From cf963b1336eaad219db6e091eb81b6b1d1fdb8b9 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Wed, 9 Nov 2022 09:10:17 +0100
Subject: [PATCH 01/21] WIP

---
 bayes_opt/bayesian_optimization.py |  50 ++-
 bayes_opt/constraint.py            |   6 +-
 bayes_opt/logger.py                |  24 +-
 bayes_opt/parameter.py             | 145 +++++++++
 bayes_opt/target_space.py          | 263 +++++++++------
 bayes_opt/util.py                  |   1 -
 examples/parameter_types.ipynb     | 497 +++++++++++++++++++++++++++++
 7 files changed, 856 insertions(+), 130 deletions(-)
 create mode 100644 bayes_opt/parameter.py
 create mode 100644 examples/parameter_types.ipynb

diff --git a/bayes_opt/bayesian_optimization.py b/bayes_opt/bayesian_optimization.py
index 7f37331c5..1dc083ed7 100644
--- a/bayes_opt/bayesian_optimization.py
+++ b/bayes_opt/bayesian_optimization.py
@@ -1,7 +1,5 @@
 import warnings
 
-from bayes_opt.constraint import ConstraintModel
-
 from .target_space import TargetSpace
 from .event import Events, DEFAULT_EVENTS
 from .logger import _get_default_logger
@@ -9,9 +7,12 @@
 
 from sklearn.gaussian_process.kernels import Matern
 from sklearn.gaussian_process import GaussianProcessRegressor
+from .parameter import wrap_kernel
+from icecream import ic
 
 
 class Queue:
+
     def __init__(self):
         self._queue = []
 
@@ -116,15 +117,6 @@ def __init__(self,
 
         self._queue = Queue()
 
-        # Internal GP regressor
-        self._gp = GaussianProcessRegressor(
-            kernel=Matern(nu=2.5),
-            alpha=1e-6,
-            normalize_y=True,
-            n_restarts_optimizer=5,
-            random_state=self._random_state,
-        )
-
         if constraint is None:
             # Data structure containing the function to be optimized, the
             # bounds of its domain, and a record of the evaluations we have
@@ -132,20 +124,22 @@ def __init__(self,
             self._space = TargetSpace(f, pbounds, random_state=random_state)
             self.is_constrained = False
         else:
-            constraint_ = ConstraintModel(
-                constraint.fun,
-                constraint.lb,
-                constraint.ub,
-                random_state=random_state
-            )
-            self._space = TargetSpace(
-                f,
-                pbounds,
-                constraint=constraint_,
-                random_state=random_state
-            )
+            self._space = TargetSpace(f,
+                                      pbounds,
+                                      constraint=constraint,
+                                      random_state=random_state)
             self.is_constrained = True
 
+        # Internal GP regressor
+        self._gp = GaussianProcessRegressor(
+            kernel=wrap_kernel(Matern(nu=2.5),
+                               transform=self._space.kernel_transform),
+            alpha=1e-6,
+            normalize_y=True,
+            n_restarts_optimizer=5,
+            random_state=self._random_state,
+        )
+
         self._verbose = verbose
         self._bounds_transformer = bounds_transformer
         if self._bounds_transformer:
@@ -219,9 +213,8 @@ def suggest(self, utility_function):
                              gp=self._gp,
                              constraint=self.constraint,
                              y_max=self._space.target.max(),
-                             bounds=self._space.bounds,
+                             bounds=self._space.float_bounds,
                              random_state=self._random_state)
-
         return self._space.array_to_params(suggestion)
 
     def _prime_queue(self, init_points):
@@ -230,7 +223,8 @@ def _prime_queue(self, init_points):
             init_points = max(init_points, 1)
 
         for _ in range(init_points):
-            self._queue.add(self._space.random_sample())
+            self._queue.add(
+                self._space.array_to_params(self._space.random_sample()))
 
     def _prime_subscriptions(self):
         if not any([len(subs) for subs in self._events.values()]):
@@ -307,8 +301,8 @@ def maximize(self,
             if self._bounds_transformer and iteration > 0:
                 # The bounds transformer should only modify the bounds after
                 # the init_points points (only for the true iterations)
-                self.set_bounds(
-                    self._bounds_transformer.transform(self._space))
+                self.set_bounds(self._bounds_transformer.transform(
+                    self._space))
 
         self.dispatch(Events.OPTIMIZATION_END)
 
diff --git a/bayes_opt/constraint.py b/bayes_opt/constraint.py
index a31142310..173e30acb 100644
--- a/bayes_opt/constraint.py
+++ b/bayes_opt/constraint.py
@@ -2,7 +2,7 @@
 from sklearn.gaussian_process.kernels import Matern
 from sklearn.gaussian_process import GaussianProcessRegressor
 from scipy.stats import norm
-
+from .parameter import wrap_kernel
 
 class ConstraintModel():
     """
@@ -37,7 +37,7 @@ class ConstraintModel():
     is a simply the product of the individual probabilities.
     """
 
-    def __init__(self, fun, lb, ub, random_state=None):
+    def __init__(self, fun, lb, ub, transform, random_state=None):
         self.fun = fun
 
         self._lb = np.atleast_1d(lb)        
@@ -48,7 +48,7 @@ def __init__(self, fun, lb, ub, random_state=None):
             raise ValueError(msg)
 
         basis = lambda: GaussianProcessRegressor(
-            kernel=Matern(nu=2.5),
+            kernel=wrap_kernel(Matern(nu=2.5), transform),
             alpha=1e-6,
             normalize_y=True,
             n_restarts_optimizer=5,
diff --git a/bayes_opt/logger.py b/bayes_opt/logger.py
index e2f47f173..61b81c594 100644
--- a/bayes_opt/logger.py
+++ b/bayes_opt/logger.py
@@ -5,7 +5,10 @@
 from .observer import _Tracker
 from .event import Events
 from .util import Colours
+from .parameter import FloatParameter, IntParameter, CategoricalParameter
 
+import numpy as np
+from icecream import ic
 def _get_default_logger(verbose, is_constrained):
     return ScreenLogger(verbose=verbose, is_constrained=is_constrained)
 
@@ -66,7 +69,7 @@ def _format_bool(self, x):
         )
         return s
 
-    def _format_key(self, key):
+    def _format_str(self, key):
         s = "{key:^{s}}".format(
             key=key,
             s=self._default_cell_size
@@ -86,20 +89,29 @@ def _step(self, instance, colour=Colours.black):
 
 
         for key in instance.space.keys:
-            cells.append(self._format_number(res["params"][key]))
+            val = res["params"][key]
+            if type(instance.space._params_config[key]) == FloatParameter:
+                cells.append(self._format_number(val))
+            elif type(instance.space._params_config[key]) == IntParameter:
+                cells.append(self._format_number(val))
+            elif type(instance.space._params_config[key]) == CategoricalParameter:
+                cells.append(self._format_str(str(val)))
+            else:
+                raise TypeError
+                
 
         return "| " + " | ".join(map(colour, cells)) + " |"
 
     def _header(self, instance):
         cells = []
-        cells.append(self._format_key("iter"))
-        cells.append(self._format_key("target"))
+        cells.append(self._format_str("iter"))
+        cells.append(self._format_str("target"))
 
         if self._is_constrained:
-            cells.append(self._format_key("allowed"))
+            cells.append(self._format_str("allowed"))
 
         for key in instance.space.keys:
-            cells.append(self._format_key(key))
+            cells.append(self._format_str(key))
 
         line = "| " + " | ".join(cells) + " |"
         self._header_length = len(line)
diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
new file mode 100644
index 000000000..0b3b93518
--- /dev/null
+++ b/bayes_opt/parameter.py
@@ -0,0 +1,145 @@
+from typing import Callable
+import numpy as np
+from sklearn.gaussian_process import kernels
+from inspect import signature
+
+from icecream import ic
+
+def is_numeric(value):
+    return type(value) in [float, int, complex]
+
+def pfloat(*args, **kwargs):
+    return FloatParameter(*args, **kwargs)
+
+
+def pint(*args, **kwargs):
+    return IntParameter(*args, **kwargs)
+
+
+def pcat(*args, **kwargs):
+    return CategoricalParameter(*args, **kwargs)
+
+
+class BayesParameter():
+
+    def __init__(self, name: str, domain) -> None:
+        self.name = name
+        self.domain = domain
+
+    @property
+    def float_bounds(self):
+        pass
+
+    def to_float(self, value) -> np.ndarray:
+        pass
+
+    def to_param(self, value):
+        pass
+
+    def kernel_transform(self, value):
+        pass
+
+    @property
+    def dim(self) -> int:
+        pass
+
+
+class FloatParameter(BayesParameter):
+
+    def __init__(self, name: str, domain) -> None:
+        super().__init__(name, domain)
+
+    @property
+    def float_bounds(self):
+        return np.array(self.domain)
+
+    def to_float(self, value) -> np.ndarray:
+        return value
+
+    def to_param(self, value):
+        return float(value)
+
+    def kernel_transform(self, value):
+        return value
+
+    @property
+    def dim(self) -> int:
+        return 1
+
+
+class IntParameter(BayesParameter):
+
+    def __init__(self, name: str, domain) -> None:
+        super().__init__(name, domain)
+
+    @property
+    def float_bounds(self):
+        # adding/subtracting ~0.5 to achieve uniform probability of integers
+        return np.array(
+            [self.domain[0] - 0.4999999, self.domain[1] + 0.4999999])
+
+    def to_float(self, value) -> np.ndarray:
+        return float(value)
+
+    def to_param(self, value):
+        return int(np.round(np.squeeze(value)))
+
+    def kernel_transform(self, value):
+        return np.round(value)
+
+    @property
+    def dim(self) -> int:
+        return 1
+
+
+class CategoricalParameter(BayesParameter):
+
+    def __init__(self, name: str, domain) -> None:
+        super().__init__(name, domain)
+
+    @property
+    def float_bounds(self):
+        # to achieve uniform probability after rounding
+        lower = np.zeros(self.dim)
+        upper = np.ones(self.dim)
+        return np.vstack((lower, upper)).T
+
+    def to_float(self, value) -> np.ndarray:
+        res = np.zeros(len(self.domain))
+        one_hot_index = [i for i, val in enumerate(self.domain) if val==value]
+        if len(one_hot_index) != 1:
+            raise ValueError
+        res[one_hot_index] = 1
+        return res.astype(float)
+
+    def to_param(self, value):
+        return self.domain[np.argmax(value)]
+
+    def kernel_transform(self, value):
+        value = np.atleast_2d(value)
+        res = np.zeros(value.shape)
+        res[np.argmax(value, axis=0)] = 1
+        return res
+
+    @property
+    def dim(self) -> int:
+        return len(self.domain)
+
+def wrap_kernel(kernel: kernels.Kernel, transform: Callable) -> kernels.Kernel:
+    class WrappedKernel(type(kernel)):
+        @copy_signature(getattr(kernel.__class__.__init__, "deprecated_original", kernel.__class__.__init__))
+        def __init__(self, **kwargs) -> None:
+            super().__init__(**kwargs)
+
+        def __call__(self, X, Y=None, eval_gradient=False):
+            X = transform(X)
+            return super().__call__(X, Y, eval_gradient)
+
+    return WrappedKernel(**kernel.get_params())
+
+def copy_signature(source_fct):
+    """https://stackoverflow.com/a/58989918/"""
+    def copy(target_fct): 
+        target_fct.__signature__ = signature(source_fct)
+        return target_fct 
+    return copy 
\ No newline at end of file
diff --git a/bayes_opt/target_space.py b/bayes_opt/target_space.py
index b2de951c7..acaf3570a 100644
--- a/bayes_opt/target_space.py
+++ b/bayes_opt/target_space.py
@@ -1,5 +1,8 @@
 import numpy as np
 from .util import ensure_rng, NotUniqueError
+from icecream import ic
+from .parameter import pfloat, pint, pcat, is_numeric, CategoricalParameter
+from .constraint import ConstraintModel
 
 
 def _hashable(x):
@@ -22,7 +25,12 @@ class TargetSpace(object):
     >>> y = space.register_point(x)
     >>> assert self.max_point()['max_val'] == y
     """
-    def __init__(self, target_func, pbounds, constraint=None, random_state=None):
+
+    def __init__(self,
+                 target_func,
+                 pbounds,
+                 constraint=None,
+                 random_state=None):
         """
         Parameters
         ----------
@@ -41,13 +49,12 @@ def __init__(self, target_func, pbounds, constraint=None, random_state=None):
         # The function to be optimized
         self.target_func = target_func
 
-        # Get the name of the parameters
         self._keys = sorted(pbounds)
-        # Create an array with parameters bounds
-        self._bounds = np.array(
-            [item[1] for item in sorted(pbounds.items(), key=lambda x: x[0])],
-            dtype=float
-        )
+        self._params_config = self.make_params(pbounds)
+        self._dim = sum([self._params_config[key].dim for key in self._keys])
+
+        self._masks = self.make_masks()
+        self._float_bounds = self.calculate_float_bounds()
 
         # preallocated memory for X and Y points
         self._params = np.empty(shape=(0, self.dim))
@@ -56,15 +63,20 @@ def __init__(self, target_func, pbounds, constraint=None, random_state=None):
         # keep track of unique points we have seen so far
         self._cache = {}
 
-
-        self._constraint = constraint
-
         if constraint is not None:
+            self._constraint = ConstraintModel(constraint.fun,
+                                               constraint.lb,
+                                               constraint.ub,
+                                               transform=self.kernel_transform,
+                                               random_state=random_state)
             # preallocated memory for constraint fulfillement
-            if constraint.lb.size == 1:
+            if self._constraint.lb.size == 1:
                 self._constraint_values = np.empty(shape=(0), dtype=float)
             else:
-                self._constraint_values = np.empty(shape=(0, constraint.lb.size), dtype=float)
+                self._constraint_values = np.empty(
+                    shape=(0, self._constraint.lb.size), dtype=float)
+        else:
+            self._constraint = None
 
     def __contains__(self, x):
         return _hashable(x) in self._cache
@@ -87,16 +99,12 @@ def target(self):
 
     @property
     def dim(self):
-        return len(self._keys)
-
-    @property
-    def keys(self):
-        return self._keys
+        return self._dim
 
     @property
     def bounds(self):
-        return self._bounds
-    
+        return [self._params_config[key].domain for key in self.keys]
+
     @property
     def constraint(self):
         return self._constraint
@@ -106,40 +114,103 @@ def constraint_values(self):
         if self._constraint is not None:
             return self._constraint_values
 
-    def params_to_array(self, params):
-        try:
-            assert set(params) == set(self.keys)
-        except AssertionError:
-            raise ValueError(
-                "Parameters' keys ({}) do ".format(sorted(params)) +
-                "not match the expected set of keys ({}).".format(self.keys)
-            )
-        return np.asarray([params[key] for key in self.keys])
+    @property
+    def keys(self):
+        return self._keys
+
+    @property
+    def float_bounds(self):
+        return self._float_bounds
 
-    def array_to_params(self, x):
+    @property
+    def masks(self):
+        return self._masks
+
+    def make_params(self, pbounds) -> dict:
+        params = {}
+        for key in sorted(pbounds):
+            pbound = pbounds[key]
+            if len(pbound) == 2 and is_numeric(pbound[0]) and is_numeric(
+                    pbound[1]):
+                res = pfloat(name=key, domain=pbound)
+            elif len(pbound) == 3 and pbound[-1] == float:
+                res = pfloat(name=key, domain=(pbound[0], pbound[1]))
+            elif len(pbound) == 3 and pbound[-1] == int:
+                res = pint(name=key, domain=(int(pbound[0]), int(pbound[1])))
+            else:
+                # assume categorical variable with pbound as list of possible values
+                res = pcat(name=key, domain=pbound)
+            params[key] = res
+        return params
+
+    def make_masks(self):
+        masks = {}
+        pos = 0
+        for key in self._keys:
+            mask = np.zeros(self._dim)
+            mask[pos:pos + self._params_config[key].dim] = 1
+            masks[key] = mask.astype(bool)
+            pos = pos + self._params_config[key].dim
+        return masks
+
+    def calculate_float_bounds(self):
+        bounds = np.empty((self._dim, 2))
+        for key in self._keys:
+            bounds[self.masks[key]] = self._params_config[key].float_bounds
+        return bounds
+
+    def params_to_array(self, value) -> np.ndarray:
+        if type(value
+                ) == dict:  # assume the input is one single set of parameters
+            return self._to_float(value)
+        else:
+            return np.vstack([self._to_float(x) for x in value])
+
+    def _to_float(self, value) -> np.ndarray:
         try:
-            assert len(x) == len(self.keys)
+            assert set(value) == set(self.keys)
         except AssertionError:
             raise ValueError(
-                "Size of array ({}) is different than the ".format(len(x)) +
-                "expected number of parameters ({}).".format(len(self.keys))
-            )
-        return dict(zip(self.keys, x))
-
-    def _as_array(self, x):
-        try:
-            x = np.asarray(x, dtype=float)
-        except TypeError:
-            x = self.params_to_array(x)
-
-        x = x.ravel()
+                "Parameters' keys ({}) do ".format(sorted(value)) +
+                "not match the expected set of keys ({}).".format(self.keys))
+        res = np.zeros(self._dim)
+        for key in self._keys:
+            p = self._params_config[key]
+            res[self._masks[key]] = p.to_float(value[key])
+        return res
+
+    def array_to_params(self, value: np.ndarray):
         try:
-            assert x.size == self.dim
+            assert value.shape[-1] == self._dim
         except AssertionError:
             raise ValueError(
-                "Size of array ({}) is different than the ".format(len(x)) +
-                "expected number of parameters ({}).".format(len(self.keys)))
-        return x
+                "Size of array ({}) is different than the ".format(
+                    value.shape[-1]) +
+                "expected number of parameters ({}).".format(self._dim))
+        if len(value.shape) == 1:
+            return self._to_params(value)
+        else:
+            return [self._to_params(v) for v in value]
+
+    def _to_params(self, value: np.ndarray) -> dict:
+        res = {}
+        for key in self._keys:
+            p = self._params_config[key]
+            mask = self._masks[key]
+            res[key] = p.to_param(value[mask])
+        return res
+
+    def kernel_transform(self, value: np.ndarray) -> np.ndarray:
+        """Transform floating-point suggestions to values used in the kernel.
+        
+        Vectorized."""
+        value = np.atleast_2d(value)
+        res = []
+        for p in self._keys:
+            par = self._params_config[p].kernel_transform(value[:,
+                                                                self.masks[p]])
+            res.append(par)
+        return np.hstack(res)
 
     def register(self, params, target, constraint_value=None):
         """
@@ -174,26 +245,33 @@ def register(self, params, target, constraint_value=None):
         >>> len(space)
         1
         """
-        x = self._as_array(params)
+
+        if type(params) == np.ndarray:
+            x = params
+        else:
+            assert type(params) == dict
+            x = self.params_to_array(params)
+
         if x in self:
             raise NotUniqueError('Data point {} is not unique'.format(x))
 
-
         self._params = np.concatenate([self._params, x.reshape(1, -1)])
-        self._target = np.concatenate([self._target, [target]])
+        self._target = np.concatenate([self._target, np.atleast_1d(target)])
 
         if self._constraint is None:
             # Insert data into unique dictionary
             self._cache[_hashable(x.ravel())] = target
         else:
             if constraint_value is None:
-                msg = ("When registering a point to a constrained TargetSpace" +
+                msg = (
+                    "When registering a point to a constrained TargetSpace" +
                     " a constraint value needs to be present.")
                 raise ValueError(msg)
             # Insert data into unique dictionary
             self._cache[_hashable(x.ravel())] = (target, constraint_value)
-            self._constraint_values = np.concatenate([self._constraint_values,
-                                                 [constraint_value]])
+            self._constraint_values = np.concatenate(
+                [self._constraint_values,
+                 np.atleast_1d(constraint_value)])
 
     def probe(self, params):
         """
@@ -214,12 +292,16 @@ def probe(self, params):
         y : float
             target function value.
         """
-        x = self._as_array(params)
+        if type(params) == np.ndarray:
+            x = params
+            params = self.array_to_params(params)
+        else:
+            assert type(params) == dict
+            x = self.params_to_array(params)
 
         try:
             return self._cache[_hashable(x)]
         except KeyError:
-            params = dict(zip(self._keys, x))
             target = self.target_func(**params)
 
             if self._constraint is None:
@@ -230,7 +312,6 @@ def probe(self, params):
                 self.register(x, target, constraint_value)
                 return target, constraint_value
 
-
     def random_sample(self):
         """
         Creates random points within the bounds of the space.
@@ -238,7 +319,7 @@ def random_sample(self):
         Returns
         ----------
         data: ndarray
-            [num x dim] array points with dimensions corresponding to `self._keys`
+            [num x dim] array points with dimensions corresponding to `self.keys`
 
         Example
         -------
@@ -248,8 +329,8 @@ def random_sample(self):
         >>> space.random_points(1)
         array([[ 55.33253689,   0.54488318]])
         """
-        data = np.empty((1, self.dim))
-        for col, (lower, upper) in enumerate(self._bounds):
+        data = np.empty((1, self._dim))
+        for col, (lower, upper) in enumerate(self._float_bounds):
             data.T[col] = self.random_state.uniform(lower, upper, size=1)
         return data.ravel()
 
@@ -261,10 +342,10 @@ def max(self):
         if self._constraint is None:
             try:
                 res = {
-                    'target': self.target.max(),
-                    'params': dict(
-                        zip(self.keys, self.params[self.target.argmax()])
-                    )
+                    'target':
+                    self.target.max(),
+                    'params':
+                    self.array_to_params(self.params[self.target.argmax()])
                 }
             except ValueError:
                 res = {}
@@ -279,46 +360,33 @@ def max(self):
                 # there must be a better way to do this, right?
                 res = {
                     'target': self.target[idx],
-                    'params': dict(
-                        zip(self.keys, self.params[idx])
-                    ),
+                    'params': dict(zip(self.keys, self.params[idx])),
                     'constraint': self._constraint_values[idx]
                 }
             else:
-                res = {
-                    'target': None,
-                    'params': None,
-                    'constraint': None
-                }
+                res = {'target': None, 'params': None, 'constraint': None}
             return res
 
     def res(self):
         """Get all target values and constraint fulfillment for all parameters.
         """
         if self._constraint is None:
-            params = [dict(zip(self.keys, p)) for p in self.params]
+            params = [self.array_to_params(p) for p in self.params]
 
-            return [
-                {"target": target, "params": param}
-                for target, param in zip(self.target, params)
-            ]
+            return [{
+                "target": target,
+                "params": param
+            } for target, param in zip(self.target, params)]
         else:
-            params = [dict(zip(self.keys, p)) for p in self.params]
-
-            return [
-                {
-                    "target": target,
-                    "constraint": constraint_value,
-                    "params": param,
-                    "allowed": allowed
-                }
-                for target, constraint_value, param, allowed in zip(
-                    self.target,
-                    self._constraint_values,
-                    params,
-                    self._constraint.allowed(self._constraint_values)
-                    )
-            ]
+            params = params = [self.array_to_params(p) for p in self.params]
+            return [{
+                "target": target,
+                "constraint": constraint_value,
+                "params": param,
+                "allowed": allowed
+            } for target, constraint_value, param, allowed in zip(
+                self.target, self._constraint_values, params,
+                self._constraint.allowed(self._constraint_values))]
 
     def set_bounds(self, new_bounds):
         """
@@ -329,6 +397,17 @@ def set_bounds(self, new_bounds):
         new_bounds : dict
             A dictionary with the parameter name and its new bounds
         """
+        new__params_config = self.make_params(new_bounds)
+
+
         for row, key in enumerate(self.keys):
             if key in new_bounds:
-                self._bounds[row] = new_bounds[key]
+                if isinstance(self._params_config[key], CategoricalParameter):
+                    if set(self._params_config[key].domain) == set(new_bounds[key]):
+                        msg = "Changing bounds of categorical parameters is not supported"
+                        raise NotImplementedError(msg)
+                self._params_config[key] = new__params_config[key]
+        
+        self._dim = sum([self._params_config[key].dim for key in self._keys])
+        self._masks = self.make_masks()
+        self._float_bounds = self.calculate_float_bounds()
diff --git a/bayes_opt/util.py b/bayes_opt/util.py
index f5ad6b152..f2a1c2d0b 100644
--- a/bayes_opt/util.py
+++ b/bayes_opt/util.py
@@ -3,7 +3,6 @@
 from scipy.stats import norm
 from scipy.optimize import minimize
 
-
 def acq_max(ac, gp, y_max, bounds, random_state, constraint=None, n_warmup=10000, n_iter=10):
     """
     A function to find the maximum of the acquisition function
diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
new file mode 100644
index 000000000..5e6522a50
--- /dev/null
+++ b/examples/parameter_types.ipynb
@@ -0,0 +1,497 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from bayes_opt import BayesianOptimization\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretized_function(x, y):\n",
+    "    y = np.round(y)\n",
+    "    return (-1*np.cos(x) + -1*np.cos(y))/((x/3)**2 + (y/3)**2 + 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Bounded region of parameter space\n",
+    "c_pbounds = {'x': (-5, 5), 'y': (-5, 5)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = [\"All-float Optimizer\", \"Typed Optimizer\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "continuous_optimizer = BayesianOptimization(\n",
+    "    f=discretized_function,\n",
+    "    pbounds=c_pbounds,\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "d_pbounds = {'x': (-5, 5), 'y': (-5, 5, int)}\n",
+    "discrete_optimizer = BayesianOptimization(\n",
+    "    f=discretized_function,\n",
+    "    pbounds=d_pbounds,\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================  All-float Optimizer  ====================\n",
+      "\n",
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[0m1        \u001b[0m | \u001b[0m-0.1702  \u001b[0m | \u001b[0m-0.8298  \u001b[0m | \u001b[0m2.203    \u001b[0m |\n",
+      "| \u001b[95m2        \u001b[0m | \u001b[95m0.03165  \u001b[0m | \u001b[95m-4.999   \u001b[0m | \u001b[95m-1.977   \u001b[0m |\n",
+      "| \u001b[95m3        \u001b[0m | \u001b[95m0.04415  \u001b[0m | \u001b[95m-4.947   \u001b[0m | \u001b[95m-2.138   \u001b[0m |\n",
+      "| \u001b[95m4        \u001b[0m | \u001b[95m0.1741   \u001b[0m | \u001b[95m-4.491   \u001b[0m | \u001b[95m-4.256   \u001b[0m |\n",
+      "| \u001b[0m5        \u001b[0m | \u001b[0m-0.1392  \u001b[0m | \u001b[0m-1.299   \u001b[0m | \u001b[0m-5.0     \u001b[0m |\n",
+      "| \u001b[0m6        \u001b[0m | \u001b[0m-0.08152 \u001b[0m | \u001b[0m-4.962   \u001b[0m | \u001b[0m-4.987   \u001b[0m |\n",
+      "| \u001b[95m7        \u001b[0m | \u001b[95m0.2105   \u001b[0m | \u001b[95m-4.333   \u001b[0m | \u001b[95m-3.837   \u001b[0m |\n",
+      "| \u001b[95m8        \u001b[0m | \u001b[95m0.368    \u001b[0m | \u001b[95m-3.598   \u001b[0m | \u001b[95m-4.108   \u001b[0m |\n",
+      "| \u001b[95m9        \u001b[0m | \u001b[95m0.4317   \u001b[0m | \u001b[95m-3.069   \u001b[0m | \u001b[95m-3.524   \u001b[0m |\n",
+      "| \u001b[95m10       \u001b[0m | \u001b[95m0.5767   \u001b[0m | \u001b[95m-2.566   \u001b[0m | \u001b[95m-2.495   \u001b[0m |\n",
+      "=================================================\n",
+      "Max: 0.5767225405731152\n",
+      "\n",
+      "\n",
+      "====================  Typed Optimizer  ====================\n",
+      "\n",
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[0m1        \u001b[0m | \u001b[0m-0.1702  \u001b[0m | \u001b[0m-0.8298  \u001b[0m | \u001b[0m2        \u001b[0m |\n",
+      "| \u001b[95m2        \u001b[0m | \u001b[95m0.03165  \u001b[0m | \u001b[95m-4.999   \u001b[0m | \u001b[95m-2       \u001b[0m |\n",
+      "| \u001b[95m3        \u001b[0m | \u001b[95m0.08123  \u001b[0m | \u001b[95m-4.803   \u001b[0m | \u001b[95m-2       \u001b[0m |\n",
+      "| \u001b[95m4        \u001b[0m | \u001b[95m0.2658   \u001b[0m | \u001b[95m-4.201   \u001b[0m | \u001b[95m-2       \u001b[0m |\n",
+      "| \u001b[95m5        \u001b[0m | \u001b[95m0.5262   \u001b[0m | \u001b[95m-3.313   \u001b[0m | \u001b[95m-2       \u001b[0m |\n",
+      "| \u001b[95m6        \u001b[0m | \u001b[95m0.6115   \u001b[0m | \u001b[95m-2.149   \u001b[0m | \u001b[95m-3       \u001b[0m |\n",
+      "| \u001b[0m7        \u001b[0m | \u001b[0m-0.07779 \u001b[0m | \u001b[0m-1.004   \u001b[0m | \u001b[0m-2       \u001b[0m |\n",
+      "| \u001b[0m8        \u001b[0m | \u001b[0m0.4368   \u001b[0m | \u001b[0m-2.948   \u001b[0m | \u001b[0m-4       \u001b[0m |\n",
+      "| \u001b[95m9        \u001b[0m | \u001b[95m0.6648   \u001b[0m | \u001b[95m-2.948   \u001b[0m | \u001b[95m-3       \u001b[0m |\n",
+      "| \u001b[0m10       \u001b[0m | \u001b[0m-0.08566 \u001b[0m | \u001b[0m4.993    \u001b[0m | \u001b[0m5        \u001b[0m |\n",
+      "=================================================\n",
+      "Max: 0.6647526708201366\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "kappas = [2., 2.]\n",
+    "for lbl, optimizer, kappa in zip(labels, [continuous_optimizer, discrete_optimizer], kappas):\n",
+    "    print(f\"====================  {lbl}  ====================\\n\")\n",
+    "    optimizer.maximize(\n",
+    "        init_points=2,\n",
+    "        n_iter=8,\n",
+    "        kappa=kappa\n",
+    "    )\n",
+    "    print(f\"Max: {optimizer.max['target']}\\n\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(c_pbounds['x'][0], c_pbounds['x'][1], 1000)\n",
+    "y = np.linspace(c_pbounds['y'][0], c_pbounds['y'][1], 1000)\n",
+    "\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "Z = discretized_function(X, Y)\n",
+    "\n",
+    "params = [{'x': x_i, 'y': y_j} for y_j in y for x_i in x]\n",
+    "c_pred = continuous_optimizer._gp.predict(continuous_optimizer._space.params_to_array(params)).reshape(X.shape)\n",
+    "d_pred = discrete_optimizer._gp.predict(discrete_optimizer._space.params_to_array(params)).reshape(X.shape)\n",
+    "\n",
+    "vmin = np.min([np.min(Z), np.min(c_pred), np.min(d_pred)])\n",
+    "vmax = np.max([np.max(Z), np.max(c_pred), np.max(d_pred)])\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 3)\n",
+    "\n",
+    "axs[0].set_title('Actual function')\n",
+    "axs[0].contourf(X, Y, Z, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
+    "\n",
+    "\n",
+    "axs[1].set_title(labels[0])\n",
+    "axs[1].contourf(X, Y, c_pred, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
+    "axs[1].scatter(continuous_optimizer._space.params[:,0], continuous_optimizer._space.params[:,1], c='k')\n",
+    "\n",
+    "axs[2].set_title(labels[1])\n",
+    "axs[2].contourf(X, Y, d_pred, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
+    "axs[2].scatter(discrete_optimizer._space.params[:,0], discrete_optimizer._space.params[:,1], c='k')\n",
+    "\n",
+    "\n",
+    "def make_plot_fancy(ax: plt.Axes):\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    ax.set_xlabel('x (float)')\n",
+    "    ax.set_xticks([-5.0, -2.5, 0., 2.5, 5.0])\n",
+    "    ax.set_ylabel('y (int)')\n",
+    "    ax.set_yticks([-5, -3, 0, 3, 5])\n",
+    "\n",
+    "for ax in axs:\n",
+    "    make_plot_fancy(ax)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Categorical variables\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f1(x1, x2):\n",
+    "    return -1*(x1 - np.sqrt(x1**2 + x2**2) * np.cos(np.sqrt(x1**2 + x2**2))**2 + 0.5 * np.sqrt(x1**2 + x2**2))\n",
+    "\n",
+    "def f2(x1, x2):\n",
+    "    return -1*(x2 - np.sqrt(x1**2 + x2**2) * np.sin(np.sqrt(x1**2 + x2**2))**2 + 0.5 * np.sqrt(x1**2 + x2**2))\n",
+    "\n",
+    "def SPIRAL(x1, x2, k):\n",
+    "    \"\"\"cf Ladislav-Luksan\n",
+    "    \"\"\"\n",
+    "    if k=='1':\n",
+    "        return f1(x1, x2)\n",
+    "    elif k=='2':\n",
+    "        return f2(x1, x2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |     k     |    x1     |    x2     |\n",
+      "-------------------------------------------------------------\n",
+      "| \u001b[0m1        \u001b[0m | \u001b[0m8.698    \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m-9.998   \u001b[0m | \u001b[0m-3.953   \u001b[0m |\n",
+      "| \u001b[0m2        \u001b[0m | \u001b[0m6.796    \u001b[0m | \u001b[0m    1    \u001b[0m | \u001b[0m-6.275   \u001b[0m | \u001b[0m-3.089   \u001b[0m |\n",
+      "| \u001b[0m3        \u001b[0m | \u001b[0m7.978    \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m-9.97    \u001b[0m | \u001b[0m-3.781   \u001b[0m |\n",
+      "| \u001b[95m4        \u001b[0m | \u001b[95m10.21    \u001b[0m | \u001b[95m    2    \u001b[0m | \u001b[95m-10.0    \u001b[0m | \u001b[95m-4.999   \u001b[0m |\n",
+      "| \u001b[0m5        \u001b[0m | \u001b[0m-14.69   \u001b[0m | \u001b[0m    1    \u001b[0m | \u001b[0m9.182    \u001b[0m | \u001b[0m6.094    \u001b[0m |\n",
+      "| \u001b[95m6        \u001b[0m | \u001b[95m15.21    \u001b[0m | \u001b[95m    2    \u001b[0m | \u001b[95m-5.001   \u001b[0m | \u001b[95m-10.0    \u001b[0m |\n",
+      "| \u001b[0m7        \u001b[0m | \u001b[0m2.929    \u001b[0m | \u001b[0m    1    \u001b[0m | \u001b[0m-10.0    \u001b[0m | \u001b[0m-10.0    \u001b[0m |\n",
+      "| \u001b[0m8        \u001b[0m | \u001b[0m2.288    \u001b[0m | \u001b[0m    1    \u001b[0m | \u001b[0m-0.2839  \u001b[0m | \u001b[0m-10.0    \u001b[0m |\n",
+      "| \u001b[0m9        \u001b[0m | \u001b[0m10.01    \u001b[0m | \u001b[0m    1    \u001b[0m | \u001b[0m-5.544   \u001b[0m | \u001b[0m-7.449   \u001b[0m |\n",
+      "| \u001b[0m10       \u001b[0m | \u001b[0m-2.929   \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m-10.0    \u001b[0m | \u001b[0m10.0     \u001b[0m |\n",
+      "| \u001b[95m11       \u001b[0m | \u001b[95m17.07    \u001b[0m | \u001b[95m    2    \u001b[0m | \u001b[95m10.0     \u001b[0m | \u001b[95m-10.0    \u001b[0m |\n",
+      "| \u001b[0m12       \u001b[0m | \u001b[0m3.042    \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m10.0     \u001b[0m | \u001b[0m-6.849   \u001b[0m |\n",
+      "| \u001b[0m13       \u001b[0m | \u001b[0m3.855    \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m7.475    \u001b[0m | \u001b[0m-9.986   \u001b[0m |\n",
+      "| \u001b[0m14       \u001b[0m | \u001b[0m9.141    \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m-6.365   \u001b[0m | \u001b[0m-10.0    \u001b[0m |\n",
+      "| \u001b[0m15       \u001b[0m | \u001b[0m7.252    \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m-3.837   \u001b[0m | \u001b[0m-9.241   \u001b[0m |\n",
+      "| \u001b[0m16       \u001b[0m | \u001b[0m7.252    \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m-3.837   \u001b[0m | \u001b[0m-9.241   \u001b[0m |\n",
+      "| \u001b[0m17       \u001b[0m | \u001b[0m-16.29   \u001b[0m | \u001b[0m    1    \u001b[0m | \u001b[0m9.945    \u001b[0m | \u001b[0m-9.748   \u001b[0m |\n",
+      "| \u001b[0m18       \u001b[0m | \u001b[0m8.905    \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m0.3066   \u001b[0m | \u001b[0m-8.598   \u001b[0m |\n",
+      "| \u001b[0m19       \u001b[0m | \u001b[0m-4.946   \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m6.071    \u001b[0m | \u001b[0m2.961    \u001b[0m |\n",
+      "| \u001b[0m20       \u001b[0m | \u001b[0m2.591    \u001b[0m | \u001b[0m    2    \u001b[0m | \u001b[0m7.782    \u001b[0m | \u001b[0m1.343    \u001b[0m |\n",
+      "=============================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "pbounds = {'x1': (-10, 10), 'x2': (-10, 10), 'k': ('1', '2')}\n",
+    "\n",
+    "categorical_optimizer = BayesianOptimization(\n",
+    "    f=SPIRAL,\n",
+    "    pbounds=pbounds,\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "categorical_optimizer.maximize(\n",
+    "        init_points=2,\n",
+    "        n_iter=18,\n",
+    "        kappa=2.\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = categorical_optimizer._space.res()\n",
+    "k1 = np.array([[p['params']['x1'], p['params']['x2']] for p in res if p['params']['k']=='1'])\n",
+    "k2 = np.array([[p['params']['x1'], p['params']['x2']] for p in res if p['params']['k']=='2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x1 = np.linspace(pbounds['x1'][0], pbounds['x1'][1], 1000)\n",
+    "x2 = np.linspace(pbounds['x2'][0], pbounds['x2'][1], 1000)\n",
+    "\n",
+    "X1, X2 = np.meshgrid(x1, x2)\n",
+    "Z1 = SPIRAL(X1, X2, '1')\n",
+    "Z2 = SPIRAL(X1, X2, '2')\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2)\n",
+    "\n",
+    "vmin = np.min([np.min(Z1), np.min(Z2)])\n",
+    "vmax = np.max([np.max(Z1), np.max(Z2)])\n",
+    "\n",
+    "axs[0].contourf(X1, X2, Z1, vmin=vmin, vmax=vmax)\n",
+    "axs[0].set_aspect(\"equal\")\n",
+    "axs[0].scatter(k1[:,0], k1[:,1], c='k')\n",
+    "axs[1].contourf(X1, X2, Z2, vmin=vmin, vmax=vmax)\n",
+    "axs[1].scatter(k2[:,0], k2[:,1], c='k')\n",
+    "axs[1].set_aspect(\"equal\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Saving, loading and restarting\n",
+    "\n",
+    "By default you can follow the progress of your optimization by setting `verbose>0` when instanciating the `BayesianOptimization` object. If you need more control over logging/alerting you will need to use an observer. For more information about observers checkout the advanced tour notebook. Here we will only see how to use the native `JSONLogger` object to save to and load progress from files.\n",
+    "\n",
+    "### 4.1 Saving progress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bayes_opt.logger import JSONLogger\n",
+    "from bayes_opt.event import Events"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The observer paradigm works by:\n",
+    "1. Instantiating an observer object.\n",
+    "2. Tying the observer object to a particular event fired by an optimizer.\n",
+    "\n",
+    "The `BayesianOptimization` object fires a number of internal events during optimization, in particular, everytime it probes the function and obtains a new parameter-target combination it will fire an `Events.OPTIMIZATION_STEP` event, which our logger will listen to.\n",
+    "\n",
+    "**Caveat:** The logger will not look back at previously probed points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger = JSONLogger(path=\"./logs.json\")\n",
+    "optimizer.subscribe(Events.OPTIMIZATION_STEP, logger)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[0m11       \u001b[0m | \u001b[0m0.379    \u001b[0m | \u001b[0m-3.532   \u001b[0m | \u001b[0m-4       \u001b[0m |\n",
+      "| \u001b[0m12       \u001b[0m | \u001b[0m0.5579   \u001b[0m | \u001b[0m-3.137   \u001b[0m | \u001b[0m-2       \u001b[0m |\n",
+      "| \u001b[0m13       \u001b[0m | \u001b[0m-0.08256 \u001b[0m | \u001b[0m4.97     \u001b[0m | \u001b[0m-5       \u001b[0m |\n",
+      "| \u001b[0m14       \u001b[0m | \u001b[0m-0.08628 \u001b[0m | \u001b[0m-4.998   \u001b[0m | \u001b[0m5        \u001b[0m |\n",
+      "| \u001b[0m15       \u001b[0m | \u001b[0m-0.3398  \u001b[0m | \u001b[0m5.0      \u001b[0m | \u001b[0m0        \u001b[0m |\n",
+      "=================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer.maximize(\n",
+    "    init_points=2,\n",
+    "    n_iter=3,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 Loading progress\n",
+    "\n",
+    "Naturally, if you stored progress you will be able to load that onto a new instance of `BayesianOptimization`. The easiest way to do it is by invoking the `load_logs` function, from the `util` submodule."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bayes_opt.util import load_logs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'black_box_function' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [16], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m new_optimizer \u001b[39m=\u001b[39m BayesianOptimization(\n\u001b[0;32m----> 2\u001b[0m     f\u001b[39m=\u001b[39mblack_box_function,\n\u001b[1;32m      3\u001b[0m     pbounds\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m: (\u001b[39m-\u001b[39m\u001b[39m2\u001b[39m, \u001b[39m2\u001b[39m), \u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m: (\u001b[39m-\u001b[39m\u001b[39m2\u001b[39m, \u001b[39m2\u001b[39m)},\n\u001b[1;32m      4\u001b[0m     verbose\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m,\n\u001b[1;32m      5\u001b[0m     random_state\u001b[39m=\u001b[39m\u001b[39m7\u001b[39m,\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      7\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mlen\u001b[39m(new_optimizer\u001b[39m.\u001b[39mspace))\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'black_box_function' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "new_optimizer = BayesianOptimization(\n",
+    "    f=black_box_function,\n",
+    "    pbounds={\"x\": (-2, 2), \"y\": (-2, 2)},\n",
+    "    verbose=2,\n",
+    "    random_state=7,\n",
+    ")\n",
+    "print(len(new_optimizer.space))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_logs(new_optimizer, logs=[\"./logs.json\"]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"New optimizer is now aware of {} points.\".format(len(new_optimizer.space)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_optimizer.maximize(\n",
+    "    init_points=0,\n",
+    "    n_iter=10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "This tour should be enough to cover most usage scenarios of this package. If, however, you feel like you need to know more, please checkout the `advanced-tour` notebook. There you will be able to find other, more advanced features of this package that could be what you're looking for. Also, browse the examples folder for implementation tips and ideas."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.6 ('bopt')",
+   "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": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "49851069de08cc5bbf068d7713ecb1523f4cab708013d75e8e72826d85a7e48d"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 81321f330d1c35dc9a44df5d150b9036dd94e2e4 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Wed, 9 Nov 2022 09:18:55 +0100
Subject: [PATCH 02/21] Add ML example

---
 examples/parameter_types.ipynb | 180 +++++++++------------------------
 1 file changed, 48 insertions(+), 132 deletions(-)

diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
index 5e6522a50..ad4f4c144 100644
--- a/examples/parameter_types.ipynb
+++ b/examples/parameter_types.ipynb
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -316,134 +316,64 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. Saving, loading and restarting\n",
-    "\n",
-    "By default you can follow the progress of your optimization by setting `verbose>0` when instanciating the `BayesianOptimization` object. If you need more control over logging/alerting you will need to use an observer. For more information about observers checkout the advanced tour notebook. Here we will only see how to use the native `JSONLogger` object to save to and load progress from files.\n",
-    "\n",
-    "### 4.1 Saving progress"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bayes_opt.logger import JSONLogger\n",
-    "from bayes_opt.event import Events"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The observer paradigm works by:\n",
-    "1. Instantiating an observer object.\n",
-    "2. Tying the observer object to a particular event fired by an optimizer.\n",
-    "\n",
-    "The `BayesianOptimization` object fires a number of internal events during optimization, in particular, everytime it probes the function and obtains a new parameter-target combination it will fire an `Events.OPTIMIZATION_STEP` event, which our logger will listen to.\n",
-    "\n",
-    "**Caveat:** The logger will not look back at previously probed points."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "logger = JSONLogger(path=\"./logs.json\")\n",
-    "optimizer.subscribe(Events.OPTIMIZATION_STEP, logger)"
+    "## 4. Use in ML"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|   iter    |  target   |     x     |     y     |\n",
-      "-------------------------------------------------\n",
-      "| \u001b[0m11       \u001b[0m | \u001b[0m0.379    \u001b[0m | \u001b[0m-3.532   \u001b[0m | \u001b[0m-4       \u001b[0m |\n",
-      "| \u001b[0m12       \u001b[0m | \u001b[0m0.5579   \u001b[0m | \u001b[0m-3.137   \u001b[0m | \u001b[0m-2       \u001b[0m |\n",
-      "| \u001b[0m13       \u001b[0m | \u001b[0m-0.08256 \u001b[0m | \u001b[0m4.97     \u001b[0m | \u001b[0m-5       \u001b[0m |\n",
-      "| \u001b[0m14       \u001b[0m | \u001b[0m-0.08628 \u001b[0m | \u001b[0m-4.998   \u001b[0m | \u001b[0m5        \u001b[0m |\n",
-      "| \u001b[0m15       \u001b[0m | \u001b[0m-0.3398  \u001b[0m | \u001b[0m5.0      \u001b[0m | \u001b[0m0        \u001b[0m |\n",
-      "=================================================\n"
+      "|   iter    |  target   |     C     |  degree   |  kernel   |\n",
+      "-------------------------------------------------------------\n",
+      "| \u001b[0m1        \u001b[0m | \u001b[0m0.8166   \u001b[0m | \u001b[0m3.808    \u001b[0m | \u001b[0m3        \u001b[0m | \u001b[0m   rbf   \u001b[0m |\n",
+      "| \u001b[0m2        \u001b[0m | \u001b[0m0.1887   \u001b[0m | \u001b[0m1.645    \u001b[0m | \u001b[0m1        \u001b[0m | \u001b[0m  poly   \u001b[0m |\n",
+      "| \u001b[95m3        \u001b[0m | \u001b[95m0.8244   \u001b[0m | \u001b[95m3.871    \u001b[0m | \u001b[95m3        \u001b[0m | \u001b[95m   rbf   \u001b[0m |\n",
+      "| \u001b[0m4        \u001b[0m | \u001b[0m0.6588   \u001b[0m | \u001b[0m4.869    \u001b[0m | \u001b[0m3        \u001b[0m | \u001b[0m  poly   \u001b[0m |\n",
+      "| \u001b[0m5        \u001b[0m | \u001b[0m0.6005   \u001b[0m | \u001b[0m3.553    \u001b[0m | \u001b[0m3        \u001b[0m | \u001b[0m  poly   \u001b[0m |\n",
+      "| \u001b[95m6        \u001b[0m | \u001b[95m0.9827   \u001b[0m | \u001b[95m9.711    \u001b[0m | \u001b[95m3        \u001b[0m | \u001b[95m   rbf   \u001b[0m |\n",
+      "| \u001b[0m7        \u001b[0m | \u001b[0m0.8715   \u001b[0m | \u001b[0m4.768    \u001b[0m | \u001b[0m1        \u001b[0m | \u001b[0m   rbf   \u001b[0m |\n",
+      "| \u001b[0m8        \u001b[0m | \u001b[0m0.7914   \u001b[0m | \u001b[0m9.389    \u001b[0m | \u001b[0m3        \u001b[0m | \u001b[0m  poly   \u001b[0m |\n",
+      "| \u001b[95m9        \u001b[0m | \u001b[95m0.985    \u001b[0m | \u001b[95m9.756    \u001b[0m | \u001b[95m3        \u001b[0m | \u001b[95m   rbf   \u001b[0m |\n",
+      "| \u001b[0m10       \u001b[0m | \u001b[0m0.985    \u001b[0m | \u001b[0m9.863    \u001b[0m | \u001b[0m3        \u001b[0m | \u001b[0m   rbf   \u001b[0m |\n",
+      "| \u001b[95m11       \u001b[0m | \u001b[95m0.9872   \u001b[0m | \u001b[95m9.989    \u001b[0m | \u001b[95m3        \u001b[0m | \u001b[95m   rbf   \u001b[0m |\n",
+      "| \u001b[0m12       \u001b[0m | \u001b[0m0.9872   \u001b[0m | \u001b[0m9.999    \u001b[0m | \u001b[0m3        \u001b[0m | \u001b[0m   rbf   \u001b[0m |\n",
+      "=============================================================\n"
      ]
     }
    ],
    "source": [
-    "optimizer.maximize(\n",
-    "    init_points=2,\n",
-    "    n_iter=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.2 Loading progress\n",
+    "from sklearn.datasets import load_diabetes\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.metrics import f1_score\n",
+    "from bayes_opt import BayesianOptimization\n",
     "\n",
-    "Naturally, if you stored progress you will be able to load that onto a new instance of `BayesianOptimization`. The easiest way to do it is by invoking the `load_logs` function, from the `util` submodule."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bayes_opt.util import load_logs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'black_box_function' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [16], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m new_optimizer \u001b[39m=\u001b[39m BayesianOptimization(\n\u001b[0;32m----> 2\u001b[0m     f\u001b[39m=\u001b[39mblack_box_function,\n\u001b[1;32m      3\u001b[0m     pbounds\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m: (\u001b[39m-\u001b[39m\u001b[39m2\u001b[39m, \u001b[39m2\u001b[39m), \u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m: (\u001b[39m-\u001b[39m\u001b[39m2\u001b[39m, \u001b[39m2\u001b[39m)},\n\u001b[1;32m      4\u001b[0m     verbose\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m,\n\u001b[1;32m      5\u001b[0m     random_state\u001b[39m=\u001b[39m\u001b[39m7\u001b[39m,\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      7\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mlen\u001b[39m(new_optimizer\u001b[39m.\u001b[39mspace))\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'black_box_function' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "new_optimizer = BayesianOptimization(\n",
-    "    f=black_box_function,\n",
-    "    pbounds={\"x\": (-2, 2), \"y\": (-2, 2)},\n",
-    "    verbose=2,\n",
-    "    random_state=7,\n",
-    ")\n",
-    "print(len(new_optimizer.space))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "load_logs(new_optimizer, logs=[\"./logs.json\"]);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"New optimizer is now aware of {} points.\".format(len(new_optimizer.space)))"
+    "data = load_diabetes() #load_iris()\n",
+    "\n",
+    "kernels = ['rbf', 'poly']\n",
+    "\n",
+    "def f_target(kernel, C, degree):\n",
+    "    \n",
+    "    model = SVC(C=C, kernel=kernel, degree=degree)\n",
+    "    model.fit(data['data'], data['target'])\n",
+    "\n",
+    "    weighted_f1 = f1_score(model.predict(data['data']), data['target'], average='weighted')\n",
+    "    return weighted_f1\n",
+    "\n",
+    "\n",
+    "params_svm ={\n",
+    "    'kernel': ['rbf', 'poly'],\n",
+    "    'C':(1e-1, 1e+1),\n",
+    "    'degree':(1, 3, int),\n",
+    "}\n",
+    "\n",
+    "optimizer = BayesianOptimization(f_target, params_svm, random_state=42, verbose=2)\n",
+    "\n",
+    "optimizer.maximize(init_points=2, n_iter=10, kappa=1.)"
    ]
   },
   {
@@ -451,21 +381,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "new_optimizer.maximize(\n",
-    "    init_points=0,\n",
-    "    n_iter=10,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "This tour should be enough to cover most usage scenarios of this package. If, however, you feel like you need to know more, please checkout the `advanced-tour` notebook. There you will be able to find other, more advanced features of this package that could be what you're looking for. Also, browse the examples folder for implementation tips and ideas."
-   ]
+   "source": []
   }
  ],
  "metadata": {

From 4106850142c025f85e6199e13503315f2c2fce9e Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Tue, 23 May 2023 16:44:10 +0200
Subject: [PATCH 03/21] Save for merge

---
 bayes_opt/bayesian_optimization.py  |  4 +--
 bayes_opt/logger.py                 |  2 +-
 bayes_opt/parameter.py              | 12 ---------
 bayes_opt/target_space.py           | 19 +++++++--------
 tests/test_bayesian_optimization.py | 16 ++++++------
 tests/test_constraint.py            |  2 +-
 tests/test_seq_domain_red.py        | 23 ++---------------
 tests/test_target_space.py          | 38 ++++++++++++++---------------
 8 files changed, 41 insertions(+), 75 deletions(-)

diff --git a/bayes_opt/bayesian_optimization.py b/bayes_opt/bayesian_optimization.py
index 1dc083ed7..7298437a7 100644
--- a/bayes_opt/bayesian_optimization.py
+++ b/bayes_opt/bayesian_optimization.py
@@ -8,7 +8,7 @@
 from sklearn.gaussian_process.kernels import Matern
 from sklearn.gaussian_process import GaussianProcessRegressor
 from .parameter import wrap_kernel
-from icecream import ic
+from .domain_reduction import DomainTransformer
 
 
 class Queue:
@@ -145,7 +145,7 @@ def __init__(self,
         if self._bounds_transformer:
             try:
                 self._bounds_transformer.initialize(self._space)
-            except (AttributeError, TypeError):
+            except (AttributeError, TypeError) as e:
                 raise TypeError('The transformer must be an instance of '
                                 'DomainTransformer')
 
diff --git a/bayes_opt/logger.py b/bayes_opt/logger.py
index 61b81c594..7c35cd6a2 100644
--- a/bayes_opt/logger.py
+++ b/bayes_opt/logger.py
@@ -8,7 +8,7 @@
 from .parameter import FloatParameter, IntParameter, CategoricalParameter
 
 import numpy as np
-from icecream import ic
+
 def _get_default_logger(verbose, is_constrained):
     return ScreenLogger(verbose=verbose, is_constrained=is_constrained)
 
diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index 0b3b93518..f3208845b 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -3,22 +3,10 @@
 from sklearn.gaussian_process import kernels
 from inspect import signature
 
-from icecream import ic
 
 def is_numeric(value):
     return type(value) in [float, int, complex]
 
-def pfloat(*args, **kwargs):
-    return FloatParameter(*args, **kwargs)
-
-
-def pint(*args, **kwargs):
-    return IntParameter(*args, **kwargs)
-
-
-def pcat(*args, **kwargs):
-    return CategoricalParameter(*args, **kwargs)
-
 
 class BayesParameter():
 
diff --git a/bayes_opt/target_space.py b/bayes_opt/target_space.py
index acaf3570a..07f5b6b4d 100644
--- a/bayes_opt/target_space.py
+++ b/bayes_opt/target_space.py
@@ -1,7 +1,6 @@
 import numpy as np
 from .util import ensure_rng, NotUniqueError
-from icecream import ic
-from .parameter import pfloat, pint, pcat, is_numeric, CategoricalParameter
+from .parameter import FloatParameter, IntParameter, is_numeric, CategoricalParameter
 from .constraint import ConstraintModel
 
 
@@ -132,14 +131,14 @@ def make_params(self, pbounds) -> dict:
             pbound = pbounds[key]
             if len(pbound) == 2 and is_numeric(pbound[0]) and is_numeric(
                     pbound[1]):
-                res = pfloat(name=key, domain=pbound)
+                res = FloatParameter(name=key, domain=pbound)
             elif len(pbound) == 3 and pbound[-1] == float:
-                res = pfloat(name=key, domain=(pbound[0], pbound[1]))
+                res = FloatParameter(name=key, domain=(pbound[0], pbound[1]))
             elif len(pbound) == 3 and pbound[-1] == int:
-                res = pint(name=key, domain=(int(pbound[0]), int(pbound[1])))
+                res = IntParameter(name=key, domain=(int(pbound[0]), int(pbound[1])))
             else:
                 # assume categorical variable with pbound as list of possible values
-                res = pcat(name=key, domain=pbound)
+                res = CategoricalParameter(name=key, domain=pbound)
             params[key] = res
         return params
 
@@ -160,8 +159,8 @@ def calculate_float_bounds(self):
         return bounds
 
     def params_to_array(self, value) -> np.ndarray:
-        if type(value
-                ) == dict:  # assume the input is one single set of parameters
+        if type(value) == dict:
+            # assume the input is one single set of parameters
             return self._to_float(value)
         else:
             return np.vstack([self._to_float(x) for x in value])
@@ -171,8 +170,8 @@ def _to_float(self, value) -> np.ndarray:
             assert set(value) == set(self.keys)
         except AssertionError:
             raise ValueError(
-                "Parameters' keys ({}) do ".format(sorted(value)) +
-                "not match the expected set of keys ({}).".format(self.keys))
+                f"Parameters' keys ({sorted(value)}) do " +
+                f"not match the expected set of keys ({self.keys}).")
         res = np.zeros(self._dim)
         for key in self._keys:
             p = self._params_config[key]
diff --git a/tests/test_bayesian_optimization.py b/tests/test_bayesian_optimization.py
index 1607c4f70..2f767dcc8 100644
--- a/tests/test_bayesian_optimization.py
+++ b/tests/test_bayesian_optimization.py
@@ -80,8 +80,8 @@ def test_suggest_at_random():
     for _ in range(50):
         sample = optimizer.space.params_to_array(optimizer.suggest(util))
         assert len(sample) == optimizer.space.dim
-        assert all(sample >= optimizer.space.bounds[:, 0])
-        assert all(sample <= optimizer.space.bounds[:, 1])
+        assert all(sample >= optimizer.space.float_bounds[:, 0])
+        assert all(sample <= optimizer.space.float_bounds[:, 1])
 
 
 def test_suggest_with_one_observation():
@@ -93,8 +93,8 @@ def test_suggest_with_one_observation():
     for _ in range(5):
         sample = optimizer.space.params_to_array(optimizer.suggest(util))
         assert len(sample) == optimizer.space.dim
-        assert all(sample >= optimizer.space.bounds[:, 0])
-        assert all(sample <= optimizer.space.bounds[:, 1])
+        assert all(sample >= optimizer.space.float_bounds[:, 0])
+        assert all(sample <= optimizer.space.float_bounds[:, 1])
 
     # suggestion = optimizer.suggest(util)
     # for _ in range(5):
@@ -209,13 +209,13 @@ def test_set_bounds():
 
     # Ignore unknown keys
     optimizer.set_bounds({"other": (7, 8)})
-    assert all(optimizer.space.bounds[:, 0] == np.array([0, 0, 0, 0]))
-    assert all(optimizer.space.bounds[:, 1] == np.array([1, 2, 3, 4]))
+    assert all(optimizer.space.float_bounds[:, 0] == np.array([0, 0, 0, 0]))
+    assert all(optimizer.space.float_bounds[:, 1] == np.array([1, 2, 3, 4]))
 
     # Update bounds accordingly
     optimizer.set_bounds({"p2": (1, 8)})
-    assert all(optimizer.space.bounds[:, 0] == np.array([0, 1, 0, 0]))
-    assert all(optimizer.space.bounds[:, 1] == np.array([1, 8, 3, 4]))
+    assert all(optimizer.space.float_bounds[:, 0] == np.array([0, 1, 0, 0]))
+    assert all(optimizer.space.float_bounds[:, 1] == np.array([1, 8, 3, 4]))
 
 
 def test_set_gp_params():
diff --git a/tests/test_constraint.py b/tests/test_constraint.py
index 29c38739a..736381d89 100644
--- a/tests/test_constraint.py
+++ b/tests/test_constraint.py
@@ -1,5 +1,5 @@
 import numpy as np
-from bayes_opt import BayesianOptimization, ConstraintModel
+from bayes_opt import BayesianOptimization
 from pytest import approx, raises
 from scipy.optimize import NonlinearConstraint
 
diff --git a/tests/test_seq_domain_red.py b/tests/test_seq_domain_red.py
index ae71a9669..48a01246c 100644
--- a/tests/test_seq_domain_red.py
+++ b/tests/test_seq_domain_red.py
@@ -15,25 +15,6 @@ def black_box_function(x, y):
 
 
 def test_bound_x_maximize():
-
-    class Tracker:
-        def __init__(self):
-            self.start_count = 0
-            self.step_count = 0
-            self.end_count = 0
-
-        def update_start(self, event, instance):
-            self.start_count += 1
-
-        def update_step(self, event, instance):
-            self.step_count += 1
-
-        def update_end(self, event, instance):
-            self.end_count += 1
-
-        def reset(self):
-            self.__init__()
-
     bounds_transformer = SequentialDomainReductionTransformer()
     pbounds = {'x': (-10, 10), 'y': (-10, 10)}
     n_iter = 10
@@ -64,8 +45,8 @@ def reset(self):
     )
 
     assert len(standard_optimizer.space) == len(mutated_optimizer.space)
-    assert not (standard_optimizer._space.bounds ==
-                mutated_optimizer._space.bounds).any()
+    assert not (standard_optimizer._space.float_bounds ==
+                mutated_optimizer._space.float_bounds).any()
 
 def test_minimum_window_is_kept():
     bounds_transformer = SequentialDomainReductionTransformer(minimum_window=1.0)
diff --git a/tests/test_target_space.py b/tests/test_target_space.py
index 83fa8d38d..59cb38d8b 100644
--- a/tests/test_target_space.py
+++ b/tests/test_target_space.py
@@ -25,8 +25,8 @@ def test_keys_and_bounds_in_same_order():
     assert space.dim == len(pbounds)
     assert space.empty
     assert space.keys == ["p1", "p2",  "p3",  "p4"]
-    assert all(space.bounds[:, 0] == np.array([0, 0, 0, 0]))
-    assert all(space.bounds[:, 1] == np.array([1, 2, 3, 4]))
+    assert all(space.float_bounds[:, 0] == np.array([0, 0, 0, 0]))
+    assert all(space.float_bounds[:, 1] == np.array([1, 2, 3, 4]))
 
 
 def test_params_to_array():
@@ -52,25 +52,23 @@ def test_array_to_params():
         space.array_to_params(np.array([2, 3, 5]))
 
 
-def test_as_array():
+def test_to_float():
     space = TargetSpace(target_func, PBOUNDS)
 
-    x = space._as_array([0, 1])
+    x = space._to_float({"p2": 0, "p1": 1})
     assert x.shape == (2,)
-    assert all(x == np.array([0, 1]))
-
-    x = space._as_array({"p2": 1, "p1": 2})
-    assert x.shape == (2,)
-    assert all(x == np.array([2, 1]))
+    assert all(x == np.array([1, 0]))
 
     with pytest.raises(ValueError):
-        x = space._as_array([2, 1, 7])
+        x = space._to_float([0, 1])
+    with pytest.raises(ValueError):
+        x = space._to_float([2, 1, 7])
     with pytest.raises(ValueError):
-        x = space._as_array({"p2": 1, "p1": 2, "other": 7})
+        x = space._to_float({"p2": 1, "p1": 2, "other": 7})
     with pytest.raises(ValueError):
-        x = space._as_array({"p2": 1})
+        x = space._to_float({"p2": 1})
     with pytest.raises(ValueError):
-        x = space._as_array({"other": 7})
+        x = space._to_float({"other": 7})
 
 
 def test_register():
@@ -96,7 +94,7 @@ def test_register():
 
 
 def test_register_with_constraint():
-    constraint = ConstraintModel(lambda x: x, -2, 2)
+    constraint = ConstraintModel(lambda x: x, -2, 2, transform=lambda x: x)
     space = TargetSpace(target_func, PBOUNDS, constraint=constraint)
 
     assert len(space) == 0
@@ -159,8 +157,8 @@ def test_random_sample():
     for _ in range(50):
         random_sample = space.random_sample()
         assert len(random_sample) == space.dim
-        assert all(random_sample >= space.bounds[:, 0])
-        assert all(random_sample <= space.bounds[:, 1])
+        assert all(random_sample >= space.float_bounds[:, 0])
+        assert all(random_sample <= space.float_bounds[:, 1])
 
 
 def test_max():
@@ -204,13 +202,13 @@ def test_set_bounds():
 
     # Ignore unknown keys
     space.set_bounds({"other": (7, 8)})
-    assert all(space.bounds[:, 0] == np.array([0, 0, 0, 0]))
-    assert all(space.bounds[:, 1] == np.array([1, 2, 3, 4]))
+    assert all(space.float_bounds[:, 0] == np.array([0, 0, 0, 0]))
+    assert all(space.float_bounds[:, 1] == np.array([1, 2, 3, 4]))
 
     # Update bounds accordingly
     space.set_bounds({"p2": (1, 8)})
-    assert all(space.bounds[:, 0] == np.array([0, 1, 0, 0]))
-    assert all(space.bounds[:, 1] == np.array([1, 8, 3, 4]))
+    assert all(space.float_bounds[:, 0] == np.array([0, 1, 0, 0]))
+    assert all(space.float_bounds[:, 1] == np.array([1, 8, 3, 4]))
 
 
 if __name__ == '__main__':

From 5d34efa5676778528a1bbb7489ef80f3f3e7b034 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Sun, 6 Oct 2024 21:13:26 +0200
Subject: [PATCH 04/21] Update

---
 bayes_opt/acquisition.py            |  40 ++--
 bayes_opt/bayesian_optimization.py  |   6 +-
 bayes_opt/domain_reduction.py       |  13 +-
 bayes_opt/parameter.py              | 355 +++++++++++++++++++++++++---
 bayes_opt/target_space.py           | 103 ++++++--
 examples/parameter_types.ipynb      | 139 +++++------
 tests/test_acquisition.py           |  12 +-
 tests/test_bayesian_optimization.py |  16 +-
 tests/test_constraint.py            |   4 +-
 tests/test_parameter.py             |  17 +-
 tests/test_seq_domain_red.py        |   2 +-
 tests/test_target_space.py          |  16 +-
 12 files changed, 533 insertions(+), 190 deletions(-)

diff --git a/bayes_opt/acquisition.py b/bayes_opt/acquisition.py
index 5b079399c..1d4eb5854 100644
--- a/bayes_opt/acquisition.py
+++ b/bayes_opt/acquisition.py
@@ -127,7 +127,7 @@ def suggest(
             self._fit_gp(gp=gp, target_space=target_space)
 
         acq = self._get_acq(gp=gp, constraint=target_space.constraint)
-        return self._acq_min(acq, target_space._float_bounds, n_random=n_random, n_l_bfgs_b=n_l_bfgs_b)
+        return self._acq_min(acq, target_space, n_random=n_random, n_l_bfgs_b=n_l_bfgs_b)
 
     def _get_acq(
         self, gp: GaussianProcessRegressor, constraint: ConstraintModel | None = None
@@ -182,7 +182,7 @@ def acq(x: NDArray[Float]) -> NDArray[Float]:
     def _acq_min(
         self,
         acq: Callable[[NDArray[Float]], NDArray[Float]],
-        bounds: NDArray[Float],
+        space: TargetSpace,
         n_random: int = 10_000,
         n_l_bfgs_b: int = 10,
     ) -> NDArray[Float]:
@@ -197,10 +197,8 @@ def _acq_min(
         acq : Callable
             Acquisition function to use. Should accept an array of parameters `x`.
 
-        bounds : np.ndarray
-            Bounds of the search space. For `N` parameters this has shape
-            `(N, 2)` with `[i, 0]` the lower bound of parameter `i` and
-            `[i, 1]` the upper bound.
+        space : TargetSpace
+            The target space over which to optimize.
 
         n_random : int
             Number of random samples to use.
@@ -217,15 +215,15 @@ def _acq_min(
         if n_random == 0 and n_l_bfgs_b == 0:
             error_msg = "Either n_random or n_l_bfgs_b needs to be greater than 0."
             raise ValueError(error_msg)
-        x_min_r, min_acq_r = self._random_sample_minimize(acq, bounds, n_random=n_random)
-        x_min_l, min_acq_l = self._l_bfgs_b_minimize(acq, bounds, n_x_seeds=n_l_bfgs_b)
+        x_min_r, min_acq_r = self._random_sample_minimize(acq, space, n_random=n_random)
+        x_min_l, min_acq_l = self._l_bfgs_b_minimize(acq, space, n_x_seeds=n_l_bfgs_b)
         # Either n_random or n_l_bfgs_b is not 0 => at least one of x_min_r and x_min_l is not None
         if min_acq_r < min_acq_l:
             return x_min_r
         return x_min_l
 
     def _random_sample_minimize(
-        self, acq: Callable[[NDArray[Float]], NDArray[Float]], bounds: NDArray[Float], n_random: int
+        self, acq: Callable[[NDArray[Float]], NDArray[Float]], space: TargetSpace, n_random: int
     ) -> tuple[NDArray[Float] | None, float]:
         """Random search to find the minimum of `acq` function.
 
@@ -234,10 +232,8 @@ def _random_sample_minimize(
         acq : Callable
             Acquisition function to use. Should accept an array of parameters `x`.
 
-        bounds : np.ndarray
-            Bounds of the search space. For `N` parameters this has shape
-            `(N, 2)` with `[i, 0]` the lower bound of parameter `i` and
-            `[i, 1]` the upper bound.
+        space : TargetSpace
+            The target space over which to optimize.
 
         n_random : int
             Number of random samples to use.
@@ -252,14 +248,14 @@ def _random_sample_minimize(
         """
         if n_random == 0:
             return None, np.inf
-        x_tries = self.random_state.uniform(bounds[:, 0], bounds[:, 1], size=(n_random, bounds.shape[0]))
+        x_tries = space.random_sample(n_random, random_state=self.random_state)
         ys = acq(x_tries)
         x_min = x_tries[ys.argmin()]
         min_acq = ys.min()
         return x_min, min_acq
 
     def _l_bfgs_b_minimize(
-        self, acq: Callable[[NDArray[Float]], NDArray[Float]], bounds: NDArray[Float], n_x_seeds: int = 10
+        self, acq: Callable[[NDArray[Float]], NDArray[Float]], space: TargetSpace, n_x_seeds: int = 10
     ) -> tuple[NDArray[Float] | None, float]:
         """Random search to find the minimum of `acq` function.
 
@@ -268,10 +264,8 @@ def _l_bfgs_b_minimize(
         acq : Callable
             Acquisition function to use. Should accept an array of parameters `x`.
 
-        bounds : np.ndarray
-            Bounds of the search space. For `N` parameters this has shape
-            `(N, 2)` with `[i, 0]` the lower bound of parameter `i` and
-            `[i, 1]` the upper bound.
+        space : TargetSpace
+            The target space over which to optimize.
 
         n_x_seeds : int
             Number of starting points for the L-BFGS-B optimizer.
@@ -286,14 +280,14 @@ def _l_bfgs_b_minimize(
         """
         if n_x_seeds == 0:
             return None, np.inf
-        x_seeds = self.random_state.uniform(bounds[:, 0], bounds[:, 1], size=(n_x_seeds, bounds.shape[0]))
+        x_seeds = space.random_sample(n_x_seeds, random_state=self.random_state)
 
         min_acq: float | None = None
         x_try: NDArray[Float]
         x_min: NDArray[Float]
         for x_try in x_seeds:
             # Find the minimum of minus the acquisition function
-            res: OptimizeResult = minimize(acq, x_try, bounds=bounds, method="L-BFGS-B")
+            res: OptimizeResult = minimize(acq, x_try, bounds=space.bounds, method="L-BFGS-B")
 
             # See if success
             if not res.success:
@@ -306,11 +300,11 @@ def _l_bfgs_b_minimize(
 
         if min_acq is None:
             min_acq = np.inf
-            x_min = np.array([np.nan] * bounds.shape[0])
+            x_min = np.array([np.nan] * space.bounds.shape[0])
 
         # Clip output to make sure it lies within the bounds. Due to floating
         # point technicalities this is not always the case.
-        return np.clip(x_min, bounds[:, 0], bounds[:, 1]), min_acq
+        return np.clip(x_min, space.bounds[:, 0], space.bounds[:, 1]), min_acq
 
 
 class UpperConfidenceBound(AcquisitionFunction):
diff --git a/bayes_opt/bayesian_optimization.py b/bayes_opt/bayesian_optimization.py
index 6a206086f..ed1ade680 100644
--- a/bayes_opt/bayesian_optimization.py
+++ b/bayes_opt/bayesian_optimization.py
@@ -249,7 +249,7 @@ def probe(
     def suggest(self) -> dict[str, float]:
         """Suggest a promising point to probe next."""
         if len(self._space) == 0:
-            return self._space.array_to_params(self._space.random_sample())
+            return self._space.array_to_params(self._space.random_sample(random_state=self._random_state))
 
         # Finding argmax of the acquisition function.
         suggestion = self._acquisition_function.suggest(gp=self._gp, target_space=self._space, fit_gp=True)
@@ -268,7 +268,9 @@ def _prime_queue(self, init_points: int) -> None:
             init_points = max(init_points, 1)
 
         for _ in range(init_points):
-            self._queue.append(self._space.array_to_params(self._space.random_sample()))
+            self._queue.append(
+                self._space.array_to_params(self._space.random_sample(random_state=self._random_state))
+            )
 
     def _prime_subscriptions(self) -> None:
         if not any([len(subs) for subs in self._events.values()]):
diff --git a/bayes_opt/domain_reduction.py b/bayes_opt/domain_reduction.py
index 3bb305027..fa06ba356 100644
--- a/bayes_opt/domain_reduction.py
+++ b/bayes_opt/domain_reduction.py
@@ -63,11 +63,14 @@ class SequentialDomainReductionTransformer(DomainTransformer):
 
     def __init__(
         self,
+        parameters: Iterable[str] | None = None,
         gamma_osc: float = 0.7,
         gamma_pan: float = 1.0,
         eta: float = 0.9,
         minimum_window: NDArray[Float] | Sequence[float] | float | Mapping[str, float] | None = 0.0,
     ) -> None:
+        # TODO: Ensure that this is only applied to continuous parameters
+        self.parameters = parameters
         self.gamma_osc = gamma_osc
         self.gamma_pan = gamma_pan
         self.eta = eta
@@ -87,7 +90,7 @@ def initialize(self, target_space: TargetSpace) -> None:
             TargetSpace this DomainTransformer operates on.
         """
         # Set the original bounds
-        self.original_bounds = np.copy(target_space.float_bounds)
+        self.original_bounds = np.copy(target_space.bounds)
         self.bounds = [self.original_bounds]
 
         # Set the minimum window to an array of length bounds
@@ -97,12 +100,12 @@ def initialize(self, target_space: TargetSpace) -> None:
                 raise ValueError(error_msg)
             self.minimum_window = self.minimum_window_value
         else:
-            self.minimum_window = [self.minimum_window_value] * len(target_space.float_bounds)
+            self.minimum_window = [self.minimum_window_value] * len(target_space.bounds)
 
         # Set initial values
-        self.previous_optimal = np.mean(target_space.float_bounds, axis=1)
-        self.current_optimal = np.mean(target_space.float_bounds, axis=1)
-        self.r = target_space.float_bounds[:, 1] - target_space.float_bounds[:, 0]
+        self.previous_optimal = np.mean(target_space.bounds, axis=1)
+        self.current_optimal = np.mean(target_space.bounds, axis=1)
+        self.r = target_space.bounds[:, 1] - target_space.bounds[:, 0]
 
         self.previous_d = 2.0 * (self.current_optimal - self.previous_optimal) / self.r
 
diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index b1d1895a1..dbd3ae421 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -1,40 +1,114 @@
+"""Parameter classes for Bayesian optimization."""
+
 from __future__ import annotations
 
 import abc
+from collections.abc import Sequence
 from inspect import signature
-from typing import Callable
+from typing import Any, Callable
 
 import numpy as np
 from sklearn.gaussian_process import kernels
 
+from bayes_opt.util import ensure_rng
+
 
 def is_numeric(value):
+    """Check if a value is numeric."""
     return np.issubdtype(type(value), np.number)
 
 
 class BayesParameter(abc.ABC):
-    def __init__(self, name: str, domain) -> None:
+    """Base class for Bayesian optimization parameters.
+
+    Parameters
+    ----------
+    name : str
+        The name of the parameter.
+    """
+
+    def __init__(self, name: str, bounds) -> None:
         self.name = name
-        self.domain = domain
+        self._bounds = bounds
 
     @property
-    @abc.abstractmethod
-    def float_bounds(self):
-        pass
+    def bounds(self):
+        """The bounds of the parameter in float space."""
+        return self._bounds
+
+    def random_sample(self, n_samples: int, random_state: np.random.RandomState | int | None) -> np.ndarray:
+        """Generate random samples from the parameter.
+
+        Parameters
+        ----------
+        n_samples : int
+            The number of samples to generate.
+
+        random_state : np.random.RandomState | int | None
+            The random state to use for sampling.
+
+        Returns
+        -------
+        np.ndarray
+            The samples.
+        """
+        random_state = ensure_rng(random_state)
+        return random_state.uniform(self.bounds[0], self.bounds[1], n_samples)
 
     @abc.abstractmethod
     def to_float(self, value) -> np.ndarray:
-        pass
+        """Convert a parameter value to a float.
+
+        Parameters
+        ----------
+        value : Any
+            The value to convert, should be the canonical representation of the parameter.
+        """
 
     @abc.abstractmethod
     def to_param(self, value):
-        pass
+        """Convert a float value to a parameter.
+
+        Parameters
+        ----------
+        value : np.ndarray
+            The value to convert, should be a float.
+
+        Returns
+        -------
+        Any
+            The canonical representation of the parameter.
+        """
 
     @abc.abstractmethod
     def kernel_transform(self, value):
-        pass
+        """Transform a parameter value for use in a kernel.
+
+        Parameters
+        ----------
+        value : np.ndarray
+            The value(s) to transform, should be a float.
+
+        Returns
+        -------
+        np.ndarray
+        """
 
     def repr(self, value, str_len) -> str:
+        """Represent a parameter value as a string.
+
+        Parameters
+        ----------
+        value : Any
+            The value to represent.
+
+        str_len : int
+            The maximum length of the string representation.
+
+        Returns
+        -------
+        str
+        """
         s = value.__repr__()
 
         if len(s) > str_len:
@@ -46,26 +120,67 @@ def repr(self, value, str_len) -> str:
     @property
     @abc.abstractmethod
     def dim(self) -> int:
-        pass
+        """The dimensionality of the parameter."""
 
 
 class FloatParameter(BayesParameter):
-    def __init__(self, name: str, domain) -> None:
-        super().__init__(name, domain)
+    """A parameter with float values.
 
-    @property
-    def float_bounds(self):
-        return np.array(self.domain)
+    Parameters
+    ----------
+    name : str
+        The name of the parameter.
+
+    bounds : tuple[float, float]
+        The bounds of the parameter.
+    """
+
+    def __init__(self, name: str, bounds: tuple[float, float]) -> None:
+        super().__init__(name, np.array(bounds))
 
     def to_float(self, value) -> np.ndarray:
+        """Convert a parameter value to a float.
+
+        Parameters
+        ----------
+        value : Any
+            The value to convert, should be the canonical representation of the parameter.
+        """
         return value
 
     def to_param(self, value):
+        """Convert a float value to a parameter.
+
+        Parameters
+        ----------
+        value : np.ndarray
+            The value to convert, should be a float.
+
+        Returns
+        -------
+        Any
+            The canonical representation of the parameter.
+        """
         if isinstance(value, np.ndarray) and value.size != 1:
-            raise ValueError("FloatParameter scalars")
+            msg = "FloatParameter value should be scalar"
+            raise ValueError(msg)
         return value.flatten()[0]
 
     def repr(self, value, str_len) -> str:
+        """Represent a parameter value as a string.
+
+        Parameters
+        ----------
+        value : Any
+            The value to represent.
+
+        str_len : int
+            The maximum length of the string representation.
+
+        Returns
+        -------
+        str
+        """
         s = f"{value:<{str_len}.{str_len}}"
         if len(s) > str_len:
             if "." in s:
@@ -74,29 +189,99 @@ def repr(self, value, str_len) -> str:
         return s
 
     def kernel_transform(self, value):
+        """Transform a parameter value for use in a kernel.
+
+        Parameters
+        ----------
+        value : np.ndarray
+            The value(s) to transform, should be a float.
+
+        Returns
+        -------
+        np.ndarray
+        """
         return value
 
     @property
     def dim(self) -> int:
+        """The dimensionality of the parameter."""
         return 1
 
 
 class IntParameter(BayesParameter):
-    def __init__(self, name: str, domain) -> None:
-        super().__init__(name, domain)
+    """A parameter with int values.
 
-    @property
-    def float_bounds(self):
-        # adding/subtracting ~0.5 to achieve uniform probability of integers
-        return np.array([self.domain[0] - 0.4999999, self.domain[1] + 0.4999999])
+    Parameters
+    ----------
+    name : str
+        The name of the parameter.
+
+    bounds : tuple[int, int]
+        The bounds of the parameter.
+    """
+
+    def __init__(self, name: str, bounds: tuple[int | float, int | float]) -> None:
+        super().__init__(name, np.array(bounds))
+
+    def random_sample(self, n_samples: int, random_state: np.random.RandomState | int | None) -> np.ndarray:
+        """Generate random samples from the parameter.
+
+        Parameters
+        ----------
+        n_samples : int
+            The number of samples to generate.
+
+        random_state : np.random.RandomState | int | None
+            The random state to use for sampling.
+
+        Returns
+        -------
+        np.ndarray
+            The samples.
+        """
+        random_state = ensure_rng(random_state)
+        return random_state.randint(self.bounds[0], self.bounds[1] + 1, n_samples).astype(float)
 
     def to_float(self, value) -> np.ndarray:
+        """Convert a parameter value to a float.
+
+        Parameters
+        ----------
+        value : Any
+            The value to convert, should be the canonical representation of the parameter.
+        """
         return float(value)
 
     def to_param(self, value):
+        """Convert a float value to a parameter.
+
+        Parameters
+        ----------
+        value : np.ndarray
+            The value to convert, should be a float.
+
+        Returns
+        -------
+        Any
+            The canonical representation of the parameter.
+        """
         return int(np.round(np.squeeze(value)))
 
     def repr(self, value, str_len) -> str:
+        """Represent a parameter value as a string.
+
+        Parameters
+        ----------
+        value : Any
+            The value to represent.
+
+        str_len : int
+            The maximum length of the string representation.
+
+        Returns
+        -------
+        str
+        """
         s = f"{value:<{str_len}}"
         if len(s) > str_len:
             if "." in s:
@@ -105,42 +290,127 @@ def repr(self, value, str_len) -> str:
         return s
 
     def kernel_transform(self, value):
+        """Transform a parameter value for use in a kernel.
+
+        Parameters
+        ----------
+        value : np.ndarray
+            The value(s) to transform, should be a float.
+
+        Returns
+        -------
+        np.ndarray
+        """
         return np.round(value)
 
     @property
     def dim(self) -> int:
+        """The dimensionality of the parameter."""
         return 1
 
 
 class CategoricalParameter(BayesParameter):
-    def __init__(self, name: str, domain) -> None:
-        super().__init__(name, domain)
+    """A parameter with categorical values.
 
