6JlSvR8bOf+90cQooC2uIRQraYtu/fr+Z7BSdOQL2MfhFCCBmzBCoqMN5Zhqft3nuRbGnxu0mEjHiGVXx9/OMfV0NxLrfddhs2btyYybe3t2OPPfYYziYRQrYQ8WzY/tBDKt149dXK4xUhhJCxjcz7jS6YrywjWu+6y+/mEDLiGVbx9cILLyAej2fyt9xyCzo6OjJ5cT8v62wRQkYWMjV0w/XXy48U5UccgYojjvC7SYQQQkYAhmmi8epvyqR9tf5X/2uv+d0kQkY0wyq+8n17+ODrgxCyDXT9+mnE/r0IRkkJGq+60u/mEEIIGUGU7DkP1ad8WqWbbrwJNufvEzIknPNFCNkkqc5OtNxxh0qP++pXEJo0ye8mEUIIGWGM+8Y3YFZWIr5kCTY+8YTfzSFkxDKs4kvcyEvILyOEjFxav/2/SHd2IrLrLNSefbbfzSGEEDICCdbWYtxFF6l0693fUR13hBCfF1kWM8NzzjkHkUhE5QcGBnD++ednFln2zgcjhPhP/2v/wcYnn1TpxmuvhREK+d0kQgghI5Saz5yuRr3i776L1u98BxOuu87vJhEythdZPvfcc7eo3kOOR7WRDBdZJqMdO5XCqk+fgviyZaj61Kcw8Zab/W4SIYSQEU7///0f1nz+LOWAY/pTT6Jk7ly/m0TIiNIGwzryVQyiihCikQUzRXgFqqow/rJL/W4OIYSQIqB0v/1Qefzx6P7d79B8082Y9ugjnGJCiAc63CCEDCK5YQNav/c9lRbhFayp8btJhBBCioTxl18Go7QUsf/8B93PPut3cwgZUVB8EUIG0XzLrbD7+1HyoQ8pk0NCCCFkSwk1NKD+gvNVuvmOO5Hu7fW7SYSMGCi+CCE59PzlL+j54x+BQACN112rFtAkhBBCtgbxjhueNg3ptja03Xuf380hZMTAtypCSAYrFkPzjTdlHpzR3Xbzu0mEEEKKEDMcRsNVV6p0x89+hviKFX43iZARAcUXISRD2/0/QHLdOgQnTFALKhNCCCHbSvlhh6H8iCOAVArNN9+slhwiZKxD8UUIUUivZPuDD6p0w5ULYTrr7xFCCCHbSsPCK2CEw+j75yvapJ2QMQ7FFyFE9UY2XX8DkEyi/KMfRcVRR/ndJEIIIaOA8JQpqD3vCyrdctvtyrydkLEMxRchRLkC7n/1VRjRKBquvpprshBCCNlh1H/5ywhOnIDk+vVof+BHfjeHEF+h+CJkjJPu6kLz7d9S6foLLkB48iS/m0QIIWQUYZaUoOHy/1Hp9h/9CIkPPvC7SYT4BsUXIWOclm//L9IdHQjPnIm6c8/xuzmEEEJGIRXHHoPSAw+EnUig+bbb/G4OIb5B8UXIGCb2+uvY+MQTKt147TVqUjQhhBCyoxFz9sarrwKCQfS++BJ6//4Pv5tEiC9QfBEyRrFTKWy47nrxtoGqk05C2f77+90kQggho5jIrFmoPeMMlVau5xMJv5tEyLBD8UXIGKXzkUcQX7oUZlUVxl9+md/NIYQQMgaov/CrCNTXI7F6tVp8mZCxBsUXIWOQZHMzWr/zXZUef8klCNbV+d0kQgghY4BARQXG//d/q3Tbvfch2dzid5NIMdPbCvR3oJig+CJkDNJ8y62w+vtRsmABqk89xe/mEEIIGUNUnXSiev7Ic6jlzjv9bg4pNuI9wBuPAT//NHDXbsCin6CYoPgiZIzR+/e/o+eFF4BAAI3XXwfD5G2AEELI8CHPnYZvflO8cKD7N79B/7//7XeTyEgnFQeW/g548hzgjl2BX/8XsPxFwE4DLUtQTAT9bgAhZPiwBgbQdMONKl175pmIzpnjd5MIIYSMQUrmzUX1qacqj7tNN92MGb98CkYg4HezyEjCsoA1LwOLnwTeeQYY2JjdVjcL2PNUYN4pQP0sFBMUX4SMIdp+8AMk338fwYYG1H/ta343hxBCyBhm3De+ju4XXlDOnzoffxy1n/uc300ifmPbwIY3tOB661dAz/rstooJwLxPA3ueAkzYS42cFiMUX4SMEeIrV6H9Rz9W6YYrr0SgvMzvJhFCCBnDBGtqMO7ii9B8w43KCVTlccepMjIGaV8BLH5Ki67297Ll0Spgj5P0KNe0QwCz+EdHR81kj3vvvRfTp09HNBrFAQccgFdffXXIug888AA+/OEPo6amRoWjjjpqk/UJKXZs20bTDTcAySTKDvswKo452u8mEUIIIag5/XRE5syB1dWF1v+92+/mkOGkpwl45T7gh4cD39sb+MstWngFo8DcTwKfeRS49D3gxO8BMw4bFcJr1Iivxx9/HJdccgmuvfZavPbaa1iwYAGOPfZYtLQUdl/6l7/8BZ/97Gfx5z//Ga+88gqmTJmCY445BuvWrRv2thMyHHT/9rfo/9e/YEQiaPzmN2EU6VA9IYSQ0YXM82q8+iqV3vjkk4i99bbfTSI7k9hG4LWfAQ+fCHx7d+CFhcD61+RCAGYdBXzyB1pwnfoTYM7xQDCC0YZhS5d4kSMjXfvttx/uuecelbcsSwmqr33ta7jiiis2u386nVYjYLL/WWedtUWf2d3djaqqKnR1daGysnK7vwMhO4t0dzdWHPdxpNvbMe7rF6P+/PP9bhIhhBCSw7rLLleeD0v22gvTHn2EnnhHE8kY8N4fgDef0HE6kd025QBtUrjHyUD5OBQzW6oNin7OVyKRwKJFi7Bw4cJMmWmaypRQRrW2hP7+fiSTSdTW1g5ZJx6Pq+D9AxNSDLTefbcSXuEZM1D7hS/43RxCCCFkEOMvvRS9L72E2Ouvo+uZZ1H9yZP9bhLZHtIpYPXf9DyuJb8B4p735nFztOASxxk10zHWKHrx1dbWpkauGhoacsolv3Tp0i06xv/8z/9g4sSJSrANxa233orrr79+u9tLyHASe/NNdP7iMZVuvPZamOGw300ihBBCBhFqGI/6r1yAljvvQstdd6HiqCMRqKjwu1lkaxBjunWLsp4K+zzTf6qmOJ4KTwUa5hatp8IdQdGLr+3ltttuw2OPPabmgYmzjqGQkTWZV+Yd+RLTRkJGKnY6jabrrlc3w8oTT0DZgQf43SRCCCFkSGrPOgsbn/olEqtXo+2ee9GwcPNTR8gIoHWZFlwSOldny0tqteMMEVxiXkhT0tEhvurr6xEIBNDc3JxTLvnGxsZN7nvnnXcq8fXiiy9i/vz5m6wbiURUIKRY6Hz0Fxh45x2YlZVouPxyv5tDCCGEbBIjHEbDVVfh/S99CR2PPILqU09BZFZxLaA7Zuj6AHjrl1pwNS3OlofKtKMMEVwzDwcCIT9bOSIpevEVDoexzz774KWXXsLJJ5+ccbgh+QsvvHDI/b71rW/h5ptvxgsvvIB99913GFtMyM4n2dyi5noJ4y/5BoL19X43iRBCCNks5R8+FOVHHqnmfzXdfDOmPvggPfSOFPo7gHee0YJrzcvZcjOoPRWK4NrtOCDMdURHtfgSxBzw7LPPViJq//33x913342+vj6ce+65art4MJw0aZKatyXcfvvtuOaaa/Doo4+qtcGamppUeXl5uQqEFDstt98Gq68P0fnzUX3aaX43hxBCCNlixNyw7+9/R/8r/0LPC39A5ceO9btJY5dEH7DsOe04Y/mLgJXMbpNFj8VphngqLB3aaR0ZheLr9NNPR2trqxJUIqT22msvPP/88xknHGvXrlUeEF2+//3vKy+Jp5xySs5xZJ2w6667btjbT8iOpPcfL6P7988p2+oJ111Ld72EEEKKivDkyaj74hfRdt99aP7W7Sj/yGEwS0r8btbYIZ0EVvxZj3At/R2Q7Mtua9xTj3CJ84yqyX62smgZFet8+QHX+SIjESsex8oTT0RyzVrUnPV5NF55pd9NIoQQQrYaKxbDiuOPR2r9BuUFcdxFF/ndpNGNZQHv/z8tuN7+NRDryG4Td/BKcJ0CjJ/jZytHNGNmnS9CSJb2Hz6ghFdw/Hg+qAghhBQtMtLVcMUVWHfRxWj/0Y9R9clPIkwv0zsWGX9pfttxDf9LoOv97LaycVnX8JP2GdOu4Xc0FF+EjBLiq1ah/Yc/VOmGKxciwPmLhBBCipiKo49G2cEHoe+fr6D51tsw5b57/W7S6EDcwcscLgmtS7Ll4QpgjxP1PK7phwEByoSdAf+qhIwCxHq4+cYbYSeTKDv0UFQcy8nJhBBCihvxciiu51eedDJ6//Qn9P7tbyg/7DC/m1Wc9LYC7zytR7nEvNAlEAZ2PQaYf5qOQ5xbt7Oh+CJkFND9+9+rnkFZI6Xxm1fTLe8IxLItrO9dj/JQOaqj1X43hxBCioLIzJmo/fzn0fHQQ2i++RaUHnggzHDY72YVB/Ee7TBDBJc40LDTzgYDmHGYNinc/QSghM+k4YTii5AiJ93Tg+bbblPpuvP/C+Fp0/xuEsb6KGT7QDve7XwXyzuX472N7+G9zvewsmslYqmYqjO9cjrmj5uPBeMWqHhW9SwEZZ0UQgghg6j/6lfQ9dvfILFmDToefhj1X/qS300auaTi2iW8CC5xEZ8ayG6buLfjOONTQEWjn60c09Db4TZCb4dkpNB0403ofOQRhKdPx4xnn2GP4DDSl+xTwmr5xuUqFqElgqsz3lmwfsgMIeldI8WhJFiCefXzML8+K8jqSuqG4RsQQkhxsPHpp7HhioUwSksx87nfI+QsJ0TEtCKtFz0WwSWLIA90ZbfVzQL2PE3P46qb6WcrRz3dW6gNKL62EYovMhKILX4Lq08/XbmInfrQgyg76CC/mzQqSaaTWN29OkdgSbyud13B+iZM1Ecnojo4FWFrItLxRvR0j0NLRzlKIgmMq29GuPR99Jsr0RR/FwNpzxoqDpPLJysRJmGvcXthdu1sJd4IIWQsYlsW1pxxJmL/+Q8qjz8ek+66E2MaeX3f8Lp2miGeCns2ZLdVTMh6KpywgJ4KhwmKr50MxRfxGzudxurTTsfA22+j8hOfwKQ77/C7SaNiXtaGvg1aZDlCS2IRXikrVXCfErMGUXsy7EQDervHoau7HlZ8PGBvqVCyYIZbUVr5ASqq1sGOrEGfvV7OcE6tSCCCuXVzc8wVx5eO3wHfmhBCioPY229j9SmnKuEx7Wc/Rel++2HM0b5Cj3BJaF+eLY9WAXucrAXXtIMBM+BnK8ck3RRfOxeKL+I3HY88guYbb4JZUYGZv/8dguPG+d2koqJzoDNHYLkjWv2p/oL1TTsKJBox0N8AK94Ia6AB6UQDkC4bVLcsHMCU2lJMrpFQ4gSd7hlIYWVbL1a29mFlay9WtvXh/Y5+WN47sTmAQMn7CJSsVSFY+j5gDm7XhLIJenRMzBXHL8DutbsjLJ6rCCFklLLh2uuw8fHHEdltN8z45VMwgmNgvmxPkx7dEsG1/j/Z8mAU2O04bVY460ggGPGzlWOeboqvnQvFF/GTVGsrVhz3cVi9vWi45puo/dzn/G7SiEWcXKzYuCIjsN7teA/LOt/FxnhHwfq2HYAVH6cFVrxRmQxaA42wU1XaQxSA0nBACakpGXGlYy24SlBVEtoqj5PxVBpr2/uVEPOKMok7+2WOmA0j3JYRYxLMSBMMI/f2HTCCmFGxG/ZuXID9JnwIC+oXoLGskd4vCSGjhlRnJ1Z+7Diku7rQcPXVqD3zDIxKYhuBJb/RgmvV37LWEEYAmHm4HuGaczwQqfC7pcSB4msnQ/FF/GTdf1+K7t/9DtF58zD98cdgBGheIGaBa7vXYlnHu/hP0xIsaX8Xa3tXoiMhdvCFb3NWolaLK2ckS8WJekRDoSGFleRrSrdOXG0PnX0JNVK2ojVXmK3p6IAVzo6OmRKCg+eORYxqTC7ZHXPr5uHgyfvgI9M+hPJI6bC0nRBCdgadv/gFmq6/AWZlJWY+/xyCtbUYFSRjwLsvaMH13h+AdCK7bcoBWnCJaWE5LV1GIhRfOxmKL+IXff/8J9Z+4TzANDH9iSdQMm8uxhLJVBpvtazFv9cvwTtty7CqezlaBtag11oH2yg8L8tKlecIrEBqAiaUTsfUmppBwkriurLwiB8tSqUtfNAZy5gwLm/pxbL2VVjTuxR9xgotyKIbYBhWzn62bSKUmoy60K6YWTkXe49fgL0nzsTM8RWoLaPJIiGkOOY8rzrlVMSXLEH1qadiwo03oGhJp4BVf9WOM2SkK9GT3TZud2C+uIb/NFAz3c9Wki2A4msnQ/FF/MCKx7HqxJPUWic1Z56Jxquvwmgjbdlo7h5QwuK9tma81fIuVna9h6aB1ehKv49UYAOMgF4vKx/bCsOKN6i5WZWBqWgsmY5dKmdhVn1jzvyrceWRES+utofugSRWtfZhaUsbFm1YjKWdb2PDwFLEzJUwgp4Hu0ecpmNTEE7NwMTobphTOxezx9dhl/pyzBxXhql1pYgEObpKCBk59L/2GtZ87gzlyU91RO45D0WDvHp/8G89wvX2r4C+1uy2qinaLbyMcjWMrc7VYofiaydD8UX8oPXee9H2vXsQGFePmb//PQIVxWfrbVk2Wnri+KCzXwkscTaxpqNLLUK8PrYKXam1QLhJzWkyQ90FjyGjN8F0AyrMyWiITseMylnYo3425jVMx7TactSXR2Cao1dcbSvptIXXm1bhb2sW4fWWN7Cq9x1sTK2CbaRz6tm2oee7xaaqYMemYlLFVMysL8cu4ySUZYTZuIrRLWQJISOXdZdfju5nf4PogvmY/otfwDBNjGhalmrB9dZTQOfqbHlpHTD3k1pwTd5fWbaQ4oPiaydD8UWGGxntWnnCibATCUy8605UHX88Rqq4auuN4/3OWEZgZYRWZx829H6AdGiDFleRZh2H2wY5j3ApMeoxLjIN0ypmYve62dhnwh7Ye8JsREP06rQjiKfjWNK+BIuaXse/1v8HSzoWoyvp6YV1sFOlSA9M0YKsf6pKw4qiPBJ0xFiZEmYzVKzFWUmYo2WEkJ1HsqVFOd+w+vsx4ZZbUP2pT2LE0fWB9lT45pNA8+JseahMO8wQwSUONAJcx7HYofjayVB8keFEfqbvf/FL6Hv5ZZQdfDCm/PhHvo02SFvaehNKUOUKrBg+6OjHBxtjSKTSyrzNFVgBFUtogWGK977BlAQqMLV8Jnar3RXzx8/BbrWzMat6FsrD5cP+Hcc6zX3NeLPtTbzR8gbebH0Tb7W/jaTlmfgtqNGx8UjFpsJyRsishEwCz/bYTqyKekbKyjLpiVUlHJkkhOwQ2n/8IFruuAOBujrlfGNEWIT0dwDvPK3nca15OVtuBoFZR2uzQnERHx68VAkpXii+djIUX2Q46X7uOaz7xiUwwmHs8uwzCE/feRNv5ZbQ0ZcYNHL1fkc2H095nDiYA3kCqwmBSDOMYOH1ssJmBDOrZ2J2za7YVUK1jutL6mm+NkJJppNY1rkMb7S+oYIIsnW96wbVC6IU4dR0xHonoa97sppHBmuwZ8VoyMT0ujLMdIWZM1ImcUWUvb+EkC1HrEFWnnQyEqtWofbss9CwcKE/DUn0Acue02aFy1+UybTZbdMO1YJrj5OA0lHimXET7xC2nYJlJWDbSRVbVhK2LXEClp2E7ZTl1pFtCdhOeU49t9w5hreOxPV1H8XkyWcWjTYYAyvTEVLcpHt70XzLrSpd96UvbbfwkhujrB01yCSwIzuCFUvmzgHSpGBGWmGWNiMSbUJJWQvMcDOSZnvBzzENE1MrpuYILAmTyycjYI49c7R0ykJ/dwKBoImS8hCMIhr5CQVCmFc/T4Uzdtdr6rTF2pQIkyCC7O32txFL9SMVfAeofgel1XrfuvAUVJkzYQ9MRVfnRKxvrcJA0sLSph4V8pE5ZO4o2UyPMBNHKcEA50GQrfcKKgubqxBPZtK9nrQ4qOl16wzo8t54Chb7pouG3XY/AV9Z9V20/vTnuKx/OppqJxasFwqYqC4NqbUYq0rCKl1dEnLKnLwq0+loaDPPqnQSWPEnLbiW/g5IejodG+drk8J5nwKqJu/Q72vblke4xAcLGSVcnG1DiR0n1ts8+Ux5crN1lKjKKddlQy3vsrOIRhpRTHDkaxvhyBcZLppuvgWdP/sZQtOmYpdnn4UZ2fRcJ/lJd8WSQworKetLFBJXLhbMcCdqqztRUdEKM9qEhLke3an1sFB4v/Gl43MElqRnVM1ANBjFWCAxkELfxjh6N8Z13JkfDyDWkzW3NEygtDKM8pooyqoiKKsKo6xGYidUSwgjXBIsmtFAWWdNFrJ2xZiYLa7pXjOoXmmwFLOq9kBDeDai1i4Y6J2Ede0BtXZZa098yOOHAgam1eWaLypxVl+OGrrIH3XIfUxEulcw9XhEkhJM8cGCqdubH0gN0ZFERiNX/7+f4JANb+H1+plYeMj5ygvitmMjYKRRFrZRWwbUlohoA6pLgJqohV3t5Zjd+womdS9C0OqFZRiwTSBZNg7WlH1hTP4Q7LLaQSM52VGb+GbETnaUp9AIkm0X13VtmrJ8S1jFpkqHnHQIphGGoWJdZjh1JG/k13HKM/XUMUMoLZuJ6qp9/P6aNDvc2VB8keEg9vbbWH3qaeLFQs3zKj/kEFWuxVVWUGXFVT/WdcbQEy+83pWX8RURNNakUFXdjlC0CcnABmxMrUVzbA1i6cImgxWhCsyqmZURWjInS+KqSBVG7QtgX3KwmBKR1RFDb3sMfV0JJBJbdhs1rBRsUV4StoBg0EBpVQjlNSWOIHOCR6BJOjhCHVt0DnRicdvijLniW21voS85eCFoGSGdP24+ZlfPRU1gV6QGGrCmLa7WL1vR2otVbX25pq55yKLXSpDlCbOptWUIBzla5ofTnd5EYcHkiiSvQMoRTJ5tKWvHvZ6UhAKoiAZRHg0q09ZKFQeVwxjJS1rFKq/rBYpodJoAZvMGVJ5/JoxEAj2Xno/4gdNhp9php9uc0A4r3Y20lUA6nR3JgRMMJGEaKQSMFILm5p+hIwktZkJZUeMVLl7hkxEuoSEETuE6WcEknoRDg+s5IigrsLL7GEbxdCJuLxRfY0R8yUNOFlklo494PInUBechvHwp1i44BL88/vyMwJKXlc0h7tbdBYQbqw2ES1rUGlk91vtoiq3Giq730DHQUXDfkBnCLlW7ZEayRGTNrpmNhtKGUXMTtdLaDNArrGSEqrelB73t/ejrSqKvX0w7NvV9bQSQQMgYQNTaiLJUK0pTbSi1OhBNdyJqd6sQsXsRDsYRLjXU+i7JuIW0EUbKjOoQkFCCpFmCVKAUyUAp0mZExiBhqxCAZbtpHbz5QMBEuDSMcHkU0cpSRMoiiEi6IoKoKosiKu73g0HACABi9pkTm3oi+KBtZoG6Em/bNZC20ljRtSI7Otb6plpiIJ9oIIq59XOVIFswbgH2rJuPRKJMibGVrb1qlMxNr+8aGPLz5N1Zrn+vKKOL/E2TSImZXnZUKSOYNjXC5BFMbn5HIadIBFJlRiAVEExOud6Wzcs+UleElJibkeJFRnwSiVbEE61IxFs8cQsScSlvRjzeisivO1DxOxPpahst1yZh7yinuDZgWjZM24asWy/OeZMoQQzl6LPLEE8HkUibGEiZSKSDSNsBJK0g0pYT28HcvBVE0vbms/UioQgioShKQhFEIyUoC0dRFi1BeSSK8pJSVERLUFVaiqrSElSVlKO6rATVpRFe4yMAiq8xIr4G4gm8dfMhiNlhxBBRod/WcQxh9NtRDEjslA9InJeWfVWMKJKQHnS+kIwEjl/1T1z4xq/QF4ziy0deho6S3NGlurIwJtdmFw6WRYQnVoVghtuVwFrTu0KZgUkQ5wh2ARtsAwYmV0zOjmTVzMLs6tmYWjkVQXkZL1JSibQeneqMo8cRVD1N3ehr60G8uxvJWAxWKq5EU8iM6dgYQNiI5ZS5+YjdgzB61S9JyoJmHMFAQvWOGuZYvIUauWJMCbdCQs0cWuw5+W7DwGIzjTfNJN5AHG8ijh4MHuWaaISxIFCJ+cFKLAhWYU6oSnUSJGGie8DCxgELnbE0OgfSaO+XkIL0UaRFqMLMiSWEgkHUVJSirrwE9ZUlGFdZqkNVKSKh0JaJ0Pw6hlvXzMt7t7t/u0LbJWzb/Vce5WJi5442iTjKn8fUkzfilGvSp7dtaoRxawkHzKwokjhSWDBJXguq/G0hlIUDFMmjmHQ6hni8xRFWLXnCKhsnk4U7CgeRAMbfGEKw3UD8xBrgjDkIh8cjEh6HcGQ8QqFqZzRnsPlbZjQn1gPjvRdhvvMbmB8sUmJLyZpAGJh9rJ7HtesxQKikYBMGkmls7E9iYyyh4/4kutx0LC+v0hInNjMdYPPIb0jmsnnnrVV55rTl5vWcNqm/2XltZIuh+Boj4ivW3YH0pXN22PHSMBCHvNCEEUdQxQkEkVD5kPTx54S4EUYSQcRVPU+5OoaOveUpEXd8kG6WgJXGBa88grJkDH899ix0HHuyHsWq0WJrYnUU3ck2vLdRiys3XtW1Ckk12XUwddG6nFEsScvoVmlosDe6EYHcmpIxINGrQ7wXdqIXye4uxDduRLyjHYm2ViS7NiLd3w073gekYjDsOIKGI6wc8RR2xFTQyHOXviORv6O4DRbX+BIi5U5eQkU2LS/aVhoQm30VW3l5iS0nTsFOJ2EPDMCOx5wQh50YgJ2QOA47lVSLJ8tInq1e7E3Y6uVf0hIkKTLbgmFYKjY96YJ5edvwCfnk1aEg3ohE8GYkjDeiESwPhfR38xC2bOyRSGB+PI75A3EsiCfQmC6ueRCFsGHAdkSaZQRU3h0B1QLS0LFtIGVLGXRsGyq4AlP2c+s7ZzgjQGXU1M0Prif7GzDNAMxAAAEVgioOOulQMIBgMKTywVAQ4aCUBRGSdCiEsFMWdEdaNys48wWqmzeGELDbK3C38POkU0F1LPCZtTXIa2U63atEVXZkKl9YtajtUm9LEfO1cLgekfB4JaRcQZUfx//6BtZf9HUYoRB2+e1vEJ42bfMHH+jWDjPEccbKv+j7r/pQE5hxmBZccz4BlDiehHbSqLMIMa8w02It4Qi0Avn+xBZZwmwK8T7rOhjJiDc370mLaFP50rCqN1wdI7ZlK4dVOjjppJvX6ZKKMKob/H+XofgaI+LL6u3Gsn0P8O3zyc4lOncuan72QyzvXpkRWMs3LsfyzuXoSQ72FJdxaOCZlyWx5GujO9m9rXh9ivdod7tKMDlx3E072/LzTpkIKzvWDXtAl5tWTF5Dd0pTbXn5NMKAGc2KppIqGKVVMMprYUQrsiJKtikhVSjvii1HVPmMNTCAVFs70m2tSLW3I9XahlRbG5Ktrehv70Vf5wD6etLoHwDiZhnikSokwtUqjoerkBSRqNCv/F4xptPpTDpkpFASTqK0BCitCKC0Oozy2lKU1ZeipDKEkjITJaUBBGRk0BWW4no5R1xuToTq8t5kDG8NbMCb/U14Y6AJbw60YqM12DnHeDOKBaFqNTImo2S7B0oRkUso79jpdBKxeAL9AwkVxxJJxOMJxJPiuUtLkYAjRSQWCSTpoGEhGgAiARth00bIsBEydR3bspQHssx3cYJhp+VBqyQNKVKUCAvpRXAlrWInn0lvok5O3q0Xzttna44x1D6bO8b23aPk+k4mOwuY/A2OLWtgy/+8ZtQRVOOGiLWwCoVqYGzBfFnvupjlH/0optz//cIVU3HgvT9qwfXu80DK0+ZJ+2jBNfeTQMXI9qSXtmx0x7LCTOIuR5hlR9o8os3dHkuqfQuaWEonsMw7tnUcsA3lHj3g5mEgbBioCgdQEQqiPBxEedBEaTCA0kAAJQET0YCBiGmqeiEYCBmG2t+05TZpI51MZ8WUV1CpOFsudTfHvI9Mwkc+uxv8huJrrIiveBxrPvu5HXAkO/vCoF6MdmCQ4+2sl2j1r9vz4ryquz2X0qvr6elUaXjyarvh9DTLMTz5TA+0PnYmr2I7u5aF+5/zM3LT+XW82zJpTx33qN5/4yHgJ8eEsaShsMgKIIDGwCRMDk3H1PAMTItOx7SSXdAQaVC90eLS3AwYKlbpoKHmBqm0aSNgxxGwYzCtfgRSfQhYfTCSEhxxVFA0uaFvcFl6540qJawoknYJkrYTp0Ow0gEVbEt6poMwAhEEI1EESysQqalCsGYcArUNCNRPQGDcRJgVdUDEGYEK7qiJAMWJXHNWXx/SbVqcqdDahnhrO/pae5Qzkb7uJPpjQCwVRDxYiYQINEekpYOFzW0KETYSKAmlUFpqoLRSOw8pH1+Biok1KojjEOm13JpFl6X9a3vW5swde7fzXaTzPICJ6ezutbtn5o5JPLFs4iZ7a2WNOzWvTJx9tOlY8ms7+pHcgpeAzbTcEXVazGVFns5HgwaqIiYqIiYqnbgibKIsLGkD5eEAykMGysKS1uWlIaBMykKSNpQwFLGnhGbO/dwrcL335/RWbh/qeBKrt6rc7Zk63jZt6TE31catO56IYxkttCwTacuJnbyMBMpcG3U27BDSCMKytVDJ7XzQI8TuOOHgfBqGke24yNmvQH74B9QMR/TlCjMrEEAyHEQ8EkAiZCAeBuJBIBGyEA9YSATTiAeSSJjiMGjLfwMBhBExyhE2KxAxKxEJVCEcrEIkWINwsBaRUJ0KgWAljEFi1CscC2zbzB8vvnIlVp54EpBKYcoP7kf5Rz6iN8i1svofWnAteRYY6MruVLcrMP80YN6ngbqZGO75+/kjOlbKRso7wjNoxKeAcMkbHUqJwFGxWz+lYve4KQkJS1lPSN6We5x/hg9bh+cSaJheiVP+Z1/4DcXXGBFf6UQSSy+/RdyiwXZvVm5axQF101J5M6DzUi7mLM6cBRElersWKBLShoGUYSNtiCmiOBi3VT4FKUvrWMqQ1tutlHrxSdoplU7ZaZWXCfZSW5WJhyFVL+nEem8Vq7Q8HJ0ySUtsSKz/k8/VsQVrUEhnYjsvn1MuRzYt1XqJi4XygVrU9U9ArYSYxBNRHRuPgL2j52XJ64d4e5KZNNrrU07a4w0qk5b1vzz13BcRQd9dXPOprKMIK/OiE1QvOml54bFDSNlBmMkUjFQaQQPKU12kJITyijAq6qOobKxC5eRaRCeMQ7C+HmZFxYibD6JuqTIKIiZwKTEbTMNOyeSjtC4TE8BQSIdgUMcB/0fNhkJeWtNdXR6h1o5Yczt6m7u0SOtKoL/fRiwRwIAVyQg0iW15WdoCDNtCxBhASTClR9LKAyiriaJ8XLkSaJWT61DeWI1I6dBes/qT/WqtMVeQSSjkUEYW855fPz8jyPao22OLTG9lvShZeNwVZuLoaIUSZn1o64175iwNMYfJk9ce97JOJFwHEsXimVGJd8tWL4di7prOi1WvttNj7ZZn84O3DbWPJS+Q6nPceluwz5DbR96rjurqMzxB7pAqLfdQHWdNgnMNRA1DRlRF0GmBqY1FnWDrmY2GmYQZ7YUR7YUZ7VPBiPYBkX4YUQkxIDKgw1bcRo14EGY8DDMeUiEQDyKQCCEwEEAgHkAwoeOAZeeJTnuwYPWaQrvfq4ApdM7fwxGu0mFjBAxtVm0YWctRCaaJ5ldNdLxpIFQJ7HKaocqs/i7YyQHl4CKFCFKhWqRq5yFZNw+pyASkZHqEFVRB1Uk76bSpzXvTTrAMpNIG0mkR8AYsSVvqNq8H+Z0+ASWqnOtP0iJuxIxO/X4kr/oJRt61WWyU7VGNcy7a2+9mUHyNFfHV1daGc35y6ZCiw87J6zrpTN2hxYpy5TNGMWwZGg/AtA2YtqmG2yWWfABO7IRMGoXyesje9A7Xq20S25nhfImD0iPu5EOGiYAhfYYhTBgIYUJfCBEZ6RGhkumVFdEigkVEjC73ChldlpeGewxPGsW3PpK8pMggiZizyYuKctSnHspO3nlA5wT35UReSjzBsFMwLTfWQdzBG+kEzLTESRhWEmYqqdJmOgFDpRMwUwnx7AEjlYCZjKsYyQEYycTWu6wxzawQk3kyYYnzBJobqxAEQpuqo2NdR9LhQdvcPDLlTh312fl1sp/rFY4I5Nr8i8hMdXQg3d6uTB371negt6kLvWLy2JVEf28asbiBWDqMeKBMibREuHKLXe+bVhJRxFASSKAkaqO0LKBGzsrry1DRWIWKyXWomN6ASHmJEgjiaEYtBN32Jt5oeQNLO5YiZefOj5DfmsyBdMWYhCkVU7ZK1MtnbWsngBodd1/QHKGQFRGDBYua37cVokfF6tjOvs7nbG6fnM/MfHa2jaMB+a3n3AfcvLonZOf8aIuIbOckcvLqTT+Tz27T5Tul3cEBBKNdOpS48UZPmU4HIoWXDCmELWIiXol0rAqpAQnVSLnpmCcfrwTE2mDEYyOQiuHAV29EJNGNFTNOwJppx9KhmIMW/M70RhGvahqlzPWUPnkRsmIhY8BUFjQmDLGaEaEr20xtVZMt03m9v+zrlDl5vY8utwwbibSNeNrCgBPEUZDE/cm0Cn1O6BXHWYmUE6QzXlshSfe5dO+KI2IVA/jEPpNw42cW+P1npfgaM+Krsx2HPvvRYfks0WOm5YgPSwsN08qmVazyjshQeWRjEReWN7h1bU+ZiBIbgTQQVG5ddazKnaCES9ots1Q+lJbjWAim02r/UFq7hFUCSOqKyBGPRer4QCAt4kh/lrRNtgVEaDk9aOpFSh6iMundlGCr540aaArasAM2rIANW6WRycvBLPGnINskyH6y3XTSQRuWCXU8VRZw00BKPks5PZGbik7LY8IxiNR62DKU5yV5UBpyJ7LcYGbTaROGtyxtQu5aVsoAkgbk3dNOGEgng0imRfZFkEYUlhFF2ozCMktgmaWwAhHY4g1KGildivLlDT2BXv6ihph3qtkwAX1tiMCRl1CJLZnros+fMtiU2JncbhvBTFAjYqaTVqOwEuuRWR07o7fFhPo7OC9yIuRE6KmXOknL30VOoHvbzd5+c18JPLflnDv0ltQvfEsfcv5cgfq5dTfdFve3ogSkSutzrdKikj2/J5331FNWtrYyB1MmYBJnHELo2VaWey1shfdN9yVaFkkNGDYCAQmAHU6htXIDNlR8gA9K12JteA26Ax6zI4cyqwxTk1MwLTEFUxOTMTkxAVEr5DGlS6v5L3JulZGwGgmSdgeQdmLL0ssDKJM2y1TzDNU2N3jyowElYLydGUrUSCdHnsBx8u5vJNMh4qSzvxdXBLn7OXWUSMruL7EMMajfl8ROXSOdPYY+tj5f2c+REZRctNE3tNhyNsq9yx3JVy+mpq08nGrrdW+sKsIIZMv0zVt6ilRvUeZ3oifUyKK86s3XcZIDWFETqTIb6TIbqVLodImNVImFtBNSUQt2aCte2+T5q0anQjAHQjDiYRjxCMx4BMZABIhHgIEoEI+q+7l0UyFvKQvXLDPbged0AjodexlLBsdcU82SlN+C83xQc2ydmZOSdsb4nKDHw7JPux1LQ9OrmLv0YaTNMP61/zWIR2uGqJl/z8udbOA1J9WjcB73N6rDT482yreW0UiVN8QSRKxCJO+1IklmrErEgkTnEyodVGUJfQznuPqz3GOmB31mtsx1qZPKtC+zzdOubTV51aN0ho7lB+LmlQWwxHIP13Oqddotc8YrHYdAViafjd2WqdFFQ48ySl1ZfS1tm4jDRK9Rio1GFN1GKXqMUnSbUfQaUfSZUewSSuOb3/42ikUbFEP3BdkEVn83Pte0AAHLRNCWYX4TgbSkg0oIhWQER0SOGrmRcnl5N2T8BkEVZ7fpURsdyz561MfJy35qvpQ7R8l9SHnSmeDcxNxeCueeKr0Vqq6UG3Zmm/uQU2lnPxEpbl312FO6wpkRperZznHcvA6OvkCf1M2UZ+u6n5FpQ6Ey93PVU9ed85X9ni6FXLdny+VfucE5JiTycFYe5OSY2XJ5squbu9wgHdMT+cZqH2mLnTsHDe7NzPkDq+1ys0NurF8X9B/etWjIHEvFbl6pouw38ZxP/W980Pf0zmHL+Zt4Rkuz0iJXZOSXu+c+t062rp67IsJOX6OGXN+2iD19fRrONSoCUV+nEpuidtVvQV3plhqvVPu6eXXlS30lHANqm0qrcuez1DZ9bLVfJq3UtKdOvjh0RWnhUUV7U2n9dTdZ13s+tnwf7JB9Nrl9a2d2upV3wjuXCDU1CuwWuN2jSSDSV43pG3bHdGdTb3gjmstXoblitQqtZe+jz+zDkshSFVQTbQM1/Y1o6J2Bhp5paOiZgeqBRn1P3AkY6uVJv7hJrIN+gXPThjctsZPX9xInLX8BOY4ql7z0QOm6Ug9iQC6xsoDQeWVknsk7FhNOLHltOeGU247FhNquWqDuP2K2rqxvVSyvfm6Ptb7XuIbJKi/3M/dZIfekTG929p6mtrv1nCkpejQqe0ydzr4ue9OZZ40tnU3SqVPg9dpT5pwE52JyLlHnvure5vTdVPJO7HQyeWWE3s9GSchGWdhCaTiN0pCFEpXWsZuOhi2lz7aUVFrWgjSRSJhIxANIJIJIDoSRjIeQGogiNVCCZKIUdiIK047AsEMwbOlMk/uSTsMpA6RcOhf0du82tR+Gd45s2kghbSSQMiUkVZx20mkzjrSRhGUmYJtJWEYStinzjZNAIAkYEhIwROgYKayakkB9RxQNLQOY1Hwz/nFEFZJmHEkzqeaxqXQgjaQp0ygMJA0dJK2nX8ivQscqr9qn0wWRTl/HukVbs7h5J62cWegy0edST+lz9ZzTaaXbJS8dR66GF6maSbvXl77m3MsmWy7ItamFtLi70CVZvGn9FpBNZ+7nng5hN50dcdK/m5Tz29ESz/nbOL8rN69+727s+f3bme2e37n6z7nH5P2GbaNvyLl+fRuLaxxp1Iive++9F3fccQeampqwYMECfO9738P+++8/ZP0nn3wS3/zmN7F69WrsuuuuuP322/Hxj38cxcbSZf/C5P3WOhen7kXI9iqJC2Ld6yQPHRm50Gk1DqRiXe7UyZS5ddSQju4Js+ShpcvVbcPtyVI/7uw+md4sb++W8iynfzDaOMztO9K3DP1gdMsLPFSdH32mRzLzQ9aoB7yRHYZWaY/w0+LNu919eOfWyf0c5/OdOPM5brucdG6bjQLnwRsKlQ8+Z7m9gV5p5Iw05JXluhvJ1smms3WGKstzWVIwbeS8hTsvG54bd6a+ekC4pZ5je97gCz4EMvt56w9Vx/3kwRay2XZm2yoPxAI6L3sU57iZh9BQyiTnb6BfRrMKcssUjfehmR+rh2cm1r+QnHInrUYYVaxf/10PUnJW3LQ2k82LHZNY/dDXeemQyaYdCerxduV+hhbB9pBty34H5zs5fyf9cqr30XX0H0OOJR05ys+Ge37U27auo0aAHYc1Eqdtd+6phaQzK1RLBSuTTjnb1UxT9dKkzaz1C5NTX+ayZuJ0Nm+PR3X3OJR374VYaCMGIu2IhduRCHciHYiho2yDCksa/qmvHSsIM1WJQLISRqpM+/9S14IOknZ79HW55xXJduO8V3V1P9U3KfnP3a7PgBYqbrmKc46V3VderuVF2vuZuv4QFHxvGfwrzehl51ajO0g2J7qHHKPNbt/U781bVrBeYfXuvQXkv2gO1bb8z1Z/ZZlLpP7ezl/R+T2pT5XRB1NGVyVOwQzo0Y5AIAVT5luZkk4iZtqIGxY2KmsAG2bCRiBpIdDvHENesuXJKp1uYmKRDsNIRYFUBEZS4iisZBR2UsRUCaxEmdqmv5u0yhlUc17oPVdhdpvnJd37Aq/39lwp7gu+OfRfVnUSqg5Cx2TF7WRUA9wi5rUViNvBqOqKiYkzFUJPdUiJCYru8FSiSraLiJcOAecZrEaYnf3Fw6oSNrpj07kzaLNdsaBQ16FsjTrlcjztofEfB1Xhk8/+C9NXxbB4/Rysn1CtBrFDKW3yL2N++ouKlUva+Ws4brG02YbujJX/nZuh5N33GnkrUO8Zapt8R6fTWVXVP5ZMJ7BjNJf5FrbU13m5vyWdtwxV15Z58JmbKWyZr+46+lIdxa7jrty8fofxfG7ms731c7Z68vr7ZT/DW0//4D1HGTFWnN1GPYqJUSG+Hn/8cVxyySW4//77ccABB+Duu+/Gsccei2XLlmH8+PGD6v/zn//EZz/7Wdx66634xCc+gUcffRQnn3wyXnvtNcybNw/FxHMv/g0PTroht3BTlqRb0jngeTEdcid7+4+/Vcfd4jp53fM77Pi5xzW2an9vv6xbVLgh2R7WLcTTkM29v2wz2+zQYlta4f1CeULQNQPK7Z/Oe0UsLB43VW9TbR18DG87hqqnhWmhzx2y0Pv+6bwEZFrm/fs7SdUBstlj6ET2BcEtch+veUrTqw/y2pK/xla2HUaB9rrb3GvHGPIYGUnsaa/3GLnbpNzc/DHU/Cudl5dat4br3CDbvaEUVHabN6iv5XS7yIul3QXDWgvbeh+w1gL2ev3CGO5QgZCdiTLxyhvzyHaJ5QfvuAgK7pfZniP+h6iTX2YPtT1v/8wNJnc//TMdfExHJjrf2Knl7czwbsscS/XcOCM8epuuJZ3Kns+qKEPlvBk4evEqHPz3JVhTX+V5Lrvdpuovnekwyqh2J+12P+TUzWzLdmrm9xS4x8rvRNXbvN82d3Pm2xZ4NA3ar0BH5Sa3bcUxc27F+Z0TQ3SQ6nx2T90RlTmryO6XWyevpMDfYvDT1B2Rfm2POhQTo2LOlwiu/fbbD/fcc4/KW5aFKVOm4Gtf+xquuOKKQfVPP/109PX14be//W2m7MADD8Ree+2lBFwxzfm65tIv46tlL2zTvtvTYbF96y9t+75+tNkYAe3dlJAYSohs6hjbWg/bIG42VU+/HBPiL2LuVojCgzCGGDjhvXAIi6NhLI6EsTYU9LyebdlxvMcbelueqN7K/Qt99pb9ujddtq2fvenPKdCezdxAh+7C2vTnb26buz0TcswUPVYTOWm7YP2MWbtjPUH8oSxm4zs/SKMy5ndLyM6gdXYNDntWWyX4yZiZ85VIJLBo0SIsXLgwUyaeWI466ii88sorBfeRchkp8yIjZU8//fSQnxOPx1Xw/oFHAuIwosHY6HczCCGkaNm6TgAx0AbmJ+MqoPAyfIQUxGsH4Yqz7Fw4r2DLzrHJmMHn7OOpkxF4rhl8dprjIDP9zNwaTx2vyf0m6gy1T7YNnjqe7+AVoZnvWuA76PYNXccrerPTAjzz+zL13TZ4/oYw8JtTgAnrtRGvM2FCjZy4s3Rdk25n0oUzN0vPtRKfKblztPQIerauM8/LMz8r65LKOb5nDpg3nTfwn590CjZxjypgjTH0gQrtV2gobNP7FG5jofqb6Dg2Nv8ZgzYN8XdoLqlFMVH04qutrQ3pdBoNDQ055ZJfulRPls5H5oUVqi/lQyEmitdffz1GGkceewJ+/ZvOTD7zI1LOHVyyQ+F66Ffn3UtYOuPcrbmXda47Bu827+B6jrWAp252RlB2wDk7q2bwMb1mQ9leUNe62L1LePbISVpDmCzlTUgo1Is8xA/d8xcquNUdWPcaJeQOnGcNFfR8Iq/ZRX4DBhkLZCdW5H1XdT4yTXP+js4Adqbv3Zl7k93NeSzmfSXXcCS7v/u9sp+rWu49vnIEkt/a7N9fW2p46qsTknvedZs9p3wT91v193Ob7trdqy3yDS2v8Une3yv7N805597rJu/aUL8aOb4zBzHza8kcVh7w7jwbvZOeU5g1PMn+8vINVbSJXs6VX3RQAAAOBUlEQVQ8KWcOg/vQzjXmyZ4zPTMw+/eXeRo5v4vM4bWxnGs65zlpTgX3j577W5F5Ler7ZhY69rY5O78x4wVOebp0q+q2WDJJxDmu/huI90rHeM99MZO6jgdE9aqjjuWZL2lqj1fuXyC7n8QmDKd9Oq9NKt2vo+tnzSAzV5zb/syNLmNQmL1iHLMY15GN3s8198reJNULYuZazB5P13VFXPa7aXfj7jGzn6uvC8dUKu886ZmfudeL907jFrpzdTJXXObG57y65l30as6dczz9nb2mVO7vXH+Ee3UJ6j7iTOKTY3ivOvEem/+MEK+n6lhOkzNXhPPyqn/32c/W3iKdY+TcIvQ++pJ33DXlzAvV81TEFCn7a3B/fXn3G/Fs6DYo/ynl+djc2SzZe3z23lZodNN7py002zV7XblnLXt3HOy2qeAIoGd+9OA6WadKmRY659gVK7n3Q/d43s8389L5T//sd8v//uqcZ6+gjMMn7zEy137Oc8ptp3NcdWG691v3mtR3umzb9L1BPKPqGvq6yMyddn/7Rt75cJyiSJd593g557qd+q/lnh23pd6ndObHnnMm3b909jmphVjuFaOfiep4nnnBes6mvqe7ptiZ5272ZHl+f97z6j7v3TOWPQc5bxaeudHZbzjozSfz+8z8bT0PxNw3t9w3G+/RMsfPe5jm3ily/462e3UUmI+pWpzzjlHg7cCz3cWyohgev987hqIXX8OFjKx5R8tk5EtMG/3mkGNOBCQQQgghhBBCRjRFL77q6+sRCATQ3NycUy75xsbGgvtI+dbUFyKRiAqEEEIIIYQQsi0U/eqO4XAY++yzD1566aVMmTjckPxBBx1UcB8p99YX/vjHPw5ZnxBCCCGEEEIw1ke+BDEHPPvss7Hvvvuqtb3E1bx4Mzz33HPV9rPOOguTJk1S87aEiy++GB/5yEdw11134fjjj8djjz2Gf//73/jhD3/o8zchhBBCCCGEjFZGhfgS1/Gtra245pprlNMMcRn//PPPZ5xqrF27VnlAdDn44IPV2l5XX301rrzySrXIsng6LLY1vgghhBBCCCHFw6hY58sPRso6X4QQQgghhJDi0AZFP+eLEEIIIYQQQooBii9CCCGEEEIIGQZGxZwvP3CtNWWIkRBCCCGEEDJ26XY0weZmdFF8bSM9PT0qHgkLLRNCCCGEEEJGhkaQuV9DQYcb24isJbZ+/XpUVFTAMAzflbaIwPfff5/OP0YJPKejE57X0QfP6eiE53X0wXM6OukeQedVJJUIr4kTJ+Z4Wc+HI1/biPxRJ0+ejJGEXHR+X3hkx8JzOjrheR198JyOTnheRx88p6OTyhFyXjc14uVChxuEEEIIIYQQMgxQfBFCCCGEEELIMEDxNQqIRCK49tprVUxGBzynoxOe19EHz+nohOd19MFzOjqJFOF5pcMNQgghhBBCCBkGOPJFCCGEEEIIIcMAxRchhBBCCCGEDAMUX4QQQgghhBAyDFB8EUIIIYQQQsgwQPE1Crj33nsxffp0RKNRHHDAAXj11Vf9bhLZDv72t7/hhBNOUCukG4aBp59+2u8mke3g1ltvxX777YeKigqMHz8eJ598MpYtW+Z3s8h28v3vfx/z58/PLOx50EEH4bnnnvO7WWQHctttt6l78Ne//nW/m0K2g+uuu06dR2+YM2eO380i28m6detw5plnoq6uDiUlJdhzzz3x73//G8UAxVeR8/jjj+OSSy5RbjZfe+01LFiwAMceeyxaWlr8bhrZRvr6+tR5FFFNip+//vWv+OpXv4p//etf+OMf/4hkMoljjjlGnWdSvEyePFm9nC9atEg98I844gicdNJJePvtt/1uGtkB/N///R9+8IMfKIFNip+5c+diw4YNmfCPf/zD7yaR7aCzsxOHHHIIQqGQ6vR65513cNddd6GmpgbFAF3NFzky0iW96vfcc4/KW5aFKVOm4Gtf+xquuOIKv5tHthPpofv1r3+tRkvI6KC1tVWNgIkoO+yww/xuDtmB1NbW4o477sB5553nd1PIdtDb24u9994b9913H2666SbstddeuPvuu/1uFtmOkS+xIHn99df9bgrZQVxxxRV4+eWX8fe//x3FCEe+iphEIqF6XY866qhMmWmaKv/KK6/42jZCSGG6uroyL+pkdJBOp/HYY4+p0UwxPyTFjYxUH3/88TnPVlLcvPfee8qUf5dddsEZZ5yBtWvX+t0ksh08++yz2HfffXHqqaeqzswPfehDeOCBB1AsUHwVMW1tbeqh39DQkFMu+aamJt/aRQgpjIxMy/wRMZeYN2+e380h28nixYtRXl6OSCSC888/X41S77HHHn43i2wHIqLFhF/mapLRYyH0k5/8BM8//7yaq7lq1Sp8+MMfRk9Pj99NI9vIypUr1bncdddd8cILL+CCCy7ARRddhIcffhjFQNDvBhBCyFjqUX/rrbc432CUsNtuuylTJhnNfOqpp3D22Wcrc1IKsOLk/fffx8UXX6zmZooDKzI6OO644zJpmcMnYmzatGl44oknaCJcxB2Z++67L2655RaVl5Evebbef//96j480uHIVxFTX1+PQCCA5ubmnHLJNzY2+tYuQshgLrzwQvz2t7/Fn//8Z+WsgRQ/4XAYs2bNwj777KNGSsRRzne+8x2/m0W2ETHjF2dVMt8rGAyqIGL6u9/9rkqLpQkpfqqrqzF79mwsX77c76aQbWTChAmDOrl23333ojEnpfgq8ge/PPRfeumlnN4AyXPeASEjA/FpJMJLTNL+9Kc/YcaMGX43iewk5P4bj8f9bgbZRo488khlSiqjmW6Q3nWZIyRp6ewko8OhyooVK9QLPClODjnkkEFLtrz77rtqRLMYoNlhkSNu5mWIVR4Q+++/v/LIJJO+zz33XL+bRrbjweDtkRP7dHnwi4OGqVOn+to2sm2mho8++iieeeYZtdaXOx+zqqpKrU1CipOFCxcqcyb5TcrcETnHf/nLX9T8A1KcyO8zfy5mWVmZWkeIczSLl0svvVStnSkv5uvXr1dL84iQ/uxnP+t308g28o1vfAMHH3ywMjs87bTT1Pq2P/zhD1UoBii+ipzTTz9dua6+5ppr1EuduMSVSaX5TjhI8SBrBh1++OE5AlsQkS2ThklxIZOChY9+9KM55Q899BDOOeccn1pFthcxTzvrrLPUmkEipGUuiQivo48+2u+mEUI8fPDBB0potbe3Y9y4cTj00EPVuouSJsXJfvvtp6xJpBPshhtuUBYlMvggo9TFANf5IoQQQgghhJBhgHO+CCGEEEIIIWQYoPgihBBCCCGEkGGA4osQQgghhBBChgGKL0IIIYQQQggZBii+CCGEEEIIIWQYoPgihBBCCCGEkGGA4osQQgghhBBChgGKL0IIIYQQQggZBii+CCGEEEIIIWQYoPgihBAypjnnnHNgGIYKoVAIDQ0NOProo/Hggw/Csiy/m0cIIWQUQfFFCCFkzPOxj30MGzZswOrVq/Hcc8/h8MMPx8UXX4xPfOITSKVSfjePEELIKIHiixBCyJgnEomgsbERkyZNwt57740rr7wSzzzzjBJiP/nJT1Sdb3/729hzzz1RVlaGKVOm4Ctf+Qp6e3vVtr6+PlRWVuKpp57KOe7TTz+t6vf09CCRSODCCy/EhAkTEI1GMW3aNNx6662+fF9CCCH+QPFFCCGEFOCII47AggUL8Ktf/UrlTdPEd7/7Xbz99tt4+OGH8ac//QmXX3652iYC6zOf+QweeuihnGNI/pRTTkFFRYXa99lnn8UTTzyBZcuW4ZFHHsH06dN9+W6EEEL8IejT5xJCCCEjnjlz5uDNN99U6a9//euZchFNN910E84//3zcd999quyLX/wiDj74YGW+KKNbLS0t+P3vf48XX3xRbV+7di123XVXHHrooWp+mYx8EUIIGVtw5IsQQggZAtu2lVASREQdeeSRyjRRRrI+//nPo729Hf39/Wr7/vvvj7lz56pRMeHnP/+5EliHHXZYxrHH66+/jt122w0XXXQR/vCHP/j4zQghhPgBxRchhBAyBEuWLMGMGTOUIw5xvjF//nz88pe/xKJFi3DvvfeqOjKXy0VGv9w5YmJyeO6552bEm8wlW7VqFW688UbEYjGcdtppyiSREELI2IHiixBCCCmAzOlavHgxPv3pTyuxJW7n77rrLhx44IGYPXs21q9fP2ifM888E2vWrFHzu9555x2cffbZOdvFKcfpp5+OBx54AI8//rgSch0dHcP4rQghhPgJ53wRQggZ88TjcTQ1NSGdTqO5uRnPP/+88kQoo11nnXUW3nrrLSSTSXzve9/DCSecgJdffhn333//oOPU1NTgU5/6FC677DIcc8wxmDx5cmabeEuUuWAf+tCHlPOOJ598UnlYrK6uHuZvSwghxC848kUIIWTMI2JLhJE40pA1v/785z+r0StxNx8IBJTXQxFPt99+O+bNm6c8FQ7lJv68885Tpohf+MIXcsplnti3vvUt7Lvvvthvv/2UKaM45BAhRgghZGxg2DKbmBBCCCE7hJ/97Gf4xje+ocwSw+Gw380hhBAygqDZISGEELIDEK+H4mb+tttuw3/9139ReBFCCBkEbR0IIYSQHYCYFMq6YDKPa+HChX43hxBCyAiEZoeEEEIIIYQQMgxw5IsQQgghhBBChgGKL0IIIYQQQggZBii+CCGEEEIIIWQYoPgihBBCCCGEkGGA4osQQgghhBBChgGKL0IIIYQQQggZBii+CCGEEEIIIWQYoPgihBBCCCGEEOx8/j/Dsh2AYpK1PgAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -431,9 +430,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 9, diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb index 8e3c24a5935..7eb00022787 100644 --- a/docs/tutorials/spin-chain-vqe.ipynb +++ b/docs/tutorials/spin-chain-vqe.ipynb @@ -141,9 +141,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 3, @@ -201,9 +200,8 @@ "outputs": [ { "data": { - "image/png": 