-    @property
-    def float_bounds(self):
-        # to achieve uniform probability after rounding
+    Parameters
+    ----------
+    name : str
+        The name of the parameter.
+
+    categories : Sequence[Any]
+        The categories of the parameter.
+    """
+
+    def __init__(self, name: str, categories: Sequence[Any]) -> None:
+        self.categories = categories
         lower = np.zeros(self.dim)
         upper = np.ones(self.dim)
-        return np.vstack((lower, upper)).T
+        bounds = np.vstack((lower, upper)).T
+        super().__init__(name, bounds)
+
+    def random_sample(self, n_samples: int, random_state: np.random.RandomState | int | None) -> np.ndarray:
+        """Generate random float-format samples from the parameter.
+
+        Parameters
+        ----------
+        n_samples : int
+            The number of samples to generate.
+
+        random_state : np.random.RandomState | int | None
+            The random state to use for sampling.
+
+        Returns
+        -------
+        np.ndarray
+            The samples.
+        """
+        res = random_state.randint(0, len(self.categories), n_samples)
+        one_hot = np.zeros((n_samples, len(self.categories)))
+        one_hot[np.arange(n_samples), res] = 1
+        return one_hot.astype(float)
 
     def to_float(self, value) -> np.ndarray:
-        res = np.zeros(len(self.domain))
-        one_hot_index = [i for i, val in enumerate(self.domain) if val == value]
+        """Convert a parameter value to a float.
+
+        Parameters
+        ----------
+        value : Any
+            The value to convert, should be the canonical representation of the parameter.
+        """
+        res = np.zeros(len(self.categories))
+        one_hot_index = [i for i, val in enumerate(self.categories) if val == value]
         if len(one_hot_index) != 1:
             raise ValueError
         res[one_hot_index] = 1
         return res.astype(float)
 
     def to_param(self, value):
-        return self.domain[np.argmax(value)]
+        """Convert a float value to a parameter.
+
+        Parameters
+        ----------
+        value : np.ndarray
+            The value to convert, should be a float.
+
+        Returns
+        -------
+        Any
+            The canonical representation of the parameter.
+        """
+        return self.categories[np.argmax(value)]
 
     def repr(self, value, str_len) -> str:
+        """Represent a parameter value as a string.
+
+        Parameters
+        ----------
+        value : Any
+            The value to represent.
+
+        str_len : int
+            The maximum length of the string representation.
+
+        Returns
+        -------
+        str
+        """
         s = f"{value:^{str_len}}"
         if len(s) > str_len:
             return s[: str_len - 3] + "..."
         return s
 
     def kernel_transform(self, value):
+        """Transform a parameter value for use in a kernel.
+
+        Parameters
+        ----------
+        value : np.ndarray
+            The value(s) to transform, should be a float.
+
+        Returns
+        -------
+        np.ndarray
+        """
         value = np.atleast_2d(value)
         res = np.zeros(value.shape)
         res[np.argmax(value, axis=0)] = 1
@@ -148,10 +418,31 @@ def kernel_transform(self, value):
 
     @property
     def dim(self) -> int:
-        return len(self.domain)
+        """The dimensionality of the parameter."""
+        return len(self.categories)
 
 
 def wrap_kernel(kernel: kernels.Kernel, transform: Callable) -> kernels.Kernel:
+    """Wrap a kernel to transform input data before passing it to the kernel.
+
+    Parameters
+    ----------
+    kernel : kernels.Kernel
+        The kernel to wrap.
+
+    transform : Callable
+        The transformation function to apply to the input data.
+
+    Returns
+    -------
+    kernels.Kernel
+        The wrapped kernel.
+
+    Notes
+    -----
+    See https://arxiv.org/abs/1805.03463 for more information.
+    """
+
     class WrappedKernel(type(kernel)):
         @copy_signature(getattr(kernel.__class__.__init__, "deprecated_original", kernel.__class__.__init__))
         def __init__(self, **kwargs) -> None:
diff --git a/bayes_opt/target_space.py b/bayes_opt/target_space.py
index 020e5df40..167076102 100644
--- a/bayes_opt/target_space.py
+++ b/bayes_opt/target_space.py
@@ -86,7 +86,7 @@ def __init__(
         self._dim = sum([self._params_config[key].dim for key in self._keys])
 
         self._masks = self.make_masks()
-        self._float_bounds = self.calculate_float_bounds()
+        self._bounds = self.calculate_bounds()
 
         # preallocated memory for X and Y points
         self._params: NDArray[Float] = np.empty(shape=(0, self.dim))
@@ -186,7 +186,7 @@ def bounds(self) -> NDArray[Float]:
         -------
         np.ndarray
         """
-        return [self._params_config[key].domain for key in self.keys]
+        return self._bounds
 
     @property
     def constraint(self) -> ConstraintModel | None:
@@ -199,14 +199,30 @@ def constraint(self) -> ConstraintModel | None:
         return self._constraint
 
     @property
-    def float_bounds(self):
-        return self._float_bounds
+    def masks(self) -> dict:
+        """Get the masks for the parameters.
 
-    @property
-    def masks(self):
+        Returns
+        -------
+        dict
+        """
         return self._masks
 
     def make_params(self, pbounds) -> dict:
+        """Create a dictionary of parameters from a dictionary of bounds.
+
+        Parameters
+        ----------
+        pbounds : dict
+            A dictionary with the parameter names as keys and a tuple with minimum
+            and maximum values.
+
+        Returns
+        -------
+        dict
+            A dictionary with the parameter names as keys and the corresponding
+            parameter objects as values.
+        """
         params = {}
         for key in sorted(pbounds):
             pbound = pbounds[key]
@@ -214,18 +230,28 @@ def make_params(self, pbounds) -> dict:
             if isinstance(pbound, BayesParameter):
                 res = pbound
             elif len(pbound) == 2 and is_numeric(pbound[0]) and is_numeric(pbound[1]):
-                res = FloatParameter(name=key, domain=pbound)
+                res = FloatParameter(name=key, bounds=pbound)
             elif len(pbound) == 3 and pbound[-1] is float:
-                res = FloatParameter(name=key, domain=(pbound[0], pbound[1]))
+                res = FloatParameter(name=key, bounds=(pbound[0], pbound[1]))
             elif len(pbound) == 3 and pbound[-1] is int:
-                res = IntParameter(name=key, domain=(int(pbound[0]), int(pbound[1])))
+                res = IntParameter(name=key, bounds=(int(pbound[0]), int(pbound[1])))
             else:
                 # assume categorical variable with pbound as list of possible values
-                res = CategoricalParameter(name=key, domain=pbound)
+                res = CategoricalParameter(name=key, categories=pbound)
             params[key] = res
         return params
 
-    def make_masks(self):
+    def make_masks(self) -> dict:
+        """Create a dictionary of masks for the parameters.
+
+        The mask can be used to select the corresponding parameters from an array.
+
+        Returns
+        -------
+        dict
+            A dictionary with the parameter names as keys and the corresponding
+            mask as values.
+        """
         masks = {}
         pos = 0
         for key in self._keys:
@@ -235,10 +261,11 @@ def make_masks(self):
             pos = pos + self._params_config[key].dim
         return masks
 
-    def calculate_float_bounds(self):
+    def calculate_bounds(self) -> NDArray[Float]:
+        """Calculate the float bounds of the parameter space."""
         bounds = np.empty((self._dim, 2))
         for key in self._keys:
-            bounds[self.masks[key]] = self._params_config[key].float_bounds
+            bounds[self.masks[key]] = self._params_config[key].bounds
         return bounds
 
     def params_to_array(self, params: Mapping[str, float]) -> NDArray[Float]:
@@ -346,10 +373,9 @@ def mask(self) -> NDArray[np.bool_]:
             mask &= self._constraint.allowed(self._constraint_values)
 
         # mask points that are outside the bounds
-        if self._float_bounds is not None:
+        if self._bounds is not None:
             within_bounds = np.all(
-                (self._float_bounds[:, 0] <= self._params) & (self._params <= self._float_bounds[:, 1]),
-                axis=1,
+                (self._bounds[:, 0] <= self._params) & (self._params <= self._bounds[:, 1]), axis=1
             )
             mask &= within_bounds
 
@@ -429,10 +455,17 @@ def register(
                 raise NotUniqueError(error_msg)
 
         # if x is not within the bounds of the parameter space, warn the user
-        if self._float_bounds is not None and not np.all(
-            (self._float_bounds[:, 0] <= x) & (x <= self._float_bounds[:, 1])
-        ):
-            warn(f"\nData point {x} is outside the bounds of the parameter space. ", stacklevel=2)
+        if self._bounds is not None and not np.all((self._bounds[:, 0] <= x) & (x <= self._bounds[:, 1])):
+            for key in self.keys:
+                if not np.all(
+                    (self._params_config[key].bounds[..., 0] <= x[self.masks[key]])
+                    & (x[self.masks[key]] <= self._params_config[key].bounds[..., 1])
+                ):
+                    msg = (
+                        f"\nData point {x} is outside the bounds of the parameter {key}."
+                        f"\n\tBounds:\n{self._params_config[key].bounds}"
+                    )
+                    warn(msg, stacklevel=2)
 
         # Make copies of the data, so as not to modify the originals incase something fails
         # during the registration process. This prevents out-of-sync data.
@@ -509,10 +542,22 @@ def probe(
         self.register(x, target, constraint_value)
         return target, constraint_value
 
-    def random_sample(self) -> NDArray[Float]:
+    def random_sample(
+        self, n_samples: int = 0, random_state: np.random.RandomState | int | None = None
+    ) -> NDArray[Float]:
         """
         Sample a random point from within the bounds of the space.
 
+        Parameters
+        ----------
+        n_samples : int, optional
+            Number of samples to draw. If 0, a single sample is drawn,
+            and a 1D array is returned. If n_samples > 0, an array of
+            shape (n_samples, dim) is returned.
+
+        random_state : np.random.RandomState | int | None
+            The random state to use for sampling.
+
         Returns
         -------
         data: ndarray
@@ -526,10 +571,16 @@ def random_sample(self) -> NDArray[Float]:
         >>> space.random_sample()
         array([[ 0.54488318,   55.33253689]])
         """
-        data = np.empty((1, self._dim))
-        for col, (lower, upper) in enumerate(self._float_bounds):
-            data.T[col] = self.random_state.uniform(lower, upper, size=1)
-        return data.ravel()
+        random_state = ensure_rng(random_state)
+        flatten = n_samples == 0
+        n_samples = max(1, n_samples)
+        data = np.empty((n_samples, self._dim))
+        for key, mask in self._masks.items():
+            smpl = self._params_config[key].random_sample(n_samples, random_state)
+            data[:, mask] = smpl.reshape(n_samples, self._params_config[key].dim)
+        if flatten:
+            return data.ravel()
+        return data
 
     def _target_max(self) -> float | None:
         """Get the maximum target value within the current parameter bounds.
@@ -639,4 +690,4 @@ def set_bounds(self, new_bounds: Mapping[str, NDArray[Float] | Sequence[float]])
                     )
                     raise ValueError(msg)
                 self._params_config[key] = new__params_config[key]
-        self._float_bounds = self.calculate_float_bounds()
+        self._bounds = self.calculate_bounds()
diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
index c1bfd783e..961432a95 100644
--- a/examples/parameter_types.ipynb
+++ b/examples/parameter_types.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from bayes_opt import BayesianOptimization\n"
+    "from bayes_opt import BayesianOptimization, BayesParameter\n"
    ]
   },
   {
@@ -51,7 +51,7 @@
     "    f=discretized_function,\n",
     "    pbounds=c_pbounds,\n",
     "    verbose=2,\n",
-    "    random_state=1,\n",
+    "    random_state=42,\n",
     ")\n",
     "\n",
     "\n",
@@ -60,7 +60,7 @@
     "    f=discretized_function,\n",
     "    pbounds=d_pbounds,\n",
     "    verbose=2,\n",
-    "    random_state=1,\n",
+    "    random_state=42,\n",
     ")"
    ]
   },
@@ -77,36 +77,36 @@
       "\n",
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1702  \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m2.2032449\u001b[39m |\n",
-      "| \u001b[35m2        \u001b[39m | \u001b[35m0.03165  \u001b[39m | \u001b[35m-4.998856\u001b[39m | \u001b[35m-1.976674\u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m0.04415  \u001b[39m | \u001b[35m-4.946870\u001b[39m | \u001b[35m-2.137915\u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.002676\u001b[39m | \u001b[39m-4.441393\u001b[39m | \u001b[39m-4.515654\u001b[39m |\n",
-      "| \u001b[35m5        \u001b[39m | \u001b[35m0.6544   \u001b[39m | \u001b[35m-3.051440\u001b[39m | \u001b[35m-2.571351\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m0.4494   \u001b[39m | \u001b[39m-2.023482\u001b[39m | \u001b[39m-2.342706\u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m0.4207   \u001b[39m | \u001b[39m-2.516753\u001b[39m | \u001b[39m-3.549863\u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m0.2129   \u001b[39m | \u001b[39m-3.015779\u001b[39m | \u001b[39m-1.270144\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.1227  \u001b[39m | \u001b[39m-0.103683\u001b[39m | \u001b[39m-3.639513\u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.06719 \u001b[39m | \u001b[39m4.8593667\u001b[39m | \u001b[39m4.9971018\u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1504  \u001b[39m | \u001b[39m-1.254598\u001b[39m | \u001b[39m4.5071430\u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m0.08233  \u001b[39m | \u001b[35m2.3199394\u001b[39m | \u001b[35m0.9865848\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.2095   \u001b[39m | \u001b[35m0.9907151\u001b[39m | \u001b[35m-2.811653\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.286    \u001b[39m | \u001b[35m4.0043956\u001b[39m | \u001b[35m-4.018452\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.08174 \u001b[39m | \u001b[39m-4.963953\u001b[39m | \u001b[39m-4.888396\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.03501  \u001b[39m | \u001b[39m4.9846486\u001b[39m | \u001b[39m-1.821879\u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m0.1011   \u001b[39m | \u001b[39m2.3883784\u001b[39m | \u001b[39m-4.558775\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.009009\u001b[39m | \u001b[39m4.4807948\u001b[39m | \u001b[39m-4.842764\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m0.1525   \u001b[39m | \u001b[39m4.5858949\u001b[39m | \u001b[39m-3.783561\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m0.2783   \u001b[39m | \u001b[39m4.0389974\u001b[39m | \u001b[39m-4.031531\u001b[39m |\n",
       "=================================================\n",
-      "Max: 0.6544320709931273\n",
+      "Max: 0.2859884589036788\n",
       "\n",
       "\n",
       "====================  Typed Optimizer  ====================\n",
       "\n",
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1702  \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[35m2        \u001b[39m | \u001b[35m0.03165  \u001b[39m | \u001b[35m-4.998856\u001b[39m | \u001b[35m-2       \u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m0.08123  \u001b[39m | \u001b[35m-4.803093\u001b[39m | \u001b[35m-2       \u001b[39m |\n",
-      "| \u001b[35m4        \u001b[39m | \u001b[35m0.2941   \u001b[39m | \u001b[35m-4.115548\u001b[39m | \u001b[35m-2       \u001b[39m |\n",
-      "| \u001b[35m5        \u001b[39m | \u001b[35m0.5668   \u001b[39m | \u001b[35m-3.073842\u001b[39m | \u001b[35m-2       \u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m0.4001   \u001b[39m | \u001b[39m-1.478073\u001b[39m | \u001b[39m-3       \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.007857\u001b[39m | \u001b[39m-2.126598\u001b[39m | \u001b[39m-1       \u001b[39m |\n",
-      "| \u001b[35m8        \u001b[39m | \u001b[35m0.6648   \u001b[39m | \u001b[35m-2.947338\u001b[39m | \u001b[35m-3       \u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m0.1439   \u001b[39m | \u001b[39m-2.847256\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.08434 \u001b[39m | \u001b[39m4.9833729\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1504  \u001b[39m | \u001b[39m-1.254598\u001b[39m | \u001b[39m5        \u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m0.2024   \u001b[39m | \u001b[35m2.79691  \u001b[39m | \u001b[35m-1       \u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.6476   \u001b[39m | \u001b[35m-2.349882\u001b[39m | \u001b[35m-3       \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m0.126    \u001b[39m | \u001b[39m-2.594630\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.3138  \u001b[39m | \u001b[39m-0.431955\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[35m6        \u001b[39m | \u001b[35m0.6741   \u001b[39m | \u001b[35m-2.731757\u001b[39m | \u001b[35m-3       \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m0.1951   \u001b[39m | \u001b[39m-2.737932\u001b[39m | \u001b[39m-1       \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.2396   \u001b[39m | \u001b[39m-4.647873\u001b[39m | \u001b[39m-3       \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m0.1508   \u001b[39m | \u001b[39m4.9876130\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.1084  \u001b[39m | \u001b[39m-0.947769\u001b[39m | \u001b[39m-2       \u001b[39m |\n",
       "=================================================\n",
-      "Max: 0.6647727296561663\n",
+      "Max: 0.6741133751365254\n",
       "\n",
       "\n"
      ]
@@ -136,7 +136,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -229,26 +229,26 @@
      "text": [
       "|   iter    |  target   |     k     |    x1     |    x2     |\n",
       "-------------------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m8.698    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.997712\u001b[39m | \u001b[39m-3.953348\u001b[39m |\n",
-      "| \u001b[39m2        \u001b[39m | \u001b[39m6.796    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-6.274795\u001b[39m | \u001b[39m-3.088785\u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m7.978    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.970416\u001b[39m | \u001b[39m-3.781190\u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m4.331    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-9.784631\u001b[39m | \u001b[39m-5.210773\u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m-14.69   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m9.1818929\u001b[39m | \u001b[39m6.0937003\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m0.5244   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.947053\u001b[39m | \u001b[39m4.9142403\u001b[39m |\n",
-      "| \u001b[35m7        \u001b[39m | \u001b[35m17.04    \u001b[39m | \u001b[35m    2    \u001b[39m | \u001b[35m9.9626887\u001b[39m | \u001b[35m-9.994737\u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m-10.26   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m4.8389550\u001b[39m | \u001b[39m-9.994760\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m2.323    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m9.9704528\u001b[39m | \u001b[39m-7.076212\u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m13.23    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-9.169301\u001b[39m | \u001b[39m-0.486971\u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m-3.706   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-5.889131\u001b[39m | \u001b[39m1.1635320\u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m12.81    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m9.1816596\u001b[39m | \u001b[39m-9.986873\u001b[39m |\n",
-      "| \u001b[39m13       \u001b[39m | \u001b[39m2.761    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-1.480697\u001b[39m | \u001b[39m9.9724447\u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m15.44    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-4.812190\u001b[39m | \u001b[39m-9.939661\u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m9.753    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-3.349925\u001b[39m | \u001b[39m-7.682301\u001b[39m |\n",
-      "| \u001b[39m16       \u001b[39m | \u001b[39m13.62    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-7.480940\u001b[39m | \u001b[39m-9.985028\u001b[39m |\n",
-      "| \u001b[39m17       \u001b[39m | \u001b[39m-2.073   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m2.2439153\u001b[39m | \u001b[39m-0.924723\u001b[39m |\n",
-      "| \u001b[39m18       \u001b[39m | \u001b[39m7.228    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-6.166753\u001b[39m | \u001b[39m-8.036643\u001b[39m |\n",
-      "| \u001b[39m19       \u001b[39m | \u001b[39m10.01    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-2.211731\u001b[39m | \u001b[39m-9.971190\u001b[39m |\n",
-      "| \u001b[39m20       \u001b[39m | \u001b[39m16.82    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.944579\u001b[39m | \u001b[39m-9.916347\u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-10.87   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m9.9436962\u001b[39m | \u001b[39m8.6511471\u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m10.65    \u001b[39m | \u001b[35m    2    \u001b[39m | \u001b[35m-3.953348\u001b[39m | \u001b[35m-7.064882\u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m1.911    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-3.430812\u001b[39m | \u001b[39m-7.728198\u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m9.726    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-4.862044\u001b[39m | \u001b[39m-5.999132\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m0.8457   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-1.919685\u001b[39m | \u001b[39m-3.730800\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m3.984    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-7.008155\u001b[39m | \u001b[39m-7.841077\u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m4.096    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-7.357018\u001b[39m | \u001b[39m-3.570538\u001b[39m |\n",
+      "| \u001b[35m8        \u001b[39m | \u001b[35m12.62    \u001b[39m | \u001b[35m    1    \u001b[39m | \u001b[35m-9.879071\u001b[39m | \u001b[35m0.8692765\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m10.67    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-9.766583\u001b[39m | \u001b[39m2.6526599\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m4.147    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-7.515377\u001b[39m | \u001b[39m1.2085635\u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-1.594   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.888163\u001b[39m | \u001b[39m-0.986066\u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m12.6     \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-9.789574\u001b[39m | \u001b[39m1.5273833\u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m0.3688   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.862455\u001b[39m | \u001b[39m5.0749913\u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m-2.888   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.949469\u001b[39m | \u001b[39m1.6633608\u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m0.3513   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-0.825539\u001b[39m | \u001b[39m0.9079267\u001b[39m |\n",
+      "| \u001b[39m16       \u001b[39m | \u001b[39m-1.711   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m2.195066 \u001b[39m | \u001b[39m1.2996733\u001b[39m |\n",
+      "| \u001b[39m17       \u001b[39m | \u001b[39m0.3682   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m1.5699874\u001b[39m | \u001b[39m0.4413354\u001b[39m |\n",
+      "| \u001b[39m18       \u001b[39m | \u001b[39m8.667    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-4.855364\u001b[39m | \u001b[39m-5.736939\u001b[39m |\n",
+      "| \u001b[39m19       \u001b[39m | \u001b[39m-0.03433 \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m1.0870723\u001b[39m | \u001b[39m3.4490001\u001b[39m |\n",
+      "| \u001b[39m20       \u001b[39m | \u001b[39m-8.418   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m7.8951162\u001b[39m | \u001b[39m-3.354534\u001b[39m |\n",
       "=============================================================\n"
      ]
     }
@@ -287,7 +287,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -333,21 +333,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|   iter    |  target   |     C     |  degree   |  kernel   |\n",
-      "-------------------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m0.8166   \u001b[39m | \u001b[39m3.8079471\u001b[39m | \u001b[39m3        \u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
-      "| \u001b[39m2        \u001b[39m | \u001b[39m0.1887   \u001b[39m | \u001b[39m1.6445845\u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m  poly   \u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m0.6176   \u001b[39m | \u001b[39m3.8018183\u001b[39m | \u001b[39m3        \u001b[39m | \u001b[39m  poly   \u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m0.6639   \u001b[39m | \u001b[39m4.9294903\u001b[39m | \u001b[39m3        \u001b[39m | \u001b[39m  poly   \u001b[39m |\n",
-      "| \u001b[35m5        \u001b[39m | \u001b[35m0.9169   \u001b[39m | \u001b[35m5.7324339\u001b[39m | \u001b[35m1        \u001b[39m | \u001b[35m   rbf   \u001b[39m |\n",
-      "| \u001b[35m6        \u001b[39m | \u001b[35m0.9536   \u001b[39m | \u001b[35m7.0665290\u001b[39m | \u001b[35m1        \u001b[39m | \u001b[35m   rbf   \u001b[39m |\n",
-      "| \u001b[35m7        \u001b[39m | \u001b[35m0.9827   \u001b[39m | \u001b[35m9.6695859\u001b[39m | \u001b[35m1        \u001b[39m | \u001b[35m   rbf   \u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m0.9807   \u001b[39m | \u001b[39m9.3918632\u001b[39m | \u001b[39m3        \u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m0.9706   \u001b[39m | \u001b[39m7.8178252\u001b[39m | \u001b[39m3        \u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m0.9749   \u001b[39m | \u001b[39m8.5973444\u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m0.8493   \u001b[39m | \u001b[39m9.9999466\u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m  poly   \u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m0.9749   \u001b[39m | \u001b[39m8.5770975\u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
-      "=============================================================\n"
+      "|   iter    |  target   |     C     |  kernel   |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.8166   \u001b[39m | \u001b[39m3.8079471\u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m0.5419   \u001b[39m | \u001b[39m1.9160044\u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m0.5927   \u001b[39m | \u001b[39m3.4819990\u001b[39m | \u001b[39m  poly3  \u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.8463   \u001b[39m | \u001b[35m9.6815942\u001b[39m | \u001b[35m  poly2  \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m0.7862   \u001b[39m | \u001b[39m7.1327138\u001b[39m | \u001b[39m  poly2  \u001b[39m |\n",
+      "| \u001b[35m6        \u001b[39m | \u001b[35m0.8688   \u001b[39m | \u001b[35m4.6671691\u001b[39m | \u001b[35m   rbf   \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m0.6765   \u001b[39m | \u001b[39m2.5627368\u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.7324   \u001b[39m | \u001b[39m6.7594238\u001b[39m | \u001b[39m  poly3  \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m0.8585   \u001b[39m | \u001b[39m4.4567931\u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m0.6446   \u001b[39m | \u001b[39m4.5126050\u001b[39m | \u001b[39m  poly3  \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m0.8446   \u001b[39m | \u001b[39m9.1220066\u001b[39m | \u001b[39m  poly2  \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m0.4482   \u001b[39m | \u001b[39m1.9270948\u001b[39m | \u001b[39m  poly3  \u001b[39m |\n",
+      "=================================================\n"
      ]
     }
    ],
@@ -361,8 +361,16 @@
     "\n",
     "kernels = ['rbf', 'poly']\n",
     "\n",
-    "def f_target(kernel, C, degree):\n",
-    "    \n",
+    "def f_target(kernel, C):\n",
+    "    if kernel == 'poly2':\n",
+    "        kernel = 'poly'\n",
+    "        degree = 2\n",
+    "    elif kernel == 'poly3':\n",
+    "        kernel = 'poly'\n",
+    "        degree = 3\n",
+    "    elif kernel == 'rbf':\n",
+    "        degree = 3 # not used, equal to default\n",
+    "\n",
     "    model = SVC(C=C, kernel=kernel, degree=degree)\n",
     "    model.fit(data['data'], data['target'])\n",
     "\n",
@@ -371,9 +379,8 @@
     "\n",
     "\n",
     "params_svm ={\n",
-    "    'kernel': ['rbf', 'poly'],\n",
+    "    'kernel': ['rbf', 'poly2', 'poly3'],\n",
     "    'C':(1e-1, 1e+1),\n",
-    "    'degree':(1, 3, int),\n",
     "}\n",
     "\n",
     "optimizer = BayesianOptimization(f_target, params_svm, random_state=42, verbose=2)\n",
diff --git a/tests/test_acquisition.py b/tests/test_acquisition.py
index 50546216d..860d4f42f 100644
--- a/tests/test_acquisition.py
+++ b/tests/test_acquisition.py
@@ -12,27 +12,27 @@
 from bayes_opt.target_space import TargetSpace
 
 
-@pytest.fixture()
+@pytest.fixture
 def target_func():
     return lambda x: sum(x)
 
 
-@pytest.fixture()
+@pytest.fixture
 def random_state():
     return np.random.RandomState()
 
 
-@pytest.fixture()
+@pytest.fixture
 def gp(random_state):
     return GaussianProcessRegressor(random_state=random_state)
 
 
-@pytest.fixture()
+@pytest.fixture
 def target_space(target_func):
     return TargetSpace(target_func=target_func, pbounds={"x": (1, 4), "y": (0, 3.0)})
 
 
-@pytest.fixture()
+@pytest.fixture
 def constrained_target_space(target_func):
     constraint_model = ConstraintModel(fun=lambda params: params["x"] + params["y"], lb=0.0, ub=1.0)
     return TargetSpace(
@@ -114,7 +114,7 @@ def fun(x):
         except IndexError:
             return np.nan
 
-    _, min_acq_l = acq._l_bfgs_b_minimize(fun, bounds=target_space._float_bounds, n_x_seeds=1)
+    _, min_acq_l = acq._l_bfgs_b_minimize(fun, space=target_space, n_x_seeds=1)
     assert min_acq_l == np.inf
 
 
diff --git a/tests/test_bayesian_optimization.py b/tests/test_bayesian_optimization.py
index 55c127562..d035f8b4e 100644
--- a/tests/test_bayesian_optimization.py
+++ b/tests/test_bayesian_optimization.py
@@ -93,8 +93,8 @@ def test_suggest_at_random():
     for _ in range(50):
         sample = optimizer.space.params_to_array(optimizer.suggest())
         assert len(sample) == optimizer.space.dim
-        assert all(sample >= optimizer.space.float_bounds[:, 0])
-        assert all(sample <= optimizer.space.float_bounds[:, 1])
+        assert all(sample >= optimizer.space.bounds[:, 0])
+        assert all(sample <= optimizer.space.bounds[:, 1])
 
 
 def test_suggest_with_one_observation():
@@ -106,8 +106,8 @@ def test_suggest_with_one_observation():
     for _ in range(5):
         sample = optimizer.space.params_to_array(optimizer.suggest())
         assert len(sample) == optimizer.space.dim
-        assert all(sample >= optimizer.space.float_bounds[:, 0])
-        assert all(sample <= optimizer.space.float_bounds[:, 1])
+        assert all(sample >= optimizer.space.bounds[:, 0])
+        assert all(sample <= optimizer.space.bounds[:, 1])
 
     # suggestion = optimizer.suggest(util)
     # for _ in range(5):
@@ -195,13 +195,13 @@ def test_set_bounds():
 
     # Ignore unknown keys
     optimizer.set_bounds({"other": (7, 8)})
-    assert all(optimizer.space.float_bounds[:, 0] == np.array([0, 0, 0, 0]))
-    assert all(optimizer.space.float_bounds[:, 1] == np.array([1, 2, 3, 4]))
+    assert all(optimizer.space.bounds[:, 0] == np.array([0, 0, 0, 0]))
+    assert all(optimizer.space.bounds[:, 1] == np.array([1, 2, 3, 4]))
 
     # Update bounds accordingly
     optimizer.set_bounds({"p2": (1, 8)})
-    assert all(optimizer.space.float_bounds[:, 0] == np.array([0, 1, 0, 0]))
-    assert all(optimizer.space.float_bounds[:, 1] == np.array([1, 8, 3, 4]))
+    assert all(optimizer.space.bounds[:, 0] == np.array([0, 1, 0, 0]))
+    assert all(optimizer.space.bounds[:, 1] == np.array([1, 8, 3, 4]))
 
 
 def test_set_gp_params():
diff --git a/tests/test_constraint.py b/tests/test_constraint.py
index 586773ca2..495dc9d17 100644
--- a/tests/test_constraint.py
+++ b/tests/test_constraint.py
@@ -7,12 +7,12 @@
 from bayes_opt import BayesianOptimization, ConstraintModel
 
 
-@pytest.fixture()
+@pytest.fixture
 def target_function():
     return lambda x, y: np.cos(2 * x) * np.cos(y) + np.sin(x)
 
 
-@pytest.fixture()
+@pytest.fixture
 def constraint_function():
     return lambda x, y: np.cos(x) * np.cos(y) - np.sin(x) * np.sin(y)
 
diff --git a/tests/test_parameter.py b/tests/test_parameter.py
index 58983a8ee..64e9eaf76 100644
--- a/tests/test_parameter.py
+++ b/tests/test_parameter.py
@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import numpy as np
-import pytest
 
 from bayes_opt.parameter import CategoricalParameter, FloatParameter, IntParameter
 from bayes_opt.target_space import TargetSpace
@@ -22,9 +21,9 @@ def target_func(**kwargs):
     assert isinstance(space._params_config["p1"], FloatParameter)
     assert isinstance(space._params_config["p2"], FloatParameter)
 
-    assert all(space.float_bounds[:, 0] == np.array([0, 1]))
-    assert all(space.float_bounds[:, 1] == np.array([1, 2]))
-    assert (space.bounds == space.float_bounds).all()
+    assert all(space.bounds[:, 0] == np.array([0, 1]))
+    assert all(space.bounds[:, 1] == np.array([1, 2]))
+    assert (space.bounds == space.bounds).all()
 
     point1 = {"p1": 0.2, "p2": 1.5}
     target1 = 1.7
@@ -56,10 +55,6 @@ def target_func(**kwargs):
     assert isinstance(space._params_config["p1"], IntParameter)
     assert isinstance(space._params_config["p3"], IntParameter)
 
-    assert pytest.approx(space.float_bounds[:, 0], abs=0.001) == np.array([-0.5, -1.5])  # sub 0.5
-    assert pytest.approx(space.float_bounds[:, 1], abs=0.001) == np.array([5.5, 3.5])  # add 0.5
-    assert (space.bounds != space.float_bounds).any()  # bounds are modified from float bounds
-
     point1 = {"p1": 2, "p3": 0}
     target1 = 2
     space.probe(point1)
@@ -90,9 +85,9 @@ def target_func(fruit: str):
 
     assert isinstance(space._params_config["fruit"], CategoricalParameter)
 
-    assert space.float_bounds.shape == (len(fruits), 2)
-    assert (space.float_bounds[:, 0] == np.zeros(len(fruits))).all()
-    assert (space.float_bounds[:, 1] == np.ones(len(fruits))).all()
+    assert space.bounds.shape == (len(fruits), 2)
+    assert (space.bounds[:, 0] == np.zeros(len(fruits))).all()
+    assert (space.bounds[:, 1] == np.ones(len(fruits))).all()
 
     point1 = {"fruit": "banana"}
     target1 = 2.0
diff --git a/tests/test_seq_domain_red.py b/tests/test_seq_domain_red.py
index 85e01d172..265d3ee5b 100644
--- a/tests/test_seq_domain_red.py
+++ b/tests/test_seq_domain_red.py
@@ -57,7 +57,7 @@ def reset(self):
     mutated_optimizer.maximize(init_points=2, n_iter=n_iter)
 
     assert len(standard_optimizer.space) == len(mutated_optimizer.space)
-    assert not (standard_optimizer._space.float_bounds == mutated_optimizer._space.float_bounds).any()
+    assert not (standard_optimizer._space.bounds == mutated_optimizer._space.bounds).any()
 
 
 def test_minimum_window_is_kept():
diff --git a/tests/test_target_space.py b/tests/test_target_space.py
index c83f957f6..e949b19cf 100644
--- a/tests/test_target_space.py
+++ b/tests/test_target_space.py
@@ -23,8 +23,8 @@ def test_keys_and_bounds_in_same_order():
     assert space.dim == len(pbounds)
     assert space.empty
     assert space.keys == ["p1", "p2", "p3", "p4"]
-    assert all(space.float_bounds[:, 0] == np.array([0, 0, 0, 0]))
-    assert all(space.float_bounds[:, 1] == np.array([1, 2, 3, 4]))
+    assert all(space.bounds[:, 0] == np.array([0, 0, 0, 0]))
+    assert all(space.bounds[:, 1] == np.array([1, 2, 3, 4]))
 
 
 def test_params_to_array():
@@ -173,8 +173,8 @@ def test_random_sample():
     for _ in range(50):
         random_sample = space.random_sample()
         assert len(random_sample) == space.dim
-        assert all(random_sample >= space.float_bounds[:, 0])
-        assert all(random_sample <= space.float_bounds[:, 1])
+        assert all(random_sample >= space.bounds[:, 0])
+        assert all(random_sample <= space.bounds[:, 1])
 
 
 def test_y_max():
@@ -273,13 +273,13 @@ def test_set_bounds():
 
     # Ignore unknown keys
     space.set_bounds({"other": (7, 8)})
-    assert all(space.float_bounds[:, 0] == np.array([0, 0, 0, 0]))
-    assert all(space.float_bounds[:, 1] == np.array([1, 2, 3, 4]))
+    assert all(space.bounds[:, 0] == np.array([0, 0, 0, 0]))
+    assert all(space.bounds[:, 1] == np.array([1, 2, 3, 4]))
 
     # Update bounds accordingly
     space.set_bounds({"p2": (1, 8)})
-    assert all(space.float_bounds[:, 0] == np.array([0, 1, 0, 0]))
-    assert all(space.float_bounds[:, 1] == np.array([1, 8, 3, 4]))
+    assert all(space.bounds[:, 0] == np.array([0, 1, 0, 0]))
+    assert all(space.bounds[:, 1] == np.array([1, 8, 3, 4]))
 
 
 def test_no_target_func():

From 2b64ff044f46bbf27e7e93d119b4689277298b8c Mon Sep 17 00:00:00 2001
From: phi-friday <phi.friday@gmail.com>
Date: Wed, 9 Oct 2024 16:22:18 +0900
Subject: [PATCH 05/21] Parameter types more (#13)

* fix: import error from exception module (#525)

* fix: replace list with sequence (#524)

* Fix min window type check (#523)

* fix: replace dict with Mapping

* fix: replace list with Sequence

* fix: add type hint

* fix: does not accept None

* Change docs badge (#527)

* fix: parameter, target_space

* fix: constraint, bayesian_optimization

* fix: ParamsType

---------

Co-authored-by: till-m <36440677+till-m@users.noreply.github.com>
---
 README.md                          |   2 +-
 bayes_opt/acquisition.py           |  10 +--
 bayes_opt/bayesian_optimization.py |  18 ++---
 bayes_opt/constraint.py            |   2 +-
 bayes_opt/domain_reduction.py      |  12 ++--
 bayes_opt/parameter.py             | 107 +++++++++++++++++++----------
 bayes_opt/target_space.py          |  50 ++++++--------
 tests/test_acquisition.py          |  20 +++---
 8 files changed, 125 insertions(+), 96 deletions(-)

diff --git a/README.md b/README.md
index 0750475b8..37e7e5b4d 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 # Bayesian Optimization
 
 ![tests](https://github.com/bayesian-optimization/BayesianOptimization/actions/workflows/run_tests.yml/badge.svg)
-[![docs - stable](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fraw.githubusercontent.com%2Fbayesian-optimization%2FBayesianOptimization%2Fgh-pages%2Fversions.json&query=%24%5B%3F(%40.aliases%20%26%26%20%40.aliases.indexOf('stable')%20%3E%20-1)%5D.version&prefix=stable%20(v&suffix=)&label=docs)](https://bayesian-optimization.github.io/BayesianOptimization/)
+[![docs - stable](https://img.shields.io/badge/docs-stable-blue)](https://bayesian-optimization.github.io/BayesianOptimization/index.html)
 [![Codecov](https://codecov.io/github/bayesian-optimization/BayesianOptimization/badge.svg?branch=master&service=github)](https://codecov.io/github/bayesian-optimization/BayesianOptimization?branch=master)
 [![Pypi](https://img.shields.io/pypi/v/bayesian-optimization.svg)](https://pypi.python.org/pypi/bayesian-optimization)
 ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/bayesian-optimization)
diff --git a/bayes_opt/acquisition.py b/bayes_opt/acquisition.py
index 1d4eb5854..b025d228d 100644
--- a/bayes_opt/acquisition.py
+++ b/bayes_opt/acquisition.py
@@ -40,7 +40,7 @@
 from bayes_opt.target_space import TargetSpace
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
+    from collections.abc import Callable, Sequence
 
     from numpy.typing import NDArray
     from scipy.optimize import OptimizeResult
@@ -906,18 +906,18 @@ class GPHedge(AcquisitionFunction):
 
     Parameters
     ----------
-    base_acquisitions : List[AcquisitionFunction]
-        List of base acquisition functions.
+    base_acquisitions : Sequence[AcquisitionFunction]
+        Sequence of base acquisition functions.
 
     random_state : int, RandomState, default None
         Set the random state for reproducibility.
     """
 
     def __init__(
-        self, base_acquisitions: list[AcquisitionFunction], random_state: int | RandomState | None = None
+        self, base_acquisitions: Sequence[AcquisitionFunction], random_state: int | RandomState | None = None
     ) -> None:
         super().__init__(random_state)
-        self.base_acquisitions = base_acquisitions
+        self.base_acquisitions = list(base_acquisitions)
         self.n_acq = len(self.base_acquisitions)
         self.gains = np.zeros(self.n_acq)
         self.previous_candidates = None
diff --git a/bayes_opt/bayesian_optimization.py b/bayes_opt/bayesian_optimization.py
index ed1ade680..46944cf1e 100644
--- a/bayes_opt/bayesian_optimization.py
+++ b/bayes_opt/bayesian_optimization.py
@@ -21,7 +21,7 @@
 from bayes_opt.util import ensure_rng
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Iterable, Mapping, Sequence
+    from collections.abc import Callable, Iterable, Mapping
 
     import numpy as np
     from numpy.random import RandomState
@@ -31,6 +31,7 @@
     from bayes_opt.acquisition import AcquisitionFunction
     from bayes_opt.constraint import ConstraintModel
     from bayes_opt.domain_reduction import DomainTransformer
+    from bayes_opt.parameter import BoundsMapping, ParamsType
 
     Float = np.floating[Any]
 
@@ -114,7 +115,7 @@ def __init__(
     ):
         self._random_state = ensure_rng(random_state)
         self._allow_duplicate_points = allow_duplicate_points
-        self._queue: deque[Mapping[str, float] | Sequence[float] | NDArray[Float]] = deque()
+        self._queue: deque[ParamsType] = deque()
 
         if acquisition_function is None:
             if constraint is None:
@@ -203,10 +204,7 @@ def res(self) -> list[dict[str, Any]]:
         return self._space.res()
 
     def register(
-        self,
-        params: Mapping[str, float] | Sequence[float] | NDArray[Float],
-        target: float,
-        constraint_value: float | NDArray[Float] | None = None,
+        self, params: ParamsType, target: float, constraint_value: float | NDArray[Float] | None = None
     ) -> None:
         """Register an observation with known target.
 
@@ -224,9 +222,7 @@ def register(
         self._space.register(params, target, constraint_value)
         self.dispatch(Events.OPTIMIZATION_STEP)
 
-    def probe(
-        self, params: Mapping[str, float] | Sequence[float] | NDArray[Float], lazy: bool = True
-    ) -> None:
+    def probe(self, params: ParamsType, lazy: bool = True) -> None:
         """Evaluate the function at the given points.
 
         Useful to guide the optimizer.
@@ -246,7 +242,7 @@ def probe(
             self._space.probe(params)
             self.dispatch(Events.OPTIMIZATION_STEP)
 
-    def suggest(self) -> dict[str, float]:
+    def suggest(self) -> dict[str, float | NDArray[Float]]:
         """Suggest a promising point to probe next."""
         if len(self._space) == 0:
             return self._space.array_to_params(self._space.random_sample(random_state=self._random_state))
@@ -321,7 +317,7 @@ def maximize(self, init_points: int = 5, n_iter: int = 25) -> None:
 
         self.dispatch(Events.OPTIMIZATION_END)
 
-    def set_bounds(self, new_bounds: Mapping[str, NDArray[Float] | Sequence[float]]) -> None:
+    def set_bounds(self, new_bounds: BoundsMapping) -> None:
         """Modify the bounds of the search space.
 
         Parameters
diff --git a/bayes_opt/constraint.py b/bayes_opt/constraint.py
index e029f8abd..120169bdb 100644
--- a/bayes_opt/constraint.py
+++ b/bayes_opt/constraint.py
@@ -57,7 +57,7 @@ def __init__(
         fun: Callable[..., float] | Callable[..., NDArray[Float]] | None,
         lb: float | NDArray[Float],
         ub: float | NDArray[Float],
-        transform=None,
+        transform: Callable[[Any], Any] | None = None,
         random_state: int | RandomState | None = None,
     ) -> None:
         self.fun = fun
diff --git a/bayes_opt/domain_reduction.py b/bayes_opt/domain_reduction.py
index fa06ba356..1c6d99d9e 100644
--- a/bayes_opt/domain_reduction.py
+++ b/bayes_opt/domain_reduction.py
@@ -8,6 +8,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from collections.abc import Iterable, Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 from warnings import warn
 
@@ -16,8 +17,6 @@
 from bayes_opt.target_space import TargetSpace
 
 if TYPE_CHECKING:
-    from collections.abc import Iterable, Mapping, Sequence
-
     from numpy.typing import NDArray
 
     Float = np.floating[Any]
@@ -67,14 +66,16 @@ def __init__(
         gamma_osc: float = 0.7,
         gamma_pan: float = 1.0,
         eta: float = 0.9,
-        minimum_window: NDArray[Float] | Sequence[float] | float | Mapping[str, float] | None = 0.0,
+        minimum_window: NDArray[Float] | Sequence[float] | Mapping[str, float] | float = 0.0,
     ) -> None:
         # TODO: Ensure that this is only applied to continuous parameters
         self.parameters = parameters
         self.gamma_osc = gamma_osc
         self.gamma_pan = gamma_pan
         self.eta = eta
-        if isinstance(minimum_window, dict):
+
+        self.minimum_window_value: NDArray[Float] | Sequence[float] | float
+        if isinstance(minimum_window, Mapping):
             self.minimum_window_value = [
                 item[1] for item in sorted(minimum_window.items(), key=lambda x: x[0])
             ]
@@ -93,8 +94,9 @@ def initialize(self, target_space: TargetSpace) -> None:
         self.original_bounds = np.copy(target_space.bounds)
         self.bounds = [self.original_bounds]
 
+        self.minimum_window: NDArray[Float] | Sequence[float]
         # Set the minimum window to an array of length bounds
-        if isinstance(self.minimum_window_value, (list, np.ndarray)):
+        if isinstance(self.minimum_window_value, (Sequence, np.ndarray)):
             if len(self.minimum_window_value) != len(target_space.bounds):
                 error_msg = "Length of minimum_window must be the same as the number of parameters"
                 raise ValueError(error_msg)
diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index dbd3ae421..4566cf2d2 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -5,17 +5,41 @@
 import abc
 from collections.abc import Sequence
 from inspect import signature
-from typing import Any, Callable
+from numbers import Number
+from typing import TYPE_CHECKING, Any, Callable, Union
 
 import numpy as np
 from sklearn.gaussian_process import kernels
 
 from bayes_opt.util import ensure_rng
 
+if TYPE_CHECKING:
+    from collections.abc import Mapping
 
-def is_numeric(value):
+    from numpy.typing import NDArray
+
+    Float = np.floating[Any]
+    Int = np.integer[Any]
+
+    FloatBoundsWithoutType = tuple[float, float]
+    FloatBoundsWithType = tuple[float, float, type[float]]
+    FloatBounds = Union[FloatBoundsWithoutType, FloatBoundsWithType]
+    IntBounds = tuple[Union[int, float], Union[int, float], type[int]]
+    CategoricalBounds = Sequence[Any]
+    Bounds = Union[FloatBounds, IntBounds, CategoricalBounds]
+    BoundsMapping = Mapping[str, Bounds]
+
+    # FIXME: categorical parameters can be of any type.
+    # This will make static type checking for parameters difficult.
+    ParamsType = Union[Mapping[str, Any], Sequence[Any], NDArray[Float]]
+
+
+def is_numeric(value: Any) -> bool:
     """Check if a value is numeric."""
-    return np.issubdtype(type(value), np.number)
+    return isinstance(value, Number) or (
+        isinstance(value, np.generic)
+        and (np.isdtype(value.dtype, np.number) or np.issubdtype(value.dtype, np.number))
+    )
 
 
 class BayesParameter(abc.ABC):
@@ -27,16 +51,18 @@ class BayesParameter(abc.ABC):
         The name of the parameter.
     """
 
-    def __init__(self, name: str, bounds) -> None:
+    def __init__(self, name: str, bounds: NDArray[Any]) -> None:
         self.name = name
         self._bounds = bounds
 
     @property
-    def bounds(self):
+    def bounds(self) -> NDArray[Any]:
         """The bounds of the parameter in float space."""
         return self._bounds
 
-    def random_sample(self, n_samples: int, random_state: np.random.RandomState | int | None) -> np.ndarray:
+    def random_sample(
+        self, n_samples: int, random_state: np.random.RandomState | int | None
+    ) -> NDArray[Float]:
         """Generate random samples from the parameter.
 
         Parameters
@@ -56,7 +82,7 @@ def random_sample(self, n_samples: int, random_state: np.random.RandomState | in
         return random_state.uniform(self.bounds[0], self.bounds[1], n_samples)
 
     @abc.abstractmethod
-    def to_float(self, value) -> np.ndarray:
+    def to_float(self, value: Any) -> float | NDArray[Float]:
         """Convert a parameter value to a float.
 
         Parameters
@@ -66,7 +92,7 @@ def to_float(self, value) -> np.ndarray:
         """
 
     @abc.abstractmethod
-    def to_param(self, value):
+    def to_param(self, value: float | NDArray[Float]) -> Any:
         """Convert a float value to a parameter.
 
         Parameters
@@ -81,7 +107,7 @@ def to_param(self, value):
         """
 
     @abc.abstractmethod
-    def kernel_transform(self, value):
+    def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:
         """Transform a parameter value for use in a kernel.
 
         Parameters
@@ -94,7 +120,7 @@ def kernel_transform(self, value):
         np.ndarray
         """
 
-    def repr(self, value, str_len) -> str:
+    def repr(self, value: Any, str_len: int) -> str:
         """Represent a parameter value as a string.
 
         Parameters
@@ -109,7 +135,7 @@ def repr(self, value, str_len) -> str:
         -------
         str
         """
-        s = value.__repr__()
+        s = repr(value)
 
         if len(s) > str_len:
             if "." in s:
@@ -138,7 +164,7 @@ class FloatParameter(BayesParameter):
     def __init__(self, name: str, bounds: tuple[float, float]) -> None:
         super().__init__(name, np.array(bounds))
 
-    def to_float(self, value) -> np.ndarray:
+    def to_float(self, value: float) -> float:
         """Convert a parameter value to a float.
 
         Parameters
@@ -148,7 +174,7 @@ def to_float(self, value) -> np.ndarray:
         """
         return value
 
-    def to_param(self, value):
+    def to_param(self, value: float | NDArray[Float]) -> float:
         """Convert a float value to a parameter.
 
         Parameters
@@ -164,9 +190,11 @@ def to_param(self, value):
         if isinstance(value, np.ndarray) and value.size != 1:
             msg = "FloatParameter value should be scalar"
             raise ValueError(msg)
+        if isinstance(value, (int, float)):
+            return value
         return value.flatten()[0]
 
-    def repr(self, value, str_len) -> str:
+    def repr(self, value: float, str_len: int) -> str:
         """Represent a parameter value as a string.
 
         Parameters
@@ -188,7 +216,7 @@ def repr(self, value, str_len) -> str:
             return s[: str_len - 3] + "..."
         return s
 
-    def kernel_transform(self, value):
+    def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:
         """Transform a parameter value for use in a kernel.
 
         Parameters
@@ -220,10 +248,12 @@ class IntParameter(BayesParameter):
         The bounds of the parameter.
     """
 
-    def __init__(self, name: str, bounds: tuple[int | float, int | float]) -> None:
+    def __init__(self, name: str, bounds: tuple[int, int]) -> None:
         super().__init__(name, np.array(bounds))
 
-    def random_sample(self, n_samples: int, random_state: np.random.RandomState | int | None) -> np.ndarray:
+    def random_sample(
+        self, n_samples: int, random_state: np.random.RandomState | int | None
+    ) -> NDArray[Float]:
         """Generate random samples from the parameter.
 
         Parameters
@@ -242,7 +272,7 @@ def random_sample(self, n_samples: int, random_state: np.random.RandomState | in
         random_state = ensure_rng(random_state)
         return random_state.randint(self.bounds[0], self.bounds[1] + 1, n_samples).astype(float)
 
-    def to_float(self, value) -> np.ndarray:
+    def to_float(self, value: int | float) -> float:
         """Convert a parameter value to a float.
 
         Parameters
@@ -252,7 +282,7 @@ def to_float(self, value) -> np.ndarray:
         """
         return float(value)
 
-    def to_param(self, value):
+    def to_param(self, value: int | float | NDArray[Int] | NDArray[Float]) -> int:
         """Convert a float value to a parameter.
 
         Parameters
@@ -267,7 +297,7 @@ def to_param(self, value):
         """
         return int(np.round(np.squeeze(value)))
 
-    def repr(self, value, str_len) -> str:
+    def repr(self, value: int, str_len: int) -> str:
         """Represent a parameter value as a string.
 
         Parameters
@@ -289,7 +319,7 @@ def repr(self, value, str_len) -> str:
             return s[: str_len - 3] + "..."
         return s
 
-    def kernel_transform(self, value):
+    def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:
         """Transform a parameter value for use in a kernel.
 
         Parameters
@@ -328,7 +358,9 @@ def __init__(self, name: str, categories: Sequence[Any]) -> None:
         bounds = np.vstack((lower, upper)).T
         super().__init__(name, bounds)
 
-    def random_sample(self, n_samples: int, random_state: np.random.RandomState | int | None) -> np.ndarray:
+    def random_sample(
+        self, n_samples: int, random_state: np.random.RandomState | int | None
+    ) -> NDArray[Float]:
         """Generate random float-format samples from the parameter.
 
         Parameters
@@ -344,12 +376,13 @@ def random_sample(self, n_samples: int, random_state: np.random.RandomState | in
         np.ndarray
             The samples.
         """
+        random_state = ensure_rng(random_state)
         res = random_state.randint(0, len(self.categories), n_samples)
         one_hot = np.zeros((n_samples, len(self.categories)))
         one_hot[np.arange(n_samples), res] = 1
         return one_hot.astype(float)
 
-    def to_float(self, value) -> np.ndarray:
+    def to_float(self, value: Any) -> NDArray[Float]:
         """Convert a parameter value to a float.
 
         Parameters
@@ -364,7 +397,7 @@ def to_float(self, value) -> np.ndarray:
         res[one_hot_index] = 1
         return res.astype(float)
 
-    def to_param(self, value):
+    def to_param(self, value: float | NDArray[Float]) -> Any:
         """Convert a float value to a parameter.
 
         Parameters
@@ -377,9 +410,9 @@ def to_param(self, value):
         Any
             The canonical representation of the parameter.
         """
-        return self.categories[np.argmax(value)]
+        return self.categories[int(np.argmax(value))]
 
-    def repr(self, value, str_len) -> str:
+    def repr(self, value: Any, str_len: int) -> str:
         """Represent a parameter value as a string.
 
         Parameters
@@ -399,7 +432,7 @@ def repr(self, value, str_len) -> str:
             return s[: str_len - 3] + "..."
         return s
 
-    def kernel_transform(self, value):
+    def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:
         """Transform a parameter value for use in a kernel.
 
         Parameters
@@ -422,7 +455,7 @@ def dim(self) -> int:
         return len(self.categories)
 
 
-def wrap_kernel(kernel: kernels.Kernel, transform: Callable) -> kernels.Kernel:
+def wrap_kernel(kernel: kernels.Kernel, transform: Callable[[Any], Any]) -> kernels.Kernel:
     """Wrap a kernel to transform input data before passing it to the kernel.
 
     Parameters
@@ -442,27 +475,31 @@ def wrap_kernel(kernel: kernels.Kernel, transform: Callable) -> kernels.Kernel:
     -----
     See https://arxiv.org/abs/1805.03463 for more information.
     """
+    kernel_type = type(kernel)
 
-    class WrappedKernel(type(kernel)):
-        @copy_signature(getattr(kernel.__class__.__init__, "deprecated_original", kernel.__class__.__init__))
-        def __init__(self, **kwargs) -> None:
+    class WrappedKernel(kernel_type):
+        @copy_signature(getattr(kernel_type.__init__, "deprecated_original", kernel_type.__init__))
+        def __init__(self, **kwargs: Any) -> None:
             super().__init__(**kwargs)
 
-        def __call__(self, X, Y=None, eval_gradient=False):
+        def __call__(self, X: Any, Y: Any = None, eval_gradient: bool = False) -> Any:
             X = transform(X)
-            return super().__call__(X, Y, eval_gradient)
+            return kernel(X, Y, eval_gradient)
+
+        def __reduce__(self) -> str | tuple[Any, ...]:
+            return (wrap_kernel, (kernel, transform))
 
     return WrappedKernel(**kernel.get_params())
 
 
-def copy_signature(source_fct):
+def copy_signature(source_fct: Callable[..., Any]) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
     """Clones a signature from a source function to a target function.
 
     via
     https://stackoverflow.com/a/58989918/
     """
 
-    def copy(target_fct):
+    def copy(target_fct: Callable[..., Any]) -> Callable[..., Any]:
         target_fct.__signature__ = signature(source_fct)
         return target_fct
 
diff --git a/bayes_opt/target_space.py b/bayes_opt/target_space.py
index 167076102..4cef05482 100644
--- a/bayes_opt/target_space.py
+++ b/bayes_opt/target_space.py
@@ -13,14 +13,16 @@
 from bayes_opt.util import ensure_rng
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Mapping, Sequence
+    from collections.abc import Callable, Mapping
 
     from numpy.random import RandomState
     from numpy.typing import NDArray
 
     from bayes_opt.constraint import ConstraintModel
+    from bayes_opt.parameter import BoundsMapping, ParamsType
 
     Float = np.floating[Any]
+    Int = np.integer[Any]
 
 
 def _hashable(x: NDArray[Float]) -> tuple[float, ...]:
@@ -67,7 +69,7 @@ class TargetSpace:
     def __init__(
         self,
         target_func: Callable[..., float] | None,
-        pbounds: Mapping[str, tuple[float, float]],
+        pbounds: BoundsMapping,
         constraint: ConstraintModel | None = None,
         random_state: int | RandomState | None = None,
         allow_duplicate_points: bool | None = False,
@@ -199,7 +201,7 @@ def constraint(self) -> ConstraintModel | None:
         return self._constraint
 
     @property
-    def masks(self) -> dict:
+    def masks(self) -> dict[str, NDArray[np.bool_]]:
         """Get the masks for the parameters.
 
         Returns
@@ -208,7 +210,7 @@ def masks(self) -> dict:
         """
         return self._masks
 
-    def make_params(self, pbounds) -> dict:
+    def make_params(self, pbounds: BoundsMapping) -> dict[str, BayesParameter]:
         """Create a dictionary of parameters from a dictionary of bounds.
 
         Parameters
@@ -223,16 +225,16 @@ def make_params(self, pbounds) -> dict:
             A dictionary with the parameter names as keys and the corresponding
             parameter objects as values.
         """
-        params = {}
+        params: dict[str, BayesParameter] = {}
         for key in sorted(pbounds):
             pbound = pbounds[key]
 
             if isinstance(pbound, BayesParameter):
                 res = pbound
-            elif len(pbound) == 2 and is_numeric(pbound[0]) and is_numeric(pbound[1]):
-                res = FloatParameter(name=key, bounds=pbound)
-            elif len(pbound) == 3 and pbound[-1] is float:
-                res = FloatParameter(name=key, bounds=(pbound[0], pbound[1]))
+            elif (len(pbound) == 2 and is_numeric(pbound[0]) and is_numeric(pbound[1])) or (
+                len(pbound) == 3 and pbound[-1] is float
+            ):
+                res = FloatParameter(name=key, bounds=(float(pbound[0]), float(pbound[1])))
             elif len(pbound) == 3 and pbound[-1] is int:
                 res = IntParameter(name=key, bounds=(int(pbound[0]), int(pbound[1])))
             else:
@@ -241,7 +243,7 @@ def make_params(self, pbounds) -> dict:
             params[key] = res
         return params
 
-    def make_masks(self) -> dict:
+    def make_masks(self) -> dict[str, NDArray[np.bool_]]:
         """Create a dictionary of masks for the parameters.
 
         The mask can be used to select the corresponding parameters from an array.
@@ -268,7 +270,7 @@ def calculate_bounds(self) -> NDArray[Float]:
             bounds[self.masks[key]] = self._params_config[key].bounds
         return bounds
 
-    def params_to_array(self, params: Mapping[str, float]) -> NDArray[Float]:
+    def params_to_array(self, params: Mapping[str, float | NDArray[Float]]) -> NDArray[Float]:
         """Convert a dict representation of parameters into an array version.
 
         Parameters
@@ -303,19 +305,16 @@ def constraint_values(self) -> NDArray[Float]:
 
         return self._constraint_values
 
-    def kernel_transform(self, value: np.ndarray) -> np.ndarray:
+    def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:
         """Transform floating-point suggestions to values used in the kernel.
 
         Vectorized.
         """
         value = np.atleast_2d(value)
-        res = []
-        for p in self._keys:
-            par = self._params_config[p].kernel_transform(value[:, self.masks[p]])
-            res.append(par)
+        res = [self._params_config[p].kernel_transform(value[:, self.masks[p]]) for p in self._keys]
         return np.hstack(res)
 
-    def array_to_params(self, x: NDArray[Float]) -> dict[str, float]:
+    def array_to_params(self, x: NDArray[Float]) -> dict[str, float | NDArray[Float]]:
         """Convert an array representation of parameters into a dict version.
 
         Parameters
@@ -336,7 +335,7 @@ def array_to_params(self, x: NDArray[Float]) -> dict[str, float]:
             raise ValueError(error_msg)
         return self._to_params(x)
 
-    def _to_float(self, value) -> np.ndarray:
+    def _to_float(self, value: Mapping[str, float | NDArray[Float]]) -> NDArray[Float]:
         if set(value) != set(self.keys):
             msg = (
                 f"Parameters' keys ({sorted(value)}) do " f"not match the expected set of keys ({self.keys})."
@@ -348,8 +347,8 @@ def _to_float(self, value) -> np.ndarray:
             res[self._masks[key]] = p.to_float(value[key])
         return res
 
-    def _to_params(self, value: np.ndarray) -> dict:
-        res = {}
+    def _to_params(self, value: NDArray[Float]) -> dict[str, float | NDArray[Float]]:
+        res: dict[str, float | NDArray[Float]] = {}
         for key in self._keys:
             p = self._params_config[key]
             mask = self._masks[key]
@@ -397,10 +396,7 @@ def _as_array(self, x: Any) -> NDArray[Float]:
         return x
 
     def register(
-        self,
-        params: Mapping[str, float] | Sequence[float] | NDArray[Float],
-        target: float,
-        constraint_value: float | NDArray[Float] | None = None,
+        self, params: ParamsType, target: float, constraint_value: float | NDArray[Float] | None = None
     ) -> None:
         """Append a point and its target value to the known data.
 
@@ -495,9 +491,7 @@ def register(
         self._target = target_copy
         self._cache = cache_copy
 
-    def probe(
-        self, params: Mapping[str, float] | Sequence[float] | NDArray[Float]
-    ) -> float | tuple[float, float | NDArray[Float]]:
+    def probe(self, params: ParamsType) -> float | tuple[float, float | NDArray[Float]]:
         """Evaluate the target function on a point and register the result.
 
         Notes
@@ -664,7 +658,7 @@ def res(self) -> list[dict[str, Any]]:
             )
         ]
 
-    def set_bounds(self, new_bounds: Mapping[str, NDArray[Float] | Sequence[float]]) -> None:
+    def set_bounds(self, new_bounds: BoundsMapping) -> None:
         """Change the lower and upper search bounds.
 
         Parameters
diff --git a/tests/test_acquisition.py b/tests/test_acquisition.py
index 860d4f42f..f0a7efde2 100644
--- a/tests/test_acquisition.py
+++ b/tests/test_acquisition.py
@@ -7,7 +7,7 @@
 from scipy.spatial.distance import pdist
 from sklearn.gaussian_process import GaussianProcessRegressor
 
-from bayes_opt import acquisition
+from bayes_opt import acquisition, exception
 from bayes_opt.constraint import ConstraintModel
 from bayes_opt.target_space import TargetSpace
 
@@ -94,7 +94,7 @@ def test_upper_confidence_bound(gp, target_space, random_state):
 
     # Test that the suggest method raises an error if the GP is unfitted
     with pytest.raises(
-        acquisition.TargetSpaceEmptyError, match="Cannot suggest a point without previous samples"
+        exception.TargetSpaceEmptyError, match="Cannot suggest a point without previous samples"
     ):
         acq.suggest(gp=gp, target_space=target_space)
 
@@ -122,7 +122,7 @@ def test_upper_confidence_bound_with_constraints(gp, constrained_target_space, r
     acq = acquisition.UpperConfidenceBound(random_state=random_state)
 
     constrained_target_space.register(params={"x": 2.5, "y": 0.5}, target=3.0, constraint_value=0.5)
-    with pytest.raises(acquisition.ConstraintNotSupportedError):
+    with pytest.raises(exception.ConstraintNotSupportedError):
         acq.suggest(gp=gp, target_space=constrained_target_space)
 
 
@@ -157,11 +157,11 @@ def test_probability_of_improvement_with_constraints(gp, constrained_target_spac
     with pytest.raises(ValueError, match="y_max is not set"):
         acq.base_acq(0.0, 0.0)
 
-    with pytest.raises(acquisition.TargetSpaceEmptyError):
+    with pytest.raises(exception.TargetSpaceEmptyError):
         acq.suggest(gp=gp, target_space=constrained_target_space)
 
     constrained_target_space.register(params={"x": 2.5, "y": 0.5}, target=3.0, constraint_value=3.0)
-    with pytest.raises(acquisition.NoValidPointRegisteredError):
+    with pytest.raises(exception.NoValidPointRegisteredError):
         acq.suggest(gp=gp, target_space=constrained_target_space)
 
     constrained_target_space.register(params={"x": 1.0, "y": 0.0}, target=1.0, constraint_value=1.0)
@@ -199,11 +199,11 @@ def test_expected_improvement_with_constraints(gp, constrained_target_space, ran
     with pytest.raises(ValueError, match="y_max is not set"):
         acq.base_acq(0.0, 0.0)
 
-    with pytest.raises(acquisition.TargetSpaceEmptyError):
+    with pytest.raises(exception.TargetSpaceEmptyError):
         acq.suggest(gp=gp, target_space=constrained_target_space)
 
     constrained_target_space.register(params={"x": 2.5, "y": 0.5}, target=3.0, constraint_value=3.0)
-    with pytest.raises(acquisition.NoValidPointRegisteredError):
+    with pytest.raises(exception.NoValidPointRegisteredError):
         acq.suggest(gp=gp, target_space=constrained_target_space)
 
     constrained_target_space.register(params={"x": 1.0, "y": 0.0}, target=1.0, constraint_value=1.0)
@@ -250,11 +250,11 @@ def test_constant_liar_with_constraints(gp, constrained_target_space, random_sta
     base_acq = acquisition.UpperConfidenceBound(random_state=random_state)
     acq = acquisition.ConstantLiar(base_acquisition=base_acq, random_state=random_state)
 
-    with pytest.raises(acquisition.TargetSpaceEmptyError):
+    with pytest.raises(exception.TargetSpaceEmptyError):
         acq.suggest(gp=gp, target_space=constrained_target_space)
 
     constrained_target_space.register(params={"x": 2.5, "y": 0.5}, target=3.0, constraint_value=0.5)
-    with pytest.raises(acquisition.ConstraintNotSupportedError):
+    with pytest.raises(exception.ConstraintNotSupportedError):
         acq.suggest(gp=gp, target_space=constrained_target_space)
 
     mean = random_state.rand(10)
@@ -338,7 +338,7 @@ def test_gphedge_integration(gp, target_space, random_state):
 
     acq = acquisition.GPHedge(base_acquisitions=base_acquisitions, random_state=random_state)
     assert acq.base_acquisitions == base_acquisitions
-    with pytest.raises(acquisition.TargetSpaceEmptyError):
+    with pytest.raises(exception.TargetSpaceEmptyError):
         acq.suggest(gp=gp, target_space=target_space)
     target_space.register(params={"x": 2.5, "y": 0.5}, target=3.0)
 

From 3920e0fdf684ddc25fc242434b39e26899e16dfa Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Wed, 9 Oct 2024 09:30:36 +0200
Subject: [PATCH 06/21] Use `.masks` not `._masks`

---
 bayes_opt/target_space.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/bayes_opt/target_space.py b/bayes_opt/target_space.py
index 4cef05482..212045493 100644
--- a/bayes_opt/target_space.py
+++ b/bayes_opt/target_space.py
@@ -344,14 +344,14 @@ def _to_float(self, value: Mapping[str, float | NDArray[Float]]) -> NDArray[Floa
         res = np.zeros(self._dim)
         for key in self._keys:
             p = self._params_config[key]
-            res[self._masks[key]] = p.to_float(value[key])
+            res[self.masks[key]] = p.to_float(value[key])
         return res
 
     def _to_params(self, value: NDArray[Float]) -> dict[str, float | NDArray[Float]]:
         res: dict[str, float | NDArray[Float]] = {}
         for key in self._keys:
             p = self._params_config[key]
-            mask = self._masks[key]
+            mask = self.masks[key]
             res[key] = p.to_param(value[mask])
         return res
 
@@ -569,7 +569,7 @@ def random_sample(
         flatten = n_samples == 0
         n_samples = max(1, n_samples)
         data = np.empty((n_samples, self._dim))
-        for key, mask in self._masks.items():
+        for key, mask in self.masks.items():
             smpl = self._params_config[key].random_sample(n_samples, random_state)
             data[:, mask] = smpl.reshape(n_samples, self._params_config[key].dim)
         if flatten:

From 241e5c7b72785f7178b7618a4d5d4e8570a900f8 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Wed, 9 Oct 2024 18:48:04 +0200
Subject: [PATCH 07/21] User `super` to call kernel

---
 bayes_opt/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index 4566cf2d2..876c3517b 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -484,7 +484,7 @@ def __init__(self, **kwargs: Any) -> None:
 
         def __call__(self, X: Any, Y: Any = None, eval_gradient: bool = False) -> Any:
             X = transform(X)
-            return kernel(X, Y, eval_gradient)
+            return super().__call__(X, Y, eval_gradient)
 
         def __reduce__(self) -> str | tuple[Any, ...]:
             return (wrap_kernel, (kernel, transform))

From 68909ad027988e38dbb11f031bba3ee31db15718 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Sat, 12 Oct 2024 11:48:33 +0200
Subject: [PATCH 08/21] Update logging for parameters

---
 bayes_opt/logger.py     |  2 +-
 bayes_opt/parameter.py  | 46 ++++++++++++++---------------------------
 tests/test_parameter.py | 38 +++++++++++++++++++++++++++++++---
 3 files changed, 52 insertions(+), 34 deletions(-)

diff --git a/bayes_opt/logger.py b/bayes_opt/logger.py
index 02874d9ee..0f5cd41c2 100644
--- a/bayes_opt/logger.py
+++ b/bayes_opt/logger.py
@@ -169,7 +169,7 @@ def _step(self, instance: BayesianOptimization, colour: str = _colour_regular_me
             cells[2] = self._format_bool(res["allowed"])
         params = res.get("params", {})
         cells[3:] = [
-            instance.space._params_config[key].repr(val, self._default_cell_size)
+            instance.space._params_config[key].to_string(val, self._default_cell_size)
             for key, val in params.items()
         ]
 
diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index 876c3517b..bc30d51d4 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -120,7 +120,7 @@ def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:
         np.ndarray
         """
 
-    def repr(self, value: Any, str_len: int) -> str:
+    def to_string(self, value: Any, str_len: int) -> str:
         """Represent a parameter value as a string.
 
         Parameters
@@ -135,11 +135,9 @@ def repr(self, value: Any, str_len: int) -> str:
         -------
         str
         """
-        s = repr(value)
+        s = f"{value!r:<{str_len}}"
 
         if len(s) > str_len:
-            if "." in s:
-                return s[:str_len]
             return s[: str_len - 3] + "..."
         return s
 
@@ -194,7 +192,7 @@ def to_param(self, value: float | NDArray[Float]) -> float:
             return value
         return value.flatten()[0]
 
-    def repr(self, value: float, str_len: int) -> str:
+    def to_string(self, value: float, str_len: int) -> str:
         """Represent a parameter value as a string.
 
         Parameters
@@ -211,7 +209,7 @@ def repr(self, value: float, str_len: int) -> str:
         """
         s = f"{value:<{str_len}.{str_len}}"
         if len(s) > str_len:
-            if "." in s:
+            if "." in s and "e" not in s:
                 return s[:str_len]
             return s[: str_len - 3] + "..."
         return s
@@ -297,28 +295,6 @@ def to_param(self, value: int | float | NDArray[Int] | NDArray[Float]) -> int:
         """
         return int(np.round(np.squeeze(value)))
 
-    def repr(self, value: int, str_len: int) -> str:
-        """Represent a parameter value as a string.
-
-        Parameters
-        ----------
-        value : Any
-            The value to represent.
-
-        str_len : int
-            The maximum length of the string representation.
-
-        Returns
-        -------
-        str
-        """
-        s = f"{value:<{str_len}}"
-        if len(s) > str_len:
-            if "." in s:
-                return s[:str_len]
-            return s[: str_len - 3] + "..."
-        return s
-
     def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:
         """Transform a parameter value for use in a kernel.
 
@@ -352,6 +328,13 @@ class CategoricalParameter(BayesParameter):
     """
 
     def __init__(self, name: str, categories: Sequence[Any]) -> None:
+        if len(categories) != len(set(categories)):
+            msg = "Categories must be unique."
+            raise ValueError(msg)
+        if len(categories) < 2:
+            msg = "At least two categories are required."
+            raise ValueError(msg)
+
         self.categories = categories
         lower = np.zeros(self.dim)
         upper = np.ones(self.dim)
@@ -412,7 +395,7 @@ def to_param(self, value: float | NDArray[Float]) -> Any:
         """
         return self.categories[int(np.argmax(value))]
 
-    def repr(self, value: Any, str_len: int) -> str:
+    def to_string(self, value: Any, str_len: int) -> str:
         """Represent a parameter value as a string.
 
         Parameters
@@ -427,7 +410,10 @@ def repr(self, value: Any, str_len: int) -> str:
         -------
         str
         """
-        s = f"{value:^{str_len}}"
+        if not isinstance(value, str):
+            value = repr(value)
+        s = f"{value:<{str_len}}"
+
         if len(s) > str_len:
             return s[: str_len - 3] + "..."
         return s
diff --git a/tests/test_parameter.py b/tests/test_parameter.py
index 64e9eaf76..dd9d94802 100644
--- a/tests/test_parameter.py
+++ b/tests/test_parameter.py
@@ -70,13 +70,13 @@ def target_func(**kwargs):
 
 
 def test_cat_parameters():
-    fruit_ratings = {"apple": 1.0, "banana": 2.0, "mango": 5.0, "honeydew melon": -10.0, "straberry": np.pi}
+    fruit_ratings = {"apple": 1.0, "banana": 2.0, "mango": 5.0, "honeydew melon": -10.0, "strawberry": np.pi}
 
     def target_func(fruit: str):
         return fruit_ratings[fruit]
 
-    fruits = ("apple", "banana", "mango", "honeydew melon", "straberry")
-    pbounds = {"fruit": ("apple", "banana", "mango", "honeydew melon", "straberry")}
+    fruits = ("apple", "banana", "mango", "honeydew melon", "strawberry")
+    pbounds = {"fruit": ("apple", "banana", "mango", "honeydew melon", "strawberry")}
     space = TargetSpace(target_func, pbounds)
 
     assert space.dim == len(fruits)
@@ -101,3 +101,35 @@ def target_func(fruit: str):
     assert (space.params[1] == np.array([0, 0, 0, 1, 0])).all()
 
     assert (space.target == np.array([target1, target2])).all()
+
+
+def test_to_string():
+    pbounds = {"p1": (0, 1), "p2": (1, 2)}
+    space = TargetSpace(None, pbounds)
+
+    assert space._params_config["p1"].to_string(0.2, 5) == "0.2  "
+    assert space._params_config["p2"].to_string(1.5, 5) == "1.5  "
+    assert space._params_config["p1"].to_string(0.2, 3) == "0.2"
+    assert space._params_config["p2"].to_string(np.pi, 5) == "3.141"
+    assert space._params_config["p1"].to_string(1e-5, 6) == "1e-05 "
+    assert space._params_config["p2"].to_string(-1e-5, 6) == "-1e-05"
+    assert space._params_config["p1"].to_string(1e-15, 5) == "1e-15"
+    assert space._params_config["p1"].to_string(-1.2e-15, 7) == "-1.2..."
+
+    pbounds = {"p1": (0, 5, int), "p3": (-1, 3, int)}
+    space = TargetSpace(None, pbounds)
+
+    assert space._params_config["p1"].to_string(2, 5) == "2    "
+    assert space._params_config["p3"].to_string(0, 5) == "0    "
+    assert space._params_config["p1"].to_string(2, 3) == "2  "
+    assert space._params_config["p3"].to_string(-1, 5) == "-1   "
+    assert space._params_config["p1"].to_string(123456789, 6) == "123..."
+
+    pbounds = {"fruit": ("apple", "banana", "mango", "honeydew melon", "strawberry")}
+    space = TargetSpace(None, pbounds)
+
+    assert space._params_config["fruit"].to_string("apple", 5) == "apple"
+    assert space._params_config["fruit"].to_string("banana", 5) == "ba..."
+    assert space._params_config["fruit"].to_string("mango", 5) == "mango"
+    assert space._params_config["fruit"].to_string("honeydew melon", 10) == "honeyde..."
+    assert space._params_config["fruit"].to_string("strawberry", 10) == "strawberry"

From 1a03b05763e1411032113b7ddcf6922378f2a9f8 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Sat, 12 Oct 2024 11:49:16 +0200
Subject: [PATCH 09/21] Disable SDR when non-float parameters are present

---
 bayes_opt/domain_reduction.py | 5 +++++
 tests/test_seq_domain_red.py  | 8 ++++++++
 2 files changed, 13 insertions(+)

diff --git a/bayes_opt/domain_reduction.py b/bayes_opt/domain_reduction.py
index 1c6d99d9e..a5179a7a9 100644
--- a/bayes_opt/domain_reduction.py
+++ b/bayes_opt/domain_reduction.py
@@ -14,6 +14,7 @@
 
 import numpy as np
 
+from bayes_opt.parameter import FloatParameter
 from bayes_opt.target_space import TargetSpace
 
 if TYPE_CHECKING:
@@ -90,6 +91,10 @@ def initialize(self, target_space: TargetSpace) -> None:
         target_space : TargetSpace
             TargetSpace this DomainTransformer operates on.
         """
+        any_not_float = any([not isinstance(p, FloatParameter) for p in target_space._params_config.values()])
+        if any_not_float:
+            msg = "Domain reduction is only supported for all-FloatParameter optimization."
+            raise ValueError(msg)
         # Set the original bounds
         self.original_bounds = np.copy(target_space.bounds)
         self.bounds = [self.original_bounds]
diff --git a/tests/test_seq_domain_red.py b/tests/test_seq_domain_red.py
index 265d3ee5b..82c7c87bc 100644
--- a/tests/test_seq_domain_red.py
+++ b/tests/test_seq_domain_red.py
@@ -179,6 +179,14 @@ def test_minimum_window_dict_ordering():
     )
 
 
+def test_mixed_parameters():
+    """Ensure that the transformer errors when providing non-float parameters"""
+    pbounds = {"x": (-10, 10), "y": (-10, 10), "z": (1, 10, int)}
+    target_space = TargetSpace(target_func=black_box_function, pbounds=pbounds)
+    with pytest.raises(ValueError):
+        _ = SequentialDomainReductionTransformer().initialize(target_space)
+
+
 if __name__ == "__main__":
     r"""
     CommandLine:

From f17c96ac69fdd3f2e551b28dea27f1bdaf3ba957 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Sat, 12 Oct 2024 11:50:43 +0200
Subject: [PATCH 10/21] Add demo script for typed optimization

---
 examples/typed_hyperparameter_tuning.py | 94 +++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 examples/typed_hyperparameter_tuning.py

diff --git a/examples/typed_hyperparameter_tuning.py b/examples/typed_hyperparameter_tuning.py
new file mode 100644
index 000000000..376974eb1
--- /dev/null
+++ b/examples/typed_hyperparameter_tuning.py
@@ -0,0 +1,94 @@
+import numpy as np
+from bayes_opt import BayesianOptimization, acquisition
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.datasets import load_digits
+from sklearn.model_selection import KFold
+from sklearn.metrics import log_loss
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+
+N_FOLDS = 10
+N_START = 2
+N_ITER = 25 - N_START
+# Load data
+data = load_digits()
+    
+
+# Define the hyperparameter space
+continuous_pbounds = {
+    'log_learning_rate': (-10, 0),
+    'max_depth': (1, 6),
+    'min_samples_split': (2, 6)
+}
+
+discrete_pbounds = {
+    'log_learning_rate': (-10, 0),
+    'max_depth': (1, 6, int),
+    'min_samples_split': (2, 6, int)
+}
+
+kfold = KFold(n_splits=N_FOLDS, shuffle=True, random_state=42)
+
+res_continuous = []
+res_discrete = []
+
+METRIC_SIGN = -1
+
+for i, (train_idx, test_idx) in enumerate(tqdm(kfold.split(data.data), total=N_FOLDS)):
+    def gboost(log_learning_rate, max_depth, min_samples_split):
+        clf = GradientBoostingClassifier(
+            n_estimators=10,
+            max_depth=int(max_depth),
+            learning_rate=np.exp(log_learning_rate),
+            min_samples_split=int(min_samples_split),
+            random_state=42 + i
+        )
+        clf.fit(data.data[train_idx], data.target[train_idx])
+        #return clf.score(data.data[test_idx], data.target[test_idx])
+        return METRIC_SIGN * log_loss(data.target[test_idx], clf.predict_proba(data.data[test_idx]), labels=list(range(10)))
+    
+    continuous_optimizer = BayesianOptimization(
+        f=gboost,
+        acquisition_function=acquisition.ExpectedImprovement(1e-1),
+        pbounds=continuous_pbounds,
+        verbose=0,
+        random_state=42,
+    )
+
+    discrete_optimizer = BayesianOptimization(
+        f=gboost,
+        acquisition_function=acquisition.ExpectedImprovement(1e-1),
+        pbounds=discrete_pbounds,
+        verbose=0,
+        random_state=42,
+    )
+    continuous_optimizer.maximize(init_points=2, n_iter=N_ITER)
+    discrete_optimizer.maximize(init_points=2, n_iter=N_ITER)
+    res_continuous.append(METRIC_SIGN * continuous_optimizer.space.target)
+    res_discrete.append(METRIC_SIGN * discrete_optimizer.space.target)
+
+score_continuous = []
+score_discrete = []
+
+for fold in range(N_FOLDS):
+    best_in_fold = min(np.min(res_continuous[fold]), np.min(res_discrete[fold]))
+    score_continuous.append(np.minimum.accumulate((res_continuous[fold] - best_in_fold)))
+    score_discrete.append(np.minimum.accumulate((res_discrete[fold] - best_in_fold)))
+
+mean_continuous = np.mean(score_continuous, axis=0)
+quantiles_continuous = np.quantile(score_continuous, [0.1, 0.9], axis=0)
+mean_discrete = np.mean(score_discrete, axis=0)
+quantiles_discrete = np.quantile(score_discrete, [0.1, 0.9], axis=0)
+
+
+plt.figure(figsize=(10, 5))
+plt.plot((mean_continuous), label='Continuous best seen')
+plt.fill_between(range(N_ITER + N_START), quantiles_continuous[0], quantiles_continuous[1], alpha=0.3)
+plt.plot((mean_discrete), label='Discrete best seen')
+plt.fill_between(range(N_ITER + N_START), quantiles_discrete[0], quantiles_discrete[1], alpha=0.3)
+
+plt.xlabel('Number of iterations')
+plt.ylabel('Score')
+plt.legend(loc='best')
+plt.grid()
+plt.savefig('discrete_vs_continuous.png')

From 3c4c298ab3f75ccd3a0b26c4d3070b6a881903d8 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Tue, 15 Oct 2024 16:42:19 +0200
Subject: [PATCH 11/21] Update parameters, testing

---
 bayes_opt/bayesian_optimization.py      |   6 +-
 bayes_opt/constraint.py                 |   4 +-
 bayes_opt/parameter.py                  |  89 ++++----
 examples/parameter_types.ipynb          | 285 ++++++++++++++++--------
 examples/typed_hyperparameter_tuning.py |   2 -
 tests/test_parameter.py                 |  50 +++++
 6 files changed, 295 insertions(+), 141 deletions(-)

diff --git a/bayes_opt/bayesian_optimization.py b/bayes_opt/bayesian_optimization.py
index 46944cf1e..826f1cfbb 100644
--- a/bayes_opt/bayesian_optimization.py
+++ b/bayes_opt/bayesian_optimization.py
@@ -16,7 +16,7 @@
 from bayes_opt.constraint import ConstraintModel
 from bayes_opt.event import DEFAULT_EVENTS, Events
 from bayes_opt.logger import _get_default_logger
-from bayes_opt.parameter import wrap_kernel
+from bayes_opt.parameter import WrappedKernel
 from bayes_opt.target_space import TargetSpace
 from bayes_opt.util import ensure_rng
 
@@ -152,7 +152,7 @@ def __init__(
 
         # Internal GP regressor
         self._gp = GaussianProcessRegressor(
-            kernel=wrap_kernel(Matern(nu=2.5), transform=self._space.kernel_transform),
+            kernel=WrappedKernel(Matern(nu=2.5), transform=self._space.kernel_transform),
             alpha=1e-6,
             normalize_y=True,
             n_restarts_optimizer=5,
@@ -329,4 +329,6 @@ def set_bounds(self, new_bounds: BoundsMapping) -> None:
 
     def set_gp_params(self, **params: Any) -> None:
         """Set parameters of the internal Gaussian Process Regressor."""
+        if "kernel" in params:
+            params["kernel"] = WrappedKernel(params["kernel"], self._space.kernel_transform)
         self._gp.set_params(**params)
diff --git a/bayes_opt/constraint.py b/bayes_opt/constraint.py
index 120169bdb..f643d47fe 100644
--- a/bayes_opt/constraint.py
+++ b/bayes_opt/constraint.py
@@ -9,7 +9,7 @@
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import Matern
 
-from bayes_opt.parameter import wrap_kernel
+from bayes_opt.parameter import WrappedKernel
 
 if TYPE_CHECKING:
     from collections.abc import Callable
@@ -71,7 +71,7 @@ def __init__(
 
         self._model = [
             GaussianProcessRegressor(
-                kernel=wrap_kernel(Matern(nu=2.5), transform) if transform is not None else Matern(nu=2.5),
+                kernel=WrappedKernel(Matern(nu=2.5), transform) if transform is not None else Matern(nu=2.5),
                 alpha=1e-6,
                 normalize_y=True,
                 n_restarts_optimizer=5,
diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index bc30d51d4..b83faff3a 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -4,7 +4,6 @@
 
 import abc
 from collections.abc import Sequence
-from inspect import signature
 from numbers import Number
 from typing import TYPE_CHECKING, Any, Callable, Union
 
@@ -375,8 +374,6 @@ def to_float(self, value: Any) -> NDArray[Float]:
         """
         res = np.zeros(len(self.categories))
         one_hot_index = [i for i, val in enumerate(self.categories) if val == value]
-        if len(one_hot_index) != 1:
-            raise ValueError
         res[one_hot_index] = 1
         return res.astype(float)
 
@@ -432,7 +429,7 @@ def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:
         """
         value = np.atleast_2d(value)
         res = np.zeros(value.shape)
-        res[np.argmax(value, axis=0)] = 1
+        res[:, np.argmax(value, axis=1)] = 1
         return res
 
     @property
@@ -441,52 +438,68 @@ def dim(self) -> int:
         return len(self.categories)
 
 
-def wrap_kernel(kernel: kernels.Kernel, transform: Callable[[Any], Any]) -> kernels.Kernel:
-    """Wrap a kernel to transform input data before passing it to the kernel.
+class WrappedKernel(kernels.Kernel):
+    """Wrap a kernel with a parameter transformation.
+
+    The transform function is applied to the input before passing it to the base kernel.
 
     Parameters
     ----------
-    kernel : kernels.Kernel
-        The kernel to wrap.
+    base_kernel : kernels.Kernel
 
-    transform : Callable
-        The transformation function to apply to the input data.
+    transform : Callable[[Any], Any]
+    """
 
-    Returns
-    -------
-    kernels.Kernel
-        The wrapped kernel.
+    def __init__(self, base_kernel: kernels.Kernel, transform: Callable[[Any], Any]) -> None:
+        super().__init__()
+        self.base_kernel = base_kernel
+        self.transform = transform
 
-    Notes
-    -----
-    See https://arxiv.org/abs/1805.03463 for more information.
-    """
-    kernel_type = type(kernel)
+    def __call__(self, X: NDArray[Float], Y: NDArray[Float] = None, eval_gradient: bool = False) -> Any:
+        """Return the kernel k(X, Y) and optionally its gradient after applying the transform.
 
-    class WrappedKernel(kernel_type):
-        @copy_signature(getattr(kernel_type.__init__, "deprecated_original", kernel_type.__init__))
-        def __init__(self, **kwargs: Any) -> None:
-            super().__init__(**kwargs)
+        For details, see the documentation of the base kernel.
 
-        def __call__(self, X: Any, Y: Any = None, eval_gradient: bool = False) -> Any:
-            X = transform(X)
-            return super().__call__(X, Y, eval_gradient)
+        Parameters
+        ----------
+        X :  ndarray of shape (n_samples_X, n_features)
+            Left argument of the returned kernel k(X, Y).
 
-        def __reduce__(self) -> str | tuple[Any, ...]:
-            return (wrap_kernel, (kernel, transform))
+        Y : ndarray of shape (n_samples_Y, n_features), default=None
+            Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated.
 
-    return WrappedKernel(**kernel.get_params())
+        eval_gradient : bool, default=False
+            Determines whether the gradient with respect to the kernel hyperparameter is calculated.
 
+        Returns
+        -------
+        K : ndarray of shape (n_samples_X, n_samples_Y)
 
-def copy_signature(source_fct: Callable[..., Any]) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
-    """Clones a signature from a source function to a target function.
+        K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims)
+        """
+        X = self.transform(X)
+        return self.base_kernel(X, Y, eval_gradient)
 
-    via
-    https://stackoverflow.com/a/58989918/
-    """
+    def is_stationary(self):
+        """Return whether the kernel is stationary."""
+        return self.base_kernel.is_stationary()
 
-    def copy(target_fct: Callable[..., Any]) -> Callable[..., Any]:
-        target_fct.__signature__ = signature(source_fct)
-        return target_fct
+    def diag(self, X: NDArray[Float]) -> NDArray[Float]:
+        """Return the diagonal of k(X, X).
 
-    return copy
+        This method allows for more efficient calculations than calling
+        np.diag(self(X)).
+
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples,)
+            Left argument of the returned kernel k(X, Y)
+
+        Returns
+        -------
+        K_diag : ndarray of shape (n_samples_X,)
+            Diagonal of kernel k(X, X)
+        """
+        X = self.transform(X)
+        return self.base_kernel.diag(X)
diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
index 961432a95..a77934f47 100644
--- a/examples/parameter_types.ipynb
+++ b/examples/parameter_types.ipynb
@@ -8,23 +8,87 @@
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from bayes_opt import BayesianOptimization, BayesParameter\n"
+    "from bayes_opt import BayesianOptimization\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def target_function_1d(x):\n",
+    "    return np.sin(np.round(x)) - np.abs(np.round(x) / 5)\n",
+    "\n",
+    "c_pbounds = {'x': (-10, 10)}\n",
+    "bo_cont = BayesianOptimization(target_function_1d, c_pbounds, verbose=0)\n",
+    "\n",
+    "d_pbounds = {'x': (-10, 10, int)}\n",
+    "bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0)\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 1, figsize=(10, 6), sharex=True, sharey=True)\n",
+    "\n",
+    "bo_cont.maximize(init_points=2, n_iter=10)\n",
+    "bo_cont.acquisition_function._fit_gp(bo_cont._gp, bo_cont.space)\n",
+    "\n",
+    "y_mean, y_std = bo_cont._gp.predict(np.linspace(-10, 10, 1000).reshape(-1, 1), return_std=True)\n",
+    "axs[0].set_title('Continuous')\n",
+    "axs[0].plot(np.linspace(-10, 10, 1000), target_function_1d(np.linspace(-10, 10, 1000)), 'k--', label='True function')\n",
+    "axs[0].plot(np.linspace(-10, 10, 1000), y_mean, label='Predicted mean')\n",
+    "axs[0].fill_between(np.linspace(-10, 10, 1000), y_mean - y_std, y_mean + y_std, alpha=0.3, label='Predicted std')\n",
+    "axs[0].plot(bo_cont.space.params, bo_cont.space.target, 'ro')\n",
+    "\n",
+    "bo_disc.maximize(init_points=2, n_iter=10)\n",
+    "bo_disc.acquisition_function._fit_gp(bo_disc._gp, bo_disc.space)\n",
+    "\n",
+    "y_mean, y_std = bo_disc._gp.predict(np.linspace(-10, 10, 1000).reshape(-1, 1), return_std=True)\n",
+    "axs[1].set_title('Discrete')\n",
+    "axs[1].plot(np.linspace(-10, 10, 1000), target_function_1d(np.linspace(-10, 10, 1000)), 'k--', label='True function')\n",
+    "axs[1].plot(np.linspace(-10, 10, 1000), y_mean, label='Predicted mean')\n",
+    "axs[1].fill_between(np.linspace(-10, 10, 1000), y_mean - y_std, y_mean + y_std, alpha=0.3, label='Predicted std')\n",
+    "axs[1].plot(bo_disc.space.params, bo_disc.space.target, 'ro')\n",
+    "\n",
+    "for ax in axs:\n",
+    "    ax.grid(True)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We can see, that the discrete optimizer is aware that the function is discrete and does not try to predict values\n",
+    "# between the integers. The continuous optimizer tries to predict values between the integers, despite the fact that these are known.\n",
+    "# We can also see that the discrete optimizer predicts blocky mean and standard deviations, which is a result of the discrete nature of the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def discretized_function(x, y):\n",
     "    y = np.round(y)\n",
-    "    return (-1*np.cos(x) + -1*np.cos(y))/((x/3)**2 + (y/3)**2 + 1)"
+    "    return (-1*np.cos(x)**np.abs(y) + -1*np.cos(y)) + 0.1 * (x + y) - 0.01 * (x**2 + y**2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,12 +107,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "continuous_optimizer = BayesianOptimization(\n",
     "    f=discretized_function,\n",
+    "    #acquisition_function=acquisition.ExpectedImprovement(xi=1., random_state=42),\n",
     "    pbounds=c_pbounds,\n",
     "    verbose=2,\n",
     "    random_state=42,\n",
@@ -58,15 +123,17 @@
     "d_pbounds = {'x': (-5, 5), 'y': (-5, 5, int)}\n",
     "discrete_optimizer = BayesianOptimization(\n",
     "    f=discretized_function,\n",
+    "    #acquisition_function=acquisition.ExpectedImprovement(xi=1., random_state=42),\n",
     "    pbounds=d_pbounds,\n",
     "    verbose=2,\n",
     "    random_state=42,\n",
-    ")"
+    ")\n",
+    "#discrete_optimizer.set_gp_params(alpha=1e-3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -77,36 +144,46 @@
       "\n",
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1504  \u001b[39m | \u001b[39m-1.254598\u001b[39m | \u001b[39m4.5071430\u001b[39m |\n",
-      "| \u001b[35m2        \u001b[39m | \u001b[35m0.08233  \u001b[39m | \u001b[35m2.3199394\u001b[39m | \u001b[35m0.9865848\u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m0.2095   \u001b[39m | \u001b[35m0.9907151\u001b[39m | \u001b[35m-2.811653\u001b[39m |\n",
-      "| \u001b[35m4        \u001b[39m | \u001b[35m0.286    \u001b[39m | \u001b[35m4.0043956\u001b[39m | \u001b[35m-4.018452\u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.08174 \u001b[39m | \u001b[39m-4.963953\u001b[39m | \u001b[39m-4.888396\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m0.03501  \u001b[39m | \u001b[39m4.9846486\u001b[39m | \u001b[39m-1.821879\u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m0.1011   \u001b[39m | \u001b[39m2.3883784\u001b[39m | \u001b[39m-4.558775\u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.009009\u001b[39m | \u001b[39m4.4807948\u001b[39m | \u001b[39m-4.842764\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m0.1525   \u001b[39m | \u001b[39m4.5858949\u001b[39m | \u001b[39m-3.783561\u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m0.2783   \u001b[39m | \u001b[39m4.0389974\u001b[39m | \u001b[39m-4.031531\u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1778  \u001b[39m | \u001b[39m-1.254598\u001b[39m | \u001b[39m4.5071430\u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m0.4089   \u001b[39m | \u001b[35m2.3199394\u001b[39m | \u001b[35m0.9865848\u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-1.787   \u001b[39m | \u001b[39m3.0783145\u001b[39m | \u001b[39m-0.082060\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.7095   \u001b[39m | \u001b[35m1.5913220\u001b[39m | \u001b[35m1.8823587\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.09673 \u001b[39m | \u001b[39m2.7779629\u001b[39m | \u001b[39m2.3840590\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-1.034   \u001b[39m | \u001b[39m0.8481708\u001b[39m | \u001b[39m0.8669483\u001b[39m |\n",
+      "| \u001b[35m7        \u001b[39m | \u001b[35m1.277    \u001b[39m | \u001b[35m1.2462273\u001b[39m | \u001b[35m3.1071194\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.9953   \u001b[39m | \u001b[39m1.261569 \u001b[39m | \u001b[39m4.2461872\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m0.3207   \u001b[39m | \u001b[39m0.2574759\u001b[39m | \u001b[39m3.3743200\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m0.7984   \u001b[39m | \u001b[39m2.3822527\u001b[39m | \u001b[39m4.0972752\u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m0.2769   \u001b[39m | \u001b[39m4.0866405\u001b[39m | \u001b[39m4.9990993\u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-1.783   \u001b[39m | \u001b[39m-4.990362\u001b[39m | \u001b[39m-4.900993\u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m-0.2363  \u001b[39m | \u001b[39m-4.975007\u001b[39m | \u001b[39m1.5082626\u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m0.1458   \u001b[39m | \u001b[39m2.0323084\u001b[39m | \u001b[39m4.9763438\u001b[39m |\n",
+      "| \u001b[35m15       \u001b[39m | \u001b[35m1.374    \u001b[39m | \u001b[35m1.8577640\u001b[39m | \u001b[35m3.3266304\u001b[39m |\n",
       "=================================================\n",
-      "Max: 0.2859884589036788\n",
+      "Max: 1.3739320944970081\n",
       "\n",
       "\n",
       "====================  Typed Optimizer  ====================\n",
       "\n",
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1504  \u001b[39m | \u001b[39m-1.254598\u001b[39m | \u001b[39m5        \u001b[39m |\n",
-      "| \u001b[35m2        \u001b[39m | \u001b[35m0.2024   \u001b[39m | \u001b[35m2.79691  \u001b[39m | \u001b[35m-1       \u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m0.6476   \u001b[39m | \u001b[35m-2.349882\u001b[39m | \u001b[35m-3       \u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m0.126    \u001b[39m | \u001b[39m-2.594630\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.3138  \u001b[39m | \u001b[39m-0.431955\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
-      "| \u001b[35m6        \u001b[39m | \u001b[35m0.6741   \u001b[39m | \u001b[35m-2.731757\u001b[39m | \u001b[35m-3       \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m0.1951   \u001b[39m | \u001b[39m-2.737932\u001b[39m | \u001b[39m-1       \u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m0.2396   \u001b[39m | \u001b[39m-4.647873\u001b[39m | \u001b[39m-3       \u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m0.1508   \u001b[39m | \u001b[39m4.9876130\u001b[39m | \u001b[39m3        \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.1084  \u001b[39m | \u001b[39m-0.947769\u001b[39m | \u001b[39m-2       \u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1778  \u001b[39m | \u001b[39m-1.254598\u001b[39m | \u001b[39m5        \u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m0.4923   \u001b[39m | \u001b[35m2.79691  \u001b[39m | \u001b[35m-1       \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-0.3589  \u001b[39m | \u001b[39m3.6456361\u001b[39m | \u001b[39m-2       \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-1.817   \u001b[39m | \u001b[39m2.4028434\u001b[39m | \u001b[39m0        \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m0.07317  \u001b[39m | \u001b[39m2.1170379\u001b[39m | \u001b[39m-2       \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.1352   \u001b[39m | \u001b[39m-3.550760\u001b[39m | \u001b[39m5        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m0.1517   \u001b[39m | \u001b[39m-4.945271\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[35m8        \u001b[39m | \u001b[35m1.563    \u001b[39m | \u001b[35m-3.487143\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.7904  \u001b[39m | \u001b[39m-3.462641\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.475   \u001b[39m | \u001b[39m-3.027560\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m0.7741   \u001b[39m | \u001b[39m-4.139354\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-1.785   \u001b[39m | \u001b[39m-4.999737\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[35m13       \u001b[39m | \u001b[35m1.658    \u001b[39m | \u001b[35m-2.773927\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m1.012    \u001b[39m | \u001b[39m-1.617889\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-0.07162 \u001b[39m | \u001b[39m-0.713869\u001b[39m | \u001b[39m2        \u001b[39m |\n",
       "=================================================\n",
-      "Max: 0.6741133751365254\n",
+      "Max: 1.6582609813341627\n",
       "\n",
       "\n"
      ]
@@ -117,26 +194,19 @@
     "    print(f\"====================  {lbl}  ====================\\n\")\n",
     "    optimizer.maximize(\n",
     "        init_points=2,\n",
-    "        n_iter=8\n",
+    "        n_iter=13\n",
     "    )\n",
     "    print(f\"Max: {optimizer.max['target']}\\n\\n\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -199,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -220,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -229,26 +299,26 @@
      "text": [
       "|   iter    |  target   |     k     |    x1     |    x2     |\n",
       "-------------------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-10.87   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m9.9436962\u001b[39m | \u001b[39m8.6511471\u001b[39m |\n",
-      "| \u001b[35m2        \u001b[39m | \u001b[35m10.65    \u001b[39m | \u001b[35m    2    \u001b[39m | \u001b[35m-3.953348\u001b[39m | \u001b[35m-7.064882\u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m1.911    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-3.430812\u001b[39m | \u001b[39m-7.728198\u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m9.726    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-4.862044\u001b[39m | \u001b[39m-5.999132\u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m0.8457   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-1.919685\u001b[39m | \u001b[39m-3.730800\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m3.984    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-7.008155\u001b[39m | \u001b[39m-7.841077\u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m4.096    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-7.357018\u001b[39m | \u001b[39m-3.570538\u001b[39m |\n",
-      "| \u001b[35m8        \u001b[39m | \u001b[35m12.62    \u001b[39m | \u001b[35m    1    \u001b[39m | \u001b[35m-9.879071\u001b[39m | \u001b[35m0.8692765\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m10.67    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-9.766583\u001b[39m | \u001b[39m2.6526599\u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m4.147    \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-7.515377\u001b[39m | \u001b[39m1.2085635\u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m-1.594   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.888163\u001b[39m | \u001b[39m-0.986066\u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m12.6     \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-9.789574\u001b[39m | \u001b[39m1.5273833\u001b[39m |\n",
-      "| \u001b[39m13       \u001b[39m | \u001b[39m0.3688   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.862455\u001b[39m | \u001b[39m5.0749913\u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m-2.888   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-9.949469\u001b[39m | \u001b[39m1.6633608\u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m0.3513   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m-0.825539\u001b[39m | \u001b[39m0.9079267\u001b[39m |\n",
-      "| \u001b[39m16       \u001b[39m | \u001b[39m-1.711   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m2.195066 \u001b[39m | \u001b[39m1.2996733\u001b[39m |\n",
-      "| \u001b[39m17       \u001b[39m | \u001b[39m0.3682   \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m1.5699874\u001b[39m | \u001b[39m0.4413354\u001b[39m |\n",
-      "| \u001b[39m18       \u001b[39m | \u001b[39m8.667    \u001b[39m | \u001b[39m    2    \u001b[39m | \u001b[39m-4.855364\u001b[39m | \u001b[39m-5.736939\u001b[39m |\n",
-      "| \u001b[39m19       \u001b[39m | \u001b[39m-0.03433 \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m1.0870723\u001b[39m | \u001b[39m3.4490001\u001b[39m |\n",
-      "| \u001b[39m20       \u001b[39m | \u001b[39m-8.418   \u001b[39m | \u001b[39m    1    \u001b[39m | \u001b[39m7.8951162\u001b[39m | \u001b[39m-3.354534\u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-5.62    \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m5.9308597\u001b[39m | \u001b[39m-6.331304\u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m7.509    \u001b[39m | \u001b[35m2        \u001b[39m | \u001b[35m1.9731696\u001b[39m | \u001b[35m-6.879627\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m8.825    \u001b[39m | \u001b[35m2        \u001b[39m | \u001b[35m1.1484948\u001b[39m | \u001b[35m-7.207232\u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-3.945   \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m1.4977824\u001b[39m | \u001b[39m-8.189324\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m1.978    \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m1.0306587\u001b[39m | \u001b[39m-6.389038\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m8.229    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m1.3033840\u001b[39m | \u001b[39m-7.107783\u001b[39m |\n",
+      "| \u001b[35m7        \u001b[39m | \u001b[35m9.632    \u001b[39m | \u001b[35m2        \u001b[39m | \u001b[35m-0.198942\u001b[39m | \u001b[35m-7.391484\u001b[39m |\n",
+      "| \u001b[35m8        \u001b[39m | \u001b[35m11.54    \u001b[39m | \u001b[35m2        \u001b[39m | \u001b[35m-1.136689\u001b[39m | \u001b[35m-7.696695\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m7.76     \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-1.535976\u001b[39m | \u001b[39m-7.007879\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.5064  \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-1.153247\u001b[39m | \u001b[39m-8.358024\u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m9.011    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-0.860207\u001b[39m | \u001b[39m-7.266433\u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-1.8     \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-1.227306\u001b[39m | \u001b[39m-7.434615\u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m4.911    \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-1.825508\u001b[39m | \u001b[39m-6.159445\u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m11.25    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-0.643026\u001b[39m | \u001b[39m-7.648759\u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m3.807    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-0.603103\u001b[39m | \u001b[39m-6.548109\u001b[39m |\n",
+      "| \u001b[39m16       \u001b[39m | \u001b[39m8.803    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-2.366971\u001b[39m | \u001b[39m-6.942279\u001b[39m |\n",
+      "| \u001b[39m17       \u001b[39m | \u001b[39m2.363    \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-1.839455\u001b[39m | \u001b[39m-6.747216\u001b[39m |\n",
+      "| \u001b[39m18       \u001b[39m | \u001b[39m-0.7103  \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-3.198436\u001b[39m | \u001b[39m-7.144806\u001b[39m |\n",
+      "| \u001b[39m19       \u001b[39m | \u001b[39m0.9779   \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-2.068342\u001b[39m | \u001b[39m-4.863568\u001b[39m |\n",
+      "| \u001b[39m20       \u001b[39m | \u001b[39m-1.224   \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-0.663333\u001b[39m | \u001b[39m-7.308690\u001b[39m |\n",
       "=============================================================\n"
      ]
     }
@@ -258,10 +328,12 @@
     "\n",
     "categorical_optimizer = BayesianOptimization(\n",
     "    f=SPIRAL,\n",
+    "    #acquisition_function=acquisition.ExpectedImprovement(1e-2),\n",
     "    pbounds=pbounds,\n",
     "    verbose=2,\n",
-    "    random_state=1,\n",
+    "    random_state=42,\n",
     ")\n",
+    "discrete_optimizer.set_gp_params(alpha=1e-3)\n",
     "\n",
     "categorical_optimizer.maximize(\n",
     "        init_points=2,\n",
@@ -271,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,12 +354,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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7+LG78GHRVtNuLCxAefXYJu4LKtp8TJD1T/VLVq5cicLCQlx33XWajguHw/jHP/6B4uJiTjkzBk/xo9b7QyEv7Zjp0jc7PKZULru5z622G24TPyKEuqxYjkKkJTD0PAO97VpN3bJzOEwID1AkEsHKlSsxZ84c9OmTmKXZs2fjggsuwPLlywEAjz32GL7yla/goosuQmtrK55++mkcOHAA3/ve9zRf9w+nByAnt/f3rLw/oogfrbAQPjw3/4yHt0hzqkdIBE/QiSGGLmGZ3YhiZ/GjR/hYhdWCIxUizPjUapv0eoPU7iTPCjM9QUIIoA8++ABNTU347ne/2+u3pqYmeL3nK9epU6cwb948NDc3Y8CAAZgwYQLWr1+PUaNGMcmLmeJHb8hLJPFjltDRcn0eosiJQshqERTYZ+xeWmk3Wiv8KY2nmeJHL1o6M6uEj4iiR4n4/Jp9z/QIIdYiyKzV31tHB9B/5zFm5xNqELRZBINBBAIB/HrbBOTkZiT8xkIA2VX8AMriwWqxoxcegsgsA23WDDXZ3zgOjO7u6sTWt5YwGcxoFlHbMWHmE+jTN3EladbPirX3R3ThYzfRkw473EPWdciMF4D+O48xGwQtzBggESDxkygUOstDCR+7wqMcTpo+z309JAs2ObQ7bhI/Ioyr4YEVY4a02iWtoS0hpsdXsHtZIgH0JW4XP4BzBE86WAshM7CqkzAz9m9nRB33o2VdH7MHODtV+KRCpIkOvdIybuN22jeMBBAj7C5+3AgrsWf2jDEeWD0zzM6IOu5HVK+Pm4RPMmaWXYtd0iKUzW7LPOsmCSAY9/6Q+LE/rISQGZAIEgfTw0Uq76GIXh83C59kzBZCqtMyEkGsy8arjrpeABnZQR3gK37UQuKHHUaFkN3HBtF4IPWIOuhZi/gxAxI+8ph1b7R6g9RgtgjigesFkBxqRAVv8aNGnJH44QMLIWQGpo4roPFA3GAhfrSO9zEDO3SCIiCaN0jEts6jzrpaAL197LKU35P4IaIYEUJmeoNYQqGw9LQNF8t0ihbyIq+Pdsz0BqlKp0JQ2z0UJlYrtgkkfuSJ7ngf/TgF0b1BrI0nhcLMw6j3h7w+zsJuITGzRRDLlw8hVoIWCRbCwg3iR624iaYTIc9GiYogPQsrKq26zBIzVmRltSR93u4gg9zYG6N1QiTxQ8KHHSKsBp+QjuOGpFZCHiCNpOv4nTzg2YmeHT0YCYmZAauOiHcojEiPUqdD4sf5iBYSk8OuA6JJAMWRTlzwFj/pvD9WiB8SPalxy7ggq3f+djJGQl8kftyDXUSQHSHrphI3iR8SPeqww7ggnjjNGNoBtTO9zBDaNNDZPOwgguzoBSIB9CVKAsNqIWCW+OEhej5ruNAR43/kcIMIIi8Qe/R4f8jr427sIILSIVq9IcvGAN7eH97w8vY4WfjE42YRRF4gcyDxQwBibY6cyhtpt4HSJIBgzPtj59AXzzCXW8RPFDeIIDlIBGlD67Mg8UMkI4oIAnTsKC9QHaJp8Ao4Xfzwwirxo2d6ejysdojXgxnT5I1OkTdrKr8daN+1A5n5g+CZegE83p57qqb+qbl/8W/RJH4IOXgveaGlvfOaJm9k6RE1uF4Aieqp4JUvJwgfXo1B6bxmiCM7iCA51BjA8M7dyKisYH5tK2j505sAgIw/BpB/+/XImTiG+TVI/GiH5b2wQ5lFFEF5u9rSL56oMd+8hJDlIbBHH30UHo8n4TNy5EjFY9566y2MHDkSWVlZGDt2LNasWcM8X1Z7f1jDe1YXT/GT1eBL+FhBch5SfVggemdmNH/hnbsNHR9FFLsRPtWG4y+8ijNbatOm1dKRkPhRJjrLLfljt2uwQLRwGM+QOOsXUSGe5ujRo3H06NHY55NPPpFNu379etx222248847sX37dsyYMQMzZsxAbW16A5RMbWNJyu+dttgh71lsPMSP1YLHSuzaqaUzfFHvDysRZJXdSMXJV9+HFDFXKNi1nuhBJBEiUl6iiCSC1CJC3RLi6fXp0wdFRUWxz6BBg2TT/vznP8f06dPxwAMPoKKiAo8//jguu+wy/PKXv2SSFzPEj5L3x07ih/UUdzeLnmRE7txY5I2FCBLJboRPtuFsU4Ps7yIYe61YnWfRRIYcdsmnUUQpX6iMnRdIiBLt3bsXJSUlKC8vx6xZs9DU1CSbdsOGDZg2bVrCd9XV1diwYYPsMaFQCMFgMOFjFWaFvuwU8iLRYx3Md2vW4P4O19YZuhZvuwFosx3d7ebZFTMWObQCu4sJK/Mv0jpBakmXZ979guW1rKqqCqtWrUJNTQ1eeukl7N+/H1dccQXa29tTpm9ubsbgwYMTvhs8eDCam5tlr7F8+XIEAoHYp7S0NGU6p4S+7BLyIuGjjB5jEx2EqOkYHYZTryFkNQjaDLsBqLcdANAn16+9IDqwqziQw+6iRw4rymVHEWQllpfkmmuuwcyZM1FZWYnq6mqsWbMGra2tePPNN5ldY/HixWhra4t9Dh48yOzcWjAj9GUn8UOkR6+xsXJBMjPWBTLDbgDqbUcffx6yh5Sn/I1lpyRyaFQrThQ9cphZVruJICtDrcLVvry8PFxyySWor69P+XtRURFaWloSvmtpaUFRUZHsOX0+H/x+f8InGau9Pyywg/ghr4929C5KpkUEmekF4gEPuwGosx0AUDh9Rmw9IF44Rfy4SfgkY7fNkJVwwjMUrgSnT5/Gvn37UFxcnPL3yZMnY926dQnfrV27FpMnT9Z9TScMfBZd/JDwMYYZniCmngoFLxCPtYCssBsAkJEfQMnNc5FbUZnyd1b31AmdppuFTzJ0L9TDs9+w/An8x3/8Bz7++GM0NjZi/fr1uPHGG5GRkYHbbrsNADB79mwsXrw4lv7ee+9FTU0NnnnmGezZswePPvootmzZgnvuuceqIqSF98BnO4gfwjyM7M+jeZsGi4y41XYj/7vfwuAH5+GCZ34kK36IHqizl4fnvbGTF8iqMJjltfLQoUO47bbbMGLECNx8880YOHAgPv30UxQUFAAAmpqacPTo0Vj6KVOm4LXXXsMrr7yCcePG4Q9/+APeffddjBmjbyVWq0NfRsUFiR/3YMTYWDEmiOdYIKvtRr9JY5FVUa4Y9nK794eEj3pIBFmDR5IkyepMmE0wGOyZ0fHyUowf/YViWp4rPpP4IfSg1qilEj28VhiWy5OS8Ap9thMf4T20tbXJjq0RjXjb4c3OUqzjLDofO4sfQh/Ml6aw0eBrpbxGV4GOnO3EwbuXMbEbrq6lY8qOKP4u8sBnEj+EHtR6gqxeBI+wp4ggr49xWN9DasvyUE3lCC/vD4kfd6PWOMp5e3iEw+TyZMaUeBGxS6djtyn6bsJuIsiOz99+OTYJUb0/oosfwhzsGhqJJ2PMCK7n5w1PoW+n50teH37Y7b4aza/ZLw59TL0aAUC/yHCK+FG7oy95kviRt6tNlXcmb2/EsFFrHR2wdGFGu2GnTs9OebUr0XtsVBywaMtWktXgY74bPAmgFPBe90cPdhI/rCqp0nmsFEfJhsgqo9J6sTetUVQSH2pFEMv8uAE73AO7DNAmzmOX9mWXfAIUAjMdEUNMIuYpHZ3loYSPleTtjdimwevByWUTDbWiwur6T+LHGqwIMempZ4aW7DDR3lAtTsKN3h8noNRApUgEZxrrEfzHNpxprIcUMdbA5Bq3FUJIjaFR8vKYFZpy2mBoX6N1HkijgsdoHSXxYy1W3n8RXjhZQiEwwbFT6EsE4htnVoMP7bt34ljNO+gOnu/o+/gDKJx+I7cVfKMdjFmGyqjLWU0oTO34ATu5v3lgpOxq7q+VnQ8JH3Ew0s5YjAXqLA+lHYbAwxZkNfhwpriT2fmoRsch2p5fJH6McfLkVhx5c1WC+AGA7mAbjry5Cu27d+o+txoDIlJozGkeGKdhlvjRWx9J/IiHk5+JWXbTuXeQcDVSJIKTq99XTHOs5l1D4TC1BsgOa3CoCYUZDp2QCNONlRMCnNzR2h3dmyQzsElqBLnodUfs3JmIaHt+kffHGKG6RoRPKXfq3cFWnG1qMCU/IniCzBIgohs90RD5fomcN6IH3s9ISWDbfTwQ1W6GsBr8TOLHOOHWoKp03e3q0smhxfjwFkF28AI5GV73hlUnozV/JH7sg55nxaq+pqufrOsRywkIVMMJR5KRp26TvD65xjfhFEkEGYX3rDAKgyWSru5Y9YZN4sd+iPzMrBRoSoh7x0xEpPAXeX/Y4BtRhowByp1tH38esoeUm5Sj81g5OJqFAFGTd5GNsRsRXXgTbLCq3dk1FEZWihGs1/7hgVvEDwB4vF7k3369YpoBc66Dx8umCYjS4bPIB21bYQ4svD88BkCLUpcJfVjlkTY7FMYC8XJkIlI4gi+2H8b6P53A5xuDiIQlS/NDCx6yJWfiGBQsmNXLE5SRH0DBglnImTiG6ZuL1gZu57dyQ+vdODAMxvpZUuiLMALr5+jUfRldvRDih7N/j9CJM1j/5d/5RZmYvWQILq/OZ3YNUbwuouTDbHImjkH2ZaN6ZoW1BpGR54dvRBkzz49ReGxQmG4BMjWbk7LeJ4xIREShIWKeCP1YsShpugUSteaJ9waurq7xoRNnEv4+2XIOzy+ox+a/nNR0HhbhL/L+8MPj9SKrohz9Jl+KrIpyruJH1MF+PEi7ESt1qLpg7f2hMVvuhZ6rMpbfneXLl+Pyyy9Hbm4uCgsLMWPGDNTV1Skes2rVKng8noRPVlaW8cx8GQH73ZNNlofDWOJW749VuEkEWYVQdkMjonVKouWHMBenhG/1YHnN//jjjzF//nx8+umnWLt2Lbq6uvCNb3wDHR0disf5/X4cPXo09jlw4ACbDEnAyaPnsGdLu+FTqRUe5P0hALaGKF2npia8xXMwtNHwmnB2gxFaOg+njssg2GKFwFWqxyIJbsvHANXU1CT8vWrVKhQWFmLr1q342te+Jnucx+NBUVGRqmuEQiGEQucfSDCYfvG71mNdqs4t+uwv8v7Iw7MDccumoLxj9HKYYTcAfbZDCZGMPyBefgg+sLBHWQ0+W3l31CBc7W9r63nrzM9XHoh8+vRpDB06FKWlpfjmN7+JXbt2yaZdvnw5AoFA7FNaWpo2H3mFfbVlXCfk/eFDVoMv7UdERBNNRr1AZnWwPOwGoM52mLWiLmtI/LgLs5+3HbxAYuTiSyKRCO677z78y7/8C8aMGSObbsSIEfjtb3+L9957D6tXr0YkEsGUKVNw6FBqMbF48WK0tbXFPgcPHpTPhAfIL87EyIm5hspCnhdzEVHcWLlRoZrrs5rlZbVo42U3AI22Q0Dkno3RDqizPOQ4b4DouPWe87QvlofA4pk/fz5qa2vxySefKKabPHkyJk+eHPt7ypQpqKiowK9+9Ss8/vjjvdL7fD74fCo6RU/PP7P/cwi8GR5NedcDrfpsDFGEDqGP1go/UGv8PLzsBqDBdqhASXTYpWOzSz6dRvx9j/5fj/0TKTQvQl6E8QDdc889eP/99/Hhhx/iwgu1ddx9+/bF+PHjUV9fr+m4AUlhrvyiTNz3wkWq1wESffyPUxHJy5MOq71ALBB5ZWgr7IYIGKn/eutkKvFDgsg69N57M8NPotcPyz1AkiRhwYIFeOedd/DRRx9h2LBhms8RDofxj3/8A9dee62m4366ZiwO7DmD1mNdyCvsi5ETc03x/JiBE70/dhE9ImHWW5bZg6GttBt6EMX7w1L8ENaTbuFBOUTwvoiA5R6g+fPnY/Xq1XjttdeQm5uL5uZmNDc34+zZs7E0s2fPxuLFi2N/P/bYY/jf//1fNDQ0YNu2bbj99ttx4MABfO9739N0bY/Xg1FVfky5YSBGVfmZiR814oMGP2vDjeLHDANl19WerbQbAHD2QAOkiL06EBI/zsRMT5AeOyzyYGjLPUAvvfQSAGDq1KkJ369cuRJz584FADQ1NcEbt3rvqVOnMG/ePDQ3N2PAgAGYMGEC1q9fj1GjRmm6dt2WIM6ejjjO++M07C5+nPC2ZWRrDB7lt9JuAMDh3/8/9PEHUDj9RuT1kR94bTXRe0/ix9mQJ0gfHkmSnLPksUqCwSACgURjrnUfMKXxP1Z7gJwU/rK7+IliaPNQg29J6a6tdoxPOgGklM9Ueeju6sTWt5agra0Nfr9fVR6sJpXtuPiKOcgvHSt7DI/wlxntQm3enNJGRUbNszDrOeips+nypmVbnXCoE/UrHmJiNywPgYmC3n3A9EDhL3U4ybBa6eo169pufZM8sPU924XDCOchsrdO1LyRAIri0H3ACH1IkQjONNYj+I9tONNYb2kH51ZhYRfOnWlF+/EGq7PBFPL+2BNRhYaoWD4GSCji9gEbVaXPtZYcfpIiEYTqGhFuDSIjzw/fiDIGGXU+VhrW9t07cazmHXQHz4eGouM9cisqLcsXL1pHB1SFwYyMA3I6586m3jtQlNlfWhA1X4RzsWosEgmgFKjdBywdZ7bU4uTq9xE+db5zyRgQQOGiySi5cjiTayTjpPE/VtC+eyeOvLmq1/fdwTYceXMVSm6eq1sEGWnkVu25RagjM9vYyvEEwQq9A6J5I2K+yKKmgMU+YGe21OL4C68miB8ACJ9qw5YlNTjy8T7D1yDYIkUiOFbzjmKaYzXv0ngPBdwYrsvMyUNuQbnV2WACeX+cAT1HdZAAikflPmDpVoCWIhGcXP2+YpraX3wCKey+ziIdUiSCzt0NONGy0fSxN2ebGhLCXqnoDrbibJP9xntY7T2y+vo8GTrhm/B4e5ePV/hLtLdogj9ueOZW2AgKgUVhuA9YqK6xl+cnmc5jp3Fi51EMGn+BoWs5iVQhQzPH3nS3B5mmI5xNH38eCqfPQL7A6wBpgbwGzkLEkJNoeXLua5lG+gf6aNoHTIlwq7oOsvNEh+FrxWPn8T9yIcPo2Jv23Tu556FPrrqB72rTpcLIW44I4SWR9wUzkwtu+x7K713iyEHxBOEWSAB9yenWbmbnyshT10FmDezH7Jp2Rk3IsOWDd3C27KxiGqNkDylHH7/yLKc+/jxkD3HGeI94aHaXNrKHlqcMe0WxW8iPvD+EG7FXK+WJh90aQL4RZcgYoNyhZBX2x8DKYsPXcgJqQobhk20I1TWiszzEzVh7vF4UTr9RMU3h9BmKHR9BpIPEBmEWdqtrZr84kCWPErcGkF6iISiP14v8269XTDvmh1+FJ4NuP6A+ZBifjlfDzq2oRMnNc3t5gvr48wxNgXcTIoTqCIIQE5FEGQ2CToLVGkA5E8egYMGsXoN6swr7Y8wPv8ptHSA7ojZkqDadUXIrKtF/xJieWWHtQfTJ9SN7iHLIwyxoPSDxsdvzEalDSoVc/qwYTNtZHuq1uG2g7wjutsHIMxJt4LFIkABKgsUaQFFyJo5B9mWjYo3lklEhDKwsJs9PEtGQoVIYLCM/0GsVbZ4N2+P1IqfsIi7ntgq37/xM2A+ljt/sjr2zPJRypuoXAwLIv/165OdPMC0vTibZTvF8oaCeOIrKNYA0n9brRVZFOfpNvhSDxl9A4icFakKG+bOuT/mWJfrbayrs5iEgxMKOdV4PIpUzKn7kFrc9/sKrOHlyq0W5I/RClhhgugaQVdh5CjxwPmSYPHg8Iz+AggWzkDPRGWutEM4jXtCmE7cideqA8fzwKo/a85p1P9XMVD356vvcZ6rqRbR6lw6zXhIpBAYgvygTs/9ziKo1gNKtAk3oJzlkGN08VoSxNwRBmINonXVneQih3epnqnoqymnMTRpEGZfkagE07yfDUDjEh5ETc23r+XEa0ZAhQdgJCmuyQY/4MaMz1TNTlTCGGW1KiFb74osvoqysDFlZWaiqqsKmTZsU07/11lsYOXIksrKyMHbsWKxZs0bXdSdNz8eoKj+JH4LQgEirQVtlO5yAaJ4WI/AOxWmdqeqke+tkLBdAb7zxBhYuXIilS5di27ZtGDduHKqrq3Hs2LGU6devX4/bbrsNd955J7Zv344ZM2ZgxowZqK2tNTnnBEFYCdkOZyGaaIjPj5rFbZNnqopcHqIHywXQs88+i3nz5uGOO+7AqFGj8PLLLyMnJwe//e1vU6b/+c9/junTp+OBBx5ARUUFHn/8cVx22WX45S9/aXLOCREQIY5sNjSVvQeyHUQ8LDv45HMZmanKOi8EOywVQOfOncPWrVsxbdq02HderxfTpk3Dhg0bUh6zYcOGhPQAUF1dLZseAEKhEILBYMKHIAj7Ykfb4dSOjEW5WN2bdFvlRH9PlyYVemaqOvWZOwVLB0F/8cUXCIfDGDx4cML3gwcPxp49e1Ie09zcnDJ9c3Oz7HWWL1+OZcuWGc8wQRBCQLaDUCIqPKIe4lRCRE2aZNw+U9Vpgs4VT23x4sVoa2uLfQ4ePGh1lghCNzTjyDxEtR1O6oh4lkXN5slaN1iOX9w2q4LvFjlOes4iYqkHaNCgQcjIyEBLS0vC9y0tLSgqKkp5TFFRkab0AODz+eDzuW+siBsQZT0JwlzIdhiDdcdK7VAeujepEeG+WPoqmZmZiQkTJmDdunWx7yKRCNatW4fJkyenPGby5MkJ6QFg7dq1sukJgmBL62jl2TBmQLaDIAijWL4Q4sKFCzFnzhxMnDgRkyZNwvPPP4+Ojg7ccccdAIDZs2fjggsuwPLlywEA9957L6688ko888wzuO666/D6669jy5YteOWVV6wsBkEQJkO2g3AyPMJfInhdRMJyAXTLLbfg+PHjeOSRR9Dc3IxLL70UNTU1scGKTU1N8MbFWKdMmYLXXnsNS5YswUMPPYSLL74Y7777LsaMMWevqFuKNtN2GAQhAHazHU5HT+dKY1zsgxOflUeSJMnqTJhNMBhEIBDAr7dNQE5uhubj5QRQug1Jx5Uf0nwtLdh9Q1S92PGNxshaPkYGQStdV+0Kz2pCYKnymHzt7q5ObH1rCdra2uD3q1tp12qitmPwDTcjM38QsoeoHwTLowPRW/d5dWYkgFKj5zmJ8oyiiFJ/w6FO1K94iIndsNwDRBBGcJv4IcSg5U9vAgD6+AMonH4jcisqLc6RGFCIJTVa74tbhKHV0HxagnAJJLzY0x1sw5E3V6F9906rs0I4BBI/5kECiLAt9KZJiMKxmnchRUhgAtSBOxGnPlMSQAzhPcaHOI9bxY8dFkG0Qx5Z0x1sxdmmBtOvK2rHJGq+RIfum7nQGCAT+azhQhJJBnGr8CHEp7vd+B6D8fbBrZMaCPtit/rrvlc1wpZkNfhI/HBE7QwwQp4+ufaYyWYW5M3Qhqj3S22+7PhyTwLIQdixAqrBScLH7gORRVgFWkT6+POQPaTc0DmS26/a9ixqxwmInTeRoPtkDSSACGEhr08ibhxbYxcKp8/gsimmE15q5Dp36vR7cMJ9SFVPedXdUBm7+0UWVQe3FG22OguOh4SPs7C750uOPv48lNw8l9YBSoMTOnmjpLoHot8X0fNnFBoEbTI0EJqwArMEiFu8VHpWgtbLuPJDaQeU2mEBwmhnKno+zcBJwsLO/RkJIIehxlgS1mDV9hcEe3JHX4oMX5bV2bAlTur8teLmsosIWVXG2FkNiwQZCvNguQcYYR3UZgiWqKlPZvd3rOs4CSCCEBzy/ogHy4GYaqAXK4IFFH5MhCyrBVCISh1OeqO1chAwjf9xD05qM4R1mOH9EUGMkcXSicgzwZz0tuh2g06igojCsl2L0PkQhBZ49AVkXQnhcbsIEgEj43+cOgVe1Hopar4IeyDi2B9ekADigJrKQWEwbdjZqOsVAGZ4f2gLDPNg4XVxSsdDECJgmQBqbGzEnXfeiWHDhiE7OxvDhw/H0qVLce7cOcXjpk6dCo/Hk/C5++67Tcq1fXCioewsD9laCFmBE8f/kO1ID7UTQg+ien941WfL1gHas2cPIpEIfvWrX+Giiy5CbW0t5s2bh46ODvzsZz9TPHbevHl47LHHYn/n5OTwzm5KbinajDeaL7fk2m7GTguqiez9UYto099Fsh1WLEBIa30RPHCjaLZMAE2fPh3Tp0+P/V1eXo66ujq89NJLaY1YTk4OioqKeGeRO7xXhXa6oYxvsHYQQ0QiebuDuo4j26EOO6wOTZiH2SFYO/Q94rxmAmhra0N+fn7adK+++ioGDRqEMWPGYPHixThz5oxi+lAohGAwmPDhjRNDUCITDY+JFCYT3fvDavyPCN4qJ9kONThhp3hCHESuJzzzJsxWGPX19XjhhRfSvsH9+7//O4YOHYqSkhLs3LkTP/7xj1FXV4e3335b9pjly5dj2bJlrLPMBNobjA/pGo2ob8YsxQSL8T9Gw19mjEGy2naw8rRotQVGPbxZDT6hOz7CHNTWASf2U8wF0IMPPoif/vSniml2796NkSNHxv4+fPgwpk+fjpkzZ2LevHmKx951112x/48dOxbFxcW4+uqrsW/fPgwfPjzlMYsXL8bChQtjfweDQZSWlqopTlpEHwfk9DCYXniHz5w69ZsnbrMdZsErFJbcIZKdYY9T77EoL6DMBdCiRYswd+5cxTTl5eWx/x85cgRXXXUVpkyZgldeeUXz9aqqqgD0vAXKGTGfzwefr/cNf/vYZbg99zPN11SLWvFBXiACMNf7oyb8pcb7ozfPebva0J30nZ1sRzJqRQZrr4taG2PGeKCoDXNKJ20lZvUHont/eHsomQuggoICFBQUqEp7+PBhXHXVVZgwYQJWrlwJr1e7Md2xYwcAoLi4WPOxboG8QOaix/sjwjgaqyHbwRezBkWTvdGPmUJDdPFjBpZZ3cOHD2Pq1KkYMmQIfvazn+H48eNobm5Gc3NzQpqRI0di06ZNAIB9+/bh8ccfx9atW9HY2Ig//vGPmD17Nr72ta+hsrLSqqIwgQyGM3CK+JGkCIIt9fiicTuCLfWQIr3LlS7fvMKAZDsS0dJBmTXmZ1z5IUd3nDwQUfzohUV/JpfHMWVHDJ87imWDoNeuXYv6+nrU19fjwgsTb5YkSQCArq4u1NXVxWZqZGZm4oMPPsDzzz+Pjo4OlJaW4qabbsKSJUt05+ON5ssN7+sl+jgggN7K3ITR8FdL6258fnwtzu08ny4zJ4ChE2Ygv3QskzwaQRTbkYxdpp2bmU+yO+oQVfw4XcR6pKjFcBHBYBCBQADX1MxD336ZTDY2VRJAWgwAzwpHhqg3LDsCUbw/RgRQS+tu7DjwB9nfL75iDvJLx6rKt1w+otfvDoewrvYptLW1we/3pz2fCERtR+nLS+HNzur1u5r6lK4D0msDzG7fWvNJ9qc3oq+rw6MuGm0jowY34M/Tf83Ebojne3coUiSCzt0N6NiwA527G1KGFHjjdDWvFTeKHyUkKYLdR/6imObA1vcsqbuiIeeGV/N2zcv7Ynb71tohk/1JRPT7IWL+WOdJmHWArIR3GKzg4MfY/swGhE+df/POGBBA/u3XI2fimIS0tDq0OVgdqrBq3I+S9+dURxNCXe2Kx58704r24w3AiEuUr0PLABApoJliYgqLZIzk0eizNXNtKvIAcebIx/uwZUlNgvgBgPCpNhx/4VWc2VJrUc74I4LXKxWsxY+Wzr71Yq+Qg54BINR1WlW61oC6dKlww+7zRg24kQ5EdC9QFDcOkjZSZjcLxig86gt5gBiS7AWSwhHU/vz/FI85+er7yL5sFDxx03id4AU6s6UWJ1e/r8rrZSZWix+eGPW6+Pr2V5WuT649xuvwRlRvqqj5SoWd8qoXuwk9K/Nr9srkYr6KWgCPWVwndh5F5/EOxTThk20I1TX2+p63UeBZyc9sqcXxF14VyuuV1eCzNOwlgtcnnffFc/lYZOYoL37Yx5+H7CHlimko/GX93kpmdmJGbZVTvUGsymWmQOT9HPTaYF75st4qO5jOE8riJ0q4VYwNFlkgRSI4ufp9xTQnX33f1HAYL+GjtqM3Q/wYFR2towPweL0YOmGGYrrC6TMSvJVacGL4S8kwK4kgq8egiYhThBDLcthN/BjJrxUvDSSA4mDhBYofTJ01sJ+qYzLyUocU7OgFCtU19vL8JCPn9WINT6+PGsEh0ngfteIjv3QsLr5iTi9PUGZOHkpunovcCnsvGmgXWLR9O3mB4okKCLuJITvm2Q7wvKc0BogjAyuLkVXQTzEMlpEfgG9EmezvdhsPpNabxdPrZfXbtdmih4X3J5780rEYcMFotB9vwLmz7cjMzkV4ykWqPD9uDH8ptSGrF0c0c4wND1sVfz4Rxwo5Zd02q4WbVSFjMV5PBYKlF8iT4cWYe69QTJs/6/q0HYuIDV8OOW+W3nRqiXp7zOhs5Dp5kTw+8Sh5f+Q2PPV4vfAPvgiDysbDP1id+NGbB4IvVndurBDFM2RGPuwoftLlWc42K4kf3s9aPGvtMEquHI6JT0xHVkFiOCwjP4CCBbMsnREVhWUl840oQ8YA5YG06bxeaogXPCJ4fCxb18cEj4uIok409IwFSldv7fTiE8WsPMeLEN6dpJnXMhunlUcrFAIzgZIrh6P4q8N6ZoWd6MDhUBl8I8o0vVXbJRTm8Xox+Os34sibq2TTqPF6RbFa3KRCFEHAOvRlFDeGv9RCoTC+yF1PbZlFEgJmikhW6M2zld4fgARQSnisDO3J8GLQ+AsAABcC+KxBeydqFxGUW1GJkpvn4ljNO+gOng999PHnoXD6DOTmVwINhi9DqEAu9KRW/LAQe24Jf/EQGSzbvNNFUCpEyIMW7Oj1U0Mq8W/1UhEACSDbYScR1H/EGJxtakB3exB9cv3IHlJueCwJ0QOFvsRE64DorAafqR2BG0WQXbDjuB+98NoQWCtk4WRgPSU+GSv3WkmH0coXrdwerxc5ZRfBP/Yy5JQZH0hL9KBW/Bj1/mgh3c7vqQjX1jHPh+joETus27tdp8c7GTuLHzs/Y1f3SLWNJYq/81gdmhV2EUGEWJgZ+nIr6dqOCG2DRJA42Fn86EEU7w/gcgFkBry8QACJIDdixPvDS/zoCceFd+7WfIyTEKFtkAiyHruLH635ZyF+vlW4TdM1lXC9AEr3AHl7gUgEEWoxMu6HR9grHU4e/JzOCKtpN1raBq92TiLIOuwuftQSHffGoi8wOjkpGUsFUFlZGTweT8JnxYoVisd0dnZi/vz5GDhwIPr374+bbroJLS0tJuVYH6wfWjIkgoh4koWHFvFjhveHBSLYjnTtmrUI4gWJIHP5rOFCR4gftWXoLA+pqufp8smjH7XcA/TYY4/h6NGjsc+CBQsU099///3405/+hLfeegsff/wxjhw5gm9961uG8mCGF4hnKAywhwgSwdjbFb1Cg6f4UUJx8DOj8JcItsNMeLZxs0WQW4WQ2eUWYcwPC3g5ESwXQLm5uSgqKop9+vWT30C0ra0Nv/nNb/Dss8/iX//1XzFhwgSsXLkS69evx6effmooH05okKKLIECMN167oUX8xAsP3uLH6oUPRbAdLLxAomB2Xp1gc7VgttfHTlt1WNVOLBdAK1aswMCBAzF+/Hg8/fTT6O7ulk27detWdHV1Ydq0abHvRo4ciSFDhmDDhg2yx4VCIQSDwYSPVuzgBQLsI4JICKnDDM8Pa8wa+yOK7TBTBJnRvskbxBanhLx4YUXoK4qlAuiHP/whXn/9dXz44Yf4/ve/j5/85Cf40Y9+JJu+ubkZmZmZyMvLS/h+8ODBaG5ulj1u+fLlCAQCsU9paWnKdHYfEB3FDiIIICHEg9bRAc3ix0zvD6vwl2i2g/c4v3jM6EzJG2QcK8SdGc/NzDLxblfMBdCDDz7Ya3Bi8mfPnj0AgIULF2Lq1KmorKzE3XffjWeeeQYvvPACQiG2neLixYvR1tYW+xw8eJDp+bVglqHk3fhYNjQSQqmx42rPWrw/GWNGJPztZNtht7dywBoR5BQhZEU5nFbHzOgrmW+FsWjRIsydO1cxTXl5ecrvq6qq0N3djcbGRowYMaLX70VFRTh37hxaW1sT3uRaWlpQVFQkez2fzwefT91GhOmWb2exT5gSrJeq57kcffS8rPIbL4JE3ATVTEQWPyzyllFZge5wolixu+1I3v8vGZZt26xtJszcOiNK9Hp269CtEm92ncUnwvNlLoAKCgpQUFCg69gdO3bA6/WisLAw5e8TJkxA3759sW7dOtx0000AgLq6OjQ1NWHy5Mmarzem7Ag+b+ltUHmLIDMNJWCf/cPicbMYEln8KJHO+xMNf2VUVqT83U62Q450bdsOSOEITuw8is4THcga2A+VlRF4MryWCSFAjM5SDiu9Vk4VP2ZFSizbDHXDhg3YuHEjrrrqKuTm5mLDhg24//77cfvtt2PAgAEAgMOHD+Pqq6/G7373O0yaNAmBQAB33nknFi5ciPz8fPj9fixYsACTJ0/GV77yFauKoguzDSXvtyqeb4rJ4TErBJGaEB2LfIkufozmT078aEF026HUtkX3Ah35eB9qf/5/6DzeEfsuq6Afxtx7BcZdaV1nL5pXSIRQnSj3gjVmjqezTAD5fD68/vrrePTRRxEKhTBs2DDcf//9WLhwYSxNV1cX6urqcObMmdh3zz33HLxeL2666SaEQiFUV1fjv/7rv3TnQ84gOS0UFoV3SMwMw5BKjLAQH1aPQxJd/CihZuwPC/EDiGM7lLCjCDry8T5sWVLT6/vO4x3YsqQGE5+YjnFXnr+uFSRf164eECNYIXzM8v6YKX4AwCNJkmTqFQUgGAwiEAjgmpp56NsvU/Hh8nbVpfMC2WUgczKiGAszMSLC7CB+lPKoZ+p7dziEdbVPoa2tDX6/30jWTCNqO369bQJycjMU05rVtlm0YykcwdqZv0vw/CSTVdgfX3/zO/Bk9NQjEdu4XWbS6sEqj49o4mf1vnH48/RfM7EblnmARELpjcxp44Hi4elWtmLwpJU4Xfwo4eQ9v4xgVpibhRfoxM6jiuIHADqPncaJnUcxaPwFANhPgmCBSHlhiRPCXSKWwfKFEAnr4TX9VMQKzwM3iB+5fJL4UcaMxU8B4x1/5wll8aOUzuzFE92E1ffWLEGp1onA+oWCBNCXKFUy3gskirKcPg8h5HTD6Gbx42bePnaZ6rRmiSAj+PKyDaezurN2EiLcS9FCXzwgAaQSt4gggL0Qsroh88IN4kcJ8v6oR/htcDweZumc2t7NQAThwxpRxQ9AAiiBdBXPTSIIOC+EnBpXN4JbxA95f+Rh6Y63WgSFTp1Jn0hDumhH7rTOnBcs79UtRZsNCwszBuhrzSOP8XQkgDTilP3CtBIvhtwuiPSKn7y9EceIH/L+aMesFxw97TNrYD+m6eIhISQPq3sTFT0sPCoiih9ekABKgkVlNCKC1FQMEYxJsiBSmkXnFMGU1eAzJH7MgHfYC2Anflor7DH1XQmtbV1UETSwshj5RZmAQoQrq7A/BlYW684TCaHzsBY+rBBpaYZ4eDkWSADpQE0lcYMISkatKLIjooe8ALbih3eete5Y7yREefuNx5PhRfk9//rlH8k/9nzufqQIt16w1fC13BoeY1lu1sIHME/8iFT/XS2AvlW4LeX3aiqoCCKI4I9Rr49ZIS+zxA+Fvnqjp52LOCi65MrhmPj4dOQPzkz4Pr8oE/e9cBEur84HwLbzdboYYl0+tffeqv3oeIgfnmWhhRANoGYBMiMLJVq5SKLbMbq1hh29PulgFvpysfcnHjO2y9C6SGLJlcNx77dPYM+WdrQe60JeYV+MnJgLb0bv2FjUrrHqoOLzaVe7xkvIaelD9DwPFvfbTp6fKK72AAHyD0VtRebtCUqHU9+crMKIxyeKncUPhb70obeNi+gJeuv4JIyq8mPKDQMxqsqfUvzEwyMcE+85EdnG8c6n1nvrNPHD25PlegEEiC2C7DoeyG6wEj52DHlF4R36cqr4MYqIIkiNvYqEJXy+MYj1fzqBzzcGMbNgE7e3/GShYYUwMvP6PERlKtwsfgAKgTGDZzhMzZ5CIu7LYwdY7CJv5lo53HZzp3E/huEV7mbVtrWGw5TKs/kvJ/G7J5pwsvlc7Lv8okzMXjIEt1SfP0aEZUHU3DcRXiJ5b6ydjMjiR4naxhJm53L1bvDJOzrLVSAtFUVNQ+KtiPVW7Hgx0Fke0nUOO8BC9ESxc7grHt57faXy/nR3dWLrW0tsuRv8NTXz0LdfZq/feXdiZnRaySSXafNfTuL5BfVAcs/xZaQsfsB0FKsG5YoOC4HgtLAXoNwXR8524uDdy5jYDQqBxWE0FAZYHw4D9L/NxIueaEgo/mNnWJfD7uGueGjcj3bk3kLtsBq8kXBYJCzhd0809RY/QOy73z3ZhEg4MQHLhfqcAKt74UTxYyYkgDhgZxGkhJ1EEa98miV8AJMWNaRxP8xxqgh6o/ly7NnSnhD26oUEnDx6Dnu2tMsmcasYYl1up4ofFpEYtZAASoKFFwiwrwjSGvoSQRSZkQezhY8bxE/bcHubH57j7UQUQQDw572lqtK1HutSlS5eFDhNEPEsm9vEDy8ss0AfffQRPB5Pys/mzfI3cerUqb3S33333UzzZrYIYr2QWjxmiKBkUgkSpY/Rc/DGacIHsF78GCmnSLZDro2zMOQiiiC1e4HlFfbVkx1bCyKz8u5U8aMEr5cNy2aBTZkyBUePHk347uGHH8a6deswceJExWPnzZuHxx57LPZ3Tk6Orjx8u/8prMEgXceqRe3MCyMzSNKhZxZJVASZITBEDaU5KdQVj53FDyCG7VADizZtxmKoWmaHDawsRlZBP3Qe70idwNMzG2zkxFxDeYqS6v6JMKDaCnEmqvAB+A7m5ulptUwAZWZmoqioKPZ3V1cX3nvvPSxYsAAej/LCWzk5OQnHGuHW/qfw+ukBvb6XMzx6DI4WERS9thrUTI+PR0vepUgEobpGdLQGkX12ILKHlMPjtXfIQg1OmNKuhJnlSwWLMotiO6JonV6uFTUiKJoPvUSPTVcOT4YXY+69AluW1KT4seef2f85JO3iiUZQso8sxZFIHihRxY+V45lYIMw0+P/5n//BzTffjAMHDuDCC+Uf3NSpU7Fr1y5IkoSioiLccMMNePjhhxXf5EKhEEKh82GdYDCI0tJSnPpnOfy5GSkFUBSWqjS+MknhCE7sPIrOEx3IGtgPAyuL4ck43znwXP0zXd7PbKnFydXvI3zqvDegjz+Awuk3IreiUtO17IJTvT3JWOn9SS57ONSJ+hUPGZ7OaoXtKH15KbzZWQlp5ToLMzsJs974j3y8D7U//78ET1B+cSZm/+eQXlPgCf3oFQZ2Ej+Atn6W5TR4YQTQtddeCwBYs2aNYrpXXnkFQ4cORUlJCXbu3Ikf//jHmDRpEt5++23ZYx599FEsW7as1/dRAQRAVgQpVUC9lazg4Me9jEdWQT+MufcKlFw5PCGt2krG6g3hzJZaHH/hVdljSm6e6ygR5BbhA4glfgB2AsgK26FFAAHmdBZRzBJBqV7iWOwWTxjziBh9/maFvKJodTIILYAefPBB/PSnP1VMs3v3bowcOTL296FDhzB06FC8+eabuOmmmzRd769//Suuvvpq1NfXY/jw4SnTpPMARdEqgvRUtHQCY+IT0y0TQVIkgsMLn0rw/CSTkR/A8PkP2z4cZtYaPqIgmvgBegsgO9mOVAII4O8FimKGN8hIWE+k8JHdENnrA5gn5s0QQMzHAC1atAhz585VTFNeXp7w98qVKzFw4ED827/9m+brVVVVAYCiEfP5fPD59A+0ZTUeSIpEcHL1+4ppan/xCYq/OiwhHKZ1bJAW4scPhOoaFcUPAIRPtqGtqw5ZFeXCDl5OB0/xI5LoiSKi+EmFE2yH3Hgg1pMc1G6PY8a4oFTwtFlOxUqvD2C++FHCrC2dmAuggoICFBQUqE4vSRJWrlyJ2bNno29f7VMnd+zYAQAoLi7WfGwycgOiATYiSI3A6Dx2Gid2HsWg8Rf0+i2dUdE6KDqeceWHsH5DUFXacGtPOjNnirGCtfgRUfDEYxfxA9jbdqjBKhEEGB8grdcbREIoPUYHANst5BXFillfyVhuvf/6179i//79+N73vtfrt8OHD2PkyJHYtGkTAGDfvn14/PHHsXXrVjQ2NuKPf/wjZs+eja997WuorGQzLuXW/qdkfzO6PlBUOKSj84TMFNMvia4dxHrk/CWj1K3/k5GX6HbsLA/ZZu8wI4Iluk5P/Edk7CR+9CCa7YiiZMBZt1m1a84YnaX2WcOFhjomHvbK7hi9J0afCSCe+DEby3eD/81vfoMpU6YkxPWjdHV1oa6uDmfOnAHQM/31gw8+wPPPP4+Ojg6UlpbipptuwpIlS8zOdi/UeIKShYMcahcbA9hWpLRrfKBnDJBvRFnK3+ziERJduBglnZfLCeIHENt2KHlNeKz5ZQdvEJBor9zoFWJlr80a32W2+DHT+wMINAvMTKI7OicPgo6Hx9R4tYOMr3373xPGAJnJkY/3pV7j40sKFsxCzsQxqs8nuhhyGrzFD8tFDlnNAjOTqO2QGwSdjFmDoqOo7WCt2FVeDjcIIRI+PbAQP7QbvAnwCIV5vF7k3369Ypr8Wddj54Eh6TPIiZIrh2PiE9ORVZDohcrID2gWP4C9wmNmEr0vyR8j2En8uB1eIQAtITGrw2JReIX0rYZlucwKdwFiix/WkAdIxgMUhYcnKNVCgxn5AeTPur6XwOC5wqwScgs1sqiobvQKqRU3eu+NHcWPGzxAgHnrAyWjpeO1csq8EnbyDvEQcGZ66njfa1aDnoVeB8gOsBJAgLFwWKiuEeHWIDLy/PCNKJNdW8cqEaQEK8XuVDGk15uj537YUfwAQP/Pz2DrW0scL4AA60QQ4AwhFEUkQcTbY2Xms7CyDpIAMpmoEfv1tgn4bnH6mVm8RJBWnCyEothVELEI87lJ/OTtjaC7q9M1AgiwjwgCxBdC8Yh274ziJOEDsA99jRrcgD9P/zUJIL3EC6Cc3AzF8T5RSAQpwzOGK5Io4jWeSWsZ1axnJLL4AeA6AQRYK4IAZwshu2P2vba6vundT7Or4xwzAWT5NHgReP30AFUiSAmWu8crYWR1Vp7E54e1GFISHTzEkdmDtkUTP+mED0ADnseUHcHnLeXpEyZh9vT4ZKLnVyuEjE6dF9VeiYIVs/HMCiPyED+sIQH0JelEkNIq0elgLYIAsQ0Li/VG1GL3GWZ2Ez9GhY9Zm8+aAY96boYIArSvGs9KCMWfy804WfgA9hA/AIXAkJNiELSSENIbCgP4CgLRjYpV0xxFhYfwAewlfuwcArumZh769suMfa+1fqdrr6J0VkpQeEwbVq29ZPbAcd7ih0JgnDESElN6s+LhCYoiskcI4Bsisxt28/oA5PlJh9a2nW5FZbM8QYD2sFgU8gqlx+oFJ50mflhDHiCFafByIkhNKMzqRZ/sYlDcJoa0iB8RvD4Am/E+qcrSf+cxrKt9yhEeoCh29gRFsXpXcsA+9isVItwDEesNq7ZxQ79PMe+yreQB4k1U6OjxBlnlCYpil7crt3iGRAt5mTnQ2enen3js7AmKotcjBLBrz3axXwBbu2W0rFatk2SW+LmlaDPOtGs6lSKu9gBdUzMPtw//TPVxUSGkZTC01Z6geEQ3JPE4RQzxEj6AvcVP3q42dIdDtvUARb3HrJa/ENETFEWUfazisdKWiVYOqxeHNFP8AMCZ9jAzD5DrBVDffpmWrodgVUdPYogvPBY1jKXjKHwAc8QPAEcIIIBd2xZZBAHsFwTk1a5Z2DaR8xbF6voAmC9+ABJAhpGL49tpOXCW2EUM2UUIieb1MVv4AOnFD+AcAQS4RwRFEXXfK9HgYVvtUge0PE8tC4SSADKI0kBGt4qgKKKLIRHukRw8hQ+gXfyoFT6A+eIHcJYAAtwnggBztokQuc1HoT3QEuElfgC2AogGQSfBe9Ch1YOj05F8fdEFkdXwDHUlHMNJ/LBe0Vmt+HEiSqvBA+o7Bd4Do5Mndehd4BVI7KB4iSE1Nsipa6yx6ovin7mR5221+GENeYBSTGUF7OcJinbEvFdGdsrgQ6Po3YLDqV4fQLv4CX22Ex/hPcd4gKKwattq2poeO2VkoVe1mL2BqJPgIXri0fuMRRA/t/Y/hWB7GAMuaWBiN7ht6PPkk09iypQpyMnJQV5eXso0TU1NuO6665CTk4PCwkI88MAD6O7uVjzvyZMnMWvWLPj9fuTl5eHOO+/E6dOndeWxtrFE9jfeDVipko8rP6RbaGQ1+BI+rPms4cKUH16YcQ0t6L2veXsj3MRP6+hA7KMq/cVey8VPeOdu2XPZwXZ8W0FEpGvbalFT5/XYKaUO8Nb+pwzviwj03IPoh0gPq/sVfX4snmE8LMVPuv5NSfywhlsI7Ny5c5g5cyYmT56M3/zmN71+D4fDuO6661BUVIT169fj6NGjmD17Nvr27Yuf/OQnsuedNWsWjh49irVr16Krqwt33HEH7rrrLrz22mu68mnl5oTp9uPREhLrLA+l7Jjjv+PpHRJFoPDAiJDUuwaOGvGjxdsDmL+BqR7xA9jHdigti5Eu1A2oazPpwmGAPjulZu/D+LRGSM4beYfYRxjUigOtz1IErw/AR/wAJoTAVq1ahfvuuw+tra0J3//5z3/G9ddfjyNHjmDw4MEAgJdffhk//vGPcfz4cWRm9g5N7d69G6NGjcLmzZsxceJEAEBNTQ2uvfZaHDp0CCUl8h6deKJu7NKXl8KbnQXA+lgkK7e5ls7a7huJ8sQK0QPwET4AX/Gj1/vTLXUphsBEth2n/lkOf1wITK5jYTVTRq3nSKut0tqxsAqRRXGLGOLRh/B+dqKKH5YhMMsGQW/YsAFjx46NGTAAqK6uxg9+8APs2rUL48ePT3lMXl5ezIABwLRp0+D1erFx40bceOONKa8VCoUQCp3v7Nvaegxz5Oz577bvGoQxZUdSHr963zgAwLcKt2kooTZu6Pcp3j52WcrfRg1uAKAcsotyprgTAOBrTN+B9016EQ+VuVsQxd+zMDo1Hx/Y1yMElAMx8uTtDsoe21oR19C71OetbfiXwofTow3si6TMs1xZwrV1sf93owsAoPUdTATbETydKPquxRf4Q4oO5oZ+nwKAYttW06637xoEALI2KsrqfeM02anftvfUK6WQXjzX4ovY/1OVVyvR+xOP3L2yE8nPgNXqxfHPKajynOefU1j1dXqewTnFND31Nr0titbZro7Uv3+rcJvs/fl2/1O9yhlteyx8N5YJoObm5gQDBiD2d3Nzs+wxhYWFCd/16dMH+fn5sscAwPLly7Fs2bJe3x++f0XC3wfT5PnPaX43zlbuVyBsSq3VGeDHiRMnEAio92qJYDuGXtaoOr89sGnb6WwUoM9OzdNxDD/sbwd59RXmPSd2z8BIv6pUXq12IxWaBNCDDz6In/70p4ppdu/ejZEjRxrKFGsWL16MhQsXxv5ubW3F0KFD0dTUZPgGikgwGERpaSkOHjxom9k1WnF6GZ1WvqVLl+L5559P+dvw4cMBkO0QAafVu2ScXj7A+WVsa2vDkCFDkJ+fb/hcmgTQokWLMHfuXMU05eXlqs5VVFSETZs2JXzX0tIS+03umGPHjiV8193djZMnT8oeAwA+nw8+X++QUCAQcGQFieL3+x1dPsD5ZXRK+R566CF8//vfT/ju9OnTuPzyy7F582b079+fbIdAOKXeyeH08gHOL6PXa3xcoyYBVFBQgIKCAsMXBYDJkyfjySefxLFjx2Ku6bVr18Lv92PUqFGyx7S2tmLr1q2YMGECAOCvf/0rIpEIqqqqmOSLIAj2pLIdwWAQAHDJJZdoMtRkOwiCYAG3qSFNTU3YsWMHmpqaEA6HsWPHDuzYsSO27sY3vvENjBo1Ct/5znfw2Wef4S9/+QuWLFmC+fPnx964Nm3ahJEjR+Lw4cMAgIqKCkyfPh3z5s3Dpk2b8Pe//x333HMPbr31VtWzOAiCEBuyHQRBmILEiTlz5kgAen0+/PDDWJrGxkbpmmuukbKzs6VBgwZJixYtkrq6umK/f/jhhxIAaf/+/bHvTpw4Id12221S//79Jb/fL91xxx1Se3u7prx1dnZKS5culTo7O40WU0icXj5Jcn4ZnV4+SZIvI9kO66Dy2R+nl5Fl+Vy5FQZBEARBEO7G3KVhCYIgCIIgBIAEEEEQBEEQroMEEEEQBEEQroMEEEEQBEEQroMEEEEQBEEQrsN1AujJJ5/ElClTkJOTg7y8vJRpmpqacN111yEnJweFhYV44IEH0N2td4tL6ykrK4PH40n4rFixIv2BgvLiiy+irKwMWVlZqKqq6rUqsJ159NFHez0r0baH0MLf/vY33HDDDSgpKYHH48G7776b8LskSXjkkUdQXFyM7OxsTJs2DXv37rUmswqQ3bC/3QCcazucZjcAc2yH6wTQuXPnMHPmTPzgBz9I+Xs4HMZ1112Hc+fOYf369fjv//5vrFq1Co888ojJOWXLY489hqNHj8Y+CxYssDpLunjjjTewcOFCLF26FNu2bcO4ceNQXV3da5sDOzN69OiEZ/XJJ59YnSXddHR0YNy4cXjxxRdT/v7UU0/hF7/4BV5++WVs3LgR/fr1Q3V1NTo71e94bwZkN+xtNwDn2w4n2Q3AJNtheCUhm7Jy5UopEAj0+n7NmjWS1+uVmpubY9+99NJLkt/vl0KhkIk5ZMfQoUOl5557zupsMGHSpEnS/PnzY3+Hw2GppKREWr58uYW5YsfSpUulcePGWZ0NLgCQ3nnnndjfkUhEKioqkp5++unYd62trZLP55N+//vfW5DD9JDdsC9Oth1OthuSxM92uM4DlI4NGzZg7NixGDx4cOy76upqBINB7Nq1y8KcGWPFihUYOHAgxo8fj6efftqWrvlz585h69atmDZtWuw7r9eLadOmYcOGDRbmjC179+5FSUkJysvLMWvWLDQ1NVmdJS7s378fzc3NCc8zEAigqqrKds+T7IbYuMF2uMVuAOxsh6bNUN1Ac3NzghEDEPu7ubnZiiwZ5oc//CEuu+wy5OfnY/369Vi8eDGOHj2KZ5991uqsaeKLL75AOBxO+Xz27NljUa7YUlVVhVWrVmHEiBE4evQoli1bhiuuuAK1tbXIzc21OntMibanVM/Tbm2N7IbYON12uMluAOxshyM8QA8++GCvAWDJHydU8ni0lHnhwoWYOnUqKisrcffdd+OZZ57BCy+8gFAoZHEpiGSuueYazJw5E5WVlaiursaaNWvQ2tqKN9980+qsOQ6yG2Q3nALZDX04wgO0aNEizJ07VzFNeXm5qnMVFRX1mhnQ0tIS+00UjJS5qqoK3d3daGxsxIgRIzjkjg+DBg1CRkZG7HlEaWlpEerZsCQvLw+XXHIJ6uvrrc4Kc6LPrKWlBcXFxbHvW1pacOmll3K/PtmN1DjNbgDusx1OthsAO9vhCAFUUFCAgoICJueaPHkynnzySRw7dgyFhYUAgLVr18Lv92PUqFFMrsECI2XesWMHvF5vrHx2ITMzExMmTMC6deswY8YMAEAkEsG6detwzz33WJs5Tpw+fRr79u3Dd77zHauzwpxhw4ahqKgI69atixmtYDCIjRs3ys62YgnZDW3Y1W4A7rMdTrYbAEPbwXKkth04cOCAtH37dmnZsmVS//79pe3bt0vbt2+X2tvbJUmSpO7ubmnMmDHSN77xDWnHjh1STU2NVFBQIC1evNjinOtj/fr10nPPPSft2LFD2rdvn7R69WqpoKBAmj17ttVZ08Xrr78u+Xw+adWqVdLnn38u3XXXXVJeXl7C7Bs7s2jRIumjjz6S9u/fL/3973+Xpk2bJg0aNEg6duyY1VnTRXt7e6yNAZCeffZZafv27dKBAwckSZKkFStWSHl5edJ7770n7dy5U/rmN78pDRs2TDp79qzFOU+E7Ia97YYkOdt2OM1uSJI5tsN1AmjOnDkSgF6fDz/8MJamsbFRuuaaa6Ts7Gxp0KBB0qJFi6Suri7rMm2ArVu3SlVVVVIgEJCysrKkiooK6Sc/+YnU2dlpddZ088ILL0hDhgyRMjMzpUmTJkmffvqp1Vlixi233CIVFxdLmZmZ0gUXXCDdcsstUn19vdXZ0s2HH36Ysr3NmTNHkqSe6awPP/ywNHjwYMnn80lXX321VFdXZ22mU0B2w/52Q5KcazucZjckyRzb4ZEkSTLqjiIIgiAIgrATjpgFRhAEQRAEoQUSQARBEARBuA4SQARBEARBuA4SQARBEARBuA4SQARBEARBuA4SQARBEARBuA4SQARBEARBuA4SQARBEARBuA4SQARBEARBuA4SQARBEARBuA4SQARBEARBuI7/D0OtqSRDWco2AAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -326,42 +398,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|   iter    |  target   |     C     |  kernel   |\n",
+      "|   iter    |  target   |  kernel   |  log10_C  |\n",
       "-------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m0.8166   \u001b[39m | \u001b[39m3.8079471\u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
-      "| \u001b[39m2        \u001b[39m | \u001b[39m0.5419   \u001b[39m | \u001b[39m1.9160044\u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m0.5927   \u001b[39m | \u001b[39m3.4819990\u001b[39m | \u001b[39m  poly3  \u001b[39m |\n",
-      "| \u001b[35m4        \u001b[39m | \u001b[35m0.8463   \u001b[39m | \u001b[35m9.6815942\u001b[39m | \u001b[35m  poly2  \u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m0.7862   \u001b[39m | \u001b[39m7.1327138\u001b[39m | \u001b[39m  poly2  \u001b[39m |\n",
-      "| \u001b[35m6        \u001b[39m | \u001b[35m0.8688   \u001b[39m | \u001b[35m4.6671691\u001b[39m | \u001b[35m   rbf   \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m0.6765   \u001b[39m | \u001b[39m2.5627368\u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m0.7324   \u001b[39m | \u001b[39m6.7594238\u001b[39m | \u001b[39m  poly3  \u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m0.8585   \u001b[39m | \u001b[39m4.4567931\u001b[39m | \u001b[39m   rbf   \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m0.6446   \u001b[39m | \u001b[39m4.5126050\u001b[39m | \u001b[39m  poly3  \u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m0.8446   \u001b[39m | \u001b[39m9.1220066\u001b[39m | \u001b[39m  poly2  \u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m0.4482   \u001b[39m | \u001b[39m1.9270948\u001b[39m | \u001b[39m  poly3  \u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1446  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.5930859\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-0.1474  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.4639878\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m-0.139   \u001b[39m | \u001b[35mpoly3    \u001b[39m | \u001b[35m0.9998496\u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.139   \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.9994641\u001b[39m |\n",
+      "| \u001b[35m5        \u001b[39m | \u001b[35m-0.1072  \u001b[39m | \u001b[35mrbf      \u001b[39m | \u001b[35m0.9996179\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-0.1607  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m-0.805461\u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.1372  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.0678008\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.1547  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.1300376\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.1257  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m-0.162077\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.1634  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m-0.999886\u001b[39m |\n",
       "=================================================\n"
      ]
     }
    ],
    "source": [
-    "from sklearn.datasets import load_diabetes\n",
+    "from sklearn.datasets import load_breast_cancer\n",
     "from sklearn.svm import SVC\n",
-    "from sklearn.metrics import f1_score\n",
+    "from sklearn.metrics import f1_score, log_loss\n",
+    "from sklearn.model_selection import train_test_split\n",
     "from bayes_opt import BayesianOptimization\n",
     "\n",
-    "data = load_diabetes()\n",
-    "\n",
+    "data = load_breast_cancer()\n",
+    "X_train, y_train = data['data'], data['target']\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
     "kernels = ['rbf', 'poly']\n",
     "\n",
-    "def f_target(kernel, C):\n",
+    "def f_target(kernel, log10_C):\n",
     "    if kernel == 'poly2':\n",
     "        kernel = 'poly'\n",
     "        degree = 2\n",
@@ -371,23 +443,47 @@
     "    elif kernel == 'rbf':\n",
     "        degree = 3 # not used, equal to default\n",
     "\n",
-    "    model = SVC(C=C, kernel=kernel, degree=degree)\n",
-    "    model.fit(data['data'], data['target'])\n",
+    "    C = 10**log10_C\n",
+    "\n",
+    "    model = SVC(C=C, kernel=kernel, degree=degree, probability=True, random_state=42)\n",
+    "    model.fit(X_train, y_train)\n",
     "\n",
-    "    weighted_f1 = f1_score(model.predict(data['data']), data['target'], average='weighted')\n",
-    "    return weighted_f1\n",
+    "    # Package looks for maximum, so we return -1 * log_loss\n",
+    "    loss = -1 * log_loss(y_val, model.predict_proba(X_val))\n",
+    "    return loss\n",
     "\n",
     "\n",
     "params_svm ={\n",
     "    'kernel': ['rbf', 'poly2', 'poly3'],\n",
-    "    'C':(1e-1, 1e+1),\n",
+    "    'log10_C':(-1, +1),\n",
     "}\n",
     "\n",
-    "optimizer = BayesianOptimization(f_target, params_svm, random_state=42, verbose=2)\n",
+    "optimizer = BayesianOptimization(\n",
+    "    f_target,\n",
+    "    params_svm,\n",
+    "    #acquisition_function=acquisition.ExpectedImprovement(1e-2, random_state=42),\n",
+    "    random_state=42,\n",
+    "    verbose=2\n",
+    ")\n",
+    "discrete_optimizer.set_gp_params(alpha=1e-3)\n",
     "\n",
-    "optimizer.maximize(init_points=2, n_iter=10)"
+    "optimizer.maximize(init_points=2, n_iter=8)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -398,7 +494,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.6 ('bopt')",
+   "display_name": "bayesian-optimization-t6LLJ9me-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -413,11 +509,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.10.13"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "49851069de08cc5bbf068d7713ecb1523f4cab708013d75e8e72826d85a7e48d"
-   }
   }
  },
  "nbformat": 4,
diff --git a/examples/typed_hyperparameter_tuning.py b/examples/typed_hyperparameter_tuning.py
index 376974eb1..592c82b82 100644
--- a/examples/typed_hyperparameter_tuning.py
+++ b/examples/typed_hyperparameter_tuning.py
@@ -49,7 +49,6 @@ def gboost(log_learning_rate, max_depth, min_samples_split):
     
     continuous_optimizer = BayesianOptimization(
         f=gboost,
-        acquisition_function=acquisition.ExpectedImprovement(1e-1),
         pbounds=continuous_pbounds,
         verbose=0,
         random_state=42,
@@ -57,7 +56,6 @@ def gboost(log_learning_rate, max_depth, min_samples_split):
 
     discrete_optimizer = BayesianOptimization(
         f=gboost,
-        acquisition_function=acquisition.ExpectedImprovement(1e-1),
         pbounds=discrete_pbounds,
         verbose=0,
         random_state=42,
diff --git a/tests/test_parameter.py b/tests/test_parameter.py
index dd9d94802..3fe5eecae 100644
--- a/tests/test_parameter.py
+++ b/tests/test_parameter.py
@@ -1,7 +1,9 @@
 from __future__ import annotations
 
 import numpy as np
+import pytest
 
+from bayes_opt import BayesianOptimization
 from bayes_opt.parameter import CategoricalParameter, FloatParameter, IntParameter
 from bayes_opt.target_space import TargetSpace
 
@@ -38,6 +40,11 @@ def target_func(**kwargs):
 
     assert (space.target == np.array([target1, target2])).all()
 
+    p1 = space._params_config["p1"]
+    assert p1.to_float(0.2) == 0.2
+    assert p1.to_float(np.array(2.3)) == 2.3
+    assert p1.to_float(3) == 3.0
+
 
 def test_int_parameters():
     def target_func(**kwargs):
@@ -68,6 +75,15 @@ def target_func(**kwargs):
 
     assert (space.target == np.array([target1, target2])).all()
 
+    p1 = space._params_config["p1"]
+    assert p1.to_float(0) == 0.0
+    assert p1.to_float(np.array(2)) == 2.0
+    assert p1.to_float(3) == 3.0
+
+    assert p1.kernel_transform(0) == 0.0
+    assert p1.kernel_transform(2.3) == 2.0
+    assert p1.kernel_transform(np.array([1.3, 3.6, 7.2])) == pytest.approx(np.array([1, 4, 7]))
+
 
 def test_cat_parameters():
     fruit_ratings = {"apple": 1.0, "banana": 2.0, "mango": 5.0, "honeydew melon": -10.0, "strawberry": np.pi}
@@ -102,6 +118,23 @@ def target_func(fruit: str):
 
     assert (space.target == np.array([target1, target2])).all()
 
+    p1 = space._params_config["fruit"]
+    for i, fruit in enumerate(fruits):
+        assert (p1.to_float(fruit) == np.eye(5)[i]).all()
+
+    assert (p1.kernel_transform(np.array([0.8, 0.2, 0.3, 0.5, 0.78])) == np.array([1, 0, 0, 0, 0])).all()
+    assert (p1.kernel_transform(np.array([0.78, 0.2, 0.3, 0.5, 0.8])) == np.array([0, 0, 0, 0, 1.0])).all()
+
+
+def test_cateogrical_valid_bounds():
+    pbounds = {"fruit": ("apple", "banana", "mango", "honeydew melon", "banana", "strawberry")}
+    with pytest.raises(ValueError):
+        TargetSpace(None, pbounds)
+
+    pbounds = {"fruit": ("apple",)}
+    with pytest.raises(ValueError):
+        TargetSpace(None, pbounds)
+
 
 def test_to_string():
     pbounds = {"p1": (0, 1), "p2": (1, 2)}
@@ -133,3 +166,20 @@ def test_to_string():
     assert space._params_config["fruit"].to_string("mango", 5) == "mango"
     assert space._params_config["fruit"].to_string("honeydew melon", 10) == "honeyde..."
     assert space._params_config["fruit"].to_string("strawberry", 10) == "strawberry"
+
+
+def test_integration_mixed_optimization():
+    fruit_ratings = {"apple": 1.0, "banana": 2.0, "mango": 5.0, "honeydew melon": -10.0, "strawberry": np.pi}
+
+    pbounds = {
+        "p1": (0, 1),
+        "p2": (1, 2),
+        "p3": (-1, 3, int),
+        "fruit": ("apple", "banana", "mango", "honeydew melon", "strawberry"),
+    }
+
+    def target_func(p1, p2, p3, fruit):
+        return p1 + p2 + p3 + fruit_ratings[fruit]
+
+    optimizer = BayesianOptimization(target_func, pbounds)
+    optimizer.maximize(init_points=2, n_iter=10)

From 264b79e5bb565341d1bc47699510ad78c0d3fb8f Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Tue, 29 Oct 2024 17:56:36 +0100
Subject: [PATCH 12/21] Remove sorting, gradient optimize only continuous
 params

---
 bayes_opt/acquisition.py            |  62 +++++++---
 bayes_opt/bayesian_optimization.py  |  14 ++-
 bayes_opt/domain_reduction.py       |  13 +--
 bayes_opt/parameter.py              |  21 ++++
 bayes_opt/target_space.py           |  42 ++++---
 examples/parameter_types.ipynb      | 171 +++++++++++++++-------------
 scripts/format.sh                   |   2 +-
 tests/test_acquisition.py           |   2 +-
 tests/test_bayesian_optimization.py |   8 +-
 tests/test_target_space.py          |  20 ++--
 10 files changed, 216 insertions(+), 139 deletions(-)

diff --git a/bayes_opt/acquisition.py b/bayes_opt/acquisition.py
index b025d228d..167bd5dc0 100644
--- a/bayes_opt/acquisition.py
+++ b/bayes_opt/acquisition.py
@@ -215,15 +215,22 @@ def _acq_min(
         if n_random == 0 and n_l_bfgs_b == 0:
             error_msg = "Either n_random or n_l_bfgs_b needs to be greater than 0."
             raise ValueError(error_msg)
-        x_min_r, min_acq_r = self._random_sample_minimize(acq, space, n_random=n_random)
-        x_min_l, min_acq_l = self._l_bfgs_b_minimize(acq, space, n_x_seeds=n_l_bfgs_b)
-        # Either n_random or n_l_bfgs_b is not 0 => at least one of x_min_r and x_min_l is not None
-        if min_acq_r < min_acq_l:
-            return x_min_r
-        return x_min_l
+        x_min_r, min_acq_r, x_seeds = self._random_sample_minimize(
+            acq, space, n_random=max(n_random, n_l_bfgs_b), n_x_seeds=n_l_bfgs_b
+        )
+        if n_l_bfgs_b:
+            x_min_l, min_acq_l = self._l_bfgs_b_minimize(acq, space, x_seeds=x_seeds)
+            # Either n_random or n_l_bfgs_b is not 0 => at least one of x_min_r and x_min_l is not None
+            if min_acq_r > min_acq_l:
+                return x_min_l
+        return x_min_r
 
     def _random_sample_minimize(
-        self, acq: Callable[[NDArray[Float]], NDArray[Float]], space: TargetSpace, n_random: int
+        self,
+        acq: Callable[[NDArray[Float]], NDArray[Float]],
+        space: TargetSpace,
+        n_random: int,
+        n_x_seeds: int = 0,
     ) -> tuple[NDArray[Float] | None, float]:
         """Random search to find the minimum of `acq` function.
 