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NJzNypIdXy2tMb0sT29PkuGhP34sL8Gfvb8ovVC56/MVizYF+2CH9tNNbkZnf35rYp720vnihssWeG0n6YHN+wbI7OfEcQYHP58frz7hkTRnbQ29OPlmjH16kg6meKfZzat7IWcXQp/kmE/PGZ803YwMAAADgAysrb9q0SQsXLtT7779f7PHQ0NDCYuVJkybZqy0HBATYqxW7UvS7d+9eXX755SU+17JlSz3zzDN2QXR1FC2XJSEhQXfeead+/PFHBQUFVfj3hw4daueoQK2cEN2gNoX3T3vzDuVmZis4IkyJ6+L0zXkPKCVht5bd/5a9sztswdP2dtmph7To9ulK25n/tVqWpA0JSpizQuf99JwOJR7QjoVr1Ljf4aUdMg+kaecvfyg77ZD9+0caeMZAHQgq/cDXkbGWxPpKr6yU/IrHjP0pWvrAWzrp2TH66sy7VK9ra8We3Udf9i+/jcqLpUBAdBOF3zlXFdX0yke1/rYeiu57gQ6uWaC2k+dV6PcHnnGG8pK2l7udKzmrrry5mrPK5q4qOXM1X5WJqyqxVSQux4uqr4h7fij16dzsDA2c8LKyf3y9Wt+WvwH3fNYCmnVS+NiPSn0+LTNLvUfeq5zV/1N1oj3Lb8/KjkvVFVtFYqU9C2JjjAKqU2D7UxR6xQsKCDx6X8kq6tibnqvjz71euRsXV+v70qe5p+8I6n2xQs65SwHBoUc9ZxXobEiWOp1ytvL2Hv0tSFVh4r6nr805PDXfMLk9PTmuu3vOYVK/4SThU1bZ/3bs2N3bocDHhN39vQJrlfxVEjm5ORr/3i/KnDHasXMhd/e3RvVpQSEKf3BZiXMhS152pi59/FNlzfq/Kr2NSecIymvfTNWSgq51+f9206OLFf/txfp03hZ9t2ibKuKMgQMVqgMuf+ZNPbdiSs5M+6w58XyU4/s0D82FOD7qW/ueVY2rKrH5874KAMAzODYEAOVr3bq1Zs2aJWOLlVevXq3o6Gi1aNGi8LH09HRt3rzZLlbeuXOnXcj855/5V6C2b99e11xzjerXr1/m6wYHBys2NrbE53bv3m3/W69evSrFHhMTYxcgF11FueD1e/XqZf+8atUq+/mOHTsWPm+txmytHv36669rxYoVlX7/T3uNKfU5ayd48cRXyv29pfe8rqWlvEZgSLDqd2uj7695UtXFuoK409hztfLh90p8PuHrpWo1tJ+633GJmg8+QYvvfLVwh96TOr9W/OR5eOM26vbfZMVPu1GZe7fqr/tOsx8Pjqyr1nfPdHs8TsibaTlzSmymCoiMKWeDQAVEVa0PrQj+BqomILKe8nKyFRBUyvCem6OAqLLH1epEe/pWbLSn78YGeGqMUk62VEKxsi03mzmHg/oNu62sr3Qoc5v61V6s7NS2NLk9TYuL9vTNuADkC6hRp/TnAoMUULv4t/r5e39rdJ8WHllqobLFei6gVvW2pxPPEZTljL5NlHggQx1b11FQUIBy3PQ1K76UN3JWOvo032Va3vis+W5sAAAAADwjIC+vnDOM1WTmzJkaNWqUkpKSFBgYaD82depUjR8/3l4dOTIyUgMGDNA333xjP2cVAS9btsx+vDQ//PCD/ZpxcUefALV+94wzztDkyZM1btw4l2LcsGGDOnTooC1bthxVAN2zZ0+ddtppevLJ/AMoKSkpdiG1VWBtrQJ98OBBxcfHF/udQYMG2QXXI0eOtKvJK+LgP7v1WRkHhapLizN7qdfkq7Tli0Va+X8l71xfsOxlRTUvedWPsmLtOv5CbXhnjkJqhCskMkL71xfPT1hMLQ1fPk1xs5do4S0vuBRvebEU2J4mDa38BdWVNmuA1KRG1dq3uvPmas68kTtX82VyXE6XlCEN/C5/BbyShARI4zpKlxxTve/L34B7PmubDkgXl75QtgIDpCd6SqdW8zep0Z7lt6e3xqXKxEp75mOMAqrX8j3SmMX5qyiX5o2TpK7Fv0G4yujT3NN3zE6QHv7t6K+xL7bNAKlRNfdXJu57mtaeJs05yvvcmdqens4hcw7z9J2d/+/ic7wdCXzNufOkrWklPxcUIJ3aSHr8BOfOhUzub6u7T8vOlfp/I6XllH48bURr6ZbjqvY+Jp0jKK9947YdVKszP3bp/RrUDdeqj87VGTd8qyk3ddeKdXv12Bu/uxzvlm8uUmzTKK/mrDrOrZiSM9M+a048H+X0Ps1TcyGT9lUsHB8tH/sqAAB/xbEhAHAfj62sbBX7WispT5kyRVdeeaXmzZunRx55RI0bN7ZXLrZYxcXWispW/bRVYFxWobIlPDxczZo1K7Xw+J577nGpUDkxMVEJCQmFRc9//PGHXVTdpk2bwhhuueUWjR49Wj169FCnTp304IMPqmnTpjrnnPzRKSoqyn68qJCQEDVp0qTChcqelPDNMvvmDlsX/Kpm/bvZX+v1z3fLj3q+yX+62F+LFN2umQKCApWXU1q5pH8hb/Ck6DDptMbSjztLKTYJkM4suZt1G/4GKq91Lem4aOnPZOnIxVUCrK+vC5FO8txCTTba07fQngAqq0c9qVGEtCv96IukrEtZm9WUupS+2KBb0KdV3oAm0hNrpezso58LDvi3vT144pC29C20JwBfZV0IPnVdycdfcvOki1p5Nh7628oLDpTOj5U+3Fxye2blSece/oJJ47nzHEFJXr6vn1788A+t35yksf+3WKs+GqbP58frz7hkOYkn80bOykefBk/hswYAAAAAlZe/xLEHtGjRQi+++KKmT5+ubt26acWKFbrkkkvUpUsX+/n58+frxx9/1ObNm+2VjefMmaOlS0v7oqh8ffr00cKFC0t8ziqIvvPOO12KbdasWXZM5513nn3/7LPPLoyx6OtZqzTffvvtduG1VeBsrQIdGlr61735u32/bVK9riUXaofXq62e91+hOSOmKOWfPeo0ZpjH4zMVeYOn3dklv3jIKiwpYP1srcL7f92l2h7u5vgbqBqrzayiZGsVnwLWz6GB0lMn5J9Q8yTa07fQngAqy5pXPHmCFBEshRQZi6yfa4ZIj/eUAoqMXZ5An1Z54cH539Zgtd+Rc466YdL9x3s2HtrSt9CeAHzVhbFS3wb586KAIisqWz+Papt/sY8n0d9WzQ3tpHa189uw6PE06+5dXaQWZa/D4rcuGtRKLRtH6skZa+z7u/al6+7nV+iNh072+P6AU5Az19CnwVP4rAEAAABA5Xm0ZOmGG27Qjh077FWLX3nlFbsouaBYOScnR3Xq1LGLf61b7dq1tW/fPo/ENWrUKHs15yNvp556arHtrELlrVu36tChQ1qwYIHatm1b5utaKzVbqzH7MyuPORmZRz3e57HrtOGtb5X811Ytvft1dbj6TNVu08Tj8aX8cXSxe+a+7Yp/8TrFvXCtshJ3aO/8GYp7bpTycrKVsTtemx45z76/d95bStu0Sn/cerzf5Q2+xSooef9U6daOUsdoqXWUNKyl9OGpUn8vfbz4G6i85pHSJ6dJ17c//NiIY6RP+kvH53+RgcfRnu4dm5KWfWU/t+nx4UpPWOf2sYr2BFBZ7aPzx6iRbaS2taRja0lXtc1/rG1t78REn1Z5fRpIH58qXRB7+LGxHfLnkNaFcJ5GW7p3vmHJPrBXa29oo4xdccw3AKASrIuHn+olPdJDOqGe1CpSOqWR9HLf/DHUG+hvK8+6CO/VE6V7ux4uPh/YVJpxcn5hOkr28XdbdMKIWcop8pVg78zaqJNGzlZeSd/6BnJWAfRp/ru/kpeTo23v3quEV29W0pIv2F8BAAAAAEN5eH3F4tauXVtYrDxgwADVq1dPffv2tW8xMTEaNGiQN8NDNdj2/WrtWfl3scdih/ZTZLP6Wvvyl/b99D1JWvnI++r39JhqX05t/+LPte7mTtr4f8O0/rYehSdZCxz4bd5Rv7Nv3htqPOJBNRnxoPbOe8N+LKb/KAUEBStj25+K6T9SLW9+U6l/LVWN1t1Vo9XxPpe3kiSv/EbxL4/WxkfO1da37ypxm11fPK19379brGgOzlAzOP/rSN/+j/TRadLdXaRjorwXj4l/A64y4e8gOiy/+Mte7TBQuqWj1MSDX8Xu9PZ0dxtW99gU3WuIWt70mhpfdJ8O/Pqd28cqE9uTMQpwjgYR0uj20n9PlT44Vbq+nVQv3HvxmNinucqEfs26SOr2zofnHJe3kWp56cuHnNaWTptv2DHPmqroPvnfSOWP8w0Lcw4AVWWtwjugifRyv/yLip84QTqhvvfiMbW/dYUJ/W1YkDS0RX4hujUXmtxd6ljH42EAcGifZkI/5i3Vvb+StOxLZR/Yo4DAIIXWb+G3+ysAAAAAYLr8M05ekJqaqvj4+MJi5cDAQE2fPt1b4ThWQGCgjr/jYoVGRuhg/C61GNxLiyZM08G4nfbz1o5xm4tP085f1imyeX1t/PgHtTirt9oMP0ULrnrC7fFtW/DrUY/FzfrFvhW16ZMf7Vt1sw5KNDjrJoU1bqPs5N2FJ1nT49Zo99cvKW3jCmXt36nQBi3V+MK77ecy923Vjo+mFL5GSEyzwp8jjummzU9cpJ0zn1CTyw5v42t5K0ntHmfat+0fPqQ6fc63DyZt/+B+hTVqrax9W9X+yaXK2r9DDc+doJjTrlDalt/sormIFh3dFpN1Jf2O/06yr2IPa9zaPgDV8qbXtXfO69oz51W1vmumwhrGKmn5bCX++L7ysrPU+KJ7rcveFffC1Tpu6mq3xQbf+xsoiQl/B05gense2Y5RnU9zaxtW99hkycvN1Z5vpqnxxQ8oIDjErWOVie3ppDFq15fP6NC2P5WXeUix499V8sqvGaMALzKxTyuJCf2a6UxvS6fPN6wi3cj2/ZT69zKP7Bub2p5OmnPEv3itAkLCFVSztpqNfFypf6/Qzs8es+eKVuFGeJNj7TlH7C0z7P1mAP7J1P72SCb0tyZq0LuDWp9/smo0qqvfpn6mtpecpuAaYQoMDtaPNz6roXOf1KLbp2vfb5sKf+e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zo{BsJGiur6*O~q&MRqGg>;6Bm92U7_xBO+NI{87`0nH7Ehd9_{Yhh(WBlybIf}5K0IbntoC}~3Om@%U{(AS z2|W@qp(MaUwhy_$fx*6Is45qS`9sfjQA>x_*m-F|e?>+zQ(~wCQr9jVV_S?m7y;;S zs}FL3ihDT4d2lKc0=GrK_xL_U^ne9;(Lc1EB;20p5m2>9 mH?bFRi_j!!I}+m~b%DJvV2xl*p=)W&0FezQeUU2bxW7pH^J#1V From dddaf5a3adee4225c9577d3388c9565d946b9d48 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Thu, 7 Aug 2025 15:15:19 -0500 Subject: [PATCH 12/26] delete folder --- ...ime-benchmarking-for-qubit-selection.ipynb | 776 ------------------ 1 file changed, 776 deletions(-) delete mode 100644 Untitled Folder/real-time-benchmarking-for-qubit-selection.ipynb diff --git a/Untitled Folder/real-time-benchmarking-for-qubit-selection.ipynb b/Untitled Folder/real-time-benchmarking-for-qubit-selection.ipynb deleted file mode 100644 index 3d7f317cd22..00000000000 --- a/Untitled Folder/real-time-benchmarking-for-qubit-selection.ipynb +++ /dev/null @@ -1,776 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Real-time benchmarking for qubit selection\n", - "\n", - "*Usage estimate: 4 minutes on ibm\\_cusco (NOTE: This is an estimate only. Your runtime might vary.)*\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Background\n", - "\n", - "This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", - "\n", - "The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Requirements\n", - "\n", - "Before starting this tutorial, be sure you have the following installed:\n", - "\n", - "* Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "* Qiskit Runtime 0.29 or later ( `pip install qiskit-ibm-runtime` )\n", - "* Qiskit Experiments 0.7 or later ( `pip install qiskit-experiments` )\n", - "* Rustworkx graph library (`pip install rustworkx`)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import SamplerV2\n", - "from qiskit.transpiler import generate_preset_pass_manager\n", - "from qiskit.quantum_info import hellinger_fidelity\n", - "from qiskit.transpiler import InstructionProperties\n", - "\n", - "\n", - "from qiskit_experiments.library import (\n", - " T1,\n", - " T2Hahn,\n", - " LocalReadoutError,\n", - " StandardRB,\n", - ")\n", - "from qiskit_experiments.framework import BatchExperiment, ParallelExperiment\n", - "\n", - "from qiskit_ibm_runtime import QiskitRuntimeService\n", - "from qiskit_ibm_runtime import Session\n", - "\n", - "from datetime import datetime\n", - "from collections import defaultdict\n", - "import numpy as np\n", - "import rustworkx\n", - "import matplotlib.pyplot as plt\n", - "import copy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Map classical inputs to a quantum problem\n", - "\n", - "To benchmark the difference in performance, we consider a circuit that prepares a Bell state across a linear chain of varying length. The fidelity of the Bell state at the ends of the chain is measured.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting up backend and coupling map\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# To run on hardware, select the backend with the fewest number of jobs in the queue\n", - "service = QiskitRuntimeService()\n", - "backend = service.least_busy(\n", - " operational=True, simulator=False, min_num_qubits=127\n", - ")\n", - "\n", - "qubits = list(range(backend.num_qubits))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "coupling_graph = backend.coupling_map.graph.to_undirected(multigraph=False)\n", - "\n", - "# Get unidirectional coupling map\n", - "one_dir_coupling_map = coupling_graph.edge_list()\n", - "\n", - "# Get layered coupling map\n", - "edge_coloring = rustworkx.graph_bipartite_edge_color(coupling_graph)\n", - "layered_coupling_map = defaultdict(list)\n", - "for edge_idx, color in edge_coloring.items():\n", - " layered_coupling_map[color].append(\n", - " coupling_graph.get_edge_endpoints_by_index(edge_idx)\n", - " )\n", - "layered_coupling_map = [\n", - " sorted(layered_coupling_map[i])\n", - " for i in sorted(layered_coupling_map.keys())\n", - "]\n", - "\n", - "flattened_layered_coupling_map = []\n", - "for layer in layered_coupling_map:\n", - " flattened_layered_coupling_map += layer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Characterization experiments\n", - "\n", - "A series of experiments is used to characterize the main properties of the qubits in a QPU. These are $T_1$, $T_2$, readout error, and single-qubit and two-qubit gate error. We'll briefly summarize what these properties are and refer to experiments in the [`qiskit-experiments`](https://qiskit-community.github.io/qiskit-experiments/index.html) package that are used to characterize them.\n", - "\n", - "#### T1\n", - "\n", - "$T_1$ is the characteristic time it takes for an excited qubit to fall to the ground state due to amplitude-damping decoherence processes. In a [$T_1$ experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t1.html), we measure an excited qubit after a delay. The larger the delay time is, the more\n", - "likely is the qubit to fall to the ground state. The goal of the\n", - "experiment is to characterize the decay rate of the qubit towards the\n", - "ground state.\n", - "\n", - "#### T2\n", - "\n", - "$T_2$ represents the amount of time required for a single qubit's Bloch\n", - "vector projection on the XY plane to fall to approximately 37% ($\\frac{1}{e}$) of\n", - "its initial amplitude due to dephasing decoherence processes. In a [$T_2$ Hahn Echo experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t2hahn.html), we can estimate the rate of this decay.\n", - "\n", - "#### State preparation and measurement (SPAM) error characterization\n", - "\n", - "In a [SPAM-error characterization experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/measurement/readout_mitigation.html) qubit are prepared in a certain state ($\\vert 0 \\rangle$ or $\\vert 1 \\rangle$) and measured. The probability of measuring a state different than the one prepared then gives the probability of the error.\n", - "\n", - "#### Single-qubit and two-qubit randomized benchmarking\n", - "\n", - "[Randomized benchmarking](https://qiskit-community.github.io/qiskit-experiments/manuals/verification/randomized_benchmarking.html) is a popular protocol for characterizing the error rate of\n", - "quantum processors. An RB experiment consists of the generation of random Clifford\n", - "circuits on the given qubits such that the unitary computed by the circuits is the\n", - "identity. After running the circuits, the number of shots resulting in an error (that is, an output different from the ground state) are counted, and from this data one can infer error estimates for the quantum device, by calculating the Error Per Clifford.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Create T1 experiments on all qubit in parallel\n", - "t1_exp = ParallelExperiment(\n", - " [\n", - " T1(\n", - " physical_qubits=[qubit],\n", - " delays=np.linspace(\n", - " 1e-6, 2 * backend.properties().t1(qubit), 5, endpoint=True\n", - " ),\n", - " )\n", - " for qubit in qubits\n", - " ],\n", - " backend,\n", - " analysis=None,\n", - ")\n", - "\n", - "# Create T2-Hahn experiments on all qubit in parallel\n", - "t2_exp = ParallelExperiment(\n", - " [\n", - " T2Hahn(\n", - " physical_qubits=[qubit],\n", - " delays=np.linspace(\n", - " 1e-6, 2 * backend.properties().t2(qubit), 5, endpoint=True\n", - " ),\n", - " )\n", - " for qubit in qubits\n", - " ],\n", - " backend,\n", - " analysis=None,\n", - ")\n", - "\n", - "# Create readout experiments on all qubit in parallel\n", - "readout_exp = LocalReadoutError(qubits)\n", - "\n", - "# Create single-qubit RB experiments on all qubit in parallel\n", - "singleq_rb_exp = ParallelExperiment(\n", - " [\n", - " StandardRB(\n", - " physical_qubits=[qubit], lengths=[10, 100, 500], num_samples=10\n", - " )\n", - " for qubit in qubits\n", - " ],\n", - " backend,\n", - " analysis=None,\n", - ")\n", - "\n", - "# Create two-qubit RB experiments on the three layers of disjoint edges of the heavy-hex\n", - "twoq_rb_exp_batched = BatchExperiment(\n", - " [\n", - " ParallelExperiment(\n", - " [\n", - " StandardRB(\n", - " physical_qubits=pair,\n", - " lengths=[10, 50, 100],\n", - " num_samples=10,\n", - " )\n", - " for pair in layer\n", - " ],\n", - " backend,\n", - " analysis=None,\n", - " )\n", - " for layer in layered_coupling_map\n", - " ],\n", - " backend,\n", - " flatten_results=True,\n", - " analysis=None,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### QPU properties over time\n", - "\n", - "Looking at the reported QPU properties over time (we'll consider a single week below), we see how these can fluctuate on a scale of a single day. Small fluctuations can happen even within a day. In this scenario, the reported properties (updated once per day) will not accurately capture the current status of the QPU. Moreover, if a job is transpiled locally (using current reported properties) and submitted but executed only at a later time (minutes or days), it may run the risk of having used outdated properties for qubit selection in the transpilation step. This highlights the importance of having updated information about the QPU at execution time. First, let's retrieve the properties over a certain time range.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "errors_list = []\n", - "for day_idx in range(10, 17):\n", - " calibrations_time = datetime(\n", - " year=2024, month=10, day=day_idx, hour=0, minute=0, second=0\n", - " )\n", - " targer_hist = backend.target_history(datetime=calibrations_time)\n", - "\n", - " t1_dict, t2_dict = {}, {}\n", - " for qubit in range(targer_hist.num_qubits):\n", - " t1_dict[qubit] = targer_hist.qubit_properties[qubit].t1\n", - " t2_dict[qubit] = targer_hist.qubit_properties[qubit].t2\n", - "\n", - " errors_dict = {\n", - " \"1q\": targer_hist[\"sx\"],\n", - " \"2q\": targer_hist[\"ecr\"],\n", - " \"spam\": targer_hist[\"measure\"],\n", - " \"t1\": t1_dict,\n", - " \"t2\": t2_dict,\n", - " }\n", - "\n", - " errors_list.append(errors_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, let's plot the values\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(5, 1, figsize=(10, 20), sharex=False)\n", - "\n", - "\n", - "# Plot for T1 values\n", - "for qubit in range(targer_hist.num_qubits):\n", - " t1s = []\n", - " for errors_dict in errors_list:\n", - " t1_dict = errors_dict[\"t1\"]\n", - " t1s.append(t1_dict[qubit] / 1e-6)\n", - "\n", - " axs[0].plot(t1s)\n", - "\n", - "axs[0].set_title(\"T1\")\n", - "axs[0].set_ylabel(r\"Time ($\\mu s$)\")\n", - "axs[0].set_xlabel(\"Days\")\n", - "\n", - "# Plot for T2 values\n", - "for qubit in range(targer_hist.num_qubits):\n", - " t2s = []\n", - " for errors_dict in errors_list:\n", - " t2_dict = errors_dict[\"t2\"]\n", - " t2s.append(t2_dict[qubit] / 1e-6)\n", - "\n", - " axs[1].plot(t2s)\n", - "\n", - "axs[1].set_title(\"T2\")\n", - "axs[1].set_ylabel(r\"Time ($\\mu s$)\")\n", - "axs[1].set_xlabel(\"Days\")\n", - "\n", - "# Plot SPAM values\n", - "for qubit in range(targer_hist.num_qubits):\n", - " spams = []\n", - " for errors_dict in errors_list:\n", - " spam_dict = errors_dict[\"spam\"]\n", - " spams.append(spam_dict[tuple([qubit])].error)\n", - "\n", - " axs[2].plot(spams)\n", - "\n", - "axs[2].set_title(\"SPAM Errors\")\n", - "axs[2].set_ylabel(\"Error Rate\")\n", - "axs[2].set_xlabel(\"Days\")\n", - "\n", - "# Plot 1Q Gate Errors\n", - "for qubit in range(targer_hist.num_qubits):\n", - " oneq_gates = []\n", - " for errors_dict in errors_list:\n", - " oneq_gate_dict = errors_dict[\"1q\"]\n", - " oneq_gates.append(oneq_gate_dict[tuple([qubit])].error)\n", - "\n", - " axs[3].plot(oneq_gates)\n", - "\n", - "axs[3].set_title(\"1Q Gate Errors\")\n", - "axs[3].set_ylabel(\"Error Rate\")\n", - "axs[3].set_xlabel(\"Days\")\n", - "\n", - "# Plot 2Q Gate Errors\n", - "for pair in one_dir_coupling_map:\n", - " twoq_gates = []\n", - " for errors_dict in errors_list:\n", - " twoq_gate_dict = errors_dict[\"2q\"]\n", - " twoq_gates.append(twoq_gate_dict[pair].error)\n", - "\n", - " axs[4].plot(twoq_gates)\n", - "\n", - "axs[4].set_title(\"2Q Gate Errors\")\n", - "axs[4].set_ylabel(\"Error Rate\")\n", - "axs[4].set_xlabel(\"Days\")\n", - "\n", - "plt.subplots_adjust(hspace=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can see that over several days some of the qubit properties can change considerably. This highlights the importance of having fresh information of the QPU status, to be able to select the best performing qubits for an experiment.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit import QuantumCircuit\n", - "\n", - "ideal_dist = {\"00\": 0.5, \"11\": 0.5}\n", - "\n", - "num_qubits_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 127]\n", - "circuits = []\n", - "for num_qubits in num_qubits_list:\n", - " circuit = QuantumCircuit(num_qubits, 2)\n", - " circuit.h(0)\n", - " for i in range(num_qubits - 1):\n", - " circuit.cx(i, i + 1)\n", - " circuit.barrier()\n", - " circuit.measure(0, 0)\n", - " circuit.measure(num_qubits - 1, 1)\n", - " circuits.append(circuit)\n", - "\n", - "circuits[-1].draw(output=\"mpl\", style=\"clifford\", fold=-1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Optimize problem for quantum hardware execution\n", - "\n", - "No optimization of the circuits or operators is done in this tutorial.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Execute using Qiskit primitives\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Execute a quantum circuit with default qubit selection\n", - "\n", - "As a reference result of performance, we'll execute a quantum circuit on a QPU by using the default qubits, which are the qubits selected with the requested backend properties. We will use `optimization_level = 3`. This setting includes the most advanced transpilation optimization, and uses target properties (like operation errors) to select the best performing qubits for execution.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pm = generate_preset_pass_manager(target=backend.target, optimization_level=3)\n", - "isa_circuits = pm.run(circuits)\n", - "initial_qubits = [\n", - " [\n", - " idx\n", - " for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", - " if qb._register.name != \"ancilla\"\n", - " ]\n", - " for circuit in isa_circuits\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Execute a quantum circuit with real-time qubit selection\n", - "\n", - "In this section, we'll investigate the importance of having updated information on qubit properties of the QPU for optimal results. First, we'll carry out a full suite of QPU characterization experiments ($T_1$, $T_2$, SPAM, single-qubit RB and two-qubit RB), which we can then use to update the backend properties. This allows the pass manager to select qubits for the execution based on fresh information about the QPU, possibly improving execution performances. Second, we execute the Bell pair circuit and we compare the fidelity obtained after selecting the qubits with update QPU properties to the fidelity we obtained before when we use the default reported properties for qubit selection.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare characterization experiments\n", - "batches = [t1_exp, t2_exp, readout_exp, singleq_rb_exp, twoq_rb_exp_batched]\n", - "batches_exp = BatchExperiment(batches, backend) # , analysis=None)\n", - "run_options = {\"shots\": 1e3, \"dynamic\": False}\n", - "\n", - "with Session(backend=backend) as session:\n", - " sampler = SamplerV2(mode=session)\n", - "\n", - " # Run characterization experiments\n", - " batches_exp_data = batches_exp.run(\n", - " sampler=sampler, **run_options\n", - " ).block_for_results()\n", - "\n", - " EPG_sx_result_list = batches_exp_data.analysis_results(\"EPG_sx\")\n", - " EPG_sx_result_q_indices = [\n", - " result.device_components.index for result in EPG_sx_result_list\n", - " ]\n", - " EPG_x_result_list = batches_exp_data.analysis_results(\"EPG_x\")\n", - " EPG_x_result_q_indices = [\n", - " result.device_components.index for result in EPG_x_result_list\n", - " ]\n", - " T1_result_list = batches_exp_data.analysis_results(\"T1\")\n", - " T1_result_q_indices = [\n", - " result.device_components.index for result in T1_result_list\n", - " ]\n", - "\n", - " T2_result_list = batches_exp_data.analysis_results(\"T2\")\n", - " T2_result_q_indices = [\n", - " result.device_components.index for result in T2_result_list\n", - " ]\n", - "\n", - " Readout_result_list = batches_exp_data.analysis_results(\n", - " \"Local Readout Mitigator\"\n", - " )\n", - "\n", - " EPG_ecr_result_list = batches_exp_data.analysis_results(\"EPG_ecr\")\n", - "\n", - " # Update target properties\n", - " target = copy.deepcopy(backend.target)\n", - " for i in range(target.num_qubits - 1):\n", - " qarg = (i,)\n", - "\n", - " if qarg in EPG_sx_result_q_indices:\n", - " target.update_instruction_properties(\n", - " instruction=\"sx\",\n", - " qargs=qarg,\n", - " properties=InstructionProperties(\n", - " error=EPG_sx_result_list[i].value.nominal_value\n", - " ),\n", - " )\n", - " if qarg in EPG_x_result_q_indices:\n", - " target.update_instruction_properties(\n", - " instruction=\"x\",\n", - " qargs=qarg,\n", - " properties=InstructionProperties(\n", - " error=EPG_x_result_list[i].value.nominal_value\n", - " ),\n", - " )\n", - "\n", - " err_mat = Readout_result_list.value.assignment_matrix(i)\n", - " readout_assignment_error = (\n", - " err_mat[0, 1] + err_mat[1, 0]\n", - " ) / 2 # average readout error\n", - " target.update_instruction_properties(\n", - " instruction=\"measure\",\n", - " qargs=qarg,\n", - " properties=InstructionProperties(error=readout_assignment_error),\n", - " )\n", - "\n", - " if qarg in T1_result_q_indices:\n", - " target.qubit_properties[i].t1 = T1_result_list[\n", - " i\n", - " ].value.nominal_value\n", - " if qarg in T2_result_q_indices:\n", - " target.qubit_properties[i].t2 = T2_result_list[\n", - " i\n", - " ].value.nominal_value\n", - "\n", - " for pair_idx, pair in enumerate(flattened_layered_coupling_map):\n", - " qarg = tuple(pair)\n", - " try:\n", - " target.update_instruction_properties(\n", - " instruction=\"ecr\",\n", - " qargs=qarg,\n", - " properties=InstructionProperties(\n", - " error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", - " ),\n", - " )\n", - " except:\n", - " target.update_instruction_properties(\n", - " instruction=\"ecr\",\n", - " qargs=qarg[::-1],\n", - " properties=InstructionProperties(\n", - " error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", - " ),\n", - " )\n", - "\n", - " # transpile circuits to updated target\n", - " pm = generate_preset_pass_manager(target=target, optimization_level=3)\n", - " isa_circuit_updated = pm.run(circuits)\n", - " updated_qubits = [\n", - " [\n", - " idx\n", - " for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", - " if qb._register.name != \"ancilla\"\n", - " ]\n", - " for circuit in isa_circuit_updated\n", - " ]\n", - "\n", - " n_trials = 3 # run multiple trials to see variations\n", - "\n", - " # interleave circuits\n", - " interleaved_circuits = []\n", - " for original_circuit, updated_circuit in zip(\n", - " isa_circuits, isa_circuit_updated\n", - " ):\n", - " interleaved_circuits.append(original_circuit)\n", - " interleaved_circuits.append(updated_circuit)\n", - "\n", - " # Run circuits\n", - " # Set simple error suppression/mitigation options\n", - " sampler.options.dynamical_decoupling.enable = True\n", - " sampler.options.dynamical_decoupling.sequence_type = \"XY4\"\n", - "\n", - " job_interleaved = sampler.run(interleaved_circuits * n_trials)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Post-process and return result in desired classical format\n", - "\n", - "Finally, let's compare the fidelity of the Bell state obtained in the two different settings:\n", - "\n", - "* `original`, that is with the default qubits chosen by the transpiler based on reported properties of the backend.\n", - "* `updated`, that is with the qubits chosen based on updated properties of the backend after characterization experiments have run.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "results = job_interleaved.result()\n", - "all_fidelity_list, all_fidelity_updated_list = [], []\n", - "for exp_idx in range(n_trials):\n", - " fidelity_list, fidelity_updated_list = [], []\n", - "\n", - " for idx, num_qubits in enumerate(num_qubits_list):\n", - " pub_result_original = results[\n", - " 2 * exp_idx * len(num_qubits_list) + 2 * idx\n", - " ]\n", - " pub_result_updated = results[\n", - " 2 * exp_idx * len(num_qubits_list) + 2 * idx + 1\n", - " ]\n", - "\n", - " fid = hellinger_fidelity(\n", - " ideal_dist, pub_result_original.data.c.get_counts()\n", - " )\n", - " fidelity_list.append(fid)\n", - "\n", - " fid_up = hellinger_fidelity(\n", - " ideal_dist, pub_result_updated.data.c.get_counts()\n", - " )\n", - " fidelity_updated_list.append(fid_up)\n", - " all_fidelity_list.append(fidelity_list)\n", - " all_fidelity_updated_list.append(fidelity_updated_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(8, 6))\n", - "plt.errorbar(\n", - " num_qubits_list,\n", - " np.mean(all_fidelity_list, axis=0),\n", - " yerr=np.std(all_fidelity_list, axis=0),\n", - " fmt=\"o-.\",\n", - " label=\"original\",\n", - " color=\"b\",\n", - ")\n", - "# plt.plot(num_qubits_list, fidelity_list, '-.')\n", - "plt.errorbar(\n", - " num_qubits_list,\n", - " np.mean(all_fidelity_updated_list, axis=0),\n", - " yerr=np.std(all_fidelity_updated_list, axis=0),\n", - " fmt=\"o-.\",\n", - " label=\"updated\",\n", - " color=\"r\",\n", - ")\n", - "# plt.plot(num_qubits_list, fidelity_updated_list, '-.')\n", - "plt.xlabel(\"Chain length\")\n", - "plt.xticks(num_qubits_list)\n", - "plt.ylabel(\"Fidelity\")\n", - "plt.title(\"Bell pair fidelity at the edge of N-qubits chain\")\n", - "plt.legend()\n", - "plt.grid(\n", - " alpha=0.2,\n", - " linestyle=\"-.\",\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With increasing chain length, and thus less freedom to choose physical qubits, the fact that we have updated information about qubit properties becomes less substantial, with there being no difference when all of the qubits are used.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