@@ -238,6 +245,8 @@ def _random_sample_minimize(
         n_random : int
             Number of random samples to use.
 
+        n_x_seeds : int
+            Number of top points to return, for use as starting points for L-BFGS-B.
         Returns
         -------
         x_min : np.ndarray
@@ -252,10 +261,18 @@ def _random_sample_minimize(
         ys = acq(x_tries)
         x_min = x_tries[ys.argmin()]
         min_acq = ys.min()
-        return x_min, min_acq
+        if n_x_seeds != 0:
+            idxs = np.argsort(ys)[-n_x_seeds:]
+            x_seeds = x_tries[idxs]
+        else:
+            x_seeds = []
+        return x_min, min_acq, x_seeds
 
     def _l_bfgs_b_minimize(
-        self, acq: Callable[[NDArray[Float]], NDArray[Float]], space: TargetSpace, n_x_seeds: int = 10
+        self,
+        acq: Callable[[NDArray[Float]], NDArray[Float]],
+        space: TargetSpace,
+        x_seeds: NDArray[Float] | None = None,
     ) -> tuple[NDArray[Float] | None, float]:
         """Random search to find the minimum of `acq` function.
 
@@ -267,8 +284,8 @@ def _l_bfgs_b_minimize(
         space : TargetSpace
             The target space over which to optimize.
 
-        n_x_seeds : int
-            Number of starting points for the L-BFGS-B optimizer.
+        x_seeds : int
+            Starting points for the L-BFGS-B optimizer.
 
         Returns
         -------
@@ -278,24 +295,35 @@ def _l_bfgs_b_minimize(
         min_acq : float
             Acquisition function value at `x_min`
         """
-        if n_x_seeds == 0:
-            return None, np.inf
-        x_seeds = space.random_sample(n_x_seeds, random_state=self.random_state)
+        continuous_dimensions = space.continuous_dimensions
+        continuous_bounds = space.bounds[continuous_dimensions]
+
+        if not continuous_dimensions.any():
+            min_acq = np.inf
+            x_min = np.array([np.nan] * space.bounds.shape[0])
+            return x_min, min_acq
 
         min_acq: float | None = None
         x_try: NDArray[Float]
         x_min: NDArray[Float]
         for x_try in x_seeds:
-            # Find the minimum of minus the acquisition function
-            res: OptimizeResult = minimize(acq, x_try, bounds=space.bounds, method="L-BFGS-B")
 
+            def continuous_acq(x: NDArray[Float], x_try=x_try) -> NDArray[Float]:
+                x_try[continuous_dimensions] = x
+                return acq(x_try)
+
+            # Find the minimum of minus the acquisition function
+            res: OptimizeResult = minimize(
+                continuous_acq, x_try[continuous_dimensions], bounds=continuous_bounds, method="L-BFGS-B"
+            )
             # See if success
             if not res.success:
                 continue
 
             # Store it if better than previous minimum(maximum).
             if min_acq is None or np.squeeze(res.fun) >= min_acq:
-                x_min = res.x
+                x_try[continuous_dimensions] = res.x
+                x_min = x_try
                 min_acq = np.squeeze(res.fun)
 
         if min_acq is None:
diff --git a/bayes_opt/bayesian_optimization.py b/bayes_opt/bayesian_optimization.py
index 826f1cfbb..b887f5b19 100644
--- a/bayes_opt/bayesian_optimization.py
+++ b/bayes_opt/bayesian_optimization.py
@@ -14,6 +14,7 @@
 
 from bayes_opt import acquisition
 from bayes_opt.constraint import ConstraintModel
+from bayes_opt.domain_reduction import DomainTransformer
 from bayes_opt.event import DEFAULT_EVENTS, Events
 from bayes_opt.logger import _get_default_logger
 from bayes_opt.parameter import WrappedKernel
@@ -162,11 +163,10 @@ def __init__(
         self._verbose = verbose
         self._bounds_transformer = bounds_transformer
         if self._bounds_transformer:
-            try:
-                self._bounds_transformer.initialize(self._space)
-            except (AttributeError, TypeError) as exc:
-                error_msg = "The transformer must be an instance of DomainTransformer"
-                raise TypeError(error_msg) from exc
+            if not isinstance(self._bounds_transformer, DomainTransformer):
+                msg = "The transformer must be an instance of DomainTransformer"
+                raise TypeError(msg)
+            self._bounds_transformer.initialize(self._space)
 
         super().__init__(events=DEFAULT_EVENTS)
 
@@ -330,5 +330,7 @@ def set_bounds(self, new_bounds: BoundsMapping) -> None:
     def set_gp_params(self, **params: Any) -> None:
         """Set parameters of the internal Gaussian Process Regressor."""
         if "kernel" in params:
-            params["kernel"] = WrappedKernel(params["kernel"], self._space.kernel_transform)
+            params["kernel"] = WrappedKernel(
+                base_kernel=params["kernel"], transform=self._space.kernel_transform
+            )
         self._gp.set_params(**params)
diff --git a/bayes_opt/domain_reduction.py b/bayes_opt/domain_reduction.py
index a5179a7a9..243a51bd5 100644
--- a/bayes_opt/domain_reduction.py
+++ b/bayes_opt/domain_reduction.py
@@ -75,13 +75,7 @@ def __init__(
         self.gamma_pan = gamma_pan
         self.eta = eta
 
-        self.minimum_window_value: NDArray[Float] | Sequence[float] | float
-        if isinstance(minimum_window, Mapping):
-            self.minimum_window_value = [
-                item[1] for item in sorted(minimum_window.items(), key=lambda x: x[0])
-            ]
-        else:
-            self.minimum_window_value = minimum_window
+        self.minimum_window_value = minimum_window
 
     def initialize(self, target_space: TargetSpace) -> None:
         """Initialize all of the parameters.
@@ -91,6 +85,11 @@ def initialize(self, target_space: TargetSpace) -> None:
         target_space : TargetSpace
             TargetSpace this DomainTransformer operates on.
         """
+        if isinstance(self.minimum_window_value, Mapping):
+            self.minimum_window_value = [self.minimum_window_value[key] for key in target_space.keys]
+        else:
+            self.minimum_window_value = self.minimum_window_value
+
         any_not_float = any([not isinstance(p, FloatParameter) for p in target_space._params_config.values()])
         if any_not_float:
             msg = "Domain reduction is only supported for all-FloatParameter optimization."
diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index b83faff3a..836a72799 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -59,6 +59,11 @@ def bounds(self) -> NDArray[Any]:
         """The bounds of the parameter in float space."""
         return self._bounds
 
+    @property
+    @abc.abstractmethod
+    def is_continuous(self) -> bool:
+        """Whether the parameter is continuous."""
+
     def random_sample(
         self, n_samples: int, random_state: np.random.RandomState | int | None
     ) -> NDArray[Float]:
@@ -161,6 +166,11 @@ class FloatParameter(BayesParameter):
     def __init__(self, name: str, bounds: tuple[float, float]) -> None:
         super().__init__(name, np.array(bounds))
 
+    @property
+    def is_continuous(self) -> bool:
+        """Whether the parameter is continuous."""
+        return True
+
     def to_float(self, value: float) -> float:
         """Convert a parameter value to a float.
 
@@ -248,6 +258,11 @@ class IntParameter(BayesParameter):
     def __init__(self, name: str, bounds: tuple[int, int]) -> None:
         super().__init__(name, np.array(bounds))
 
+    @property
+    def is_continuous(self) -> bool:
+        """Whether the parameter is continuous."""
+        return False
+
     def random_sample(
         self, n_samples: int, random_state: np.random.RandomState | int | None
     ) -> NDArray[Float]:
@@ -340,6 +355,11 @@ def __init__(self, name: str, categories: Sequence[Any]) -> None:
         bounds = np.vstack((lower, upper)).T
         super().__init__(name, bounds)
 
+    @property
+    def is_continuous(self) -> bool:
+        """Whether the parameter is continuous."""
+        return False
+
     def random_sample(
         self, n_samples: int, random_state: np.random.RandomState | int | None
     ) -> NDArray[Float]:
@@ -478,6 +498,7 @@ def __call__(self, X: NDArray[Float], Y: NDArray[Float] = None, eval_gradient: b
         K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims)
         """
         X = self.transform(X)
+        Y = self.transform(Y) if Y is not None else None
         return self.base_kernel(X, Y, eval_gradient)
 
     def is_stationary(self):
diff --git a/bayes_opt/target_space.py b/bayes_opt/target_space.py
index 212045493..edc2f5f53 100644
--- a/bayes_opt/target_space.py
+++ b/bayes_opt/target_space.py
@@ -82,7 +82,7 @@ def __init__(
         self.target_func = target_func
 
         # Get the name of the parameters
-        self._keys: list[str] = sorted(pbounds)
+        self._keys: list[str] = list(pbounds.keys())
 
         self._params_config = self.make_params(pbounds)
         self._dim = sum([self._params_config[key].dim for key in self._keys])
@@ -180,6 +180,11 @@ def keys(self) -> list[str]:
         """
         return self._keys
 
+    @property
+    def params_config(self) -> dict[str, BayesParameter]:
+        """Get the parameters configuration."""
+        return self._params_config
+
     @property
     def bounds(self) -> NDArray[Float]:
         """Get the bounds of this TargetSpace.
@@ -210,6 +215,20 @@ def masks(self) -> dict[str, NDArray[np.bool_]]:
         """
         return self._masks
 
+    @property
+    def continuous_dimensions(self) -> NDArray[np.bool_]:
+        """Get the continuous parameters.
+
+        Returns
+        -------
+        dict
+        """
+        result = np.zeros(self.dim, dtype=bool)
+        masks = self.masks
+        for key in self.keys:
+            result[masks[key]] = self._params_config[key].is_continuous
+        return result
+
     def make_params(self, pbounds: BoundsMapping) -> dict[str, BayesParameter]:
         """Create a dictionary of parameters from a dictionary of bounds.
 
@@ -226,7 +245,7 @@ def make_params(self, pbounds: BoundsMapping) -> dict[str, BayesParameter]:
             parameter objects as values.
         """
         params: dict[str, BayesParameter] = {}
-        for key in sorted(pbounds):
+        for key in pbounds:
             pbound = pbounds[key]
 
             if isinstance(pbound, BayesParameter):
@@ -285,8 +304,7 @@ def params_to_array(self, params: Mapping[str, float | NDArray[Float]]) -> NDArr
         """
         if set(params) != set(self.keys):
             error_msg = (
-                f"Parameters' keys ({sorted(params)}) do "
-                f"not match the expected set of keys ({self.keys})."
+                f"Parameters' keys ({params}) do " f"not match the expected set of keys ({self.keys})."
             )
             raise ValueError(error_msg)
         return self._to_float(params)
@@ -337,9 +355,7 @@ def array_to_params(self, x: NDArray[Float]) -> dict[str, float | NDArray[Float]
 
     def _to_float(self, value: Mapping[str, float | NDArray[Float]]) -> NDArray[Float]:
         if set(value) != set(self.keys):
-            msg = (
-                f"Parameters' keys ({sorted(value)}) do " f"not match the expected set of keys ({self.keys})."
-            )
+            msg = f"Parameters' keys ({value}) do " f"not match the expected set of keys ({self.keys})."
             raise ValueError(msg)
         res = np.zeros(self._dim)
         for key in self._keys:
@@ -389,8 +405,7 @@ def _as_array(self, x: Any) -> NDArray[Float]:
         x = x.ravel()
         if x.size != self.dim:
             error_msg = (
-                f"Size of array ({len(x)}) is different than the "
-                f"expected number of parameters ({len(self.keys)})."
+                f"Size of array ({len(x)}) is different than the " f"expected number of ({len(self.dim)})."
             )
             raise ValueError(error_msg)
         return x
@@ -666,8 +681,7 @@ def set_bounds(self, new_bounds: BoundsMapping) -> None:
         new_bounds : dict
             A dictionary with the parameter name and its new bounds
         """
-        print(new_bounds)
-        new__params_config = self.make_params(new_bounds)
+        new_params_config = self.make_params(new_bounds)
 