\n", - " Call to Action: Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tutorial survey\n", - "\n", - "Please take this short survey to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", - "\n", - "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_0w6FZ9QrWkKfTQq)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "© IBM Corp., 2017-2025" - ] - } - ], - "metadata": { - "description": "Run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU.", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - }, - "title": "Real-time benchmarking for qubit selection\n" - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 3303e27f403b7bfae0bf119c9283a677b4bee4da Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Tue, 26 Aug 2025 12:28:15 -0500 Subject: [PATCH 13/26] comments --- docs/tutorials/circuit-transpilation-settings.ipynb | 2 +- docs/tutorials/grovers-algorithm.ipynb | 2 +- docs/tutorials/operator-back-propagation.ipynb | 2 +- docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb | 2 +- docs/tutorials/quantum-kernel-training.ipynb | 2 +- docs/tutorials/spin-chain-vqe.ipynb | 2 +- learning/courses/quantum-machine-learning/qvc-qnn.ipynb | 2 +- .../examples-and-applications.ipynb | 4 ++-- .../modules/computer-science/quantum-key-distribution.ipynb | 4 ++-- 9 files changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/tutorials/circuit-transpilation-settings.ipynb b/docs/tutorials/circuit-transpilation-settings.ipynb index 1132eedcfe1..716a09011f2 100644 --- a/docs/tutorials/circuit-transpilation-settings.ipynb +++ b/docs/tutorials/circuit-transpilation-settings.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Compare transpiler settings\n", - "*Usage estimate: under one minute on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: under one minute on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/docs/tutorials/grovers-algorithm.ipynb b/docs/tutorials/grovers-algorithm.ipynb index 9a454848e26..fbbd8b4a6da 100644 --- a/docs/tutorials/grovers-algorithm.ipynb +++ b/docs/tutorials/grovers-algorithm.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Grover's algorithm\n", - "*Usage estimate: under one minute on ibm_brisbane (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: under one minute on Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/docs/tutorials/operator-back-propagation.ipynb b/docs/tutorials/operator-back-propagation.ipynb index 9c010793a10..a14b2a317da 100644 --- a/docs/tutorials/operator-back-propagation.ipynb +++ b/docs/tutorials/operator-back-propagation.ipynb @@ -8,7 +8,7 @@ "{/* cspell:ignore simeq // This in an equation and isn't being ignored correctly */}\n", "\n", "# Operator backpropagation (OBP) for estimation of expectation values\n", - "*Usage estimate: 16 minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 16 minutes on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb index 943408f9d3b..043d59a5429 100644 --- a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb +++ b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb @@ -9,7 +9,7 @@ "\n", "# Pauli Correlation Encoding to reduce Maxcut requirements\n", "\n", - "*Usage estimate: 30 minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 30 minutes on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/docs/tutorials/quantum-kernel-training.ipynb b/docs/tutorials/quantum-kernel-training.ipynb index 4eafc8e77fa..aa861e8eb05 100644 --- a/docs/tutorials/quantum-kernel-training.ipynb +++ b/docs/tutorials/quantum-kernel-training.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Quantum kernel training\n", - "*Usage estimate: under one minute on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: under one minute on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb index 7eb00022787..01de08d28bd 100644 --- a/docs/tutorials/spin-chain-vqe.ipynb +++ b/docs/tutorials/spin-chain-vqe.ipynb @@ -8,7 +8,7 @@ }, "source": [ "# Ground-state energy estimation of the Heisenberg chain with VQE\n", - "*Usage estimate: Two minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: Two minutes on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/learning/courses/quantum-machine-learning/qvc-qnn.ipynb b/learning/courses/quantum-machine-learning/qvc-qnn.ipynb index 13c3c2ddb90..d7b87334b80 100644 --- a/learning/courses/quantum-machine-learning/qvc-qnn.ipynb +++ b/learning/courses/quantum-machine-learning/qvc-qnn.ipynb @@ -1729,7 +1729,7 @@ "metadata": {}, "outputs": [], "source": [ - "# This was run on an Eagle processor on 10-4-24, and took 7 min.\n", + "# This was run on an Eagle r3 processor on 10-4-24, and took 7 min.\n", "\n", "from qiskit_ibm_runtime import EstimatorV2 as Estimator, Session\n", "\n", diff --git a/learning/courses/variational-algorithm-design/examples-and-applications.ipynb b/learning/courses/variational-algorithm-design/examples-and-applications.ipynb index 3e46135f995..536d69205f0 100644 --- a/learning/courses/variational-algorithm-design/examples-and-applications.ipynb +++ b/learning/courses/variational-algorithm-design/examples-and-applications.ipynb @@ -1345,7 +1345,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Estimated compute resource usage: 25 minutes. Benchmarked at 24 min, 30 sec on an Eagle processor on 5-30-24\n", + "# Estimated compute resource usage: 25 minutes. Benchmarked at 24 min, 30 sec on an Eagle r3 processor on 5-30-24\n", "\n", "k = 2\n", "betas = [30, 50, 80]\n", @@ -1788,7 +1788,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Estimated hardware usage: 20 min benchmarked on an Eagle processor on 5-30-24\n", + "# Estimated hardware usage: 20 min benchmarked on an Eagle r3 processor on 5-30-24\n", "\n", "real_prev_states = []\n", "real_prev_opt_parameters = []\n", diff --git a/learning/modules/computer-science/quantum-key-distribution.ipynb b/learning/modules/computer-science/quantum-key-distribution.ipynb index 703e87461d8..f8706245f33 100644 --- a/learning/modules/computer-science/quantum-key-distribution.ipynb +++ b/learning/modules/computer-science/quantum-key-distribution.ipynb @@ -986,7 +986,7 @@ "source": [ "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", "\n", - "# This calculation was run on an Eagle processor on 11-7-24 and required 3 sec to run, with 127 qubits.\n", + "# This calculation was run on an Eagle r3 processor on 11-7-24 and required 3 sec to run, with 127 qubits.\n", "# Qiskit patterns step 1: Mapping your problem to a quantum circuit\n", "\n", "bit_num = 127\n", @@ -1095,7 +1095,7 @@ "source": [ "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", "\n", - "# This calculation was run on an Eagle processor on 11-7-24 and required 2 s to run, with 127 qubits.\n", + "# This calculation was run on an Eagle r3 processor on 11-7-24 and required 2 s to run, with 127 qubits.\n", "# Qiskit patterns step 1: Mapping your problem to a quantum circuit\n", "\n", "bit_num = 127\n", From 7ad7c981f9b3148b1eaf6ccfd141aa95dff1879f Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Tue, 26 Aug 2025 12:35:45 -0500 Subject: [PATCH 14/26] unused --- .../cf99fe0e-3b93-4bd1-ac27-8d75fc68fba7-0.avif | Bin 19411 -> 0 bytes .../extracted-outputs/0fda3611-0.avif | Bin 4006 -> 0 bytes .../33135970-8bc4-4fb2-ab87-08726a432ce4-0.avif | Bin 2681 -> 0 bytes .../extracted-outputs/4381a2b3-0.avif | Bin 5133 -> 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zGt6m|N0ZmMCq;tv`m1F&*Cq9&J?%7%FlLL(o9trqtE^T*JUu60gf8Omrpf>A>b_;B zE*r9bHl+&LxHK_51<{UfeO}i6`I=lKNKzq>ULM8exAB+)pZv)dA`@H* zC*JknhYp$BKrRa^jZTi_gdOquWSnqGtLkitmR1v;1jHaOJ;Kf>tN=0@jLi`|EX*^2 zLPtDG$=r3kn?){L-d`jX!gi>JDO%jOqUAI+=jB!fI}{gCkX~cSh$J^#+7NEzY-L$t z_ymW&?p1A@%jLB{ZoqO}%EnVhO;$$ zrU@(=LeW@E)<+aEBA7*a%DY(Bv_&0ncNn~Y_4OR(09ezW+1}FceF(D&Ym>5K1LsyG zeIk`^U#P6D$?39_w}H9HZs1zE)a$LYU2Ln}9&j3& z_m4O#9$8%MTQ#Qcy{FvLk=xtMV%83N5%OyO!zXmWwhFNYcmw4#@R(weZ8-y zwlXHlMwi{QaW&IzfHB{*VP?6m~ zU1bx2(>#*PUpe>iJG%Zt1YsS-c^x4{7vueuF5#>zB7-i0-ggSIty9l@L6i3QlVyA57IbN%Q4=0tOY2*mo-eoE}_GtQ|> zC@FI!vY_^OiK8#V zc3upsb(_Ql_SY>F>%H|cvucz?)~>N#i$#6NhiXMz%m;!ff2{d&ar^q7*7WL4i3i=I zZ8u4tSL&1vNL?pUVNFhi_T!Wz7g57QAQ1PN2x5@#74mcyozU4~mqG-m_8K_H)+Nq_ k6SBWQ>LIDhM;XtT#>`cs+6l;cIVhL6*;$Egez79d$oWMx6aWAK From 8c1f9fdbd057b15e06a0c3356890209c145b49dd Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Tue, 26 Aug 2025 14:06:22 -0500 Subject: [PATCH 15/26] remove div --- .../real-time-benchmarking-for-qubit-selection.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 9a2cc9d56ed..64829eeab54 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -791,9 +791,9 @@ "id": "b8c3bc0f-9cbe-4842-ac9a-eded8edfdf74", "metadata": {}, "source": [ - "

\n", - "Call to Action: Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", - "
" + "\n", + "Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", + "\n" ] }, { From 55673a1b8def7b68009a28f6938ba7dbf8c984bb Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Tue, 26 Aug 2025 15:17:13 -0500 Subject: [PATCH 16/26] Don't specify CRN --- .../real-time-benchmarking-for-qubit-selection.ipynb | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 64829eeab54..0577aad468b 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "c24b5540-f94e-4fa8-96cb-a626573510ef", "metadata": {}, "outputs": [ @@ -160,9 +160,7 @@ } ], "source": [ - "service = QiskitRuntimeService(\n", - " instance=\"crn:v1:bluemix:public:quantum-computing:us-east:a/26b15df5f5684154b2c791c32e07e69b:08dcfec2-410b-45a4-8185-10fd26ce26f6::\"\n", - ")\n", + "service = QiskitRuntimeService()\n", "\n", "# To run on hardware, select a backend that supports the ecr gate\n", "service.backends(filters=lambda x: (\"ecr\" in x.basis_gates))" From fbc9bd69700bce5e1fc00b1288c12eb21742bac4 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Tue, 26 Aug 2025 16:17:47 -0500 Subject: [PATCH 17/26] replace with main --- ...ime-benchmarking-for-qubit-selection.ipynb | 1605 ++++++++--------- 1 file changed, 774 insertions(+), 831 deletions(-) diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 0577aad468b..bb0526b08cd 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -1,833 +1,776 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "c60285b4-72df-4a5d-9ee1-41efc0123fce", - "metadata": {}, - "source": [ - "# Real-time benchmarking for qubit selection\n", - "*Usage estimate: 4 minutes on ibm_brisbane (NOTE: This is an estimate only. Your runtime might vary.)*" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "643fe0b3-d024-4d94-9603-6cdf680d297c", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "# This cell is hidden from users – it disables some lint rules\n", - "# ruff: noqa: E722" - ] - }, - { - "cell_type": "markdown", - "id": "cda4fc0d-a40e-4fcd-bad1-9f373aef8395", - "metadata": {}, - "source": [ - "## Background\n", - "This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", - "\n", - "The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine." - ] - }, - { - "cell_type": "markdown", - "id": "0d2ab297-142e-4919-9a17-519b407c8019", - "metadata": {}, - "source": [ - "## Requirements\n", - "Before starting this tutorial, be sure you have the following installed:\n", - "\n", - "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "- Qiskit Runtime 0.29 or later ( `pip install qiskit-ibm-runtime` )\n", - "- Qiskit Experiments 0.7 or later ( `pip install qiskit-experiments` )" - ] - }, - { - "cell_type": "markdown", - "id": "af357ab3-1992-402f-869c-47302bd228ef", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0d86f001-2679-4ce9-98e8-1d9a687f5c79", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import SamplerV2\n", - "from qiskit.transpiler import generate_preset_pass_manager\n", - "from qiskit.quantum_info import hellinger_fidelity\n", - "from qiskit.transpiler import InstructionProperties\n", - "\n", - "\n", - "from qiskit_experiments.library import (\n", - " T1,\n", - " T2Hahn,\n", - " LocalReadoutError,\n", - " StandardRB,\n", - ")\n", - "from qiskit_experiments.framework import BatchExperiment, ParallelExperiment\n", - "\n", - "from qiskit_ibm_runtime import QiskitRuntimeService\n", - "from qiskit_ibm_runtime import Session\n", - "\n", - "from datetime import datetime\n", - "from collections import defaultdict\n", - "import numpy as np\n", - "import rustworkx\n", - "import matplotlib.pyplot as plt\n", - "import copy" - ] - }, - { - "cell_type": "markdown", - "id": "9bb8631d-d401-4ee6-aae6-02e6bd0585f6", - "metadata": {}, - "source": [ - "## Step 1: Map classical inputs to a quantum problem\n", - "\n", - "To benchmark the difference in performance, we consider a circuit that prepares a Bell state across a linear chain of varying length. The fidelity of the Bell state at the ends of the chain is measured." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "64c25da9-a728-4ae4-a377-3078a1dc618d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from qiskit import QuantumCircuit\n", - "\n", - "ideal_dist = {\"00\": 0.5, \"11\": 0.5}\n", - "\n", - "num_qubits_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 127]\n", - "circuits = []\n", - "for num_qubits in num_qubits_list:\n", - " circuit = QuantumCircuit(num_qubits, 2)\n", - " circuit.h(0)\n", - " for i in range(num_qubits - 1):\n", - " circuit.cx(i, i + 1)\n", - " circuit.barrier()\n", - " circuit.measure(0, 0)\n", - " circuit.measure(num_qubits - 1, 1)\n", - " circuits.append(circuit)\n", - "\n", - "circuits[-1].draw(output=\"mpl\", style=\"clifford\", fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "45f6acb2-11b6-44de-b578-911312869b96", - "metadata": {}, - "source": [ - "### Setting up backend and coupling map" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c24b5540-f94e-4fa8-96cb-a626573510ef", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "service = QiskitRuntimeService()\n", - "\n", - "# To run on hardware, select a backend that supports the ecr gate\n", - "service.backends(filters=lambda x: (\"ecr\" in x.basis_gates))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d14e123a-df7f-4fa9-9c8c-cf5a57e9f0b7", - "metadata": {}, - "outputs": [], - "source": [ - "backend = service.backend(\"ibm_brisbane\")\n", - "\n", - "qubits = list(range(backend.num_qubits))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "406fd2a9-ef54-4f81-861c-b90e4d7434f0", - "metadata": {}, - "outputs": [], - "source": [ - "coupling_graph = backend.coupling_map.graph.to_undirected(multigraph=False)\n", - "\n", - "# Get unidirectional coupling map\n", - "one_dir_coupling_map = coupling_graph.edge_list()\n", - "\n", - "# Get layered coupling map\n", - "edge_coloring = rustworkx.graph_bipartite_edge_color(coupling_graph)\n", - "layered_coupling_map = defaultdict(list)\n", - "for edge_idx, color in edge_coloring.items():\n", - " layered_coupling_map[color].append(\n", - " coupling_graph.get_edge_endpoints_by_index(edge_idx)\n", - " )\n", - "layered_coupling_map = [\n", - " sorted(layered_coupling_map[i])\n", - " for i in sorted(layered_coupling_map.keys())\n", - "]\n", - "\n", - "flattened_layered_coupling_map = []\n", - "for layer in layered_coupling_map:\n", - " flattened_layered_coupling_map += layer" - ] - }, - { - "cell_type": "markdown", - "id": "8ff27f9d-dbf4-43b8-ad73-3ee464a9d4d5", - "metadata": {}, - "source": [ - "### Characterization experiments\n", - "\n", - "A series of experiments is used to characterize the main properties of the qubits in a QPU. These are $T_1$, $T_2$, readout error, and single-qubit and two-qubit gate error. We'll briefly summarize what these properties are and refer to experiments in the [`qiskit-experiments`](https://qiskit-community.github.io/qiskit-experiments/index.html) package that are used to characterize them.\n", - "\n", - "#### T1\n", - "$T_1$ is the characteristic time it takes for an excited qubit to fall to the ground state due to amplitude-damping decoherence processes. In a [$T_1$ experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t1.html), we measure an excited qubit after a delay. The larger the delay time is, the more\n", - "likely is the qubit to fall to the ground state. The goal of the\n", - "experiment is to characterize the decay rate of the qubit towards the\n", - "ground state.\n", - "\n", - "#### T2\n", - "$T_2$ represents the amount of time required for a single qubit's Bloch\n", - "vector projection on the XY plane to fall to approximately 37% ($\\frac{1}{e}$) of\n", - "its initial amplitude due to dephasing decoherence processes. In a [$T_2$ Hahn Echo experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t2hahn.html), we can estimate the rate of this decay.\n", - "\n", - "#### State preparation and measurement (SPAM) error characterization\n", - "In a [SPAM-error characterization experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/measurement/readout_mitigation.html) qubit are prepared in a certain state ($\\vert 0 \\rangle$ or $\\vert 1 \\rangle$) and measured. The probability of measuring a state different than the one prepared then gives the probability of the error.\n", - "\n", - "#### Single-qubit and two-qubit randomized benchmarking\n", - "[Randomized benchmarking](https://qiskit-community.github.io/qiskit-experiments/manuals/verification/randomized_benchmarking.html) is a popular protocol for characterizing the error rate of\n", - "quantum processors. An RB experiment consists of the generation of random Clifford\n", - "circuits on the given qubits such that the unitary computed by the circuits is the\n", - "identity. After running the circuits, the number of shots resulting in an error (that is, an output different from the ground state) are counted, and from this data one can infer error estimates for the quantum device, by calculating the Error Per Clifford." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d7cb8ad7-ea83-49ed-bde2-ef6953fdc128", - "metadata": {}, - "outputs": [], - "source": [ - "# Create T1 experiments on all qubit in parallel\n", - "t1_exp = ParallelExperiment(\n", - " [\n", - " T1(\n", - " physical_qubits=[qubit],\n", - " delays=np.linspace(\n", - " 1e-6, 2 * backend.properties().t1(qubit), 5, endpoint=True\n", - " ),\n", - " )\n", - " for qubit in qubits\n", - " ],\n", - " backend,\n", - " analysis=None,\n", - ")\n", - "\n", - "# Create T2-Hahn experiments on all qubit in parallel\n", - "t2_exp = ParallelExperiment(\n", - " [\n", - " T2Hahn(\n", - " physical_qubits=[qubit],\n", - " delays=np.linspace(\n", - " 1e-6, 2 * backend.properties().t2(qubit), 5, endpoint=True\n", - " ),\n", - " )\n", - " for qubit in qubits\n", - " ],\n", - " backend,\n", - " analysis=None,\n", - ")\n", - "\n", - "# Create readout experiments on all qubit in parallel\n", - "readout_exp = LocalReadoutError(qubits)\n", - "\n", - "# Create single-qubit RB experiments on all qubit in parallel\n", - "singleq_rb_exp = ParallelExperiment(\n", - " [\n", - " StandardRB(\n", - " physical_qubits=[qubit], lengths=[10, 100, 500], num_samples=10\n", - " )\n", - " for qubit in qubits\n", - " ],\n", - " backend,\n", - " analysis=None,\n", - ")\n", - "\n", - "# Create two-qubit RB experiments on the three layers of disjoint edges of the heavy-hex\n", - "twoq_rb_exp_batched = BatchExperiment(\n", - " [\n", - " ParallelExperiment(\n", - " [\n", - " StandardRB(\n", - " physical_qubits=pair,\n", - " lengths=[10, 50, 100],\n", - " num_samples=10,\n", - " )\n", - " for pair in layer\n", - " ],\n", - " backend,\n", - " analysis=None,\n", - " )\n", - " for layer in layered_coupling_map\n", - " ],\n", - " backend,\n", - " flatten_results=True,\n", - " analysis=None,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "39d1b87e-54c2-44fe-bd2d-0159467475a9", - "metadata": {}, - "source": [ - "### QPU properties over time\n", - "Looking at the reported QPU properties over time (we'll consider a single week below), we see how these can fluctuate on a scale of a single day. Small fluctuations can happen even within a day. In this scenario, the reported properties (updated once per day) will not accurately capture the current status of the QPU. Moreover, if a job is transpiled locally (using current reported properties) and submitted but executed only at a later time (minutes or days), it may run the risk of having used outdated properties for qubit selection in the transpilation step. This highlights the importance of having updated information about the QPU at execution time. First, let's retrieve the properties over a certain time range." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8bbb0de5-f450-4d8d-9508-3a979f25c994", - "metadata": {}, - "outputs": [], - "source": [ - "errors_list = []\n", - "for day_idx in range(10, 17):\n", - " calibrations_time = datetime(\n", - " year=2024, month=10, day=day_idx, hour=0, minute=0, second=0\n", - " )\n", - " targer_hist = backend.target_history(datetime=calibrations_time)\n", - "\n", - " t1_dict, t2_dict = {}, {}\n", - " for qubit in range(targer_hist.num_qubits):\n", - " t1_dict[qubit] = targer_hist.qubit_properties[qubit].t1\n", - " t2_dict[qubit] = targer_hist.qubit_properties[qubit].t2\n", - "\n", - " errors_dict = {\n", - " \"1q\": targer_hist[\"sx\"],\n", - " \"2q\": targer_hist[\"ecr\"],\n", - " \"spam\": targer_hist[\"measure\"],\n", - " \"t1\": t1_dict,\n", - " \"t2\": t2_dict,\n", - " }\n", - "\n", - " errors_list.append(errors_dict)" - ] - }, - { - "cell_type": "markdown", - "id": "0e1bdb14-dca9-4256-9ae5-e1f6acc93c42", - "metadata": {}, - "source": [ - "Then, let's plot the values" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b1914005-8f5c-4e72-91f8-304eefe77018", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(5, 1, figsize=(10, 20), sharex=False)\n", - "\n", - "\n", - "# Plot for T1 values\n", - "for qubit in range(targer_hist.num_qubits):\n", - " t1s = []\n", - " for errors_dict in errors_list:\n", - " t1_dict = errors_dict[\"t1\"]\n", - " t1s.append(t1_dict[qubit] / 1e-6)\n", - "\n", - " axs[0].plot(t1s)\n", - "\n", - "axs[0].set_title(\"T1\")\n", - "axs[0].set_ylabel(r\"Time ($\\mu s$)\")\n", - "axs[0].set_xlabel(\"Days\")\n", - "\n", - "# Plot for T2 values\n", - "for qubit in range(targer_hist.num_qubits):\n", - " t2s = []\n", - " for errors_dict in errors_list:\n", - " t2_dict = errors_dict[\"t2\"]\n", - " t2s.append(t2_dict[qubit] / 1e-6)\n", - "\n", - " axs[1].plot(t2s)\n", - "\n", - "axs[1].set_title(\"T2\")\n", - "axs[1].set_ylabel(r\"Time ($\\mu s$)\")\n", - "axs[1].set_xlabel(\"Days\")\n", - "\n", - "# Plot SPAM values\n", - "for qubit in range(targer_hist.num_qubits):\n", - " spams = []\n", - " for errors_dict in errors_list:\n", - " spam_dict = errors_dict[\"spam\"]\n", - " spams.append(spam_dict[tuple([qubit])].error)\n", - "\n", - " axs[2].plot(spams)\n", - "\n", - "axs[2].set_title(\"SPAM Errors\")\n", - "axs[2].set_ylabel(\"Error Rate\")\n", - "axs[2].set_xlabel(\"Days\")\n", - "\n", - "# Plot 1Q Gate Errors\n", - "for qubit in range(targer_hist.num_qubits):\n", - " oneq_gates = []\n", - " for errors_dict in errors_list:\n", - " oneq_gate_dict = errors_dict[\"1q\"]\n", - " oneq_gates.append(oneq_gate_dict[tuple([qubit])].error)\n", - "\n", - " axs[3].plot(oneq_gates)\n", - "\n", - "axs[3].set_title(\"1Q Gate Errors\")\n", - "axs[3].set_ylabel(\"Error Rate\")\n", - "axs[3].set_xlabel(\"Days\")\n", - "\n", - "# Plot 2Q Gate Errors\n", - "for pair in one_dir_coupling_map:\n", - " twoq_gates = []\n", - " for errors_dict in errors_list:\n", - " twoq_gate_dict = errors_dict[\"2q\"]\n", - " twoq_gates.append(twoq_gate_dict[pair].error)\n", - "\n", - " axs[4].plot(twoq_gates)\n", - "\n", - "axs[4].set_title(\"2Q Gate Errors\")\n", - "axs[4].set_ylabel(\"Error Rate\")\n", - "axs[4].set_xlabel(\"Days\")\n", - "\n", - "plt.subplots_adjust(hspace=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "fb1d269a-349a-4b73-9f5f-abfa5ed6b71e", - "metadata": {}, - "source": [ - "You can see that over several days some of the qubit properties can change considerably. This highlights the importance of having fresh information of the QPU status, to be able to select the best performing qubits for an experiment." - ] - }, - { - "cell_type": "markdown", - "id": "779527fd-02bc-41bd-88e3-70a17d99acb4", - "metadata": {}, - "source": [ - "## Step 2: Optimize problem for quantum hardware execution\n", - "No optimization of the circuits or operators is done in this tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "8cc1a29c-b5b4-4b6c-8cc9-5b7e90e51871", - "metadata": {}, - "source": [ - "## Step 3: Execute using Qiskit primitives" - ] - }, - { - "cell_type": "markdown", - "id": "e7bb078c-9dca-4215-aa93-55ec5761e81a", - "metadata": {}, - "source": [ - "### Execute a quantum circuit with default qubit selection\n", - "As a reference result of performance, we'll execute a quantum circuit on a QPU by using the default qubits, which are the qubits selected with the requested backend properties. We will use `optimization_level = 3`. This setting includes the most advanced transpilation optimization, and uses target properties (like operation errors) to select the best performing qubits for execution." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4250450d-6596-4473-973a-211fd4578289", - "metadata": {}, - "outputs": [], - "source": [ - "pm = generate_preset_pass_manager(target=backend.target, optimization_level=3)\n", - "isa_circuits = pm.run(circuits)\n", - "initial_qubits = [\n", - " [\n", - " idx\n", - " for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", - " if qb._register.name != \"ancilla\"\n", - " ]\n", - " for circuit in isa_circuits\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "0eb66644-5588-4724-8de2-e1d8af0f0b6c", - "metadata": {}, - "source": [ - "### Execute a quantum circuit with real-time qubit selection\n", - "\n", - "In this section, we'll investigate the importance of having updated information on qubit properties of the QPU for optimal results. First, we'll carry out a full suite of QPU characterization experiments ($T_1$, $T_2$, SPAM, single-qubit RB and two-qubit RB), which we can then use to update the backend properties. This allows the pass manager to select qubits for the execution based on fresh information about the QPU, possibly improving execution performances. Second, we execute the Bell pair circuit and we compare the fidelity obtained after selecting the qubits with update QPU properties to the fidelity we obtained before when we use the default reported properties for qubit selection." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a260f4ee-8307-431c-bc9b-8cf333a42b91", - "metadata": {}, - "outputs": [ - { - "ename": "RequestsApiError", - "evalue": "\"('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\"", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mRemoteDisconnected\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:787\u001b[39m, in \u001b[36mHTTPConnectionPool.urlopen\u001b[39m\u001b[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[39m\n\u001b[32m 786\u001b[39m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m787\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 788\u001b[39m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 789\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 790\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 791\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 792\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 793\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 794\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 795\u001b[39m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 796\u001b[39m \u001b[43m \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 797\u001b[39m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 798\u001b[39m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 799\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 800\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 802\u001b[39m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:534\u001b[39m, in \u001b[36mHTTPConnectionPool._make_request\u001b[39m\u001b[34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[39m\n\u001b[32m 533\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m534\u001b[39m response = \u001b[43mconn\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connection.py:565\u001b[39m, in \u001b[36mHTTPConnection.getresponse\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 564\u001b[39m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m565\u001b[39m httplib_response = \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 567\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:1423\u001b[39m, in \u001b[36mHTTPConnection.getresponse\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1422\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1423\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1424\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:331\u001b[39m, in \u001b[36mHTTPResponse.begin\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m version, status, reason = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m status != CONTINUE:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:300\u001b[39m, in \u001b[36mHTTPResponse._read_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 297\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m line:\n\u001b[32m 298\u001b[39m \u001b[38;5;66;03m# Presumably, the server closed the connection before\u001b[39;00m\n\u001b[32m 299\u001b[39m \u001b[38;5;66;03m# sending a valid response.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RemoteDisconnected(\u001b[33m\"\u001b[39m\u001b[33mRemote end closed connection without\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 301\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m response\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 302\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[31mRemoteDisconnected\u001b[39m: Remote end closed connection without response", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[31mProtocolError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\adapters.py:667\u001b[39m, in \u001b[36mHTTPAdapter.send\u001b[39m\u001b[34m(self, request, stream, timeout, verify, cert, proxies)\u001b[39m\n\u001b[32m 666\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m667\u001b[39m resp = \u001b[43mconn\u001b[49m\u001b[43m.\u001b[49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 668\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 669\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m=\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 670\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 671\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m.\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 672\u001b[39m \u001b[43m \u001b[49m\u001b[43mredirect\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 673\u001b[39m \u001b[43m \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 674\u001b[39m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 675\u001b[39m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 676\u001b[39m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 677\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 678\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 679\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 681\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:841\u001b[39m, in \u001b[36mHTTPConnectionPool.urlopen\u001b[39m\u001b[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[39m\n\u001b[32m 839\u001b[39m new_e = ProtocolError(\u001b[33m\"\u001b[39m\u001b[33mConnection aborted.\u001b[39m\u001b[33m\"\u001b[39m, new_e)\n\u001b[32m--> \u001b[39m\u001b[32m841\u001b[39m retries = \u001b[43mretries\u001b[49m\u001b[43m.\u001b[49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 842\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnew_e\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m=\u001b[49m\u001b[43msys\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m 843\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 844\u001b[39m retries.sleep()\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:125\u001b[39m, in \u001b[36mPostForcelistRetry.increment\u001b[39m\u001b[34m(self, method, url, response, error, _pool, _stacktrace)\u001b[39m\n\u001b[32m 116\u001b[39m logger.debug(\n\u001b[32m 117\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mRetrying method=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, url=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, status=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, error=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, data=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, headers=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m,\n\u001b[32m 118\u001b[39m method,\n\u001b[32m (...)\u001b[39m\u001b[32m 123\u001b[39m headers,\n\u001b[32m 124\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m125\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 126\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 127\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m=\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 128\u001b[39m \u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 129\u001b[39m \u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m=\u001b[49m\u001b[43merror\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 130\u001b[39m \u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 131\u001b[39m \u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 132\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\util\\retry.py:474\u001b[39m, in \u001b[36mRetry.increment\u001b[39m\u001b[34m(self, method, url, response, error, _pool, _stacktrace)\u001b[39m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m read \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_method_retryable(method):\n\u001b[32m--> \u001b[39m\u001b[32m474\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43merror\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 475\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m read \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\util\\util.py:38\u001b[39m, in \u001b[36mreraise\u001b[39m\u001b[34m(tp, value, tb)\u001b[39m\n\u001b[32m 37\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m value.__traceback__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tb:\n\u001b[32m---> \u001b[39m\u001b[32m38\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m value.with_traceback(tb)\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m value\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:787\u001b[39m, in \u001b[36mHTTPConnectionPool.urlopen\u001b[39m\u001b[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[39m\n\u001b[32m 786\u001b[39m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m787\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 788\u001b[39m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 789\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 790\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 791\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 792\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 793\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 794\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 795\u001b[39m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 796\u001b[39m \u001b[43m \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 797\u001b[39m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 798\u001b[39m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 799\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 800\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 802\u001b[39m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connectionpool.py:534\u001b[39m, in \u001b[36mHTTPConnectionPool._make_request\u001b[39m\u001b[34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[39m\n\u001b[32m 533\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m534\u001b[39m response = \u001b[43mconn\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\urllib3\\connection.py:565\u001b[39m, in \u001b[36mHTTPConnection.getresponse\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 564\u001b[39m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m565\u001b[39m httplib_response = \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 567\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:1423\u001b[39m, in \u001b[36mHTTPConnection.getresponse\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1422\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1423\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1424\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:331\u001b[39m, in \u001b[36mHTTPResponse.begin\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 330\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m version, status, reason = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 332\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m status != CONTINUE:\n", - "\u001b[36mFile \u001b[39m\u001b[32mC:\\Python312\\Lib\\http\\client.py:300\u001b[39m, in \u001b[36mHTTPResponse._read_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 297\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m line:\n\u001b[32m 298\u001b[39m \u001b[38;5;66;03m# Presumably, the server closed the connection before\u001b[39;00m\n\u001b[32m 299\u001b[39m \u001b[38;5;66;03m# sending a valid response.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RemoteDisconnected(\u001b[33m\"\u001b[39m\u001b[33mRemote end closed connection without\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 301\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m response\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 302\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[31mProtocolError\u001b[39m: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[31mConnectionError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:327\u001b[39m, in \u001b[36mRetrySession.request\u001b[39m\u001b[34m(self, method, url, bare, **kwargs)\u001b[39m\n\u001b[32m 326\u001b[39m \u001b[38;5;28mself\u001b[39m._log_request_info(final_url, method, kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m327\u001b[39m response = \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfinal_url\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 328\u001b[39m response.raise_for_status()\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\sessions.py:589\u001b[39m, in \u001b[36mSession.request\u001b[39m\u001b[34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[39m\n\u001b[32m 588\u001b[39m send_kwargs.update(settings)\n\u001b[32m--> \u001b[39m\u001b[32m589\u001b[39m resp = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 591\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\sessions.py:703\u001b[39m, in \u001b[36mSession.send\u001b[39m\u001b[34m(self, request, **kwargs)\u001b[39m\n\u001b[32m 702\u001b[39m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m703\u001b[39m r = \u001b[43madapter\u001b[49m\u001b[43m.\u001b[49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 705\u001b[39m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\adapters.py:682\u001b[39m, in \u001b[36mHTTPAdapter.send\u001b[39m\u001b[34m(self, request, stream, timeout, verify, cert, proxies)\u001b[39m\n\u001b[32m 681\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m--> \u001b[39m\u001b[32m682\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request=request)\n\u001b[32m 684\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m MaxRetryError \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "\u001b[31mConnectionError\u001b[39m: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[31mRequestsApiError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m batches_exp = BatchExperiment(batches, backend) \u001b[38;5;66;03m# , analysis=None)\u001b[39;00m\n\u001b[32m 4\u001b[39m run_options = {\u001b[33m\"\u001b[39m\u001b[33mshots\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m1e3\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mdynamic\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m}\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mSession\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[32m 7\u001b[39m sampler = SamplerV2(mode=session)\n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m# Run characterization experiments\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:132\u001b[39m, in \u001b[36mSession.__init__\u001b[39m\u001b[34m(self, backend, max_time, create_new)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28mself\u001b[39m._instance = \u001b[38;5;28mself\u001b[39m._backend._instance\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.configuration().simulator:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28mself\u001b[39m._session_id = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_create_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcreate_new\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcreate_new\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:137\u001b[39m, in \u001b[36mSession._create_session\u001b[39m\u001b[34m(self, create_new)\u001b[39m\n\u001b[32m 135\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Create a session.\"\"\"\u001b[39;00m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m._service, QiskitRuntimeService) \u001b[38;5;129;01mand\u001b[39;00m create_new:\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m session = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_service\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_api_client\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_instance\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate_session\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 138\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_instance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_max_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_service\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchannel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdedicated\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 139\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 140\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m session.get(\u001b[33m\"\u001b[39m\u001b[33mid\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 141\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\clients\\runtime.py:237\u001b[39m, in \u001b[36mRuntimeClient.create_session\u001b[39m\u001b[34m(self, backend, instance, max_time, channel, mode)\u001b[39m\n\u001b[32m 224\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate_session\u001b[39m(\n\u001b[32m 225\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 226\u001b[39m backend: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m (...)\u001b[39m\u001b[32m 230\u001b[39m mode: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 231\u001b[39m ) -> Dict[\u001b[38;5;28mstr\u001b[39m, Any]:\n\u001b[32m 232\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Create a session.\u001b[39;00m\n\u001b[32m 233\u001b[39m \n\u001b[32m 234\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m 235\u001b[39m \u001b[33;03m mode: Execution mode.\u001b[39;00m\n\u001b[32m 236\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m237\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_api\u001b[49m\u001b[43m.\u001b[49m\u001b[43mruntime_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43msession_id\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minstance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\n\u001b[32m 239\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\rest\\runtime_session.py:65\u001b[39m, in \u001b[36mRuntimeSession.create\u001b[39m\u001b[34m(self, backend, instance, max_time, channel, mode)\u001b[39m\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 64\u001b[39m payload[\u001b[33m\"\u001b[39m\u001b[33mmax_ttl\u001b[39m\u001b[33m\"\u001b[39m] = max_time \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m65\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msession\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpayload\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_HEADER_JSON_CONTENT\u001b[49m\u001b[43m)\u001b[49m.json()\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\requests\\sessions.py:637\u001b[39m, in \u001b[36mSession.post\u001b[39m\u001b[34m(self, url, data, json, **kwargs)\u001b[39m\n\u001b[32m 626\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\u001b[38;5;28mself\u001b[39m, url, data=\u001b[38;5;28;01mNone\u001b[39;00m, json=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 627\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33mr\u001b[39m\u001b[33;03m\"\"\"Sends a POST request. Returns :class:`Response` object.