         for key in self.keys:
             if key in new_bounds:
@@ -676,12 +690,12 @@ def set_bounds(self, new_bounds: BoundsMapping) -> None:
                 ) == set(new_bounds[key]):
                     msg = "Changing bounds of categorical parameters is not supported"
                     raise NotImplementedError(msg)
-                if not isinstance(new__params_config[key], type(self._params_config[key])):
+                if not isinstance(new_params_config[key], type(self._params_config[key])):
                     msg = (
-                        f"Parameter type {type(new__params_config[key])} of"
+                        f"Parameter type {type(new_params_config[key])} of"
                         " new bounds does not match parameter type"
                         f" {type(self._params_config[key])} of old bounds"
                     )
                     raise ValueError(msg)
-                self._params_config[key] = new__params_config[key]
+                self._params_config[key] = new_params_config[key]
         self._bounds = self.calculate_bounds()
diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
index a77934f47..2c9903e49 100644
--- a/examples/parameter_types.ipynb
+++ b/examples/parameter_types.ipynb
@@ -8,7 +8,10 @@
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from bayes_opt import BayesianOptimization\n"
+    "from bayes_opt import BayesianOptimization\n",
+    "from bayes_opt import acquisition\n",
+    "\n",
+    "from sklearn.gaussian_process.kernels import Matern"
    ]
   },
   {
@@ -18,7 +21,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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j64IgCIIgjGh7oxW/PHHp3gFfAMMPbkJVVY7cC0lVUZEo/vmPucc4A6tPwe2TkVUVRVFRVDDoNJh0Gkx6LYlGHWkJelLNetIS9GSYtJzw0x/RF30fTlVBkjDf8kNc73w2bEo8BFPs97Xa6XB4OaYwVYyyC4IgCGMmgnVBEARBEIakHJz/7fHJSNrwXTZ8+ParbF73Ad/9ya8wGIwAvPWf59mzbfOgyx/T3soZbS1Drk9CJaO7DeXDD9lVMm9U+3J8/Q6ubh9m3aqKqbWZ9M2f0rPkhJDW2e3w8VltNwtL0sdV1V4QBEE4eolgXRAEQRCEIR3ocIR9naqqcu+Pv4/X4+aqG27FkBEM1nd8tp41/31h0NekGRNDWrd548t0fv4uN9x2J5lZ2Wg1Em/+61k+fO9tNAYzkiEBbUIK2qSMvv+K3T0hrXvt2h1U6UspSjNTkZ1IRqIBSRo628DlDc6JX1SajlEnAnZBEARhdESwLgiCIAjCoFqsblp63GFfb8Dvx+sZuN6TVp5NYWl5v8fsUiL1ukLkphb4x09GXPf0Yyu4oKiU48pzSEpJBcC7dB75Jv8gS8tAB2cfNwteHnm/P/MZ+fTz1r6f0xL0VOQkUZGdyNyCVPJTTQOCd5dXZnuDlcWl6aJqvCAIgjAqIlgXBEEQBGEAly/A3hZ7RNYdCBwKnE0J5r5/Lzv1TJadeiYADd0uXtnezNYGCwCdxZm0p2aTbe1EYuDcclWS8Obms/jO+1h8xLzyhcefxMLjTxp6fxQZf34ButYWpEHmrSuANT2L8nO/gMHuo67bRUO3C4vbz+a6HjbX9fACjWQlGZhXlMbCkjSm5yajORi429x+drfYmFuYGvLfSBAEQRBEsC4IgiAIQj+qqrKzyTZiv/KxCvgPBet6vaHfcza3n5e2NvFJZScqIAHHlWVw5pxcWgp/Q/ZN16Ai9Q+qDwbFjt/ch9Gkx+sPvQd8drKR8uxE9H98KFhpXpL6FZrr3YfPrr2e0+YW9D3uDcjUdbmobHewr83OvlY7nQ4f7+1t57297WQkGlhensnJ07PJSDTQavWQnmigMC1h1L3cBUEQhKNTVEqU/ulPf6KsrAyTycTSpUvZuHHjkMs++eSTSJLU7z+TyRSN3RQEQRAEAajpdGJzD5Y2Hh6HB+uag0FqQFFYs7uNn7y8k48PBuqLS9O568tzuO7kcsoyE+ladRae5/+JVFjYf4VFRUgvvkjWFZdwQkUWcwtTyUwyoB0i7dxs1FKWZWb51EyOLU4j2aQP9lF/8UU4Yt1ScTHKv14k9/ob+j1u1GmZnpvM6mPyuemM6Txw0XyuP3UqJ07NIkGvpdvp47XPW7jtpc959KNqGrpd7G+1433hX1BWBqeeCpdeGvx/WVmwx7sgCIIgHCbiI+v//Oc/ufnmm3n44YdZunQp999/P6tWrWLfvn3k5OQM+pqUlBT27dvX9/NwxVsEQRAEQQgfu8dPbZczotvoTYPX64MF2uq7XPxtXQ0NB+fHl2aaueS4EqbmJPV7XUlGIgkXXQDnf3XIkWmNRiIv1UReqglFUXH5Zbx+GUUFvVbCbNBh0A0xVvHVr8I55wxYt1arZZ6qUtnuoK5r8B7qRr2W+cVpzC9O49IlJWxrsPDB/g72tdnZUNPNhppuvmfZwYpHfjKwRVxTU3BU/8UXg/sgCIIgCEQhWP/973/Ptddey1VXXQXAww8/zOuvv84TTzzBrbfeOuhrJEkiLy8v0rsmCIIgCMJhVFVlT4sdJfQs8jHpHVnXGU28vK2JNz9vRVZVkow6vrqwkBMrsgYUYzMbtJRnHawIr9XCihUjbkejkUgy6kgyjuJyZ5B1y7KMw+FgWm4qqQl6drfYCMhDTxEw6DQsmZLBkikZ1HY6eWtXK1tqOrnkud8P2ie+t5e7esMNdJ72BZwBFW9AQVZUJAmMOg1JJh3pZoPo2y4IgnAUiWiw7vP52Lx5M7fddlvfYxqNhjPOOIP169cP+TqHw0FpaSmKorBw4UJ+/etfM2fOnEjuqiAIgiAc9Rq63RFNf++VnVfA3S98zEt77by2I9jffFFpOpctKSElQT/oa2bkJU9INfWXX36Z888/n9zcXFauXAmArKhY3X48fpnv/vhXJCYlA7Dmvy+wdcPHg67nbKubAnvn0BtSVaTGRu45+yx2ZOXwjRtuIys3H4BP3n2TT95982DgrsVs0Pbr3X7HHXdQXl4+1JoFQRCEOBXRYL2zsxNZlsnNze33eG5uLnv37h30NTNmzOCJJ55g3rx5WK1W7rvvPpYvX86uXbsoKioasLzX68Xr9fb9bLPZAPD7/fj9kb/gGI/e/Yv1/RTEsYon4ljFB3GcYo/XL1PZZkE9YsRYVeR+/x8vRVX5394O/rPNgqxCslHHpccVsbg0LbgdOTDgNVnJRlKMmgl5vxQUFCDLMs3NzTz11FMDnr/5xz/r2+c92zcP2Sc+M8TtNX2yljXABVd8k8ysbAAqd+8Ycr0A3/zmNykuLhafqzgijlX8EMcqPsTTcRrNPkqqOkiPkjBpbm6msLCQdevWsWzZsr7Hb7nlFj744AM2bNgw4jr8fj+zZs3ikksu4Re/+MWA5++8807uuuuuAY8/99xzmM3mAY8LgiAIgjAxbD74e6WGvdZgKvexGQoXliskDT6YHjPWrVtHW1vboM+tXr0ao9EIwLZt26ipqRl0uZmtrdz69tsjbus3q1axNy+P008/nZSUFAD27Nkz5CAHwCmnnEJrayt///vfKS4u5lvf+taI2xEEQRAmhsvl4tJLL8VqtfZ9zw8losG6z+fDbDbz4osvcu655/Y9fsUVV2CxWPjvf/8b0nouuOACdDodzz///IDnBhtZLy4uprOzc8RffqL5/X7WrFnDypUr0etj/ErlKCeOVfwQxyo+iOMUW2xuP1vqegZ9TlVk3LVbSShbgKQZe3uxXS02Hl9Xj90TQKeBYutOZiR6OO/r1wz7upLMRMqzE8e83WhTFBW/rCCrKlpJQq/VBNP3ZRnd1KnQ3DxkL3d3Tj7r3v501G3c8tJM7N+4lvPPP58ZM2awZcsW8bmKceI7MH6IYxUf4uk42Ww2srKyQgrWI5oGbzAYWLRoEe+++25fsK4oCu+++y7XX399SOuQZZnPP/+c1atXD/q80Wjsu6N9OL1eH/MHqlc87evRThyr+CGOVXwQxyk2VDfZkbTDXxJIGu2IywwmICv8Z1sTb+8KjkwXpiVwSoaNu795K1Xl0zj/yqFHgY16DVPzUodswRarBl6VAHo9PPjgoL3ce+v53Xby1Uzv9jIzb3SDDW32AC2O4DQFWZbF5yqOiGMVP8Sxig/xcJxGs38RLyl688038+ijj/LUU0+xZ88evv3tb+N0Ovuqw19++eX9CtD9/Oc/55133qG6upotW7bwta99jbq6Oq65Zvg774IgCIIgjF6r1YPVFZk5fh12L/e8va8vUF8xPZufrJ5FmvZg6zbd8BcsFdlJcReoD2uIXu6e3ALuuuIuXqk4nj+sOcCm2u5Rr9rhCwb/shye2gKCIAjCxIt467aLLrqIjo4O7rjjDlpbW5k/fz5vvfVWX9G5+vp6NJpD9wx6enq49tpraW1tJT09nUWLFrFu3Tpmz54d6V0VBEEQhKOKoqhUdTgisu5Ndd08ta4Ot1/GbNByxbIyFpWmA+D3+wDQ6oe+DEk26ShIS4jIvk2oQXq511Ycy/EWD7s+rmFTXQ9//agav6yyrCLUsnSgOZg6L4J1QRCEySPiwTrA9ddfP2Ta+9q1a/v9/Ic//IE//OEPUdgrQRAEQTi6NVncuH3hDe78ssILmxp4f18HABXZiVx3UjmZSYeSw3v7rOv1hiHXMz03Oaz7FVOO6OVe7pdpdfq47qRyTPo6Pq7s5IlPavDLCidPzw5xlcFgXVEUmi1uSrNjOw1UEARBGFlUgnVBEARBEGKLrKjUdDrDus52u4eHP6imvtsFwBfn5nHO/AJ0mv6z7gKBYLCuG2LeXnaykfTEoQP5ycak11KUbqa+y8Xly0rRayXe39fBM5/WYdRpWFo+8gi79mA9AUVRqGy3k5aUQOoQPesFQRCE+CCCdUEQBEE4CtV3u/AFlJEXDNGm2m6eWh9Me08y6rj6xCkcU5g66LK9I+u6QUbWJQmm5iSFbb/iRWmmmaYeNyhw6ZISAN7f18Hjn9Rg1GuZX5w27Ot1Oh1GkwmdToeiwK4mK0umZKDTRrw8kSAIghAhIlgXBEGIEVa3ny6HF6vbj9sn45ODgZReq8Gk15KaoCPdbCDdbAi2ghKEMQrICnVd4RlVPzLtfVpOEteeVE7GMCPjvXPWdbqBlyGF6QkkGo++yxOjTkthegL1XS4kSeKSJSW4/TKfVnfz8AdV3HjGtGGrxM+ct5BXN1biqt4EgMsns7/NweyC2G5jKwiCIAzt6DsbCoIw6aiqisev4AsoeA8GAR6fjE6nQ5JiO6hVFJVmq5v6bhcu7+BzhwOyjNsn0+P0UYsLvU5DfqqJkgwzJv3Y+14LR6/6bhcBeWCv79Fqs3n464eH0t5Xz83jnPmFI1ZwP+G0LzLt3/MwJZj7Pa7VSpRnHX2j6r16R9dlRUUjSVy5vAyPT2Fbo4WH3qvkR6tmUpJpHnlFBzVb3OSmGPvVCxAEQRDihwjWBUGIS3aPn3a7lx6nD5vHj3Iwm1eVAwB8Wt2FTq8nJUFHZqKRvFRTzAW2HXYv+9vsoy7w5Q8o1He5aOh2UZCWQHl2IkZdbP1uQuwKyEpfcD0eG2u6efrTWjx+hSSjjmtOnMLcIdLej5ScmkZyatqAx8syEzHojt607cNH1wF0Gg3fPKWcB949wN5WOw++d4CfnDWLdHPo8/n3tto5vtwwuVrgCYIgHCVEsC4IQtxQFJUWm4eGbhcOT2DE5WVFpcfpp8fpp6rDQVaSkSnZiaSYJrboUkBW2Ndmp8XiGdd6VBWaety02TxMy02mcDK2uYojflnBG1DwBxQUVUWSJLQaCaNOE1M3ihp63OMaVfcGZF7Y1MgH+w+lvV93cvmoAsjBmPRaSjNCHzWerEoyzDT2uPpuQOq1Gr6zooLfvLmXZquHh96r5JZVMwa8pzpam7n/zh+ikz3c+fALfY+7fTI1nc6jsg6AIAhCvBPBuiAIMU9VVVqsHqo7nHj8Y2szparBkewOu5f8NBPTcpInZATP5QuwvcGK0zvyzYZQBWSVPc02OuxeZuenHNUjk9FkdfvpdvqwuHzYPYFhi7VpNRKJRh1pZj3pZgOZiRNTd0BW1HGNqld3Onj84xrabF4AVh+TxznHjpz2fqTPN3/K9o3rmDZnHktPPgOAipxEUYuB4E2LgrQEGrvdfY+ZDTq+d9o0fv3mHuq7XTz2UQ3fWVHR7+/l9XrY+NF7mM0Db3jUdzspSDNhNojLPkEQhHgirugEQYhpNo+fz2p72N1sG3OgfqQWi4dPq7vodHjDsr5QWd3B3yWcgfrhOu1eNtZ0Y/P4I7J+AZzeAAfa7Hx0oIPParqpanfQ5fCNWFVdVlRsbj/1XS62N1j44EAHu5qtWFy+KO15UFOPG/8YKsDLCvx3Rwu/eXMvbTYv6WY9N50xja8uKBpTevX2jet46o/3sv69twFISdCTnyoyQ3qVZSZyRLc7spONXH/qVHQaiW2NFv69pbHf84f3WT+SosCBNkfE9lcQBEGIDHGLVRCEmKSqwR7QNZ1O1PHXwRrAF1DYVm+hPDuR8uzIp4daXX62NPQgh6Go13A8fpnNtT3MKUwhJ9kU0W0dTbqdPmq7nHQ7whNcy7JKi8VDi8VDSoKesixzxI+XMsZR9Rarh0d3amlwtgGwdEoGly4pGVfF9kOt24JTUqbnihTtw5n0WnJTTAOmylRkJ/GNE6bwyEfVvL27jdLMRJZMyQBAqwkG67I8+E3NjoM1Po6m/vWCIAjxTgTrgiDEHI9fZmeTFYsr8iPE1R1OXD6ZOQUpEascb/f42RqFQL2XrKh83mhlRp5CUbqYAzweVrefynY7Pc7IvRdtbj87Gqykml1Mz00mNSEyNRVabZ5RZacoqsp7e9v595ZG/LKE2aDla0tL+4LD8QgEDgbrOj05KUbSxjnffTKakpVIq9Uz4GblkikZNPS4eHNnK0+ur6UwLYHC9AQ0B9vgDRWsA1R2ODgucfzHTxAEQYgOkQYvCEJMsbr8bKzpjkqg3qvV6mFHoxVFCX8w7fHLbK23hKVN1mioKuxtsfdVlRZGxxdQ2NVs5bOa7ogG6oezuvx8VtPNnhYbfnn0qeojqRvFe6Hb6eMPa/bzj88a8MsqM1MV7jprZlgCdTg0sq436JmWkxyWdU42ZoOO3JTBsy2+Mr+QWfnJ+AIKf15bicsX6JcGrw6RjmR1+Wm3j6+wpSAIghA9IlgXBCFmtFo9bK7vHnH+byR02L183mQd8iJ3LAKywtZ6y4T8Pr32t9lpCEObrqNJq9XD+uqucVfrH6umHjefVnfRFcaaCp0Ob0i1ElRVZX11Fz97ZRd7Wu0YdBouO66Ib81SSDOHb8Tf7w9OJ8hMSSTBEDuV8mNNWVbioI9rNBLXnVRORqKBNruXxz+uQdIc+jsONm+9V3WHM+z7KQiCIESGCNYFQYgJ9V0udjZZGeYaM+I67F52t9jCtr7dLbaIFZMbjX2tdpot7pEXPMr5ZYWdTVZ2NlnHVIQtnLz+4I2eynZ7WG4ghTKqbvf4efjDah7/uAa3X6Y8K5GfnT2bFdOzCPcMETkQ/Fxkp4hpGsNJMurITjYO+lyySc93VlSg00hsb7TybqUVAI1GgywP/b3j8ARot4nRdUEQhHgg5qwLgjDhqjscMTPa02LxkKDXjrvoXH2Xi3ZbdKvND2dPiw29VjPkhf/Rzu7x83mjFZcvPB0HwqW204XV7eeYwrQxt+Sze/z0OIcvjLej0cJT6+uwuv1oJYkvHZvPF+fmo9VIqMMEfmPVO7JuMon340jKshLpsA/+XVKWmcjXji/lyXW1vLWni/vf3k2Zez8Gw/B/1+pOJzlDpNgLgiAIsUOMrAuCMKGqYihQ71Xd4RzXvE6bx09lhz2MezR+qgo7m6zYRVu3AVqtHjbV9sRcoN6rxxms4zDWYzfcqLrHL/P0+loefK8Sq9tPfqqJ21bP5Ox5BWNqyRaq79x8C5988gmXXXZZxLYxWaQm6MlIGroA34lTszhxahYq8MS6euwhvE0cnkDUW1cKgiAIoydG1gVBmDA1nU5qYixQ77Wr2UZimW7U7akURZ3wdP6hyIrKtgYLS6ZkYNSJecIQvFkUq+/Bw3n8MpvqephXmEpmUuij0d6APOSNp8p2B49/UtM3artyVi5fWVA45hH8UEkSnL5kHknjaP12tCnPShy2beAlS4qp7nDQbPXw7AENN01XGekTXtvpJGsU7yVBEAQh+sTIuiAIE6Kh20VVu2Oid2NIsqzyedPoK8RXdThweWNzhBaCc6E/j1Dl+3iiqiq7m21xEaj3kuXgzZYWa+j1Bxp73ANuHAVkhZe2NHLP23vpsHvJMBv44ZnTuei44ogH6gAlGWYRqI9SmtlAeuLQBf6MOi3fPLkCSQmw16rh1a31I67T4vJjjWLXDUEQBGH0RLAuCELUtds87G+LrTTxwTg8Afa3h76fVpef+jiovG5x+Uf1e002iqKyo9Eal0X3VBV2NdlCasmnKCpNPf1/x6YeN796Yw9v7GxFVWFZeSZ3fnk2M/NSIrXL/SQYgvUgXnzxRf7whz+wZ8+eqGx3MpiSNXwdjcL0BLr/91cA3tjbQ1XHyDdD67rj52aVIAjC0UgE64IgRJXF5WNns5UwdkiLqMZud0hzOxVFZU+rLa5+r7ajsCK0oqhsb7QMWbArXuxvs1M9QjDWavP0tQ1UFJW3d7Xyi9d309DjJsmo49unVHD1iVMwG6I3yj0zLxmtRuKvf/0rN998M1u2bInatuNdRqJhxPZ57l3v4dzzIYoKj3xYjcs3fHHADrsXd4zWahAEQRBEsC4IQhS5fTLbG2NzPvdw9rTY8MvD73RDjwuHZ+LbtI3G7hbbiBfzk4miqGxrtNA1zNzfeFLd4aTy8AwJWYa1a+H552HtWhoOFjnsdHi5b80+/rW5kYCiMq8wlbu+PIdFpelR3d/8NFPffHufL3gMDIahC6cJA00Zou96L61WS9dbD5Fu0tDl9PHUurphW/+pavC7SxAEQYhNUQnW//SnP1FWVobJZGLp0qVs3Lhx2OX/9a9/MXPmTEwmE8cccwxvvPFGNHZTEIQICsgK2xstE96/eiy8fmXYtH2PX6a6M/7SSWVZZWeT7aiYv66qwRoEwxXpike1na7ge/Oll6CsDE49FS69FE49lWNPmk/XM89z56u72N/mwKjTcPnxpXzvtKmkJgw/QhtuRr2G6bnJfT/7/cG50iJYH53MJOOwo+sarRbV5+b8GQloNRKb63v4uLJz2HU2W9zIR8F3gCAIQjyKeLD+z3/+k5tvvpmf/exnbNmyhWOPPZZVq1bR3t4+6PLr1q3jkksu4eqrr2br1q2ce+65nHvuuezcuTPSuyoIQgTtbrHF3cjz4VosHrqGSIevbHcgy/F5sWtz++PyRsNo7Wq2xX3q+1Dc//gX6vnnQ2Njv8cNbS1c8JubOGXnx0zNTuJnX5rNydOzkaTItWQbyuz8FPTaQ5ccvSPren10bxpMBsONrmu1wRrweWb46oJCAP7xWcOwU14CsjqqooWCIAhC9EQ8WP/973/Ptddey1VXXcXs2bN5+OGHMZvNPPHEE4Mu/8ADD/CFL3yB//u//2PWrFn84he/YOHChfzxj3+M9K4KghAhdV1O2m3xHyjtbbUj+wP9Uo0tdjet1vie+13X5cTimlwjzoc70GaP+2M0JFlmxt0/ZbBiCb0n+N989Di3nDGVnGRTdPftoJJM84B2cyINfuyGG13XaoP1B+RAgJWzc5mZl4w3oPD4xzXDjp439ohgXRAEIRZFtKqMz+dj8+bN3HbbbX2PaTQazjjjDNavXz/oa9avX8/NN9/c77FVq1bx8ssvD7q81+vF6z0UBNhsNiCYYtebZherevcv1vdTEMdqPKxuHwdaLFErvKYqcr//h1PSq6+g/PZOtC3NfY8l5OWTfctdtJ+xOuzbixYV2NnQzeKyDLSa6Iy6Rusz1dTjpnYSV75P/2wdpraWIZ/XAOldbWRsXkfPccvxuN1848snD7n88SefwfdvvxsITh247MwlwX8HfEi6/oH1/CUncMuv7u/7+cqzT8Tn7X9TTiNJ6LUajj9+Kc8//3zf459//jkAkiSJ79UxKE030mMfGGBrDmYvBAI+JEXmquOLufP1vVR3OnltexNfnpc36PrszgCdNiepCeLmSbSI64r4IY5VfIin4zSafYxosN7Z2Yksy+Tm5vZ7PDc3l7179w76mtbW1kGXb21tHXT5u+++m7vuumvA4++88w5ms3mMex5da9asmehdEEIkjlX8cNduDev68tevZ9499wx43NDawrybr+OzH/2IlmXLwrrNaHIBbw/+tRxR4jM1Pum7Pg1twV2f4so04PF46BwmuO9uqsJVvQkIBuvDLdvTXNO3LEBna3PfiPmR9u1L61d/JikpCY/HQ11dnahLE0Z/fuhBNBoNOp0XV/UmTMCFZRJPHdDy+uctTFUbKUse/LWfVEd1V4WDxHdg/BDHKj7Ew3FyuUIv7Bm9fi0Rctttt/UbibfZbBQXF3PmmWeSkhKdvrFj5ff7WbNmDStXrhTz9mKcOFZjs7PJSucQ84TdLhefvPcmnkG+sBYuO4mC4jIAmupr2Prpx0Nu49jjllM8pQKAtuZGNn70Lv6uBvSZxUhS/5k+cxcuoWzqDAA621r49IP/DbneWfMWUjFzDgA+l5OZf70KgCPHnSWCI9Oz//Y0red9E1X2sXvbJoaSV1hCUVk5AB63m51bNgy5bE5+ISXl04L74POy47PBM5IAMnPymDJt5pDPh0KSYEFJOilRKD4W6c+Uyxdgc11P3NYSCFlXiNMX5hyPuXwxRlnmj/8YOjhOTknFXFQKBIP1P/7jDVAUPM17MRXMBM2hz1RiYhLm0vK+n//w9H9RCf69JaAiJ7kvXTsxMZEZM2b0Lfv++++Tl5c34Oa8EDqbx8+W2p5+jyUoMu7arSSULUDSBOevn1gOe/x1bKzt4dlaM3esnoFJrx2wPo0Gji/PwqATjYKiQVxXxA9xrOJDPB2n3kzwUEQ0WM/KykKr1dLW1tbv8ba2NvLyBk/FysvLG9XyRqMRo9E44HG9Xh/zB6pXPO3r0U4cq9A1dLvocslI2sG/Zv7z3BM8+eBvBn3u9t8/SmHZVAAq9+7iwV/eNuhyAP/3qwcoORiA11bt56Ff/WTIZb9/+2+YMiMYgDc11A273ut+eAdT5xyL1y9T8+LLnGXpGXJZCUhqb+G5P/yDvdOOpXHLVrwNO3FVbkBxWvote/E13+Pqm4L72N3VyY+//bUh13vuZVfz3R//CgCHo2vYZb9w3qX84Oe/H/L5UO3vcLN0SgKaKKXDR+Iz5ZcVdrVYUdAiDYxJJg1fQOHPahG/TM4iz945aBEaVZLw5uZjOe4EJK0WnVbHjGMWhrR+CZhxzEJUOYDLHMBcPn/IzzPA9GMW9P17ak4SZcMUQlu8eHFI+yAMLVOvJzfdN2g9EEmj7XesvnZ8KZXtTjocPl7Y0sIVy8sGvEYFulwyJZkDr6mEyBHXFfFDHKv4EA/HaTT7F9Fg3WAwsGjRIt59913OPfdcABRF4d133+X6668f9DXLli3j3Xff5cYbb+x7bM2aNSyL4/RSQTjaOL0BKtsdwy7T0xnsCFFcPo3S8un9nsvKzT/s3wWceMZZQ64nJ7+w79/pWTmcePoXCTh70CWmwxEj63kHRwwBUjMyh12vIX86T6+vZWNtN2fuG771Ud++OHr41KuSNOdUkuacSqb6XaSuGjQNW5GatiMpATJzDo0k6g0Gps46Zsj1Hf530Gq1wy6bk1c45HOj4fQGqOt2jdjPOZbtarbh8oW/ZkEsaba4+euH1TRZ3Nx1+nU8/PKvUSUJ6fDiEAervh+47Regjd5di8L0hGEDdSF8KrKT6LB7+2qC/O3Be2g+sIOv3fgzyqbP7lvObNDxjRPL+N07+/mospN5RaksKEkfsL4mi5uSzPiYQihEhi+gYHH5sLr92L0BPD4Zr6ygKCqSBFqNBoNWg9mgJdmkI81sIC1BH7UbvPFGVlQcngAOXwCvXyagqCiq2lfPw6TXkGjUkWTQib+hMKiIp8HffPPNXHHFFSxevJglS5Zw//3343Q6ueqqYErp5ZdfTmFhIXffHSxoc8MNN3DKKafwu9/9jrPOOot//OMfbNq0iUceeSTSuyoIQhioqsquZtuIfXuXnHw6KWnpHLNoGQuOP3HI5eYuXMLchUtC2vaMufO54w+P4qrehLl88bCjgFOmzeJnDzw+4PEDbXZe2dHMqy12aA8G6d7snJC2f9GXj6OibBqV7Q52Ntuo6XSiZlUgZ1WQdPzFnDw9i9NnHQrWc/IL+cuLoc2tSsvIGnHZd17+J9aeLlZ88Vyy8wpCWu9gajod5KYYMRvib6ZUTadzyKkXk4GqqnxU2ck/NjbgkxWSTTpmfecKdqyoYM69d6Brbjq0cFER0v33U7TqbLoaLQSiMCUgN8XEzLwhJkULYZdo1FGQlkDTwWrun36whpoDe1l9WXu/YB1gZl4Kq+bk8dauVp5aX0d5dhKpR0x5cXoDWF1+Uofp5S5MPgFZodXmoc3mweLyD1kQVlWDg27+gILTGzjYDtOJViuRk2ykIDWB9ERRpNDjl2mzeeiwe7F5/CjKyK/RaiRSEvRkJxnJSTEOOlVFODpF/ErsoosuoqOjgzvuuIPW1lbmz5/PW2+91TdPrb6+Hs1hc+CWL1/Oc889x09/+lN+/OMfM23aNF5++WXmzp0b6V0VBCEMartc2NwjV7lcctLpLDnp9CjsUWja7R7+sbGBHU1WALSSxOKydE6alsX0rPl4Xv89xvbW/iOXB/WmGruXnsAcrZY5BamcM7+QToeXjTXdrN3fQbfTxxuft/LunnZOn5nDmXPySDKG9yv4H4//kYbqA0yfO39cwbqiBNvULRxk5C2WdTt9VHcMn9ERz1y+AM98WsdnB+cpz8pP5poTy0lN0NNdfDZ8++uw7hNoaYH8fDjpJNBqSQeOK8tge4MlohkHOSlG5hSkTEgf96NZeXYirTYPsqyiOZhBIcuBQZc9Z34Bu5qtNPS4eWZ9Hd89tWLA8WqyuEWwfpTw+GXqulw0W9wj3mAfjiyrtFg8tFg8JJt0TMlKJCdlYlpFTiSLy0dtl4suh3fUHXBkRaXH6aPH6eNAu52MRAMlGQPbXgpHn6gMm1x//fVDpr2vXbt2wGMXXHABF1xwQYT3ShCEcHN4A9R0xlew5JcV3vi8hTd3thJQVLSSxAlTM1l9TD5Zh50k9932S+bddM2AVGP14IXuvlsHphpnJRlZfUw+q+bksa3Bwps7W6jtcvHGzlbW7u/gKwsKOWVadthS33S6Qz2Wx6vb4aPd5ombCy5vQGZnkzVqLQKjrbrDwSMfVdPp8KGVJM5dUMCqOXloDr7/clNM6Ax6qktKUIqKKCoqwnTY+zHRqOO4KRnsarZFJPOgIC2BWfnJIlCfAEadlimZiVS2O/r6rCtDtK7UazVcfeIUfvH6HrY1WlhX3cUJFVn9lmmze5ihJEetjaMQfb6AQk2nkyaLK6RR39GwewLsaLSSanYxPTd5QPbGZGT3+DnQ7qDbEWLBzxGoKnQ5fHQ5fCSbdFTkJPW7HhGOLqLkpyAIYaGqKrubbSGf+NubG6mt3IttmMJtkdbY4+JXb+zh1R0tBBSV2fkp3HXOHC5fVjbgxNix8ix2PvA4FB4xN7yoiL0PPkHHyqHnv2s1EotK0/nJ6ll8d0UFhWkJuHwyf99Qzy9e382BMPUB1+mCF0WBQHh6jO5vc4xrtCWadjXb8AXCfNUZAxRV5c2dLdzz1j46HT6ykgzc8oUZfHFufl+gDlCckQDAmWeeybRp09iyZcuAdem1GuYXpzE9N/nwou7jIkkwLTeJ2WJEfUKVZJgxG7Roe0fWA0NnUBSlmznn2GDmzT82NtDt7B9gyLJKu90TuZ0VJlRjj4t1VZ00dIc/UD+c1eVnU203+9vscXMeGS1ZUdnXamdjTXfYAvUj2T0BttVb2FLfg8s3/hvxQvwRwbogCGHR0O0OKf2916O//yXXnrOCd1/7dwT3anCqqrJmdxu/fH0PjT1ukk06vnlyOTedMY28YUaSEy46H6m2Ft5/H557Dt5/H6mmhtSvXRTSdiVJYkFJOnecPZtLl5RgNmhp6HFzz1v7ePbTOjz+8aUoaw+OrAf84Tmhe/wytV3OsKwrkuq7XBG7UJpIVref+/93gH9vaUJWVY4rC753KrKT+i2XataTbOq9URM89r1ZFoMpyTSzZEpmX1u1sTIbtCwqTac0UxSTm2gajcS03OS+kfWh0uB7rZqTR3lWIm6/zN/W1aAckZLSbBHB+mTj8ctsrutmb4s9KvUrIDhCXN/lYmNNNw7v5Ao0rS4/G6q7aOh2RSWjq9vh49PqLmo7naiTNYVMGFT8VQ8SBCHmePwyVaOcKywfHP3VRrFKNYDXL/PU+jo21nYDMK8olSuWlY2YqqfVSJRkJIJWAytW9HsuP9VEXZcLZ4gXI1qNxGkzc1hcms5LW5v4uLKTtfs72Nls5crlZczMSxnT76bXBwv7BPzhC1zru1wUpiXEbLEbu8dPZUd4MhNiyc4mK49/UoPdE8Cg1XDJkmJOnJo16Oh1UXpC379lOXjDZ6TPVZJRx+KyDNptHqo6nCG/d4PrlijNMFOWmSiqF8eQ7GQjRkNvsD78jT+tRuIbJ07h56/uZk+LnQ/2dXDqzEOFNHucPjx+OWY/98LotNs87G6xRS1IP5LTG+Czmm5mF6SQGydTq4bT0O3iQLs9opkJg1EUqGx30OnwMrcwVXw+jxJiZF0QhHHb2zr6NLdDQUX07hm22z38+s29bKztRitJXLqkhO+dOjWkOXWF6QkYdIN/ZUqSxNScpEGfG05Kgp4rl5fxg5XTyUw00Onwcd87+3l+Yz1+efRXAX0j62GYs95LVtQR2/BNFEUJdh6I9gVTJAVkhX9tauD+dw9g9wQoSk/gp2fN4qRp2YMG6nqdhtzkQxe/oYysHy4nxcSyikwWlKSRl2pCP8R7HCDFrGd6bjInTs2iPDtJBOoxKDkhOH0nlLoVeSkmvrowOK3nX1saabP1H01vsYrR9Xinqir72+zsaLROWKDeS1ZUPm+0UtMZ+9laQ+md7revNfqB+uEsLj8barrpckzezifCIWJkXRCEcWm3e8ZUsKo3TVOrj87X0N5WG39eW4XLJ5Ni0vHtUyqYlhtaiymNJjgndDjZyUbSzHosrtHPF5+Vn8JdX57DvzY38sH+Dt7d287+NjvXnVxOfmrCyCs4qHfOuhymOeu9Wq0eitPNMVchurrTgcMzeVIr2+0eHvmwmtouFwCnzsjmwsXF6LVDB9CFaaZ+QXOoI+tHykwy9lUddvkCuHwyAVlFI4EGmU+qYWFJOnp9bL0HhP7++c9/8Pbbb5M5Z+h2mIc7bWYO2xos7G2187dParll1Yy+91OL1c2ULDHFIV4FZIUdTdaYmyJU1e7AG5DHnEE2kXY0WbG4Y+PusD+gsK3BwrScZEoyh78+EeKbGFkXBGHMZEVlf+vYRl17CyBFY2R9Y0039//vAC6fTHlWIrefPTvkQB0gPzW0NPAj5xKPhkmv5evHl3LD6dNINulo6HHzi9f38PGBzpDnp33jxtu45/F/sWj5ijHvx1D2h6kIXrhYXX7qDga1k8GGmi5+/tpuartcmA1avrOigsuWlg4bqEsSFKb1v0gb7cj6YMwGHVlJRvJSTeSkmEhNEH2T40V6ejopKSlkpYYWZGskiSuXl2HSa6jscLBmT1vfcy6vjHUUdUiE2OHxy2yq64m5QL1XY7ebXc3WuJl7HTiY6dYTY39PVYX9bXb2tdrj5m8pjJ4I1gVBGLOaTueYi6L1jaxHOFh/Z3crj3xUTUBRWVSazv+tmkG6OfTgQ5KgLMQCWumJBjKSxhfYHFOYys/Ons2svGR8AYUn19fy6Ec1If2dp82ex8LjTyIjO2fEZUfL6vIPSJOdKIqisqtlcrRp8/pl/vZJzcFjrDAtJ4mfnT07pB73GYkGEgz9byKNdWRdmFxm5oXeei0rychFi4sB+M/WJpos7r7nYuUzL4TO5Quwua4n5rOOWiwe9rTE1k3gwfRmKMSyhm4Xu5ptImCfpESwLgjCmLh8Aeq7xz73rC9Y10UmqFBVlX9tbuCFTY1AMN3zmyeVDztSOZjcFNOAgGg44xld75VmNnDTyul8dUEhGgk21nbzqzf20GJ1j/ziCKpqd6DEQAue6k4HLu/4KufHgvpuF794fQ+fVHUhSfClefn88MwZfenoIylKH5j6+O1vf5vvfe97pKePHOwLk88TTzzBX/7yFzau/4RpuaF/F504NYtjClMJKCqPf1xD4OCE3FarRwQAcaQ3UHf74uP7sdniZn9b7AbssqKyvdGCbQzT26Kt1ephR6M1Js7RQniJYF0QhDE50OYYV4GVFV88lwuu+jbFZVPDt1MHqSr8Y3MTb+8KpnSet7CQS44rHlNBrNJRzgVLTdCTkxJasDUcjSSx+ph8/m/VDNIS9LRYPfzy9T1srhu6L/32jZ/w6j+epHLP5+Pe/mBcPrnfqNtEsHniP/1dVVXe3dPGr9/YQ6vNQ7pZzw9XzuCc+YUhj4YmGLRkDZLF8Zvf/IYHH3yQrKyscO+2EAfWrFnD22+/zc6dOylKN4f8XSRJElcsK8Vs0FLf7eKNz1sB8AWUAX3YhdjUG6h7/bExpzpU9V0u6mKwRaiqquxsstLjjP1AvVeH3cvnTfEzvUAIjQjWBUEYtW6nj44xFJU73JcuuoLrfvgzymfMDtNeBSmqygvVGt7b14kEfP34Ur44N3/QStojyUwy9PWvHo3y7CTGsLlBTctJ5vazZzM9NwlvQOEvH1Txr00Ng1bfX/PKv3jwF7ey6eO14dn4IKo7nX3z96JNVVX2NNviOv3d5vbz0HuVPP9ZAwFF5diiVO44ezYz8kKvoQBQmJYwpve0MLn11ironQ4xKz8Fc4iZQWlmA19bWgrA6ztaqD0YQImq8LHP45fZUmeJu0C914E2B+0xNuViX5t93Nc5E6HD7mVXs22id0MIIxGsC4IwKr2tYGKRoqo8/WkD69o1SMCVy8s4ZXr2mNcX6lz1IyUZdWHtJZuaoOcHK2ewanYuAG/vbuN3a/YNKP7UWw0+EOZq8IfzBxTquidmZLu+24U9xudhDmdnk5U7X93FjiYrOo3EJccVc/2pU0d9Q0ijgYK0gV0CVFWlsbGRlpYWlMnUz04Ime6I9o16rYZ5xWlotaHd2DmuLJ3FpenIqsoTn9TglxU6HN5Rt+YUoscvK2yp7xlz/ZhYsavZht0TG6PYDd0uGrsnNotsPFqtnpi9ThNGTwTrQtgpiorTG+gbfW23e+h2+nB4A+KEPwk0Wz1hKVzT3txIa1M9Pm947qarqsqzn9bxSXU3GlSuXl7KCVPHngqckqAnPXHsxeLKsxPDNroOoNVIXLC4mG+dUo5Rp2F/m4NfvLa7Xw/0Q33WI3vBU9/twheIbjDo9slUd8ReqmQo/LLCPw/2Trd5AhSkmfjpWbM4fVbumEbHc1NMGAbph64oCsXFxRQUFNDTM/R0CWHyOjJYh+DNw7kFqSF9H0mSxGVLS0g26Wi2ePjvtmZkWaVT9HOOSYqisr3BMilqeMiKyo5Ga9TPLUfqcfomRaBb3+WiPs6njAlBos+6MG6KotLl9NHl9NLj9OPyBYZMU5WkYFugNLOezCQDWYnGMc0jFiaGrKhUd4ytVduRfvytS6mr2s9vn3iR+UtD6wk8FFVVeWFTIx8eOJj6Pk1h6ZTxFdgqG2ffUrNBR35qAs1hnuO9uDSDwrQE/ry2iharh9++vY+Ljivm1BnZh/VZj+zosyyr1HY5mT6K9nfjtbfVFpc3+5otbh79qJqGnuD74NQZ2VywqHjQYDtUxRmDvzd7U59hfK3bhPjV2wXg8PcCQHaykZn5KewJIT022aTn8uNL+dPaKt7e3cqCkjSyk41hzRYSwmN3iw1LHBQ/C5XbJ7Oz2RpSN4xI8PhldjRNjk4jAAfa7ZgMGnKSxWc3nomzuTBmLl+Ahm43LVY3ATm0bzZVBac3gNMboKnHjVYrkZdiojjDTJJRvB1jXX23K2xz4vpaTIUhqHhle3Nff+DLjy9moVQzrvWZDVqyk8dfJK48O5FWm3tchfgGk5+awE9Wz+LJdbVsquvhuY31VHc6kHTBTIBIj6wDNPa4KMkwh9R/frzabR66Yqy/7UhUVeXDA53887MGfLJCklHHlcvLmF+cNq71ppr1pAyRNn/4aKpo3XZ0GmxkvVdhWgKyHNo0pgUl6RxfnsGn1d387ZNaSjPNzClIQTfKbhpC5FR3OGidhPUEuh0+ajqdTMka2zS0seotKOef4JH9cFLV4PQCc5lOXGPHMfGtK4yayxfg80Yr66u6aOh2hRyoD0aWVZp63Hxa1cX2BkvMzFcSBvIFlL6CQ+EQrj7rb+5s4dUdLQBcclwxJ1ZkjnvfSjLNYSneZdJrKUwb3wj9cOv+5snlXLCoCI0En1Z3syfjBHSpuQT8kf8cKQrUdEY+LT0gK+yLs5REq9vPn9ZW8cyndfhkhdn5Kdz5pdnjDtQBigdp19br8NFUEawfnY4sMHekkkwzM/KSQ0qJv+S4ElIT9LTaPPx7cxMdIhU+ZrTbPXE7LSgU1R0OLK7o3qCt6nBOqiyFXrKssqPBgn+CCsMK4yeCdSFkAVlhf5ud9VVdtNk8YU8T6rB72VjTzZ4Wm/hSiUG1XU7kcdyYOVJvqvZ4gooP93fw7y1NAHx1QSGnz8od937pdRryUwcW7xqrsixzyO24RkuSJFbNyePmldNJNulw6pLJu/IBuvXjv2ERimaLG5cvsin3NZ3OuKpwvKm2m5+9sottDZZgnYFFRdx4xjTSzGOvf9DLoNOQM0zGx+GjqSIN/uh0++238+ijj3LzzTcPuUxxhpljClNH/F5KNOq4YlmwOvz/9rTx3p72sO6rMDYuX2DSV/vuHRGOVueRHqcvJtvHhYvLJ0/698xkJoJ1ISTdTh+fVndT3+WK6FweVYWmHjfrq7rismXGZOXxyzT2hLdQyaE0+NG3RgPY3mjhmQ11AKyem8fqY/LDsl/F6QlhDa6NOi3FGeEL/gczMy+F28+aTWGyFq0pibrck3llezNKhCfeqSoRHd1xeAM0hPl9FykOT4BHPqzm4Q+rcXgDFKcn8NOzZrFqTh6aMFUaLExPGLbGhxhZFzIzM8nOziYlJWXY5XJSTCwuS8dsHP59Mq8ojROnZqEC9797AKvDDWvXwvPPB/8/xAi+EBmyorK9wRrWG+exyu2To5JV5ZcVdsV5S9BQdNq91EYhG04IPxGsC8NSVZXKdjtb6qLbFsQXUNjeYGFvqw0lDotKTTbVHc6wz7vuS4PXjT6oqOl08tcPq1FVOKEik68sKAzLPmk1EkXDpBmPVWlmYsitk8YqI9HAT798bF+rule2N/PH9ypxeiM78t1m80RsG/ta7WF/30XCjkYLP3t1Fxtru9FIcPa8fH6yetawKeujpdEE5xwP5/CRdY1GnN6F4SWb9Cydknlw2s/Qy124uIgMs4GFm95HLZ0Cp54Kl14a/H9ZGbz0UtT2+Wi3t9UW8e/0WNJi8dBuj+y8/H2t9rhvexeqqg4H1kmY6j/ZibO5MCRfINi7s7Zz4ka2GrvdbIryjQKhP5cvQIs1/P1G5cDBkfVRzllvs3l48L0D+AIKcwpS+Pqy0rDMLwfITxu8JdZ46bUaSoeo4B3u7Xz9+FKuWl6GTiOxo8nKL9/YE9HRaVWNzNz1NpuHHmdsF5VzeAI8/nEND75XidXtJy/VxG1fnMW58wvDXogrJ9k0YjE/k8nEtddeyzXXXBPWbQvxY82aNfztb3/jpRADaK1GYnpuMkvLM8lJGXyKhdmg4+fyPv7y8q9J6W7r/2RTE5x/vgjYo6DV6qHFMvkKyo1kb4s9YlMj2+2eSVmkbyiqCjubrVGbXiCER0QntXV3d/O9732PV199FY1Gw3nnnccDDzxAUlLSkK9ZsWIFH3zwQb/HvvnNb/Lwww9HcleFIzi9AbY1WHD7Jj5Itrn9fFbbzfziNJKHqIIsRE51hzMi6WFfPO9S3E4HySlpIb/G7vHzwLsHsHsClGSY+fYpFejCNIIoSVASwYC6JMNMQ487opVmm+pq2L19E1k5+dz2xQX8eW0VHXYvd7+xl8uXlXJ8eWTmsrfZPEzJSiQxTNVmZUXlQFt4WgRGgqqqbKjp5h+fNeDwBpCAM2bn8pX5hRG52QNDt2s7XFpaGo888khEti/Eh/Xr1/Pf//4XnU7HjBkz+h43GAzMnj277+d9+/bhdg+8CZvoD9DjUkgrLMd18PzfVH2A8/50FxIw4Laoqga/PG+8Ec45h6raWuz2oVOX58+f3/fv7u5uMjIyxvBbHn3cPpk9rUfnnGNfQGFfq525halhXa9fVtjbEl/FS8Ohd3rBnILw/j2FyIlosH7ZZZfR0tLCmjVr8Pv9XHXVVVx33XU899xzw77u2muv5ec//3nfz2Zz5EekhEOsbj/bGiwx1b7C61fYVNfD/KI00hPHX6hJCI3LF6DNFpm7ztf+4PZRLe8NyDz0XiXtdi9ZSQZuOH1aWNuGZScbMRsi95Wo02qYkpkYUtuksdq+8RP+cOcPWXbqKn7+x6e4/azZPPpRNbtabDz2cQ3VHU4uWFyEPsyjvr1z148pCs/Jv6bTGbPZNJ0OL89+WsfOg8V6CtJMXLGsjIrsoW9Cj1eqWU9qgrhRKYxMrw++T/7973/z73//u+/xsrIyamoOtbS87LLL2Lx586DryMnJoa2tDY9fxur28+IF3+aKrs6hN6qq0NAAH33E9ffcw1tvvTXMosE7v7Isc9111/HVr36VSy+9dDS/4lFHVVV2NR8d89SH0mr1kJ9qIjNp/C1Ve+1vs+OLoevcaGqxeMhONor+63EiYleme/bs4a233uKzzz5j8eLFADz00EOsXr2a++67j4KCgiFfazabycvLi9SuCcOwuHxsbbDE5ElBllW2NvQwryiNrDB+YQtDi9So+mgpqsrjH9dQ3ekk0aDlxtOnhz14Kc2IfE/XovQE6rtdEQtEdQcv1AP+YPp4kknHDadP47/bm3n98xbe29dOZYeDb55cTm5KeE/S7XYPDm/iuHu5un0y9d2xVwRHUVT+t7eNl7c14wso6DQSZ8/L5wtz8iLeezrUjA9ZlrFareh0uhELjAmT0+rVq3nssccGtG7Lze3fKSM7O3vI67CsrCwg2B7SpNcyIzG0Apm7N+9FMSSRnZuPJIFGCnasGGyS0j333MOBAwfYtWtXSOs+mtV2uSZlS7HR2ttq5/hyQ1gKwHY7fUfllILD7W2xk5ZgiFg2mBA+EQvW169fT1paWl+gDnDGGWeg0WjYsGEDX/nKV4Z87d///neeffZZ8vLy+NKXvsTtt98+5Oi61+vF6z1UNdxmC452+P1+/FHoNTwevfsXK/tpdfvY0WBFjuGCbrIM2+s6mVuYRkYUR9hj7VhFg9sXoLXHEbFgvaujDa1WR0pa+ojFsF7e1sKWegs6jcR3T5lCbpIOVR68yI6qyP3+H4pUsx6zPjrHtyTdyL6WyKQzag/+HQN+f9/fRwLOnZdLeaaJv62vp77bxc9f283XlhRx/JTwpaCqQFWrldkFoQWJQ32m9jZbkf2xVUDpQLuD5zc10dATTBuenpPI15cWk5diAhTUCM7/M+q1pJs0Ib039+zZw7HHHktGRgatra1h24ej8fsvXs2ePZuHHnqIlStX9o2y9zr8+L3yyivDrufwZb/361/DypUjbtuVnskPf3n/gMcTDFqyk03kpxr71tve3s6OHTs488wzj9r3VSifK4fXT3WbBfXoHADux+UOUNlqpTx7fDfWFUVld2M36ig6GYzluiLWeWXY29zDrPzJc2M3ns5Vo9nHiAXrra2t5OTk9N+YTjfiRcSll15KaWkpBQUF7Nixgx/96Efs27dvyGIpd999N3fdddeAx9955524SZ9fs2bNRO9C3Pm0cmK2K45VeCiKwiVf/SoATz/99LCjgJs6JN6oDKa7X1weoNCxF1cI05ndtVtD3h8X0LIz5MVjltxdD4DP0YOrelO/56YC/zcHnqnUUmlTeHxdPZ8fqOX8KQojdG8KWS1Qu210r4nlz5TVB6/UadjUGbwJkqBVOadUYWmOFU2nFdcwmcHh4gLe3BfasnV1wVaGsizzxhtvhH1fYvlYCf2F9VjJMmdmZmLq6hp0lFwF3FlZNKVp4YjvHQi+h7uAvYc91vtePXDgQETeq/FEfK5Ct7e6//so2kZzXREPaoCayfUrAfHxmXK5Qi/8O+pg/dZbb+Wee+4Zdpk9e/aMdrV9rrvuur5/H3PMMeTn53P66adTVVVFRUXFgOVvu+02br755r6fbTYbxcXFnHnmmTGfBuj3+1mzZs2gd8Cjye0LsKU+tuaoh0KnlZhfkkaSMfJ/u1g5VtHi8clsqOmK2Ki633+oyndyxWLMyYN/Vqs6nTy/oRJQ+eLsHE5ZMPT0mV6qIuOu3UpC2QIkzchRaIJBy5IpGWGrKB+KDoeXXY3WsK/XXBuMHlWdCXP54oHPA/83Q+W1nW28trOVjR0a6jwJXL28hClZ4ZkGkJtqCulO/ZGfKVVV+ay2B1cMtCUKyArv7uvk1c9b8QYUJODEqZl85dh8kk0RLfXSj1Yrsaw8M+Q0+23btgHBqWSrV68O234cbd9/8SxSx0r685/h4otRAemwE4MCSEgc+PGvMU9bGvL60vPfA6C0tDSs79V4MtKxqutyUdMRu4U2J0paooH5xWljeq3HJ7OxtmvULUFHe10RT4z64DVQOKYXTLR4Olf1ZoKHYtRXHT/4wQ+48sorh12mvLycvLw82tvb+z0eCATo7u4e1Xz0pUuDX/6VlZWDButGoxGjceD8Zb1eH/MHqtdE7qs3IPN5i5OAqkGK8LzLcJOBnS1OjivLCGuhseHE0/tqPA50uEGjG3QUJRwU36FgXWswIg3Svq3L4eXPH9QQUFTmF6fxlUXFowqoJY120PUeqTQ7GYMhukULC9L1NFl9Ye93qjcEvwsDgcCQv7tWC+csKGJWQSqPflRNm93Lb945wOq5+Zw9L3/c8687nAEqFCnkyvC9n6mGbhfuACEds0hRVZXNdT28tLWJdntwelV5ViKXLimhLEw3M0ajKNNMgin0+hy900l0Ol1EvqeOlu+/ySDsx+rCC0GngxtugMbGvodbk7P47Re/xSmnrCZpFJ9djxz8Lnd6/Uf9e2qwY+X0Bqi3eCb0+zBWWT0KPW6ZnDHUXdnV6kSVdEhjvGQM9boinvgUaLB4mZabPNG7EjbxcK4azf6N+h2XnZ1Ndnb2iMstW7YMi8XC5s2bWbRoEQDvvfceiqL0BeCh6B0pyM/PH+2uCiNQFJUdjdaYaM82Vl6/wvYGC4vLJsddwVjg8cu02sLfV/1wcuDQ6KlWN/BryOOX+eP7ldg8AYrSE7jmxCloIjDyrddpKEgLrXhSuE3LSWJTbU9Y16nTB286BEKYCzU9N5mffWkOz2+sZ0NNN6993sL2RgtXnziFovSxTyHq7bs+mjY7flmhaoJHkPa32XlxcyPVB3vGp5h0nLewiGUVmRF5741kLK0EAwc/V1rt5Br5EWLEV78K55wDH30ELS1U65L5+n4DTXY/TRvrue7k8pBX1fsebbe42NtqY3pOMhpxDu+zp8U26tHfo8n+NgeZScZRXfd1Orx02r0jL3gUqu92kZ+WMO4CsUJkROyozJo1iy984Qtce+21PPzww/j9fq6//nouvvjivgqkTU1NnH766Tz99NMsWbKEqqoqnnvuOVavXk1mZiY7duzgpptu4uSTT2bevHmR2tWj1u4WW9hH9iaC3RNgd7MtbG2jjnb13a6IXyTIhxWH0x5xl1pVVf72SS0NPW6STTq+d+rUiGVOFKUnTNhNnjSzgexkIx1hvHiYMm0Wt937F1LS0kNaPsmo49qTyllQksazn9bT0OPmF6/vYfXcPFYfkz/mFm+j7bte3eEkMEEdKJosbl7a0sj2g9MSjDoNq+bkcebs3Khl7AwmN8U06u33VgDXDXIDTBDCQquFFSsAyHD5uDK3kbvf3MPG2m4WlqaxuDS0opWag6nEiqLQ2O3G6Q0wrygt7G0l41GTxS2qv4/A45ep63JSHmLLTEVR2d969PVUD5Wqwr5WG4tC/PwK0RXRM/rf//53rr/+ek4//XQ0Gg3nnXceDz74YN/zfr+fffv29U2yNxgM/O9//+P+++/H6XRSXFzMeeedx09/+tNI7uZRqaHbRat18rStaLN5SOnSUZoZ/VTVycQvKzT1RHZUHUAOBIMKSZIGjAK+tauVzfU9aDUS310xNax9VQ+n1UgUj2MEORym5SbR6fCGrTZARnYOp501dKeNoSwuzWBaTjLPrK9jW6OFV3e0sLGmm8uWloZc3f1woxldd/oCNPaEXmglXBp7XLy2o4XNdT2oBNtMnTQtmy8fWxATPc1LM0f/3hQj60I0pZkNzMxP5otz83n98xae/bSe6TnJpITw+Vly0mkkp6YxZdpMAHqcfjbV9rCgJG1Cb5JNNF9A4UCbCCpDUdfloiAtIaT3S0OPC1eYskhVVe1Xd+dIGknT10Y1UssC+HyD3+jXanVjOgf0OP202Txhb+sqjF9Eg/WMjAyee+65IZ8vKytDPewqtbi4mA8++CCSuyQQ7KV+oH3ynQwq2x2kmPSkR7Gl22TT0O2KSuu+3pH1I0fVdzVbeWlrEwCXLilhak5od83HIi/VNOH9Rc0GHUXpZhq6ox+sHik1Qc93T61gc10Pz3/WQJvdy+//t5+lUzK4cHHxqAPYNpuH8uxEzIbhTzOV7ZFrDziY+m4Xr+1oZku9pe+xRSXpfGVBIXmpsXGRkplkINk0+hsG2dnZXHbZZaOqCyMI45GbYuLseflsa7DQZHHz7IY6vn1KxYj1RWYcs4AZxyzo95jTG2BLXQ8LS9OP2oD9QLt9wrKM4o2sqFS2O0a8KewLKNQcnN4UDp1tLVx6+sIhn199/te46a77AHDYrHx1+cwhlz397PO49Z4/AcHpa2ctKB1y2RNO/yJ3Pvi3vp+/tLgcZZD2c2mZWfzpn2+Tk1844u9ypANtDrJGOb1AiDyRK3eU8QUUPm+yTsq5UKoKO5utLJ2SOeFBWDySFbWvj3SkGYxGvvDVS4ITcw/qcnh59KMaVBVOnJrFydOyIrZ9SRrbyGUkTMlKpMXqDssFmtNuY8unH6HV6lh+2qpRv16SJBaXZTCnIJX/bGvi/b3tbKjpZluDhS/MzePMWbkYQ7yIVtVgevtIF1I9Dl/EC/aoqsq+Njtrdrf1pbtLwKLSdM6elz+uOfqRMNbK/LNmzeLZZ58N894IwtByk03Ud7m4+sQp/Or1PWypt7Cxppul5ZljWp/LJ7OlvofFpRlH3Xnc6vLTYpk8GY/R0Gr1UJxhHvZmcnWnY9zn1/27tqN01FNeOHdc64kGS1cn+3dtH1OwPtrpBUJ0iGD9KLO7xYbXPwkj9YO8foXdLbYxt/U4mjVb3FFr35eSlsEPfvGHvp99AYU/ra3C4Q1QlmnmsqUlEW2llpVkHHHEN1oMOg3lWUnsD0PqY3trEz+/8WrSMjJZftquMa8nwaDl0iUlLC/P5O8b66npdPLfbc18sK+DL88vYHlFJjrNyBfSw42uK1HI4PDLChtru/nf7ra+G1EScFxZBmfNy6dwgooLDifNrCfNLLKDhPiQatZj0mspyTBz9rx8/ru9mb9vrGdGXvKw7+POthaa6qpJzcikbGr/kUeXV2Zbg4VFpelHzQifqqrsbQ29lZNwSGW7fci51g5vICxT+35z6/dorKvmt4/nMW/Jiby84cCQyx5eMyQpJTXkZXV6/bDLHpna/tK6gR3n//fKv3DYbZRWTB9yPSMZzfQCITpi42pViIqGbtdRUQmz0+6lodtF8SgrKR/NVFWlfoJSsVVV5dkNddR3u0gy6vj2KRURLzJUFmO1DYrSE2i0uHB5xzenTqcLji74Q6gGH4qyrER+/MWZfFbbw0tbG+l0+Hh6fR1vfN7CF+fms7wic9hjNdzoemMEszjabB4+OtDJuqpObJ7glAuDVsOyikxWzsqNmXT3wYynRZwsywQCAXS6sc1ZFISxyEkxUt/l4ovH5LG1wUJ9t4unP63je6dOHfKm68f/e50//fqnnPKFL/PT3z0y4Hmb28/OJivzilIjeuM2VjRbPdg9gZEXFAbocfppt3vISR74vR6OaVaqqtLZ3gpAVk4eGo2GxKTQ2pxJkhSRZYFBlz3n0m+E/PqhyIpKdYdzTPVqhMgQwfpRwuENTMp56kOpbHeQmWSImdHTWNdh90a1hZ8sy3jcLrRaLZ/WO1hX1YUkwTdPLo9YQbleaWY9qeaJLyB2OI1GYnpuMtsOm0c9Fr3BuhwIXyVhSZJYMiWDBSVpvL+vnTd3ttLp8PHMp3W8tqOZU2fkcOLUrCGLSrXZPJRnmDBvWA8tLZCfj+f45dR1h28OIQTT97bU9/BxZSf72w61gUtL0HPazBxOnpZNkim2vw9SEvRkjeP9/+qrr/KVr3yF448/nvXr14dxzwRhaL2p8DqNhqtPmMIvXt/NjkYr66q6OGHq4NOZeqvB9xYbHUyH3UtVhzOitUtigV9WqGyf2NaV8a6y3UF2gg7p44/7zjPdC5eGZYDK5bDjcQcHMzJzjo420i1WNyWZZtHKLUaIo3AUUBSVXZN0nvpQZEVld7ONRaXpR8Vd+fGqi/Koes3+PXz7/DPImr2clHN+AsD5C4uYlR/5O7klMTJX/UhZSUayko3jurjorRQb8Id/hEav1XDm7DxOmZ7NRwc6eXtXKz0uPy9tbeK/25tZVJLOSdOymJ6b3C91Neud19Hdczu0NPc9pskvIPP/7sRenjOufXL5AmxvsLK5roedzVYCB1PrJQnmFqRy4tQsji1KRRcn7aDKssb33hSt24SJkGrWk2DQ4vbJFKYn8OVjC3hpaxP/+KyBWfkpZAxS9FWj7W3dNvxN4tpOJykJukFHTSeL+i5X1KagTVaJr72CfO8d6JqbDj2WV0D2rb+gY+VZ41p3R1vw3JWUlIQpIfamTh2upbEOm6WHnPxC0jOzx7weVYWqdgfHiimlMUGc0Y8CNV3OozK9yuLy09DtjtngLFZYXX6sUe7pKssBNAkpJJz2LWRFZXFpOmfOzo34ds1GbUxf9E3PTaLb6R3zjbXekfVAwI+qqhG5UWXUaTljVi6nTM9mY003a/d3UNPpZGNtNxtru0k26VhYks7CkjRO3PER8354LUfmIepbW5j3g2/iveUWrOWLQ962rKjUdTnZ02pnT4uNA+2Oft0LcpONLKvIZHlF1qABQixLMo0/IBGt24SJkpNspK4reNN31Zw8tjVYqO508tS6Wm48Y9qA76LeTiCDVbM+0q5mG8lTgjcEJqMmiwskcTk+VtlrXmfeTdcMOM8Y2lqYd9M17PjDY+MK2DvbginwmZljK5oYTX/97V188r83+P7tv+FLF185rnV12L1YXD5RQyUGiG+HSc7u8VMbxpYV8aaqw0FWskiHH06405FDEQgEyDzrJjSJGeSlmLhyeVlUMiBiba76kcwGHaWZidR0jO2YaA8bUZUDAXR6PT6fl7amhiFfk5SS2ncHPuD309JYN/SyyamkZwWX1agKJVoLl8/S0+JIYlOLjz2dfuyeAB/s7+Cjva1c+PCtoKoceWQlVUUF5j72GJ9c/B04ohq8qqo4vAE6HF7arF7qup3Ud7uo73bhOaJAZkGaiUUl6SwqTacwLSFuM2nKs8f/3hQj68JEyU019QXrWo3EN06Ywl2v7WJXi40PD3RyyvT+o3yagwUq5RFG1gFkWeXzJiuLS9PRTMKCc4oC0uS8DzGigN9P4IhpW1qtDr0hGCCqqorXM3R9Ew0w4+6fDnOekZh+909pWHYyGoMBg8EY2nq12r5lOw+OrMdDsN773d9743a8qjocQxbvE6JHnNEnMVUNpoJHs4dxrJEVlb2tdhaWpE/0rsQkj1+mYwKKDm5sVTBXHIca8PHNU2ZHpeqoUa8hLyV2R9V7TclMpM3qwTWGGgJ6ffACp6is4mB6qZ7m+hquPWfFkK+58KrvcO0P7wCgs72Fb5x94pDLfuniK/n+7b8BwGbtGbisRoup5BjMM05kVUom+fbOIdclAeauLtY89hK7pi/CF1Dw+GU8AQWr249viLRQs0HLjLxkZuWlMDs/JaaLxYUqOQyj6iBG1oWJk2LSYzZo+7638lJNfGVBIS9sauSFTQ3MKUjpV4+h98aiHMLIOgQLzlV3OpiaE3oBrlhndfsmehcm1O7tm7n1mgtxu/rfnD7nsqv5+k130m730NTaxYP3/RptUsbB/9LRGBLRGBOQDGZO6qxndVvLkNuQUEloa+HJu/7Mjop5VFSUk5ZgINmk5R8P/RLZ0U3A1kmgpxnFfaga//ErzuQXf3oagJS0YJCekRH7Qevh2XXh0OP00+XwRryWkDA8EaxPYnVdrqMy/f1I3Q4fLVY3+amxPddoIjR0u6J+M+dAm5117QfvgW99ieJvLI/KdksyzHExKqPRSMzMT2FLXc+oX2sym1nxxXOomDkXg9F0cH1aklPShnyNwXQoSNRImmGXPXy+niRJgy/bXYt/fS0L5swLaZ899c3sSpo24HGJYDHArCQjJRlmSjLNlGaYKUhNiIvjOBoVYSqgJUbWhYmUk2Lql8l3xsxcttZbONDu4Ml1tdy8cjqag5kvvQXmQkmD71XX5SIryThp0nKrxphBNVns2rIBn8ZIwtQ5GHLK0WUUok8vZEfeFH7wr+19y2Wccd2Q68hu2BnStgq0OjaZs9nTcqjQcsbp/dcru+0EeprwdzXhSNGzt9VGSYaZ5aetwpyUzKJFi0b5G0Zfby0IOUwj6wDVnU4RrE8wcUafpNw+mZqjOP39SAfaHGQlGSPeEiyeyIpKkyVy7bMGY/f4eeSjalQkHDvfI7lxc1S2q9NKMdlTeygZiQbyUk20Wj2jep0kSfzkvr/2e6ykfBovrR/Yj3UwOQVFIS+blpE17LLpGz+B9R+OuJ7jls4ic14ZRp0Gk06LUachJUFPRqLhqPi89t6QCAcxsi5MpNwUY79gXaORuOqEMu58dTd7W+2s3dfBaTODRSUrZs7h6pt+Ql5hccjrV1XY3WxjaXlm3Pdfb7d5sEW5VsxE8/plqjqcVHU4qO1yskczn6LvPj1gOYXgzdqMRAPpZj3JRg2pJh2pCXpSTDoS9BpMOg0mvYbygh54deRtn3TCLIrmluJVNFjcPiwuP90ONzZPgG6nnx53AG1CMtqEmRgLZmIH7ntnPwDZyUbOuvcN0HbTavOQl5YYs9OtDnWECV+wbnX56XR4w3aeEkZPBOuT1N5WW7/CS0c7X0ChqsPBzDzRN7JXs8VNQI7ee0RRVR7/pIYel59UvUz9O38mY9r0qGy7KN0cNxXBe83IS6bb6RsyHTzW9Sw6Hk9uPsb2VqRB0jcUoNNoJOeLp5Jvip8bKeFWkR2+tlSlpaWce+65LF4cetE+QQiXZJOeRKMOp/dQoJCTbOL8hUU8t7GeF7c0MqcghdwUE6UV0ymtGP33v8snU93hYFpu/KbDq6p6VLRqCyjBlnR7Wuzsa7VT0+Xsf10qGUBVSMLDMRXF5KeayE0xkZdiIiclxMGVvNOGPc+okoQ3N5/01atIH+YmpjcQnBLYZvPSbHUHa6R0uehy+uiwe+mwe9mIFqr3kpqgZ2ZeMscWpTGnIIXEGGpvdmjOenhvBNV0OkWwPoFi5x0mhE27zUOX4+ieCzWYph43hWkJJJtiq8f2RGnoiW67trd2trKzyYZeK3HeNCMZZ6wir7Ak4tvVaiRKMuKvI4Beq2FmXjI7Gq0TvStjo9Wy77ZfMu+ma1Alqd+FlCpJSEDtjTei0R+9n8fMJAPpYaxaf+aZZ3LmmWeGbX2CMFp5qSaqjghEV8zIZkt9D3tb7fztk1puWTVjXFNZ6rtd5KaaSInTc3mTxT2mmiTxwO7xs7PJxvZGC7uabbj9/X/PDLOBablJTMlKpDTTTEm6GeN4ataMcJ4B2HfrL2CEbCOjTktRupmidDOLOFTjyOEJUNftZH+rjb11rdQ5NVjdfjbUdLOhphuNBNNykjm2OJXjyjJIn+ApGqOtBREqq0vMXZ9IIlifZGRFZV+bfeQFj0KqCvvb7KKyJdDl8OLyRu9i4UCbnZe3BfufXrqkhOXTslm++K8jvCo8CtISMOjia1S9V06KibxU76jT4WNFx8qz6HjiWXJu/xE0NvY9rhQWsvPmO2gpzyH+bqOEhyTB1DDNVReEWJGbYhwQrGskiSuXl/GzV3ZR2eHgf3vbOKHYTGNdNQajkSnTZo1qG6oKe5ptLJmSEbPpyEORFXXSTVF0+QJsqbewsaabPa39ixonm3TMKUhhZm4KM/KSyUoyhP2Ydaw8i30PPcGM39ze/zxTUMjO/7trXG3bkkw65hSkMjs3EVdyE/rSY6nu9rCzycaORgvNVg/72uzsa7Pzr02NzMhL5vgpmSwsTZuQLkSLTzyVxOQU5i1eFvZ114i56xNGBOuTTE2nA68/PtNmo6HH6afN5iE3DqqCR1JjT/Tmqju9AR75qBpFhePLMzhxalbUtq3RQGlmfIeDM/KS6XH54vJznZKgJ/uKS+DrF8FHH0FLC+Tnoz3pJDw1HXi3fIhJUUYa9JiU8lJNIstHmHTMBh0pCXps7v5puFlJRi5aXMzTn9bx0pYmaLHx2+9fwrTZ8/jzv94Z9XbsngCNPW6K4yxrqqHbFZff5UeSFZUdjRY+qepiZ5OVwGHp7cXpCRxblMa8olTKshL7igpGUvrXL0b61tcHnGd8DVYIY22AYMZbCjPzUjh/UREddi/bGy1squ2hssPB3lY7e1vtPLtBYsmUDE6bkUNZVvRaxi49+QyWnnxGRNZtcfnpcfrCmg0mhEYE65OIyxegvju6qc3xqLLdQXaScdJVlA6V2yfT6YhOuzZVVXn60zp6XH5yko18bWkpkiShKAqqqqLRaCI6MpKbYopKW7hI0ms1zM5PYWu9ZaJ3ZVQ0GphdkBI8vlotrFjR7/lT5pbh9Xp59p2N5EZhOkQs0WqksM5V73Xfffdx6623cuWVV/LYY4+Fff2CEIq8FNOAYB3gpGlZbK7vYVezjbWdZpA040rXrepwkJtiipvMKb+sUNsV36PqXQ4vHx3o5KPKTqyHHeOCNBNLyjJYMiUj5DaUj/7uF7z54t+54Bvf4ZJrvz/mfUpJ0B8agDniPDM1O4nNY+isEqrsZCNnzMrljFm5dDq8wfT46i6arR7WVXWxrqqLKVmJnDYjh8Vl6XFfNLWmyymC9QkggvVJZF+rHSX+b9hGnNsn09DjojQzenc7Y0mTJXrt2j6p7GJzXQ9aSeLak8r7Aud3X/s39972PRafcCp3P/J8RLYtSTAline0IykzyUhpppm6rvi5GVeRnUTSMIV3DAYDXq8Xvy86N45iSWmmOSI3kfx+P7Iso0a7H6MgHCYnxciBdvuA84wkSVyxLJgO3+6FlKXnoXTuGPN2AnKwUNvsgvgoHFvf7YpqUddwUdXg9Mp3drfxeaOV3t8g2aRjeUUmy8ozKUoffYaD2+nAbrOM+xww3HSi9EQDmUmGqNRxykoyctYx+ayem0dNp5P393XwWW03NZ1OHu+s4cUtjayak8sp07LHN09/GDZLDz1dHSQmJZOVmx/29Xc7fNg8/ritFxGvRLA+SXQ6vKKo3CjUdDopSEuI+7uco6UoKk2W6Mx/brV5eP6zegDOXVDQL3CW5WC1YE0E//7ZyaYJmTMWKRXZSfS4/IOOWMWa9ET9iEX9DIbg3XlFjv3fJ5xMem3EbhSKPutCLDDptaSZDfQ4B16TZCQauHhJMX/7pJa0Ey9F/l/buLbVYnVTlJEQ88GDL6DEXeajrKhsruvh7d2t/W4Uz8pP5pRp2cwvThtXl5W+VpO6sR+7jCQDGSOM9E7NSaLb2R21QQpJkijPTqI8O4kLFhXxUWUna/e10+Py88KmRt74vJWVs3M5Y2ZO2IP21154mr89cDdfOO9SfvDz34d13b3qOl0cU5QakXULgxNn9ElAVVX2i6JyoxKQg0Vepsdx+5fBqKrKxo0baWpq4rTTTiMtLQ2AvXv3snv3bnqcXuq7B85XP/a4ZaSkBQvvNdXVUL1/95DbmLtgCelZ2QC0NNZRuWfngGVkFd7sysAbCLY4WTUnj/aWJvbt3AbA/s+D/9eN4yQ9kpLMydUOTKORmFeUyqfVXTE9OqPTSswpSB1xeoPRGCxUk2E6uk5D03KTItYjWvRZF2JFXqpp0GAdYHl5Jh/sqKHarofjLiGgKOg0Ywv6VDVYwDTWC8fWdTmRY/h7+3ABRWFdVRdvfN5C58FBIINWwwlTMzljVm7Yav70thcb681FSYJpIRTpTDYF0+QnolBrSoKes47JZ9XsXNZVd/HmzlY67F7+s7WJ9/a28+VjCzhxalbYzgm9f0vZH7mb4O12D25fEgkGcZ6JlqPrKmmSauxxR7Wy92TR2OOiJCMy6agTZePGjRx//PEAbN26lfnz5wPw0ksv8ZOf/GTI1/3hmVeYu3AJABs+WMNf7rljyGXvfuQfLM5aEdzG+o/4w50/HLBM2ilXkHr8BRgkhW+cMAWNJLFz8wbu/tF3+i2nN0Ru7lOSMbZHWsbCpNdyTGEq2xosURslGK05BakhfaZ6R9bTTSDpNHHbT3400hMNES1uKUbWhViRk2xkn4ZBp+ZJksTKYok/b7ahzSjmjc9b+fKxBWPeVo/TT7vdE/Jc6WjzBuSoFnUdK1lR+bSmi9e2t9BxsK5NsknHaTNzWDE9O+wFMeWDNxfHetM+NyX0Ip0V2Um02z0TNlVUp9Vw8rRsTqjIYmNtN//d1kSnw8czn9axZncbFy4uYl5R2ri3o9VGpnXb4VQV6rqdzMyLj+knk0HEzui/+tWveP3119m2bRsGgwGLxTLia1RV5Wc/+xmPPvooFouFE044gb/85S9MmzYtUrsZ9/yyQvUkawMSLYoSLFAzp2DypPM0NDQAkJKSQmLioVTb4uJili0/AbsnMOjrEpMOZRhk5uQyd+HSIbeRlHzoCzo9K2fAsoH0MtyLzgdgZZHal6KWmpHZb1m9wcA5l34j1F9NOCgzycjUnCQOtDlGXjjKyrLMZCeH1tpFf7C/uiIHmJGTxO5mWyR3bcJpNMH00UgSI+tCrNBrNWQlGWm3DT4fOcWopXvNw2R/+RZe39HCsUWp45oeUtkWLBwbi63c6rpcyEqM3l0leO29pd7CS1saabMfCtJXz83nlOnZESvg1zuyrh3DzUWNZnStLxMMwT7q9RNc90WrkVhWnslxpems3d/BaztaaLV5ePC9ShYUp3HxccXjao/W+7fs/dtGSovFQ3lWUtwUd4x3EQvWfT4fF1xwAcuWLePxxx8P6TX33nsvDz74IE899RRTpkzh9ttvZ9WqVezevRuTKTbvmE602k4n/qNgRCpSWq0eyjITSRymEFY86b1YX7RoUb+bXF//+tdZdPo5NFtGvrt/yhfO4ZQvnBPS9padeibLTj2z72eHJ8Cdr+7C7fZz8rQsvrKsrO+5RctPYdHyU0L8TcYuPclAfM0MHL3SzEScXjmk4xktGUmGUVU4702D9/l8FKQl0GRxYw1ji51YU5qZGPEaCmJkXYgleammIYP1zNx8vrJiMZX00Kqm88Qntfz0rFljriPj8skx2crN45dp7IndM1Jtp5N/bmrgQHvw5m+SUccX5uRx6ozIFUHr1Teyrh/9yPpYsiLLMhNptrhjYhqZTqvhjFm5nFCRxWs7mvnfnna2NljY1WzjS8fmc+bsvDGlxvf+LXv/tpEiKypNFvekKeIb6yJ2Rr/rrrsAePLJJ0NaXlVV7r//fn76059yzjnBQOHpp58mNzeXl19+mYsvvjhSuxq3PP5gVXNh7FQ1OLoejvSjWNB7sX7kyFpAVmizRXa+lqqqPPNpHRa3n7wUExctLo7o9oZSlmmmaUK2HF0z85LxBGS6Y6CwpNkYTM8fzajWaaedRlJSEhkZwbmmM/KS+awmekWAosls1DIlCt0npk6dysqVK5k+fXrEtyUII8lKNKLXaQYdUMjKyePK62/B7vFzxyu7aLK4eXVHM19dUDTm7fUWjo1UTYixqO1yxmSXnh6Xj5e2NLG+ugsIzklfNSeXVXPyojY1sLBkCjOPWUBGVs6oXqfXacaUhWE4+Lqq9tjJSkswaLlgcTHLp2bx9w117G9z8O8tTWypt3DV8jIK0kZXe6cvDT7CwTpAQ7eL0gzzUdsGOZpi5vZ7TU0Nra2tnHHGGX2PpaamsnTpUtavXy+C9UFUtjti8iQQb9ptXuwef9jnY02EoYL1Fqsn4ml4n9Z0s7m+t03blIjflR9MRpKB1ISjoweoRiMxrzCVrQ2WCR2RNug0zC9OG/WI2H333ccbb7zRV1chxaSnMD2BxkEKIMYzSYLZ+SlRuaC57rrruO666yK+HUEIhUYjkZtiHPYznWzS87Wlpfzlgyre3NnK/OI0yrNCz9A5XG/F9VgZ7fP4Yyv7CYIjou/tbeflbU14D95EWVaeyVcWFI5YVT3crv3h0LVxhlOelTjmDIySDDONPS68/ti6eC5MS+D/zpzBuqou/vFZAzWdTn7+2m6+sqCQlbNyQz5/6PrS4CMfrPsCCm12D/mpk6uYbyyKmWC9tbUVgNzc3H6P5+bm9j03GK/Xi9d7KM3KZgvOe/T7/fgjWA0xHHr3byz76fD6aemOnbuD8a6y1crcwqHnro/nWEWTzxccZdVoNP32taHLjipH7su72+XjuQ11AJx9TC4lacaIbm8oJWlJcXOswmVOXiLbGyw4hqhHEElarcTcgmT0kjrqv/dgx6k0zUi7xRlzF1LjUZhhJlEvxfX78Wj7TMWzWDtW2WYdDR0Dv5v8fh/N9bUALKyYzpLSNDbWWXji4xruWD1jzMFYbbuV3CRdTLRlrWqzI/uH/l5WFbnf/yOttsvFMxsb+jrClGeZuXhxEVMyg1MHJuKcPVoJBi05ibpxvb/L0k3sbRldjZRoHavlU9KYmWvm6U8b2NVi51+bG9nZZOGa5aWkJIw8oFRSPpWvfv1aSqZMjcrxrG23kWWOmVAy5r7/hjOafZRUNfSkw1tvvZV77rln2GX27NnDzJkz+35+8sknufHGG0csMLdu3TpOOOEEmpubyc/P73v8wgsvRJIk/vnPfw76ujvvvLMv5f5wzz33HGZzbM1dEoRIe++993j44YdZuHAht956a1S2qarwlz0a9lk1lCap3DBXRiuyooQQ9J5+YrEo1FBkWUYZJqVJq9WiOdiGKpzLajSavowZRVGGrPZ7+HKCEKuam5v5zne+g9ls5rnnnsPph99s12LzS5yWr3BO2eS5YTfRvDK8Xq/hw1YJFYkErcqXSxWOz1ERGcyxSVVhfbvEf2o1+BSJFL3K16cpTE+dhPPEjlIul4tLL70Uq9VKSsrwlfVHFax3dHTQ1dU17DLl5eV9LXkg9GC9urqaioqKfu2mAE455RTmz5/PAw88MOjrBhtZLy4uprOzc8RffqL5/X7WrFnDypUr+yojh6LH5WN7vSVyO3aUyko2Djm6PtZjFQv2ttpotURuvvr7+zt57rNGDFqJ21fPIC+CramGs6A0jdQEQ1wfq/GQFZVdzdaozGHXaSXmFaWFdKd/KBdccAGvvPIKf/zjH7n22mv7Pbe72UZ7hGssjNWLT/2VR373iyGfv+fRf7Jg6QloNLDtf//mhzfeMOSyL730EmeffTYAzzzzDFdfffWQyz7zzDNcdNFFfa8bbmrYvffey4033jjCbxK6o/UzFY9i8VjVdbmo6eifCdjSWM8Vq5djSjDzyob9AGxvtPLHD2qQgP9bOTWkHtqD0WoklpZnYNBN3E2r/W12mkdo16YqMu7arSSULUDSRGZfKzuc/G19He324HlhSVk6Fy0sGNd3d7jcfv2VVO3bxQ9+/jsWLTt5xOXTEg3ML04Ly7ZHex0djWM1mGarh79+VEuz1YMEfHleHqvn5qKJoRvc2SnGmOmqFIvff0Ox2WxkZWWFFKyPKnchOzub7Ozsce3cUKZMmUJeXh7vvvtuX7Bus9nYsGED3/72t4d8ndFo7KsqfDi9Xh/zB6rXaPe1ttuOpI2dtJPJossl45EZdu56PL2vINjar9MpR+z90mbz8OKWZgDOW1hEfvrYLq7GKyPJQFZK/3mK8XasxksPLCzLYn+bg4buyBWeTDBoObY4jaRxdlDQaDSoqoosywOO06zCdKzertjsva4ZPr1W0miRtDoqcpI4YBx+DqhOp+v73Ueq4B7qsiaTieOOOy4i7/2j7TMVz2LpWBVlJlHX4+lXPFKnD342ZPnQ+Wl+aSbLK2ysq+risXX13Pml2WPqoKAATTY/03Mn5saxxy/T5vCHfN7t/c4IJ7+s8N9tzby9uxVVhXSzniuWlQ073S/aurva6WxrQVaUEX9/SYKZBWlhe0/npOrJTvPTaR+8W8GQ+xGBYzWcwowkfnLWLJ7f2MDHlZ38d0crjVYv31heNmhdIJ/Xg6WrE41WS1Zu/iBrDL8ul4yMJmqFCUMRS99/QxnN/kXsHVdfX093dzf19fXIssy2bduAYLXapKTgBf3MmTO5++67+cpXvoIkSdx444388pe/ZNq0aX2t2woKCjj33HMjtZtxp93mweaO/bkY8aqm0zlpKsNDsDVdpArLyYrKE5/U4JMVZuYlc+rM0VV0DaeKMRYkmmwkSWJGXjIpCTr2ttqRw9yiJiPJwNyC1LD0Vu3NwDo8M6rvOZ2GmfnJ7Giwjns74fDS04+wY/OnnHnOhZxz6TdYfd5lQy5rSjCTmWSgLCuRq666iksuuWTIZQ+fqnXxxRf3dUIZTELCoSI+X/7yl4fMVjMajaLVqRBTTHotGYkGug7L+tEcvOGkHDEH+JLjSjjQ5qDD4eXp9XV88+TyMU2TaepxU5ppxjgBo+sTXQG+rsvJ45/U0Hwwo255RSYXH1cc8daRo9U7n1+nGzloyU9NCHsR4Gk5SXQ7vTFfqNmo03Ll8jIqshN5dkM9m+t66LB7+e6KigE92bd/tp4ff/MSymfM4a8vvRuV/VNVaOxxj6rvvTA6Efvk3nHHHTz11FN9Py9YsACA999/nxUrVgCwb98+rNZDF2O33HILTqeT6667DovFwoknnshbb70lLjwOUlWVyg5RVC6S2m1eHN7AuEcNJ8pLL73EY489xumnn84PfvADmiJYifbtXa1UdThJ0Gu5annZhKVlZSYZSDXH9h3UaMtPTSAtwcDuFhs9zvGnxWs0UJGdREmGOWzzyw/vsz6YnGQThek+mkZIJY2GPTu28Mn/3mDe4mUYDEYMhoHZXL1Mem1fSuBQmV+DMRgM/aaQDUev15OaGjsjZIIwkoK0hH7BuvZgKvGRLaYSDFquPXkK97y5j011Pcyp7OSkaaPP6JQVlbouF9Nzk8e346M0kRXgFVVlze42XtrShKyqJJt0XH58KQtK0idkf0YSCAQHnkbMKtJKEQkEE406itLN1HfFRwvkk6Zlk5di4s8fVFHf7eKXb+zhhtOmUXZY94PeGx9KlIsFNlnclGclijZuERKxcplPPvkkqqoO+K83UIdg8HnllVf2/SxJEj//+c9pbW3F4/Hwv//9T/SLPUyL1YPLG52qoUez2k7nRO/CmFVVVfHmm2+yY8cOrC5/xCqEN3S7+O/2YPr7xUuKB9zdjaYKcTd3UAkGLYtK05lXlIrZMPbRpZwUI8vKsyjNTAxrIbjewHSoYB1gem4yiTFw48zltANgThy+JZRGA8cUhSfzQBAmk+ykYM/1XprDqrUfWVixPCuJcxcUAPD8Zw1jDn6betx4A9G9Zqrrck3ISK3d4+eh9yr51+ZGZFVlYUkaP//ynJgN1OFQezHtCCPr5VlJEftOnZKV2O99Geum5Sbz09WzKEpPwO4J8Nt39rGr+dCgp/ZgJkk0Wrcdzn+wjZsQGfHzDj3KKYpKdUf8BpHxpM3mwe2Lz5sih/dZj9Soul9WeOzjGmRFZX5xGsvLMyOynVDkpBhJCXNq3GSTk2JiWUUm84pSyUgyEEq8rdVK5KeZWFqewbyiNBLGEewPpXfEebA0+L790EjMK0pFO8HtBdzO4HevOXH4UbpZ+SmkxkDhJkGINRqNRH7qoSxJ7WHzfpVBOhusmpPHrPxkfAGFRz+qxi+PPgLuHV2PFo9fpskS/VHafa127np1N583WdFpJL5+fCnfPqUi7Gnj4SYfHFnXDjOynmjUUZwRuT7eeq2G8qzhb8LGmswkIz9aNZNZecl4AwoPvlfJxppu4NDIem/WQjQ1xkAW3GQ18UMWQkiaLG48/vgMIOONqgbnnM3Kj+1uAoPpDdYljYa2CFXTfnVHM00Wd1+K3US13ZIkKM8Wo+qhkCSJnBQTOSkmfAEFi8uHzRPA7ZPxKwoSwYuWRKOOFJOOdLMh4ulsoYysQ/BibU5+CjsaJ27+utNxcGQ9aehgvSwrkfzUyF1UCkK8K0hL6Es5NhiNnHfFN/vS4Y+kkSSuPmEKd722m4YeNy9ubuSSJSWj3mY0565He1RdUVRe/7yFV3Y0o6qQl2rimyeXU5weH22Le0d/h0uDn5mXHPFrjKL0BJos7ohlIkZCgkHL90+fxuMf17CprodHPqrG4Q1QePBvKUc5owTA6vJj9/hj/iZRPBLBehyQFZWaOE7NjkctVjfl2YkTUpxmPHqDdZ9MRArL1XU5eWtnKwBfW1o6oe1fclNMcVtbYCIZdJqDgfvE7sfMmTNZsGAB06ZNG3HZnBQTU7ID1ExQdpHbFawVYk4c/OZQXqpJFNcRhBEkGXWkmfVYXH4MRhPfuuWuYZdPMxu4ankZD75Xybt725mVnzLq1l2yotLQ7WJqTmTnrnsD0R1Vd3gDPPZRNTubbUCwiNylS0piqiL3SIqnTCU5JZUE8+Aj23mpJtITQ6vjMR6SJDEzL5lNtT0R31Y46bUarju5nJSNDby3r53nNtZzenFvsD4xhagbe9zMyhfBeriJK9040NDtis0WRpOYohCVE3y49Qbr7kD4A/WArPC3dbUoKiwuTWdR6cTNhesteCbEr6uuuorc3FxWr14d0vIV2Um4fTKt1ujPi3P1jawPfM9lJRuZUxB/WTiCMBEK0xOwuEIPJOYVpbFyVi5r9rTxxCc13H7WbLKTR1cjpaHHTUlGYkRrSURzVL2h28Wf11bR4fBi0Gq47PgSTqjIis7Gw+i3T7w45HM6rcS03Oid49PMBvJSTRNyfhkPjSRxyZJiTHoNb+xs5d2GAMmLzyFQ+dGE7E+rzcO0nCR0WjHLOpzEXzPG+WWF2i4xqj4RGnvcBMYwT24i9Qbrshr+tLE3d7XS2OMmyajj0jGkI4ZTYZo5IvOohdg2Oz+FjKTIj7QcyesJzsUzm/tfPGYkGZhXmDphU0EEId7kJpvQaSVUVaWtuYGWhrq+89ZQzltYSHlWIi6fzF8+qBr1/HVZVqnvjtyotzcgR61rxYbqLu5+cy8dDi9ZSQZu/eLMuAzUR1KRnRT1zMZpuUnoJrg+ylhIksRXFhRy1jHBvuoZp1/LvEtvnZB9kWWV1ghNwTyaiWA9xtV1uQiEuVeyEJqArMZdwYzeqrqaIeYBjlVTj5vXdrQAcMmS4glNf9dqJabEWUEYITw0Golji9JIi3Krvlc+q+aVz6rIyivoeywzycD8ojTRqkYQRkGjkShIC9Z2+NrK47j8C0uxWbqHfY1Oq+Fbp1SQZNRR3+3i+Y31o95uY48rYjff67pcEZl2driAovCPz+p59OMafLLCnIIUfnrWbEoy4mN++mikJOgpSo9+/Q+jThu3GXuSJHHu/AK+NC8YsDckz+b9fe0Tsi/xdt0cD0QafAzzBRQaemKv/2NAUei0++h2+nB4Azh9wUJViho8Wem1GhINOhKNWjISDWQnGzEb4vOt1tDjiquT4S9/+SvOvOJGfGEsRigrKn9bd6j6+5KyjLCteyxKM8yiNdYk8MQTT3DTTTdxzjnn8Pzzz4f8Oq1GYkFJOtsaeuhxRmdeniRJ/eZV5qWamJ2fIgJ1QRiDovRgoTmNRoOiKANatw0mI9HAtSdN4f7/HeDDA52UZydx4tTQR5QDskpDjzvsN3qjMapudfv564dV7G8L1s4465h8zjm2IO6/f65cvRyNRsMfnvkvqenBrjKSBLPyI19UbihF6Qk0W9zY46jYXC9JkjhnfiGqCq993sJzG+pJNOhYMiW612wOTwCr2y86o4RRfEZQR4naLifyBI+q+wIKdV1OKjscVHc6abF46LB7kdXR7VeKSUdZViJTshKZnpNMRU4iOk3sB1xev0KrzUN2Ynx8VDocXgKyiiaMf9t3drdS2+XCbNDytaUlE5rya9Bp4urmiTA0RVFwu904naOf5qPVSCwoTmdns5V229Ct3yJhSnZi3I6+CEIsMBt0ZCQZ0Gp1KIpv0NZtg5lTkMo58wt4eVszf99QR0mGeVTng/ru4M13bRiD3EiPqtd2OvnT2kp6XH5Meg3fOGEKC2O4d3qoZFmmqa4aoN81RWlm4oRWE5ckiZn5KWyq7WaUl7kxQVEUTi0z0W1LZl2dncc/rsFs0DK3MDWq+9HU4xbBehjFRwRyFPL4ZRonYFRdVVWarR4+b7Syo8lCVYdz0BORQachM9FAklFHklGH2aBFc/AL1ycruHwyDm+ATocXuyeAzRNgR6O1r/2SSa9hdn4Kx5VlcGxRWkyPlNZ1uchOjI8CUuHurd5q9fDfbc0AXLS4mDRz9OcLH648O1EULpkkQm3dNhSNRuKYwlSqDA5qOyP3XdnSUMcj9/2cvMJC/vTgA+SkmEZ+kSAIwyrJMKPRasEPshz6KObqY/Kp6nDyeZOVP6+t5CerZ4Uc3PkDCk09bkoyw3PDN9LXaeuru3hqXS0BRSUvxcR3T62YNO0hD69Wrj3YG9xs1MZEz/PUBD1F6WYaIljnIFIcNgvnnzgHkDj/zx/zWV0Pf15bxc0rp0e1Y0mb3cN0WRSaCxcRrMeo6g5nVPt1NvW42VDTxcbabjod/S+eUxP0VBwcTSpKTyAvJdhOQxPiCKvbJ9NsdVPT6aS6w8meVht2T4At9Ra21Fsw6TUcV5rBabNyYrI/qNMboMs5toAimjx+mWeffILN6z7glFVf5uRVXxrX+pSD6e8BRWVOQQrLKzLDtKdjYzZqKUybHBcqwviDdQiOgkzNSSYlQc+eFjv+CHTN6Gpv5eP/vc7UqdNEoC4IYZKVZERz8EJeGcVcco0kcfWJU/jV63vocHj5ywdV3HzG9JCDgrpuJ0XpCWFJIY9UBXhZUfn3lkbe2d0GwLyiVK49sXxSFVXt7bEOoNVqkSRiampRRXYiHXYvnjBOKYwGrbY3rFO5fGkh7oDMziYbf3y/kh+vnklOcnTOYbKs0mb3imu2MBHBegxy+QK0WCNfoMHi8rGuqosNNd39RmR1GomZ+cnMK0xjTkEKOcnGcaU+JxiCRTsqspNgFiiqSn2Xiy31PWyo6abL6eOjyk4+quxkRm4yq4/JY3Z+SkxVWJ6ILIfRara42b9rBx++/SplU2eOe33v7WunqsOJUafh8uNLJ/x4TMuZuHlsQvj1Bute7/jT2HOSTaQm6NnXag9rWrxOK5FhDF5UpqbGR3aNIMQLvVaHG1CU0QVESUYd1582lbvf3MP+Ngd/31DP5ctCO0d5/QotNs+4gwiPPzJ91Z3eAI98WM2ulmD/9LOOyeec+QUhD47EC/mwYF2n01OcYZ7wzL3D6bQaZuYns63eMtG7Mipa3WFhnSzz7ZMruPedfdR1uXjw3Upu++JMEo3RCf2aLW4RrIeJCNZjUHWHM2JzZVRVZV+bnbX7Othab+mbe647mFK6ZEoG8wpTMeojdwdXI0mUZSVSlpXIuQsKqWx38N7edrbU97Cvzc6+NjsV2YmcO7+QWfmxcYHc44j9kfUWq6dv7p9GO77j12H38tLWJgAuWFREZtLo+tqGW/rBQoXC5GE0Bo/npk2beP311znrrLMAeOutt7j++uuHfN3dd9/NBRdcAMAHH3zA1Vdf3e95RVXxyyqKovK1b9/MynMuBGDvji3cfct3hlzvBd/4DmdfeDkA9VV7uev730CvlXA4gkWdkpOTx/ibCoIwGL0+eAk6Uuu2wRSmJXDdSeU89H4lH1V2UpCWwMrZuSG9tq7TSUGqaVw3f2u7wp/92Gxx88f3K2m3ezHoNHxjeRmLJ7iga6QEDkuDT040MjUG64BkJRnjrve6TndoSkgg4Cc5MZHvnTqVX7+xl1abhz+vreKmM6ZFJT3d6vLj8AZIitLNgclM/AVjjN3jj8gXg8sXYH1VF2v3d9By2PqnZidxwtRMFpakR+1u2+E0ksT03GSm5ybT7fTxzu5WPtjfQVWHk9+t2c+C4jQuXFwsArURdDt9wYr8B0cotONo3aaqKk+tr8UXUJiZl8zJ07PDtZtjIknB/qfC5DJt2jQkScLn8/UFxABOp5OqqqohX2e32/v+7XK5hl1WE3Cj1UjIiorP56W5oXbIZR02KykJevJTTSQ7zNTXVvd7fu7cuSH8VoIghOob3/gGLV0WEpPHdlN+XlEaFywq4oVNjbywuYHsZCPzi9NGfJ3LJ9Nm85KXOraUYLdPpjnM9WG21vfw2Mc1eAMKmYkGrj91KsWTuJhq78i6VqdjbmHstsCckRe8NvXGSTb84SPrvbUg0swGvnf6VO55ay/72uw8/WkdVy0vi0qmYrPFzfRccaN7vESwHmOqOkZfGXk49d0u1u5rZ0NNN96D8zmNOg3Hl2eyYkZ2TM0Rz0g0cPFxJXxhTh5vfN7K2v3tbG2wsLPZyhfm5PHFufkTXojOF5DR62OvwmXvhcOhkfWx/50+PNDJ3lY7Bp2Gy5eVTnj6XV6qiZQJrA4rRMb06dN55JFHmDFjBrNmzep7fMWKFaxbt27I11VUVPT9+/jjjx922bKyMnJzs7G4/eQkLOWZ/76DP6CgAhLBNHeTTkOCQcesaeVUHBzFSp89q9969Xo9CxYsGPsvKwjCAPfeey8ev8y6qs4xj1KvnJVLi8Xz/+zdeXhU1fkH8O+9d7ZM9n0PSQgQ9l0EQRbZ3KqiVmt/blXUWuuCrYKtC1prbV1Qa7WuqBVtq9RqXSMCooIsiuxIgBCyQfY9k1nu74/JDAnMJJNkJnNP8v08j4/MzJmbN7lz79z3nnPegw35Ffj7lwdxx9yhPiUHBZWNPU7WD1U0+K1X3aEC/9tZhv/uKAMADEsMx00zs4NaEb2vpA7KRohRr+mq4fq24fA/FFQGOxSfSJIEWVHgsNs71AVIjzbjpjMH4+kvDuCbg5XIiDFj7nDfRqL0RmltC3LiwzR7M0YUTNY1pKapFRX1vZ9vabU7sO1INda2zTl2SYkyYfbQBJyeHavpQiVRZgOumJKBmUPj8daWQuwrq8cHO0qx9Ug1rjsjC5lBrBZaVN2CYSHaKjJltTtwvN45WsI1nFDuYc96ZYMF/952FACwaHxqnxUj8UZRJC6T1Y/Fx8fj9NNP73ADLDY2FlOnTvXp/dHR0T61jQk1ICYrGWOykn3ablhYmM8xEFHPmfQKkiJCetxTLUkS/u/0QahtsWJHUS2e+SIfdy8chrQuOiIaWmw4Xt/S7e+4RovNb6MfW6x2rPxRxg9VzkR9zrAE/HRymhDL2vZWXGIy3lu3BRMHaX8ZuoRwE5KiTDjUdVNN0On0aLXbO1TcB4BRqZG4xDUSZetRpEebMSwpsL3eVpsDFQ0WFmftJSbrGpJ/vKHrRp04VteCL38sx9cHK9FgaRtiJEmYMCgKs4clYEhCmFAFulKjQ3DnvKHYVliNtzYfRWltC/748V6cOzoZ545JDsoXWmltM4YkRWrqLuGxuhb3XX7XMPiezFlXVRVvbDqCFqsDg+NDMWdYgj/D7JHM2FCYAlg/gYiIgqO6uhqtra1IiQhHaS16XKtHkSXceGY2nsw7gPzyBjz5+QEsOzsXcV3UWimoaOp2sn6wvMEvNYVKaprx3Lp8lNbJUGQJ/zclAzOGBHfKWV/SKRJGpkQKc02aEx8mTLI+57xFcNjtMBhNqDheBke7HvaxkSr2J5nwQ1kL/rbuAO4/fxRiQp2F/arKj8Nm9VyfSZJlxCeluB9XV5TD2uqlc1GSkJCc6n646+BR5MR6PhaTkpLcxWbJOybrwWK3Q1q/HqlffgkpNBTHJ01FTZO16/edxOZw4IejtVj343HsLT0xlzParMfMofGYMSRe00OMuiJJEiYNikFuYgTe3HwEWwqq8cGOUuwsrsUvZw7u88JnVpt/Ksn6U0nNibv8J3rWu38j45tDldhVUgedLOGaaZlBvyERYlAwqB/P2SMiGsgmT56MgwcP4uuvv0bioJG96rE26hT8ek4O/vzpfhTXNOOxz/bjt/OHdXqNUNdsRWWDxefriNpmq19Wm9h8uAqvbSyAxeZAhF7FL2cPwZCkyF5vVyTDkyM0PcLzZK6CbCLcW7jzwSfc//7F+TNw9NCBDq9LOiOS/u8vQGI2/rYuH3cvzIVekfH7X/0fDuze4XGbUbFx+PeXu9yPH7pzMXZu3eSxrSnEjA+2nri1sfTWG7Hlq7Ue2w4ZMgR79uyBTsd0tDP9f6yNFq1eDWRmQjdvHiY98QR08+YhcvhQxOd96NPbVVXFwfIGvPntEfzm3zvw3PqD2FtaDwnA6NRI3DI7B39aNAbnjUkROlFvL8ykw41nDsYNM7JhNigoqGzCg//bg13FtX0eS2GldpZxq2+xoq75xE2epY8+i/c3H8S5l17Zre3UNLXin1ucw98vGJeC5Mjg34wYksh5TkRE/ZXSNgLMbrcjMy6014lQqFGH2+cOQXy4ERUNrfjzp/tR3sXUwoJK3+sE9Xb0o83uwKrNhXhhwyFYbA4MSwzDb8fYkRMfvKl9wZAWE4KKowcxZswY9yogohCt6J9eb4DBaOrwn16RUPPhY1AtjSiobHJf++n1xlPauv8zGLvcbvv/2tPpDTCaTDC1+8/Vm97c3Iy6urq++WMIjLcy+trq1cAll5wy3stwrBRj7rgeO558CeXzTj15tdoc2H+sHjuLarGjuAYV7ZYSizDpMH1IHM4cEt/lsC/RnZYVg+z4UDy3/iCOVDbhqTUHcP7YFJw3JrnPCqE1WmzduhsfSKUn9UQYDEbA0L24VFXFP74tRFOrHZmxZswfkeTPEHskJswQ9PnyREQUOK7eNJvNubxTYkTvl8mKNhvw2/nD8Phn+3Gs3oK/fLofS+YN9VpMrrrRipqm1i7X+C6vt6C6sedLuB6ra8GLGw6hoO1m/zmjkvCT0YmwFGzr8TZFFG7SYWhCOL49XIedO3d2WAlEBJmxoai1qB06SbTs76vXeH1tV3EtVqw5gHU/liM3KRxPvfmBz9t99KV/+dz2wb++BrNRwbTBcT6/hzoKWM/6ww8/jGnTpsFsNiMqKsqn91xzzTWQJKnDfwsXLgxUiH3Pbgduu83jxCyp7bmhf7oXDY3NOFzegG8PVeDf247iz5/uw23//B5PrTmAL/YfR0VDq7Oie1YMbpuTg0cXjcaFY1P6faLuEhdmxNKFuThzSBxUAO//UIK/f3kIrTY/L3raiaPV/l22pSccDvWUZL0nthRUY/vRGihtw9+VIPdmy7KzIi4REfVf7XvWASDLD73rgLOg5G8XDENihBFVTa145OO9OHC83mv7QxWd966rqtrjXnVVVfHlj+VY/r89KKhsgtmg4JbZOVg0IS3o37V9Ta+TMSbNuUyb1epMdrW4uk5nZFnCqNQIKIr4+25UaiTOHuXsnHlt45EuR6H0RpPFjlpBbnBoUcB61ltbW3HppZdi6tSpePnll31+38KFC/Hqq6+6HxuN/SgB3bABKCry+rKkqggpK8HbT76FTRljTnnd0ViFxh+/RfOhLWg58gN+tFrwz7bXsoeN7PQOWn+jV2RcNTUT2fFheGPTEWw7Uo3qxlb8anZOnwz9r6i3oKnVBrMheINTKhossJ50g+JfrzyLgvz9OPfSKzFy/OQut1HfYsWqzYUAgHNHJ3dZQbcvpEebEWrkoB8iov7M1bPuStZDjTokR/a8Mnx7UWYD7l6Qi2fW5uNwRSMe/+xHXDc9C5Pblmdsr6qhFbVNVkSaPV87FNc0o9Fi8/haZ2qaWvHmt4X4/mgNAOdN6OumZ7kLeg0kkgSMSjkxT921rJhoyToAmA06DE+KCMo0TH+7YFwKfjxWj4Pljfj7lwfd89cDobS2ud9Mze1rAbsiXr58OQBg5cqV3Xqf0WhEUlLwh+EGRGmpT80SGqphb6iG3tqAGVMmICs2FNnxoVhy0VRUHS8LcJBimZ4Th/gwI55dl49DFY145OO9uHXOEKT0QQG4o1XNAV/2ojPFHi5otn2zHt9t/BITp830KVl/89tCNFhsSIsOwTmjgn/cGfUysoK4NB8REfUNV896+/Wgs+NDUVbX7Jd1zCNC9PjNvKF4ccNhbC+qwd+/PIT84w24dGKau2CYy6GKBozPOHUZMavd0WEJXF84VBXrfyzH6u+K0Wy1Q5ElXDQuFfNHJgZ8up6qqvjPGy+i8HA+ACAmLh5X/eq37tff+NvjqCw/5vG94ZFRuO72e9yP337xGZSVHPXY1hQSgpvuWu5+/M7K51F0xHO9dJ1Oh6eefto9dfDZZ5/FRx99BEDMZB0AkiJNqG5qRbEGRln2hk6WceOZg7H8g90oqGzCu98V4fLJGQH5WWW1LRiaEO6uRXTWWWehoaEB//nPf5CSktLFuwc2zXVfrVu3DgkJCYiOjsacOXPwhz/8AbGxscEOyz+SfVvj9ydzhmPm5AlQFB3CIk5UCH3hP2uhqp6/wWRZQUH+Puzc9i2SUtIxecYcv4QsgmFJ4bjnnOF4es0BHK+34NFP9uH2uUMDnvSV1DYjOz40YHchO9NitaPKw/w5RzfWWd92pBpbj1RDloBrp2WecvESDEMTwzURBxERBdbJw+AB57rrGTGhKOhiaLqvjHoFN88ajHe/L8Knu49hzb7jOFjegOumZ3UopFrZ0IraZisiDbJzFGRpKZCcjMNDx50ygq0zRyob8ea3he6h9ZmxZlw1NRMZfVSY7MjB/Xju0fvcj9Ozh3RI1r/89H0U5O/3+N6E5NQOyfrXaz7Cvp3fe2wbERXTIVnftO4z/LDlG49tjUYTBr15YoTtxx9/7E7W4+LEncc8LDEcdc1W1Ld0f9SFlsSEGnDtGVn469p8fL73OMakRmFESoTff47NrnZYc33z5s1oaGhAc7PYNzz6gqaS9YULF2LRokXIysrCwYMHcc899+Dss8/Gxo0b3Sf1k1ksFlgsJ+ZZuKoKWq1W95wYzTj9dOhSU4GSEvcc9fZUSUJLYjLUWWchou33Ve0nTgLh4Z334q798Bs88/A9mH7W2Zg07Uz/xq5xiaE6LJ0/BM+sO4TDlU147LP9+PWsLL/NfVbb1i93/R8AbHagqKIBaTF9Xzm9qLKpw9qZLva2z4ssdfzsnKzBYsOb3x4BACwckYiMKGOn7ftCdJgBMSFKr49b1/s1d/xTB9xP4uC+EodI++rss8/GsGHDkJSU1CHe1Ag9iisdfqtDIwG4ZFwyhsSZ8crGQhRUNuGBD/Zg4YgEnDMyEQad8wZxxcp/IOLheyEVF7vfm5GYjOa7l+P43HM6/RnH6y1474dSbDlSAwAw6mRcNC4Zs4fEQZYlj9+vnq4requ+ugqAs5f8op9fh4iomA4/+/zLrkZtdaXH94aGhXdoe/bFV+A0Lx0/BqOpQ9t5P7kUYydPPaWdUa8gLSasw/697LLLMHHiRCiKgosvvliIz6q342pEYii2HqmCzX7qNb1IxqaEYdaQOKw7UIFXvj6MB84dFpDpiMVVDYgOceY3rryupaXFb58Bkc5/3YlRUlUPWaMXS5cuxaOPPtppm7179yI3N9f9eOXKlbj99ttRU1Pjc1Auhw4dwuDBg/H555/jrLPO8tjmgQcecA+5b2/VqlUwm4M///ZkyRs3YnLb37D9YCjXTthy990onXrqCc8XeXl5ePbZZzF58mT87ne/612ggmqxAy/tk3GgToZeUnHtMAdGRot9Eu2OZcuWYe/evbj77rsxtZPP0RsHZGytkJEUouK3Y+zQsTObiIj6uSoL8O9DMvbUOL/0og0q5qU5cNHBb3D6n7t3baaqwKF6YH2pjB1VEtS2d06Mc+AnGQ5EBaHk0g8//ID7778fGRkZePrpp/s+ABKWxQ48tkPB8RYJE2IduHpoYIs2X3nllaivr8czzzyD9PT0gP4sLWpqasIVV1yB2tpaRER0PpKhW7dN7rzzTlxzzTWdtsnOzu7OJrvcVlxcHPLz870m68uWLcOSJUvcj+vq6pCeno758+d3+csHxTnnwD5hApQlS4B2d29tKanY85v7UTv3HPT0FkNI0mEAgGoIhTl7kh+CFY8ZwB3ZDvx9QwF+KK7Dy/sV/GLaIJyWeepctO5QHXY0F3yPkMzxkE4aYj4yLRLxfViJv6apFdsLazy+puqcw4vMKcO8fgZ+KKrF1orDkCTg2jOHIkIDc8Qz40KR6ac4rFYr8vLyMG/ePGHnww0E3E/i4L4SR3/aV98XVqO2yb89ZGYAt+eq+P5oLd7aWozqZiveyQeW/N05TPvkGeUSnKMeR7/2Bmovvxl2SUZhVRO2F9Xhu6M1KKs7MbJzdEoELhyb5POQ986uK3pKLnIWPTOGRQX1OlCvkzEhIwohQSzC609dHVdHKptwuFysZehOZgZwfXQj/vTZAXxXKWOCnIUpvbx29mRIYjhSo0NgMplQX1+PM844A6NGjfLLtkU6/3VnffluHUXx8fGIj4/vdkA9VVRUhMrKSiR3MtfbaDR6rBiv1+u1u6N++lPg4othW7sW2z/+GOPOPhvyzFmoK6iGZO35nSx92/radrsdktI/TpA9YVCAX87OwatfF+Dbw1V46ZsjgCxjSlbvax9IsnLK37a0zoqU6LBeb9tXxxqavO5fR1tVHkWv99imqdWGf2x2rkgwf3giBidGntKmr5mNCgYnRrqLjviLps8B5Mb9JA7uK3GIsK8sFgvWr1+PK6+8ssO8dZe09HQ88dYn7mJzv/3FJTi0f4/HbUXGxOKVDza4H9/7q6uwZ/tWj21NISF48/NtGJ0egy8PlKPw7XeQVFfhNU7XSj0fPv8uPorNRav9xHWaXpEwNTsWZ+UmIjW6Z1PiPF1X9JS1bXqcwWgK2nWgIksYPygaEf2w8re34yonKRJNNhXH6wK3/FlfyE6MxHljUvD+DyVYtaUIQ5Mi/b56wfFGGzIT9O5h8LIs+/1cJcL5rzvxBexILiwsRFVVFQoLC2G327F9+3YAQE5ODsLCnIlNbm4uHnnkEVx00UVoaGjA8uXLcfHFFyMpKQkHDx7EXXfdhZycHCxYsCBQYQaPokCdORPFjY0YO3MmdHodBseHYU+J73daTtmk3rk7bTbtz9UINJ0s47rpWTAoMjbkV+Clrw5DliSPy7b0VnVjK+pbrAg3Bf7EYLM7Ol0L05Wseysw96+tRahptiIx3IgLxqUGJMbuGp4U4fdEnYiItO33v/89HnvsMa+vh4eHIysuDAfb1jhvbKhDXU2Vx7bySYVJO2vbanH2fBt0MuYOT8Tane/6FK+jpBStUUMRolcwIiUCEzKiMDo1MqhLuJ5s0rRZeOV/X0EJUqIuy8CYtMgBuUTXyJRINLdWCV9w7pzRSdhZXIvDFY149ZvDuGPuUL+uYlDXbEWjxeZeutHmof4SdRSwo/m+++7Da6+95n48fvx4AMDatWsxa9YsAMD+/ftRW+scsqMoCnbs2IHXXnsNNTU1SElJwfz58/HQQw/1r7XWO5EcacLRqqYeH+iuk7Pd5r9iJSKTJQlXTh0Eh6ri64OVeHHDIciShImD/D+s52hVM0akBP7LqayuBXaH9zn4f3nlHVitrQgNP3UKyO6SWnyVXwEJwDXTMt1FdYIpJSoE0QNwzVkiooHuz3/+M2688UavhZb0ej0yY80or7egrtmKe594Ea0WzzerTy5CfPcjz6DFS5Vp6aTEY/biW4Hf3NRlvFOnj8Kk6SORFGkK+BJsPRUSGor0rJyg/GznWuqR7iXaBhpFljA2PQpbCqpg6cUo2WBzdXY9+MEe7C2tx4YDFZg51L+jqktrWxAdHY2mpqZTjkc6VcCS9ZUrV3a5xnr72nYhISH49NNPAxWOECRJwtDEcGw7Ut2j97vuUtnZs+4mSxKunpoJhwpsPFSJF748hJtmZntcT7U3yuqakZMQFvAEuKSmpdPX2y/1116L1Y7XNjqrv8/JTcAQP1XJ7w2DTsaQxL6bPkBERNohSRJycrpOLEelRuDbw1VIThvk87YTU7pRsGr++WhJXA7j8TKPK/U4AJTp9DgcCpwW1ferv4hAkoARKRHuZbkGKpNewZi0KHx3pLrTjhWtS4owYdGEVLy95Sj+ve0oRqf6dzh8WW0LduzY4bft9XfB71qjDqJDDUjs4clu2KjxWP7Ma7jp7gf9HJXYZFnCtdMyMSUrBnZVxfNfHsLO4lq//gyHAyiuCexakfUtVtQ19+xGzD+3HEVVYyviwgxYNF4bw99zk8KDskY9ERGJw2zQYURyAAsGKwoO3POw898n9fKpkgQJwC02Kyoqvc9r14qd2zZh5dOP4us1H/fZz5QkYHhyRId16weyyBA9RqZGnPxREs6cYQkYHB+KFqsDb2w6gm4sHtalFqsd1Y2tfttef8crZQ0akhgGpQdzeKPj4jFtzgKMmnBaAKISmyxL+MUZWZicGQ27Q8Vz6w7ix2P1fv0ZRdVNcATwTmpXveoAsPKZR/H0Q0tRevSI+7ntR2uwoW34+7XTsmDU+6fqbG8kRBgH/B14IiLyTWKECYNiA7ccb/zVP4P0zjtAaseb2ZbEZNw7bhL+A6DVEtgb8v6w+7stePPvT2Lj2r4ZqerqUU/hiIMOEsJNGKqBEYy9IcvOkak6WcLO4lpsOuy5BkRPldZ2fU1LTkzWNcikV5ClgeW0+htFlnDd9CyMSY1Eq92BZ77IR2Flk9+2b7E6cKw+MCcfh0NFaW3XFwpr/vcuPnh7JaqrnD0A9S1WvLaxAAAwb0QihiUF/8tDp0iaiIOIiMSRkxCG+HD/z4fOig9FUqQJWLQIKCgA1q4FVq0C1q5F0Xd7sDkjCwBgadF+cmG1Ouf06w2BrwUjy8Co1Ej2qHuRHmMO6A2mvpASFYLzx6YAAN7eXIjaHo7u9OTuO27BnDlz8N133/ltm/0Vk3WNyogxI9TYvZICtdWVyHv/31j/6fsBikp8OlnGTTMHY2hiGJqtdjy55keU+fHunj+T//aO11tgs3fda+9oW/5GkWWoqoo3Nh1BfYsNKVEmXKSZ4e8RMOqC37tPRETikCQJo1Mj/VqUNC0mBIPj29VOURRg1izgZz8DZs1CVmIEQszOZLTVov1k3VWAz2AI7Mg1RZYwOjWqx9M2B4ohieHOG0ECWzAyEenRIWhstWPV5kK/bXfPD99h7dq1qKjQ/vSSYGOyrlGyLGF4cvd6H8uKj+LPy36Nv/95eYCi6h8MOhm/nj0EGTFm1LfY8ETej6hs8M/amPUtNtQ0+X8eTnGNbzcBXGvVyoqCTYeq8F1hDRRJwvVnZGtifnh8uFH4Ly4iIgoOWZYwLj0KMWG9T9jTY8zITep8LrwiS0iMdrYRoWfdnawHcBUlnSJhfEZUQEY59EcjUyIQJ/DfSifLuHZaFmQJ2HakGt8V9qwI9slcKzi4rlvJu+BfvZNXUWYD0mJ8H16k0zmXDmM1+K6FGBTcMXcIkiJNqGpqxROf/9jj4m0nO+Ln3vVGiw3Vjb7F5upZb7BK7jug549NRoYGhmLpdTJyu3kDioiIqD1FljAuLarH86QlyTmk3tfpWHFRznYWAXrWra2BHQZv0iuYlBmDKDOXXPWVa0RIlFncteczYs1YODIJAPDW5kK0WHufYMuyMwVtaWXO0hUm6xqXEx+GEINvQ4ZdS7fZbD1bp32gCTfpsWTuUMSEGnCszoInP/8RTa29/9tVNFj8sh2Xkm5UmXc47IAk44PDVjRb7ciOC8XZo5L9Fktv5CaFc/g7ERH1mixLGJESgZGpEdApvhfkDTEomJARjcxu1AWKiopCTGwsDH0wD7y3WlsDNww+3KTDpMxohHVziia13WBKj0K4Sdy/3XljUhAfZkR1kxX/3V7S6+3JbT3rVQGq9dSfMFnXOJ0iY0Syb0tAKHrnXTsbe9Z9FhNqwJ3zhiLCpMPR6mY8vSYfFlvv7hiqKnC0yj9VYx0OFSXdmFPvsDsQOfWnKKxzwKiTcd30rB6tLOBviREmzm0jIiK/So4MwbTBcRgUa4bSSdJu0MkYnBCG07Njuz3n/a677kJlRQX++KdHextuwAVqGHxChBGTMmNg0sBqMqLSKTLGZ0R3ux6VVhh0Mn4+JQMA8Pm+Y72u0STLzs9SZYP2V1kINibrAogONfhUUdI1/8NhZ896dyRGmHDH3KEwGxTklzfgb2sPwmp39GqbJTXNvd4G4CwsZ7X5vh05MQeRZ/wMAPB/pw/SRIJs1HP4OxERBYZBJ2NIYjjOHBKPMemRyIwLRXKUCSlRIciOD8X4jCjMGBKHrLjQXt28zooN1Xyidf0dv8Oz//oUs86+0G/bzI4PxZi0KE3c+BedQSdjfEYUzD6OmNWaUamRmDQoGqoKvPHtkV4tVyy31VGqb27162jU/ojJuiCy48IQEdL5fBfXnHWblR/67kqPMePWOUNg0MnYXVqHFzccgr0XJyG7Q0VRde/vFhZV+37nsqHFhkE/exCSrOD0zGhMzY7t9c/3hxHJEZoobkdERP2XIktICDchJyEMI1MiMSIlAtnxYYgNM0LyZXhiF1xD7/2wqYBJSEnD0JFjEROf0Ott6RQJY9OjkN2+Wj71mkmvYMKgaJ+nuGrN5ZPTEaJXcLiiEesPlPd4OwaDCXqDEVBVv67K1B/xCloQsuwsUNHZ3Cx3sm6zQlV7nmgOVDkJYbhlVg50soTvCmvw2sYCOHrxdyyqburVXccGiw01Tb5NaVBVFSs3FqDW4kBiuBH/NzWzxz/Xn9JjzIgNE7cKKhERDWzr16/H7NmzccsttyAyRI+sbsx3F1W4SYcpWbGs+B4gJr2zdoKI0wqizAb3UsCrvyvu8drrf3rxbXz0/RHMXHgBk/UuMFkXSIhBwYgU78uMhEZEYNmfn8O9T7zYh1H1LyNSInDjmdmQJeCbg5V4e8vRHt/4sFgdKK3r+QmouBs98x/vKsP2ozVQZAk3nJmtiS+AUKMOQxJ4R56IiMRVXV2NdevWYdu2bQCArLhQzVb2/mT1Krz94jMoPnK4x9tIiwnB5MwYYXt+RRFiUDBxkJgJ+6yh8ciMNaPZasc/txzt9faaWu09TvoHAibrgkkINyEr3vNdXYPBiDnnXoQzF5zvlyFfA9X4jGhce0YWAOCLfcd7VfXySGVjj95nd6gorfUtWd9VXIv/fF8MAEir+QGfvfo4mhobevRz/UWWgVGpEZA5x42IiAQWEuJcIq652fmdLEkSRqVGQq/T3iX0B2+/hpdXPIyigvxuv1evkzEmPRK5Sfzu7iuiJuyyLOHK0wdBkoDNBVXYXVLb622yd9077Z1pqEuD48OQEMGhSYE0NTsWPz/NWfXyfztL8eme4z3aTpPFjuM9WJaitLYZNnvXPfrH61vwwoZDUAHMyInDl8/dg3+9+jdYWoJbXXNIQjjCTdrseSAiIvLVyck64BzGPEqD89ddS7fpjd0rLhsbZsCUrBgkhAe/KO1AI2rCPig2FGflOmsjvPltYbeLKr/14tO49+YrsWXDFwCAY3UtnMLrBZN1QY1MiUTkScOwVFXFhrwPse7j99wnbOq52bkJWNQ2L+ed70uwtqRn38pHerC8hS/F6SxWO55dexBNrc711C+bmOJ+TVGCV7E2PtyI9JiuVy8gIiLSOk/JOgDEhhkxJEFbK51YW1sBAAa9b8vTKYqE4SkRGC/o/On+wpWwizb14IKxqYgM0eN4vQWf7Crr1nv379qOTevzUFZcCABotTlQ2dgaiDCFx2RdUIosYWxaFMzGjgf2g7dfh4d/cxMa6+qCFFn/cs7oZJw3OhkA8N4RBZ/t7X4Pe22TFdXdOAFVN7aioaXziv4Oh4oXNxxGcU0zIkw6/HLWYMjSiTuSrmX8+ppJ33ldBSIiIpF4S9YBICPWjNTokL4OySvXOut6H9ZZjws3Ymp2LFKjtBP/QCZiwh5iUHDZpHQAwIc7S7s1klSWnSmo3W53P8eh8J4xWReYQSdjQka0e71GSZI6VIQn/7hgXArOG5UIAPj3dyX4eFdpt7dxuBtz1492sVybqqpYtbkQ24tqoJMl/HLWYESbDbDbTiT4stz3J3tJAkanRnKZNiIi6jc6S9YBIDcpXDNTE61WZ7JuMHiPx6iXMSYtEuPSo9ibrjEmvTNhF2kd9smZ0RieHA6bQ8Wqbwt9Hsru6lRyOE4Mny+vt8DWzeH0AwGvqgXnWq/RdWDr9M7hz0zW/UeSJFwwNhkL05x3/979rhjv/1DSrbk1VQ2tPlW6bLHaUV7f+RSGj3eVYd2P5ZAALJ6R7R6G1/6EJwchYc5JCDtlagYREZHIzGYzDAYDjEajx+99SZIwKiUScRpY5qyznnVZBjLjzJiaHYuECM5N1yr3db1RjIRdkiT8fMog6GQJu0rq8F1hjU/vc3UqOdr1rNsdKsobOI33ZEzW+wHXgR1m0kFum6vcflgJ+cfZ6SouGJMEAHj/hxK8sekI7N1YR/1wRde960XVTejsHsCavcewuq3y+2WT0zFxULT7tfYnvL6esx4fbsSg2P6/9iwREQ0sycnJsFgsqKys9LrSjixLGJMaGfQedvec9ZN61hMijDg9OxY5CeHQcfSb5rl62EONwas/1B1JESYsHOW8Pn57SyFarF3nIK5OJYejY9tSDoU/RcCO2IKCAlx33XXIyspCSEgIBg8ejPvvvx+trZ3P3W1pacGvfvUrxMbGIiwsDBdffDGOHTsWqDD7DdeBrdc7ezbtVvasB8J5o5NwxWkZkAB8eaACz60/iFabb0N2KuotqGvxvl/sDrXTwnJf7DuOt9rWszxnVBLmDk/s8Hr7E57ch3PWzQbOUyciooFNliWMTo0MaoHVx1/7Dx5buRpRsXEAgOhQPSZlRmNMWhTMBjESP3Iy6pzX9WEmMfbbOaOSER9mRHWTFe//0PWSxyd61jteQ1c3tvqU7A8kAfsE7Nu3Dw6HA3//+9+Rk5ODXbt2YfHixWhsbMRjjz3m9X133HEHPvzwQ/z73/9GZGQkbrnlFixatAhff/11oELtN/SKjBCjAbUAbLbOC5RRz83JTUBkiB4vbjiE7Udr8Nhn+3HzrMGIMnddffVweSPGpkd5fK2kxvNybaqq4rM9x/DvbUUAgIUjk3BRW5X69sLCI/HS+1/C4bC7C3cEmiJLGJ3GeepERNS/vfrqq8jLy/P42sSJE3HnnXdCkiQMSwrHPbfdiJrGFo8j5TKH5OKKG25zP37i/t/AYvE89Dd1UBauvuUu9+OnH1qKhjrPa1onpqThujt+BwCIMuuRFReK2LDgD82nnjPoZEwcFI3vC2tQ58NUymAy6GRcMSUDT605gM/3HsO0wbFIi/Z+48rVqWQ/qWddVZ3LuHG05gkBS9YXLlyIhQsXuh9nZ2dj//79eO6557wm67W1tXj55ZexatUqzJkzB4Dz5Dh8+HBs2rQJp59+eqDC7TdcPevp0QYostStYdrku4mDohFuGoq/rs3HoYpGPPi/PbjxzMEYltT5Mi7l9RbUNlsRGXLqsnuFVacWlrM7nMXk1v9YDsCZqF88IdXjUDxFp8OgwUN78Vt134iUCK6nTkRE/dpNN92EPXv2YMOGDR5fb2pqwp133ul+/MF/3oHVywjHCVPP7JCsb/j8IzTWe17BZ8S4SR2S9W+++ASVxz0vkTV42Egsve9BDIo1+9R5QGLQKzImZETh+6M1qG3SdsI+OjUSEzKi8F1hDf6xqRB3LRwG2cvUkdvv+zNuv/8vHjuXSmuZrLfXp2MramtrERMT4/X1bdu2wWq1Yu7cue7ncnNzkZGRgY0bNzJZ98Ff/vIXtLS04LTRuTCFR2FXcW2Xy4BRzwxNDMfvzhmO59YfRFF1Mx7P24/zxqTgnFFJnc4JO1jegAmpEcCGDUBpKZCcjONjJ6O5tePdxfoWK17acBi7S+sgAbh0UhrmDU/0Omeur2XGmZHIIjVERNTP3XzzzVi7di0uvvhij69nZ2d3ePz444/D4XBAVVXUNFlR0WiBqwZsfFJKh7a/uHUpbF7qDMXGJ3R4fNWvfouW5o439hVZQmSIDtnpyV5H7pHYdIqM8elR+KGoBtWN2k7YL5+cgd0ldcgvb8A3BysxPSfOYztF5z0FbWixob7Fys6gNn2WrOfn5+OZZ57pdAh8WVkZDAYDoqKiOjyfmJiIsjLPdxItFkuH4UN1beuLW61Wr3c1tcIVnz/jXLRoUbtHKsanhuNodTOOVDbAwdUQekxtG6ajnjRcJyFUh6Xzh+Afm49i0+FqvP9DCb47UoVrTs/AoFjPw3+Ud9+H/bHlUEqK3c9FJSUj/q7lOD73HADArpI6rNxUiNpmGwyKjMVnDMK49EjAYYe3sRL1dTVY/caLMBhD8LPrb+n9L92JmDADMqKMmjzGAnFckf9xP4mD+0oc3FeBMXz4cAwfPrzTNu3/5jfddFPH1+wOFFc3o6SmGa02B1S7zX09cd6lP4fUyXKrqv1Eh8vZF10GAFAUCbGhRsRHGBFrNkCWpVNiIP/RynE1MikMO4trUdPYef2vYIo2yTh/dBLe+b4E72w7irEpYQjrQaG84soGDE4I69Z7tLKffNGdGCW1O+tPAVi6dCkeffTRTtvs3bsXubm57sfFxcWYOXMmZs2ahZdeesnr+1atWoVrr732lLk7p512GmbPnu3x5z7wwANYvny5x22ZzcEr8kEDi6oC31dKeOewjEabBAkqJsWrODvNgdh2nc/JGzdictvnuH3/uOsgXHPH3fhr0hnYXuXsmU8MUXH1EDtSfRgNVFpail/+8pcICQnBW2+95Z9fjIiIiIjIR3YH8OcdCsqaJUxLcOCywaf2Fq5btw5bt27F5MmTMXPmzCBEGVxNTU244oorUFtbi4iIzos0d/tWx5133olrrrmm0zbthwOVlJRg9uzZmDZtGl544YVO35eUlITW1lbU1NR06F0/duwYkpKSPL5n2bJlWLJkiftxXV0d0tPTMX/+/C5/+WCzWq3Iy8vDvHnz3HPNe2vTpk2orKzEpEmTkJiYeMrrtc2tOFTeqPl5L1qjOuxoLvgeIZnjvd4Bnz4YGDPGin9uLcbmIzXYUi5ha4WMsamROD0rGiMTzBiz8pcAOibqrscOACNeehk7bpoGWQHmDIvHhWOTYdT5VrzNIB0EACg6A8zZk3r4m3ZOr5MxMSMaJoN21/8MxHFF/sf9JA7uK3FwX4nDta8mTJsJq0OC1e6AqzC2IjvnKhv1CkIMMkL1OnfvOfU9rR1XDoeKXSW1qGrQbg/7lREN+EtePr45LuPM8cMwOK5jr1PRfz7GV199haTBI71es45Jj0JMqO/1F7S2nzrjGgnui24n6/Hx8YiPj/epbXFxMWbPno2JEyfi1Vdf7bJC9cSJE6HX67FmzRr3vKD9+/ejsLAQU6dO9fgeo9EIo/HUapd6vV7zO8rFn7Heeeed2LJlCz744AOcd955p7wep9cjLiIU1Y2tOFLVhIp6zxVIqaM3nnsCO75Zg3mXXI0Fi64AABwvLcbj997hsX1kWBKMEy/CcTUc24tqsb2oFqcf+QHnHyv1+jNkACn1Fbi06RA+2f8Vvl9fju89tBs/ZQYuX/xrAM41VX9/8/8BAJqbnOu4y4oCKQDrrMsyMC4jGuGCFK4R6RwwkHE/iYP7ShzcV+JIigrlvhKElo6r8YPisKO4VrPX8cOSozBtcCy+OViJNzcX4ffnjoDS7oaTonP+HVUVXq9ZyxttSIzqfqE5Le0nb7oTX8DmrBcXF2PWrFkYNGgQHnvsMZSXl7tfc/WSFxcX46yzzsLrr7+O0047DZGRkbjuuuuwZMkSxMTEICIiAr/+9a8xdepUFpfzkWvndzUXIjrUgOhQA5pabSiubkZpbYvP64UPNHU1VXjjuScAACOnHHE/b2lpxncbv/T6vouTo3HLjUvxVX4Fth6uQEJjjU8/7+J4O/787LteX4+OO1FwRoV6SgxxCZ5HofSGJAGjUiJZYZaIiIgoyGRZwpjUSOwsrkW5RhP2SyemYfvRGhytbsba/ccxd/iJEb/elm5rr7zeApvd0WnR5oEgYMl6Xl4e8vPzkZ+fj7S0tA6vuabJW61W7N+/H01NJypbPvnkk5BlGRdffDEsFgsWLFiAv/3tb4EKs9/RtVVX9HWddbNBhyGJ4chJCENFQyuO1bWgvMECu4f1vgeqluZmAIAsy5gx71z38zHxiVj2qPfPZnp2DlKiQvDTSek4b3g0jhR5Xp/1ZI7UtE63m5ia7v63ouhOaTtmsudRKL0xNDEcCaz8TkRERKQJsixhtIYT9nCTHhdPSMMbm47gve3FmDQo2t3p4xpt7fCyEgLgXL74WL0FqVEhfRKvVgUsWb/mmmu6nNuemZmJk+vbmUwmPPvss3j22WcDFVq/5mvP+skkSUJ8uBHx4UY4HCqqmlpR0WBBRX0rWqzeD6SBwNrqnBNkMBgweNgI9/OhYeGYc94ib2/rwBwahuG33omWD96C8XgZJA91HVVJgiUxGQ1TZmCO4tuccEVRfI6hpzLjQpEew2KNRERERFqi9YR9Rk4cvsqvwOGKRvxraxFuONNZ10xpu851dNKzDgClNc0DPlkf2OMK+qGeJuvtybKEuDAjcpMiMH1IHKYOjsWwpHDEhxuhUwZegROr1Xny6/X8F0XB/mV/AOBMzNtzPd6/9CHAx0S9L6TFhCCnm0tnEBEREVHfcCXs8eGn1vAKNlmW8H9TMiBJwOaCKuwpqWt7vi1Zt3c+BbemyYqmVt9GC/dXTNb7GX8k6ycLNeqQHmPG2PQozBwaj9OyYzA00Zm8632sVC4yV8+6P4pVlM87FztXvAQ1JaXjC6mp2P3UyyhvN8w+2JKjTMhN0vaKCkREREQDnSthj9Ngwj4oNhSzhznrLb25+Qisdod7znpXPesAUFrbEtD4tC5gw+ApOAKRrLcnSRIiTHpEmPTIiHUOja5vsaKmyYrqplZUN1lh7WeF6lx/S39VltRdcgnkX10NbNgAlJYCycmQZsxAXIMVZcW1fvkZvZUUacKIZCbqRERERCJwFZ37oagGlRpb1u3CcSnYWlCFY3UWfLq7DBdfdQN+8rNrfbq2Lq1pweD4gTvKk8l6P3P99dfjrLPOwowZM/rsZ4ab9Ag36d3zmutbrKhutKKy0YKaJivsDrGL1SUkpeD62++B0lzZ620pioTBCaHOoe6zZnV4LSlSQUWDBWVBvoOYHOVM1CVp4E15ICIiIhKVLEsYmxaF74/WoLpROwm72aDDZZPS8eJXh/HhzlJMyYpFfHi4T+9tsdpR2WBBbJj2Rg30BSbr/czZZ58d7BDcyXtGrBkOh4rqplZUNraiot6CplbxitXFJSbjp7+4GU2HtvZ6W4PjwmDUeZ+TPjw5AvUtNjRagjM/JyPWjKGJvp08iYiIiEhbZFnCuPQobD9ajerGwIy07YnTsmKwIb8C+8rqsWpzIW6dk+Nzx1BpbQuTdeo/du7cie+//97r6wsXLkRCgnPuyN69e7FlyxavbefOnYuUtvnVP/74IzZt2uS17axZs5CRkQEAOHToEL766qtT2rRY7ahvsSFr5AREJKSd8np/FmbSIT2m84qWiixhbHokNh+ugq2Pl88bnBCGrLjQPv2ZRERERORfSrse9tombSTskiTh51My8MAHe7CzuBYPPPoEJmRE44IrftHle4/Xt8BqD4d+AK65zmS9H/rggw/wu9/9zuvrX331lTtZ/+yzz3D77bd7bfvpp5+6k/X169fjhhtu8Np29erV7mR906ZNuPrqq722/cc//oGF08biWJ0FpbXNaLJot8e9rqYKJUcOQVdbisHZPduGJDl7zX25g2g26DAmzXlH1NEH0/9lGRiRHImkSK6jTkRERNQf6BQZ49Kj8N2RatS3aKOienJkCBaMTMRHO8twJGw4sOW/PiXrDgdQVtsyIJcSZrLeD2VnZ2PhwoVeX4+KinL/e9CgQZ22jY2Ndf87LS2t07aJiYnufycnJ3faNjk5GWaDDllxOmTFhaKuxYrSmhaU1bVorkDd1q/W4ZG7b8aYMWPw2Bnn92gbGTFmRIb4XqAuJtSAUSnOdTM9LMnuNya9gtFpkd2KjYiIiIi0T6/IGJ8Rja1HqjTTMXbu6GSs21WIpogE1CRN8Pl9xTXNTNapf7j88stx+eWX+9T2wgsvxIUXXuhT27PPPtvnOfGzZ8/G7NmzO22jqiocDgcURXFWmE/SY0hCGMobLCiqbtZMYYzWXq6zHmbS9aiKZUKECaMA7C6pDUgPe2yYASNTImEYAMvvEREREQ1EBp2MCRnR2HakGs0aqB1l1CkYZyzHNy2paEwaj0PlDcj24Tq5ocWG2iYrIs0Dq4OJV+kUFLfddhsURcEf/vCHDs/LsoTECBMmDorG1MGxSI8xQ1GCW5Xctc66Ttf9e1uKLGFUaiRkuWe/Q2KECWPToqDz499AUSQMSwrH+IxoJupERERE/ZxJr2BCRjSMem1c96UZWtC4Zx0gyXj568Ow2Hy7iVBc0xzYwDRIG3uMBhxFUaCqKpqbvR90oUYdhiWFY0ZOHIYlhcNs8F5FPZBcyXpPetZzk8MRZuzdAJbYMCNOy4pBhB+GqidEGDE1O3ZADiMiIiIiGqhCDArGZ0RDr4GOGllRUPXZc5As9ThWZ8G73xX79L5jdS2w2rU1XTbQgr+3aEAym53JYlNTU5dtdYqM9Bgzpg6OxZj0SESH9u3wF5u1Z8l6RqwZyZGdV3/3ldmgw+TMaAxNDO9RL3tMmAGTMqMxJi0KJn1wbnoQERERUfCEGXUYlx4V9FGrsqzAYWlE6L4PAQBf7DuO3SW1Xb7P7lBRVtsS6PA0hck6BUVIiDOJ9SVZd5EkCQnhJkwcFIPJWTFIjDDBx+UZe6Unw+Djw40YktD9eeqdkSQJGbFmTM+Jw9DEcISbOo/HbFCQGee8yTEhIxpRZoNf4yEiIiIisUSG6DE2LQpyELNApW0JNqXyIGYPiwcArPymAI2WrqvWF1UPrKHwLDBHQeHqWe9sGHxnIkP0GJ0WiebWMBypakRpTQvsjsCUTe/uMPjoUD1GpUb6tExbT+gUGRmxZmTEmmGxOdetb7Ha4XA4l2Ez6RWEGXXsQSciIiKiU8SEGjAqNRI7iwK76pA3p8+aj7fX/QCDwQiDORx7SupwrN6Cld8U4OZZgzu9hm602FDd2Iro0IHRCcVknYKiO8PgOxNiUJCbFIHsuDAcrW5CUXWz35d+G3vaGZCgItOHSpXRoQbn8KIeFpTrLqNOgTGMSTkRERER+S4h3IThySr2lNT1+c82mkJgNJ2YKrr4zGz86eN9+P5oDfL2HsP8EUmdvr+ounnAJOscBk9B4RoG39Oe9ZMZdDIGx4dhek4ccpPDYTb6L4Edf/p0XPWr32DSpEmdtkuKNGF8HybqREREREQ9lRIVgiGJ/p222ROZsaG4bHI6AODdbcXIP97Qafvj9S1osQZ/Gbq+wGSdgiIjIwPz58/HxIkT/bpdRZaQFm3GtMFxGJcRhdgwQ8DntcsyMCQxrFdLtBERERER9bVBsaEYFNu3qwQdOfgjnv3j7/D2i8+4n5s1NB6nZcbArqp4fv1BVDW2en2/qg6cuescBk9BMWvWLMyaNSugPyMuzIi4MCOaW+0ormlGaW0zLNbuD5E/XlKEpoY6hDQ24ORTWbhJh+EpEYgw9W2FeiIiIiIifxiSGI5WuwOlNX1Taf14aTHee/NlDM4dhcsX/xqAs5DyVVMHoaimCSU1LXjmiwO4e2Gu1xpMxTXNyIoL7fcjWtmzTv1eiEFBToJziPz4jCgkR5m6tfzZS08+jMUXzcGaNWvcz5kNCkakRDjXP2eiTkREREQCG5EcgfhwY5/8LEVxJuB2e8fq7ya9glvnDEG4SYej1c14YcMhOLwUkLbaHCit7f+960zWacCQJAmxYUaMTInEzKHxmDgoGplxoYgO1Xd6V679OutJUSaMz4jCtJw4pESFBKziOxERERFRX5EkCaNTIxFlDnwnlCtZd9hPnXceF2bEr2fnQK9I2FFUi9c2FsDhpWR9YVXvClWLIGDJekFBAa677jpkZWUhJCQEgwcPxv3334/WVu/zDwDn8GhJkjr8d9NNNwUqTAqSPXv2IDIyEmlpaUH5+ZIkITrUgJyEMEwcFINZw+IxLScW4zOiMDI1AsOSwjEsKRwjUiIQojhPEHq9HrlJEYgN65u7jkREREREfUWWJYxNj0KYKbAzpWVXsu7wPD01Oz4M10/PhiQBXx+sxD82HfGYsDdZ7Dhe3zdD94MlYHti3759cDgc+Pvf/46cnBzs2rULixcvRmNjIx577LFO37t48WI8+OCD7seuZb6o/zAYDKirq4MajMUdPZAkCWaDDmbDqYeEarcCAHQ6lnggIiIiov5Lr8gYnxGFrQXVaG4NTMV1Wfbes+4ycVA0rj8jCy99dRhfHqhAi9WBa8/IhF7p2Nd8pLIJCeGmgMSpBQHLPhYuXIiFCxe6H2dnZ2P//v147rnnukzWzWYzkpI6X1+PxOavddb7gsViAeDsWSciIiIi6s+MOgUTMqKx9UhVj4ozd0X2Mmf9ZFOyYwEJeOXrAmwuqEJ1UytuPDMbUeYTa6zXNllRVdeMiM1fI/XLLyGFhgKzZwOK/5ZxDqY+7Sqsra1FTExMl+3efPNN/OMf/0BSUhLOP/983HvvvV571y0WizuZAoC6ujoAgNVqhdVq9U/gAeKKT+txBoIr8bXb7WhqatJ0Itw+WR+I+0o0A/m4Egn3kzi4r8TBfSUO7itxDNR9pZOAUcmh2F5YA5vdvyNhZTi3Z7fboXaRsJ+WEYkwfTae+/IwDhxvwPIP9uD/pqRhfFokJElCwucfIfTPD0BXVoJJAPDEE1BTU2F/4gmoF13k17j9pTufJUnto3HI+fn5mDhxIh577DEsXrzYa7sXXngBgwYNQkpKCnbs2IG7774bp512GlavXu2x/QMPPIDly5ef8vyqVas4fF7DrFYrLr30UgDOmzOhoaFBjsi7JUuW4NChQ7jvvvswYcKEYIdDRERERCSs1tZWVFZWQqfTIT4+3qf3HG8GVv6ooLjJWdw5N9KBX5Z+jZ888ygAoH3JZ1dyu+Xuu1E6daofI/ePpqYmXHHFFaitrUVERESnbbudrC9duhSPPvpop2327t2L3Nxc9+Pi4mLMnDkTs2bNwksvvdSdH4cvvvgCZ511FvLz8zF48OBTXvfUs56eno6Kioouf/lgs1qtyMvLw7x58zTdsxwIqqoiJCQEDocDhYWFmp728Mwzz6CgoAC5ubm45pprBty+Es1APq5Ewv0kDu4rcXBfiYP7ShzcV0BVYyt2FdfASz24HjtWUoR9O7/3+nru6PFITHEWo644Vood32/FzqYI7GmOABwOfPX8dUiqr/BYMV2VJCA1FbYDBzQ3JL6urg5xcXE+JevdHgZ/55134pprrum0TXZ2tvvfJSUlmD17NqZNm4YXXnihuz8OU6ZMAQCvybrRaITReGp1br1eL8wBJVKs/mQ2m9HQ0ACr1arp33/JkiWwWq346KOPBuy+EhH3lRi4n8TBfSUO7itxcF+JYyDvq8QoPSRFwc6iWvhzTPbuH7bhT3f/yuvrSx99FknpmQCAH/fuwp/u+iUAQBeVjLOHTEFKfYXX90qqChQVQb9pEzBrlv+C9oPufI66nazHx8f7PFyhuLgYs2fPxsSJE/Hqq69Clru/Utz27dsBAMnJyd1+L2nbmWeeCYvF4l5rkYiIiIiItCch3ISRKcDuEv8l7NGx8Rg7eVqnr7tERsV0aDuicrdvP6S0tMfxaUHACswVFxdj1qxZGDRoEB577DGUl5e7X3MNeS4uLsZZZ52F119/HaeddhoOHjyIVatW4ZxzzkFsbCx27NiBO+64A2eeeSbGjBkTqFApSD788EMAQFlZGW688Uav7ebOneue315VVYVly5Z5bXvmmWfi5z//OQCgoaEBd955p9e2U6ZMwS9+8QsAziFOt9xyi8d2DocDd911F2y2zgtgEBERERH1V0mRJjhUFXtK6vyyvQlTz8SEqWf61HbUxCl4bOWJGmbRm78Grr246zcK3uEbsGQ9Ly8P+fn5yM/PR1paWofXXNPkrVYr9u/f716+y2Aw4PPPP8eKFSvQ2NiI9PR0XHzxxfj9738fqDBJA2pqajqdIhEWFuZO1hsbGzttK0mSO1m3WCydtm1qanIn63a7vdO2L7/8Ml5++eVOfw8iIiIiov4sJSoEKoC9fkrYe6p64uloSUyG8XiZc8j7ySQJSEsDZszo++D8KGDJ+jXXXNPl3PbMzEy0r2+Xnp6O9evXByok0qi4uDg89NBDXl931S0AgIiIiE7btq/WHhIS0mnb0aNHu/+t0+k6bTtixAgO1yciIiKiAS81KgRAkBN2RcH+ZX/AmDuuhypJHRN2qa02/IoVmisu1119us46kSdxcXE+j56IjIz0ua3ZbPa5rU6n67Stq8AcEREREdFAlxoVAgnA3tI6vxad647yeeei+MXXkfbAMqCo6MQLaWnORH3RouAE5kdM1omIiIiIiKhbUqJCoMgSdpfU+n1ZN1+EGBQkXXMFcM3PYFu7Fts//hjjzj4butmzhe9Rd2GyTkRERERERN2WGGGCIkvYWVQLu6PvutglCRiZEgGd4lxtTJ05E8WNjRg7c2a/SdQBeFxDnoiIiIiIiKhLcWFGTBgUDYOu71LLrLhQRJkNffbzgoXJOhEREREREfVYZIgekzNjEGoM/MDtuHAjsuPDAv5ztIDJOhEREREREfVKiEHB5MxoxIcbA/Yzwk06jEqJCNj2tYbJOhEREREREfWaTpExNj0KQxLDIPs50ww16jA+I9o9T30gGDi/KREREREREQXcoNhQTPLjsPhIsx4T+3hevBYMrN+WiIiIiIiIAi7CpMeUrBgMTgiDIks93k5KVAgmZgy8RB3g0m1EREREREQUALIsISsuFMmRJhypbEJJTbPPS7yZDQqGJIYHdA681jFZJyIiIiIiooAx6RUMSwpHdnwoympbUN5gQW2T9ZTEXZaBaLMByZEhSIwwQpJ63iPfHzBZJyIiIiIiooDTKzLSY8xIjzFDVVU0W+1otTmgqoBBJyNEr0DuxZD5/obJOhEREREREfUpSZJgNuhgNgQ7Eu0aeLP0iYiIiIiIiDSOyToRERERERGRxjBZJyIiIiIiItIYJutEREREREREGsNknYiIiIiIiEhjmKwTERERERERaQyTdSIiIiIiIiKNYbJOREREREREpDG6YAfgb6qqAgDq6uqCHEnXrFYrmpqaUFdXB71eH+xwqBPcV+LgvhID95M4uK/EwX0lDu4rcXBfiUGk/eTKU115a2f6XbJeX18PAEhPTw9yJERERERERESnqq+vR2RkZKdtJNWXlF4gDocDJSUlCA8PhyRJwQ6nU3V1dUhPT8fRo0cRERER7HCoE9xX4uC+EgP3kzi4r8TBfSUO7itxcF+JQaT9pKoq6uvrkZKSAlnufFZ6v+tZl2UZaWlpwQ6jWyIiIjT/oSIn7itxcF+JgftJHNxX4uC+Egf3lTi4r8Qgyn7qqkfdhQXmiIiIiIiIiDSGyToRERERERGRxjBZDyKj0Yj7778fRqMx2KFQF7ivxMF9JQbuJ3FwX4mD+0oc3Ffi4L4SQ3/dT/2uwBwRERERERGR6NizTkRERERERKQxTNaJiIiIiIiINIbJOhEREREREZHGMFknIiIiIiIi0hgm60REREREREQaw2SdiIiIiIiISGOYrBMRERERERFpDJN1IiIiIiIiIo1hsk5ERERERESkMUzWiYiIiIiIiDSGyToRERERERGRxjBZJyIiIiIiItIYJutEREREREREGsNknYiIqB954IEHIElSsMMgIiKiXmKyTkREpGErV66EJEnu/0wmE1JSUrBgwQI8/fTTqK+vD3aIPtuzZw8eeOABFBQUBDsUIiIizWOyTkREJIAHH3wQb7zxBp577jn8+te/BgDcfvvtGD16NHbs2OFu9/vf/x7Nzc3BCrNTe/bswfLly5msExER+UAX7ACIiIioa2effTYmTZrkfrxs2TJ88cUXOO+88/CTn/wEe/fuRUhICHQ6HXS6vvl6t9lscDgcMBgMffLziIiIBhL2rBMREQlqzpw5uPfee3HkyBH84x//AOB5znpeXh6mT5+OqKgohIWFYdiwYbjnnns6tGlpacEDDzyAoUOHwmQyITk5GYsWLcLBgwcBAAUFBZAkCY899hhWrFiBwYMHw2g0Ys+ePQCAffv24ZJLLkFMTAxMJhMmTZqE999/3739lStX4tJLLwUAzJ492z2sf926de42H3/8MWbMmIHQ0FCEh4fj3HPPxe7du/3+dyMiIhIBe9aJiIgEduWVV+Kee+7BZ599hsWLF5/y+u7du3HeeedhzJgxePDBB2E0GpGfn4+vv/7a3cZut+O8887DmjVrcPnll+O2225DfX098vLysGvXLgwePNjd9tVXX0VLSwtuuOEGGI1GxMTEYPfu3TjjjDOQmpqKpUuXIjQ0FP/6179w4YUX4t1338VFF12EM888E7feeiuefvpp3HPPPRg+fDgAuP//xhtv4Oqrr8aCBQvw6KOPoqmpCc899xymT5+O77//HpmZmYH9QxIREWkMk3UiIiKBpaWlITIy0t0DfrK8vDy0trbi448/RlxcnMc2r7/+OtasWYMnnngCd9xxh/v5pUuXQlXVDm2LioqQn5+P+Ph493Nz585FRkYGtmzZAqPRCAC4+eabMX36dNx999246KKLkJ2djRkzZuDpp5/GvHnzMGvWLPf7GxoacOutt+L666/HCy+84H7+6quvxrBhw/DHP/6xw/NEREQDAYfBExERCS4sLMxrVfioqCgAwH//+184HA6Pbd59913ExcW5C9e1d/KQ+osvvrhDol5VVYUvvvgCP/3pT1FfX4+KigpUVFSgsrISCxYswIEDB1BcXNxp/Hl5eaipqcHPfvYz9/srKiqgKAqmTJmCtWvXdvp+IiKi/og960RERIJraGhAQkKCx9cuu+wyvPTSS7j++uuxdOlSnHXWWVi0aBEuueQSyLLznv3BgwcxbNgwnwrTZWVldXicn58PVVVx77334t577/X4nuPHjyM1NdXrNg8cOADAOQffk4iIiC7jIiIi6m+YrBMREQmsqKgItbW1yMnJ8fh6SEgIvvzyS6xduxYffvghPvnkE/zzn//EnDlz8Nlnn0FRlG79vJCQkA6PXb31v/nNb7BgwQKP7/EW28nbeOONN5CUlHTK631V3Z6IiEhL+O1HREQksDfeeAMAvCbKACDLMs466yycddZZeOKJJ/DHP/4Rv/vd77B27VrMnTsXgwcPxrfffgur1Qq9Xt+tn5+dnQ0A0Ov1mDt3bqdtTx5S7+IqYJeQkNDlNoiIiAYKzlknIiIS1BdffIGHHnoIWVlZ+PnPf+6xTVVV1SnPjRs3DgBgsVgAOOehV1RU4K9//espbU8uMHeyhIQEzJo1C3//+99RWlp6yuvl5eXuf4eGhgIAampqOrRZsGABIiIi8Mc//hFWq7XTbRAREQ0U7FknIiISwMcff4x9+/bBZrPh2LFj+OKLL5CXl4dBgwbh/fffh8lk8vi+Bx98EF9++SXOPfdcDBo0CMePH8ff/vY3pKWlYfr06QCAq666Cq+//jqWLFmCzZs3Y8aMGWhsbMTnn3+Om2++GRdccEGnsT377LOYPn06Ro8ejcWLFyM7OxvHjh3Dxo0bUVRUhB9++AGA8yaBoih49NFHUVtbC6PRiDlz5iAhIQHPPfccrrzySkyYMAGXX3454uPjUVhYiA8//BBnnHGGxxsJRERE/RmTdSIiIgHcd999AACDwYCYmBiMHj0aK1aswLXXXovw8HCv7/vJT36CgoICvPLKK6ioqEBcXBxmzpyJ5cuXIzIyEgCgKAo++ugjPPzww1i1ahXeffddxMbGuhPwrowYMQJbt27F8uXLsXLlSlRWViIhIQHjx493xw0ASUlJeP755/HII4/guuuug91ux9q1a5GQkIArrrgCKSkp+NOf/oS//OUvsFgsSE1NxYwZM3Dttdf28q9HREQkHkntanwbEREREREREfUpzlknIiIiIiIi0hgm60REREREREQaw2SdiIiIiIiISGMCmqx/+eWXOP/885GSkgJJkvDee+91+Z5169ZhwoQJMBqNyMnJwcqVKwMZIhEREREREZHmBDRZb2xsxNixY/Hss8/61P7w4cM499xzMXv2bGzfvh233347rr/+enz66aeBDJOIiIiIiIhIU/qsGrwkSfjPf/6DCy+80Gubu+++Gx9++CF27drlfu7yyy9HTU0NPvnkkz6IkoiIiIiIiCj4NLXO+saNGzF37twOzy1YsAC33367z9twOBwoKSlBeHg4JEnyc4REREREREREPaOqKurr65GSkgJZ7nygu6aS9bKyMiQmJnZ4LjExEXV1dWhubkZISMgp77FYLLBYLO7HxcXFGDFiRMBjJSIiIiIiIuqJo0ePIi0trdM2mkrWe+KRRx7B8uXLT3n+pZdegtlsDkJERERERERERKdqamrC9ddfj/Dw8C7baipZT0pKwrFjxzo8d+zYMURERHjsVQeAZcuWYcmSJe7HdXV1SE9Px4UXXoiIiIiAxttbVqsVeXl5mDdvHvR6fbDDoU5wX4mD+0oM3E/i4L4SB/eVOLivxMF9JQaR9lNdXR2uv/56n6ZsaypZnzp1Kj766KMOz+Xl5WHq1Kle32M0GmE0Gk95Xq/Xa35HuYgU60DHfSUO7isxcD+Jg/tKHNxX4uC+Egf3lRhE2E/diS+gS7c1NDRg+/bt2L59OwDn0mzbt29HYWEhAGev+FVXXeVuf9NNN+HQoUO46667sG/fPvztb3/Dv/71L9xxxx2BDJOIiIiIiIhIUwKarG/duhXjx4/H+PHjAQBLlizB+PHjcd999wEASktL3Yk7AGRlZeHDDz9EXl4exo4di8cffxwvvfQSFixYEMgwiYiIiIiIiDQloMPgZ82ahc6WcV+5cqXH93z//fcBjIqIiIiIiIhI2wLas05ERERERERE3cdknYiIiIiIiEhjmKwTERERERERaQyTdSIiIiIiIiKNYbJOREREREREpDFM1omIiIiIiIg0hsk6ERERERERkcYwWSciIiIiIiLSGCbrRERERERERBrDZJ2IiIiIiIhIY5isExEREREREWmMLtgBEBEREfWa3Q5s2ACUlgLJycCMGYCiBDsqIiKiHmOyTkRERGJbvRq47TagqOjEc2lpwFNPAYsWBS8uIiKiXuAweCIiIhLX6tXAJZd0TNQBoLjY+fzq1cGJi4iIqJfYs05ERER97sm3PsGn2wswY8YMyIqz72D/vn0oKzvm9T3Tpk2D3qAHABz48QDKiorw2t9/hzhVhXRyY1WFCgl1N/4Kvy3SobC4xOt2J02ahNCwUABAQUEBjhQc6fD62eOzcNvlC7r/SxIREfUCk3UiIiLqUw6HAyu2NEAypGPfVwXtXjEAUrrX9+3beLTdIwWnF9civr7Ga3sJKiIrylC3aTf2ZYzxvt1tJ90gOCmGvZsb8MuLbTDoedlERER9h986RERE1KdaWq2QDCEAgEvGxiMqPAwAsG//PhzrpGd96rSpMOgNAIADBw5g2K4DPv28WfoqVNoLvb4+afIkhJrbetaPdOxZ/1FKhWQwYe/hYowdOsinn0dEROQPTNaJiIioTzU2W9z/Xnp2LuKiIpwPzhvRja2MAMZHAh++2mXLm649FzfNmuX7dtsZdOs/AHM09jBZJyKiPsZknYiIiPpUU8uJZN1sMvZ8QzNmOKu+FxcDqnrq65LkfH3GjB7/iHDFjgYA1RYP2+8pLjNHREQ+YDV4IiIi6lPNllb3v01tBeN6RFGcy7MBzsS8PdfjFSt6lQhPnzAKABAal9rjbXSwejWQmQnMng1ccYXz/5mZrFpPRESn6JNk/dlnn0VmZiZMJhOmTJmCzZs3e227cuVKSJLU4T+TydQXYRIREVEfcPWsqzYrZLmXlyKLFgHvvAOknpRMp6U5n+/lOutJkc5rkNLall5tBwCXmSMiom4J+DD4f/7zn1iyZAmef/55TJkyBStWrMCCBQuwf/9+JCQkeHxPREQE9u/f734snXy3nIiIiITV1NLWs+6w+2eDixYBF1wQkKHlyW3Jelltc+82ZLcDt93mebi+qjpHAtx+u/P34JB4IiJCHyTrTzzxBBYvXoxrr70WAPD888/jww8/xCuvvIKlS5d6fI8kSUhKSgp0aERERBQEsfEJAA4iLDTEfxtVFMDnInK+Kzu8DwDw3tc7sWvbplNenzZtKvTtKtSXlHhez31s4Y94/OQe9fZUFTh6FHde8Rv8kDHUufZ7aLu1348c8frWCRPGIzw8AhJUxDX5+psREZHWBTRZb21txbZt27Bs2TL3c7IsY+7cudi4caPX9zU0NGDQoEFwOByYMGEC/vjHP2LkyJEe21osFlgsJwrV1NXVAQCsViusVquffpPAcMWn9TiJ+0ok3Fdi4H4SRyD2ld3h/H+IUa/5z0BWfDiwrx4IjUU+Yk95Pf/b4naPdICS4XE7I5oP+/TzrM165CsZyP/+eMcXvGwXAPJ/qARQ6fw5UTKu0vjflHgOFAn3lRhE2k/diTGgyXpFRQXsdjsSExM7PJ+YmIh9+/Z5fM+wYcPwyiuvYMyYMaitrcVjjz2GadOmYffu3UhLSzul/SOPPILly5ef8vxnn30Gs9nsn18kwPLy8oIdAvmI+0oc3Fdi4H4Shz/31dEGANDB1mrBRx995LftBkK4Q8Wo5r2o8DJlPXfYMOh0ziJ5JSUlqKqu8tjOplb79PNsajWSqncgJycHJqNzCP7x48dwvLzc63uys7NR1ujAodYIHCkt53ElEO4rcXBfiUGE/dTU5PsQKElVPU2e8o+SkhKkpqbim2++wdSpU93P33XXXVi/fj2+/fbbLrdhtVoxfPhw/OxnP8NDDz10yuueetbT09NRUVGBiIgI//wiAWK1WpGXl4d58+ZBr+9FNVwKOO4rcXBfiYH7SRyB2Fefbv0Rt/y3AFE6G7bcf45ftql5djt0OTlASQkkD5deqiQBqamwHTjQoznrj7z+IV45oIdUdQS7V1zD40rjeA4UB/eVGETaT3V1dYiLi0NtbW2X+WpAe9bj4uKgKAqOHTvW4fljx475PCddr9dj/PjxyM/P9/i60WiE0XjqGq16vV7zO8pFpFgHOu4rcXBfiYH7SRz+3FclbdcFtdWVA2f/6/XA0087q75LUsdCc5IECQCeegr6Hq6AE2LUu7dV1WyHYtV2cd5wkx5hxoCXTtI8ngPFwX0lBhH2U3fiC+hZ0mAwYOLEiVizZg0uvPBCAIDD4cCaNWtwyy23+LQNu92OnTt34pxzBsiddyIion6uxWIDAEiqI8iR9DHXMnO33dZx+ba0NOd68L1YZk6nyAAcUCFjb0kdJEXbifDghDAm60REXQj4WXLJkiW4+uqrMWnSJJx22mlYsWIFGhsb3dXhr7rqKqSmpuKRRx4BADz44IM4/fTTkZOTg5qaGvzlL3/BkSNHcP311wc6VCIiIuoDLW3FdWQMsGQdCNgycwadAsABSL1ct76PBHAWJhFRvxHwZP2yyy5DeXk57rvvPpSVlWHcuHH45JNP3EXnCgsLIcsnvliqq6uxePFilJWVITo6GhMnTsQ333yDESNGBDpUIiIi6gOtVjsABRIGaMIWgGXm9DodACtUQZJ1xwDd9URE3dEn449uueUWr8Pe161b1+Hxk08+iSeffLIPoiIiIqJgaLHaACiQB2qyHgDOnnUI07MO7nsioi6JckYnIiKifsLS6pyzPiCHwQeI3pWsy2Jc2nEUPBFR18Q4oxMREVG/0WqzAwAUbRcsF8roUSMBAJHRsUGOxDfM1YmIusZknYiIiPrU8JGjAAAjcocFOZL+w6B39qyLMlbBwa51IqIuMVknIiKiPhUSGg4AiI+JDnIk/YdOdg5TEKVwG3N1IqKuMVknIiKiPmW1O/t/dRwH7zeVFeUAgBZLa5Aj8Q2TdSKirjFZJyIioj51+EghAKCy/HiQI+k/LC0tAAC7IF3rKmetExF1ick6ERER9am9+w8AAAoO5gc5kv7D6F66TQluID5izzoRUdeYrBMREVGfcg2DV2QOg/cXg0Hn/AeXbiMi6jfEOKMTERFRv2FrG6qt55x1vzHq9QAASVaEqLTOYfBERF1jsk5ERER9ympnsu5vRr3O/W8R5q0LcD+BiCjomKwTERFRn3L1rOs4DN5vDO2SdZvNHsRIfCNC7z8RUbAxWSciIqI+dWIYPC9D/MVo0Lv/bbVZgxiJb5iqExF1jd+SRERE1KdszvpyMOh4GeIvURHh7n/Lir6TltrAjnUioq7xW5KIiIj6VO7wEQCAcWNGBzmS/kOvO7FkmyOIcfiO2ToRUVeYrBMREVGfCg2PBAAkJyYEOZL+Q5IkKJKzBoBDgGydPetERF1jsk5ERER9qrVtnXXOWfcv1WEDADTU1wc5kq4JULCeiCjo+C1JREREfaq07BgAoLqqIsiR9C82aysAoKm5KciRdE1l1zoRUZeYrBMREVGfOlhwBABQcDA/yJH0Mw7nkm02u/aXbmOqTkTUNSbrRERE5JndDmn9eqR++SWk9esBPyWBjrbLD2O7tcHJD1Tn9AKrTfuT1rnOOhFR1/okWX/22WeRmZkJk8mEKVOmYPPmzZ22//e//43c3FyYTCaMHj0aH330UV+ESURERC6rVwOZmdDNm4dJTzwB3bx5QGam8/lecqjOQmgGvdJFS+oWd8+6LciB+IC5OhFRlwKerP/zn//EkiVLcP/99+O7777D2LFjsWDBAhw/ftxj+2+++QY/+9nPcN111+H777/HhRdeiAsvvBC7du0KdKhEREQEOBPySy4Bioo6Pl9c7Hy+lwm72la13GRgz7pftfWs22wcBk9E1B8EPFl/4oknsHjxYlx77bUYMWIEnn/+eZjNZrzyyise2z/11FNYuHAhfvvb32L48OF46KGHMGHCBPz1r38NdKhEJLoADdn1O7sdWLcOeOst5/+1GicNTHY7cNttntfWcj13++29+ty6h8HrmKz7lUjJOrN1IqIuBfRbsrW1Fdu2bcOyZcvcz8myjLlz52Ljxo0e37Nx40YsWbKkw3MLFizAe++957G9xWKBxWJxP66rqwMAWK1WWK3WXv4GgeWKT+txEveVCKT//AfKkiXQFRdjEgA88QTU1FTYn3gC6kUXBTs8N1ecUnGx+zktxhloPKYC4/6X3sM/d1ZDbXcvPiIiAkmJiQCc84Tz870XdQsPD8OChjL8+eQe9fZUFTh6FNde8QBKT5/rfjo/P9/rPGRzSAjS0tLcj+3mOEhwLt3Gz4Afuees26BqfCi8Q3YM6H3Pc6A4uK/EINJ+6k6MAU3WKyoqYLfbkdh2keCSmJiIffv2eXxPWVmZx/ZlZWUe2z/yyCNYvnz5Kc9/9tlnMJvNPYy8b+Xl5QU7BPIR95U2JW/ciMmPPnrqC8XFUC67DFvuvhulU6f2fWAnESXOvsRjyr/e/q4MjtjsDs/VAKg51nDiiYhkr++vBdBy9DufflZ4swVr2283PMlr2wYA+9q1lXR6qKoDTeVHWZfGjxLiYlHRCkTKLWg6tDXY4XSqCcBH+4MdRfDxHCgO7isxiLCfmpp8X15T+PFny5Yt69ATX1dXh/T0dMyfPx8RERFBjKxrVqsVeXl5mDdvHvR6fbDDoU5wX/lfQ3MLnv/PWtQ2Wk55beiQIYiNjQXgvOl3oJOewCGZmZj/6usAAOmk1yQ450UOfeEl/CdtPDIHZyMp0ZlQ1NbVYs+evV63O2hQBlKSU5wPVDuqSwqgUzwXw4qLj0N8XDwAoKWlBYcPHz6ljWS3Y+TLr3Qa58iXX8WBMxZCbfs5UdFRSE5yJlZWmxX5B7z/HSIjI5GSkoLUKBNiw4xe22kFj6nA+M2nr8ABYJxShJkjMwAAMTHRGJSZCQBw2B344YcfvL4/MioSE0acDnzQ9c9aMDUdiy6a6H68fft2qA7PPeth4WEYMmSI+/HOHTswMjMZM8Yv7PoHkc/+mv81KsobYUzJhTklMtjhdEqnSJg+JD7YYQQNz4Hi4L4Sg0j7yTUS3BcBTdbj4uKgKAqOHTvW4fljx44hKcnzHfikpKRutTcajTAaT70w1ev1mt9RLiLFOtBxX/nPzX98HRubkwB4GAFTWAyguN0T3kfJnP7V57jueKnX1yUAUdWVOPzNIbxVFAagfXHLTkbfFFYAqPD+egd1AA512uL0wh34eUV5p3GGVhzHB29txKaMMe22W9iNGI7CqJPxzdI5QiTsAI8pf3MNf586LBV3XHG2xzZzRqZ0vhG7HfhdmrOYnKdh7ZIEpKXh3LtuBtrdwJqV63viPSvXey889ZxOce5/ByRIisb7YxSJxz54DhQJ95UYRNhP3YkvoGdyg8GAiRMnYs2aNbjwwgsBAA6HA2vWrMEtt9zi8T1Tp07FmjVrcPvtt7ufy8vLw9QBNjSUqL8rrbMAegCNlTDZGzu8lpmZhagoZ69QTXUNCo4UeN3OGEe1Tz8vtfwAckaNQlxcHACgoaGh07m7KSkpSEhIAAAcr6zGoRLviXtERATCw50jeaxWK44fP3ZKm4RK35LuhMpCIDYVABAaGoaoqCgAgMNhR2mp95sSZnMoLEoILDYHimuahUnWyb/Co6JR7QCSkxJ6vhFFAZ56yln1XZI6JuxtVdyxYkWHRJ20oeL4MQBmlB8rBdJjgh1Op1RWmCMi6lLAb7suWbIEV199NSZNmoTTTjsNK1asQGNjI6699loAwFVXXYXU1FQ88sgjAIDbbrsNM2fOxOOPP45zzz0Xb7/9NrZu3YoXXngh0KESUR9KTklFQTlw7vBYPHvzVT3f0Lp1wD//0mWzx393FTBr1knPzuv5z+2udRnAZ8932ezpZZfh6VPi9M3E+/+HSouEz77LR0VDeo+20Vd0XLgpIKJi4lBd0Yghgwf3bkOLFgHvvOOsCt++2FxamjNRX7Sod9ungKisKAdiBqG6qirYoXSJuToRUdcCnqxfdtllKC8vx3333YeysjKMGzcOn3zyibuIXGFhIWT5RNXaadOmYdWqVfj973+Pe+65B0OGDMF7772HUaNGBTpUIupDgzKzsLH8KEYMH9a7Dc2Y4UwguhiyixkzevdzeqsP4qyqKAfCE1BSXIRxOdpO1h0Sr9QDwWp3VgPXKSdXRuiBRYuACy6Abe1abP/4Y4w7+2zoZs9mj7qGSWhbus3hCHIkXWOyTkTUtT6Z0HTLLbd4Hfa+bt26U5679NJLcemllwY4KiIKJltbISpF7mVSIcqQ3T6IU4IDKkRZY1msK/XqxlZUNbUGO4wutbQ6l+uSVD8la4oCdeZMFDc2YuzMmcE/jqhTMlTYAdjt2k/WAed5QJL8cGOJiKif0nj1ESLqr5qaWwAADpsf1sMUZchugOOU3GssC5CsBzuAbqpuasXh8sauGwZZ+fHjkEKjsW/vHkzKOjPY4VAfk9qOLHGS9RP3KomI6FRM1okoKL7dshUIzcTmb7/FzWfl9n6DogzZbYsTGzYApaVAcrJz6Lsf4nT1qomQrIuWrQsTbtu0MqOeX+8DkStZF2EYPCDQcUVEFCT8NieioHAtx6z3x9xaF1GG7CqKh2J3veeer2rXfrKuCnaZLsyofcn5mdfrNPrZp4ByVQCye1nvXmscqgoF7FonIvJG7roJEZH/ua4lXesCU+/JbQmwVYAhsMIkv6KRnMeTgcn6gCS3FW60i9KzzvMAEVGneJVMREHBZN3/XH9JMQrMBTuC7hIkYNmZpBv0TNYHokkTJgAAMnL8MLWoD4g2woaIqK/xKpmIgsLRNvRRz2Tdb4YMzgIApGblBDmS/keYmwvuOev6IAdCwRAeFgoA0OkNQY7EN8IcV0REQcKrZCIKCneyzuG6fpMQHwcACI2IDnIk/Y8wOYWrZ53H1YCka1sKU4CZMACYrBMRdYXJOhEFhetaksm6/5y4UOcVsL+JkFQ4VBVS25z16KiIIEdDwVB4pAAAUFpS1HlDjeAweCKizjFZJ6KgiIlx9gKnJCUGOZL+o662GgBQUX4syJH0PyIkFe1v0iQlJgQxEgoWV7JecbwsuIH4SISbYEREwcRknYiCIjE5BQAwNIfzq/0l/8cfAQCHDvwY5Ej6HxGSCke7ZN01yoIGFtdKmMIMgw92AEREGsdknYiCwtULqPhznfUBTtd2Rhdl2SaRiJCs29ol64rE42ogkt3JuhjnAIcIBxYRURAxWSeioLBYrQBOrA1OvadwznrAiDAMvv2Sfa4pETSwuO59inIKYK5ORNQ5XbADIBKB2nZFUdHQCp1Ouz0WiiwhJlSMJXsOHDwEhCdh986dmDmM89b9gQXmAkeEpMJqswEAVIcdeh2/3gci10qYwvRYCxImEVGw8NucyAeu4aW7imogKdo9bEx6BdOHxAU7DB85ryoNelaD9xd3si7KhTr5lStZh8MBHZP1Ack1/UGQUfBCjFghIgomfpsT+cAhSE+lML0pANS2Jaa4dJv/6GQJUAG7OB8D8iOb3TkMXnXYoSg8rgYi11QYB06MCNMyQb5aiYiChsk6kQ8EuOYBIFqyLkECYNTzNOQvOlkC7OLcXBKJCIdWq9XVs25jsj5ATT9jKg5srUCpMR2L39gW7HA6ZdLLePKn43D26ORgh0JEpFksMEfkA7sgQ/VESCjc2nrWDRyu6zcjcocBAFKzhgQ5kv5HhOG67XvWOQx+YDpz5CAokvY/qwDQYnVgc0FVsMMgItI0fpsT+UCUgl0i9ay7k3XOWfeb9LQUYPd+RESLUrdAHCIcWlbriWRdlnkvfiCanhOLRybboUsfB0nDoyve216C9T+WC/PdSkQULEzWiXwgwtw/QIyEwk1yXkga9fogB9J/sBp84IjwF3UVvwwxirEiBPnfhg0b8P7qf2P4tDpMmTU/2OF4ZdI7byY1tdpR0WAJcjRdiwzRQ6/wBhgR9b2AJutVVVX49a9/jQ8++ACyLOPiiy/GU089hbCwMK/vmTVrFtavX9/huRtvvBHPP/98IEMl6pRdkMq6gHO+styWtGmZ3mCEDUBkZHiwQ+k3GuprAQDVVZUAsoIbTD8jwg27kFDnd2tSQnyQI6FgWbt2Ld58803MrW1B5tDh7udlWUF8Uor7cWX5MdisrR63IUkyEpJT3Y+ryo/DavWcUEuQkJCS5n5cXVGO1tYWr/ElpqQDOFG1vrqxFdsLa7r+xYJsUmY0osy8CUZEfS+gyfrPf/5zlJaWIi8vD1arFddeey1uuOEGrFq1qtP3LV68GA8++KD7sdlsDmSYRF0SaSksh6pChvaTdZ3RCJvVgeRErrHuLz/u2wsgCgWHDgCYFOxw+hURzgCuERWKADfrKDBctQo+/9+7+Px/77qfT0pNxxufbXE/vvdXV+LA7h0etxEVG4d/f7nL/fgPv7kBO7du8tjWFGLGB1sPuR//5Xe3YstXa73Gl7e7DAAgtyXrxYUFwGkZXfxWwSfQJQAR9TMBS9b37t2LTz75BFu2bMGkSc6LxmeeeQbnnHMOHnvsMaSkpHh9r9lsRlJSUqBCI+o2UdasBcRZCseVWOgUJhb+4loGz6Hyb+pvIlysu44pDtcduObOnYunnnoKjU1NQLubtnqDsUM7vd4Ig9HkcRuGU9oavLc96XldJ23b27djGyCloLqaBeaIiDoTsGR948aNiIqKcifqgPNLRJZlfPvtt7jooou8vvfNN9/EP/7xDyQlJeH888/Hvffe67V33WKxwGI5MTyrrq4OAGC1WmG1Wv302wSGKz6tx0mArW0fqQ57kCPpWmtrKyRVu4WFXGxti4E7bDa/HgMD+bjStV2bqwBUuy2osXTFdSyJsp8cdqvm/6alRYUAgP379sBqnea37Q7kY0o048ePx6uvvoqQzPGQ5I7fA+0/vyte/0+n22nf9k9/73w0ZPu2y5962ae2TXXVQGQKHA5V88cVAFhtVlit/r0JyuNKHNxXYhBpP3UnxoAl62VlZUhISOj4w3Q6xMTEoKyszOv7rrjiCgwaNAgpKSnYsWMH7r77buzfvx+rV6/22P6RRx7B8uXLT3n+s88+E2b4fF5eXrBDIB81F3wf7BC6tOZQ122CzWZ3QIVz/t8H7/8XyTH+n7c+EI+rkuIiQB4Oh8OBpkNbgx2OTwbifgqUpmPlAJLhsNnw0Ucf+X373Ffi0Px3laUBAGBvbRbiXLUxgN+rPK7EwX0lBhH2U1NTk89tu52sL126FI8++minbfbu3dvdzbrdcMMN7n+PHj0aycnJOOuss3Dw4EEMHjz4lPbLli3DkiVL3I/r6uqQnp6O+fPnIyIiosdx9AWr1Yq8vDzMmzcPelbE1rSC43XYs/Urj70VWjMlOwYhBm0v9NDQ3AJs/hIAsHD+XKQmxPpt2wP5uNp87H/YfAhQZT3M2dqes6467Ggu+F6Y/fR9YTVqm7R9t14+vhOoskOWgHPOOcdv2x3Ix5RoXPtK699VevOPAABVH6L5cxUAjEmPQkyofwvM8bgSB/eVGETaT66R4L7o9hX9nXfeiWuuuabTNtnZ2UhKSsLx48c7PG+z2VBVVdWt+ehTpkwBAOTn53tM1o1GI4xG4ynP6/V6ze8oF5FiHahcSyJJsuL+t1bJOj30em3H6Gg6US041GwOyOd/IB5XriW7VEnS/OfURZT9JCk6SIq2J663zSyBpNp5TA1wWv+ukmVnXQUVYpyrFJ0uYJ99Hlfi8Nu+stuBDRuA0lIgORmYMQNQtHtzTTQiHFPdia/bZ8j4+HjEx3e9LMzUqVNRU1ODbdu2YeLEiQCAL774Ag6Hw52A+2L79u0AgOTk5O6GSuQ3Iq1b7RCgEpal9UQPpcmg7ROqSAw6BYAdKlhgzN8EOKxga1tjUhIhWBrQXNXgRflq5SFFfrN6NXDbbUBR0Ynn0tKAp54CFi0KXlykWQG7ohs+fDgWLlyIxYsXY/Pmzfj6669xyy234PLLL3dXgi8uLkZubi42b94MADh48CAeeughbNu2DQUFBXj//fdx1VVX4cwzz8SYMWMCFSpRl0RIgF1UASrXt0/WjUzW/SZrkHMN45gErqbhbyKss+5O1oVYaI4GMsVdDFOMlStUHlPkD6tXA5dc0jFRB4DiYufzXupz0cAW0O6XN998E7m5uTjrrLNwzjnnYPr06XjhhRfcr1utVuzfv989yd5gMODzzz/H/PnzkZubizvvvBMXX3wxPvjgg0CGSdQlkdZZF+GioqVdsq7jMlN+41qzPiQsMsiR9D/aP6oAe1uFfUmEO3Y0oGVkO6c1RsUldNGSqJ+w25096p6uJ13P3X67sx1ROwGdKBQTE4NVq7wv+ZGZmdmhtyI9PR3r168PZEhEPSJCr5qLCEP2XT3rqt3mnrtIvedas16Ez4BoRDgF6E1mAK2IiYkKdihEnRqUNQQoOYyIKP8VFw0oAY5/Cpx3vyvG8s0K7t76OVRVRUtzi9e2ik4Hg2vEoAo0NzcDAE4/ugv/PLlHvT1VBY4exWU/exib0kedul1FgcF4oshhc1Ozx81ILbX4cMlcjByc7sNvRiLQflUPIg1wCNRRJcI1haxzfpHJYoyAFIat1QIAaGr2/CVOPSfCiJWY+CTgUCEmjR8f7FCIOuW6RyvKqDUxoqRAeXvjQTTbJcA11Uh/amFrFweAFuuJi0ZX28SWBp9+VmJLg8fte9vuKfQJeGfdNibr/QiTdSIfiHJBAYgxvz4yKhoAYDZ5/8Kj7jteVgoAqKmuCnIk/ZD2Dyv3iApF4V0w0jZLcyMAoKXFew+llgjwterWanOgsMr3NZyDxaSXkRZtDnYYPvnhwBEgPBHXj1Dwf2efgdLSUq9tQ0NDER3tvMZxOOwoKXG2jdhSBfgwq/fGC0bjZ5OHnPJ8SEgIYmNPjEQp8tBL/6s3t6FKikBiEoty9ydM1ol8IEIC7CJCqDZXUsGudb9yV9aXOLXA3wQ4rNw3FfU8rkjjdm/7FkAWSouPAhgb7HD6lVa7AwUVjcEOo0smvSJMsu4whkMCMDknCZnx4ciMD/f5vYPi2tqOHAw8cI+zmJynCzVJAtLSMPKaK3xaxi09Zugpz+V+W4VvDlYiJY296v0Jk3UN+OpAud/WGU2JCsHQRN9PIuQbh0BzgEW4seDqAdSzuJxfuSvry1yv1d8EOKxw+MCPAMxY83kecDmHwpN2KbIE2FkNPhBEqbFjsdnhcKiQ/XVzMUBrl5eUV0EyOG8qjM7pRRKsKM7l2S65xJmYt99PbUsZYsWKXsXs6gCxizR3k7rEK2UNsNlVv/5H/idSwS4RQt29Zx8AoLLieJAj6V9MRibrgSLCxbqtrYqww2btoiVRcCltk9aFSda1f/gLR1WBFpufKp+vXg1kZgKzZwNXXOH8f2amX5ZC25l/FADgsDQiPjqidxtbtAh45x0gNbXj82lpzud7uc6669xf16D9aRDkOybrRD4QIQF2EeGueqvNBgBQeffXr0La9ayL8Dkg/3KNquEoeNI6RRErWReJSGf+5lY/JOsBXrt8X+ExAM4q636xaBFQUACsXQusWuX8/+HDvU7UAWDXzh0AgG82bur1tkg7OAy+nxGh90dEIgwtdxEh1FarM1nnetD+5epZl2QdHHY7FJ0OdpsNdbXV3t9jMiMkNBQAYLfbUVfjvTid0RQCc2gYAMDhcKC2utJrW4PRhNAw55QcVVVRU1XR4XXVbodBhA9rGxFCdY2skvk9QBqntA37VSUxknWRjigRzlUuzdZeJutdrV0uSc61yy+4oMfDyw+VVgKIgN7qxzoAigLMmuW/7bVx3ai12Xlt1Z8wWe9nRDpJA8D2ozVotNiCHUaXLL39QulDItxYsLqGvjFZ96sQo7O6vqToYLW2QtHpcLQgH4svmOX1PT+99mYs/s19AIDysmJcOf80r23Pv/wa3HrvnwAAtdWV+OmZo722nXfBT3HXH58GALQ0N3lsO378eJx77rld/l5aIMJxxZ51EsWJFQvEGOAp1EglQUL9etsOXLflOGDoWGROURTo9K51ylVYLBav25hWvAdv+bJ2+RV/xObMsdC7tgvA0slKBLIsQ29wrmluszlvOofAexxawWS9f2KyTkHVYrX7ZxhUgIn0PS3CkH3XzQ9JlKsKQURFhLn/7VAMsNjssNpVSDrvS+TZIcPSdvOktYu2Dklp19bhe1ub57a7d+/u/BeibnGtssCeddI6XVtdDVF61kUiygjLb3bmQzJnnPK8A87vDBdJZ/C6jfhG34amJzbXQ5UUn7erdojB+RkdGunTjwoq1z0wK5P1foXJOlE/I0IPoKtnncPg/UvXrrr+rW9vd/874853vb5nK4Ctq773qe0uAL/ysW2+D22bfvzG6/u1RvtH1Yljn8usk9alpGUAeywwmsO6bqwBAnytuokSq82hAjIQ03AYL9x8tvv50NBQREY6M2O73Y5jx4553Ubcjhaf1i7/1aKxuOHMcYiJiQHgHCnR2VrpRqOxw5rm9dWV2LdD+6NAXdNLrP4q3EeawGS9nxHlJO0iWrwiEOFv6iowx551/woz6jAqNQK7iuuCHYpPzIMnwyFKkUEBPqoGkxloABLi44IdClGnMrIHA3v2OD+z5FcCnKoAAIaQUMAKJESGYtKIwV7bpcd2ckNnWCbw27Qu1y4fduXlp8xZT432/jNPZg1NxL4dPjcPGtf9+la7KkTvOpfv9Q2TdQoqUYZriUSEnnVzaDiAOoSF8kLNnyRJwnM/n4Afjvqpam2A1LVYcc9/dkFSdJBl7X9ZVzRY8M62o2jS+JSdWl0UgBYsmDc32KEQdcrVAyjC9xUgxk1wF1Hm12fljsLenWWYerr3Oild6oO1y0XiOq6qGi1Yv788yNF0bU5uAmQWWekSk/V+RrjkV7BwRSDC9/TgIUOATduQk50V7FD6HVmWYdJr+8LENbdahQSb3YF2NX806a1vC/HhzrJgh+GzKLPG/6A04FlbncW9bByuO2DZ21avMPS2d9W1dvltt3Vcvi0tzZmo+2FJNFGkpiTjSDkQl5jadWMNEOByVROYrPczIiRqFFgi9FTY25I1hXdUByR9u/3eancgJIix+KK22QoAGBwfiuHJEUGOpnPRZgMumpAW7DCIOnVg93YA4Whubg52KD4RqSNElEhdN239MhR60SLn8mwbNgClpUByMjBjxoDpUXcZNjQH35QXIHmQGB0hzlEgvA7sCpN1CipRvlRE4vdk3W73+xeg60tax2Td70T4i7YvhHf4SBHGjRgSxGi6ZmmrCjw8OQIXjtNuj8V7b76MZ59/EnX/dwWeeuqpYIdD5JXimv4iaX8aDCBWR4gose7ZuR3Qp2Drlm+BBcN6v8EArV0uEtc1lSilYMg3YpwlyWeCnKPdRPlSEYlf/6arVwOZmcDs2cAVVzj/n5npfL4Xvv7aWQV8104BKrYIRoSVkGQJUNtWAmhq0f7ata4lfLReDKe5qRHVVRVoaGgIdihEndK7bvgKULMCEOvaSpRRAC1t5/7WlqYgR9J/qHbntJKWTtaQ1xIxPqnBJ8ZZknwmSmERF1G+VETit5711audRVvazwEDnFVXL7mkVwl7S6tzWLHDpv2lUEQjCdC3PPONfQAARBlJREFULkkSYHfu+yZLa5Cj6ZprvXitjwRxtF2o6XQcNEfapuicl5+qID3r5H9tA+w0fxNUJNu2bgEA/LDt2yBHQv7Eb/R+hqkvbdq+F19tasSwYcMQYnbOBj527BhKS7yvKTpk6BCEhoYCAMqPl6OksBBX/upGhKrqqamfqkKVJLTecis+S8rF0ZISr9vNyspCZJRzvdTq6mocKTgCANhTUgPIEZAlfmIHLLsN0BnQ2MyedX+xt90AUQbYPE0Sj152fkYlSYZDVSFrfEiQUB0hgoRqb7u60Gv8JqhIXHWARKhdBHB0ra+YrFNQ8UD1r6+2/oCVe5y91tjajSHmW3d1eHh64Q78srLCa3NJVWEsLcabf/0vNmWM6WS7+zw/L2cAABR+R/udxq95T3A4E8vmVu2PrmhpS9Z1Gv/AOtrW1WXPOmmdojtxQ8nhUCFr/NgS6VJFlFhdXQEGnbZvgorEdUNZlGtrjq71TcCOkIcffhjTpk2D2WxGVFSUT+9RVRX33XcfkpOTERISgrlz5+LAgQOBCrFfEuUAdREsXM07UORcV9PR2oKMKD2GJIRhSEIY4vRWSPVlXv9Lj9C528YbbUioOOzTzxtiqex0uymhknu7SWZ0eE2pK8GV07ID+ecYkLR9yduO6hyyLdScdY3Pr2XPOomi/SgVu2gXLhonyp/T4epZ1/iIJZG4irfaBfkMkG8Cdvu9tbUVl156KaZOnYqXX37Zp/f8+c9/xtNPP43XXnsNWVlZuPfee7FgwQLs2bMHJpMpUKH2M2IdoUINLROApW0SWFj9EXz5xJKeb2hdJJD3YpfNHvr1eXhogFdf1RxBsnXJ0VYIR4Ce9VZhetY5Z53EEBMbD+AYADEqV4t0qSJKb6XD3bPOm4v+onfVghDkQkCk4yqYAnY7a/ny5bjjjjswevRon9qrqooVK1bg97//PS644AKMGTMGr7/+OkpKSvDee+8FKkyifsVid56gdY5eFu2aMQNIS/M+plqSgPR0ZzuiHjDonQmlwWQOciRdcxWY03oPUGxiMoaPHof09PRgh0LUqaTUNPe/xehZFyFGJyH+nAB0BmcnXGR4aJAj6T90baO/HIJ8Bsg3mrnyOHz4MMrKyjB37lz3c5GRkZgyZQo2btwYxMjEIspJ2kWwcDWv1ZlTQKdae7chRQFc6zSfnLC7Hq9Y0ev11sn/RKgGDwBJSckAgKwcba+xDrTrWdd4IaRFVy7GGx98gVtvvTXYoRB1qv2h5GBm4Vei/DUT0zIBADNnTA9uIP2Ivm2Ugmi5AHVOM2PlysrKAACJiYkdnk9MTHS/5onFYoHFcmLOY11dHQDAarXCau1lwhJgrvjUtuGg/mC3S5r/vdtTbTaoAnxRu/aRP/dVILS2DSdUHH74/J9/PqS334ayZAmk4mL302pqKuyPPw71/PMBDX7WXL+3SMeBP9ntNqh27Q8td9UUarK0an5fuZdukxya/9uqDtnvf8+BfkyJJBDXFYFgt9kgS84eQLvNBtWu7RthNqtNmOPK+ffU9nkKAGxt8x9kODR/bhHlHBgfFwuUtSAyLl6Iz4C11QoF/psHI8p+AroXY7eS9aVLl+LRRx/ttM3evXuRm5vbnc32yiOPPILly5ef8vxnn30Gs1n7wysBoLnge79tqwnAR7u6bEY95M99FQgtzc2APgrNdVX46KOPer9BoxF4+mnE7tkDU3U1WqKjUTlihLNH3R/bD6C8vLxgh0CdkFoVABK2ff8DHIXbgx1Op2rrnbHay/ajqTHY0XSuCUBxNxaC6A4eU+LQ+ndVSUkJbK2JkPVGNBz5AQZjsCPq3P5DwP4AbXugHlfWZud59butW9BwQPudNoD291VLkwRAQUJMFJoObQ12OF364lBgtqv1/QQATU1NPrftVrJ+55134pprrum0TXZ2z6o7JyUlAXCuB52cnOx+/tixYxg3bpzX9y1btgxLlpwopFVXV4f09HTMnz8fERERPYqlr1itVuTl5SEkczwk2T/DicND9Jg4KNov2+oLX/54XIziMg47mgu+9+u+CoTow/koLWvAsjtvxzmnDfLfhs8/33/bCjDXcTVv3jzo9fpgh9PnqhpbseNoTbDD6FJp3odAWCqqaupwzpWXBTucTj28ax1gaUVY+gh8+tEqvPK095vWf3j2NYydPA0A8PG7q/C3R+/32vbex5/HaTPOAgB88eF/8OTyu7y2/e0fnsSZ888DAHz9xSf409Jfn9LG0tIMoykEj/3lz7jxxht9+dV8MtCPKZEE4roiEEINhcCPRQAAY9pomMO1na2nxZiRkxDm120G6rgqrm7GgWP1ftteoFSsWwcYoxBmNuGcc2YHO5xOiXIObP6uGG8f2g2ERMKcPTjY4XRpSnYMQgz+G+Qtyn4CTowE90W3/kLx8fGIj4/vdkC+yMrKQlJSEtasWeNOzuvq6vDtt9/il7/8pdf3GY1GGI2nnuT1er3md5SLJCuQFP98WCVZEeb3BgDIOnHWhYZ/91UgNNucd6fjo8LE+hwEgEjnAH/S61VNf0ZdHDZnEcSWVpvm91Nr2zo4er0edlWFpaXZa1sVsvvv31Vbhyq52zq6bAt3W1WF17bWVgtycnIC8jcdqMeUiLT+XaXTG6Cqzjv1Do3HCgCyogvYZ9/fx5Wis2r+7wkAVnvb/rdZhTmvaP0c6Kqsb7XZhfgMKDo99Hr/x6n1/QSgW/EFbE8WFhaiqqoKhYWFsNvt2L59OwAgJycHYWHOu5O5ubl45JFHcNFFF0GSJNx+++34wx/+gCFDhriXbktJScGFF14YqDD7HTEGEp3AIhj+1dxWYS7cpO2TFAWOKPe+lLazlWs+uJY1NlsAyNi3fTPOufjnmD73HK9tY+IS3P+ec+4iTDpjlte20bEnbn6fMfccvD5xite2UdFx7n9Pmj4br3/6rcd26YmxmDVW+z0qNLDJigK0zasXocCcKMuhAQJdV0nOxNIUgGRtoNqz2zkP9sfdO4GFI4IcDflLwI6Q++67D6+99pr78fjx4wEAa9euxay2dZn379+P2tpad5u77roLjY2NuOGGG1BTU4Pp06fjk08+4RrrRD6qbWwCICPvw/9i6q2/CHY4FASijFRxVYO2WLWfrNscACSgrPAQQmfNRGi4b1OsQsPCERoW7lNbc2gYzKG+DbMNMYcixOx5uaOYCG0PJyYCAEXRuYvgibB0mwAhiqdtmobRj8OgBzpnNXiHMLeWRIkz2AJ2hKxcuRIrV67stI160tlPkiQ8+OCDePDBBwMVVr8n0hfKyfufes9iUwEJ2LV9KwAm66RdSluy7hoKqVV2hwpVcpauN5tDghwNUf8gK7K7Z91m0/Y5ABDs2kqQFEiVFEgATEzW/cagVwBY3d9ZWsc8wDdi7E3ymSgnaUCsLz8R2OwOONqGlRkl7fdWUmCIss66IjlPAK754FrV2i6RCAv13JutJaLsfxrYZPnEMHibnd9X/iTMtVVbz3qIgdP2/MWga6trIsoQO/IJk/X+RpSTNPldc7vhxAaZH4QBS5DvaKVtHHyrxnvVOiTrYf6tBh0IvEYjERiMRoS0La9r55x1vxImUtmZWJqYrPuNq8CcKOmdMJ/VIOPYEwoaUQ5SVVXx7PrDOFCmQPpuJ7SaDTnabqc7LE3QK2KcqMn/REnWwkJDcRyAOUzbS2y6CuCpqgNhPs4pJ6LOGU0hiI1PQFlti6aXmHMRprcaYgwtdjhUSLLzOiU8lNOL/MWgZ896f8RkvZ/R/in6BBG+UACgrsWG7UW1ACRAgMrVrWUHoMRo/+KHAkOUr+jho8bg0K5jGDVmbLBD6ZSlrWddtbUiNDw2yNEQ9R9KW0LhEORaQBQi/DVt7UZTjByeG8RI+heja5SCMHPWgx2BGJis9zMiffBFCdU1DFYvqfj9ucM13Qvwnzdewnv/fATKLb8KdihEndK5hsFrfc56WwE81W5DaKhvld2DiR0qJAJVVYG2ddZtGi8yKRoRrgNtjhP7nCMB/Scy3Dn6K0SA7yoA4iQCQcZknagLrmrVegVIiTRBUrR72JhsdYDqgKJo94YCBZYkSLZ2IlnX9oW6xeqMLzIiArEJSUGOpmssMEeiOLRvJ4zJQ1FfVwsgKtjhdEqEBFgk7esU6BWes/wlKTEBQD4iYuKCHQr5kXazDuoRoYqgCBKqO1kX4Pvk/365BHfd9VsMSY4OdigUJAJ8TAEAh/buABCPjZs2A5eND3Y4XrluJhj1Oig6fmWSGHISwrX/eXX1rAtQYE4s2v97uve5w46qqirExnKKkT/o2uoAOLR9D9xNpJwlmDR+JqfuEiUBBsQ5SF0X63oBRmoZTSGIig5DWJj2l5iiwBCkYx0OeysAoMli9d9G7XZgwwagtBRITgZmzAB6OcrENQ1GJ0jvjyj7nwIrLSYEer3Gq2y3ZRR2AZZuE+V6BRDjOtDaVv/HYWsVpn6RCFyrrNhEydbJJ0zWKWhEOT9bbc5ARUjWAXF6Vmlg0ysKYAMc/vrErl4N3HYbUFR04rm0NOCpp4BFi3q82cLiUgBAQ3VlbyPsE0zWSRQSnAlFg8WG2mY/3rQLgMgQjd/4aEeESytLq3N/q3ab9m8qCaShvhYAUF1VCYfDAVnW9oWrKHlAsDFZ72f4ufc/a9sdSp22z3kAgC/+txqv7t6Kn116Ec4777xgh0NBIMqcZX1bj7dd9UO8q1cDl1xy6jd/cbHz+Xfe6XHCXnq8HABQXyNIsi7I/idy9az/d189/rvvhyAH07lQg4I1d85CUqQp2KH0C61tyTqYrPuVsW3pNsgKbDYrDAZjcAPqAnMW3wiQflB3cDiR/1nbhsEaBDhadn63CatefwXbtm0LdigUJKL0rBra7n7Ze5tc2u3OHnVP5z7Xc7ff7mzXA3UNTQAAhZcVRH5lPfIdVJszaZM0/B8ANLbasa+sLhB/Br8T4TLQYm3rWXdYmaz7kdFgAABIkgy71RbkaLrGnMU37FmnoBHlGLXaXcPgtR+wvW0emE7rhYVowNO3fUYtIQmY9cePEBkRAQCoq6vD0fZD2U+SkpKC6KgoAEB9QwOSN+bhzU7aQ1WBo0dx581P4L9KpNdmiQmJiItzFjlqbm7BocOHAAA22QiExUGWtH/8A+LcrCGy7f0Chd+8g5fe/xKDBg8NdjhePfzRXhyuaIRN48tMuogwv97SlkiqdhuvV/zIaDjRs263az9ZJ9/wCOlntH+KPkGELxTgRIE5EYbBOxzOZJ1Lt5HWZacmAFUWSAYTCupUoK72xIsRqV7fV9gAFDacaDtK9e2zbi0th23ECK+vF7cAxUXeY4jQiVGwh7k6iWLhuT/BsapamEPDgh1KpxRJrKJdInSEWF29vg67MMuNisDk6llvGwavdQJ8VDWByXp/w0++31kFqgbvsDNZH+hEue6ZPGY4mkvfR0hcKoYMGYLY2BgAQFVVNQ4ePOj1fZmZmYiPd64hW1NTixZ5B/BB1z/vp2ePRWonOUFaWhqSk53rqDc2NmHPnj3u1+wqMGI4a0AQ+dOfn3oW+8vqgx1Gl1wVtlsF6VnfdKgSe0q0PWS/oIzTiwJB39arJCk6WK3aT9bJN0zW+xlReqsBMe7+AoIl6213/pmsD1wiFRibNCQF55xz9klzFhOBabk+biERmHwX8MbfnMXkPJ1UJAlIS8P0Gy7H9G4cF+dPzHL/u67Fis2Hqnx+bzCJcrOGSJTPqs61HJZd+z3rRyobsfyDPV031IiJ48cEO4R+xfVZBQC7TfvD4EXJA4KNyToFjSjHqGudZRGSddccJSbrA5coF8B+oyjO5dkuucT5y7f/9nf9MVas6NV663qNL3/T0UD7AJCoVFWFzWqFrCiaXmLKvXa1AD3rFQ2tAACjTsb4jKjgBtMFWZJww5nZwQ6jX1HaJeuyYkBzU6P3i20JCDGHuh+2NDdBdXhp3J22AEJCT7S1tDTD4eVGl90R7nUbdAKT9X5GpLtUolSBPFFgLsiB+MDOYfA0EC1a5FyezdM66ytW9GqddQDQKUyAifztzEljUHD4EFb84wOMHD852OF4dWIYvPZ71l0jAWNCDbh+uvYT4YmDooMdQr+ia3fTKzI2Dr9cNAtHDx3w2DYpNR1vfLbF/XjJ1RfiwO4dHttGxcbh31/ucj++56YrsHPrJo9tTSFmfLD1kPvx8tt+gS1frfXYduz4Cfhu6xZN36zTAibr/Ywg+a9QRBoGf/v9f8GKJ5/AkPSkYIdCQTJg08pFi4ALLgA2bABKS4HkZGDGjF71qLvoFfmUTnutGnAjK0hYctux6SqMqlWKQMPgXdcr7XtYtejo4XysWP5bDMsehH+9/Vaww+k32u93uwBfWD/u34fq6mrExsYGOxRNY7JOQaP904jTiWRd+xFHREUjLS0S4eGmYIdCQTKgK+sqCjBrVmA2LUtCDIMdwHufBONasstVGFWr3Ml6J8N+tcLqXr1G22eC+rpa7NiyEXXlZcEOpV9pv98dDuD5d/K8jmI9+VphxRvv+zzi9U8v/hOqj6sjPPDMSq9tJw1ORGxkiE/bGcgC1lf48MMPY9q0aTCbzYhqWxO3K9dccw0kSerw38KFCwMVYr8lyvByQcJ0D30ToWcdAK/WBzju/sDQK2KcAAb0zRoSimu6ll2QZN0qwM26VpszRq33rNvblhXrWFyUekuWT5SYtasqDEYTjKYQj/8ZjB07dTprazR1TKgNBqNf2nIomG8C1rPe2tqKSy+9FFOnTsXLL7/s8/sWLlyIV1991f3YaDQGIjzNWLx4MZpaLB5fGzZ6PB598Z/ux784fwaqyo95bJs1ZDiefOO/AJxJMD///iPSnPX//et1vFNRhOuvuRKTJk0KdjhE/YbWe6qIRCNKsq5zJ+viDIPXaXwOsK1tnXUdk3W/U2QJNocKhwA9YgKEqAkBS9aXL18OAFi5cmW33mc0GpGUNHDm2zY2NqKpqcnjay1NjR0eNzXUo7He89qZTY0Nfo8t4AQ5SK0CVYPfkPchvvtmPaafPpnJ+gDFG3WBoROlZz3YARD5SKe0DYPnnHW/cSXrZUWH8fKKdxEVHYuLr77R/frqN15EdWW5x/eGhUfgsutucT9+/61XUX6s1GNbkykEP7/pDvfjj955E6VFRzy2VRQF1/z6bvfjz977p7vgGHvW/c+drGt82saDt18HnWrFW/94HYmJicEOR9M0N2d93bp1SEhIQHR0NObMmYM//OEP/brwwOOPPw5T2khAOXVXGAwdRxU8+fp/YffypabX61FSWIADe36AvmI4Zp45IyDx+pMoa8K771QLcBXsaFtX0zUXkAYeSZKg8U4VAGKtBw8AekEqwvNmDYnC3bNuEyNZt2o8+QGAo8XO5Lr0yCHsePdppGcP6ZCsf/zOP1CQv9/jexOSUzsk63n//Rf27fzeY9uIqJgOyfoX/3sXP2z5xmNbg9HUIVn/8rMP8O36zwEAUVGRPv5m5CudLMEC7ReY2/r1OjQ3NaKhoYHJehc0dUW/cOFCLFq0CFlZWTh48CDuuecenH322di4caPXpagsFgsslhPDyOvqnD3PVqsVVqu1T+LuqRZLKxKTkhGSmgFJ9vz72dv9DglJKZ1u73//egN//ePvsPOCCzBt6ul+jTUQrFYr1LZ1wbWs/TrrqsZ7AFzrrKuqqvnPf6C4fu+B+vsDwIzBMcEOoUtWqxV5B8TZT5LqEOJ85bDZ/f435TElDpH21ZSp02AIi0JUdLSmjy3X1Zml1ebXv2sg9lVldS0AQJGAi35+HaJi4zv8beecexGqK7z0rEdEdmh75oLzMXzMBI9tTSHmDm2nzz0b2UOHe2yr6HQd2p4+cy5S0gZBVhQsuekXQnxWRTquXDeXfvefXZoeahX/q7dgOboTzc3Nfvu7irSfuhOjpHajGtnSpUvx6KOPdtpm7969yM3NdT9euXIlbr/9dtTU1PgclMuhQ4cwePBgfP755zjrrLM8tnnggQfcQ+7bW7VqFcxmc7d/Zl9askmBXfXvkaTarIjc/wEeuv4nft3uQPaXHQqKGiXclGvH8Ght36lctmwZ9u7di7vvvhtTp04NdjhERERC+7BQxmfFMs5McuDiLG0PhV+1tRTfWtOhHtmCpy8fH+xwKAhe3CdjV7UAw+va3Jx8BMMyU4MdRp9ramrCFVdcgdraWkRERHTatls963feeSeuueaaTttkZ2d3Z5NdbisuLg75+flek/Vly5ZhyZIl7sd1dXVIT0/H/Pnzu/zlg+3Ob/P8Xl1B0umhJo/AOeec49ftBkJ1Uyt+KKwJdhhdsu/eC8ACnQyEZI73OgpCE/TOKpyTJ08W4jMQCFarFXl5eZg3bx7nw2mYaPuprtmKumbt9v65RIXqEGb0799TtH01kIm0r2qbW/H9kZpgh9ElY0MZUFyG1PQMnHPOCL9tNxD76ouST4AjgCIrMGdrv27NtJxYGHQavqZqI9JxNXWmBV/+WBHsMLr0m9W7AQCTTzsN0yeN9cs2RdpPrpHgvuhWsh4fH4/4+PhuB9RTRUVFqKysRHJystc2RqPRY8V4vV6v+R218e5ZyMvLgzlznMc569316vtr8UNzNByQNP+7A4CiOCD54fcONNc8Nb2sQpIVTcfsqqprMpmE+AwEkgjnABJnP8Xq9YjV9v3fgBNlX5EY+0pvBfbs+B5P3LfEY0X4+MRk/OXVd92PH7jtFyg4sM/jtiKiovH0qg/djx+562bs37XdY1ujKQR/X73G/fjx+5Zg59ZNXuO89FFnDA41MNdW/txXzho7MmQJmr5WcdHrDdDrxOkFFuG4MhhURIVpf+1y1WGHJCtQIfv9byrCfupOfAE7kgsLC1FVVYXCwkLY7XZs374dAJCTk4OwsDAAQG5uLh555BFcdNFFaGhowPLly3HxxRcjKSkJBw8exF133YWcnBwsWLAgUGEGVZRZj1A9YDbq/HJSNbVtwuHnofWBsv1oDd789gi0XrOlrsXZmyZCNXiHwzlEz1uNByIiIi1Ydvdv8OLzz8Fm8zx3037S88dLi1B85JDHto0NcR0elx8r8drWFNJximTlsVKvbQFAkcRZui0tIxMoKsKYCZODHYpPWBBzAFMdABRYNV5gUgsClqzfd999eO2119yPx493zp1Zu3YtZs2aBQDYv38/amvbimEoCnbs2IHXXnsNNTU1SElJwfz58/HQQw/1+7XW/UXXlqA5tFxRop0/f7of+8vqgx2Gz8K0fZMOALD86VcxNN6A3MFZwQ6FiIjIq4cf+RNGnLEQNpvn6SV6g6HD4yXLn0BLs+elbk9eAeXXv3sEjQ2ery/kk5bLuOG39+OKG+/w2BYAyhRxqsHr2lYRiunHqyhR54RZacXhABTA6mFUDXUUsGR95cqVXa6x3r62XUhICD799NNAhTMguNYBFiVZb2jrsZ6RE4eYUEMXrYMrNdKASGt+sMPoUkJKGoZlxyDMJMCdBSIiGrD0ej1yvVQb9yRn+Cif22Z5qUzuSWZObqevf/D3N4GQYSgpLfN5m8Hi6v13VQTXOjGiFIwgf9QQcwharA6MHDU62KFonvYntJDPklPSgcpqZGYNDnYoPrG1DdmeOSwembGhQY6mc6rdhibvo+Q0ReK4MiIi0jhRvqkqSo8C2cNQ1+i5V19LikqcNxTKS44CE9ODHE3XeL3if6L8SeW2QO0CjFgJNgFm4ZKvomOcaytHRGt/jWUAsNqdB6hOkDvAInhn5fN46IH7UVhYGOxQiIiIvBIlqXBNMbTatD9n/cjRYuf/D+wJciS+EeQjQAHgGv3BZL1rTNb7EdcHX4QiKABga4tTJ/Nj6C//fesV/PlPf0RxcXGwQyEiIvJKlLm1urZq5a7RgFrmuv5zTYvUOlFu2IhElD9pS2MD8P/t3Xl4lNXdN/DvzGRmkpCNbGQlJKCA7IYSE1+WyBbgsiJIKYKKTREtKNvrK9gqBauA8oCWhyo+taFealvohdpWFANIXFiKCA+LkBIgxEw2IGYnyWTmvH9MZkhIZibLTOY+yfdzXVxk7jn3nV/m5GTu35wNwIXsbA9HonxytGZqE2OtZYhWaVm5hyNpG2vPuixzq2Rgblyo4/bFdoiIiJRElkRN1/h+2mBSfg+gNVnXStIJwmHwrifLa2qsqwUAlFy74eFIlE+O1kxt8uM1y1ylH/Ll6FW1fkrNZN11uHUbERHJQJKcAtrGZF2G1eBt0wsl6Vmnnszyu9rA1eCdYmvuRqxvKEKl/GoVQnDOuhtYe9aZrBMRkZLJMgxep7XcW8kww7Ch8QMFrZfy7wNl+bBGNrK8rCphaVCyTN31JOW3ZmozrbYxQVMpP1FraPIJNXvWXcdksmyHx2SdiIiUTJZkTau1bIUqQcc6rGvgaTXKnwonS/3LRp7XlT3rbcVkvRuxzquCWvmJWtO5X+xZdx2zdXEZzlknIiIFk+Wdf/L9swAAYRGRHo7EuYbGWyspetal+Q0gtxCWX1b2rDun/NZMbaZtHKolQ896fZPGqdHwD7armMwcBk9ERMqnlqQL0LbAnARd6zF94wAAg4eN9Gwg5DGyfAhiHQbfwGTdKXa/dSMy9aw3/SRNI8kbtgy2vvsxRkT7ITY21tOhEBER2SXLW791qp4Mq8Fr9T4A6hAaFu7pUJyTpP5lI0u7UtmGwTNZd4bJejeia5xXBbUGQghFb99gTdY1apWi45RNwsC7MHpQONcBICIiRZPlvf/qxfMA5NgWt+m9ldIpP0Jyp+i+/ZD3Yy3uvff/eDoUxeMw+G7Ez68XAOsnq8rWwJXg3YavKBERyUCGfL2yzLIPdJ2xwcOROHe9tAwAUPnjNc8G0gayfFhD7uHVOF3TzLtWp5isdyM+PpYkXaiU31tdL9GnvzL54O03sGnTRlRVVXk6FCIiIocUfqsCAPDR6wEAZgluma/fKAUAlBh+8HAkzklQ9VKSoU0Bt9asMAvlTy/xNOX/5aE2sya+ZgGYFb4QikxDtWQhhEDGGxvw/PPPo6amxtPhEBEROSTDYlje3pZkHSrl3zKbG2PU67QejsQ5WZJKco/Sa0UAgP89c9bDkSif8v/yUJs1XaitprbOg5E4x2HwrmduslclV4MnIiKlkyFh827sWRcSLN4rrMm6VvnJOrmHDB+AAUB1RRkAwGAo9GwgEmCy3o007aUuaRwKpVTWYfBeav4Kuop12zaA+6wTEZHyKX3KHgB4+3gDAFSNi/cqmWjculeKnnVJkkrZSNCkANxaDZ77rDvHTKkb8dLcqs66eqMHI3HO2NA4DJ57rLsMe9aJiEgmMtwB+Oi9bV8bXbV9m8kEVVYWor/8EqqsLKDJ+3enNPb+W0cDKJksSSW5hzVZN5mZrDvDZL0baTqi/GadwpP1xjc87rHuOuYmf/CYrBMRkdLJcAvg49M0WXdBUr1nD9CvH7wmT8boLVvgNXky0K+f5XhnqS2j6vR6GXrWyR1keV2tcTYofI0tJWCy3o2oVCoIkyVJr62v93A0jhkbE0sv9qy7jKnh1rYyTNaJiEjpZBgKHRoWbvu6obO5+p49wEMPAfn5zY8bDJbjnU3YJepZl6DqpSTD1BIAUKnYs95WbkvWc3NzkZ6ejvj4ePj4+KB///5Yu3Yt6p0kkbW1tViyZAlCQkLg5+eH2bNno7i42F1hdj+N85YV37PeYJ2zLscfFRmYOWediIgkIsMtgNbr1offxs4kFiYTsGwZ0Nq8d+ux5cs7PCTeZBZQNSbrvYODOxgkyU6CJgXgVpwm9qw75bY7+gsXLsBsNmPHjh0YMGAAzp49i0WLFqG6uhqbN2+2e96KFSvwySefYPfu3QgMDMTSpUsxa9YsfPPNN+4KtXtpTNhqlT5n3ToMXoZ3akn08gvAf3/wTwyLDoCaC/cREZHSSXALoFKpoFGpYBICM9fuhEa0TKZDQkIwZMgQ2+OvvvqqxWJ0I3/4D964vUe9KSGAH37Asp+vwqnYOwEAAQH+GDlylK3I0aNH7XZ6+fbqBSAUANDL17etP57HyDCqgtzHepfKZN05tyXraWlpSEtLsz1OSEhAdnY23nzzTbvJenl5Od555x188MEHuO+++wAAGRkZGDx4MI4ePYp77rnHXeF2H43Jer2xwUlBz2owc591V/PSajF8dBLG3hHm6VCIiIickiVhUzfchEnjjXxtTKvPX60Evjt69dYBbd8WZUbU5bbpe4k6Ha7q4iwPaoEzTa+LSEBn58TGPhqdl1qKKYaSjNaWjiyv6x2Dh+JEfiXGjZ/g6VAUr0vHypaXlyPYwdCcEydOwGg0YtKkSbZjgwYNQt++fXHkyBEm623g4+ODWgDBoeFOy3pSfQO3bnMHWW58iIiIZEksZt2hw/ncfLvzgUNCQzF06FDb4y+zslr0rPsHtG14u3+ACSM0BgBAQEAARt19t+25I4cP2+1Z7+Xnh9GjR8NXp5Hi3kqSqpeOLHPWdTrLp05qDadtOtNlr1BOTg62bdvmcAh8UVERdDodgoKCmh3v06cPioqKWj2nrq4OdXV1tscVFRUAAKPRCKNR4UPBG+MTZhdt2QGgV69eqK2uh6+fv6J//tp6S8+/RgUIk7JHAQCACpY6ig7SQaPQPyyVFRX48IP38b+hgXjiiSc8HY7HWH/vlfz7T6wnmbCu5CFbXQlTgxT3ANPHjsbvHg9tc/mnJ8S3PGgyQRz6G1BQAFUr89aFSgVER+O3f3wZsLNIbKvXvc2pH8pQVq3sRYYBQJhV0vyeytiulE5t3We9weSy11WmempPjO3OOlavXo1NmzY5LHP+/HkMGjTI9thgMCAtLQ1z5szBokWL2vstHdqwYQPWrVvX4vjnn38OXwnm7ADAzdyTLruW2qQBoMJX3xxGQYDLLutypwpVADTAzR9Rc/mGp8Nps4vfHfZ0CHYVFhbild/+Gj4+PoiJaX2oXk+SmZnp6RCoDVhP8mBdyYN15Vo1APb+p/PXiVywAD/ZtAkCzXuWBQAIgePz56Nw377OfyMJ1ADYe87TUbQP25XrFF6+BiAS+zL3I7Yq2qXXlqGeampq2ly23cn6qlWrsHDhQodlEhISbF8XFBQgNTUVKSkpePvttx2eFxERgfr6epSVlTXrXS8uLkZERESr56xZswYrV660Pa6oqEBsbCymTJmCgAAFZ6uwfKqSmZkJn36jbCt4dpY49b8ABAYOvgtTRvRzyTXdofjwVSA3G3r/EPgmxHk6HKd0aoGynBOYPHkytFpl7l+anZ0NANDr9Zg+fbqHo/Eca7tScl0R60kmrCt5yFZXJ/N+RHmN8nvBevvpMCImqPMXmj4dprvvhmblSst2bVYxMTD9139h1IMPYpT9s9sk70YNLl+r6uRV3C/QV4tRfXt7Oow2ka1dZWWXtLrpgJJU7v8I6AU0mEwuu2eVqZ6sI8Hbot3JelhYGMLC2raAlcFgQGpqKhITE5GRkeF0herExERotVocOHAAs2fPBmBJQPLy8pCcnNzqOXq9HvpW9pPUarWKrygrlVoDlYuGVt8oKgACI3H+wn8wY/QdLrmmO5iE5TNlL43aZT+7O+15/39wIuszVFVVYf78+QAsH0StWLHC7jkzZszAo48+CgC4fv06lixZYrfsxIkTbUPXq6qqkJ6ebrfs2LFjsXTpUgCWP0wLFiwAcKvhazQaaX733UmmvwE9GetJHqwrechSVxovL6g0Cs8qAHjrdK57PX/2M2D2bDR88QVOffopRk6bBq/UVHjZGfreXv6+eqg0tS65ljupNV5S/I42JUu7Unt5Qenbl6sbF5gWULn8NZWhntoTn9uyJIPBgAkTJiAuLg6bN2/GtWvXbM9Ze8kNBgMmTpyId999F2PGjEFgYCDS09OxcuVKBAcHIyAgAE8//TSSk5O5uFwbqWCGAFCn8NXgjSZ5VoOvLC/D7195AQAwbty4W8crK7Fr1y6750VHR9uS9Zs3bzos23QkSX19vcOyOp3OlqybzeYWZcPDlb24IBERkYXy7wEAywrrLqXRQIwfD0N1NUaMH293jnpH+Hl7IdS/ZSeW0vjpld9RIyvLYsPK/hDMevvPrducc1tLyczMRE5ODnJyclrMn7WukGk0GpGdnd1s3P7WrVuhVqsxe/Zs1NXVYerUqfjDH/7grjC7HZWwJMF1RtctWucODSZ5VoO/WVMNAFCr1c2G6oSHh2Pbtm12zxs5cqTt66CgIIdlm+7P6uvr67DswIEDbV97eXm1KDt58mS75xIRESmFJAtXQ6dR/r2Kla/OCyNjgzwdBpFDtmSdubpTbkvWFy5c6HRue79+/VpsbeHt7Y3t27dj+/bt7gqtW1M1fpJW36DsZL2+sXVqJNgL1Ni4TYpOp0NSUpLteO/evW093M74+/u3uay3t3eby2o0mjaXJSIiUhK1JNm6y3vWidxJgmZlbftmpU+uVwD+9elmZEnWjbaedeX/RWkwWpJ1pc9/ISIikony7wAsmKyTTGRoV9bbf46Cd44TRroZdeMweGODsleWkGnOupHJOhERkcvd2ccf8WG9PB2GU710vF0mciWVrWfdw4FIgH99uhmVqnE9AJMLe9ZNJuCrr4DCQiAyEhg7ttOLoRgbh8HL0LNeX89knYiIyNV8dBr4wHWLqxGRNRFWdhY8aOgI5F4oRdI9KZ4ORfGYrHcz4X0ikVcHxPR10d7le/YAy5YB+fm3jsXEAG+8Acya1eHLytSzHhIWjl/8ahmMlaWeDoWIiIiIyC7l31lb1mcCAK1O5+FIlI+TcLqZiMhoy/9RMU5KtsGePcBDDzVP1AHAYLAc37Onw5eWKVnvExWL5atfxOzZsz0dChERERGRXTKs22i9/2/gOHin2LPezVh/+TOPf49rl79vtcygwYPg7+cPACguLkZeXl6LMiqzCb9a9QT8hWj5CZ0QEAAqFi3Gm+UaCLVlCFv/Af0R3DsYAHCj9AYuX7psN87LZZY5ajJs3QYAGhn+8hERERERKdy1QgMA4GJODoARng1G4ZisdzPWFUuPlahwrMROofPZtx1omYjek/c9An68Yff7qAAEll7HqczvcbTv8MbrXgZw+bZS9tQAALQSbN1WXVWJH0rLUVZW5ulQiIiIiIjsUkkwEL60pBBAJPLyDZ4ORfGYrHcz9w4IwaWr+Si6dt1umfj4fvD1tfRsl964gcKiohZlIq5datP3i7h2CV5BIQCAvrGx8A8IAACUl5Uh32C/AcZERyM8JBjDY4La9H086VjWfmz4f09h2LBhePjhhz0dDhERERFRq+6OC4LSty9/318PlEHxcSoBk/VuJiHUDx/+3xkI89d37kKH7gT2v+O02Ou/XoDXJ0zo0Le4WFyJqzdqOnRuVzLW1wHgavBEREREpGy+Emw16K21TKE1SzAKwNPkmDBM7aLTuKBax461rPpub662SgXExlrKdcCaNWuQdFc//OV/ft+JILsG91knIiIiInINr8ZchevLOcdkvRuyzlvvFI3Gsj0b0DJhtz5+/fUO77deV1eHH0tLUV1Z0fEYu4iR+6wTEREREbmEpnGB6VaWsabbMFnvhly2aNusWcDf/w5ERzc/HhNjOd6JfdZ9fHwAAHV1tZ2JsEtwGDwRERERkWuwZ73tlD+pgdpFo1bZGoBLzJoFPPAA8NVXQGEhEBlpGfrewR51K19fXwBA3c2brojSraw9615ebC5ERERERJ3h1bjVNHvWnWP20c1oXZmoW2k0QAcXkbPH1rNeK0+yzp51IiIiIqLOGTlyBP56NRuD7xri6VAUj8l6NyPDvuVAk551CYbBDx5xN36xaDFCggI8HQoRERERkdT8e1m2kNbqOrl7VQ/AZL2bccnicl1ApmHwSeMn44mHH8ThQ/s9HQoRERERkdQ0jcPgTdxo3Sk5MjtqM1mS9YiICIwePRox/RI8HUqbqO1tYUdERERERG1WUlwEACgoLPJwJMrHnnUFmDAovMfNh54yZQqmTJmCL7JLYDIp+1O1irJSXNNXo66uztOhEBERERFJraS40PL/tesejkT55OiGpW5Lq1b+r+D2V36DwQP647PPPvN0KEREREREUvNq3FVKcOSqU+xZJ48K8tXiplHhCbvJCICrwRMRERERdZZ19ypu3eac25L13NxcvPTSSzh48CCKiooQFRWFBQsW4Ne//jV0Op3d8yZMmICsrKxmxxYvXoy33nrLXaGSB2RnZyMtLQ1+fn44c+aMp8NxyNfLMkyfyToRERERUed4WbeaVim8w04B3JasX7hwAWazGTt27MCAAQNw9uxZLFq0CNXV1di8ebPDcxctWoT169fbHltXDqfuQ61WIzc3FwEByt8Orb5xn3UvLw5EISIiIiLqDG3jPTV71p1zW/aRlpaGtLQ02+OEhARkZ2fjzTffdJqs+/r6IiIiwl2hkQJYP4CpqanxcCTOWReWY886EREREVHnaG096y5K1k0mqLKyEP3ll1D16gWkpgKN8+Jl16VdheXl5QgODnZa7v3338d7772HiIgI3H///XjhhRfs9q7X1dU1W6W7oqICAGA0GmE0Gl0TuJtY41N6nO5gTXwbGhpQU1Oj6ES4trYWgKVnvSfWlWx6cruSCetJHqwrebCu5MG6kgfryvUat1mHUKk7/bqqPvwQmpUr4WUwYDQAbNkCER0N05YtEA8+2OlY3aE9P3OXJes5OTnYtm2b0171hx9+GHFxcYiKisLp06fx3HPPITs7G3v27Gm1/IYNG7Bu3boWxz///HNphs9nZmZ6OoQu1/SX9OOPP1Z0XV27dg2A5QOGnlhXsmJdyYH1JA/WlTxYV/JgXcmDdeU6xaX1AHzh7x+IvXv3dvg6kUeO4CebNrV8wmCAZu5cHH/uORQmJ3c8UDdpz8hilRCiXZtcr169Gptae1GaOH/+PAYNGmR7bDAYMH78eEyYMAF//OMf2/PtcPDgQUycOBE5OTno379/i+db61mPjY3F9evXFT8f2mg0IjMzE5MnT1Z0z7I7CCHg7e0NIQTy8vIUPe3hd7/7HS5fvowxY8YgPT29x9WVbHpyu5IJ60kerCt5sK7kwbqSB+vK9S4UVeL+7Ufgp1cjfaD9cnFxcQgOsYzKLvuxDFeuXLE9pzKbkL7scfiVXm915rtQqYDoaDRcvKi4IfEVFRUIDQ1FeXm503y13T3rq1atwsKFCx2WSUhIsH1dUFCA1NRUpKSk4O23327vt0NSUhIA2E3W9Xo99Hp9i+NarVaaBiVTrK7k6+uL6upqNDQ0KPrnX7duHYxGI/bu3dtj60pGrCs5sJ7kwbqSB+tKHqwrebCuXMdHb9kZrKrOjDdOOyh4+gqAK60+dU/eGSwvvW73VJUQQH4+tEePAhMmdDxYN2jP71G7k/WwsDCEhYW1qazBYEBqaioSExORkZEBtbr9y/OfOnUKABAZGdnuc0nZhg8fbpsPTkRERERE3V9CaC88OCoa//6PAYWFhXbLRUZGwt/fHwBQVVWFgoIC23N9rl1u2zdzcH0ZuG3OusFgwIQJExAXF4fNmzfb5v0CsA15NhgMmDhxIt59912MGTMGly5dwgcffIDp06cjJCQEp0+fxooVKzBu3DgMHz7cXaGShxw+fBgAUFJSgtWrV9stN3bsWMyYMQMAUFZWho0bN9otm5SUhAcbF5Oorq7GSy+9ZLfsqFGjMHfuXACWIU4vvPBCq+X0ej2eeeYZmM1mxz8QERERERE5pFarsHXuSAAjO36RQwOA/W2YXi15h6/bkvXMzEzk5OQgJycHMTExzZ6zTpM3Go3Izs62TbLX6XTYv38/Xn/9dVRXVyM2NhazZ8/Gb37zG3eFSQpQWlrqcB0Eo9FoS9YrKysdll28eLEtWa+trXVYdsGCBbZk3WQyOSy7fv36dq+3QEREREREbjB2LBATAxgMQGtLsKlUlufHju362FzIbcn6woULnc5t79evH5qubxcbG4usrCx3hUQK1bt3b6xYscLu8+PGjbN97efn57BscpMVH729vR2WTUxMtH2t0Wgclh0yZAhCQ0PtPk9ERERERF1EowHeeAN46CFLYt40Ybfu3/7664pbXK69unSfdaLW9OnTB1u2bGlT2d69e7e5bK9evdpcVqvVOixrXWCOiIiIiIgUYNYs4O9/B5YtA/Lzbx2PibEk6rNmeSw0V2GyTkRERERERPKZNQt44AE0fPEFTn36KUZOmwav1FTpe9StmKwTERERERGRnDQaiPHjYaiuxojx47tNog4A7d9LjYiIiIiIiIjcisk6ERERERERkcIwWSciIiIiIiJSGCbrRERERERERArDZJ2IiIiIiIhIYZisExERERERESkMk3UiIiIiIiIihWGyTkRERERERKQwTNaJiIiIiIiIFIbJOhEREREREZHCMFknIiIiIiIiUhgm60REREREREQKw2SdiIiIiIiISGGYrBMREREREREpDJN1IiIiIiIiIoVhsk5ERERERESkMEzWiYiIiIiIiBSGyToRERERERGRwnh5OgBXE0IAACoqKjwciXNGoxE1NTWoqKiAVqv1dDjkAOtKHqwrObCe5MG6kgfrSh6sK3mwruQgUz1Z81Rr3upIt0vWKysrAQCxsbEejoSIiIiIiIiopcrKSgQGBjosoxJtSeklYjabUVBQAH9/f6hUKk+H41BFRQViY2Pxww8/ICAgwNPhkAOsK3mwruTAepIH60oerCt5sK7kwbqSg0z1JIRAZWUloqKioFY7npXe7XrW1Wo1YmJiPB1GuwQEBCj+l4osWFfyYF3JgfUkD9aVPFhX8mBdyYN1JQdZ6slZj7oVF5gjIiIiIiIiUhgm60REREREREQKw2Tdg/R6PdauXQu9Xu/pUMgJ1pU8WFdyYD3Jg3UlD9aVPFhX8mBdyaG71lO3W2COiIiIiIiISHbsWSciIiIiIiJSGCbrRERERERERArDZJ2IiIiIiIhIYZisExERERERESkMk3U3evnll5GSkgJfX18EBQW1WiYvLw8zZsyAr68vwsPD8eyzz6KhocHhdUtLSzF//nwEBAQgKCgI6enpqKqqcsNP0HMdOnQIKpWq1X/Hjx+3e96ECRNalH/yySe7MPKep1+/fi1e840bNzo8p7a2FkuWLEFISAj8/Pwwe/ZsFBcXd1HEPVNubi7S09MRHx8PHx8f9O/fH2vXrkV9fb3D89imusb27dvRr18/eHt7IykpCf/+978dlt+9ezcGDRoEb29vDBs2DHv37u2iSHuuDRs24Cc/+Qn8/f0RHh6OmTNnIjs72+E5O3fubNF+vL29uyjinuu3v/1ti9d90KBBDs9hm/KM1u4hVCoVlixZ0mp5tqmu8+WXX+L+++9HVFQUVCoVPvroo2bPCyHw4osvIjIyEj4+Ppg0aRIuXrzo9Lrtfb/zNCbrblRfX485c+bgqaeeavV5k8mEGTNmoL6+HocPH8af//xn7Ny5Ey+++KLD686fPx/nzp1DZmYm/vWvf+HLL7/EE0884Y4focdKSUlBYWFhs3+//OUvER8fj9GjRzs8d9GiRc3Oe/XVV7so6p5r/fr1zV7zp59+2mH5FStW4J///Cd2796NrKwsFBQUYNasWV0Ubc904cIFmM1m7NixA+fOncPWrVvx1ltv4fnnn3d6LtuUe/3tb3/DypUrsXbtWnz33XcYMWIEpk6dipKSklbLHz58GPPmzUN6ejpOnjyJmTNnYubMmTh79mwXR96zZGVlYcmSJTh69CgyMzNhNBoxZcoUVFdXOzwvICCgWfu5evVqF0Xcsw0ZMqTZ6/7111/bLcs25TnHjx9vVk+ZmZkAgDlz5tg9h22qa1RXV2PEiBHYvn17q8+/+uqr+P3vf4+33noLx44dQ69evTB16lTU1tbavWZ73+8UQZDbZWRkiMDAwBbH9+7dK9RqtSgqKrIde/PNN0VAQICoq6tr9Vrff/+9ACCOHz9uO/bpp58KlUolDAaDy2Mni/r6ehEWFibWr1/vsNz48ePFsmXLuiYoEkIIERcXJ7Zu3drm8mVlZUKr1Yrdu3fbjp0/f14AEEeOHHFDhGTPq6++KuLj4x2WYZtyvzFjxoglS5bYHptMJhEVFSU2bNjQavmf/exnYsaMGc2OJSUlicWLF7s1TmqupKREABBZWVl2y9i7/yD3Wrt2rRgxYkSby7NNKceyZctE//79hdlsbvV5tinPACA+/PBD22Oz2SwiIiLEa6+9ZjtWVlYm9Hq9+Mtf/mL3Ou19v1MC9qx70JEjRzBs2DD06dPHdmzq1KmoqKjAuXPn7J4TFBTUrHd30qRJUKvVOHbsmNtj7qn+8Y9/4MaNG3j88cedln3//fcRGhqKoUOHYs2aNaipqemCCHu2jRs3IiQkBKNGjcJrr73mcCrJiRMnYDQaMWnSJNuxQYMGoW/fvjhy5EhXhEuNysvLERwc7LQc25T71NfX48SJE83ag1qtxqRJk+y2hyNHjjQrD1jeu9h+ulZ5eTkAOG1DVVVViIuLQ2xsLB544AG79xfkWhcvXkRUVBQSEhIwf/585OXl2S3LNqUM9fX1eO+99/CLX/wCKpXKbjm2Kc+7cuUKioqKmrWbwMBAJCUl2W03HXm/UwIvTwfQkxUVFTVL1AHYHhcVFdk9Jzw8vNkxLy8vBAcH2z2HOu+dd97B1KlTERMT47Dcww8/jLi4OERFReH06dN47rnnkJ2djT179nRRpD3PM888g7vvvhvBwcE4fPgw1qxZg8LCQmzZsqXV8kVFRdDpdC3WkejTpw/bUBfKycnBtm3bsHnzZofl2Kbc6/r16zCZTK2+F124cKHVc+y9d7H9dB2z2Yzly5fj3nvvxdChQ+2WGzhwIP70pz9h+PDhKC8vx+bNm5GSkoJz5845fT+jjktKSsLOnTsxcOBAFBYWYt26dRg7dizOnj0Lf3//FuXZppTho48+QllZGRYuXGi3DNuUMljbRnvaTUfe75SAyXo7rV69Gps2bXJY5vz5804XEiHP6Ej95efnY9++fdi1a5fT6zddO2DYsGGIjIzExIkTcenSJfTv37/jgfcw7amnlStX2o4NHz4cOp0OixcvxoYNG6DX690dao/XkTZlMBiQlpaGOXPmYNGiRQ7PZZsiamnJkiU4e/asw3nQAJCcnIzk5GTb45SUFAwePBg7duzASy+95O4we6xp06bZvh4+fDiSkpIQFxeHXbt2IT093YORkSPvvPMOpk2bhqioKLtl2KaoqzFZb6dVq1Y5/MQNABISEtp0rYiIiBYrEFpXpI6IiLB7zu2LIDQ0NKC0tNTuOXRLR+ovIyMDISEh+OlPf9ru75eUlATA0ovIxKLtOtPOkpKS0NDQgNzcXAwcOLDF8xEREaivr0dZWVmz3vXi4mK2oQ5ob10VFBQgNTUVKSkpePvtt9v9/dimXCs0NBQajabFbgiO2kNERES7ypNrLV261La4bHt78rRaLUaNGoWcnBw3RUetCQoKwp133mn3dWeb8ryrV69i//797R61xTblGda2UVxcjMjISNvx4uJijBw5stVzOvJ+pwRM1tspLCwMYWFhLrlWcnIyXn75ZZSUlNiGtmdmZiIgIAB33XWX3XPKyspw4sQJJCYmAgAOHjwIs9lsu4kl+9pbf0IIZGRk4NFHH4VWq2339zt16hQANPtDQs51pp2dOnUKarW6xXQRq8TERGi1Whw4cACzZ88GAGRnZyMvL6/Zp+XUNu2pK4PBgNTUVCQmJiIjIwNqdfuXTWGbci2dTofExEQcOHAAM2fOBGAZYn3gwAEsXbq01XOSk5Nx4MABLF++3HYsMzOT7cfNhBB4+umn8eGHH+LQoUOIj49v9zVMJhPOnDmD6dOnuyFCsqeqqgqXLl3CI4880urzbFOel5GRgfDwcMyYMaNd57FNeUZ8fDwiIiJw4MABW3JeUVGBY8eO2d2FqyPvd4rg6RXuurOrV6+KkydPinXr1gk/Pz9x8uRJcfLkSVFZWSmEEKKhoUEMHTpUTJkyRZw6dUp89tlnIiwsTKxZs8Z2jWPHjomBAweK/Px827G0tDQxatQocezYMfH111+LO+64Q8ybN6/Lf76eYP/+/QKAOH/+fIvn8vPzxcCBA8WxY8eEEELk5OSI9evXi2+//VZcuXJFfPzxxyIhIUGMGzeuq8PuMQ4fPiy2bt0qTp06JS5duiTee+89ERYWJh599FFbmdvrSQghnnzySdG3b19x8OBB8e2334rk5GSRnJzsiR+hx8jPzxcDBgwQEydOFPn5+aKwsND2r2kZtqmu99e//lXo9Xqxc+dO8f3334snnnhCBAUF2XYqeeSRR8Tq1att5b/55hvh5eUlNm/eLM6fPy/Wrl0rtFqtOHPmjKd+hB7hqaeeEoGBgeLQoUPN2k9NTY2tzO11tW7dOrFv3z5x6dIlceLECfHzn/9ceHt7i3PnznniR+gxVq1aJQ4dOiSuXLkivvnmGzFp0iQRGhoqSkpKhBBsU0pjMplE3759xXPPPdfiObYpz6msrLTlTgDEli1bxMmTJ8XVq1eFEEJs3LhRBAUFiY8//licPn1aPPDAAyI+Pl7cvHnTdo377rtPbNu2zfbY2fudEjFZd6PHHntMAGjx74svvrCVyc3NFdOmTRM+Pj4iNDRUrFq1ShiNRtvzX3zxhQAgrly5Yjt248YNMW/ePOHn5ycCAgLE448/bvsAgFxr3rx5IiUlpdXnrly50qw+8/LyxLhx40RwcLDQ6/ViwIAB4tlnnxXl5eVdGHHPcuLECZGUlCQCAwOFt7e3GDx4sHjllVdEbW2trczt9SSEEDdv3hS/+tWvRO/evYWvr6948MEHmyWN5HoZGRmt/j1s+pkx25TnbNu2TfTt21fodDoxZswYcfToUdtz48ePF4899liz8rt27RJ33nmn0Ol0YsiQIeKTTz7p4oh7HnvtJyMjw1bm9rpavny5rV779Okjpk+fLr777ruuD76HmTt3roiMjBQ6nU5ER0eLuXPnipycHNvzbFPKsm/fPgFAZGdnt3iObcpzrDnQ7f+s9WE2m8ULL7wg+vTpI/R6vZg4cWKLOoyLixNr165tdszR+50SqYQQoku68ImIiIiIiIioTbjPOhEREREREZHCMFknIiIiIiIiUhgm60REREREREQKw2SdiIiIiIiISGGYrBMREREREREpDJN1IiIiIiIiIoVhsk5ERERERESkMEzWiYiIiIiIiBSGyToRERERERGRwjBZJyIiIiIiIlIYJutERERERERECsNknYiIiIiIiEhh/j8dR0ugrZxX3AAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -70,9 +73,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# We can see, that the discrete optimizer is aware that the function is discrete and does not try to predict values\n",
-    "# between the integers. The continuous optimizer tries to predict values between the integers, despite the fact that these are known.\n",
-    "# We can also see that the discrete optimizer predicts blocky mean and standard deviations, which is a result of the discrete nature of the function."
+    "# We can see, that the discrete optimizer is aware that the function is discrete\n",
+    "# and does not try to predict values between the integers. The continuous optimizer\n",
+    "# tries to predict values between the integers, despite the fact that these are known.\n",
+    "# We can also see that the discrete optimizer predicts blocky mean and standard deviations,\n",
+    "# which is a result of the discrete nature of the function."
    ]
   },
   {
@@ -113,22 +118,24 @@
    "source": [
     "continuous_optimizer = BayesianOptimization(\n",
     "    f=discretized_function,\n",
-    "    #acquisition_function=acquisition.ExpectedImprovement(xi=1., random_state=42),\n",
+    "    #acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
     "    pbounds=c_pbounds,\n",
     "    verbose=2,\n",
-    "    random_state=42,\n",
+    "    random_state=1,\n",
     ")\n",
     "\n",
+    "continuous_optimizer.set_gp_params(kernel=Matern(nu=2.5, length_scale=np.ones(2)))\n",
     "\n",
     "d_pbounds = {'x': (-5, 5), 'y': (-5, 5, int)}\n",
     "discrete_optimizer = BayesianOptimization(\n",
     "    f=discretized_function,\n",
-    "    #acquisition_function=acquisition.ExpectedImprovement(xi=1., random_state=42),\n",
+    "    #acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
     "    pbounds=d_pbounds,\n",
     "    verbose=2,\n",
-    "    random_state=42,\n",
+    "    random_state=1,\n",
     ")\n",
-    "#discrete_optimizer.set_gp_params(alpha=1e-3)"
+    "\n",
+    "discrete_optimizer.set_gp_params(kernel=Matern(nu=2.5, length_scale=np.ones(2)));"
    ]
   },
   {
@@ -144,46 +151,46 @@
       "\n",
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1778  \u001b[39m | \u001b[39m-1.254598\u001b[39m | \u001b[39m4.5071430\u001b[39m |\n",
-      "| \u001b[35m2        \u001b[39m | \u001b[35m0.4089   \u001b[39m | \u001b[35m2.3199394\u001b[39m | \u001b[35m0.9865848\u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m-1.787   \u001b[39m | \u001b[39m3.0783145\u001b[39m | \u001b[39m-0.082060\u001b[39m |\n",
-      "| \u001b[35m4        \u001b[39m | \u001b[35m0.7095   \u001b[39m | \u001b[35m1.5913220\u001b[39m | \u001b[35m1.8823587\u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.09673 \u001b[39m | \u001b[39m2.7779629\u001b[39m | \u001b[39m2.3840590\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m-1.034   \u001b[39m | \u001b[39m0.8481708\u001b[39m | \u001b[39m0.8669483\u001b[39m |\n",
-      "| \u001b[35m7        \u001b[39m | \u001b[35m1.277    \u001b[39m | \u001b[35m1.2462273\u001b[39m | \u001b[35m3.1071194\u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m0.9953   \u001b[39m | \u001b[39m1.261569 \u001b[39m | \u001b[39m4.2461872\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m0.3207   \u001b[39m | \u001b[39m0.2574759\u001b[39m | \u001b[39m3.3743200\u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m0.7984   \u001b[39m | \u001b[39m2.3822527\u001b[39m | \u001b[39m4.0972752\u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m0.2769   \u001b[39m | \u001b[39m4.0866405\u001b[39m | \u001b[39m4.9990993\u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m-1.783   \u001b[39m | \u001b[39m-4.990362\u001b[39m | \u001b[39m-4.900993\u001b[39m |\n",
-      "| \u001b[39m13       \u001b[39m | \u001b[39m-0.2363  \u001b[39m | \u001b[39m-4.975007\u001b[39m | \u001b[39m1.5082626\u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m0.1458   \u001b[39m | \u001b[39m2.0323084\u001b[39m | \u001b[39m4.9763438\u001b[39m |\n",
-      "| \u001b[35m15       \u001b[39m | \u001b[35m1.374    \u001b[39m | \u001b[35m1.8577640\u001b[39m | \u001b[35m3.3266304\u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.03061  \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m2.2032449\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-0.6535  \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m-1.976674\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.2576   \u001b[39m | \u001b[35m0.1670635\u001b[39m | \u001b[35m3.0624516\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.4804   \u001b[39m | \u001b[35m1.1137325\u001b[39m | \u001b[35m2.1605226\u001b[39m |\n",
+      "| \u001b[35m5        \u001b[39m | \u001b[35m1.379    \u001b[39m | \u001b[35m1.8758251\u001b[39m | \u001b[35m3.1958494\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.3687   \u001b[39m | \u001b[39m2.7452076\u001b[39m | \u001b[39m3.6194246\u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m1.015    \u001b[39m | \u001b[39m1.4178941\u001b[39m | \u001b[39m3.9354649\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.1912   \u001b[39m | \u001b[39m2.4250498\u001b[39m | \u001b[39m2.2123493\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m1.32     \u001b[39m | \u001b[39m1.4321645\u001b[39m | \u001b[39m3.1560306\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.784   \u001b[39m | \u001b[39m4.8978481\u001b[39m | \u001b[39m-4.984869\u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.7694  \u001b[39m | \u001b[39m-4.926256\u001b[39m | \u001b[39m4.9365884\u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-1.363   \u001b[39m | \u001b[39m-0.707260\u001b[39m | \u001b[39m-4.987766\u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m-1.03    \u001b[39m | \u001b[39m-0.062037\u001b[39m | \u001b[39m4.9528772\u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m-1.75    \u001b[39m | \u001b[39m4.9885524\u001b[39m | \u001b[39m-0.432722\u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-1.992   \u001b[39m | \u001b[39m0.0847314\u001b[39m | \u001b[39m-0.145683\u001b[39m |\n",
       "=================================================\n",
-      "Max: 1.3739320944970081\n",
+      "Max: 1.3794744873707774\n",
       "\n",
       "\n",
       "====================  Typed Optimizer  ====================\n",
       "\n",
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1778  \u001b[39m | \u001b[39m-1.254598\u001b[39m | \u001b[39m5        \u001b[39m |\n",
-      "| \u001b[35m2        \u001b[39m | \u001b[35m0.4923   \u001b[39m | \u001b[35m2.79691  \u001b[39m | \u001b[35m-1       \u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m-0.3589  \u001b[39m | \u001b[39m3.6456361\u001b[39m | \u001b[39m-2       \u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-1.817   \u001b[39m | \u001b[39m2.4028434\u001b[39m | \u001b[39m0        \u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m0.07317  \u001b[39m | \u001b[39m2.1170379\u001b[39m | \u001b[39m-2       \u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m0.1352   \u001b[39m | \u001b[39m-3.550760\u001b[39m | \u001b[39m5        \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m0.1517   \u001b[39m | \u001b[39m-4.945271\u001b[39m | \u001b[39m4        \u001b[39m |\n",
-      "| \u001b[35m8        \u001b[39m | \u001b[35m1.563    \u001b[39m | \u001b[35m-3.487143\u001b[39m | \u001b[35m3        \u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.7904  \u001b[39m | \u001b[39m-3.462641\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.475   \u001b[39m | \u001b[39m-3.027560\u001b[39m | \u001b[39m4        \u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m0.7741   \u001b[39m | \u001b[39m-4.139354\u001b[39m | \u001b[39m3        \u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m-1.785   \u001b[39m | \u001b[39m-4.999737\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
-      "| \u001b[35m13       \u001b[39m | \u001b[35m1.658    \u001b[39m | \u001b[35m-2.773927\u001b[39m | \u001b[35m3        \u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m1.012    \u001b[39m | \u001b[39m-1.617889\u001b[39m | \u001b[39m3        \u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m-0.07162 \u001b[39m | \u001b[39m-0.713869\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.8025   \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-2.75    \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m0        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-0.1028  \u001b[39m | \u001b[39m0.0239500\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.4171  \u001b[39m | \u001b[39m0.0463387\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m5        \u001b[39m | \u001b[35m1.066    \u001b[39m | \u001b[35m-2.083382\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.6906   \u001b[39m | \u001b[39m-1.726489\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.4423  \u001b[39m | \u001b[39m-3.391033\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.3318   \u001b[39m | \u001b[39m-1.763948\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.7855  \u001b[39m | \u001b[39m4.9987136\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m0.2146   \u001b[39m | \u001b[39m4.997248 \u001b[39m | \u001b[39m5        \u001b[39m |\n",
+      "| \u001b[35m11       \u001b[39m | \u001b[35m1.428    \u001b[39m | \u001b[35m4.9970054\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m0.769    \u001b[39m | \u001b[39m4.4769344\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m13       \u001b[39m | \u001b[35m1.935    \u001b[39m | \u001b[35m3.7959641\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m0.299    \u001b[39m | \u001b[39m3.4532774\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-0.19    \u001b[39m | \u001b[39m2.9846175\u001b[39m | \u001b[39m2        \u001b[39m |\n",
       "=================================================\n",
-      "Max: 1.6582609813341627\n",
+      "Max: 1.9349856179084963\n",
       "\n",
       "\n"
      ]
@@ -206,7 +213,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -245,7 +252,6 @@
     "axs[2].contourf(X, Y, d_pred, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
     "axs[2].scatter(discrete_optimizer._space.params[:,0], discrete_optimizer._space.params[:,1], c='k')\n",
     "\n",
-    "\n",
     "def make_plot_fancy(ax: plt.Axes):\n",
     "    ax.set_aspect(\"equal\")\n",
     "    ax.set_xlabel('x (float)')\n",
@@ -297,28 +303,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|   iter    |  target   |     k     |    x1     |    x2     |\n",
+      "|   iter    |  target   |    x1     |    x2     |     k     |\n",
       "-------------------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-5.62    \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m5.9308597\u001b[39m | \u001b[39m-6.331304\u001b[39m |\n",
-      "| \u001b[35m2        \u001b[39m | \u001b[35m7.509    \u001b[39m | \u001b[35m2        \u001b[39m | \u001b[35m1.9731696\u001b[39m | \u001b[35m-6.879627\u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m8.825    \u001b[39m | \u001b[35m2        \u001b[39m | \u001b[35m1.1484948\u001b[39m | \u001b[35m-7.207232\u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-3.945   \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m1.4977824\u001b[39m | \u001b[39m-8.189324\u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m1.978    \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m1.0306587\u001b[39m | \u001b[39m-6.389038\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m8.229    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m1.3033840\u001b[39m | \u001b[39m-7.107783\u001b[39m |\n",
-      "| \u001b[35m7        \u001b[39m | \u001b[35m9.632    \u001b[39m | \u001b[35m2        \u001b[39m | \u001b[35m-0.198942\u001b[39m | \u001b[35m-7.391484\u001b[39m |\n",
-      "| \u001b[35m8        \u001b[39m | \u001b[35m11.54    \u001b[39m | \u001b[35m2        \u001b[39m | \u001b[35m-1.136689\u001b[39m | \u001b[35m-7.696695\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m7.76     \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-1.535976\u001b[39m | \u001b[39m-7.007879\u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.5064  \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-1.153247\u001b[39m | \u001b[39m-8.358024\u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m9.011    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-0.860207\u001b[39m | \u001b[39m-7.266433\u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m-1.8     \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-1.227306\u001b[39m | \u001b[39m-7.434615\u001b[39m |\n",
-      "| \u001b[39m13       \u001b[39m | \u001b[39m4.911    \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-1.825508\u001b[39m | \u001b[39m-6.159445\u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m11.25    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-0.643026\u001b[39m | \u001b[39m-7.648759\u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m3.807    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-0.603103\u001b[39m | \u001b[39m-6.548109\u001b[39m |\n",
-      "| \u001b[39m16       \u001b[39m | \u001b[39m8.803    \u001b[39m | \u001b[39m2        \u001b[39m | \u001b[39m-2.366971\u001b[39m | \u001b[39m-6.942279\u001b[39m |\n",
-      "| \u001b[39m17       \u001b[39m | \u001b[39m2.363    \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-1.839455\u001b[39m | \u001b[39m-6.747216\u001b[39m |\n",
-      "| \u001b[39m18       \u001b[39m | \u001b[39m-0.7103  \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-3.198436\u001b[39m | \u001b[39m-7.144806\u001b[39m |\n",
-      "| \u001b[39m19       \u001b[39m | \u001b[39m0.9779   \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-2.068342\u001b[39m | \u001b[39m-4.863568\u001b[39m |\n",
-      "| \u001b[39m20       \u001b[39m | \u001b[39m-1.224   \u001b[39m | \u001b[39m1        \u001b[39m | \u001b[39m-0.663333\u001b[39m | \u001b[39m-7.308690\u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-2.052   \u001b[39m | \u001b[39m-1.659559\u001b[39m | \u001b[39m4.4064898\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m13.49    \u001b[39m | \u001b[35m-7.437511\u001b[39m | \u001b[35m9.9808103\u001b[39m | \u001b[35m1        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m12.38    \u001b[39m | \u001b[39m-8.235396\u001b[39m | \u001b[39m8.9416358\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-3.405   \u001b[39m | \u001b[39m-6.540598\u001b[39m | \u001b[39m8.9001743\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m10.98    \u001b[39m | \u001b[39m-8.598285\u001b[39m | \u001b[39m9.9716838\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[35m6        \u001b[39m | \u001b[35m14.56    \u001b[39m | \u001b[35m-9.726922\u001b[39m | \u001b[35m8.5187646\u001b[39m | \u001b[35m1        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-6.752   \u001b[39m | \u001b[39m-9.164838\u001b[39m | \u001b[39m7.3244716\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-4.308   \u001b[39m | \u001b[39m-9.889999\u001b[39m | \u001b[39m9.6105551\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m9        \u001b[39m | \u001b[35m16.47    \u001b[39m | \u001b[35m9.8666955\u001b[39m | \u001b[35m-9.885838\u001b[39m | \u001b[35m2        \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m4.669    \u001b[39m | \u001b[39m8.7241716\u001b[39m | \u001b[39m-9.536908\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-9.977   \u001b[39m | \u001b[39m9.9692745\u001b[39m | \u001b[39m-8.882518\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m15.41    \u001b[39m | \u001b[39m-9.790780\u001b[39m | \u001b[39m-9.766374\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m4.339    \u001b[39m | \u001b[39m-9.825013\u001b[39m | \u001b[39m-8.517115\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m5.648    \u001b[39m | \u001b[39m-8.380618\u001b[39m | \u001b[39m-9.927737\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-3.121   \u001b[39m | \u001b[39m9.9064416\u001b[39m | \u001b[39m9.9099804\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m16       \u001b[39m | \u001b[39m0.6096   \u001b[39m | \u001b[39m-0.195500\u001b[39m | \u001b[39m-5.568951\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m17       \u001b[39m | \u001b[39m2.262    \u001b[39m | \u001b[39m7.4993146\u001b[39m | \u001b[39m0.7964642\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m18       \u001b[39m | \u001b[39m4.557    \u001b[39m | \u001b[39m-8.057583\u001b[39m | \u001b[39m-1.109046\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m19       \u001b[39m | \u001b[39m3.911    \u001b[39m | \u001b[39m-4.957865\u001b[39m | \u001b[39m-1.857735\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m20       \u001b[39m | \u001b[39m-2.736   \u001b[39m | \u001b[39m-9.980513\u001b[39m | \u001b[39m1.3255395\u001b[39m | \u001b[39m2        \u001b[39m |\n",
       "=============================================================\n"
      ]
     }
@@ -331,7 +337,7 @@
     "    #acquisition_function=acquisition.ExpectedImprovement(1e-2),\n",
     "    pbounds=pbounds,\n",
     "    verbose=2,\n",
-    "    random_state=42,\n",
+    "    random_state=1,\n",
     ")\n",
     "discrete_optimizer.set_gp_params(alpha=1e-3)\n",
     "\n",
@@ -359,7 +365,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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3h8Ok8ABFIhG8/PLLmDlzJnr1ii/SjBkzcOaZZ6Kurg4A8PDDD+NrX/sazj77bLS1teHxxx/Hjh078IMf/MD0ef9w5Axk5/T8npX3RxbxYxYWwofn4p+x8BZpXvUIyeAJ+mqwrVMIsxsqbhY/VoSPKEQLjmTIMOLTrG2y6g0yupI8K5z0BEkhgN5//300Nzfj+9//fo/fmpubkZ5+unEdOnQIs2fPRktLC8444wyMHTsWq1atwsiRI5mUxUnxYzXkJZP4cUromDk/D1HkRSEkWgQFt9u7liLtRlt5blLj6aT4sYqZzkyU8JFR9OgRW16nr5kVIcRaBDk1+3vbeUH027SP2fGkSoJ2ilAohGAwiN+uH4vsnIy431gIILeKH0BfPIgWO1bhIYicMtBOjVDT/I1jYnRXZwfWvXU/k2RGp1Btx9ipP0Ov3vEzSbO+V6y9P7ILH7eJnlS44RqybkNOvAD027SPWRK0NDlAMkDiJ14odJSF4z5uhUc9vDR8nvt8SAJXe3YrfhI/MuTV8EBEzpBZu2Q2tCXF8Phydi9LJIBO4XfxA3hH8KSCtRByAlGdhJOxfzcja96PmXl9nE5w9qrwSYZMAx16bMv4GXfTumEkgBjhdvHjR1iJPadHjPFA9MgwNyNr3o+sXh8/CZ9EnKy7GbtkRig7/SzzbJskgGDf+0Pix/2wEkJOQCJIHhwPFxm8hjJ6ffwsfBJxWggZ3paRCGJdN15t1PcCyM4K6gBf8WMUEj/ssCuE3J4bRPlAxpE16dmM+HECEj7aOHVtzHqDjOC0COKB7wWQFkZEBW/xY0SckfjhAwsh5ASO5hVQPhA3WIgfs/k+TuCGTlAGZPMGyfis82izvhZAb+8bk/R7Ej+Eih0h5KQ3iCUUCktN+zC5TKdsIS/y+pjHSW+Qoe0MCGq3h8LkeopdAokfbdQV79WPV5DdG8TaeFIozDnsen/I6+Mt3BYSc1oEsXz5kGImaJlgISz8IH6Miht1OxnKbBdVBFmZWFFv1mWWODEjK6sp6fO2hBiUxt3YbRMyiR8SPuyQYTb4uO04LkgqEvIAmSRVx+/lhGcvenasYCck5gSsOiLeoTAiNXqdDokf7yNbSEwLtyZEkwCKIZW44C1+Unl/RIgfEj3J8UtekOiVv72MndAXiR//4BYR5EbIuhnET+KHRI8x3JAXxBOvGUM3YHSklxNCmxKdncMNIsiNXiASQKfQExiihYBT4oeH6Pms8SxP5P9o4QcRRF4g9ljx/pDXx9+4QQSlQrZ2Q5aNAby9P7zh5e3xsvCJxc8iiLxAzkDihwDkWhw5mTfSbYnSJIBgz/vj5tAXzzCXX8SPih9EkBYkgsxh9l6Q+CESkUUEARZWlJeoDdEweB28Ln54IUr8WBmeHgurFeKt4MQwebtD5J0ayu8mYtuMkfZn5PrFvkWT+CG04D3lhZnnndcweTtTjxjB9wJIVk8Fr3J5Qfjwehj0juuEOHKDCNLCiAHs3rQFGaPKmZ9bFLzbBIkf87C8Fm6os4wiKG9ze+rJE02Wm5cQEh4Ce/DBB5GWlhb3GTFihO4+b731FkaMGIGsrCycf/75eO+995iXS7T3hzW8R3XxFD9ZjYG4jwgSy5DswwLZOzO75evetMXW/iqi7Ua41Lz4MdORkPjRRx3llvhx2zlYIFs4jGdInPVLhxR387zzzsPevXujn48++khz21WrVuH666/HTTfdhA0bNmDKlCmYMmUK6uvrTZ+3vqk46fdem+yQ9yg2HuJHtOARiVs7tVSGT/X+sBJBouyGLLi1nVhBJhEiU1lUZBJBRpGhbUlx93r16oXCwsLoZ8CAAZrbPvPMM5g8eTLuuecelJeXY/78+RgzZgx++ctfMimLE+JHz/vjJvHDeoi7n0VPIjJ3bizKxkIEyWQ3UrVZGYy9WUSXWTaRoYVbymkXWepnxfuqhRQ12rZtG4qLi1FWVobp06ejublZc9vVq1ejqqoq7rvq6mqsXr1ac59wOIxQKBT3EYVToS83hbxI9IiD+WrNJtzf3fVbbZ2Lt90A5LIdsTgxyaEI3C4mRJZfpnmCjJKqzLz7BeGtbMKECVi0aBGWLl2K559/Hl9++SW+8Y1v4PDhw0m3b2lpwaBBg+K+GzRoEFpaWjTPUVdXh2AwGP2UlJQk3c4roS+3hLxI+OhjxdioSYim9rFgOK0aQlZJ0E7YDcC47XASt4oDLdwuerQQUS83iiCRCK/JZZddhqlTp2LUqFGorq7Ge++9h7a2Nrz55pvMzlFbW4v29vboZ+fOncyObQYnQl9uEj9EaqwaG5ETkjkxL5ATdgNgYztYdkoyh0bN4kXRo4WTdXWbCBIZapWu9eXl5eHcc89FQ0ND0t8LCwvR2toa911raysKCws1jxkIBJCbmxv3SUS094cFbhA/5PUxj9VJycyIICe9QDzgYTcAY7bDqfbsFfHjJ+GTiNsWQ9bDC/dQuhocOXIE27dvR1FRUdLfJ06ciOXLl8d9t2zZMkycONHyOb2Q+Cy7+CHhYw8nPEFMPRU6XiAecwGJsBtGYHVNvdBp+ln4JELXwjg8+w3hd+C//uu/sHLlSjQ1NWHVqlW4+uqrkZGRgeuvvx4AMGPGDNTW1ka3v/POO7F06VI88cQT+OKLL/Dggw/i008/xW233SaqCinhnfjsBvFDOIed9XlML9MgyIj7wW54BersteF5bdzkBRIVBhPeKnft2oXrr78ew4cPx3e+8x30798fH3/8MQoKCgAAzc3N2Lt3b3T7SZMm4bXXXsNvfvMbVFZW4g9/+AOWLFmCiooKS+cXHfqyKy5I/PgHO8ZGRE4Qz1wg0XbDCH73/pDwMQ6JIDGkKYqiiC6E04RCoZMjOl6Yh9HnHdDdlueMzyR+CCsYNWrJRA+vGYa1yqQnvMKfbcKHeBft7e1Jc2tkJNZ2pPfJ0m3jLDofN4sfwhrMp6ZwUfK1XlnVWaAjxzuw85aHmNgNX7fSitI9ur/LnPhM4oewglFPkOhJ8Ah3igjy+tiH9TWkZ1kbaqkc4eX9IfHjb4waRy1vD49wmFaZnBgSLyNu6XTcNkTfT7hNBLnx/ruvxA4hq/dHdvFDOINbQyOxZFQM53p83vAU+m66v+T14Yfbrqvd8jr94tDL0bMRAKyLDK+IH6Mr+pIniR95m9sNeWfytkVsG7W284JCJ2Z0G27q9NxUVreiXmO74oDFsyySrMYA89XgSQAlgfe8P1Zwk/hh1Uj1jiNSHCUaIlFGpe2c9JRGUU98GBVBLMvjB9xwDdySoE2cxi3Pl1vKCVAIzHFkDDHJWKZUdJSF4z4iydsWcc0DbwUv1002jIoK0e2fxI8YRISYrLQzW1N2OGhvqBUn4EfvjxdwsiPQerhFCCEjhkbPy+NUaMprydCBJnEeSLvt3G4bJfEjFpHXX4YXTpZQS5YcN4W+ZEAGr5DTQsj2W6EBEWS0Pn7vHO3cdyPXTmS7pmRneRDtYTHSDnm0FdapD9SaY5BtzS8SP/bgKYSMPNwyhca85oHxGk6JH6vtkYSPfHj5njhlN717BQniFCJFEOCOOThYeoG0IBFmHZEDArzc0body4sku9gLxBK5S+cgsq35Rd4ftsgQFhONUwJEdqMnGzJfL5nLRpyE9z3SE9huzwei1s0QVsnPJH74wfqBNWN8eIsgN3iBvAyva8OqzZotH4kf92DlXrFqr6naJ+t2xHIAArVwwnd4WQTZhfeoMAqDxZOq7Yh6wybx4z5kvmciBZoe8l4xB5Ep/EXeH2cQHQ4TJYRYCBAjZZfZGPsR2YU3wQZRz51bQ2FkpRjBeu4fHpD4iYflQytLh8+iHLRshTOw8P7wSICWpS0T1hDlkXY6FMYC+UrkY2jCQ+cRKYLc/FZua74bD4bBWN9LCn0RdmB9H726LqPvW7tM4S+eyFIOIh4eIiiV8TO0CCp5gbgio9CQsUyEdUTcT9ZeIN4vidTiGcAi/EXeH28ga7IfD1IuxEodqiVYe38oZ8u/0H3VR/jVqaurw4UXXoicnBwMHDgQU6ZMwdatW3X3WbRoEdLS0uI+WVlZDpXYfZD3x1n8JIJE4Wa7IVunJFt5CGfxSvjWCsJb/sqVKzFnzhx8/PHHWLZsGTo7O3HppZfi6NGjuvvl5uZi79690c+OHTtMn/uageutFtsQRoUHeX8IgK0hkj0MZjcPSKTd4ImZzsOreRkEW2QLhckkuHuJLsDSpUvj/l60aBEGDhyIdevW4Zvf/KbmfmlpaSgsLDR0jnA4jHD49A0JhULWCpsE2Ud/kfdHG54dSNs56b7w6uRtiwgxaE7YDYC97ZDJ+APylYfgAwt7lNUYcJV3xwjStf729pNvnfn5+brbHTlyBEOGDEFJSQm+/e1vY/PmzZrb1tXVIRgMRj8lJSVMy2wH8v7wIasxkPIjI7KJJrteIKc6WB52AzBmO5yaUZc1JH78hdP32w1eIDlKcYpIJIK77roL//Zv/4aKigrN7YYPH46XXnoJ7777LhYvXoxIJIJJkyZh167kYqK2thbt7e3Rz86dO3lVIQp5XpxFRnEjcqFCI+dnNRxdtGjjZTcAMbaDJVr3xm4HJHptPT/i12vO074ID4HFMmfOHNTX1+Ojjz7S3W7ixImYOHFi9O9JkyahvLwcv/71rzF//vwe2wcCAQQCcnSKsdCsz/aQRegQ1mgrzwXq7R+Hl90A2NoOPdHhlo7NLeX0GrHXXf2/FfsnU2hehrJI4wG67bbb8Oc//xkrVqzAWWeZ67h79+6N0aNHo6GhgVPpkiN7/o9XkcnLkwrRXiAWyDwnkBvtBgvstH+rbTKZ+CFBJA6r197J8JPs7UO4AFIUBbfddhveeecdfPDBBxg6dKjpY3R3d+Pzzz9HUVERhxK6Ey96f9wkfGTBKWPntGBzm92QxfvDUvwQ4nGDCJIZ4Vdhzpw5WLx4MV577TXk5OSgpaUFLS0tOH78eHSbGTNmoLa2Nvr3ww8/jP/93/9FY2Mj1q9fjxtuuAE7duzAD37wAxFV6IER8UHJz+bwo/BxQlS4dVkKL9oN3pD48SZOiiArdljmZGjhAuj5559He3s7LrroIhQVFUU/b7zxRnSb5uZm7N27N/r3oUOHMHv2bJSXl+Pyyy9HKBTCqlWrMHLkSBFVIDjjdvEj+iFngZ0wGI/6k90whnrtSfx4G/IEWUN4ErSiKCm3+fDDD+P+fuqpp/DUU09xKpExZM7/8VL4y+3ixy6i5tmRHTfZDdHhLxI/hB6i7YvIZGiyrAKg8JcxvCR+RBoZr+YBEQRxGpkFq6xlIwFEEJJDwoJwGqMdlpdeUryArEJDVkgAMcZL4SeRkGF1DqOJ0DIPh5cV0eEvK8haLsK7iPKQkwDyGCTA5MXOQ05eIIIgjCCrgJWxXCSAHIbyfwgvQ0LN3cjYSRHmoftoDBJAFpB5BJhXoAeYLTKM9PAbvMJfFB72H3645yJshP+sEuEaSAQRhHPQ8+YtZLyfspWJBJCH8GL+j9dWQHZ7HhAlQhME4RVIABGuwGtCSDbcuiSGrLgt5EfPFuFH3PWUuhxKgLYPCSHC7VD7JZzCbW3N6RcHEkAM8WIISlbc9mD7CRlCdQRByIlMtlv4WmAEQRiH1gaTH7fdH5k6pGRolU/EyKhkZXGiHHbuUUdZ2BejyKzgrieVIGKQ3XDLhts6ZoLQe8adfv61zkdhebYk2imedossIuFq3Gh4SIgQdnBjm7eCTPU0UhaZyksYgyyxR6D8I4IQQ6ygTSVuZesk7ZaHV32MHtdr15MXspZLC6deEkkAmYRmgSYIguCHbJ212fLIVn4ZkeUaURI0QRCETSisyQYrHSMl+XoTJ54pKZ7a5557DqWlpcjKysKECRPwySef6G7/1ltvYcSIEcjKysL555+P9957z6GSEgQh02zQZDusI8tbOAtEh+JY7Uc4i3AB9MYbb6Cmpgbz5s3D+vXrUVlZierqauzbty/p9qtWrcL111+Pm266CRs2bMCUKVMwZcoU1NfXO1xygiBEQrbDW8gmGmTNj7KKbOWRAeEC6Mknn8Ts2bNx4403YuTIkXjhhReQnZ2Nl156Ken2zzzzDCZPnox77rkH5eXlmD9/PsaMGYNf/vKXDpeckAE/ur5posGTkO0gYmHZwcskFmQqi9cQKoBOnDiBdevWoaqqKvpdeno6qqqqsHr16qT7rF69Om57AKiurtbcHgDC4TBCoVDchyAI9+JG2+HVjoxFvVhdm1Rz8qi/OzW/kFfvuVcQKoAOHDiA7u5uDBo0KO77QYMGoaWlJek+LS0tprYHgLq6OgSDweinpKTEfuEJghAG2Q5Cj0SRk0z0GNmGiMdr10d4CMwJamtr0d7eHv3s3LlTdJEIwjI04sg5ZLUdXuqIeNbFiKiRWfjIWi6vIHQY/IABA5CRkYHW1ta471tbW1FYWJh0n8LCQlPbA0AgEEAg4L9cET9AQ2D9CdkOe7DuWOk51IauTXJkuC5CXyUzMzMxduxYLF++PPpdJBLB8uXLMXHixKT7TJw4MW57AFi2bJnm9gRBsKXtvKDoIpDtIAjCNsInQqypqcHMmTMxbtw4jB8/Hk8//TSOHj2KG2+8EQAwY8YMnHnmmairqwMA3HnnnfjWt76FJ554AldccQVef/11fPrpp/jNb34jshoEQTgM2Q7Cy/AIf8ngdZEJ4QJo2rRp2L9/Px544AG0tLTgggsuwNKlS6PJis3NzUhPP+2omjRpEl577TXcf//9uO+++3DOOedgyZIlqKiocKa8hWtpOQyCkAC32Q6vY6VzpRwX9+DFe5WmKIoiuhBOEwqFEAwG8dv1Y5Gdk2F6fy0BlGpB0sqyXabPZQa/LojqxjcaO3P52EmC1juv0RmejYTAkpUx8dxdnR1Y99b9aG9vR25urqFzi0a1HWff+ygyAlmm9uXRgVht+7w6MxJAybFyn2S5RyqytN/ucAcaFtzHxG7QcBLC1fhN/BCEzPhF0JiFFlSVExJABOETSHgRhPyQ+HEOEkCEa3Gj94cgvA514N7Dq/eUBBBDeOf4EKfxq/hxwySIbiijV5C1Y5K1XLJD181ZhI8C8xOfNZ5FIskmfhU+hD+ItQ9+HdRAuBe3tV96VSNcQVZjgMQPR4yOACMIo5A3wxyyXi+j5XLjyz0JIA/hxgZoBC8JH7cnIsswC7RXSXx+jT7PsnacgNxlkwm6TmIgAURIC3l94qHcGv/hhZcarc6dOv2TeOE6JGunvNpuuJTd9SKLaoFphWtFF8HzkPDxFm73fBH28EInb5dk10D26yJ7+exCAshh3JAYRngPpwQIeanYY+RN2g0dVUdZ2BXldAIvXQs3eynJWnkMNzdGryNq+QuCkAkvdf5m8XPdZYSsKmNIgLCBjIRzsFwDjBAHPTMES4y0J6f7O9ZtnAQQQUgOeX/kg2UiphHoxYpgAeVWxkOWVQCUB2QML73RikwCpvwf/+ClZ4YQhxPeHxnEGFksi8g8EsxLb4t+N+gkKggVls+1DJ0PQZiBR19A1pWQHr+LIBmwk//j1SHwsrZLWctFuAMZc394QQKIA0YaB4XBzOFmo25VADjh/aElMJyDhdfFKx0PQciAMAHU1NSEm266CUOHDkWfPn0wbNgwzJs3DydOnNDd76KLLkJaWlrc55ZbbnGo1O7Bi4aShpCax4v5P2Q7UkPPCWEFWb0/vNqzsNXgv/jiC0QiEfz617/G2Wefjfr6esyePRtHjx7FL37xC919Z8+ejYcffjj6d3Z2Nu/iJmVa4Vq80XKhkHP7GfVhcEMeg8zeH6PINvxdJtvRURZ2vB1Wlu0iDzLBHD+KZmECaPLkyZg8eXL077KyMmzduhXPP/98SiOWnZ2NwsJC3kXkzmeNZ3FV0143lLEPrBvEEBFP3paQpf3IdhhDhDgj5MXpEKwb+h55XjMBtLe3Iz8/P+V2r776KgYMGICKigrU1tbi2LFjutuHw2GEQqG4D2+8GIKSGTU8JlOYTHbvD6v8Hxm8VV6yHUbwwkrxhDzI3E54lk2YByiRhoYGLFy4MOUb3He/+10MGTIExcXF2LRpE37yk59g69atePvttzX3qaurw0MPPcS6yEzg7QXyK6keGlnfjFmKCRb5P3bDX07kIIm2Haw8LWZtgV0Pb1ZjQOqOj3AGo23Ai/0UcwF077334uc//7nuNlu2bMGIESOif+/evRuTJ0/G1KlTMXv2bN19b7755uj/zz//fBQVFeGSSy7B9u3bMWzYsKT71NbWoqamJvp3KBRCSUmJkeqkRPY8IK+HwazCO3zm1aHfPPGb7XAKXqGwxA6R7Ax7vHqNZXkBZS6A5s6di1mzZuluU1ZWFv3/nj17cPHFF2PSpEn4zW9+Y/p8EyZMAHDyLVDLiAUCAQQCPS/42/vG4Iacz0yf0yhGxQd5gQjAWe+PkfCXEe+P1TLnbW5HV8J3brIdiRgVGay9LkZtjBP5QKoN80onLRKn+gPZvT+8PZTMBVBBQQEKCgoMbbt7925cfPHFGDt2LF5++WWkp5s3phs3bgQAFBUVmd7XL5AXyFmseH9kyKMRDdkOvjiVFE32xjpOCg3ZxY8TCLO6u3fvxkUXXYTBgwfjF7/4Bfbv34+Wlha0tLTEbTNixAh88sknAIDt27dj/vz5WLduHZqamvDHP/4RM2bMwDe/+U2MGjVKVFWYQAbDG/hJ/KQqN68wINmOeMx0UE7l/FSW7fJ0x8kDGcWPVVj0Z1plrCjdY/vYKsKSoJctW4aGhgY0NDTgrLPiL5aiKACAzs5ObN26NTpSIzMzE++//z6efvppHD16FCUlJbj22mtx//33Wy7HGy0X2l7XS/Y8IIDeyvyEU+EvUchiOxJxy7BzJ8tJdscYsoofr4tYYQJo1qxZKeP9paWlUYMGACUlJVi5ciXnkrHFjAGgXCDnYdkRkPfHGHaH33vZdli1AWaFhpOjvyg3SBsn59WRZcSfEZvrVFndaX0JS5C4iseL4keGoe9+QMsNb8Rw8/K+OP18m+2Qyf7EI/v1kLF8rMtEAghgEr7SC6PJNHumjI1aBKJDFaI8P04tfkrTABDJoNwgd1wDO+Vzk6eKBBDhO1iLHzOdfds56VKHvXgOfQf8sfq8XQNupwOR3Quk4gYRwBo7dabwIZ+2La8ldiF2k6lVyAvED9HihyfkdXEWWZ8jWcuVDDeV1SpuE3siy+p0nhIJoFPwHsVltlGRCGJLVmNAaNhLBq9PKu8LK+8PCTHxCadOPt92bZXbBIJRWNXLSe8P7/tg1QbzKpd4q0wQnOElfIx29E6IH7uiw4nEZy+Gv/QMs54IEp2DJiNeEUIs6+E28WOnvCJeGkgAxSBTMjRAXiC78PT6GBEcMuX7sBAfstTFD7B49t3kBYpFFRBus09uLLMb4HlNyaJJDokg88gQ7nJSLDjh/TFaHz+Gv6x6gZzArSJIRXYxxLN8bvP+2EHUc0ICKAHeXiAr0AiA1Kiixwnho9XJy+TxiUXP++PUnD9eDH+5BdGdGytkEUNOlMON4idVmbVss5744X2vhc0E7VdknBpexjKlQqYcCpGixwmPi4yiTjb0niGtpSdSrQzvxpnhnSpz4jl42i+33QMzeLluRiAB5BJ4GxaWIkhPnJh1dcokdFRkEQSyJT77MfxlFNHrhDn5kiNCuGmdz2idZRICTt0nlnW2WmaR3h+ABFBSeC+QatUYuUkEaSGjoPEyWqEno+KHhdjzS/iLx/PD8pn3ughKhgxlMIPbPPFGSWb3RefHAZQD5DooKZoAKPQlK2YTop1+IXB7UrSXcWPej1VSiR+nykcWTgPZhsTHIrsIkkHZexmj4seu98cMWmXS8/50129lXg7ZsfJssH7eSQTJh5vFj5vvsa8FUH1Tse7vvGeHtoPsIuh46XEca2pA6PP1ONbUACVC+SEy4GToy6+kenZkeEEgESQPbhY/VpDF+wP4XAA5AS8vECCvCDr2aT121zyGna/8CnvfXoydr/wKjc/Mx+EtmxiX0H/Y8f7wEj9WwnHdm7aY3sdLkAgiAPeLH7PlZyF+rhm43tQ59fC9AEp1A2VbIywR2UTQsU/rsX/hq+g+FN8Bd4XasefNRSSCbGAn78ep+X5i8XLycyojbOS5MSOCeD3nJILE4XbxYxQ1z42F6Gc9x55QAVRaWoq0tLS4z4IFC3T36ejowJw5c9C/f3/069cP1157LVpbWx0qsTVY37REZBFBSiSCg4v/rLvNvqVLKBzGmUThYUb8OOH9YYEMtiPVc81aBPGCRJCzfNZ4lifEj9E6dJSFDbXzVOXk0Y8K9wA9/PDD2Lt3b/Rz++23625/9913409/+hPeeustrFy5Env27ME111xjqwxOeIF4hsIAOURQeGtTD89PIl2hNhxvbmRVLN9gVWjwFD966CY/Mwp/yWA7nMQrk/05LQBkwul6y5DzwwJeTgThAignJweFhYXRT9++fTW3bW9vx4svvognn3wS//7v/46xY8fi5ZdfxqpVq/Dxxx/bKocXHkjRIqi7LWToOF2HjW1HnMSM+IkVHrzFj+iJD2WwHSy8QLLgdFm9YHPN4LTXx01LdYh6ToQLoAULFqB///4YPXo0Hn/8cXR1dWluu27dOnR2dqKqqir63YgRIzB48GCsXr1ac79wOIxQKBT3MYsbvECAWBGUkZdr6BiRc/tI4fp3A054fljjVO6PLLbDSRHkxPNN3iC2eCXkxQsRoS8VoQLojjvuwOuvv44VK1bghz/8IR599FH8+Mc/1ty+paUFmZmZyMvLi/t+0KBBaGlp0dyvrq4OwWAw+ikpKUm6ndsTolVEiaDA8FJknKHf8WbkBxEYXgrAeGyYME7beUHT4sdJ7w+r8JdstoN3nl8sTnSm5A2yjwhx58R9c7JOvJ8r5gLo3nvv7ZGcmPj54osvAAA1NTW46KKLMGrUKNxyyy144oknsHDhQoTDbDvF2tpatLe3Rz87d+5kenwzOGUoeT98yR60tPR05N9wpe5++dOvRFp6fLMjIZQcN872bMb7k1ExPO5vL9sOt72VA2JEkFeEkIh6eK2NOdFXMl8LbO7cuZg1a5buNmVlZUm/nzBhArq6utDU1IThw4f3+L2wsBAnTpxAW1tb3Jtca2srCgsLNc8XCAQQCBibcj7VGjYs1gnTg/V6PTzX5FGPG1ve7HEVKLh9Og4u/nNcQnRGfhD5069E9rgKzePFiiC/rxkms/hhUbaMUeXo6o4XK263HXrr/wFsn20nV113ujNXz+e2Dl2UeHPrKD4Z7i9zAVRQUICCggJL+27cuBHp6ekYOHBg0t/Hjh2L3r17Y/ny5bj22msBAFu3bkVzczMmTpxo+nwVpXvwz9aeBpW3CHLSUALOL6KaPa4CfcaMPDkqrC2EjLxcBIaX9vD86OFnMSSz+NEjlfdHDX9ljCpP+rubbIcWqZ5tN5LsRccJYs8nQ2ephUivlVfFj1OREmGrwa9evRpr1qzBxRdfjJycHKxevRp33303brjhBpxxxhkAgN27d+OSSy7B7373O4wfPx7BYBA33XQTampqkJ+fj9zcXNx+++2YOHEivva1r4mqiiWcNpS836oSRVBaejqyypO/rZslMTwmQhAZCdGxKJfs4sdu+bTEjxlktx16z7YbvUAqIrxBKrJ5hWQI1clyLVjjZD6dMAEUCATw+uuv48EHH0Q4HMbQoUNx9913o6amJrpNZ2cntm7dimPHjkW/e+qpp5Ceno5rr70W4XAY1dXV+NWvfmW5HFoPtddCYSq8Q2JOGAZeq2qLzkOSXfzoYST3h4X4AeSxHXp4WQSp5xVB4nnd6gGxgwjh45T3x0nxAwBpiqIojp5RAkKhEILBIC5bOhu9+2bq3lzerrpUXiC3Tn4mi7FwEjsizA3iR6+MVoa+d3WHsbz+MbS3tyM319gUCqJRbcdv149Fdk6G7rZOPduiPAEyPuNuGUlrBS/cZxbiZ/H2Svxl8m+Z2A1hHiCZ0Hsj81o+UCw83coi3eUi8Lr40cPLa37Zwakwt9NeIBXR3qBkyFQWlngh3CVjHYRPhEiIh9fw08qyXVAiEXRsacTR1RvRsaXRk+uA+UH8aJWTxI8+Tkx+CohPxJWxc/MCoq+tU+3KqBOB9QsFCaBT6DUy3hMkyjKdPmshtGflduz78aNoXfBbHHjhDbQu+C121zyGY5/WMzuHaPwsfvzM2/vGGN7WKREkGtGdtZeQ4VrKFvriAQkgg/hFBAFshNCeldvx6f1L0bH/aNz33YfasX/hq54QQTKIHyUSQai1AQeaNiDU2hDnYeMZ9gLI+2MGLyyDYxTRHbebkUH4sEZW8QOQAIojVcPzkwgCTgshs4ZV6Y6g/pm/625z8NU/OxIO4xWCk0H8HNz5OTb+8RFsWf4Ctq96FVuWv4CNf3wEB3d+zkz8kPdHG5bueFlEEKsOSe3IvdaZ84LltZpWuNb2fXQiQd9sGXnk01EStEm8Ojw+FWaGn361aW8Pz08i3QfbEd7axGyuoGQc+7S+54zUZwSRf4P+jNSpsCp+WIqJgzs/x7a/v9Lj+xPH2rHt76+guGgWcspH2ToH61FffsepAQ9Wk6JVuxVrv1h0Ok4nSyuRiK1JWJ2EpehhhYzihxckgBJgYYTsiCAjI0dkGGGV7Pxqg+/4Sl/8qHS39VxZmxXHPq3H/oWv9jznqRBcwe3TTYsgGbw+wEkDv2PdEt1t9i1dgn7DK7gYflbip608F3B5JNTssy67CEpErZtbhBCvlx7WyCh8AHmnZuA1mlJOWSw5RhqJnRtmpFHL6FpWw2W7w6WGts/I4zP3ixKJ4ODiP+tuYzYEJ4v4AYDD+xtx4pi+COkKteF4c6Plc/AOfZldsd5LyPL2m4iezVLDKizKzis8pr70xIofQJ68Q5b1ZnUvYnFK/MjU/n0tgK4ZuD7p90YaqAwiSFYCw0uRcYZ+B5eRH0RgeCmX84e3NvUwgomoIbhUZDUGbIW8eAiJE8cPG9qu67A1DxuFvsxh5TmXNSnaSF1Ydr6sRAGPlx4WsBZ7Rq+9qPXoeIgfnnXxtQCyi2gRJKMXCDi5Dlj+DVfqbpM//UpucXmjoTW97ewIH4CvByWzT46h7XrlsPWwMQt9+dj7E4usIsgorL0QdsQCy5ceK6iDLfo3foAz29dg1JBmpvbZzLW20uewaCdu8vyo+F4Aad0Uo42XtwhKhawiSCRGQ2vJtrMrfAD+4aOcgjJkZuuLiF65eegz2HyCOYW+rGH1GZdRBJmti10hpHRHcGDDbux6/184sGE3lO5InBgych1YvPRYobJsFwp2rozOd7b+oWVYdccSLJv6O+xZud328c1eW6+JH96eLN8LIEBuEeTGfCDR7mgrIThWwseJYePtw3thwJVX624zcPIU0x423qEvr4ofu3hBBAHWhNCelduxbOrvsOqOJbriYdSQZpzZvkbTw2LnpccoiaKssmyX5nxnHfuP4tP7l1oWQTxyfJLhZ/ED0CgwZhgZdWF1dJjRkWFqOVhjdlipGXc0j2Hwaggu2SgwFTUEx2IVeSfnylHn98kpH4Xi78zCvqXvoCt0+lr3ys3DwMlTTA+Bp7wf+/Aa/cnq2TY7MsyOvYo9hhaqeEhEFQ/jfjYZxd8ahj0rt6P+mb/HiYysgr6ouPMbqPzWyb+VIb2w7MW+utNv6OUdprouJ71Ue9Hx1VFk9e+L/qOKACDlfGf1z36Eoq8PRVpG6pcR3gtrJyKz+NGjvqmY2bFIAJ1CywCZGZYqWgQB9obRxoqBjrIwAGvDSkW5o2PJHleBgtun9yx7fhCDqq5GTv4owPogqShOiZ9kExvmlI9Cv+EVON7ciK7DIfTKyUWfwWVMc6so78c5nBgi75QIUtEaRm9kstT6Zz+CElGw7oG/9vgtUSQBwJD/cx62vviJ5vFG10xE8dl7TJVf6Y7gX/+zDo1vfYbOUDj6fVZBXwz5P+elnO+sY98RfLVpLwaMPjPp7ywEgtfCXoB2nU6Wu8PycRMhARSD30VQR1k4KoKyGgM4vGUT9r9pfi4du+5oVhOZZY+rQJ8xIxHe2oT0fx1nKhBECp9Y0tLTkV16tq1zUN6PeeqbijH6vAM9vmchGLwmgoCeXiEjk6V27DuCTU+s1N2m/tmPgIiC+oUfaR4va2A/VNzx9ahQMsqeldvx2WMr4oRPtGz7j+qKrbhtk8yLxsoz4kXx4yQkgDjgZhGkokQi2Lf0Hd1tDr3y/9BnzMgegkLNwdELg2XkB5F5zmB0bGmMEzrH1/+TyURmsd6sPoFy4HzDu+oiItzFE8r7YY9XRZBaNrtMK1yLVV1fYZWBbTvb9d/2O/YdwadJPEQq537/Qpw7fQwObm7Frvf/FQ1fpQpJaYXnrJDVvy8A9qLAq+JH3/vDFhJACbDwAgHuFUGqF+h4c2NcbkkyukJtUD7cjT4JHoiOsnDKHJy+E0Zhz3/9Ik7opPfNRuTosR7bpvI4scjjSYXXhA8gXvy0D3P3GAxWsy0nQ0YRBLBb6idvYG/bxzDCl//3c+z442aED5y2K2r+kJZHyEh4zhBpQH5hJm69dDfSM8yF3lLhN/HDC2EW6MMPP0RaWlrSz9q12hfxoosu6rH9LbfcwrRsdkeFqRgdHcZ6IrVYrBjojrKw4Un0km2X1RhAfv5YFH9nFnrlxneCvXLzcMakixH6y997eIiSiZ9YDr3y/xBo6B0dscVi5JYRnAx3+UX82KmnTLZD6xlnYcidmAeM12SJqRgxLgf5hZlAmsYGaUBuvv338872jjjxA6QeoWUkPJeSU/Wa8d+DkZ6hVUlreFX86MFrPithAmjSpEnYu3dv3OcHP/gBhg4dinHjxunuO3v27Lj9HnvsMUtl+M9+hyztZwajN473XEFmjWXk3D6GttObbC+nfBTK7vwpSmb+CEXX3ICSmT/C0Nvvw+H65DNwp8Lu8g5mcWpYu5PCB3C3+AHksB1G8LoIslO/9Iw0zLh/8Mk/EvXBqb9nPThEXyTZpP7Zj6B093wWjK5lqHLtHcUnyxlDfmEm7lp4Ni6szrdVxlisXHN1eSI7GOk/eCZz85zMU1gILDMzE4WFhdG/Ozs78e677+L2229HWpp+i8/Ozo7b1w7X9TuE14+c0eN7VqEwwLir2Wyc3WgoTMVM2Y3k8RiZbC8xSfdYU0PK0JoeVpd3MIoXQ12xOFm/ZLCosyy2Q4VnKAwwFg5Ty2EVdV8nQ2IXVufjroVn43c/a8bBlhPR7/MLMzHjvwfjwup8pKen4enbG06KIMXSaTTRGqGl5uwYIb8oE1N+dCam/OhMfPHpYbTt60TewN4YMS6HqefH614fUUt3SBOE/+Mf/4ivvvoKN954Y8ptX331VQwYMAAVFRWora3FsWP6oZNwOIxQKBT3MQKrUBhgrjGaaQxmG6DRshtZzsLKZHt2BQzr5R1UnPb2iBA/qeDt/eFVZxlsB89QGODchKhOe4MurM7HMx9W4r8Xj8CcJ4fhvxePwDMrKqOeE1Uk5Q/KTHEka4zu+jxuoddphWtx66W7jXme0k6HuNIz0jByQi4mXdUfIyfkMhM/Vq+tm8SPHjy9P4BEAujFF19EdXU1zjpLv8Lf/e53sXjxYqxYsQK1tbX4n//5H9xwww26+9TV1SEYDEY/JSUlcb9fZyEUZlUEmQmJGW34VkSQkfKrc+kkzqrcKzcPxd+ZZXqyPcCegLG6vIMeXg1zJUNk6Itn3UXaDiOwFEGyhsQA6/VMJR5iRdKPflGGnDPYBS6SJWPrhudO0S+vF/MQVyx2RKVbQl4qIkJfKmmKojB1LN577734+c9/rrvNli1bMGLEiOjfu3btwpAhQ/Dmm2/i2muvNXW+Dz74AJdccgkaGhowbFjyrP5wOIxw+PRcDqFQCCUlJTj0rzLk5mREv08WCgP43CAzhkr0IniJ8/IEew+3PJeOEomg8Zn5lsJgVkVXMpwSPbIgo/jpDnegYcF9aG9vR25urqtsR8kL85DeJ6vH9lrPtYgh0Cw6Qqvw8g78c00Ij9zwhf0DnRqh9cyKSk1vzdq/HuwRnuubl4HJMwZhyo/OZJ7crCJK+ADG7rkT4gfQrk/keAd23vJQ1G7YgbkA2r9/P7766ivdbcrKypCZedqlOX/+fCxcuBC7d+9G797mhkcePXoU/fr1w9KlS1FdXW1on1AohGAwaFgAAeJFEJC64dl507RSD6sjsA5v2YQ9by7S/D29TzYix0+HJqwu76AFT/Ejk+hRkVH8AD0FkJtsh5YAArwlggC5hNCqP32F52psLjJ6SrcY8eBEuhWuuT2xOG2/E3Fa/ADW+lWWAoh5EnRBQQEKCgoMb68oCl5++WXMmDHDtAEDgI0bNwIAioqKTO+biFZCNMA2KVqF9WRkZpOiY7E6ZxBgXgilWseK5/IOqhhQIhEc3t+IE8cPI7NPDnIKrJ1DRsETi6ziJxluth1GYDWHjopTawTaSfJmOYEiwGb+oNgk61So4Tme2A2ROuHp4+HRExn6UmHuATLL8uXLUVVV1cO1DQC7d+/GJZdcgt/97ncYP348tm/fjtdeew2XX345+vfvj02bNuHuu+/GWWedhZUr9adMj0XLA6TitCcIsP6WldgwWeQbsFhLzAhKJMJ1HSstIh98hh3rluDEsdMCIDM7iCFjpyC/RHvKaNnFTiKyi59ED5BZRNoOPQ8QoP88O9mZxCLaGwTYr3ukW8GdF32Gg60njI8KSwNyz+iF6fcNRn5hJlcvjhlECx9APvEDpK6X1B4gs7z44ouYNGlSDwMGnBzeunXr1uhIjczMTLz//vt4+umncfToUZSUlODaa6/F/fff73Sxe2B3Zlarb1k8hg/aWUsMMC6E0tLT0WdwWVQEHW9u5C6CDm/ZhD1/f6XH9yeOtWPb319hmmckilQhPhnEDwtkth16zzNrTxDgDm8QEG+vrFwDNUHZ8ND4Uzrn+w+XcktYNgMre+3UC7ds4oc1wj1AIkjlAQLEeIFUeM4pYha7ddITQ4e3bEoSBgti4OSruYgQIwnYvXLzUHbn/Y54onjAW/ywnOTQrgdIBEY9QCpO5QOpGO1gZfAGqVi5FskSlPvl9QKg4Ehbd/S7/CLj4S6ekPA5CQvxI3UStBswIoAAEkGxsBZCqRKheXhijjU1YOcrv0q5XcnMH9leYT0VqrcsETtLe7hJ/AD+FkAA33lU3CiEAHPXJFmCMgDHkpZTwdI77+R9kln8AB4LgcmM00nRsVidmZUXdt3nsR1+oKF3ypXm9y1dgn7DK5h6Yuysb2YXLcHDCreJH7/gdChMxcyCyYDYsFgsiWXWuz5aCcq8k5a14JGO4AXhkwqnw16xkACyAW8RBPCfZt8sLAxme+dWQyvNH29uZOqJMToJI4vZpnkLnljcKn6C28Uuy+EUIkWQeo5U2H2ueb2wmRFETsN7+QYnB9bwvq6ilrpIha8F0B+OnIHv5+i/7et5gQDnRJB6TFmwYzC728R4YvoMLkOv3GDKHCArs02zEDxWwl9uFT952yLosrSnOxElggBz02PIKoRUktXDa523l4QPwD7puaJ0D3baKVAMvhZAwMk8n1RLYcggggD5vEGANYOZkWfMwxI5tw86ysK28mJiSUtPx8DJV+vmHqVa34yXZ8dsHY1M5iiz+PEjokWQeh4jyC6EYpHVu2AWp6dScaP4qSzbhc6jdkoUj+8FEGBMBKXCSRGkHlcmYsuTqr5GVprPyA8iMLwUgL7oYD0JY075KEfDV4B84ieV8AEo56eidA/+2WreUyhSBAHeFkJuREQSulNhRB7ihzUkgE6RSgSl8gLpwVoEAXIbllRGU11pfv/CVzWPkT/9SkMJ0FbESu+y4Si+7Mdx65sFhpciLT0dHSDxo/u7TeHjJe8Pi3y4RJwQQYD5WeNZCaHYY/kZLwsfwB3iB/D5MPjfrh+L7CTD4PWEUCoRJGqSJ9mNSrK6H/u0HgcX/znOE5SRH0T+9CuRPa7CyeI5Dg/hA7hL/HR1dmDdW/e7chj8ZUtno3ff02uSmX22nVxtOxUiVx33E6KmHHA6cZy3+Ok8egJ/mfxbmgfIKqkEEKAtgox4gUTOdOkGoxJ7DRJXmlc9MV7GbV4fgI/nx0sCCHC3CALkX4XcjYi+NjK1IVaeHxJANjEigAD3iiDAPQZF5BwQIjAjfmTw+gBs8n2S1aXfpn1YXv+YZwQQ4H4RBIhflRxwj/1KhgzXQMZ2w+rZuKrvx5g9Zh1NhMgbVehYSZDWi7HzyAlKxC0xdzPJ025GtpCXk4nOXsr7SYXZZzvVyE6ncoJiMZsoHQur59kt9gtga7dELzZrFafEz7TCtTh22NShdPG1B+iypbNxw7DPDO+nCiEzydCiPUGxyG5IYvGKGOIlfAB3i5+8ze3o6g671gOkeo9ZLYkjoydIRZZ1rGIRactkq4foySGdFD8AcOxwNzMPkO8FUO++mULnQxDV0ZMY4guPSQ2j23EUPoAz4geAJwQQwO7ZllkEAezn2+H1XLOwbTKXTUV0ewCcFz8ACSDbaMXxRc4oKrKTd4sYcosQks3r47TwAVKLH8A7AgjwjwhSkXXdK9ngYVvd0gbM3E8ziwaTALKJXiKjX0WQiuxiSIZrpAVP4QOYFz9GhQ/gvPgBvCWAAP+JIMCZWZhlfuZVeNtNt91zXuIHYCuAKAk6AZHr8jiRHJ2KxPPLLohEwzPUFbcPJ/HDekZno+LHi+jNBg8Y7xR4J0YnDuqwOsErEN9B8RJDRmyQV+dYY9UXxd5zO/dbtPhhDXmAkgxlBdznCVI7Yt7LOHgl+dAuVtcn86rXBzAvfsKfbcKHeNczHiAVVs+2kWfNip2yM9GrUbyyPpcIeIieWKzeYxnEz3X9DiF0uBtnnNvIxG5wm3HukUcewaRJk5CdnY28vLyk2zQ3N+OKK65AdnY2Bg4ciHvuuQddXfprRB88eBDTp09Hbm4u8vLycNNNN+HIkSOWyljfVKz5G+8HWK+RV5btsiw0shoDcR/WfNZ4VtIPL5w4hxmsXte8bRFu4qftvGD0Y2j7c9KFi5/uTVs0j+UG2/GfOiIi1bNtFCNt3oqd0usAr+t3yPa6iMDJa6B+iNSwul7q/WNxD2NhKX5S9W964oc13EJgJ06cwNSpUzFx4kS8+OKLPX7v7u7GFVdcgcLCQqxatQp79+7FjBkz0Lt3bzz66KOax50+fTr27t2LZcuWobOzEzfeeCNuvvlmvPbaa5bKKXqFZr2GZSYkprVqeux3PL1DsggUHtgRklbnwDEifsx4ewDnFzC1In4A99gOvWkxUoW6AWPPTKpwGGDNThlZ+zB2Wzsklo28Q+wjDEbFgdl7KYPXB+AjfgAHQmCLFi3CXXfdhba2trjv//KXv+DKK6/Enj17MGjQIADACy+8gJ/85CfYv38/MjN7hqa2bNmCkSNHYu3atRg3bhwAYOnSpbj88suxa9cuFBdre3RiUd3YJS/MQ3qfLADiY5Gs3OZmOmunVz13EyJED8BH+AB8xY9V70+X0qkbApPZdhz6VxlyY0JgWh0Lq5EyRj1HZm2V2Y6FVYhMxS9iiEcfwvveySp+WIbAhCVBr169Gueff37UgAFAdXU1br31VmzevBmjR49Ouk9eXl7UgAFAVVUV0tPTsWbNGlx99dVJzxUOhxEOn+7s29tPGubI8dPfbdg8ABWle5Luv3h7JQDgmoHrTdTQHFf1/Rhv7xuT9LeRgxoB6IfsVI4VdQAAAk2pO/DeCS/i4VJ/C6LYa9aNDtP7B7efFAL6gRht8raENPdtK4950DuNl6192Cnhw+nWBrdHkpZZqy7d9Vuj/+9CJwDA7DuYDLYjdCRe9F2OA/hDkg7mqr4fA4Dus23kud6weQAAaNoolcXbK03ZqZcOn2xXeiG9WC7Hgej/k9XXLOr1iUXrWrmJxHvAavbi2PsUMnjM0/ep2/B5Tt6DE7rbnGy3qW2R2mY7jyb//ZqB6zWvz3/2O9Sjnuqzx8J3I0wAtbS0xBkwANG/W1paNPcZOHBg3He9evVCfn6+5j4AUFdXh4ceeqjH97vvXhD3984UZf5Lit/ts477GQiXUi+6APz46quvEAwa92rJYDuGjGkyXN6TsHm2U9kowJqdmm1hH3643w7y6iucu0/s7oGdflWvvmbtRjJMCaB7770XP//5z3W32bJlC0aMGGGrUKypra1FTU1N9O+2tjYMGTIEzc3Nti+gjIRCIZSUlGDnzp2uGV1jFq/X0Wv1mzdvHp5++umkvw0bNgwA2Q4Z8Fq7S8Tr9QO8X8f29nYMHjwY+fn5to9lSgDNnTsXs2bN0t2mrKzM0LEKCwvxySefxH3X2toa/U1rn3379sV919XVhYMHD2ruAwCBQACBQM+QUDAY9GQDUcnNzfV0/QDv19Er9bvvvvvwwx/+MO67I0eO4MILL8TatWvRr18/sh0S4ZV2p4XX6wd4v47p6fbzGk0JoIKCAhQUFNg+KQBMnDgRjzzyCPbt2xd1TS9btgy5ubkYOXKk5j5tbW1Yt24dxo4dCwD44IMPEIlEMGHCBCblIgiCPclsRygUAgCce+65pgw12Q6CIFjAbWhIc3MzNm7ciObmZnR3d2Pjxo3YuHFjdN6NSy+9FCNHjsT3vvc9fPbZZ/jrX/+K+++/H3PmzIm+cX3yyScYMWIEdu/eDQAoLy/H5MmTMXv2bHzyySf4xz/+gdtuuw3XXXed4VEcBEHIDdkOgiAcQeHEzJkzFQA9PitWrIhu09TUpFx22WVKnz59lAEDBihz585VOjs7o7+vWLFCAaB8+eWX0e+++uor5frrr1f69eun5ObmKjfeeKNy+PBhU2Xr6OhQ5s2bp3R0dNitppR4vX6K4v06er1+iqJdR7Id4qD6uR+v15Fl/Xy5FAZBEARBEP7G2alhCYIgCIIgJIAEEEEQBEEQvoMEEEEQBEEQvoMEEEEQBEEQvoMEEEEQBEEQvsN3AuiRRx7BpEmTkJ2djby8vKTbNDc344orrkB2djYGDhyIe+65B11dVpe4FE9paSnS0tLiPgsWLEi9o6Q899xzKC0tRVZWFiZMmNBjVmA38+CDD/a4V7ItD2GGv/3tb7jqqqtQXFyMtLQ0LFmyJO53RVHwwAMPoKioCH369EFVVRW2bdsmprA6kN1wv90AvGs7vGY3AGdsh+8E0IkTJzB16lTceuutSX/v7u7GFVdcgRMnTmDVqlV45ZVXsGjRIjzwwAMOl5QtDz/8MPbu3Rv93H777aKLZIk33ngDNTU1mDdvHtavX4/KykpUV1f3WObAzZx33nlx9+qjjz4SXSTLHD16FJWVlXjuueeS/v7YY4/h2WefxQsvvIA1a9agb9++qK6uRkeH8RXvnYDshrvtBuB92+EluwE4ZDtszyTkUl5++WUlGAz2+P69995T0tPTlZaWluh3zz//vJKbm6uEw2EHS8iOIUOGKE899ZToYjBh/Pjxypw5c6J/d3d3K8XFxUpdXZ3AUrFj3rx5SmVlpehicAGA8s4770T/jkQiSmFhofL4449Hv2tra1MCgYDy+9//XkAJU0N2w7142XZ42W4oCj/b4TsPUCpWr16N888/H4MGDYp+V11djVAohM2bNwssmT0WLFiA/v37Y/To0Xj88cdd6Zo/ceIE1q1bh6qqquh36enpqKqqwurVqwWWjC3btm1DcXExysrKMH36dDQ3N4suEhe+/PJLtLS0xN3PYDCICRMmuO5+kt2QGz/YDr/YDYCd7TC1GKofaGlpiTNiAKJ/t7S0iCiSbe644w6MGTMG+fn5WLVqFWpra7F37148+eSTootmigMHDqC7uzvp/fniiy8ElYotEyZMwKJFizB8+HDs3bsXDz30EL7xjW+gvr4eOTk5oovHFPV5SnY/3faskd2QG6/bDj/ZDYCd7fCEB+jee+/tkQCW+PFCI4/FTJ1rampw0UUXYdSoUbjlllvwxBNPYOHChQiHw4JrQSRy2WWXYerUqRg1ahSqq6vx3nvvoa2tDW+++aboonkOshtkN7wC2Q1reMIDNHfuXMyaNUt3m7KyMkPHKiws7DEyoLW1NfqbLNip84QJE9DV1YWmpiYMHz6cQ+n4MGDAAGRkZETvh0pra6tU94YleXl5OPfcc9HQ0CC6KMxR71lrayuKioqi37e2tuKCCy7gfn6yG8nxmt0A/Gc7vGw3AHa2wxMCqKCgAAUFBUyONXHiRDzyyCPYt28fBg4cCABYtmwZcnNzMXLkSCbnYIGdOm/cuBHp6enR+rmFzMxMjB07FsuXL8eUKVMAAJFIBMuXL8dtt90mtnCcOHLkCLZv347vfe97oovCnKFDh6KwsBDLly+PGq1QKIQ1a9ZojrZiCdkNc7jVbgD+sx1ethsAQ9vBMlPbDezYsUPZsGGD8tBDDyn9+vVTNmzYoGzYsEE5fPiwoiiK0tXVpVRUVCiXXnqpsnHjRmXp0qVKQUGBUltbK7jk1li1apXy1FNPKRs3blS2b9+uLF68WCkoKFBmzJghumiWeP3115VAIKAsWrRI+ec//6ncfPPNSl5eXtzoGzczd+5c5cMPP1S+/PJL5R//+IdSVVWlDBgwQNm3b5/oolni8OHD0WcMgPLkk08qGzZsUHbs2KEoiqIsWLBAycvLU959911l06ZNyre//W1l6NChyvHjxwWXPB6yG+62G4ribdvhNbuhKM7YDt8JoJkzZyoAenxWrFgR3aapqUm57LLLlD59+igDBgxQ5s6dq3R2doortA3WrVunTJgwQQkGg0pWVpZSXl6uPProo0pHR4foollm4cKFyuDBg5XMzExl/Pjxyscffyy6SMyYNm2aUlRUpGRmZipnnnmmMm3aNKWhoUF0sSyzYsWKpM/bzJkzFUU5OZz1pz/9qTJo0CAlEAgol1xyibJ161axhU4C2Q332w1F8a7t8JrdUBRnbEeaoiiKXXcUQRAEQRCEm/DEKDCCIAiCIAgzkAAiCIIgCMJ3kAAiCIIgCMJ3kAAiCIIgCMJ3kAAiCIIgCMJ3kAAiCIIgCMJ3kAAiCIIgCMJ3kAAiCIIgCMJ3kAAiCIIgCMJ3kAAiCIIgCMJ3kAAiCIIgCMJ3/H/LbUFSSUhrewAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -407,16 +413,16 @@
      "text": [
       "|   iter    |  target   |  kernel   |  log10_C  |\n",
       "-------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.1446  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.5930859\u001b[39m |\n",
-      "| \u001b[39m2        \u001b[39m | \u001b[39m-0.1474  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.4639878\u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m-0.139   \u001b[39m | \u001b[35mpoly3    \u001b[39m | \u001b[35m0.9998496\u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.139   \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.9994641\u001b[39m |\n",
-      "| \u001b[35m5        \u001b[39m | \u001b[35m-0.1072  \u001b[39m | \u001b[35mrbf      \u001b[39m | \u001b[35m0.9996179\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m-0.1607  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m-0.805461\u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.1372  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.0678008\u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.1547  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.1300376\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.1257  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m-0.162077\u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.1634  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m-0.999886\u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.2361  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9943696\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-0.2864  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m-0.999771\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m-0.2149  \u001b[39m | \u001b[35mrbf      \u001b[39m | \u001b[35m1.0      \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.236   \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9997250\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.2532  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.9998403\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-0.2532  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.2788  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.3175170\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.2229  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m0.7279032\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.2928  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m-1.0     \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.295   \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m-0.508421\u001b[39m |\n",
       "=================================================\n"
      ]
     }
@@ -430,7 +436,7 @@
     "\n",
     "data = load_breast_cancer()\n",
     "X_train, y_train = data['data'], data['target']\n",
-    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)\n",
     "kernels = ['rbf', 'poly']\n",
     "\n",
     "def f_target(kernel, log10_C):\n",
@@ -445,7 +451,7 @@
     "\n",
     "    C = 10**log10_C\n",
     "\n",
-    "    model = SVC(C=C, kernel=kernel, degree=degree, probability=True, random_state=42)\n",
+    "    model = SVC(C=C, kernel=kernel, degree=degree, probability=True, random_state=1)\n",
     "    model.fit(X_train, y_train)\n",
     "\n",
     "    # Package looks for maximum, so we return -1 * log_loss\n",
@@ -461,12 +467,13 @@
     "optimizer = BayesianOptimization(\n",
     "    f_target,\n",
     "    params_svm,\n",
-    "    #acquisition_function=acquisition.ExpectedImprovement(1e-2, random_state=42),\n",
-    "    random_state=42,\n",
+    "    #acquisition_function=acquisition.ExpectedImprovement(1e-2, random_state=1),\n",
+    "    random_state=1,\n",
     "    verbose=2\n",
     ")\n",
-    "discrete_optimizer.set_gp_params(alpha=1e-3)\n",
     "\n",
+    "kernel = Matern(nu=2.5, length_scale=np.ones(optimizer.space.dim))\n",
+    "discrete_optimizer.set_gp_params(kernel=kernel)\n",
     "optimizer.maximize(init_points=2, n_iter=8)"
    ]
   },
diff --git a/scripts/format.sh b/scripts/format.sh
index 3f29d03e9..bff192c01 100755
--- a/scripts/format.sh
+++ b/scripts/format.sh
@@ -2,5 +2,5 @@
 set -ex
 