\u001b[39;00m\n\u001b[32m 628\u001b[39m \n\u001b[32m 629\u001b[39m \u001b[33;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 634\u001b[39m \u001b[33;03m :rtype: requests.Response\u001b[39;00m\n\u001b[32m 635\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m637\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPOST\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\1docs\\.venv\\Lib\\site-packages\\qiskit_ibm_runtime\\api\\session.py:356\u001b[39m, in \u001b[36mRetrySession.request\u001b[39m\u001b[34m(self, method, url, bare, **kwargs)\u001b[39m\n\u001b[32m 350\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m status_code == \u001b[32m503\u001b[39m: \u001b[38;5;66;03m# Planned maintenance outage\u001b[39;00m\n\u001b[32m 351\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RequestsApiError(\n\u001b[32m 352\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mUnexpected response received from server. Please check if the service \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mis in maintenance mode \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 354\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mhttps://docs.quantum.ibm.com/announcements/service-alerts \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmessage\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 355\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m356\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m RequestsApiError(message, status_code) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mex\u001b[39;00m\n\u001b[32m 358\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m response\n", - "\u001b[31mRequestsApiError\u001b[39m: \"('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\"" - ] - } - ], - "source": [ - "# Prepare characterization experiments\n", - "batches = [t1_exp, t2_exp, readout_exp, singleq_rb_exp, twoq_rb_exp_batched]\n", - "batches_exp = BatchExperiment(batches, backend) # , analysis=None)\n", - "run_options = {\"shots\": 1e3, \"dynamic\": False}\n", - "\n", - "with Session(backend=backend) as session:\n", - " sampler = SamplerV2(mode=session)\n", - "\n", - " # Run characterization experiments\n", - " batches_exp_data = batches_exp.run(\n", - " sampler=sampler, **run_options\n", - " ).block_for_results()\n", - "\n", - " EPG_sx_result_list = batches_exp_data.analysis_results(\"EPG_sx\")\n", - " EPG_sx_result_q_indices = [\n", - " result.device_components.index for result in EPG_sx_result_list\n", - " ]\n", - " EPG_x_result_list = batches_exp_data.analysis_results(\"EPG_x\")\n", - " EPG_x_result_q_indices = [\n", - " result.device_components.index for result in EPG_x_result_list\n", - " ]\n", - " T1_result_list = batches_exp_data.analysis_results(\"T1\")\n", - " T1_result_q_indices = [\n", - " result.device_components.index for result in T1_result_list\n", - " ]\n", - "\n", - " T2_result_list = batches_exp_data.analysis_results(\"T2\")\n", - " T2_result_q_indices = [\n", - " result.device_components.index for result in T2_result_list\n", - " ]\n", - "\n", - " Readout_result_list = batches_exp_data.analysis_results(\n", - " \"Local Readout Mitigator\"\n", - " )\n", - "\n", - " EPG_ecr_result_list = batches_exp_data.analysis_results(\"EPG_ecr\")\n", - "\n", - " # Update target properties\n", - " target = copy.deepcopy(backend.target)\n", - " for i in range(target.num_qubits - 1):\n", - " qarg = (i,)\n", - "\n", - " if qarg in EPG_sx_result_q_indices:\n", - " target.update_instruction_properties(\n", - " instruction=\"sx\",\n", - " qargs=qarg,\n", - " properties=InstructionProperties(\n", - " error=EPG_sx_result_list[i].value.nominal_value\n", - " ),\n", - " )\n", - " if qarg in EPG_x_result_q_indices:\n", - " target.update_instruction_properties(\n", - " instruction=\"x\",\n", - " qargs=qarg,\n", - " properties=InstructionProperties(\n", - " error=EPG_x_result_list[i].value.nominal_value\n", - " ),\n", - " )\n", - "\n", - " err_mat = Readout_result_list.value.assignment_matrix(i)\n", - " readout_assignment_error = (\n", - " err_mat[0, 1] + err_mat[1, 0]\n", - " ) / 2 # average readout error\n", - " target.update_instruction_properties(\n", - " instruction=\"measure\",\n", - " qargs=qarg,\n", - " properties=InstructionProperties(error=readout_assignment_error),\n", - " )\n", - "\n", - " if qarg in T1_result_q_indices:\n", - " target.qubit_properties[i].t1 = T1_result_list[\n", - " i\n", - " ].value.nominal_value\n", - " if qarg in T2_result_q_indices:\n", - " target.qubit_properties[i].t2 = T2_result_list[\n", - " i\n", - " ].value.nominal_value\n", - "\n", - " for pair_idx, pair in enumerate(flattened_layered_coupling_map):\n", - " qarg = tuple(pair)\n", - " try:\n", - " target.update_instruction_properties(\n", - " instruction=\"ecr\",\n", - " qargs=qarg,\n", - " properties=InstructionProperties(\n", - " error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", - " ),\n", - " )\n", - " except:\n", - " target.update_instruction_properties(\n", - " instruction=\"ecr\",\n", - " qargs=qarg[::-1],\n", - " properties=InstructionProperties(\n", - " error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", - " ),\n", - " )\n", - "\n", - " # transpile circuits to updated target\n", - " pm = generate_preset_pass_manager(target=target, optimization_level=3)\n", - " isa_circuit_updated = pm.run(circuits)\n", - " updated_qubits = [\n", - " [\n", - " idx\n", - " for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", - " if qb._register.name != \"ancilla\"\n", - " ]\n", - " for circuit in isa_circuit_updated\n", - " ]\n", - "\n", - " n_trials = 3 # run multiple trials to see variations\n", - "\n", - " # interleave circuits\n", - " interleaved_circuits = []\n", - " for original_circuit, updated_circuit in zip(\n", - " isa_circuits, isa_circuit_updated\n", - " ):\n", - " interleaved_circuits.append(original_circuit)\n", - " interleaved_circuits.append(updated_circuit)\n", - "\n", - " # Run circuits\n", - " # Set simple error suppression/mitigation options\n", - " sampler.options.dynamical_decoupling.enable = True\n", - " sampler.options.dynamical_decoupling.sequence_type = \"XY4\"\n", - "\n", - " job_interleaved = sampler.run(interleaved_circuits * n_trials)" - ] - }, - { - "cell_type": "markdown", - "id": "f1691250-bf7c-4b88-ad98-3fc2c891923b", - "metadata": {}, - "source": [ - "## Step 4: Post-process and return result in desired classical format\n", - "\n", - "Finally, let's compare the fidelity of the Bell state obtained in the two different settings:\n", - "- `original`, that is with the default qubits chosen by the transpiler based on reported properties of the backend.\n", - "- `updated`, that is with the qubits chosen based on updated properties of the backend after characterization experiments have run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a670d0f2-3908-4e69-9945-8128db30c36b", - "metadata": {}, - "outputs": [], - "source": [ - "results = job_interleaved.result()\n", - "all_fidelity_list, all_fidelity_updated_list = [], []\n", - "for exp_idx in range(n_trials):\n", - " fidelity_list, fidelity_updated_list = [], []\n", - "\n", - " for idx, num_qubits in enumerate(num_qubits_list):\n", - " pub_result_original = results[\n", - " 2 * exp_idx * len(num_qubits_list) + 2 * idx\n", - " ]\n", - " pub_result_updated = results[\n", - " 2 * exp_idx * len(num_qubits_list) + 2 * idx + 1\n", - " ]\n", - "\n", - " fid = hellinger_fidelity(\n", - " ideal_dist, pub_result_original.data.c.get_counts()\n", - " )\n", - " fidelity_list.append(fid)\n", - "\n", - " fid_up = hellinger_fidelity(\n", - " ideal_dist, pub_result_updated.data.c.get_counts()\n", - " )\n", - " fidelity_updated_list.append(fid_up)\n", - " all_fidelity_list.append(fidelity_list)\n", - " all_fidelity_updated_list.append(fidelity_updated_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2537f016-484d-4273-8f32-e5ee63598b77", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(8, 6))\n", - "plt.errorbar(\n", - " num_qubits_list,\n", - " np.mean(all_fidelity_list, axis=0),\n", - " yerr=np.std(all_fidelity_list, axis=0),\n", - " fmt=\"o-.\",\n", - " label=\"original\",\n", - " color=\"b\",\n", - ")\n", - "# plt.plot(num_qubits_list, fidelity_list, '-.')\n", - "plt.errorbar(\n", - " num_qubits_list,\n", - " np.mean(all_fidelity_updated_list, axis=0),\n", - " yerr=np.std(all_fidelity_updated_list, axis=0),\n", - " fmt=\"o-.\",\n", - " label=\"updated\",\n", - " color=\"r\",\n", - ")\n", - "# plt.plot(num_qubits_list, fidelity_updated_list, '-.')\n", - "plt.xlabel(\"Chain length\")\n", - "plt.xticks(num_qubits_list)\n", - "plt.ylabel(\"Fidelity\")\n", - "plt.title(\"Bell pair fidelity at the edge of N-qubits chain\")\n", - "plt.legend()\n", - "plt.grid(\n", - " alpha=0.2,\n", - " linestyle=\"-.\",\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9a7e3b2b-026d-46ae-9f5c-62e97769c1c0", - "metadata": {}, - "source": [ - "With increasing chain length, and thus less freedom to choose physical qubits, the fact that we have updated information about qubit properties becomes less substantial, with there being no difference when all of the qubits are used." - ] - }, - { - "cell_type": "markdown", - "id": "b8c3bc0f-9cbe-4842-ac9a-eded8edfdf74", - "metadata": {}, - "source": [ - "\n", - "Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "bf568284-ae97-4c19-8408-714e7d97b0c3", - "metadata": {}, - "source": [ - "## Tutorial survey\n", - "\n", - "Please take this short survey to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", - "\n", - "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_0w6FZ9QrWkKfTQq)" - ] - } - ], - "metadata": { - "description": "Run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU.", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - }, - "title": "Real-time benchmarking for qubit selection\n" - }, - "nbformat": 4, - "nbformat_minor": 4 +"cells": [ +{ +"cell_type": "markdown", +"id": "f69d5853-e815-4754-894d-833017217572", +"metadata": {}, +"source": [ +"# Real-time benchmarking for qubit selection\n", +"*Usage estimate: 4 minutes on an Eagle r2 processor (NOTE: This is an estimate only. Your runtime might vary.)*" +] +}, +{ +"cell_type": "code", +"execution_count": null, +"id": "2797ee64-644f-40c9-8327-094867333a69", +"metadata": { +"tags": [ +"remove-cell" +] +}, +"outputs": [], +"source": [ +"# This cell is hidden from users – it disables some lint rules\n", +"# ruff: noqa: E722" +] +}, +{ +"cell_type": "markdown", +"id": "500dc8c9-a5d8-4ef1-932f-30e400d6bdde", +"metadata": {}, +"source": [ +"## Background\n", +"This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", +"\n", +"The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine." +] +}, +{ +"cell_type": "markdown", +"id": "0babd413-d91f-4fd7-a0f5-bb46ae0bbf5b", +"metadata": {}, +"source": [ +"## Requirements\n", +"Before starting this tutorial, be sure you have the following installed:\n", +"\n", +"- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", +"- Qiskit Runtime 0.29 or later ( `pip install qiskit-ibm-runtime` )\n", +"- Qiskit Experiments 0.7 or later ( `pip install qiskit-experiments` )\n", +"- Rustworkx graph library (`pip install rustworkx`)" +] +}, +{ +"cell_type": "markdown", +"id": "3df52d5f-806a-4846-849e-633706a96d0b", +"metadata": {}, +"source": [ +"## Setup" +] +}, +{ +"cell_type": "code", +"execution_count": 1, +"id": "4766c18a-ba45-456b-8b78-6b6f1d214586", +"metadata": {}, +"outputs": [], +"source": [ +"from qiskit_ibm_runtime import SamplerV2\n", +"from qiskit.transpiler import generate_preset_pass_manager\n", +"from qiskit.quantum_info import hellinger_fidelity\n", +"from qiskit.transpiler import InstructionProperties\n", +"\n", +"\n", +"from qiskit_experiments.library import (\n", +" T1,\n", +" T2Hahn,\n", +" LocalReadoutError,\n", +" StandardRB,\n", +")\n", +"from qiskit_experiments.framework import BatchExperiment, ParallelExperiment\n", +"\n", +"from qiskit_ibm_runtime import QiskitRuntimeService\n", +"from qiskit_ibm_runtime import Session\n", +"\n", +"from datetime import datetime\n", +"from collections import defaultdict\n", +"import numpy as np\n", +"import rustworkx\n", +"import matplotlib.pyplot as plt\n", +"import copy" +] +}, +{ +"cell_type": "markdown", +"id": "65d49ed2-0581-486e-9031-a08fa9bace92", +"metadata": {}, +"source": [ +"## Step 1: Map classical inputs to a quantum problem\n", +"\n", +"To benchmark the difference in performance, we consider a circuit that prepares a Bell state across a linear chain of varying length. The fidelity of the Bell state at the ends of the chain is measured." +] +}, +{ +"cell_type": "code", +"execution_count": null, +"id": "64c25da9-a728-4ae4-a377-3078a1dc618d", +"metadata": {}, +"outputs": [ +{ +"data": { +"text/plain": [ +"\"Output" +] +}, +"metadata": {}, +"output_type": "display_data" +} +], +"source": [ +"from qiskit import QuantumCircuit\n", +"\n", +"ideal_dist = {\"00\": 0.5, \"11\": 0.5}\n", +"\n", +"num_qubits_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 127]\n", +"circuits = []\n", +"for num_qubits in num_qubits_list:\n", +" circuit = QuantumCircuit(num_qubits, 2)\n", +" circuit.h(0)\n", +" for i in range(num_qubits - 1):\n", +" circuit.cx(i, i + 1)\n", +" circuit.barrier()\n", +" circuit.measure(0, 0)\n", +" circuit.measure(num_qubits - 1, 1)\n", +" circuits.append(circuit)\n", +"\n", +"circuits[-1].draw(output=\"mpl\", style=\"clifford\", fold=-1)" +] +}, +{ +"cell_type": "markdown", +"id": "16948f21-a39b-4444-bf02-5f81331825c4", +"metadata": {}, +"source": [ +"### Setting up backend and coupling map" +] +}, +{ +"cell_type": "code", +"execution_count": null, +"id": "f968acca-9131-4f5d-aa74-70befcdda4f5", +"metadata": {}, +"outputs": [], +"source": [ +"# To run on hardware, select the backend with the fewest number of jobs in the queue\n", +"service = QiskitRuntimeService()\n", +"backend = service.least_busy(\n", +" operational=True, simulator=False, min_num_qubits=127\n", +")\n", +"\n", +"qubits = list(range(backend.num_qubits))" +] +}, +{ +"cell_type": "code", +"execution_count": 3, +"id": "62b36ded-ab4e-414e-b146-ff522786a871", +"metadata": {}, +"outputs": [], +"source": [ +"coupling_graph = backend.coupling_map.graph.to_undirected(multigraph=False)\n", +"\n", +"# Get unidirectional coupling map\n", +"one_dir_coupling_map = coupling_graph.edge_list()\n", +"\n", +"# Get layered coupling map\n", +"edge_coloring = rustworkx.graph_bipartite_edge_color(coupling_graph)\n", +"layered_coupling_map = defaultdict(list)\n", +"for edge_idx, color in edge_coloring.items():\n", +" layered_coupling_map[color].append(\n", +" coupling_graph.get_edge_endpoints_by_index(edge_idx)\n", +" )\n", +"layered_coupling_map = [\n", +" sorted(layered_coupling_map[i])\n", +" for i in sorted(layered_coupling_map.keys())\n", +"]\n", +"\n", +"flattened_layered_coupling_map = []\n", +"for layer in layered_coupling_map:\n", +" flattened_layered_coupling_map += layer" +] +}, +{ +"cell_type": "markdown", +"id": "875117af-8a2c-4aea-92d9-ffeee7ff37d5", +"metadata": {}, +"source": [ +"### Characterization experiments\n", +"\n", +"A series of experiments is used to characterize the main properties of the qubits in a QPU. These are $T_1$, $T_2$, readout error, and single-qubit and two-qubit gate error. We'll briefly summarize what these properties are and refer to experiments in the [`qiskit-experiments`](https://qiskit-community.github.io/qiskit-experiments/index.html) package that are used to characterize them.\n", +"\n", +"#### T1\n", +"$T_1$ is the characteristic time it takes for an excited qubit to fall to the ground state due to amplitude-damping decoherence processes. In a [$T_1$ experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t1.html), we measure an excited qubit after a delay. The larger the delay time is, the more\n", +"likely is the qubit to fall to the ground state. The goal of the\n", +"experiment is to characterize the decay rate of the qubit towards the\n", +"ground state.\n", +"\n", +"#### T2\n", +"$T_2$ represents the amount of time required for a single qubit's Bloch\n", +"vector projection on the XY plane to fall to approximately 37% ($\\frac{1}{e}$) of\n", +"its initial amplitude due to dephasing decoherence processes. In a [$T_2$ Hahn Echo experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t2hahn.html), we can estimate the rate of this decay.\n", +"\n", +"#### State preparation and measurement (SPAM) error characterization\n", +"In a [SPAM-error characterization experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/measurement/readout_mitigation.html) qubit are prepared in a certain state ($\\vert 0 \\rangle$ or $\\vert 1 \\rangle$) and measured. The probability of measuring a state different than the one prepared then gives the probability of the error.\n", +"\n", +"#### Single-qubit and two-qubit randomized benchmarking\n", +"[Randomized benchmarking](https://qiskit-community.github.io/qiskit-experiments/manuals/verification/randomized_benchmarking.html) is a popular protocol for characterizing the error rate of\n", +"quantum processors. An RB experiment consists of the generation of random Clifford\n", +"circuits on the given qubits such that the unitary computed by the circuits is the\n", +"identity. After running the circuits, the number of shots resulting in an error (that is, an output different from the ground state) are counted, and from this data one can infer error estimates for the quantum device, by calculating the Error Per Clifford." +] +}, +{ +"cell_type": "code", +"execution_count": 4, +"id": "9d57c42d-7a91-4e79-bc6c-6e579da929f8", +"metadata": {}, +"outputs": [], +"source": [ +"# Create T1 experiments on all qubit in parallel\n", +"t1_exp = ParallelExperiment(\n", +" [\n", +" T1(\n", +" physical_qubits=[qubit],\n", +" delays=np.linspace(\n", +" 1e-6, 2 * backend.properties().t1(qubit), 5, endpoint=True\n", +" ),\n", +" )\n", +" for qubit in qubits\n", +" ],\n", +" backend,\n", +" analysis=None,\n", +")\n", +"\n", +"# Create T2-Hahn experiments on all qubit in parallel\n", +"t2_exp = ParallelExperiment(\n", +" [\n", +" T2Hahn(\n", +" physical_qubits=[qubit],\n", +" delays=np.linspace(\n", +" 1e-6, 2 * backend.properties().t2(qubit), 5, endpoint=True\n", +" ),\n", +" )\n", +" for qubit in qubits\n", +" ],\n", +" backend,\n", +" analysis=None,\n", +")\n", +"\n", +"# Create readout experiments on all qubit in parallel\n", +"readout_exp = LocalReadoutError(qubits)\n", +"\n", +"# Create single-qubit RB experiments on all qubit in parallel\n", +"singleq_rb_exp = ParallelExperiment(\n", +" [\n", +" StandardRB(\n", +" physical_qubits=[qubit], lengths=[10, 100, 500], num_samples=10\n", +" )\n", +" for qubit in qubits\n", +" ],\n", +" backend,\n", +" analysis=None,\n", +")\n", +"\n", +"# Create two-qubit RB experiments on the three layers of disjoint edges of the heavy-hex\n", +"twoq_rb_exp_batched = BatchExperiment(\n", +" [\n", +" ParallelExperiment(\n", +" [\n", +" StandardRB(\n", +" physical_qubits=pair,\n", +" lengths=[10, 50, 100],\n", +" num_samples=10,\n", +" )\n", +" for pair in layer\n", +" ],\n", +" backend,\n", +" analysis=None,\n", +" )\n", +" for layer in layered_coupling_map\n", +" ],\n", +" backend,\n", +" flatten_results=True,\n", +" analysis=None,\n", +")" +] +}, +{ +"cell_type": "markdown", +"id": "cad4f8d3-c2d5-4bb5-92be-432a4573e14a", +"metadata": {}, +"source": [ +"### QPU properties over time\n", +"Looking at the reported QPU properties over time (we'll consider a single week below), we see how these can fluctuate on a scale of a single day. Small fluctuations can happen even within a day. In this scenario, the reported properties (updated once per day) will not accurately capture the current status of the QPU. Moreover, if a job is transpiled locally (using current reported properties) and submitted but executed only at a later time (minutes or days), it may run the risk of having used outdated properties for qubit selection in the transpilation step. This highlights the importance of having updated information about the QPU at execution time. First, let's retrieve the properties over a certain time range." +] +}, +{ +"cell_type": "code", +"execution_count": 4, +"id": "af1b6722-e77b-436a-bbcd-f272b95bb28c", +"metadata": {}, +"outputs": [], +"source": [ +"errors_list = []\n", +"for day_idx in range(10, 17):\n", +" calibrations_time = datetime(\n", +" year=2024, month=10, day=day_idx, hour=0, minute=0, second=0\n", +" )\n", +" targer_hist = backend.target_history(datetime=calibrations_time)\n", +"\n", +" t1_dict, t2_dict = {}, {}\n", +" for qubit in range(targer_hist.num_qubits):\n", +" t1_dict[qubit] = targer_hist.qubit_properties[qubit].t1\n", +" t2_dict[qubit] = targer_hist.qubit_properties[qubit].t2\n", +"\n", +" errors_dict = {\n", +" \"1q\": targer_hist[\"sx\"],\n", +" \"2q\": targer_hist[\"ecr\"],\n", +" \"spam\": targer_hist[\"measure\"],\n", +" \"t1\": t1_dict,\n", +" \"t2\": t2_dict,\n", +" }\n", +"\n", +" errors_list.append(errors_dict)" +] +}, +{ +"cell_type": "markdown", +"id": "ac75df1b-f689-475c-94a2-d70d85b1f8ca", +"metadata": {}, +"source": [ +"Then, let's plot the values" +] +}, +{ +"cell_type": "code", +"execution_count": 6, +"id": "e0ba509d-e0e0-438b-aedf-5e01919c7d4f", +"metadata": {}, +"outputs": [ +{ +"data": { +"text/plain": [ +"\"Output" +] +}, +"metadata": {}, +"output_type": "display_data" +} +], +"source": [ +"fig, axs = plt.subplots(5, 1, figsize=(10, 20), sharex=False)\n", +"\n", +"\n", +"# Plot for T1 values\n", +"for qubit in range(targer_hist.num_qubits):\n", +" t1s = []\n", +" for errors_dict in errors_list:\n", +" t1_dict = errors_dict[\"t1\"]\n", +" t1s.append(t1_dict[qubit] / 1e-6)\n", +"\n", +" axs[0].plot(t1s)\n", +"\n", +"axs[0].set_title(\"T1\")\n", +"axs[0].set_ylabel(r\"Time ($\\mu s$)\")\n", +"axs[0].set_xlabel(\"Days\")\n", +"\n", +"# Plot for T2 values\n", +"for qubit in range(targer_hist.num_qubits):\n", +" t2s = []\n", +" for errors_dict in errors_list:\n", +" t2_dict = errors_dict[\"t2\"]\n", +" t2s.append(t2_dict[qubit] / 1e-6)\n", +"\n", +" axs[1].plot(t2s)\n", +"\n", +"axs[1].set_title(\"T2\")\n", +"axs[1].set_ylabel(r\"Time ($\\mu s$)\")\n", +"axs[1].set_xlabel(\"Days\")\n", +"\n", +"# Plot SPAM values\n", +"for qubit in range(targer_hist.num_qubits):\n", +" spams = []\n", +" for errors_dict in errors_list:\n", +" spam_dict = errors_dict[\"spam\"]\n", +" spams.append(spam_dict[tuple([qubit])].error)\n", +"\n", +" axs[2].plot(spams)\n", +"\n", +"axs[2].set_title(\"SPAM Errors\")\n", +"axs[2].set_ylabel(\"Error Rate\")\n", +"axs[2].set_xlabel(\"Days\")\n", +"\n", +"# Plot 1Q Gate Errors\n", +"for qubit in range(targer_hist.num_qubits):\n", +" oneq_gates = []\n", +" for errors_dict in errors_list:\n", +" oneq_gate_dict = errors_dict[\"1q\"]\n", +" oneq_gates.append(oneq_gate_dict[tuple([qubit])].error)\n", +"\n", +" axs[3].plot(oneq_gates)\n", +"\n", +"axs[3].set_title(\"1Q Gate Errors\")\n", +"axs[3].set_ylabel(\"Error Rate\")\n", +"axs[3].set_xlabel(\"Days\")\n", +"\n", +"# Plot 2Q Gate Errors\n", +"for pair in one_dir_coupling_map:\n", +" twoq_gates = []\n", +" for errors_dict in errors_list:\n", +" twoq_gate_dict = errors_dict[\"2q\"]\n", +" twoq_gates.append(twoq_gate_dict[pair].error)\n", +"\n", +" axs[4].plot(twoq_gates)\n", +"\n", +"axs[4].set_title(\"2Q Gate Errors\")\n", +"axs[4].set_ylabel(\"Error Rate\")\n", +"axs[4].set_xlabel(\"Days\")\n", +"\n", +"plt.subplots_adjust(hspace=0.5)\n", +"plt.show()" +] +}, +{ +"cell_type": "markdown", +"id": "a3343934-9273-457c-900b-2004b3aa9d0c", +"metadata": {}, +"source": [ +"You can see that over several days some of the qubit properties can change considerably. This highlights the importance of having fresh information of the QPU status, to be able to select the best performing qubits for an experiment." +] +}, +{ +"cell_type": "markdown", +"id": "0e53f0a5-7713-4915-ae47-c1aa0a4f17cd", +"metadata": {}, +"source": [ +"## Step 2: Optimize problem for quantum hardware execution\n", +"No optimization of the circuits or operators is done in this tutorial." +] +}, +{ +"cell_type": "markdown", +"id": "862789c2-d17e-43ed-8e1b-2f37d7628ed2", +"metadata": {}, +"source": [ +"## Step 3: Execute using Qiskit primitives" +] +}, +{ +"cell_type": "markdown", +"id": "d88f925f-3ffd-43de-ac1f-2c5ba3b8cd96", +"metadata": {}, +"source": [ +"### Execute a quantum circuit with default qubit selection\n", +"As a reference result of performance, we'll execute a quantum circuit on a QPU by using the default qubits, which are the qubits selected with the requested backend properties. We will use `optimization_level = 3`. This setting includes the most advanced transpilation optimization, and uses target properties (like operation errors) to select the best performing qubits for execution." +] +}, +{ +"cell_type": "code", +"execution_count": null, +"id": "5c0d09ad-2e6a-4067-8034-8df92a475ff1", +"metadata": {}, +"outputs": [], +"source": [ +"pm = generate_preset_pass_manager(target=backend.target, optimization_level=3)\n", +"isa_circuits = pm.run(circuits)\n", +"initial_qubits = [\n", +" [\n", +" idx\n", +" for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", +" if qb._register.name != \"ancilla\"\n", +" ]\n", +" for circuit in isa_circuits\n", +"]" +] +}, +{ +"cell_type": "markdown", +"id": "6d4fb04c-a5cf-48e8-a759-dde64fd751ac", +"metadata": {}, +"source": [ +"### Execute a quantum circuit with real-time qubit selection\n", +"\n", +"In this section, we'll investigate the importance of having updated information on qubit properties of the QPU for optimal results. First, we'll carry out a full suite of QPU characterization experiments ($T_1$, $T_2$, SPAM, single-qubit RB and two-qubit RB), which we can then use to update the backend properties. This allows the pass manager to select qubits for the execution based on fresh information about the QPU, possibly improving execution performances. Second, we execute the Bell pair circuit and we compare the fidelity obtained after selecting the qubits with update QPU properties to the fidelity we obtained before when we use the default reported properties for qubit selection." +] +}, +{ +"cell_type": "code", +"execution_count": 9, +"id": "a8266467-9d60-411f-89dd-8cad6558f588", +"metadata": {}, +"outputs": [], +"source": [ +"# Prepare characterization experiments\n", +"batches = [t1_exp, t2_exp, readout_exp, singleq_rb_exp, twoq_rb_exp_batched]\n", +"batches_exp = BatchExperiment(batches, backend) # , analysis=None)\n", +"run_options = {\"shots\": 1e3, \"dynamic\": False}\n", +"\n", +"with Session(backend=backend) as session:\n", +" sampler = SamplerV2(mode=session)\n", +"\n", +" # Run characterization experiments\n", +" batches_exp_data = batches_exp.run(\n", +" sampler=sampler, **run_options\n", +" ).block_for_results()\n", +"\n", +" EPG_sx_result_list = batches_exp_data.analysis_results(\"EPG_sx\")\n", +" EPG_sx_result_q_indices = [\n", +" result.device_components.index for result in EPG_sx_result_list\n", +" ]\n", +" EPG_x_result_list = batches_exp_data.analysis_results(\"EPG_x\")\n", +" EPG_x_result_q_indices = [\n", +" result.device_components.index for result in EPG_x_result_list\n", +" ]\n", +" T1_result_list = batches_exp_data.analysis_results(\"T1\")\n", +" T1_result_q_indices = [\n", +" result.device_components.index for result in T1_result_list\n", +" ]\n", +"\n", +" T2_result_list = batches_exp_data.analysis_results(\"T2\")\n", +" T2_result_q_indices = [\n", +" result.device_components.index for result in T2_result_list\n", +" ]\n", +"\n", +" Readout_result_list = batches_exp_data.analysis_results(\n", +" \"Local Readout Mitigator\"\n", +" )\n", +"\n", +" EPG_ecr_result_list = batches_exp_data.analysis_results(\"EPG_ecr\")\n", +"\n", +" # Update target properties\n", +" target = copy.deepcopy(backend.target)\n", +" for i in range(target.num_qubits - 1):\n", +" qarg = (i,)\n", +"\n", +" if qarg in EPG_sx_result_q_indices:\n", +" target.update_instruction_properties(\n", +" instruction=\"sx\",\n", +" qargs=qarg,\n", +" properties=InstructionProperties(\n", +" error=EPG_sx_result_list[i].value.nominal_value\n", +" ),\n", +" )\n", +" if qarg in EPG_x_result_q_indices:\n", +" target.update_instruction_properties(\n", +" instruction=\"x\",\n", +" qargs=qarg,\n", +" properties=InstructionProperties(\n", +" error=EPG_x_result_list[i].value.nominal_value\n", +" ),\n", +" )\n", +"\n", +" err_mat = Readout_result_list.value.assignment_matrix(i)\n", +" readout_assignment_error = (\n", +" err_mat[0, 1] + err_mat[1, 0]\n", +" ) / 2 # average readout error\n", +" target.update_instruction_properties(\n", +" instruction=\"measure\",\n", +" qargs=qarg,\n", +" properties=InstructionProperties(error=readout_assignment_error),\n", +" )\n", +"\n", +" if qarg in T1_result_q_indices:\n", +" target.qubit_properties[i].t1 = T1_result_list[\n", +" i\n", +" ].value.nominal_value\n", +" if qarg in T2_result_q_indices:\n", +" target.qubit_properties[i].t2 = T2_result_list[\n", +" i\n", +" ].value.nominal_value\n", +"\n", +" for pair_idx, pair in enumerate(flattened_layered_coupling_map):\n", +" qarg = tuple(pair)\n", +" try:\n", +" target.update_instruction_properties(\n", +" instruction=\"ecr\",\n", +" qargs=qarg,\n", +" properties=InstructionProperties(\n", +" error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", +" ),\n", +" )\n", +" except:\n", +" target.update_instruction_properties(\n", +" instruction=\"ecr\",\n", +" qargs=qarg[::-1],\n", +" properties=InstructionProperties(\n", +" error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", +" ),\n", +" )\n", +"\n", +" # transpile circuits to updated target\n", +" pm = generate_preset_pass_manager(target=target, optimization_level=3)\n", +" isa_circuit_updated = pm.run(circuits)\n", +" updated_qubits = [\n", +" [\n", +" idx\n", +" for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", +" if qb._register.name != \"ancilla\"\n", +" ]\n", +" for circuit in isa_circuit_updated\n", +" ]\n", +"\n", +" n_trials = 3 # run multiple trials to see variations\n", +"\n", +" # interleave circuits\n", +" interleaved_circuits = []\n", +" for original_circuit, updated_circuit in zip(\n", +" isa_circuits, isa_circuit_updated\n", +" ):\n", +" interleaved_circuits.append(original_circuit)\n", +" interleaved_circuits.append(updated_circuit)\n", +"\n", +" # Run circuits\n", +" # Set simple error suppression/mitigation options\n", +" sampler.options.dynamical_decoupling.enable = True\n", +" sampler.options.dynamical_decoupling.sequence_type = \"XY4\"\n", +"\n", +" job_interleaved = sampler.run(interleaved_circuits * n_trials)" +] +}, +{ +"cell_type": "markdown", +"id": "d72e021a-3ea6-4ed2-829f-e59ce8017ef4", +"metadata": {}, +"source": [ +"## Step 4: Post-process and return result in desired classical format\n", +"\n", +"Finally, let's compare the fidelity of the Bell state obtained in the two different settings:\n", +"- `original`, that is with the default qubits chosen by the transpiler based on reported properties of the backend.