 poetry run ruff format bayes_opt tests
-poetry run ruff check bayes_opt tests --fix
+poetry run ruff check bayes_opt --fix
 
diff --git a/tests/test_acquisition.py b/tests/test_acquisition.py
index f0a7efde2..1191976df 100644
--- a/tests/test_acquisition.py
+++ b/tests/test_acquisition.py
@@ -114,7 +114,7 @@ def fun(x):
         except IndexError:
             return np.nan
 
-    _, min_acq_l = acq._l_bfgs_b_minimize(fun, space=target_space, n_x_seeds=1)
+    _, min_acq_l = acq._l_bfgs_b_minimize(fun, space=target_space, x_seeds=np.array([[2.5, 0.5]]))
     assert min_acq_l == np.inf
 
 
diff --git a/tests/test_bayesian_optimization.py b/tests/test_bayesian_optimization.py
index d035f8b4e..5c13a6703 100644
--- a/tests/test_bayesian_optimization.py
+++ b/tests/test_bayesian_optimization.py
@@ -38,7 +38,7 @@ def test_register():
     assert len(optimizer.res) == 1
     assert len(optimizer.space) == 1
 
-    optimizer.space.register(params={"p1": 5, "p2": 4}, target=9)
+    optimizer.space.register(params=np.array([5, 4]), target=9)
     assert len(optimizer.res) == 2
     assert len(optimizer.space) == 2
 
@@ -196,12 +196,12 @@ def test_set_bounds():
     # Ignore unknown keys
     optimizer.set_bounds({"other": (7, 8)})
     assert all(optimizer.space.bounds[:, 0] == np.array([0, 0, 0, 0]))
-    assert all(optimizer.space.bounds[:, 1] == np.array([1, 2, 3, 4]))
+    assert all(optimizer.space.bounds[:, 1] == np.array([1, 3, 2, 4]))
 
     # Update bounds accordingly
     optimizer.set_bounds({"p2": (1, 8)})
-    assert all(optimizer.space.bounds[:, 0] == np.array([0, 1, 0, 0]))
-    assert all(optimizer.space.bounds[:, 1] == np.array([1, 8, 3, 4]))
+    assert all(optimizer.space.bounds[:, 0] == np.array([0, 0, 1, 0]))
+    assert all(optimizer.space.bounds[:, 1] == np.array([1, 3, 8, 4]))
 
 
 def test_set_gp_params():
diff --git a/tests/test_target_space.py b/tests/test_target_space.py
index e949b19cf..b2e1af801 100644
--- a/tests/test_target_space.py
+++ b/tests/test_target_space.py
@@ -22,9 +22,9 @@ def test_keys_and_bounds_in_same_order():
 
     assert space.dim == len(pbounds)
     assert space.empty
-    assert space.keys == ["p1", "p2", "p3", "p4"]
+    assert space.keys == ["p1", "p3", "p2", "p4"]
     assert all(space.bounds[:, 0] == np.array([0, 0, 0, 0]))
-    assert all(space.bounds[:, 1] == np.array([1, 2, 3, 4]))
+    assert all(space.bounds[:, 1] == np.array([1, 3, 2, 4]))
 
 
 def test_params_to_array():
@@ -80,12 +80,18 @@ def test_register():
     assert all(space.params[0] == np.array([1, 2]))
     assert all(space.target == np.array([3]))
 
-    # registering with array
-    space.register(params={"p1": 5, "p2": 4}, target=9)
+    # registering with dict out of order
+    space.register(params={"p2": 4, "p1": 5}, target=9)
     assert len(space) == 2
     assert all(space.params[1] == np.array([5, 4]))
     assert all(space.target == np.array([3, 9]))
 
+    # registering with array
+    space.register(params=np.array([0, 1]), target=1)
+    assert len(space) == 3
+    assert all(space.params[2] == np.array([0, 1]))
+    assert all(space.target == np.array([3, 9, 1]))
+
     with pytest.raises(NotUniqueError):
         space.register(params={"p1": 1, "p2": 2}, target=3)
     with pytest.raises(NotUniqueError):
@@ -274,12 +280,12 @@ def test_set_bounds():
     # Ignore unknown keys
     space.set_bounds({"other": (7, 8)})
     assert all(space.bounds[:, 0] == np.array([0, 0, 0, 0]))
-    assert all(space.bounds[:, 1] == np.array([1, 2, 3, 4]))
+    assert all(space.bounds[:, 1] == np.array([1, 3, 2, 4]))
 
     # Update bounds accordingly
     space.set_bounds({"p2": (1, 8)})
-    assert all(space.bounds[:, 0] == np.array([0, 1, 0, 0]))
-    assert all(space.bounds[:, 1] == np.array([1, 8, 3, 4]))
+    assert all(space.bounds[:, 0] == np.array([0, 0, 1, 0]))
+    assert all(space.bounds[:, 1] == np.array([1, 3, 8, 4]))
 
 
 def test_no_target_func():

From b97c11e1a435e142302cd6e651ceaa2acb008ce4 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Tue, 29 Oct 2024 21:11:10 +0100
Subject: [PATCH 13/21] Go back to `wrap_kernel`

---
 bayes_opt/bayesian_optimization.py |  8 ++-
 bayes_opt/constraint.py            |  4 +-
 bayes_opt/parameter.py             | 87 +++++++++++++-----------------
 3 files changed, 41 insertions(+), 58 deletions(-)

diff --git a/bayes_opt/bayesian_optimization.py b/bayes_opt/bayesian_optimization.py
index b887f5b19..1beb8ae43 100644
--- a/bayes_opt/bayesian_optimization.py
+++ b/bayes_opt/bayesian_optimization.py
@@ -17,7 +17,7 @@
 from bayes_opt.domain_reduction import DomainTransformer
 from bayes_opt.event import DEFAULT_EVENTS, Events
 from bayes_opt.logger import _get_default_logger
-from bayes_opt.parameter import WrappedKernel
+from bayes_opt.parameter import wrap_kernel
 from bayes_opt.target_space import TargetSpace
 from bayes_opt.util import ensure_rng
 
@@ -153,7 +153,7 @@ def __init__(
 
         # Internal GP regressor
         self._gp = GaussianProcessRegressor(
-            kernel=WrappedKernel(Matern(nu=2.5), transform=self._space.kernel_transform),
+            kernel=wrap_kernel(Matern(nu=2.5), transform=self._space.kernel_transform),
             alpha=1e-6,
             normalize_y=True,
             n_restarts_optimizer=5,
@@ -330,7 +330,5 @@ def set_bounds(self, new_bounds: BoundsMapping) -> None:
     def set_gp_params(self, **params: Any) -> None:
         """Set parameters of the internal Gaussian Process Regressor."""
         if "kernel" in params:
-            params["kernel"] = WrappedKernel(
-                base_kernel=params["kernel"], transform=self._space.kernel_transform
-            )
+            params["kernel"] = wrap_kernel(kernel=params["kernel"], transform=self._space.kernel_transform)
         self._gp.set_params(**params)
diff --git a/bayes_opt/constraint.py b/bayes_opt/constraint.py
index f643d47fe..120169bdb 100644
--- a/bayes_opt/constraint.py
+++ b/bayes_opt/constraint.py
@@ -9,7 +9,7 @@
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import Matern
 
-from bayes_opt.parameter import WrappedKernel
+from bayes_opt.parameter import wrap_kernel
 
 if TYPE_CHECKING:
     from collections.abc import Callable
@@ -71,7 +71,7 @@ def __init__(
 
         self._model = [
             GaussianProcessRegressor(
-                kernel=WrappedKernel(Matern(nu=2.5), transform) if transform is not None else Matern(nu=2.5),
+                kernel=wrap_kernel(Matern(nu=2.5), transform) if transform is not None else Matern(nu=2.5),
                 alpha=1e-6,
                 normalize_y=True,
                 n_restarts_optimizer=5,
diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index 836a72799..bb79d6854 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -4,6 +4,7 @@
 
 import abc
 from collections.abc import Sequence
+from inspect import signature
 from numbers import Number
 from typing import TYPE_CHECKING, Any, Callable, Union
 
@@ -458,69 +459,53 @@ def dim(self) -> int:
         return len(self.categories)
 
 
-class WrappedKernel(kernels.Kernel):
-    """Wrap a kernel with a parameter transformation.
-
-    The transform function is applied to the input before passing it to the base kernel.
+def wrap_kernel(kernel: kernels.Kernel, transform: Callable[[Any], Any]) -> kernels.Kernel:
+    """Wrap a kernel to transform input data before passing it to the kernel.
 