\n", +"- `updated`, that is with the qubits chosen based on updated properties of the backend after characterization experiments have run." +] +}, +{ +"cell_type": "code", +"execution_count": 13, +"id": "5a19a5d1-5daf-4d65-9785-8f8724853821", +"metadata": {}, +"outputs": [], +"source": [ +"results = job_interleaved.result()\n", +"all_fidelity_list, all_fidelity_updated_list = [], []\n", +"for exp_idx in range(n_trials):\n", +" fidelity_list, fidelity_updated_list = [], []\n", +"\n", +" for idx, num_qubits in enumerate(num_qubits_list):\n", +" pub_result_original = results[\n", +" 2 * exp_idx * len(num_qubits_list) + 2 * idx\n", +" ]\n", +" pub_result_updated = results[\n", +" 2 * exp_idx * len(num_qubits_list) + 2 * idx + 1\n", +" ]\n", +"\n", +" fid = hellinger_fidelity(\n", +" ideal_dist, pub_result_original.data.c.get_counts()\n", +" )\n", +" fidelity_list.append(fid)\n", +"\n", +" fid_up = hellinger_fidelity(\n", +" ideal_dist, pub_result_updated.data.c.get_counts()\n", +" )\n", +" fidelity_updated_list.append(fid_up)\n", +" all_fidelity_list.append(fidelity_list)\n", +" all_fidelity_updated_list.append(fidelity_updated_list)" +] +}, +{ +"cell_type": "code", +"execution_count": 14, +"id": "656ec97a-3fd9-4635-9a98-1c5589761689", +"metadata": {}, +"outputs": [ +{ +"data": { +"text/plain": [ +"\"Output" +] +}, +"metadata": {}, +"output_type": "display_data" +} +], +"source": [ +"plt.figure(figsize=(8, 6))\n", +"plt.errorbar(\n", +" num_qubits_list,\n", +" np.mean(all_fidelity_list, axis=0),\n", +" yerr=np.std(all_fidelity_list, axis=0),\n", +" fmt=\"o-.\",\n", +" label=\"original\",\n", +" color=\"b\",\n", +")\n", +"# plt.plot(num_qubits_list, fidelity_list, '-.')\n", +"plt.errorbar(\n", +" num_qubits_list,\n", +" np.mean(all_fidelity_updated_list, axis=0),\n", +" yerr=np.std(all_fidelity_updated_list, axis=0),\n", +" fmt=\"o-.\",\n", +" label=\"updated\",\n", +" color=\"r\",\n", +")\n", +"# plt.plot(num_qubits_list, fidelity_updated_list, '-.')\n", +"plt.xlabel(\"Chain length\")\n", +"plt.xticks(num_qubits_list)\n", +"plt.ylabel(\"Fidelity\")\n", +"plt.title(\"Bell pair fidelity at the edge of N-qubits chain\")\n", +"plt.legend()\n", +"plt.grid(\n", +" alpha=0.2,\n", +" linestyle=\"-.\",\n", +")\n", +"plt.show()" +] +}, +{ +"cell_type": "markdown", +"id": "3705d85c-e11d-4a8f-bfae-20bd501124d8", +"metadata": {}, +"source": [ +"With increasing chain length, and thus less freedom to choose physical qubits, the fact that we have updated information about qubit properties becomes less substantial, with there being no difference when all of the qubits are used." +] +}, +{ +"cell_type": "markdown", +"id": "cd81b208-b13b-4988-854e-1741408f36f3", +"metadata": {}, +"source": [ +"
\n", +"Call to Action: Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", +"
" +] +}, +{ +"cell_type": "markdown", +"id": "b94755c1-1c19-434a-96b9-b83922b5d63c", +"metadata": {}, +"source": [ +"## Tutorial survey\n", +"\n", +"Please take this short survey to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", +"\n", +"[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_0w6FZ9QrWkKfTQq)" +] } +], +"metadata": { +"description": "Run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU.", +"kernelspec": { +"display_name": "Python 3", +"language": "python", +"name": "python3" +}, +"language_info": { +"codemirror_mode": { +"name": "ipython", +"version": 3 +}, +"file_extension": ".py", +"mimetype": "text/x-python", +"name": "python", +"nbconvert_exporter": "python", +"pygments_lexer": "ipython3", +"version": "3" +}, +"title": "Real-time benchmarking for qubit selection\n" +}, +"nbformat": 4, +"nbformat_minor": 4 +} \ No newline at end of file From ee1d1415ff8e2829e9664821c9eac8e6b0aeaecb Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Wed, 27 Aug 2025 09:40:15 -0500 Subject: [PATCH 18/26] remove unused image --- .../b1914005-8f5c-4e72-91f8-304eefe77018-0.avif | Bin 67939 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 public/docs/images/tutorials/real-time-benchmarking-for-qubit-selection/extracted-outputs/b1914005-8f5c-4e72-91f8-304eefe77018-0.avif diff --git a/public/docs/images/tutorials/real-time-benchmarking-for-qubit-selection/extracted-outputs/b1914005-8f5c-4e72-91f8-304eefe77018-0.avif b/public/docs/images/tutorials/real-time-benchmarking-for-qubit-selection/extracted-outputs/b1914005-8f5c-4e72-91f8-304eefe77018-0.avif deleted file mode 100644 index 639094ea7588153766f4d452feb7f57293d5ae9c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 67939 zcmYJZV~{9KkTyEDZQHhO+qQMaw(U7%+qP}nwtdgL`)%B;h{TiV?EFy?Rowsp061nY z9u9_XmSzC|=s#&|X~t-4Y530u47N3OG5inzM}!t8HctPm0suHz8oT_T{(lnA!P3R{ ze+0oUCPa+%J+c?=7+qnO$4hsP0{||yObYm0_ zfWY~u0-#trJDC2b0002%0|0<&{!{)n?{4`Y0toSsaTvO~3;$=iI@xjAI+&XO4-?`t zwlsF&a`td`F|~E(`WItqV`=E*!DVRUVDUd=`hQP>`7agW|0)7QK>Sw|&(gv6|7HmU z00;sY3JeYa1QG}g67tWBW$9%4zpwtUmw%OQ{*3_ezXd*MYhvhv1ONq&Mb>;(Kaj^n z_0OOXc)CN_t|Pb0QK%VPdnwaV}F$t_&Rm#=j9`ea)|K~6@AQ81^Q3S{|6Y_5NG)OFobeog2tzQ?- z6d2$N`V7=L4diKhXsr*B^_xlEVs{kam?<#l)nnxS8K2|!ZQYABk8x6JlJSuex*KG4 zf_w+x&s+t;x}rFtyQ|NZOg7N`kexNz(2_C94MI}@z7>QaZ@#%>*Ad6bsg#qdc+}i= zax;erq4AOXJ#ZhXTa}_e{2DwmD;_Q;?_PRs3bJ?YkXB>n0!yNF2}JJv7&k~)xXdw# zbRutAfXKQ!+otmaf1vdE+LCVaMB-Jug%(HR+Cx4sB2g~Ypp?Vvr}sDf@@fa$ z=fp8saTa%;p-8SpVg!^dvNE*K%ZIF;okaXhEj_v%1S2urep%ak^tK%ei1n6FVST17 zGOYgsAAHuS8bP`BlVVo{tuMLW?}LQ>R;y!~Tyt2H_x6C;0C%C{b?7O%jvj@9X~uEq zS!~!ukmNG$W+ot#9Xkqzwns656!nJMGH*Ech{@kG^lF=Vl-j19*ojkXnnYuK`xVr! z(Er_v4-mWl!wsL{m;HppPOT|mbwT}MTXo+JJ|rMXenH)0&5&pwQV%^B>I4EsCI~ zEezVEU-{HWINJ$hJfIh@O->vM_kEjlyvf3V38D%R9f4aJz*=8PG#|O`Fz<}0VO&`C z(48shZ{zJJ=n`R@bF$jsU@1q%S7@5sA~YB(p{drN?wK>qw~2Se?}hv(d(UX_L+GivLsNy+?lIGg&Uo~MpPZtXx&QBnnccN?R2q8E{tIiTwEq`+LXr@S105F$~&n$S_ z{PI!nXnj%e;!d&teG!O?01V2ii_r-|3I%%M9{U!MS_$%({n^^UGxYa;fe@{=<7AAA z=`dht8=I2YYCH}L!)=&{+N#Zlb2v8B4ee$ZGUSmUanjV|k+KPOING;{a&gVNT;OB% z_?Ig4+P>fZ$sOlJuCz3Ytbh^Ux-Jr6!^zZj%tZdMvUl;luMRa|rC0+YrJX9}~=F6@XD9R_U3Et^T~A-MeWfS63@PCepEPqBi|$$jUmbA!ELnj(d8 zEZ^XBjM5bE5p-wfZ5NKbaeV~C3ApNJJ;?dY(_s{kuQaq*li;^JgSpHkd|9+t4Mm>6 z4~jxJU_LSjLninbBxj%`N|j>joS$3WV>fltBu1^6D@|L0v*5P?D?AtO03RlnrtXSZ^twESVFIOe4W085 zOb!t?im{T89yR(`oMyBOPT+_`@r64oUGPDM#1`s^|ZsLc#d7{evI#KOpnG0ZF_h>qKi zLmGopLX1eWl%{TXw3i+_9hcj^%dMDcaj)a&mx=#2B7ToYYBSC`E=K!oeKs@T z-;O~{lf&x`WI`1e{6mm}?rEnjxV{@R z^&$C1>~ia5$knrVbN9~sH;MXc!;xqMvFBt2GY*Ut%3mj`nApw?4b^s@im_~c@k99H zvKuA~3gJbb7N`)mLq^W*(we7tL7V04WF1fU<#y9h9jL2Nsc)LvePCN;mzWIPa+|e+ z?KR#dLi*zdVRFsZZ)K5WiXVsW>%uO_c?zt(o_CBgbkhA`>gk-}*(_)*6q9zcsoQbL z3u1^HqlP@%9tgNOag!ugx-8mp{ic$c5Nt5~%~p}-yW;G(5ZfTH{$ajRy2U|m*tLXjo`J3# zf}E1AKwauPT=~k(v1T0(>8Pa+$)Utg*`ipOBCRSSZs^ZGbC+SJul7nEd}BO%2G^1Wgw`0P;KyARE@*OBa0TfuXYl6^?YpH^zRHVu6jk|Of7My-#TfP~g ze7&L~eO11C-e&&vXjp}b*5B6jC#JVvoRBW`n(pq}1}m`8Aw)E$j7_{3a|l9dbLi`6 z)FHJiy7I?%o2~1})J+ALdF9ErzXDI3R=m3sB?huCA65Gr)VmDK75ax^Y7*2)i!ws? 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zTW)1_9D0+?2SqJ)l8`#UwSUYkNpO5H`mQyGg$A+Ta#{(qU_xXDz|RcjJw&NbJNtlo zAfNGc-ps6@7>N+5$Vo?hh*BYUMOy~mEL7d2GPgx7-_x1766 z_L9$BB$-`^uoNp;Dl5;gwBzi&M9fw@*j70dt%5_PL&w2S^i~~iN3BuP^yw&ZX@U+0txP;d0iijfZDJ@39rtf*5e%z0+2sO-<`QzpP71!RQd7% From b6153bb81ac03e4238c631a0d5508cc87e03f62b Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Wed, 27 Aug 2025 09:58:24 -0500 Subject: [PATCH 19/26] Fix some markdown --- ...ime-benchmarking-for-qubit-selection.ipynb | 1556 +++++++++-------- 1 file changed, 782 insertions(+), 774 deletions(-) diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index bb0526b08cd..34920cd9d3f 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -1,776 +1,784 @@ { -"cells": [ -{ -"cell_type": "markdown", -"id": "f69d5853-e815-4754-894d-833017217572", -"metadata": {}, -"source": [ -"# Real-time benchmarking for qubit selection\n", -"*Usage estimate: 4 minutes on an Eagle r2 processor (NOTE: This is an estimate only. Your runtime might vary.)*" -] -}, -{ -"cell_type": "code", -"execution_count": null, -"id": "2797ee64-644f-40c9-8327-094867333a69", -"metadata": { -"tags": [ -"remove-cell" -] -}, -"outputs": [], -"source": [ -"# This cell is hidden from users – it disables some lint rules\n", -"# ruff: noqa: E722" -] -}, -{ -"cell_type": "markdown", -"id": "500dc8c9-a5d8-4ef1-932f-30e400d6bdde", -"metadata": {}, -"source": [ -"## Background\n", -"This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", -"\n", -"The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine." -] -}, -{ -"cell_type": "markdown", -"id": "0babd413-d91f-4fd7-a0f5-bb46ae0bbf5b", -"metadata": {}, -"source": [ -"## Requirements\n", -"Before starting this tutorial, be sure you have the following installed:\n", -"\n", -"- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", -"- Qiskit Runtime 0.29 or later ( `pip install qiskit-ibm-runtime` )\n", -"- Qiskit Experiments 0.7 or later ( `pip install qiskit-experiments` )\n", -"- Rustworkx graph library (`pip install rustworkx`)" -] -}, -{ -"cell_type": "markdown", -"id": "3df52d5f-806a-4846-849e-633706a96d0b", -"metadata": {}, -"source": [ -"## Setup" -] -}, -{ -"cell_type": "code", -"execution_count": 1, -"id": "4766c18a-ba45-456b-8b78-6b6f1d214586", -"metadata": {}, -"outputs": [], -"source": [ -"from qiskit_ibm_runtime import SamplerV2\n", -"from qiskit.transpiler import generate_preset_pass_manager\n", -"from qiskit.quantum_info import hellinger_fidelity\n", -"from qiskit.transpiler import InstructionProperties\n", -"\n", -"\n", -"from qiskit_experiments.library import (\n", -" T1,\n", -" T2Hahn,\n", -" LocalReadoutError,\n", -" StandardRB,\n", -")\n", -"from qiskit_experiments.framework import BatchExperiment, ParallelExperiment\n", -"\n", -"from qiskit_ibm_runtime import QiskitRuntimeService\n", -"from qiskit_ibm_runtime import Session\n", -"\n", -"from datetime import datetime\n", -"from collections import defaultdict\n", -"import numpy as np\n", -"import rustworkx\n", -"import matplotlib.pyplot as plt\n", -"import copy" -] -}, -{ -"cell_type": "markdown", -"id": "65d49ed2-0581-486e-9031-a08fa9bace92", -"metadata": {}, -"source": [ -"## Step 1: Map classical inputs to a quantum problem\n", -"\n", -"To benchmark the difference in performance, we consider a circuit that prepares a Bell state across a linear chain of varying length. The fidelity of the Bell state at the ends of the chain is measured." -] -}, -{ -"cell_type": "code", -"execution_count": null, -"id": "64c25da9-a728-4ae4-a377-3078a1dc618d", -"metadata": {}, -"outputs": [ -{ -"data": { -"text/plain": [ -"\"Output" -] -}, -"metadata": {}, -"output_type": "display_data" -} -], -"source": [ -"from qiskit import QuantumCircuit\n", -"\n", -"ideal_dist = {\"00\": 0.5, \"11\": 0.5}\n", -"\n", -"num_qubits_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 127]\n", -"circuits = []\n", -"for num_qubits in num_qubits_list:\n", -" circuit = QuantumCircuit(num_qubits, 2)\n", -" circuit.h(0)\n", -" for i in range(num_qubits - 1):\n", -" circuit.cx(i, i + 1)\n", -" circuit.barrier()\n", -" circuit.measure(0, 0)\n", -" circuit.measure(num_qubits - 1, 1)\n", -" circuits.append(circuit)\n", -"\n", -"circuits[-1].draw(output=\"mpl\", style=\"clifford\", fold=-1)" -] -}, -{ -"cell_type": "markdown", -"id": "16948f21-a39b-4444-bf02-5f81331825c4", -"metadata": {}, -"source": [ -"### Setting up backend and coupling map" -] -}, -{ -"cell_type": "code", -"execution_count": null, -"id": "f968acca-9131-4f5d-aa74-70befcdda4f5", -"metadata": {}, -"outputs": [], -"source": [ -"# To run on hardware, select the backend with the fewest number of jobs in the queue\n", -"service = QiskitRuntimeService()\n", -"backend = service.least_busy(\n", -" operational=True, simulator=False, min_num_qubits=127\n", -")\n", -"\n", -"qubits = list(range(backend.num_qubits))" -] -}, -{ -"cell_type": "code", -"execution_count": 3, -"id": "62b36ded-ab4e-414e-b146-ff522786a871", -"metadata": {}, -"outputs": [], -"source": [ -"coupling_graph = backend.coupling_map.graph.to_undirected(multigraph=False)\n", -"\n", -"# Get unidirectional coupling map\n", -"one_dir_coupling_map = coupling_graph.edge_list()\n", -"\n", -"# Get layered coupling map\n", -"edge_coloring = rustworkx.graph_bipartite_edge_color(coupling_graph)\n", -"layered_coupling_map = defaultdict(list)\n", -"for edge_idx, color in edge_coloring.items():\n", -" layered_coupling_map[color].append(\n", -" coupling_graph.get_edge_endpoints_by_index(edge_idx)\n", -" )\n", -"layered_coupling_map = [\n", -" sorted(layered_coupling_map[i])\n", -" for i in sorted(layered_coupling_map.keys())\n", -"]\n", -"\n", -"flattened_layered_coupling_map = []\n", -"for layer in layered_coupling_map:\n", -" flattened_layered_coupling_map += layer" -] -}, -{ -"cell_type": "markdown", -"id": "875117af-8a2c-4aea-92d9-ffeee7ff37d5", -"metadata": {}, -"source": [ -"### Characterization experiments\n", -"\n", -"A series of experiments is used to characterize the main properties of the qubits in a QPU. These are $T_1$, $T_2$, readout error, and single-qubit and two-qubit gate error. We'll briefly summarize what these properties are and refer to experiments in the [`qiskit-experiments`](https://qiskit-community.github.io/qiskit-experiments/index.html) package that are used to characterize them.\n", -"\n", -"#### T1\n", -"$T_1$ is the characteristic time it takes for an excited qubit to fall to the ground state due to amplitude-damping decoherence processes. In a [$T_1$ experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t1.html), we measure an excited qubit after a delay. The larger the delay time is, the more\n", -"likely is the qubit to fall to the ground state. The goal of the\n", -"experiment is to characterize the decay rate of the qubit towards the\n", -"ground state.\n", -"\n", -"#### T2\n", -"$T_2$ represents the amount of time required for a single qubit's Bloch\n", -"vector projection on the XY plane to fall to approximately 37% ($\\frac{1}{e}$) of\n", -"its initial amplitude due to dephasing decoherence processes. In a [$T_2$ Hahn Echo experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t2hahn.html), we can estimate the rate of this decay.\n", -"\n", -"#### State preparation and measurement (SPAM) error characterization\n", -"In a [SPAM-error characterization experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/measurement/readout_mitigation.html) qubit are prepared in a certain state ($\\vert 0 \\rangle$ or $\\vert 1 \\rangle$) and measured. The probability of measuring a state different than the one prepared then gives the probability of the error.\n", -"\n", -"#### Single-qubit and two-qubit randomized benchmarking\n", -"[Randomized benchmarking](https://qiskit-community.github.io/qiskit-experiments/manuals/verification/randomized_benchmarking.html) is a popular protocol for characterizing the error rate of\n", -"quantum processors. An RB experiment consists of the generation of random Clifford\n", -"circuits on the given qubits such that the unitary computed by the circuits is the\n", -"identity. After running the circuits, the number of shots resulting in an error (that is, an output different from the ground state) are counted, and from this data one can infer error estimates for the quantum device, by calculating the Error Per Clifford." -] -}, -{ -"cell_type": "code", -"execution_count": 4, -"id": "9d57c42d-7a91-4e79-bc6c-6e579da929f8", -"metadata": {}, -"outputs": [], -"source": [ -"# Create T1 experiments on all qubit in parallel\n", -"t1_exp = ParallelExperiment(\n", -" [\n", -" T1(\n", -" physical_qubits=[qubit],\n", -" delays=np.linspace(\n", -" 1e-6, 2 * backend.properties().t1(qubit), 5, endpoint=True\n", -" ),\n", -" )\n", -" for qubit in qubits\n", -" ],\n", -" backend,\n", -" analysis=None,\n", -")\n", -"\n", -"# Create T2-Hahn experiments on all qubit in parallel\n", -"t2_exp = ParallelExperiment(\n", -" [\n", -" T2Hahn(\n", -" physical_qubits=[qubit],\n", -" delays=np.linspace(\n", -" 1e-6, 2 * backend.properties().t2(qubit), 5, endpoint=True\n", -" ),\n", -" )\n", -" for qubit in qubits\n", -" ],\n", -" backend,\n", -" analysis=None,\n", -")\n", -"\n", -"# Create readout experiments on all qubit in parallel\n", -"readout_exp = LocalReadoutError(qubits)\n", -"\n", -"# Create single-qubit RB experiments on all qubit in parallel\n", -"singleq_rb_exp = ParallelExperiment(\n", -" [\n", -" StandardRB(\n", -" physical_qubits=[qubit], lengths=[10, 100, 500], num_samples=10\n", -" )\n", -" for qubit in qubits\n", -" ],\n", -" backend,\n", -" analysis=None,\n", -")\n", -"\n", -"# Create two-qubit RB experiments on the three layers of disjoint edges of the heavy-hex\n", -"twoq_rb_exp_batched = BatchExperiment(\n", -" [\n", -" ParallelExperiment(\n", -" [\n", -" StandardRB(\n", -" physical_qubits=pair,\n", -" lengths=[10, 50, 100],\n", -" num_samples=10,\n", -" )\n", -" for pair in layer\n", -" ],\n", -" backend,\n", -" analysis=None,\n", -" )\n", -" for layer in layered_coupling_map\n", -" ],\n", -" backend,\n", -" flatten_results=True,\n", -" analysis=None,\n", -")" -] -}, -{ -"cell_type": "markdown", -"id": "cad4f8d3-c2d5-4bb5-92be-432a4573e14a", -"metadata": {}, -"source": [ -"### QPU properties over time\n", -"Looking at the reported QPU properties over time (we'll consider a single week below), we see how these can fluctuate on a scale of a single day. Small fluctuations can happen even within a day. In this scenario, the reported properties (updated once per day) will not accurately capture the current status of the QPU. Moreover, if a job is transpiled locally (using current reported properties) and submitted but executed only at a later time (minutes or days), it may run the risk of having used outdated properties for qubit selection in the transpilation step. This highlights the importance of having updated information about the QPU at execution time. First, let's retrieve the properties over a certain time range." -] -}, -{ -"cell_type": "code", -"execution_count": 4, -"id": "af1b6722-e77b-436a-bbcd-f272b95bb28c", -"metadata": {}, -"outputs": [], -"source": [ -"errors_list = []\n", -"for day_idx in range(10, 17):\n", -" calibrations_time = datetime(\n", -" year=2024, month=10, day=day_idx, hour=0, minute=0, second=0\n", -" )\n", -" targer_hist = backend.target_history(datetime=calibrations_time)\n", -"\n", -" t1_dict, t2_dict = {}, {}\n", -" for qubit in range(targer_hist.num_qubits):\n", -" t1_dict[qubit] = targer_hist.qubit_properties[qubit].t1\n", -" t2_dict[qubit] = targer_hist.qubit_properties[qubit].t2\n", -"\n", -" errors_dict = {\n", -" \"1q\": targer_hist[\"sx\"],\n", -" \"2q\": targer_hist[\"ecr\"],\n", -" \"spam\": targer_hist[\"measure\"],\n", -" \"t1\": t1_dict,\n", -" \"t2\": t2_dict,\n", -" }\n", -"\n", -" errors_list.append(errors_dict)" -] -}, -{ -"cell_type": "markdown", -"id": "ac75df1b-f689-475c-94a2-d70d85b1f8ca", -"metadata": {}, -"source": [ -"Then, let's plot the values" -] -}, -{ -"cell_type": "code", -"execution_count": 6, -"id": "e0ba509d-e0e0-438b-aedf-5e01919c7d4f", -"metadata": {}, -"outputs": [ -{ -"data": { -"text/plain": [ -"\"Output" -] -}, -"metadata": {}, -"output_type": "display_data" -} -], -"source": [ -"fig, axs = plt.subplots(5, 1, figsize=(10, 20), sharex=False)\n", -"\n", -"\n", -"# Plot for T1 values\n", -"for qubit in range(targer_hist.num_qubits):\n", -" t1s = []\n", -" for errors_dict in errors_list:\n", -" t1_dict = errors_dict[\"t1\"]\n", -" t1s.append(t1_dict[qubit] / 1e-6)\n", -"\n", -" axs[0].plot(t1s)\n", -"\n", -"axs[0].set_title(\"T1\")\n", -"axs[0].set_ylabel(r\"Time ($\\mu s$)\")\n", -"axs[0].set_xlabel(\"Days\")\n", -"\n", -"# Plot for T2 values\n", -"for qubit in range(targer_hist.num_qubits):\n", -" t2s = []\n", -" for errors_dict in errors_list:\n", -" t2_dict = errors_dict[\"t2\"]\n", -" t2s.append(t2_dict[qubit] / 1e-6)\n", -"\n", -" axs[1].plot(t2s)\n", -"\n", -"axs[1].set_title(\"T2\")\n", -"axs[1].set_ylabel(r\"Time ($\\mu s$)\")\n", -"axs[1].set_xlabel(\"Days\")\n", -"\n", -"# Plot SPAM values\n", -"for qubit in range(targer_hist.num_qubits):\n", -" spams = []\n", -" for errors_dict in errors_list:\n", -" spam_dict = errors_dict[\"spam\"]\n", -" spams.append(spam_dict[tuple([qubit])].error)\n", -"\n", -" axs[2].plot(spams)\n", -"\n", -"axs[2].set_title(\"SPAM Errors\")\n", -"axs[2].set_ylabel(\"Error Rate\")\n", -"axs[2].set_xlabel(\"Days\")\n", -"\n", -"# Plot 1Q Gate Errors\n", -"for qubit in range(targer_hist.num_qubits):\n", -" oneq_gates = []\n", -" for errors_dict in errors_list:\n", -" oneq_gate_dict = errors_dict[\"1q\"]\n", -" oneq_gates.append(oneq_gate_dict[tuple([qubit])].error)\n", -"\n", -" axs[3].plot(oneq_gates)\n", -"\n", -"axs[3].set_title(\"1Q Gate Errors\")\n", -"axs[3].set_ylabel(\"Error Rate\")\n", -"axs[3].set_xlabel(\"Days\")\n", -"\n", -"# Plot 2Q Gate Errors\n", -"for pair in one_dir_coupling_map:\n", -" twoq_gates = []\n", -" for errors_dict in errors_list:\n", -" twoq_gate_dict = errors_dict[\"2q\"]\n", -" twoq_gates.append(twoq_gate_dict[pair].error)\n", -"\n", -" axs[4].plot(twoq_gates)\n", -"\n", -"axs[4].set_title(\"2Q Gate Errors\")\n", -"axs[4].set_ylabel(\"Error Rate\")\n", -"axs[4].set_xlabel(\"Days\")\n", -"\n", -"plt.subplots_adjust(hspace=0.5)\n", -"plt.show()" -] -}, -{ -"cell_type": "markdown", -"id": "a3343934-9273-457c-900b-2004b3aa9d0c", -"metadata": {}, -"source": [ -"You can see that over several days some of the qubit properties can change considerably. This highlights the importance of having fresh information of the QPU status, to be able to select the best performing qubits for an experiment." -] -}, -{ -"cell_type": "markdown", -"id": "0e53f0a5-7713-4915-ae47-c1aa0a4f17cd", -"metadata": {}, -"source": [ -"## Step 2: Optimize problem for quantum hardware execution\n", -"No optimization of the circuits or operators is done in this tutorial." -] -}, -{ -"cell_type": "markdown", -"id": "862789c2-d17e-43ed-8e1b-2f37d7628ed2", -"metadata": {}, -"source": [ -"## Step 3: Execute using Qiskit primitives" -] -}, -{ -"cell_type": "markdown", -"id": "d88f925f-3ffd-43de-ac1f-2c5ba3b8cd96", -"metadata": {}, -"source": [ -"### Execute a quantum circuit with default qubit selection\n", -"As a reference result of performance, we'll execute a quantum circuit on a QPU by using the default qubits, which are the qubits selected with the requested backend properties. We will use `optimization_level = 3`. This setting includes the most advanced transpilation optimization, and uses target properties (like operation errors) to select the best performing qubits for execution." -] -}, -{ -"cell_type": "code", -"execution_count": null, -"id": "5c0d09ad-2e6a-4067-8034-8df92a475ff1", -"metadata": {}, -"outputs": [], -"source": [ -"pm = generate_preset_pass_manager(target=backend.target, optimization_level=3)\n", -"isa_circuits = pm.run(circuits)\n", -"initial_qubits = [\n", -" [\n", -" idx\n", -" for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", -" if qb._register.name != \"ancilla\"\n", -" ]\n", -" for circuit in isa_circuits\n", -"]" -] -}, -{ -"cell_type": "markdown", -"id": "6d4fb04c-a5cf-48e8-a759-dde64fd751ac", -"metadata": {}, -"source": [ -"### Execute a quantum circuit with real-time qubit selection\n", -"\n", -"In this section, we'll investigate the importance of having updated information on qubit properties of the QPU for optimal results. First, we'll carry out a full suite of QPU characterization experiments ($T_1$, $T_2$, SPAM, single-qubit RB and two-qubit RB), which we can then use to update the backend properties. This allows the pass manager to select qubits for the execution based on fresh information about the QPU, possibly improving execution performances. Second, we execute the Bell pair circuit and we compare the fidelity obtained after selecting the qubits with update QPU properties to the fidelity we obtained before when we use the default reported properties for qubit selection." -] -}, -{ -"cell_type": "code", -"execution_count": 9, -"id": "a8266467-9d60-411f-89dd-8cad6558f588", -"metadata": {}, -"outputs": [], -"source": [ -"# Prepare characterization experiments\n", -"batches = [t1_exp, t2_exp, readout_exp, singleq_rb_exp, twoq_rb_exp_batched]\n", -"batches_exp = BatchExperiment(batches, backend) # , analysis=None)\n", -"run_options = {\"shots\": 1e3, \"dynamic\": False}\n", -"\n", -"with Session(backend=backend) as session:\n", -" sampler = SamplerV2(mode=session)\n", -"\n", -" # Run characterization experiments\n", -" batches_exp_data = batches_exp.run(\n", -" sampler=sampler, **run_options\n", -" ).block_for_results()\n", -"\n", -" EPG_sx_result_list = batches_exp_data.analysis_results(\"EPG_sx\")\n", -" EPG_sx_result_q_indices = [\n", -" result.device_components.index for result in EPG_sx_result_list\n", -" ]\n", -" EPG_x_result_list = batches_exp_data.analysis_results(\"EPG_x\")\n", -" EPG_x_result_q_indices = [\n", -" result.device_components.index for result in EPG_x_result_list\n", -" ]\n", -" T1_result_list = batches_exp_data.analysis_results(\"T1\")\n", -" T1_result_q_indices = [\n", -" result.device_components.index for result in T1_result_list\n", -" ]\n", -"\n", -" T2_result_list = batches_exp_data.analysis_results(\"T2\")\n", -" T2_result_q_indices = [\n", -" result.device_components.index for result in T2_result_list\n", -" ]\n", -"\n", -" Readout_result_list = batches_exp_data.analysis_results(\n", -" \"Local Readout Mitigator\"\n", -" )\n", -"\n", -" EPG_ecr_result_list = batches_exp_data.analysis_results(\"EPG_ecr\")\n", -"\n", -" # Update target properties\n", -" target = copy.deepcopy(backend.target)\n", -" for i in range(target.num_qubits - 1):\n", -" qarg = (i,)\n", -"\n", -" if qarg in EPG_sx_result_q_indices:\n", -" target.update_instruction_properties(\n", -" instruction=\"sx\",\n", -" qargs=qarg,\n", -" properties=InstructionProperties(\n", -" error=EPG_sx_result_list[i].value.nominal_value\n", -" ),\n", -" )\n", -" if qarg in EPG_x_result_q_indices:\n", -" target.update_instruction_properties(\n", -" instruction=\"x\",\n", -" qargs=qarg,\n", -" properties=InstructionProperties(\n", -" error=EPG_x_result_list[i].value.nominal_value\n", -" ),\n", -" )\n", -"\n", -" err_mat = Readout_result_list.value.assignment_matrix(i)\n", -" readout_assignment_error = (\n", -" err_mat[0, 1] + err_mat[1, 0]\n", -" ) / 2 # average readout error\n", -" target.update_instruction_properties(\n", -" instruction=\"measure\",\n", -" qargs=qarg,\n", -" properties=InstructionProperties(error=readout_assignment_error),\n", -" )\n", -"\n", -" if qarg in T1_result_q_indices:\n", -" target.qubit_properties[i].t1 = T1_result_list[\n", -" i\n", -" ].value.nominal_value\n", -" if qarg in T2_result_q_indices:\n", -" target.qubit_properties[i].t2 = T2_result_list[\n", -" i\n", -" ].value.nominal_value\n", -"\n", -" for pair_idx, pair in enumerate(flattened_layered_coupling_map):\n", -" qarg = tuple(pair)\n", -" try:\n", -" target.update_instruction_properties(\n", -" instruction=\"ecr\",\n", -" qargs=qarg,\n", -" properties=InstructionProperties(\n", -" error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", -" ),\n", -" )\n", -" except:\n", -" target.update_instruction_properties(\n", -" instruction=\"ecr\",\n", -" qargs=qarg[::-1],\n", -" properties=InstructionProperties(\n", -" error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", -" ),\n", -" )\n", -"\n", -" # transpile circuits to updated target\n", -" pm = generate_preset_pass_manager(target=target, optimization_level=3)\n", -" isa_circuit_updated = pm.run(circuits)\n", -" updated_qubits = [\n", -" [\n", -" idx\n", -" for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", -" if qb._register.name != \"ancilla\"\n", -" ]\n", -" for circuit in isa_circuit_updated\n", -" ]\n", -"\n", -" n_trials = 3 # run multiple trials to see variations\n", -"\n", -" # interleave circuits\n", -" interleaved_circuits = []\n", -" for original_circuit, updated_circuit in zip(\n", -" isa_circuits, isa_circuit_updated\n", -" ):\n", -" interleaved_circuits.append(original_circuit)\n", -" interleaved_circuits.append(updated_circuit)\n", -"\n", -" # Run circuits\n", -" # Set simple error suppression/mitigation options\n", -" sampler.options.dynamical_decoupling.enable = True\n", -" sampler.options.dynamical_decoupling.sequence_type = \"XY4\"\n", -"\n", -" job_interleaved = sampler.run(interleaved_circuits * n_trials)" -] -}, -{ -"cell_type": "markdown", -"id": "d72e021a-3ea6-4ed2-829f-e59ce8017ef4", -"metadata": {}, -"source": [ -"## Step 4: Post-process and return result in desired classical format\n", -"\n", -"Finally, let's compare the fidelity of the Bell state obtained in the two different settings:\n", -"- `original`, that is with the default qubits chosen by the transpiler based on reported properties of the backend.\n", -"- `updated`, that is with the qubits chosen based on updated properties of the backend after characterization experiments have run." -] -}, -{ -"cell_type": "code", -"execution_count": 13, -"id": "5a19a5d1-5daf-4d65-9785-8f8724853821", -"metadata": {}, -"outputs": [], -"source": [ -"results = job_interleaved.result()\n", -"all_fidelity_list, all_fidelity_updated_list = [], []\n", -"for exp_idx in range(n_trials):\n", -" fidelity_list, fidelity_updated_list = [], []\n", -"\n", -" for idx, num_qubits in enumerate(num_qubits_list):\n", -" pub_result_original = results[\n", -" 2 * exp_idx * len(num_qubits_list) + 2 * idx\n", -" ]\n", -" pub_result_updated = results[\n", -" 2 * exp_idx * len(num_qubits_list) + 2 * idx + 1\n", -" ]\n", -"\n", -" fid = hellinger_fidelity(\n", -" ideal_dist, pub_result_original.data.c.get_counts()\n", -" )\n", -" fidelity_list.append(fid)\n", -"\n", -" fid_up = hellinger_fidelity(\n", -" ideal_dist, pub_result_updated.data.c.get_counts()\n", -" )\n", -" fidelity_updated_list.append(fid_up)\n", -" all_fidelity_list.append(fidelity_list)\n", -" all_fidelity_updated_list.append(fidelity_updated_list)" -] -}, -{ -"cell_type": "code", -"execution_count": 14, -"id": "656ec97a-3fd9-4635-9a98-1c5589761689", -"metadata": {}, -"outputs": [ -{ -"data": { -"text/plain": [ -"\"Output" -] -}, -"metadata": {}, -"output_type": "display_data" -} -], -"source": [ -"plt.figure(figsize=(8, 6))\n", -"plt.errorbar(\n", -" num_qubits_list,\n", -" np.mean(all_fidelity_list, axis=0),\n", -" yerr=np.std(all_fidelity_list, axis=0),\n", -" fmt=\"o-.\",\n", -" label=\"original\",\n", -" color=\"b\",\n", -")\n", -"# plt.plot(num_qubits_list, fidelity_list, '-.')\n", -"plt.errorbar(\n", -" num_qubits_list,\n", -" np.mean(all_fidelity_updated_list, axis=0),\n", -" yerr=np.std(all_fidelity_updated_list, axis=0),\n", -" fmt=\"o-.\",\n", -" label=\"updated\",\n", -" color=\"r\",\n", -")\n", -"# plt.plot(num_qubits_list, fidelity_updated_list, '-.')\n", -"plt.xlabel(\"Chain length\")\n", -"plt.xticks(num_qubits_list)\n", -"plt.ylabel(\"Fidelity\")\n", -"plt.title(\"Bell pair fidelity at the edge of N-qubits chain\")\n", -"plt.legend()\n", -"plt.grid(\n", -" alpha=0.2,\n", -" linestyle=\"-.\",\n", -")\n", -"plt.show()" -] -}, -{ -"cell_type": "markdown", -"id": "3705d85c-e11d-4a8f-bfae-20bd501124d8", -"metadata": {}, -"source": [ -"With increasing chain length, and thus less freedom to choose physical qubits, the fact that we have updated information about qubit properties becomes less substantial, with there being no difference when all of the qubits are used." -] -}, -{ -"cell_type": "markdown", -"id": "cd81b208-b13b-4988-854e-1741408f36f3", -"metadata": {}, -"source": [ -"

\n", -"Call to Action: Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", -"
" -] -}, -{ -"cell_type": "markdown", -"id": "b94755c1-1c19-434a-96b9-b83922b5d63c", -"metadata": {}, -"source": [ -"## Tutorial survey\n", -"\n", -"Please take this short survey to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", -"\n", -"[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_0w6FZ9QrWkKfTQq)" -] + "cells": [ + { + "cell_type": "markdown", + "id": "f69d5853-e815-4754-894d-833017217572", + "metadata": {}, + "source": [ + "# Real-time benchmarking for qubit selection\n", + "*Usage estimate: 4 minutes on an Eagle r2 processor (NOTE: This is an estimate only. Your runtime might vary.)*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2797ee64-644f-40c9-8327-094867333a69", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# This cell is hidden from users – it disables some lint rules\n", + "# ruff: noqa: E722" + ] + }, + { + "cell_type": "markdown", + "id": "500dc8c9-a5d8-4ef1-932f-30e400d6bdde", + "metadata": {}, + "source": [ + "## Background\n", + "This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", + "\n", + "The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine." + ] + }, + { + "cell_type": "markdown", + "id": "0babd413-d91f-4fd7-a0f5-bb46ae0bbf5b", + "metadata": {}, + "source": [ + "## Requirements\n", + "Before starting this tutorial, be sure you have the following installed:\n", + "\n", + "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", + "- Qiskit Runtime 0.29 or later ( `pip install qiskit-ibm-runtime` )\n", + "- Qiskit Experiments 0.7 or later ( `pip install qiskit-experiments` )\n", + "- Rustworkx graph library (`pip install rustworkx`)" + ] + }, + { + "cell_type": "markdown", + "id": "3df52d5f-806a-4846-849e-633706a96d0b", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4766c18a-ba45-456b-8b78-6b6f1d214586", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2\n", + "from qiskit.transpiler import generate_preset_pass_manager\n", + "from qiskit.quantum_info import hellinger_fidelity\n", + "from qiskit.transpiler import InstructionProperties\n", + "\n", + "\n", + "from qiskit_experiments.library import (\n", + " T1,\n", + " T2Hahn,\n", + " LocalReadoutError,\n", + " StandardRB,\n", + ")\n", + "from qiskit_experiments.framework import BatchExperiment, ParallelExperiment\n", + "\n", + "from qiskit_ibm_runtime import QiskitRuntimeService\n", + "from qiskit_ibm_runtime import Session\n", + "\n", + "from datetime import datetime\n", + "from collections import defaultdict\n", + "import numpy as np\n", + "import rustworkx\n", + "import matplotlib.pyplot as plt\n", + "import copy" + ] + }, + { + "cell_type": "markdown", + "id": "65d49ed2-0581-486e-9031-a08fa9bace92", + "metadata": {}, + "source": [ + "## Step 1: Map classical inputs to a quantum problem\n", + "\n", + "To benchmark the difference in performance, we consider a circuit that prepares a Bell state across a linear chain of varying length. The fidelity of the Bell state at the ends of the chain is measured." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64c25da9-a728-4ae4-a377-3078a1dc618d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from qiskit import QuantumCircuit\n", + "\n", + "ideal_dist = {\"00\": 0.5, \"11\": 0.5}\n", + "\n", + "num_qubits_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 127]\n", + "circuits = []\n", + "for num_qubits in num_qubits_list:\n", + " circuit = QuantumCircuit(num_qubits, 2)\n", + " circuit.h(0)\n", + " for i in range(num_qubits - 1):\n", + " circuit.cx(i, i + 1)\n", + " circuit.barrier()\n", + " circuit.measure(0, 0)\n", + " circuit.measure(num_qubits - 1, 1)\n", + " circuits.append(circuit)\n", + "\n", + "circuits[-1].draw(output=\"mpl\", style=\"clifford\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "16948f21-a39b-4444-bf02-5f81331825c4", + "metadata": {}, + "source": [ + "### Setting up backend and coupling map" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f968acca-9131-4f5d-aa74-70befcdda4f5", + "metadata": {}, + "outputs": [], + "source": [ + "# To run on hardware, select the backend with the fewest number of jobs in the queue\n", + "service = QiskitRuntimeService()\n", + "backend = service.least_busy(\n", + " operational=True, simulator=False, min_num_qubits=127\n", + ")\n", + "\n", + "qubits = list(range(backend.num_qubits))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "62b36ded-ab4e-414e-b146-ff522786a871", + "metadata": {}, + "outputs": [], + "source": [ + "coupling_graph = backend.coupling_map.graph.to_undirected(multigraph=False)\n", + "\n", + "# Get unidirectional coupling map\n", + "one_dir_coupling_map = coupling_graph.edge_list()\n", + "\n", + "# Get layered coupling map\n", + "edge_coloring = rustworkx.graph_bipartite_edge_color(coupling_graph)\n", + "layered_coupling_map = defaultdict(list)\n", + "for edge_idx, color in edge_coloring.items():\n", + " layered_coupling_map[color].append(\n", + " coupling_graph.get_edge_endpoints_by_index(edge_idx)\n", + " )\n", + "layered_coupling_map = [\n", + " sorted(layered_coupling_map[i])\n", + " for i in sorted(layered_coupling_map.keys())\n", + "]\n", + "\n", + "flattened_layered_coupling_map = []\n", + "for layer in layered_coupling_map:\n", + " flattened_layered_coupling_map += layer" + ] + }, + { + "cell_type": "markdown", + "id": "875117af-8a2c-4aea-92d9-ffeee7ff37d5", + "metadata": {}, + "source": [ + "### Characterization experiments\n", + "\n", + "A series of experiments is used to characterize the main properties of the qubits in a QPU. These are $T_1$, $T_2$, readout error, and single-qubit and two-qubit gate error. We'll briefly summarize what these properties are and refer to experiments in the [`qiskit-experiments`](https://qiskit-community.github.io/qiskit-experiments/index.html) package that are used to characterize them.\n", + "\n", + "#### T1\n", + "\n", + "$T_1$ is the characteristic time it takes for an excited qubit to fall to the ground state due to amplitude-damping decoherence processes. In a [$T_1$ experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t1.html), we measure an excited qubit after a delay. The larger the delay time is, the more\n", + "likely is the qubit to fall to the ground state. The goal of the\n", + "experiment is to characterize the decay rate of the qubit towards the\n", + "ground state.\n", + "\n", + "#### T2\n", + "\n", + "$T_2$ represents the amount of time required for a single qubit's Bloch\n", + "vector projection on the XY plane to fall to approximately 37% ($\\frac{1}{e}$) of\n", + "its initial amplitude due to dephasing decoherence processes. In a [$T_2$ Hahn Echo experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t2hahn.html), we can estimate the rate of this decay.\n", + "\n", + "#### State preparation and measurement (SPAM) error characterization\n", + "\n", + "In a [SPAM-error characterization experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/measurement/readout_mitigation.html) qubit are prepared in a certain state ($\\vert 0 \\rangle$ or $\\vert 1 \\rangle$) and measured. The probability of measuring a state different than the one prepared then gives the probability of the error.\n", + "\n", + "#### Single-qubit and two-qubit randomized benchmarking\n", + "\n", + "[Randomized benchmarking](https://qiskit-community.github.io/qiskit-experiments/manuals/verification/randomized_benchmarking.html) is a popular protocol for characterizing the error rate of\n", + "quantum processors. An RB experiment consists of the generation of random Clifford\n", + "circuits on the given qubits such that the unitary computed by the circuits is the\n", + "identity. After running the circuits, the number of shots resulting in an error (that is, an output different from the ground state) are counted, and from this data one can infer error estimates for the quantum device, by calculating the Error Per Clifford." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9d57c42d-7a91-4e79-bc6c-6e579da929f8", + "metadata": {}, + "outputs": [], + "source": [ + "# Create T1 experiments on all qubit in parallel\n", + "t1_exp = ParallelExperiment(\n", + " [\n", + " T1(\n", + " physical_qubits=[qubit],\n", + " delays=np.linspace(\n", + " 1e-6, 2 * backend.properties().t1(qubit), 5, endpoint=True\n", + " ),\n", + " )\n", + " for qubit in qubits\n", + " ],\n", + " backend,\n", + " analysis=None,\n", + ")\n", + "\n", + "# Create T2-Hahn experiments on all qubit in parallel\n", + "t2_exp = ParallelExperiment(\n", + " [\n", + " T2Hahn(\n", + " physical_qubits=[qubit],\n", + " delays=np.linspace(\n", + " 1e-6, 2 * backend.properties().t2(qubit), 5, endpoint=True\n", + " ),\n", + " )\n", + " for qubit in qubits\n", + " ],\n", + " backend,\n", + " analysis=None,\n", + ")\n", + "\n", + "# Create readout experiments on all qubit in parallel\n", + "readout_exp = LocalReadoutError(qubits)\n", + "\n", + "# Create single-qubit RB experiments on all qubit in parallel\n", + "singleq_rb_exp = ParallelExperiment(\n", + " [\n", + " StandardRB(\n", + " physical_qubits=[qubit], lengths=[10, 100, 500], num_samples=10\n", + " )\n", + " for qubit in qubits\n", + " ],\n", + " backend,\n", + " analysis=None,\n", + ")\n", + "\n", + "# Create two-qubit RB experiments on the three layers of disjoint edges of the heavy-hex\n", + "twoq_rb_exp_batched = BatchExperiment(\n", + " [\n", + " ParallelExperiment(\n", + " [\n", + " StandardRB(\n", + " physical_qubits=pair,\n", + " lengths=[10, 50, 100],\n", + " num_samples=10,\n", + " )\n", + " for pair in layer\n", + " ],\n", + " backend,\n", + " analysis=None,\n", + " )\n", + " for layer in layered_coupling_map\n", + " ],\n", + " backend,\n", + " flatten_results=True,\n", + " analysis=None,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cad4f8d3-c2d5-4bb5-92be-432a4573e14a", + "metadata": {}, + "source": [ + "### QPU properties over time\n", + "\n", + "Looking at the reported QPU properties over time (we'll consider a single week below), we see how these can fluctuate on a scale of a single day. Small fluctuations can happen even within a day. In this scenario, the reported properties (updated once per day) will not accurately capture the current status of the QPU. Moreover, if a job is transpiled locally (using current reported properties) and submitted but executed only at a later time (minutes or days), it may run the risk of having used outdated properties for qubit selection in the transpilation step. This highlights the importance of having updated information about the QPU at execution time. First, let's retrieve the properties over a certain time range." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "af1b6722-e77b-436a-bbcd-f272b95bb28c", + "metadata": {}, + "outputs": [], + "source": [ + "errors_list = []\n", + "for day_idx in range(10, 17):\n", + " calibrations_time = datetime(\n", + " year=2024, month=10, day=day_idx, hour=0, minute=0, second=0\n", + " )\n", + " targer_hist = backend.target_history(datetime=calibrations_time)\n", + "\n", + " t1_dict, t2_dict = {}, {}\n", + " for qubit in range(targer_hist.num_qubits):\n", + " t1_dict[qubit] = targer_hist.qubit_properties[qubit].t1\n", + " t2_dict[qubit] = targer_hist.qubit_properties[qubit].t2\n", + "\n", + " errors_dict = {\n", + " \"1q\": targer_hist[\"sx\"],\n", + " \"2q\": targer_hist[\"ecr\"],\n", + " \"spam\": targer_hist[\"measure\"],\n", + " \"t1\": t1_dict,\n", + " \"t2\": t2_dict,\n", + " }\n", + "\n", + " errors_list.append(errors_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "ac75df1b-f689-475c-94a2-d70d85b1f8ca", + "metadata": {}, + "source": [ + "Then, let's plot the values" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e0ba509d-e0e0-438b-aedf-5e01919c7d4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(5, 1, figsize=(10, 20), sharex=False)\n", + "\n", + "\n", + "# Plot for T1 values\n", + "for qubit in range(targer_hist.num_qubits):\n", + " t1s = []\n", + " for errors_dict in errors_list:\n", + " t1_dict = errors_dict[\"t1\"]\n", + " t1s.append(t1_dict[qubit] / 1e-6)\n", + "\n", + " axs[0].plot(t1s)\n", + "\n", + "axs[0].set_title(\"T1\")\n", + "axs[0].set_ylabel(r\"Time ($\\mu s$)\")\n", + "axs[0].set_xlabel(\"Days\")\n", + "\n", + "# Plot for T2 values\n", + "for qubit in range(targer_hist.num_qubits):\n", + " t2s = []\n", + " for errors_dict in errors_list:\n", + " t2_dict = errors_dict[\"t2\"]\n", + " t2s.append(t2_dict[qubit] / 1e-6)\n", + "\n", + " axs[1].plot(t2s)\n", + "\n", + "axs[1].set_title(\"T2\")\n", + "axs[1].set_ylabel(r\"Time ($\\mu s$)\")\n", + "axs[1].set_xlabel(\"Days\")\n", + "\n", + "# Plot SPAM values\n", + "for qubit in range(targer_hist.num_qubits):\n", + " spams = []\n", + " for errors_dict in errors_list:\n", + " spam_dict = errors_dict[\"spam\"]\n", + " spams.append(spam_dict[tuple([qubit])].error)\n", + "\n", + " axs[2].plot(spams)\n", + "\n", + "axs[2].set_title(\"SPAM Errors\")\n", + "axs[2].set_ylabel(\"Error Rate\")\n", + "axs[2].set_xlabel(\"Days\")\n", + "\n", + "# Plot 1Q Gate Errors\n", + "for qubit in range(targer_hist.num_qubits):\n", + " oneq_gates = []\n", + " for errors_dict in errors_list:\n", + " oneq_gate_dict = errors_dict[\"1q\"]\n", + " oneq_gates.append(oneq_gate_dict[tuple([qubit])].error)\n", + "\n", + " axs[3].plot(oneq_gates)\n", + "\n", + "axs[3].set_title(\"1Q Gate Errors\")\n", + "axs[3].set_ylabel(\"Error Rate\")\n", + "axs[3].set_xlabel(\"Days\")\n", + "\n", + "# Plot 2Q Gate Errors\n", + "for pair in one_dir_coupling_map:\n", + " twoq_gates = []\n", + " for errors_dict in errors_list:\n", + " twoq_gate_dict = errors_dict[\"2q\"]\n", + " twoq_gates.append(twoq_gate_dict[pair].error)\n", + "\n", + " axs[4].plot(twoq_gates)\n", + "\n", + "axs[4].set_title(\"2Q Gate Errors\")\n", + "axs[4].set_ylabel(\"Error Rate\")\n", + "axs[4].set_xlabel(\"Days\")\n", + "\n", + "plt.subplots_adjust(hspace=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a3343934-9273-457c-900b-2004b3aa9d0c", + "metadata": {}, + "source": [ + "You can see that over several days some of the qubit properties can change considerably. This highlights the importance of having fresh information of the QPU status, to be able to select the best performing qubits for an experiment." + ] + }, + { + "cell_type": "markdown", + "id": "0e53f0a5-7713-4915-ae47-c1aa0a4f17cd", + "metadata": {}, + "source": [ + "## Step 2: Optimize problem for quantum hardware execution\n", + "\n", + "No optimization of the circuits or operators is done in this tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "862789c2-d17e-43ed-8e1b-2f37d7628ed2", + "metadata": {}, + "source": [ + "## Step 3: Execute using Qiskit primitives" + ] + }, + { + "cell_type": "markdown", + "id": "d88f925f-3ffd-43de-ac1f-2c5ba3b8cd96", + "metadata": {}, + "source": [ + "### Execute a quantum circuit with default qubit selection\n", + "\n", + "As a reference result of performance, we'll execute a quantum circuit on a QPU by using the default qubits, which are the qubits selected with the requested backend properties. We will use `optimization_level = 3`. This setting includes the most advanced transpilation optimization, and uses target properties (like operation errors) to select the best performing qubits for execution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c0d09ad-2e6a-4067-8034-8df92a475ff1", + "metadata": {}, + "outputs": [], + "source": [ + "pm = generate_preset_pass_manager(target=backend.target, optimization_level=3)\n", + "isa_circuits = pm.run(circuits)\n", + "initial_qubits = [\n", + " [\n", + " idx\n", + " for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", + " if qb._register.name != \"ancilla\"\n", + " ]\n", + " for circuit in isa_circuits\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "6d4fb04c-a5cf-48e8-a759-dde64fd751ac", + "metadata": {}, + "source": [ + "### Execute a quantum circuit with real-time qubit selection\n", + "\n", + "In this section, we'll investigate the importance of having updated information on qubit properties of the QPU for optimal results. First, we'll carry out a full suite of QPU characterization experiments ($T_1$, $T_2$, SPAM, single-qubit RB and two-qubit RB), which we can then use to update the backend properties. This allows the pass manager to select qubits for the execution based on fresh information about the QPU, possibly improving execution performances. Second, we execute the Bell pair circuit and we compare the fidelity obtained after selecting the qubits with update QPU properties to the fidelity we obtained before when we use the default reported properties for qubit selection." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a8266467-9d60-411f-89dd-8cad6558f588", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare characterization experiments\n", + "batches = [t1_exp, t2_exp, readout_exp, singleq_rb_exp, twoq_rb_exp_batched]\n", + "batches_exp = BatchExperiment(batches, backend) # , analysis=None)\n", + "run_options = {\"shots\": 1e3, \"dynamic\": False}\n", + "\n", + "with Session(backend=backend) as session:\n", + " sampler = SamplerV2(mode=session)\n", + "\n", + " # Run characterization experiments\n", + " batches_exp_data = batches_exp.run(\n", + " sampler=sampler, **run_options\n", + " ).block_for_results()\n", + "\n", + " EPG_sx_result_list = batches_exp_data.analysis_results(\"EPG_sx\")\n", + " EPG_sx_result_q_indices = [\n", + " result.device_components.index for result in EPG_sx_result_list\n", + " ]\n", + " EPG_x_result_list = batches_exp_data.analysis_results(\"EPG_x\")\n", + " EPG_x_result_q_indices = [\n", + " result.device_components.index for result in EPG_x_result_list\n", + " ]\n", + " T1_result_list = batches_exp_data.analysis_results(\"T1\")\n", + " T1_result_q_indices = [\n", + " result.device_components.index for result in T1_result_list\n", + " ]\n", + "\n", + " T2_result_list = batches_exp_data.analysis_results(\"T2\")\n", + " T2_result_q_indices = [\n", + " result.device_components.index for result in T2_result_list\n", + " ]\n", + "\n", + " Readout_result_list = batches_exp_data.analysis_results(\n", + " \"Local Readout Mitigator\"\n", + " )\n", + "\n", + " EPG_ecr_result_list = batches_exp_data.analysis_results(\"EPG_ecr\")\n", + "\n", + " # Update target properties\n", + " target = copy.deepcopy(backend.target)\n", + " for i in range(target.num_qubits - 1):\n", + " qarg = (i,)\n", + "\n", + " if qarg in EPG_sx_result_q_indices:\n", + " target.update_instruction_properties(\n", + " instruction=\"sx\",\n", + " qargs=qarg,\n", + " properties=InstructionProperties(\n", + " error=EPG_sx_result_list[i].value.nominal_value\n", + " ),\n", + " )\n", + " if qarg in EPG_x_result_q_indices:\n", + " target.update_instruction_properties(\n", + " instruction=\"x\",\n", + " qargs=qarg,\n", + " properties=InstructionProperties(\n", + " error=EPG_x_result_list[i].value.nominal_value\n", + " ),\n", + " )\n", + "\n", + " err_mat = Readout_result_list.value.assignment_matrix(i)\n", + " readout_assignment_error = (\n", + " err_mat[0, 1] + err_mat[1, 0]\n", + " ) / 2 # average readout error\n", + " target.update_instruction_properties(\n", + " instruction=\"measure\",\n", + " qargs=qarg,\n", + " properties=InstructionProperties(error=readout_assignment_error),\n", + " )\n", + "\n", + " if qarg in T1_result_q_indices:\n", + " target.qubit_properties[i].t1 = T1_result_list[\n", + " i\n", + " ].value.nominal_value\n", + " if qarg in T2_result_q_indices:\n", + " target.qubit_properties[i].t2 = T2_result_list[\n", + " i\n", + " ].value.nominal_value\n", + "\n", + " for pair_idx, pair in enumerate(flattened_layered_coupling_map):\n", + " qarg = tuple(pair)\n", + " try:\n", + " target.update_instruction_properties(\n", + " instruction=\"ecr\",\n", + " qargs=qarg,\n", + " properties=InstructionProperties(\n", + " error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", + " ),\n", + " )\n", + " except:\n", + " target.update_instruction_properties(\n", + " instruction=\"ecr\",\n", + " qargs=qarg[::-1],\n", + " properties=InstructionProperties(\n", + " error=EPG_ecr_result_list[pair_idx].value.nominal_value\n", + " ),\n", + " )\n", + "\n", + " # transpile circuits to updated target\n", + " pm = generate_preset_pass_manager(target=target, optimization_level=3)\n", + " isa_circuit_updated = pm.run(circuits)\n", + " updated_qubits = [\n", + " [\n", + " idx\n", + " for idx, qb in circuit.layout.initial_layout.get_physical_bits().items()\n", + " if qb._register.name != \"ancilla\"\n", + " ]\n", + " for circuit in isa_circuit_updated\n", + " ]\n", + "\n", + " n_trials = 3 # run multiple trials to see variations\n", + "\n", + " # interleave circuits\n", + " interleaved_circuits = []\n", + " for original_circuit, updated_circuit in zip(\n", + " isa_circuits, isa_circuit_updated\n", + " ):\n", + " interleaved_circuits.append(original_circuit)\n", + " interleaved_circuits.append(updated_circuit)\n", + "\n", + " # Run circuits\n", + " # Set simple error suppression/mitigation options\n", + " sampler.options.dynamical_decoupling.enable = True\n", + " sampler.options.dynamical_decoupling.sequence_type = \"XY4\"\n", + "\n", + " job_interleaved = sampler.run(interleaved_circuits * n_trials)" + ] + }, + { + "cell_type": "markdown", + "id": "d72e021a-3ea6-4ed2-829f-e59ce8017ef4", + "metadata": {}, + "source": [ + "## Step 4: Post-process and return result in desired classical format\n", + "\n", + "Finally, let's compare the fidelity of the Bell state obtained in the two different settings:\n", + "\n", + "- `original`, that is with the default qubits chosen by the transpiler based on reported properties of the backend.\n", + "- `updated`, that is with the qubits chosen based on updated properties of the backend after characterization experiments have run." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5a19a5d1-5daf-4d65-9785-8f8724853821", + "metadata": {}, + "outputs": [], + "source": [ + "results = job_interleaved.result()\n", + "all_fidelity_list, all_fidelity_updated_list = [], []\n", + "for exp_idx in range(n_trials):\n", + " fidelity_list, fidelity_updated_list = [], []\n", + "\n", + " for idx, num_qubits in enumerate(num_qubits_list):\n", + " pub_result_original = results[\n", + " 2 * exp_idx * len(num_qubits_list) + 2 * idx\n", + " ]\n", + " pub_result_updated = results[\n", + " 2 * exp_idx * len(num_qubits_list) + 2 * idx + 1\n", + " ]\n", + "\n", + " fid = hellinger_fidelity(\n", + " ideal_dist, pub_result_original.data.c.get_counts()\n", + " )\n", + " fidelity_list.append(fid)\n", + "\n", + " fid_up = hellinger_fidelity(\n", + " ideal_dist, pub_result_updated.data.c.get_counts()\n", + " )\n", + " fidelity_updated_list.append(fid_up)\n", + " all_fidelity_list.append(fidelity_list)\n", + " all_fidelity_updated_list.append(fidelity_updated_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "656ec97a-3fd9-4635-9a98-1c5589761689", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "plt.errorbar(\n", + " num_qubits_list,\n", + " np.mean(all_fidelity_list, axis=0),\n", + " yerr=np.std(all_fidelity_list, axis=0),\n", + " fmt=\"o-.\",\n", + " label=\"original\",\n", + " color=\"b\",\n", + ")\n", + "# plt.plot(num_qubits_list, fidelity_list, '-.')\n", + "plt.errorbar(\n", + " num_qubits_list,\n", + " np.mean(all_fidelity_updated_list, axis=0),\n", + " yerr=np.std(all_fidelity_updated_list, axis=0),\n", + " fmt=\"o-.\",\n", + " label=\"updated\",\n", + " color=\"r\",\n", + ")\n", + "# plt.plot(num_qubits_list, fidelity_updated_list, '-.')\n", + "plt.xlabel(\"Chain length\")\n", + "plt.xticks(num_qubits_list)\n", + "plt.ylabel(\"Fidelity\")\n", + "plt.title(\"Bell pair fidelity at the edge of N-qubits chain\")\n", + "plt.legend()\n", + "plt.grid(\n", + " alpha=0.2,\n", + " linestyle=\"-.\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3705d85c-e11d-4a8f-bfae-20bd501124d8", + "metadata": {}, + "source": [ + "With increasing chain length, and thus less freedom to choose physical qubits, the fact that we have updated information about qubit properties becomes less substantial, with there being no difference when all of the qubits are used." + ] + }, + { + "cell_type": "markdown", + "id": "cd81b208-b13b-4988-854e-1741408f36f3", + "metadata": {}, + "source": [ + "\n", + "Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "b94755c1-1c19-434a-96b9-b83922b5d63c", + "metadata": {}, + "source": [ + "## Tutorial survey\n", + "\n", + "Please take this short survey to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", + "\n", + "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_0w6FZ9QrWkKfTQq)" + ] + } + ], + "metadata": { + "description": "Run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU.", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + }, + "title": "Real-time benchmarking for qubit selection\n" + }, + "nbformat": 4, + "nbformat_minor": 4 } -], -"metadata": { -"description": "Run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU.", -"kernelspec": { -"display_name": "Python 3", -"language": "python", -"name": "python3" -}, -"language_info": { -"codemirror_mode": { -"name": "ipython", -"version": 3 -}, -"file_extension": ".py", -"mimetype": "text/x-python", -"name": "python", -"nbconvert_exporter": "python", -"pygments_lexer": "ipython3", -"version": "3" -}, -"title": "Real-time benchmarking for qubit selection\n" -}, -"nbformat": 4, -"nbformat_minor": 4 -} \ No newline at end of file From f2ce0aaf276ca1f1854ace5350b71829afd980da Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Wed, 27 Aug 2025 10:09:42 -0500 Subject: [PATCH 20/26] more md fixes --- docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 34920cd9d3f..7a4deff0bba 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -30,6 +30,7 @@ "metadata": {}, "source": [ "## Background\n", + "\n", "This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", "\n", "The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine." @@ -41,6 +42,7 @@ "metadata": {}, "source": [ "## Requirements\n", + "\n", "Before starting this tutorial, be sure you have the following installed:\n", "\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", From f70f56df5ef5590f8b94be9f7cda73c2278c0160 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Wed, 27 Aug 2025 11:24:05 -0400 Subject: [PATCH 21/26] temporarily restore file to test --- ...ime-benchmarking-for-qubit-selection.ipynb | 20 +++++-------------- 1 file changed, 5 insertions(+), 15 deletions(-) diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 7a4deff0bba..7d61fa6b910 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Real-time benchmarking for qubit selection\n", - "*Usage estimate: 4 minutes on an Eagle r2 processor (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 4 minutes on ibm_cusco (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -30,7 +30,6 @@ "metadata": {}, "source": [ "## Background\n", - "\n", "This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", "\n", "The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine." @@ -42,7 +41,6 @@ "metadata": {}, "source": [ "## Requirements\n", - "\n", "Before starting this tutorial, be sure you have the following installed:\n", "\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", @@ -200,24 +198,20 @@ "A series of experiments is used to characterize the main properties of the qubits in a QPU. These are $T_1$, $T_2$, readout error, and single-qubit and two-qubit gate error. We'll briefly summarize what these properties are and refer to experiments in the [`qiskit-experiments`](https://qiskit-community.github.io/qiskit-experiments/index.html) package that are used to characterize them.\n", "\n", "#### T1\n", - "\n", "$T_1$ is the characteristic time it takes for an excited qubit to fall to the ground state due to amplitude-damping decoherence processes. In a [$T_1$ experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t1.html), we measure an excited qubit after a delay. The larger the delay time is, the more\n", "likely is the qubit to fall to the ground state. The goal of the\n", "experiment is to characterize the decay rate of the qubit towards the\n", "ground state.\n", "\n", "#### T2\n", - "\n", "$T_2$ represents the amount of time required for a single qubit's Bloch\n", "vector projection on the XY plane to fall to approximately 37% ($\\frac{1}{e}$) of\n", "its initial amplitude due to dephasing decoherence processes. In a [$T_2$ Hahn Echo experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t2hahn.html), we can estimate the rate of this decay.\n", "\n", "#### State preparation and measurement (SPAM) error characterization\n", - "\n", "In a [SPAM-error characterization experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/measurement/readout_mitigation.html) qubit are prepared in a certain state ($\\vert 0 \\rangle$ or $\\vert 1 \\rangle$) and measured. The probability of measuring a state different than the one prepared then gives the probability of the error.\n", "\n", "#### Single-qubit and two-qubit randomized benchmarking\n", - "\n", "[Randomized benchmarking](https://qiskit-community.github.io/qiskit-experiments/manuals/verification/randomized_benchmarking.html) is a popular protocol for characterizing the error rate of\n", "quantum processors. An RB experiment consists of the generation of random Clifford\n", "circuits on the given qubits such that the unitary computed by the circuits is the\n", @@ -305,7 +299,6 @@ "metadata": {}, "source": [ "### QPU properties over time\n", - "\n", "Looking at the reported QPU properties over time (we'll consider a single week below), we see how these can fluctuate on a scale of a single day. Small fluctuations can happen even within a day. In this scenario, the reported properties (updated once per day) will not accurately capture the current status of the QPU. Moreover, if a job is transpiled locally (using current reported properties) and submitted but executed only at a later time (minutes or days), it may run the risk of having used outdated properties for qubit selection in the transpilation step. This highlights the importance of having updated information about the QPU at execution time. First, let's retrieve the properties over a certain time range." ] }, @@ -450,7 +443,6 @@ "metadata": {}, "source": [ "## Step 2: Optimize problem for quantum hardware execution\n", - "\n", "No optimization of the circuits or operators is done in this tutorial." ] }, @@ -467,8 +459,7 @@ "id": "d88f925f-3ffd-43de-ac1f-2c5ba3b8cd96", "metadata": {}, "source": [ - "### Execute a quantum circuit with default qubit selection\n", - "\n", + "### Execute a quantum circuit with default qubit selection\n", "As a reference result of performance, we'll execute a quantum circuit on a QPU by using the default qubits, which are the qubits selected with the requested backend properties. We will use `optimization_level = 3`. This setting includes the most advanced transpilation optimization, and uses target properties (like operation errors) to select the best performing qubits for execution." ] }, @@ -643,7 +634,6 @@ "## Step 4: Post-process and return result in desired classical format\n", "\n", "Finally, let's compare the fidelity of the Bell state obtained in the two different settings:\n", - "\n", "- `original`, that is with the default qubits chosen by the transpiler based on reported properties of the backend.\n", "- `updated`, that is with the qubits chosen based on updated properties of the backend after characterization experiments have run." ] @@ -742,9 +732,9 @@ "id": "cd81b208-b13b-4988-854e-1741408f36f3", "metadata": {}, "source": [ - "\n", - "Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", - "" + "
\n", + "Call to Action: Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", + "
" ] }, { From 2980524c0be49cac062ddae96a60022925d735d4 Mon Sep 17 00:00:00 2001 From: Rebecca Dimock Date: Wed, 27 Aug 2025 10:30:39 -0500 Subject: [PATCH 22/26] Make sure all headings have a blank line --- .../real-time-benchmarking-for-qubit-selection.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 7a4deff0bba..9165ae4aabb 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -56,7 +56,7 @@ "id": "3df52d5f-806a-4846-849e-633706a96d0b", "metadata": {}, "source": [ - "## Setup" + "## Setup\n" ] }, { @@ -142,7 +142,7 @@ "id": "16948f21-a39b-4444-bf02-5f81331825c4", "metadata": {}, "source": [ - "### Setting up backend and coupling map" + "### Setting up backend and coupling map\n" ] }, { @@ -459,7 +459,7 @@ "id": "862789c2-d17e-43ed-8e1b-2f37d7628ed2", "metadata": {}, "source": [ - "## Step 3: Execute using Qiskit primitives" + "## Step 3: Execute using Qiskit primitives\n" ] }, { From f52066830da41858d6f169ba15f0e6859a73714d Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Wed, 27 Aug 2025 11:35:22 -0400 Subject: [PATCH 23/26] bring back changes --- ...ime-benchmarking-for-qubit-selection.ipynb | 20 ++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 7d61fa6b910..7a4deff0bba 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Real-time benchmarking for qubit selection\n", - "*Usage estimate: 4 minutes on ibm_cusco (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 4 minutes on an Eagle r2 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -30,6 +30,7 @@ "metadata": {}, "source": [ "## Background\n", + "\n", "This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", "\n", "The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine." @@ -41,6 +42,7 @@ "metadata": {}, "source": [ "## Requirements\n", + "\n", "Before starting this tutorial, be sure you have the following installed:\n", "\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", @@ -198,20 +200,24 @@ "A series of experiments is used to characterize the main properties of the qubits in a QPU. These are $T_1$, $T_2$, readout error, and single-qubit and two-qubit gate error. We'll briefly summarize what these properties are and refer to experiments in the [`qiskit-experiments`](https://qiskit-community.github.io/qiskit-experiments/index.html) package that are used to characterize them.\n", "\n", "#### T1\n", + "\n", "$T_1$ is the characteristic time it takes for an excited qubit to fall to the ground state due to amplitude-damping decoherence processes. In a [$T_1$ experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t1.html), we measure an excited qubit after a delay. The larger the delay time is, the more\n", "likely is the qubit to fall to the ground state. The goal of the\n", "experiment is to characterize the decay rate of the qubit towards the\n", "ground state.\n", "\n", "#### T2\n", + "\n", "$T_2$ represents the amount of time required for a single qubit's Bloch\n", "vector projection on the XY plane to fall to approximately 37% ($\\frac{1}{e}$) of\n", "its initial amplitude due to dephasing decoherence processes. In a [$T_2$ Hahn Echo experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t2hahn.html), we can estimate the rate of this decay.\n", "\n", "#### State preparation and measurement (SPAM) error characterization\n", + "\n", "In a [SPAM-error characterization experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/measurement/readout_mitigation.html) qubit are prepared in a certain state ($\\vert 0 \\rangle$ or $\\vert 1 \\rangle$) and measured. The probability of measuring a state different than the one prepared then gives the probability of the error.\n", "\n", "#### Single-qubit and two-qubit randomized benchmarking\n", + "\n", "[Randomized benchmarking](https://qiskit-community.github.io/qiskit-experiments/manuals/verification/randomized_benchmarking.html) is a popular protocol for characterizing the error rate of\n", "quantum processors. An RB experiment consists of the generation of random Clifford\n", "circuits on the given qubits such that the unitary computed by the circuits is the\n", @@ -299,6 +305,7 @@ "metadata": {}, "source": [ "### QPU properties over time\n", + "\n", "Looking at the reported QPU properties over time (we'll consider a single week below), we see how these can fluctuate on a scale of a single day. Small fluctuations can happen even within a day. In this scenario, the reported properties (updated once per day) will not accurately capture the current status of the QPU. Moreover, if a job is transpiled locally (using current reported properties) and submitted but executed only at a later time (minutes or days), it may run the risk of having used outdated properties for qubit selection in the transpilation step. This highlights the importance of having updated information about the QPU at execution time. First, let's retrieve the properties over a certain time range." ] }, @@ -443,6 +450,7 @@ "metadata": {}, "source": [ "## Step 2: Optimize problem for quantum hardware execution\n", + "\n", "No optimization of the circuits or operators is done in this tutorial." ] }, @@ -459,7 +467,8 @@ "id": "d88f925f-3ffd-43de-ac1f-2c5ba3b8cd96", "metadata": {}, "source": [ - "### Execute a quantum circuit with default qubit selection\n", + "### Execute a quantum circuit with default qubit selection\n", + "\n", "As a reference result of performance, we'll execute a quantum circuit on a QPU by using the default qubits, which are the qubits selected with the requested backend properties. We will use `optimization_level = 3`. This setting includes the most advanced transpilation optimization, and uses target properties (like operation errors) to select the best performing qubits for execution." ] }, @@ -634,6 +643,7 @@ "## Step 4: Post-process and return result in desired classical format\n", "\n", "Finally, let's compare the fidelity of the Bell state obtained in the two different settings:\n", + "\n", "- `original`, that is with the default qubits chosen by the transpiler based on reported properties of the backend.\n", "- `updated`, that is with the qubits chosen based on updated properties of the backend after characterization experiments have run." ] @@ -732,9 +742,9 @@ "id": "cd81b208-b13b-4988-854e-1741408f36f3", "metadata": {}, "source": [ - "
\n", - "Call to Action: Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", - "
" + "\n", + "Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", + "" ] }, { From 283f95f89f9006b9ca8be9201788020299c0c001 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Wed, 27 Aug 2025 11:39:44 -0400 Subject: [PATCH 24/26] Update real-time-benchmarking-for-qubit-selection.ipynb --- .../real-time-benchmarking-for-qubit-selection.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 9165ae4aabb..7a4deff0bba 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -56,7 +56,7 @@ "id": "3df52d5f-806a-4846-849e-633706a96d0b", "metadata": {}, "source": [ - "## Setup\n" + "## Setup" ] }, { @@ -142,7 +142,7 @@ "id": "16948f21-a39b-4444-bf02-5f81331825c4", "metadata": {}, "source": [ - "### Setting up backend and coupling map\n" + "### Setting up backend and coupling map" ] }, { @@ -459,7 +459,7 @@ "id": "862789c2-d17e-43ed-8e1b-2f37d7628ed2", "metadata": {}, "source": [ - "## Step 3: Execute using Qiskit primitives\n" + "## Step 3: Execute using Qiskit primitives" ] }, { From a92101efb5c12b2e9cd7053aa87d120c70d74690 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Wed, 27 Aug 2025 11:49:31 -0400 Subject: [PATCH 25/26] restore images --- .../656ec97a-3fd9-4635-9a98-1c5589761689-0.avif | Bin 0 -> 12243 bytes .../95571eca-02ba-4452-816a-c04822675be8-0.avif | Bin 0 -> 39537 bytes .../e0ba509d-e0e0-438b-aedf-5e01919c7d4f-0.avif | Bin 0 -> 60262 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 public/docs/images/tutorials/real-time-benchmarking-for-qubit-selection/extracted-outputs/656ec97a-3fd9-4635-9a98-1c5589761689-0.avif create mode 100644 public/docs/images/tutorials/real-time-benchmarking-for-qubit-selection/extracted-outputs/95571eca-02ba-4452-816a-c04822675be8-0.avif create 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a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -732,9 +732,9 @@ "id": "cd81b208-b13b-4988-854e-1741408f36f3", "metadata": {}, "source": [ - "

\n", - "Call to Action: Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", - "
" + "\n", + "Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", + "" ] }, {