     Parameters
     ----------
-    base_kernel : kernels.Kernel
+    kernel : kernels.Kernel
+        The kernel to wrap.
 
-    transform : Callable[[Any], Any]
-    """
+    transform : Callable
+        The transformation function to apply to the input data.
 
-    def __init__(self, base_kernel: kernels.Kernel, transform: Callable[[Any], Any]) -> None:
-        super().__init__()
-        self.base_kernel = base_kernel
-        self.transform = transform
+    Returns
+    -------
+    kernels.Kernel
+        The wrapped kernel.
 
-    def __call__(self, X: NDArray[Float], Y: NDArray[Float] = None, eval_gradient: bool = False) -> Any:
-        """Return the kernel k(X, Y) and optionally its gradient after applying the transform.
+    Notes
+    -----
+    See https://arxiv.org/abs/1805.03463 for more information.
+    """
+    kernel_type = type(kernel)
 
-        For details, see the documentation of the base kernel.
+    class WrappedKernel(kernel_type):
+        @copy_signature(getattr(kernel_type.__init__, "deprecated_original", kernel_type.__init__))
+        def __init__(self, **kwargs: Any) -> None:
+            super().__init__(**kwargs)
 
-        Parameters
-        ----------
-        X :  ndarray of shape (n_samples_X, n_features)
-            Left argument of the returned kernel k(X, Y).
+        def __call__(self, X: Any, Y: Any = None, eval_gradient: bool = False) -> Any:
+            X = transform(X)
+            Y = transform(Y) if Y is not None else None
+            return super().__call__(X, Y, eval_gradient)
 
-        Y : ndarray of shape (n_samples_Y, n_features), default=None
-            Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated.
+        def __reduce__(self) -> str | tuple[Any, ...]:
+            return (wrap_kernel, (kernel, transform))
 
-        eval_gradient : bool, default=False
-            Determines whether the gradient with respect to the kernel hyperparameter is calculated.
+    return WrappedKernel(**kernel.get_params())
 
-        Returns
-        -------
-        K : ndarray of shape (n_samples_X, n_samples_Y)
-
-        K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims)
-        """
-        X = self.transform(X)
-        Y = self.transform(Y) if Y is not None else None
-        return self.base_kernel(X, Y, eval_gradient)
 
-    def is_stationary(self):
-        """Return whether the kernel is stationary."""
-        return self.base_kernel.is_stationary()
-
-    def diag(self, X: NDArray[Float]) -> NDArray[Float]:
-        """Return the diagonal of k(X, X).
-
-        This method allows for more efficient calculations than calling
-        np.diag(self(X)).
+def copy_signature(source_fct: Callable[..., Any]) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
+    """Clones a signature from a source function to a target function.
 
+    via
+    https://stackoverflow.com/a/58989918/
+    """
 
-        Parameters
-        ----------
-        X : array-like of shape (n_samples,)
-            Left argument of the returned kernel k(X, Y)
+    def copy(target_fct: Callable[..., Any]) -> Callable[..., Any]:
+        target_fct.__signature__ = signature(source_fct)
+        return target_fct
 
-        Returns
-        -------
-        K_diag : ndarray of shape (n_samples_X,)
-            Diagonal of kernel k(X, X)
-        """
-        X = self.transform(X)
-        return self.base_kernel.diag(X)
+    return copy

From 9543fb893a078bf5406356f78ab829c957c7891e Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Wed, 30 Oct 2024 13:06:22 +0100
Subject: [PATCH 14/21] Update code

---
 bayes_opt/parameter.py              |   5 --
 bayes_opt/target_space.py           |  26 +++----
 examples/parameter_types.ipynb      | 114 ++++++++++++++--------------
 ruff.toml                           |   3 +
 tests/test_acceptance.py            |  69 -----------------
 tests/test_bayesian_optimization.py |   8 --
 tests/test_observer.py              |  10 ---
 tests/test_parameter.py             |  63 ++++++++++++++-
 tests/test_seq_domain_red.py        |   8 --
 tests/test_target_space.py          |  21 +++--
 tests/test_util.py                  |   8 --
 11 files changed, 150 insertions(+), 185 deletions(-)
 delete mode 100644 tests/test_acceptance.py

diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index bb79d6854..0f69dc785 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -195,11 +195,6 @@ def to_param(self, value: float | NDArray[Float]) -> float:
         Any
             The canonical representation of the parameter.
         """
-        if isinstance(value, np.ndarray) and value.size != 1:
-            msg = "FloatParameter value should be scalar"
-            raise ValueError(msg)
-        if isinstance(value, (int, float)):
-            return value
         return value.flatten()[0]
 
     def to_string(self, value: float, str_len: int) -> str:
diff --git a/bayes_opt/target_space.py b/bayes_opt/target_space.py
index edc2f5f53..f101ae397 100644
--- a/bayes_opt/target_space.py
+++ b/bayes_opt/target_space.py
@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+from copy import deepcopy
 from typing import TYPE_CHECKING, Any
 from warnings import warn
 
@@ -125,9 +126,6 @@ def __len__(self) -> int:
         -------
         int
         """
-        if len(self._params) != len(self._target):
-            error_msg = "The number of parameters and targets do not match."
-            raise ValueError(error_msg)
         return len(self._target)
 
     @property
@@ -404,10 +402,8 @@ def _as_array(self, x: Any) -> NDArray[Float]:
 
         x = x.ravel()
         if x.size != self.dim:
-            error_msg = (
-                f"Size of array ({len(x)}) is different than the " f"expected number of ({len(self.dim)})."
-            )
-            raise ValueError(error_msg)
+            msg = f"Size of array ({len(x)}) is different than the expected number of ({self.dim})."
+            raise ValueError(msg)
         return x
 
     def register(
@@ -683,13 +679,10 @@ def set_bounds(self, new_bounds: BoundsMapping) -> None:
         """
         new_params_config = self.make_params(new_bounds)
 
+        dims = 0
+        params_config = deepcopy(self._params_config)
         for key in self.keys:
             if key in new_bounds:
-                if isinstance(self._params_config[key], CategoricalParameter) and set(
-                    self._params_config[key].domain
-                ) == set(new_bounds[key]):
-                    msg = "Changing bounds of categorical parameters is not supported"
-                    raise NotImplementedError(msg)
                 if not isinstance(new_params_config[key], type(self._params_config[key])):
                     msg = (
                         f"Parameter type {type(new_params_config[key])} of"
@@ -697,5 +690,12 @@ def set_bounds(self, new_bounds: BoundsMapping) -> None:
                         f" {type(self._params_config[key])} of old bounds"
                     )
                     raise ValueError(msg)
-                self._params_config[key] = new_params_config[key]
+                params_config[key] = new_params_config[key]
+            dims = dims + params_config[key].dim
+        if dims != self.dim:
+            msg = (
+                f"Dimensions of new bounds ({dims}) does not match" f" dimensions of old bounds ({self.dim})."
+            )
+            raise ValueError(msg)
+        self._params_config = params_config
         self._bounds = self.calculate_bounds()
diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
index 2c9903e49..29aa62feb 100644
--- a/examples/parameter_types.ipynb
+++ b/examples/parameter_types.ipynb
@@ -21,7 +21,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -153,21 +153,21 @@
       "-------------------------------------------------\n",
       "| \u001b[39m1        \u001b[39m | \u001b[39m0.03061  \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m2.2032449\u001b[39m |\n",
       "| \u001b[39m2        \u001b[39m | \u001b[39m-0.6535  \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m-1.976674\u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m0.2576   \u001b[39m | \u001b[35m0.1670635\u001b[39m | \u001b[35m3.0624516\u001b[39m |\n",
-      "| \u001b[35m4        \u001b[39m | \u001b[35m0.4804   \u001b[39m | \u001b[35m1.1137325\u001b[39m | \u001b[35m2.1605226\u001b[39m |\n",
-      "| \u001b[35m5        \u001b[39m | \u001b[35m1.379    \u001b[39m | \u001b[35m1.8758251\u001b[39m | \u001b[35m3.1958494\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m0.3687   \u001b[39m | \u001b[39m2.7452076\u001b[39m | \u001b[39m3.6194246\u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m1.015    \u001b[39m | \u001b[39m1.4178941\u001b[39m | \u001b[39m3.9354649\u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m0.1912   \u001b[39m | \u001b[39m2.4250498\u001b[39m | \u001b[39m2.2123493\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m1.32     \u001b[39m | \u001b[39m1.4321645\u001b[39m | \u001b[39m3.1560306\u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.784   \u001b[39m | \u001b[39m4.8978481\u001b[39m | \u001b[39m-4.984869\u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.7694  \u001b[39m | \u001b[39m-4.926256\u001b[39m | \u001b[39m4.9365884\u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m-1.363   \u001b[39m | \u001b[39m-0.707260\u001b[39m | \u001b[39m-4.987766\u001b[39m |\n",
-      "| \u001b[39m13       \u001b[39m | \u001b[39m-1.03    \u001b[39m | \u001b[39m-0.062037\u001b[39m | \u001b[39m4.9528772\u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m-1.75    \u001b[39m | \u001b[39m4.9885524\u001b[39m | \u001b[39m-0.432722\u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m-1.992   \u001b[39m | \u001b[39m0.0847314\u001b[39m | \u001b[39m-0.145683\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.504    \u001b[39m | \u001b[35m-4.726124\u001b[39m | \u001b[35m2.5029536\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.8909   \u001b[39m | \u001b[35m-4.025056\u001b[39m | \u001b[35m2.8839939\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m0.1895   \u001b[39m | \u001b[39m-4.768219\u001b[39m | \u001b[39m3.8258626\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-1.884   \u001b[39m | \u001b[39m-0.278106\u001b[39m | \u001b[39m-4.999266\u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.1732  \u001b[39m | \u001b[39m-2.319678\u001b[39m | \u001b[39m4.9916277\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.1496   \u001b[39m | \u001b[39m-2.960408\u001b[39m | \u001b[39m1.2794799\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m0.2145   \u001b[39m | \u001b[39m5.0      \u001b[39m | \u001b[39m5.0      \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m0.7503   \u001b[39m | \u001b[39m4.9913061\u001b[39m | \u001b[39m2.2368164\u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-1.75    \u001b[39m | \u001b[39m4.9955782\u001b[39m | \u001b[39m0.1215197\u001b[39m |\n",
+      "| \u001b[35m12       \u001b[39m | \u001b[35m1.448    \u001b[39m | \u001b[35m4.8295217\u001b[39m | \u001b[35m3.1123113\u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m0.5424   \u001b[39m | \u001b[39m2.6035879\u001b[39m | \u001b[39m3.5449777\u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m1.139    \u001b[39m | \u001b[39m4.9690678\u001b[39m | \u001b[39m3.7619280\u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-0.7848  \u001b[39m | \u001b[39m4.9731235\u001b[39m | \u001b[39m-4.953273\u001b[39m |\n",
       "=================================================\n",
-      "Max: 1.3794744873707774\n",
+      "Max: 1.4481057894148166\n",
       "\n",
       "\n",
       "====================  Typed Optimizer  ====================\n",
@@ -176,21 +176,21 @@
       "-------------------------------------------------\n",
       "| \u001b[39m1        \u001b[39m | \u001b[39m0.8025   \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m3        \u001b[39m |\n",
       "| \u001b[39m2        \u001b[39m | \u001b[39m-2.75    \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m0        \u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m-0.1028  \u001b[39m | \u001b[39m0.0239500\u001b[39m | \u001b[39m4        \u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.4171  \u001b[39m | \u001b[39m0.0463387\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[35m5        \u001b[39m | \u001b[35m1.066    \u001b[39m | \u001b[35m-2.083382\u001b[39m | \u001b[35m3        \u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m0.6906   \u001b[39m | \u001b[39m-1.726489\u001b[39m | \u001b[39m4        \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.4423  \u001b[39m | \u001b[39m-3.391033\u001b[39m | \u001b[39m4        \u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m0.3318   \u001b[39m | \u001b[39m-1.763948\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.7855  \u001b[39m | \u001b[39m4.9987136\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m0.2146   \u001b[39m | \u001b[39m4.997248 \u001b[39m | \u001b[39m5        \u001b[39m |\n",
-      "| \u001b[35m11       \u001b[39m | \u001b[35m1.428    \u001b[39m | \u001b[35m4.9970054\u001b[39m | \u001b[35m3        \u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m0.769    \u001b[39m | \u001b[39m4.4769344\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[35m13       \u001b[39m | \u001b[35m1.935    \u001b[39m | \u001b[35m3.7959641\u001b[39m | \u001b[35m3        \u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m0.299    \u001b[39m | \u001b[39m3.4532774\u001b[39m | \u001b[39m4        \u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m-0.19    \u001b[39m | \u001b[39m2.9846175\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m0.7987   \u001b[39m | \u001b[39m-0.825462\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m0.43     \u001b[39m | \u001b[39m-4.993422\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m0.7154   \u001b[39m | \u001b[39m-1.047005\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-0.7853  \u001b[39m | \u001b[39m4.9917202\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.6984  \u001b[39m | \u001b[39m-4.564365\u001b[39m | \u001b[39m5        \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.3414   \u001b[39m | \u001b[39m3.9775494\u001b[39m | \u001b[39m5        \u001b[39m |\n",
+      "| \u001b[35m9        \u001b[39m | \u001b[35m1.428    \u001b[39m | \u001b[35m4.9979954\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m1.138    \u001b[39m | \u001b[39m4.9849806\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.1651  \u001b[39m | \u001b[39m-4.981477\u001b[39m | \u001b[39m-3       \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-0.4769  \u001b[39m | \u001b[39m4.9926394\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[35m13       \u001b[39m | \u001b[35m2.413    \u001b[39m | \u001b[35m3.1997928\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m0.2625   \u001b[39m | \u001b[39m2.3496062\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m0.678    \u001b[39m | \u001b[39m2.4925340\u001b[39m | \u001b[39m4        \u001b[39m |\n",
       "=================================================\n",
-      "Max: 1.9349856179084963\n",
+      "Max: 2.4125141680884403\n",
       "\n",
       "\n"
      ]
@@ -213,7 +213,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -307,24 +307,24 @@
       "-------------------------------------------------------------\n",
       "| \u001b[39m1        \u001b[39m | \u001b[39m-2.052   \u001b[39m | \u001b[39m-1.659559\u001b[39m | \u001b[39m4.4064898\u001b[39m | \u001b[39m2        \u001b[39m |\n",
       "| \u001b[35m2        \u001b[39m | \u001b[35m13.49    \u001b[39m | \u001b[35m-7.437511\u001b[39m | \u001b[35m9.9808103\u001b[39m | \u001b[35m1        \u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m12.38    \u001b[39m | \u001b[39m-8.235396\u001b[39m | \u001b[39m8.9416358\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-3.405   \u001b[39m | \u001b[39m-6.540598\u001b[39m | \u001b[39m8.9001743\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m10.98    \u001b[39m | \u001b[39m-8.598285\u001b[39m | \u001b[39m9.9716838\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[35m6        \u001b[39m | \u001b[35m14.56    \u001b[39m | \u001b[35m-9.726922\u001b[39m | \u001b[35m8.5187646\u001b[39m | \u001b[35m1        \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m-6.752   \u001b[39m | \u001b[39m-9.164838\u001b[39m | \u001b[39m7.3244716\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m-4.308   \u001b[39m | \u001b[39m-9.889999\u001b[39m | \u001b[39m9.6105551\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[35m9        \u001b[39m | \u001b[35m16.47    \u001b[39m | \u001b[35m9.8666955\u001b[39m | \u001b[35m-9.885838\u001b[39m | \u001b[35m2        \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m4.669    \u001b[39m | \u001b[39m8.7241716\u001b[39m | \u001b[39m-9.536908\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m-9.977   \u001b[39m | \u001b[39m9.9692745\u001b[39m | \u001b[39m-8.882518\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m15.41    \u001b[39m | \u001b[39m-9.790780\u001b[39m | \u001b[39m-9.766374\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m13       \u001b[39m | \u001b[39m4.339    \u001b[39m | \u001b[39m-9.825013\u001b[39m | \u001b[39m-8.517115\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m5.648    \u001b[39m | \u001b[39m-8.380618\u001b[39m | \u001b[39m-9.927737\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m-3.121   \u001b[39m | \u001b[39m9.9064416\u001b[39m | \u001b[39m9.9099804\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m16       \u001b[39m | \u001b[39m0.6096   \u001b[39m | \u001b[39m-0.195500\u001b[39m | \u001b[39m-5.568951\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m17       \u001b[39m | \u001b[39m2.262    \u001b[39m | \u001b[39m7.4993146\u001b[39m | \u001b[39m0.7964642\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m18       \u001b[39m | \u001b[39m4.557    \u001b[39m | \u001b[39m-8.057583\u001b[39m | \u001b[39m-1.109046\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m19       \u001b[39m | \u001b[39m3.911    \u001b[39m | \u001b[39m-4.957865\u001b[39m | \u001b[39m-1.857735\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m20       \u001b[39m | \u001b[39m-2.736   \u001b[39m | \u001b[39m-9.980513\u001b[39m | \u001b[39m1.3255395\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m6.822    \u001b[39m | \u001b[39m-1.616109\u001b[39m | \u001b[39m-4.455463\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-16.13   \u001b[39m | \u001b[39m-7.462442\u001b[39m | \u001b[39m9.9962686\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m3.259    \u001b[39m | \u001b[39m-1.232832\u001b[39m | \u001b[39m-5.747412\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-7.048   \u001b[39m | \u001b[39m3.9207862\u001b[39m | \u001b[39m6.4592598\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-3.913   \u001b[39m | \u001b[39m4.5863779\u001b[39m | \u001b[39m-3.245964\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m5.802    \u001b[39m | \u001b[39m-6.913901\u001b[39m | \u001b[39m2.0971273\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m1.222    \u001b[39m | \u001b[39m-8.335282\u001b[39m | \u001b[39m0.8594578\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m4.208    \u001b[39m | \u001b[39m-2.902313\u001b[39m | \u001b[39m-1.968247\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-4.159   \u001b[39m | \u001b[39m-6.153418\u001b[39m | \u001b[39m8.7915989\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-4.333   \u001b[39m | \u001b[39m-6.356472\u001b[39m | \u001b[39m1.4090214\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m3.66     \u001b[39m | \u001b[39m0.6606721\u001b[39m | \u001b[39m-9.219624\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m1.083    \u001b[39m | \u001b[39m5.0543932\u001b[39m | \u001b[39m0.7205085\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m9.608    \u001b[39m | \u001b[39m5.0760338\u001b[39m | \u001b[39m-6.436109\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m16       \u001b[39m | \u001b[39m-10.34   \u001b[39m | \u001b[39m6.9559950\u001b[39m | \u001b[39m-3.097946\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m17       \u001b[39m | \u001b[39m4.211    \u001b[39m | \u001b[39m-8.886123\u001b[39m | \u001b[39m-8.283778\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m18       \u001b[39m | \u001b[39m7.692    \u001b[39m | \u001b[39m-7.058465\u001b[39m | \u001b[39m7.2905639\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m19       \u001b[39m | \u001b[39m-2.327   \u001b[39m | \u001b[39m3.3476177\u001b[39m | \u001b[39m4.5905557\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m20       \u001b[39m | \u001b[39m4.103    \u001b[39m | \u001b[39m-5.313351\u001b[39m | \u001b[39m4.9166311\u001b[39m | \u001b[39m1        \u001b[39m |\n",
       "=============================================================\n"
      ]
     }
@@ -365,7 +365,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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kyZNYs2YNvv/97yu+nR07dgyjRo1CWVkZrrzySmzdulXxvMFgEIFAIO4zEDV7fRnl+MYGHFz1AkJH4x9uz8FubLyrDvs/3JlwjFrxo8f74+ThrlSY4YIXZbjM6cNhvOwGoM52RHCS+NE7zOLG4S2WGLl3rIbGRBgOM2spBaFq6dq1a9HR0YGFCxfKpqmsrMQf/vAHvPHGG1izZg3C4TCmT5+OvXvl42hqa2vh9/ujn7KysrjfzRj6ksJhdL20VjFNw1MfQQr1Vy4tMT5axY+eWB+nCJ+BmGWsrRZCThZBvOwGkNp2qMHUtWoMih+jwodggxEhSSJIPR5JkiTuV1HJrFmzkJGRgTfffFP1Mb29vaiqqsL111+PBx54IGmaYDCIYPDU4nOBQABlZWU4+u8K5OWmyQoglkNfIzo/wfrb1qZMN/2pOVh8yX7V59UjfrTiROEjh5kixYoOQ6l8rDwHir/HlDkU7MGOlT9HZ2cn8vLydF+Tl90A5G3HRdU/RXraqXYhV26WzzhV3WQhfjQfYwPRo6ZNO6UcCcdwrhNm1O+BZegLBbGu4WHDdgMQIAYowu7du/Huu+/itdde03TcoEGDMGHCBOzYsUM2jc/ng8+XvBM3Q/yMr9iLve92q0o7oe9LAEM1X0MNJH5SwzNGaCBW7KrOexqq2esE8bQbgLLtiGB38WN34cOirabcWFiA8uqxTdwXVLR5TJD1T/Vrnn/+eRQVFeGyyy7TdFwoFMKXX36JkpISTjkzRmSKe+bQwarS5xcNUn1utd4fGvLSjpkufbOHx5TKZTf3udV2w23iR4ShLiuWoxBpCQw9z0Bvu1ZTt+w8HCaEBygcDuP555/HggULkJ4en6X58+djxIgRqK2tBQDcf//9+MY3voHTTz8dHR0deOSRR7B792784Ac/0HzdvxwbguzcxO9ZeX9i1/cZWlOCzMLB6Dko4wnyAAXFGRg7OUmGkqBF/GiFhfDhuflnLLxFmlM9QiJ4gg6PNHQJy+xGBDuLHz3CxyqsFhzJEGHGp1bbpNcbpHYneVaY6QkSQgC9++67aGlpwfe///2E31paWuD1nqpcR48exaJFi9Da2oohQ4Zg0qRJWL9+PcaNG8ckLzzEDwB40ryovv18bLyrLjHx1xNX5v+/kfCmpV6bRCTxY5bQ0XJ9HqLIiULIahHk32nsXlppNzqq8pIaTzPFj160dGZWCR8RRY8Ssfk1+57pEUKsRZBZq793nOVHTn07s/MJFQRtFoFAAH6/H7/bPAnZuWlxv7EQQEorO+//cCcanvxHnCeooCQD8//fSJw7qyDluXmKH0BZPFgtdvTCQxCZZaDNmqEm+xvHwOi+3h5sevUuJsGMZhGxHZOueRDpg+JXkmb9rFh7f0QXPnYTPamwwz1kXYfMeAHIqW93XhC0CPAWPwBQesEY3P7dw/hqYxc62nuRXzQIYyfnWu75ARKFgl0Fz0AGloOFINLy1iWFw+g62ISTJ7qQkZWL3MIKeLzqDIUZ3iDWK2QPxGwXuhNwk/hxmvCJYIVXiLc3SIiFEqvygAYmlyABFMEM8QNEFjb0YNxUbcqVt/gBnCN4UhEpJyshpGRsjuz5Ers3rcXJ46eMTEa2H6MmzUFB2dmqr2PFjDHA/KXp7YqocT8kfMTA7Par5aWGdRs3a/NUFggzC8zuqBc/2jFD/LiRnopg9GMEuVkZR/Z8ie3/+GOc+AGAk8c7sf0ff8SRPV9qvha3uBCLZ4bZGVHjfkQVPyLMpLIKM8uuZbaYlgUwzW7LPOsmCSAY9/6Q+LE/rIRQBCkcxu5NaxXT7970BqSwjsXNSAQJg+leE5X3UO3zcvJyDyJjthBSnZaRCGJdNl511PUCyMgO6gBf8aMWEj/sMCqEIh1K18GmBM/PQE4e70DXwSZd1+FlQLnvj+YgESRq0LMW8WMGJHzkMeveaPUGqcFsEcQD1wsgOdSICt7iR404I/HDB8NCyH9MVbqTJ7p0XwMweesOCmTmBgvxo2UYw0zxQ6RGNG+QiG2dR511tQB6rX1i0u9J/BAR9Aqh9Fx1Qe4ZWeoWvlTCTHczDYX10zlGLNMp2pAXeX20Y6Y3SFU6FYLa7kNhYrVim0DiR57xFXvjPk5BqwjKGlmB9Dxl45GRnY/cwgoj2YrC2njSUJh5GPX+kNfHWdhtSMxsEcTy5YOmwQ+AhbBwg/hRK24i6UTIs1G0TJ/3eL0omn0V9r+yWjbNqElXql4PSC1mrMjKatps/rYAg9zYG6Odg0jih4QPO0RYDT4unUOXwyAPkEZSdfxODnh2omdHD2q9QblVNSi9dmGCJygjOx9nnL9A0zpAWmDVEfEeCiNSo9TpkPhxPqINiclh14Bo8gDFkEpc8BY/qbw/Vogft4sdOXoqgqo8QblVNciprMaJlib0dQWQnpuHrJEVGLKTb/5YeYJ4L0TmZowMfZH4cQ+meHVd6gkiAaQSN4kfEj3qUCuCPF4vsstPj/uu4wz+HQdvw+k0Y2gHSPi4EzuIIFE2TNWCWLmxECWBYbUgMEv88Bje+qLpNEfE/8hhdM0g3rDopEQzWk5Aj/eHxI+7sctwmBKi1RuybAzg7f3hDa+4HicLn1jcLIIoFsgcSPwQgDlT5Y1Mk7ebR5gEEIx5f+w89MUzoNkt4ieCG0SQHCSCtKH1WZD4IQYiiggCtLd/keoQxQAp4HTxwwurxI/R3d2N7gVmBDOCjY2OwVNAdCKxdUZN/VNz/2Lfokn8EHJwj/GzcEf5CFqWHtGD6wWQqJ4KXvlygvDh1RiUzmuGOLKDCJJDjQEM1W9DWk0V82tbBe86QeJHOyzvhR3KLKIIShUMDWjPNy8hZPkQ2L333guPxxP3GTt2rOIxr776KsaOHYvMzEycffbZePvtt5nny2rvjxakcBg925rQvWELerY1Jd1hnPf6PTzFT2aTL+5jBQPzkOzDAtE7M6P5C9VvM3R8BKvtRrBcu/jR0pGQ+FEmspLxwI/drsEC0YbDeA6Js37pEMIDdNZZZ+Hdd9+N/p2eLp+t9evX4/rrr0dtbS0uv/xyvPjii5gzZw42b96M6upqTddtaC6FNyvxezstdnh8YwOOrHkLoaOn3r7ThvhRcMPlyJ7cfz94z2LjIX6sEjoiYFdPUCovUFpNFUL125h5gqyyG6LgJvEjkvAYmBcR7pFIniC1iDAtXohalZ6ejuLi4uhn2LBhsmmffPJJzJ49Gz/5yU9QVVWFBx54ABMnTsSvf/1rJnkxQ/woeX+0ip+Dq16IEz8AEDraiYOrXsDxjQ3cvT4sxY/VXh6RELlzY5E3Fp4gkexGqjorQiepFavzLLLXJRa75NMoopRPj/dVDiFKtH37dpSWlqKiogLz5s1DS0uLbNoNGzZg5syZcd/NmjULGzZskD0mGAwiEAjEfayC1dCXFA7jyJq3FNN0vfwGpBAfI8ZD+BDmw3y3Zg3u71BDo6Fr8bYbgFi2Ixbum9VaJH7sLiaszL9I6wSpJVWeefcLlteyqVOnYvXq1airq8MzzzyDXbt24fzzz0dXV1fS9K2trRg+fHjcd8OHD0dra6vsNWpra+H3+6OfsrKypOnsNPQVbGxO8PwMpKf9GA7XHzCarQRYiR+nCR8pHMbx5h0IfLkZx5t3JI3F0oIeYxMJQtR0jA7DqdcQsgqCNsNuAOpth5nYVRzIYXfRI4cV5bKjCLISy2OALrnkkuj/a2pqMHXqVIwaNQqvvPIKbrrpJibXWL58OZYuXRr9OxAIWGLIWA19AUCoQ92baM/hbk3nTQVL8eMkurbVo73udfQFTomP9Dw/imZfhdyqGt3n1Tv2rmYmBi/M2CLDDLsBsLEdLDslkYdGteKkjjQVkbKacW9F2jZDDVbGAglXA/Pz83HmmWdix44dSX8vLi5GW1tb3HdtbW0oLi6WPafP50NeXl7cZyBWe3+0kpafWIZkZA4dzOyaLMSP07w+QL/42f/K6jjxAwB9gU7sf2U1urbVGzq/3kXJtIgQM71APOBhNwB1tsOs+uwU8aPVK8Las2olZnmEyBOkDuFKcOzYMezcuRMlJSVJf582bRrWrVsX990777yDadOm6b6mnQKfI/gqy5E2RPkNP7MoB0Nrkt9HrRgVP04UPkC/cW6ve10xTXvdWkuGwwD+IkgOJe8Tj7WArLAbamB1T53Qaerp/Lu21aPpyQew54+/wYHX1mDPH3+DpicfMPxSYTVOHPLjBc9+w/In8F//9V/48MMP0dzcjPXr1+Oqq65CWloarr/+egDA/PnzsXz58mj622+/HXV1dXj00Ufx1Vdf4d5778XGjRtx6623WlWElPDY68vj9aLghssV01Tfdh48acYfMQvx41ROtDQleH4G0hfowImWJpNylCg+eIogq4y4G+yGU9Db2fP2rIoATyFkJy+QVUH3lgugvXv34vrrr0dlZSWuvfZaDB06FB9//DEKCwsBAC0tLThw4FQg7/Tp0/Hiiy/iueeew/jx4/GXv/wFa9eu1b2Wh9VDX0bERfbkakx+cDYyC+OHuTKLcjD5wdkovWCM0eyR+ElBX5e6WCy16ZQwYmys2KSQZwyS1XZDDW73/hjp3M3yrIoCiSBr8EiSJFmdCbMJBAL9MzqeXYEJZx1STMtzxWej4iIi3qRQGIfrD6DncDcyhw7G0JoS8vyYxPHmHdjzx9+kTFe24P9DdvnpTK6p1qglEz28VhiWy5OS8Ap+UY8P8AY6OzuTxtaISKzt8GZlKtZxFp2PncWPEaxoV6LAfGkKkwQK7/oeWQU6fKIHe26+j4ndsHwWmJVUl+8HkCH7u2iBz7HEeq48aV4MmzCC6flJ/Kgja2QF0vP8isNg6Xn5yBpZYWKu5FE7O0yEVVrdjh3vP6s8m+lZFQ3WM8aoLctDd4UjvLw/om9t4RbxA/THYhXNvkoxTdHsOfB42TU1tcZMTujwGA6Ty5NVU/GtxupVlNUi6hT99Fx1b/Zq09kRu23sakeRZb8cm4So3h/RxY8bya2qQem1C5GeF9/Zp+flo/TahYbWAZLDrkMjsaRVV3I9P294Cn07PV8egbwRz6oSInlWeWE3UWE0v2a/OLh6CMwq9IoMp4gftTv62smTlFtVg5zK6v5ZYV0BpOfmIWtkBVPPD0vMHAozY2FEJ2GnTo9XXiOe1f2vrJZNw9qzKiqshsTsPhSW2eRz5m7wosF73R892En8sKqkSuexUhwNNEQRo+Lxek0NyFSzGquS+GC9WrQZu9jbATvcAzsEaEc8q4krrOejaPYcLp5VkbFL+7JLPgESQKYj4hCTiHlKxUBxJIIgsvPblRJ2f3O0E2rvs9X136z6YDfPKm+Migs9bTm2rqmtZ0byaaa9cWctUsCN3h8n0FMRZO4elUOuceZvD5v+5qPGUCh5ecwamnJaMLSv2TrBbbSeG62jZovhiGc17+yJyC4/3bXiJ4KVLyNm2lkzcHdNsgF2GvoSgUgDtbKRmi2EDAceqhBBasvjdk+Rkeeu5t5ZWa9p+wZxMLQoKgPbpKYe8qgrrD2dVJtjEG3PLxI/xuAphNQ0bis8QnI4zQPjNMwSP3rrIwkf8XDyMzHLbjr3DhLE11gpggB7rMHB0gskB4kw/Vg5IcDJHa3d0b1Jso29QCwRO3cmItqeX+T9YYsIw2JWY5YAEd3oiYbI90vkvBH98H5GSgLb7vFAVLsZwir4mcQPP1g3WC3Gh7cIsoMXyMnwujes6qzW/JH4sQ96nhWr+pqqfrKuRywnIFANJ1yHk0WQUXjPCqNhsHhS1R2r3rBJ/NgPkZ+ZlQJNCXHvmImINPxF3h9zsHo4zCohxEKAqMm7yMbYjYguvAk2WNXu7DoURlaKEazX/uEBiZ94WDZaUTp8FvmgbSvMgYX3h0cAtCh1mdCHVR5ps4fCWCBejlwMLXhoPlaKIDu/lRta78aBw2CsnyUNfRFGYP0c7bQvoxZcX9tFGv7iiSj5IOLhIYJSGT9Vm6CSF4grIgoNEfNE6MeK58naC8T7JZFqPANYDH+R98cZiBrsx4OUG7FSh6oL1t4fitlyL/RclbH87tTW1uLcc89Fbm4uioqKMGfOHDQ2Nioes3r1ang8nrhPZmamSTm2H+T9MRc3iSCrsLPdEK1TEi0/hLk4ZfhWD5bX/A8//BCLFy/Gxx9/jHfeeQe9vb24+OKL0d3drXhcXl4eDhw4EP3s3r1b87W/U7RZb7ZVoVZ4kPeHANgaItGHwYzGAVlpN3iipfNwalwGwRbRhsJEEtzpVmegrq4u7u/Vq1ejqKgImzZtwje/+U3Z4zweD4qLi1VdIxgMIhg89UACgYC+zCZB9Nlf5P2Rh2cH0nGG1xVenfztYUsMmhl2A2BvO0Qy/oB4+SH4wMIeZTb5bOXdUYNwtb+zs/+ts6CgQDHdsWPHMGrUKJSVleHKK6/E1q1bZdPW1tbC7/dHP2VlZUzzrBcpHMaIzk+w991/49Dn+yCFnN9hmkVmky/lR0REE01GvUBmdbA87AagznaYtaIua0j8uAuzn7cdvEBi5OJrwuEwfvzjH+M//uM/UF1dLZuusrISf/jDH/DGG29gzZo1CIfDmD59OvbuTT6UtHz5cnR2dkY/e/bs4VWEKKk8L8c3NmDf0oex/ra12HzfO1h/21q8c82fsP/Dndzz5kREFDdWblSo5vqspqNbLdp42Q3AGtvBErlnY7QDsnpvPTfi1nvO075YPgQWy+LFi9HQ0ICPPvpIMd20adMwbdq06N/Tp09HVVUVfvvb3+KBBx5ISO/z+eDzidEpAv3i5+CqFxK+7znYjY131WHyg7NResEYw9dx+vCXKEKH0EdHVR7QYPw8vOwGwNZ2KIkOu3Rsdsmn04i975H/67F/Ig3Ni5AXYTxAt956K9566y28//77OO00bR33oEGDMGHCBOzYsYNT7pKjJ/5HCodxZM1bimkanvqIhsMUEMnLkwqrvUAsEHlNIDvaDRYYqf9662Qy8UOCyDr03nszh59Erx+WCyBJknDrrbfi9ddfx3vvvYfRo0drPkcoFMKXX36JkpISDjlkS7CxGaGjyh1KT/sxHK4/YOg6TvT+2En4iIJZxs5swWY3uyGK94el+CGsxw4iSGQsvwuLFy/GmjVr8OKLLyI3Nxetra1obW3FiRMnomnmz5+P5cuXR/++//778X//939oamrC5s2bccMNN2D37t34wQ9+YEURElASH6EOdbNIeg4rT+d1G24UPmaICrtuS+FEu8EbEj/OxEwRpMcOixwMbbkAeuaZZ9DZ2YkZM2agpKQk+nn55ZejaVpaWnDgwCmPyNGjR7Fo0SJUVVXh0ksvRSAQwPr16zFu3DgriqCJtPw8Vekyhw7mnBP7YHfxY3UjZ4GRYTAe5Xeb3dBL5N6T+HE25AnSh+VB0JIkpUzzwQcfxP39+OOP4/HHH+eUI3XoXf/HV1mOtCF+xWGwzKIcDK3R75Z30vCX3cWPUaxaZ0d07GQ3rB7+IvFDKGG1fbEyGJosq8l4vF5MWDZNMU31befBk0aPxknix0oj49Q4ICIRKRzG8eYdCHy5Gcebd0AK0zNxCyILVlHzZrkHyI2UXjAGkx+cjYYn/4Geg6difTKLclB923lMpsATzoG8QIQaurbVo73udfQFTnmX0/P8KJp9FXKrajSdS22H5aSXFCfQUxGkZ6IBEkCMUTv8VHrBGJScNxqH6w+g53A3MocOxtCaEvL8fA01YvPoOMuvKsYnf2unbYOmrcKs4a+ubfXY/8rqhO/7Ap3Y/8pqlF67ULUIEvVtnXAuVg2DUW9rIZ40L4ZNGIHTZp6JYRNGMBE/Tor/cRpGvDg0vETIIYXDaK97XTFNe91aGg5zCaIKWBHzRQLIZGjnd8LJkFAznxMtTXHDXsnoC3TgREtTynOJ2EkR2qHnqA4SQDoQfQd4J0ANmC1WxxBZfX0r4DX8NXB4uK9L3dpiatMR4uGGkAArbIT7rBJhG0gEEU5ECofRs60J3Ru2oGdbk+GhqfRcdWuLpUpH7c1ZiPg8RcsTBUE7CCfG/xjZ+E9EjAT7iTAbjAKhjZFsplbaED8Kbrgc2ZPld7JXImtkBdLz/IrDYOl5+cgaWaHr/IR9kMLh/u2WOgJIy8+Db1AlPF7yc8hBAoiwBU4TQqKhdiYYoY5kQlVuplboaCcOrnoBhUvm6RJBHq8XRbOvSnruCEWz5yh2hKK9mRPaOb6xAUfWvBW3yO5hncsguAWShiZCAdDG6akIkrEmbIeamVpHXnhL93BYblUNSq9diPS8eO9cel6+pinwhD05vrEBB1e9kLDDQGQZhK5t9RblTBtme7jJA8QQJw5BiQot+CUuIgzViYaamVqhI50INjYjs0rfUFVuVQ1yKqv7r9UVQHpuHrJGVtAQiMORwmEcWfOWYpr2urXIqawWoi6IZLtJABGEjSBxIT7Jno/aGVihDmMztTxeL7LLT9d0jOgeVbn8WdGJJsuLGflQekbBxmbFvSWBU8sgaK0bTocsKWFbRDfcokHCyTrUztRKy1eXzi0otXGz27/c9awellcrmu2yDMJAO8XTbpFFJGyNHUUQCRH3EZmppURagR++yvKU57JjndeDSOVUkxer8qtWNKsV4W6CLLFDoPgjgrCGWEErJ24jM7WUKJh3uekxGkY7bV6dvtrziiSSAGvy46ssR9qQ1OLaTssgmPWSSAJII7QKNEEQesitqkHhknkJnVVagV/3FHgnYndRY3b+PV4vCm64XDGNFeJaCVGeMQVBEwRBGETtG2v25GpkTRwXv1hdZblQnZOV6OkYRZpVZBXZk6tRuGRewjpAaQV+FMz7epHN1FvBCYUZXiAhWt3TTz+N8vJyZGZmYurUqfj0008V07/66qsYO3YsMjMzcfbZZ+Ptt982KacEQYi0YKIdbYfH60VmVQUGTzsHmVXWTVMX5S2cBVYPxbE6zgjZk6sx4rGfYvidizDs5rkYfucijHj0p+RZVMByAfTyyy9j6dKlWLFiBTZv3ozx48dj1qxZaG9vT5p+/fr1uP7663HTTTfh888/x5w5czBnzhw0NDSYnHOCIKyEbIezEE2QiRofpYSSuBbt/oqA5QLosccew6JFi3DjjTdi3LhxePbZZ5GdnY0//OEPSdM/+eSTmD17Nn7yk5+gqqoKDzzwACZOnIhf//rXJuecEAE3ur717iXmNMh2ELGw7OBFEgsi5cVpWCqATp48iU2bNmHmzJnR77xeL2bOnIkNGzYkPWbDhg1x6QFg1qxZsukBIBgMIhAIxH0IgrAvdrQdTu3IWJSL1b1JtSZP5Hez1hdy6jN3CpYKoEOHDiEUCmH48OFx3w8fPhytra1Jj2ltbdWUHgBqa2vh9/ujn7KyMuOZJwjCMsh2EEoMFDnJRI+aNEQ8Trs/lg+BmcHy5cvR2dkZ/ezZs8fqLBGEbmghRfMQ1XY4qSPiWRY1okZk4SNqvpyCpdPghw0bhrS0NLS1tcV939bWhuLi4qTHFBcXa0oPAD6fDz6f+2JF3ABNgXUnZDuMwbpjpXYoD92b5IhwXyx9lczIyMCkSZOwbt266HfhcBjr1q3DtGnTkh4zbdq0uPQA8M4778imJwiCLR1nKa86awZkOwiCMIrlCyEuXboUCxYswOTJkzFlyhQ88cQT6O7uxo033ggAmD9/PkaMGIHa2loAwO23344LLrgAjz76KC677DK89NJL2LhxI5577jkri0EQhMmQ7SCcDI/hLxG8LiJhuQCaO3cuDh48iHvuuQetra0455xzUFdXFw1WbGlpgTdmLYPp06fjxRdfxF133YWf//znOOOMM7B27VpUV5uz2NPc4s9oOwyCEAC72Q6no6dzpRgX++DEZ+WRJEmyOhNmEwgE4Pf78bvNk5Cdm6b5eDkBlGpD0vEVezVfSwtu3RDVjm80RtbyMRIErXRdtSs8qxkCS5bHgdfu6+3BplfvQmdnJ/Ly7LFTdcR2nH7nL5Dmy9R0LI8ORG/d59WZkQBKjp7nJMoziiBK/Q0Fe7Bj5c+Z2A2aTkLYGreJH4IQGbcIGq2IvqGqWyEBRBAugYQXQYgPiR/zIAFE2BY7en8IwulQB+48nPpMSQAxhHeMD3EKt4ofOyyCaIc8OgVROyZR8yU6dN/MxfJZYG7ii6bTSCQZxK3Ch3AHsfbBrZMaCPtit/pLr2qELchs8pH44YjaGWAEoRbyZmhD1PulNl92fLknAeQg7FgB1eAk4WP3QGQRVoF2KgPbr9r2LGrHCYidN5Gg+2QNJIAIYSGvTzwUW+M+nPBSI9e5U6ffjxPuQ7J6yqvuBsvZ3S+yqDqYW/yZ1VlwPCR8nIXdPV+EMZzQyRsl2T0Q/b6Inj+jkAAyGTsEhhHOwywBQl4q9qh5k7ZDR9VTEbRFPs3ASffCzl5KmgXmMMZX7OUqsqRwGMHGZoQ6AkjLz4OvshweL3V6arBq+wuCEAmndPx6cHPZRYQEEGN4CxArOb6xAUfWvIXQ0VMzhtKG+FFww+XInsx2Q0natdg8WO4BRlgHtRmCJWrEmtnen56KIHCC3fnotZJQxfGNDTi46oU48QMAoaOdOLjqBRzf2GBRzpwPeX/Eg2UgphrsPMxAiAMJ5HjIslqA3TxEUjiMI2veUkxz5IW3IIXZxpk4yV1sZRAwxf+4Bye1GcI6zPD+iCDGyGLpROSZYKzfFoONzQmen4GEjnQi2NjM9LoAGXQSFUQElu1ahM6HILTAoy8g60qkJNQRYJpOK24XQSJgJP7HqVPgRa2XouaLsAcixv7wggQQB9RUDjsNg6Xl5zFNpwc7G3W9AsAM7w9tgWEeLLwuTul4CEIELBNAzc3NuOmmmzB69GhkZWVhzJgxWLFiBU6ePKl43IwZM+DxeOI+N998s0m5tg8sDaWvshxpQ5Q9AGkFfvgqy5ldMxlOWjvDLJwY/0O2IzXUTgg9iOr94VWfLZsG/9VXXyEcDuO3v/0tTj/9dDQ0NGDRokXo7u7Gr371K8VjFy1ahPvvvz/6d3Z2Nu/sJmVu8Wd4ufVcS65tJh6vFwU3XI6Dq16QTVMw73LT1gOKNAY7xDGI7P1Ri2jT30WyHVZMPXfyUhuEdbhRNFsmgGbPno3Zs2dH/66oqEBjYyOeeeaZlEYsOzsbxcXFvLPInS+aTuOqplkayuzJ1ShcMi9xHaACPwrmsV8HSA2xDdYOYoiIJ3+bvpgxsh3qoHWBiFjMHoK1g0gXaiHEzs5OFBQUpEz3wgsvYM2aNSguLsYVV1yBu+++W/FNLhgMIhg81VkGAnyCdWNx4lta9uRqZE0cJ+RK0APfXkQw/KJ7f1jF/4jgrXKS7VCDWvtCIohQg8jeH555E0YA7dixA6tWrUr5Bvef//mfGDVqFEpLS1FfX4+f/exnaGxsxGuvvSZ7TG1tLe677z7WWWYCby8QazxeLzKrKqzORkpSNRpROwWWYoJF/I/R4S8zYpCsth2sRIZWW2D0JSuzySd0x0eYg9o6YKd+Si3MBdCdd96JX/7yl4pptm3bhrFjx0b/3rdvH2bPno1rrrkGixYtUjz2hz/8YfT/Z599NkpKSnDRRRdh586dGDNmTNJjli9fjqVLl0b/DgQCKCsrU1OclIgeB+RETxQLeA+fiTj1WwqH0XWwCSdPdCEjKxd+aSg8Huu9NxHcZjvMgpcXaGCHSHaGPU69x6K8gDIXQMuWLcPChQsV01RUnPIg7N+/HxdeeCGmT5+O5557TvP1pk6dCqD/LVDOiPl8Pvh8iTf8tfaJuCH3C83XVIta8WE3LxDBB57enyN7vsTuTWtx8vipYa+mQbmoKp2F4flVyfOjwvujN8/5WzvRN+A7O9mOgagVGay9LiINhUVsmFM6aSsxqz8Q3fvD20PJXAAVFhaisLBQVdp9+/bhwgsvxKRJk/D888/DqyOWZMuWLQCAkpISzce6BfICmYtW748UDuOArxl9XwaQnpuHrJEVTOOqjuz5Etv/8ceE74O9Xdiy+y84B9+VFUFmQraDL2bFA5G90Y+ZQkN08WMGlsUA7du3DzNmzMCoUaPwq1/9CgcPHoz+FpmlsW/fPlx00UX405/+hClTpmDnzp148cUXcemll2Lo0KGor6/HHXfcgW9+85uoqamxqihMIC+QM9Aqfo7s+RK7vliLvsApz0x6nh9Fs69CbpXxOi2Fw9i9aa1imq/2/x+K/JWah8NSeX94DQOS7YhHi+AwUwQB5A3SgojiRy8snrtcHqvL92OP4bP3Y1kAwDvvvIMdO3Zg3bp1OO2001BSUhL9ROjt7UVjYyOOHz8OAMjIyMC7776Liy++GGPHjsWyZctw9dVX480339SdDxbxOyLvCxaBxJV4RDwzseIHAPoCndj/ymp0bavXdd5Y4dF1sClu2CsZPb0BHO1uiftOtLV/YhHFdgzELgHFZuaT7I46RBU/Tn9+lnmAFi5cmHK8v7y8HJIkRf8uKyvDhx9+yDlnbNHydkZeIPNh+TasxeOhxjPTXrcWOZXVhobDTp7oUpUu2HtM03mNxCsZnX7vZNuh1wZoHXayQgSRNygRM9fVEUWgq7G5ZuVVnCkgBHdIXMVjlfgB1Hlm+gIdONHSZCgfGVm5qo7zDcqJ/l9k748oVJfvT/q9GsPNawjK7PattUMm+xOP6PdDxPyxzhMJIPAfBhNp9UwRK7UVWD0Ns8OvzuPS12Vs4b3cwgpkZCsLmsxBeRgyeKSh6wxExGUACOsZX7HX9TbIDvfASP7s5KkiAUS4DtbiR0tn33GGFx1neJGem6cqvdp0cni8XoyaNEcxzdjSi6MB0DynvgPu2H3eqAE30oGI7gWKYAcRwBojZabhQz51mwQQQ1gFQ5MXiB9Wi58IWSMrkJ6nLDbS8/KRNVL9qttyeSkoOxtnnL8gwROUOSgP54wSYwq8HRG1HYmar2TYKa96sZvYszKvZscpkQD6Gt6rOWutVCSC2JLZ5LN02Gug18Tj9aJo9lWKxxTNnsNsPaCCsrMx4/Rbce6Y76Fm5FU4d8z38M2qJXHih5X3h4a/rA84NbN9G7VVdhMIamFVLjO9P7yfg14bzCtfJIAIx8NL+Kjt6OVEQ25VDUqvXZjgCUrPy0fptQs1rQOkJi8ejxcFOeUoGVKNgpzyuHV/zAh8duLwl5JhVhJBVsegiYhThBDLcthN/BjJrxUvDcJshioCL7eea3gYS2lvMK1TVXlPi3f6iq08Oxk1gkONtyS3qgY5ldU40dKEvi4+K0EDbMSHCLu+uwUWbd/M9s3SVsWex072yQniTUR43lcSQIJDIkg7Vr9daxUKHq8X2eWn676e0SEnloHPbhz+UmpDZq28LIddRVAE0cUQT9tsN++PEawaMqZXugGIuDK0iA1fNCIxPmZ0NnKdfGSGl2goeX/MWvPHicNfdsHqzo0VkaElq8tjRj7sKH5S5VnONiuJH97PmjxAJiOix0XEPKXCai9PLFaKHjM8LiKKOtHQ4wVKtTO8HVeGNyvPA6/B037Z7RlowcllUwMJIJtgp6EwJXGi1dUpktCJIIogMGPoSwtuHP5SCw2F6UcKhXG4/gB6Dncjc+hgDK0pgSctvg3KXU9tmUUSAmY9J5Zl1ptnK70/AAmgpIgWDB3BTiJIDhEFjZORG3pSK35YiD23DH/xaD+sg4vtJoL2f7gTDU/+Az0Hu6PfZRYORvXt56P0gjEpjxdJ2KjBbp54tSSz+1YvFQFQDJDtoPWBCICGvkRF67R4s18I7LQ+0P4Pd2LjXXVx4gcAeg52Y+Ndddj/4U5D5xcNO8b96CWV+DErf2ThZBBpf7CBiC6CRFD2Tkat+DHq/dGCXJ6UvD+hhkbm+RAdPW2DdXu3gwiSQmE0PPkPxTQNT30EKeSMYVc7ix87e61cLYAamksVf+e9OrQRSAQRejBz6MutpGo7IrQN0UXQ4foDCZ6fgfS0H8Ph+gN6syUMdhY/ehDF+wO4XACZAS8vEEAiyI0Y8f7wEj96huNC9ds0H+MkRGgbIougnsPK4kdrOlGxu/jR/FwZiJ/vFG3WdE0lXC+AUj1A0fYIGwiJIPdgJO7HrPV+YnFy8HMqI6ym3WhpG7zauagiKHPoYKbpRMTu4kctkTg3Fn0B6zX2LBVA5eXl8Hg8cZ+VK1cqHtPT04PFixdj6NChyMnJwdVXX422tjaTcqwP1g9tICSCiFgGCg8t4scM7w8LRLAdqdo1axHECxFF0NCaEmQWKoubzKIcDK0pYZEtU/mi6TRHiB+1ZeipCKqq56nyyaMftdwDdP/99+PAgQPRz5IlSxTT33HHHXjzzTfx6quv4sMPP8T+/fvxne98x1AezPAC8RwKA+whgkQw9nZFr9DgKX6UUAx+ZjT8JYLtMBOnLPanRgB40ryovv18xTTVt52XsB6Q6JgdMCxCzA8LeDkRLK89ubm5KC4ujn4GD5ZX/Z2dnfj973+Pxx57DN/61rcwadIkPP/881i/fj0+/vhjQ/mwcyR7BNFFECDGG6/d0CJ+YoUHb/Fj9cKHItgOFl4gUTA7r6nsVekFYzD5wdkJnqDMohxMfnC2qnWARMJsr4+dtuqwqp1YLoBWrlyJoUOHYsKECXjkkUfQ19cnm3bTpk3o7e3FzJkzo9+NHTsWI0eOxIYNG2SPCwaDCAQCcR+t2MELBNhHBJEQUocZnh/WmBX7I4rtMFMEmdG+RfIGlV4wBt9+dT6mPzUHE1d8G9OfmoNvv/I9W4kfpwx58cKKoa8Ilgqg2267DS+99BLef/99/OhHP8IvfvEL/PSnP5VN39raioyMDOTn58d9P3z4cLS2tsoeV1tbC7/fH/2UlZUlTWf3gOgIdhBBAAkhHnSc5dcsfsz0/rAa/hLNdvCO84vFjM5UJG+QJ82LYRNG4LSZZ2LYhBG2GfYyW/gA5jw3M8vEu10xr0l33nlnQnDiwM9XX30FAFi6dClmzJiBmpoa3HzzzXj00UexatUqBINsO8Xly5ejs7Mz+tmzZw/T82vBLEPJu/GxbGgkhJJjx9WetXh/0qor4/52su2w21s5YI0IckIoAmBNSIXT6pgZfSXzvcCWLVuGhQsXKqapqKhI+v3UqVPR19eH5uZmVFZWJvxeXFyMkydPoqOjI+5Nrq2tDcXFxbLX8/l88PnULTmfag8bFvuEKcF6vx6e+4dFzssqv7EiyO17hoksfljkLa2mCn2heLFid9uhtP8fwLZtm7nrutmdeeR6duvQrRJvIs7iU4MIz5e5ACosLERhYaGuY7ds2QKv14uioqKkv0+aNAmDBg3CunXrcPXVVwMAGhsb0dLSgmnTpmm+XnX5fvyrLdGg8hZBZhpKQNxNVKVwGMHGZoQ6AkjLz4Ovshweb3+n7GYxJLL4USKV9ycy/JVWU5X0dzvZDjlStW07wvpFRy2x1xOhs5TDSq+VU8WPWSMllu0Gv2HDBnzyySe48MILkZubiw0bNuCOO+7ADTfcgCFDhgAA9u3bh4suugh/+tOfMGXKFPj9ftx0001YunQpCgoKkJeXhyVLlmDatGn4xje+YVVRdGG2oeT9VqVVBB3f2IAja95C6OipTjNtiB8FN1yO7MnVcWkHDo9ZIYjUDNGxyJfo4sdo/uTEjxZEtx1KbduOXqAIVniDIojmFRJhqE6Ue8EaM+PpLBNAPp8PL730Eu69914Eg0GMHj0ad9xxB5YuXRpN09vbi8bGRhw/fjz63eOPPw6v14urr74awWAQs2bNwm9+8xvd+ZBr1E4bCovAe0hMTZ6Pb2zAwVUvJHwfOtqJg6teQOGSeQkiKBZeu2pbHYckuvhRQk3sDwvxA4hjO5RwsgiKXNcKBl7Xrh4QI1ghfMzy/pgpfgDAI0mSZOoVBSAQCMDv9+OSukUYNDhD8eHydtWl8gLZJZB5IHL5lsJh7Fv6cJznZyBpBX6MePSn0eEwu2BEhNlB/CjlUc/U975QEOsaHkZnZyfy8vKMZM00Irbjd5snITs3TTGtWW3bKk+AKIIgFrvMpNWDE54zC/GzZud4/HX275jYDcs8QCKh9EbmtHigWHi6leXyHWxsVhQ/ABA60olgYzMyq5IHvIqI08WPEk7e88sIZg1zm+0FimC1NygZIuWFJU4Y7hKxDPZ6xSa4wGv6abIKH+pQtwil2nQi4AbxI5dPEj/KmLH4KWB9IK6InZsTsPremlWv1DoRWL9QkAD6GqVKxnuBRFGW0+chhAbmPS1fnctSbTqrcbP4cTOvtU9UndYsEWQ1VnfWTkKEeyna0BcPSACpxC0iCGAvhGLz7qssR9oQ5ZWK0wr88FWWM7s+L9wgfpQg7496nLANjlqs7rjtjAjChzWiih+ABFAcqSqem0QQcEoIsTSsHq8XBTdcrpimYN7lwgdAu0X8kPdHHpbueFFEEKsOKdKRO60z5wXLe3VN4ac4e/c67H333zj0+T5IIe1t2IwAfa11jUc8HQVBa8Sp0+NTwXL6afbkahQumZe4DlCBHwXzEtcBEg294scsMWGG+CHvj3bMmvCgNyg6Yrdi7ReLTkfEYGlRYCV6Is/ss78dwe0PtuBI60kA/dvGZBYORvXt56veQFZE8cMLmgY/OCPh91QVQE2lNfKA1RgdEY1JsvuilE+llaBFRHSvD8B22It34POhM33Y9OpdtpwGH2s7WL/JWjU9XqkcLN++RbRdZsNa+AD94ueJJTsAmR598oOzU4ogs+qekTbT232SpsFbiZo3LCOeIDXTZ61clVUOrfnxeL22mepO4octHWf5gd4ertcQFVG3y1CyWSy9QrG2UzQbxhOWw4EDn1M4JOFPD7bIih8AaHjqI5ScNxqetOR2QlTxwxNxX7dN4DtFm5N+r6aiqqksRgyFSJXEzWQ2+QwNeZkV72OW+KGhr0T0tHNRg6LVlGVu8WcUK6QS1uWTu/dfbez6ethLnp72Yzhcf4BJPuTgIX54viy4WgAZxWoR5FSjIQJGhA9gT69PKliJn46zlGcBugVRRZBaWAohwBliKLYMrD0+Sve6o71X1Xl6Dncn/Z5FPbGT5yeC6wWQ3ENRW3l5i6BU2NlYiIhR4QPYW/yYMvTlQPS2cRFFkNaysBZCAD8hwRre+VR7b/OLBqk6X+bQwQnfiSx+eA8Vu14AAWKLIDUVR2QDYRdYCR87DnlF4D305VTxYxQniCCAjxCKMFBoWCGMzLy+1ns5dnIuCooTJ/TEklmUg6E1JXHfuVn8ABQEzQyegdFqg6Ij+SDUw2IXeTPXyuG2mzvF/RiG18QHVm1b6/R4I/Yq9hw8YRWvKcJLpBHx6E3zoOLWb+HIXXWyaapvOy8uAFpk8aNEQ3Mps3O5ehr8wB2d5RqrlorCc4q8WmOit2LHioGeiqCuc9gBFqIngp2Hu2LhPeU9mfenr7fHEdPgYzFq8M2YIs9yerxaRJz1JgKs7+3+D3ei4cl/oOfgqVifzKIcVN92XtwUeNHFj1JfHD7Rgz0338fEbpAAihFAAIkgJXFgZ1HEUvQAzhE+gHVDX3YWQGXPrsCEsw4lTUMiSBkSQ+zuZ7J7KYXCOFx/AD2Hu5E5dDCG1pTYzvNjlgCiGCAOWB0TBPBx6UbiZGI/osIrn2bF+QDOFj9Oxg5b4uiJCWIlXCLxLSLOCuIJ63LLPQ9PmhfDJozAaTPPxLAJIxwlflhDAmgARgOiI9hVBGn18oggiszIg9nCxw3ip3OMvc0P7ynmSlghggD23ptYUeA0QcSzbHqeg53FDy8ss0AffPABPB5P0s9nn8nfxBkzZiSkv/nmm5nmzWwRxHohtVjMEEEDSSZIlD5Gz8EbpwkfwHrxY6ScItkOuTbOwpC7RQTFYmdBZFbenSp+lOD1smHZLLDp06fjwIH4VSnvvvturFu3DpMnT1Y8dtGiRbj//vujf2dnZ+vKw3dzjuJtDNN1rFrUzrzguYmqnlkkERFkhsAQdSjNSUNdsdhZ/ABi2A41sGjTZmygqmfz1EieeIuUZOcXIYbICnEmqvAB+AbK8/S0WiaAMjIyUFxcHP27t7cXb7zxBpYsWQKPx6N4bHZ2dtyxRrgu5yheOjYk4Xs5w6PH4GgRQZFrq0HrnkJ68t5TERRWoPDACVPalTCzfMlgUWZRbEcEvbuvq0WNCIrkQy+RY/UIIbPFgFmbtorkgRJV/PCOZ+KNMLPA/vu//xvXXnstdu/ejdNOk39wM2bMwNatWyFJEoqLi3HFFVfg7rvvVnyTCwaDCAZPDesEAgGUlZXh6L8rkJebllQARWCpSrUYFy0VS2vl0ZN3p4sgp3p7BmKl92dg2UPBHuxY+XPDszmssB1lz66ANyszLq1c+zazk7BihlgEkQSDU9ArDOwkfgBt/awjp8FfeumlAIC3335bMd1zzz2HUaNGobS0FPX19fjZz36GKVOm4LXXXpM95t5778V9992X8H1EAAGQFUFKFVBvJeMhhMx6Q3CaEHKL8AHEEj8AOwFkhe3QIoAAc6eRkwiyP0Y8Ikafv1lDXhG0OhmEFkB33nknfvnLXyqm2bZtG8aOHRv9e+/evRg1ahReeeUVXH311Zqu99577+Giiy7Cjh07MGbMmKRpUnmAImgVQUYqGokgazFrywpREE38AIkCyE62I5kAAvh7gSKY4Q0yMqxHQkg/Int9APPEvC0F0MGDB3H48GHFNBUVFcjIOLWK6gMPPIBVq1Zh3759GDRI3aZuEbq7u5GTk4O6ujrMmjVL1TGRxczUCiDAehEEpK54Zr812FUI8RQ/IomeCCKKHyBRANnJdsgJIMBZIgggIWQWVnp9APPFD6CvX2UpgJgHQRcWFqKwsFB1ekmS8Pzzz2P+/PmaDRgAbNmyBQBQUlKinFAFcgHRANug6Ah69uaJ5EVLHtWgN0AasJcQYi1+RBQ8sYgqfpJhZ9uhBtYBw2btEWgkyNus2WJ2xmgAsN2GvCJYMetrIJZb7/feew+7du3CD37wg4Tf9u3bh7Fjx+LTTz8FAOzcuRMPPPAANm3ahObmZvzP//wP5s+fj29+85uoqalhkp/rco7K/sZqfaBYvmg6TfeqrKwj5/XucNxTEbTNNhlGBEtknZ7Yj8jYSfzoQTTbESHSnqVQGIc+34e97/4bhz7fBykU5raQYCqMzlLTY6di4WGv7I7Re2L0mQDiiR+zsXw3+N///veYPn163Lh+hN7eXjQ2NuL48eMA+qe/vvvuu3jiiSfQ3d2NsrIyXH311bjrrrvMznYCRtfj0PuWxaMi6S2LXTxCogsXo6TycjlB/ABi244Nr3Sg66W18ZtSFg5G9e3n4+UL2E8dt4M3CIi3V270CrGy12bFd5ktfsz0/gACzQIzE7kYoFjMjgeKheeaIloxWibRxZDT4C1+WC5yyGoWmJmoiQE6vrEBB1e9IHuOyQ/Oxh1zj3DJH+/NkmNhZafcIIRI+PTDQvwIHQPkFMyOB4qF98JqWjD61mgXr5DZyA0ZGrlPdhI/TkUKh3FkzVuKaRqe+ggvnfc9XDdiE/PrRzous7xBsefSi1O9Qiy982YKVpHFD2tIAClgtQiKnE8EWAkhwJ1iiHeMFIkfMQg2NiN0VPle97Qfw+H6A3g5jd8qymonRIgwLBbLwDzbSRDxCEdwgvBJhVXiByABZAjeIggQyxsEsDGYbhBDZgaF21X8+Hdauy0HD0IdAVXpeg73xwbx3EpCrTcIMN6ueb2wiSyIeAfymrmOE+/7KkrQ80BcLYD+cmwIvp+rbLCUvECAeSIock5RYCGEgEShYFdBxELw6Cm7XcVP/vYw+nQdKTZp+epiEjKHDo7+n/d+WlqWxxBVCEVIVg6ndd5OEj4A+6Gv6vL92GMkQzG4WgAB/cHOSlPfATFEECCeNwhgJ4QiJBMSIokiXp4drWVUs56RyOLHqfgqy5E2xK84DJZZlIOhNfFrD5khgiLXUYPoQigWUb0LWjF75W47ip/xFXvR2506nVpcL4AAdSIoFWaKoMh5RSI2P6zHdJVEBw9xZPaaRqKJn1TCB6CYn+ry/fhXW0XC9x6vFwU3XK44C6z6tvPgSUu8f2bsrO5kIWRHrJiNZ9YwIg/xwxoSQF+TSgSl8gIpwVoEAWIbFtZeISXssgCjHHYTP0aFj5O8P3L1PHtyNQqXzMORNW/FeYLSCvwomHc5Si/Ilz2nGSII0L5qPCshFHsuN+Nk4QPYQ/wALl8H6HebJyE7yTpASkIolQiyaqqf6EbFrEh/KRzun4nTEUBafh58leXweMXzVvAQPoC9xE9fbw82vXqXLdcBuqRuEQYNPrUnWbL6rVQXU7VXUTorJazcbNWOWLX2ktmB47zFT2/3Sfx19u9oHSBeGBkSU3qz4uEJiiCyRwjgO0QW4fjGhsS37iF+FNxwObInV3O5ph7s5vUByPOTimRt2+P1IrMqcZgMSB3PZ5YnCNA+LBaBvEKpsXrBSaeJH9aQB0hmJWhA3hOkZijM6kWf7GJQWN2LVKvvFi6ZJ4QI0iJ+RPD6AGzifZKVJae+HesaHnaEByiC1voskicogtW7kgP2sV/JEOEeiFhvWLWNKwZ/jEUTN5EHiDcRoaPHG2SVJyiCXd6uWHiG1Ky+e+SFt5A1cZxlw2GiDXmZGejsdO9PLFrbtkieoAh6PUIAO0+vXewXwPaF1mhZrVonySzxM7f4Mxzv0nQqRVwtgF5rn4gbcr9ImU6vELJaBEUQfXgsgl7jqWb13dCRTgQbm2WHJHjBS/gA9hY/+Vs7bb0O0HeKNiM7N43JzE8RRRBgTAgBifaGxVCZ3LnNRCTBA1i/OKSZ4oc1rhZAgDbjomcWmJIRMVMEAfZ6q9IihtSuvqs2HQt4LGoYTcdR+ADmiB+nwGr5C1FFEGBcCEVgJYhSHcvCtvGyyyztrtXCB7C3+AFIAAE49RCtWI3VzCnjsdhRDMndI7Wr76pNZxTRvD5mCx/AHeInghtEEBBvH1ksPshSEPE4Dwt42FYRhA/AVvwo3See5SUBFIOVS9Kb7Q2KxS5iSO4eqVl9N63AD19lOcfc8RU+gHbxoFb4ACR+jOIWERSBlVcoFrlyiyRo5OBtN0V45hHUPHM7iB+ABFACbhVBEQZeX2RBFEHN6rsF8y7nEgDNc6gr7hhO4of1is5uFD8RWHl5eYuggbGMehd4Bdh7hZKhxgY5dY01Vn1R7DM38rydJH4Al0+Dl5vKCli7wZ6exhzpiHmvjCxy8GHSdYC+Xn2X9RR4vVtwONXrA2gXP8Ev6vEB3rDlNHilJTRYtW01bU2PnTKy0KtanLI/lxXwED2x6H3GIoif63KOItAVwpAzm5jYDW4C6KGHHsL//u//YsuWLcjIyEBHR0dCmpaWFtxyyy14//33kZOTgwULFqC2thbp6fKOqSNHjmDJkiV488034fV6cfXVV+PJJ59ETk6O6rxFjFjZsysw4axDsums3mVYS2WS65DN2iqClzDSKgZ5rwRtlvAB1IsfLaIH4LOPl1bxE6rfhj6pN6kAsoPtOPrvCrztGSabzs4iCGAnhAASQ2rgLXpi0fNsWYofvWtfRcpmCwG0YsUK5OfnY+/evfj973+fYMRCoRDOOeccFBcX45FHHsGBAwcwf/58LFq0CL/4xS9kz3vJJZfgwIED+O1vf4ve3l7ceOONOPfcc/Hiiy+qzlusAPJmZVrqhjNDBEWw+75ZVmFkw1W9a+CoET8iCJ8IycqpJH4AyAogO9iOo/+uQN7XHiC5DoVV27ZKBEVgKYYAEkQA+36F17MUwesDxJfPFgIowurVq/HjH/84wYj99a9/xeWXX479+/dj+PDhAIBnn30WP/vZz3Dw4EFkZCQOTW3btg3jxo3DZ599hsmTJwMA6urqcOmll2Lv3r0oLS1VlaeBAgiwfiyS1Rujls6aBJE8VogegI/wAcwXP4B+ARRBZNsRK4AAMUQQoN1WaV3bjMSQPnj0IbyfnYjiB2ArgCwLgt6wYQPOPvvsqAEDgFmzZuGWW27B1q1bMWHChKTH5OfnRw0YAMycORNerxeffPIJrrrqqqTXCgaDCAZPdfadnf2GOXzi1Hefbx2G6vL9SY9fs3M8gP7Fz3hxxeCP8Vr7xKS/jRveBABoaE5tpI+X9AAAfM2pO/BB2+L/Dpa7WxDF3rMQejQf79/ZLwT0LvCXvy0ge2xHVUxD71Wft84xXwsfTo/WvzOcNM9yZQk1NEb/34deAIDWdzARbEfgWLzouxSH8JckHcwVgz8GAMW2raZdf761f7hNzkZFWLNzvCY79Yeu/nr1XZWd6aU4FTKQrLxaidyfWOTulZ0Y+AxYrV783ThPiLpjTj2nkOrr9D+Dk4pp+uttalsUqbO93cl//07RZtn7892cownljLQ9Fr4bywRQa2trnAEDEP27tbVV9piioqK479LT01FQUCB7DADU1tbivvvuS/h+3x0r4/7ekyLPf03xu3E2cb8CYVMarM4APw4fPgy/X71XSwTbMWpis+r89sOmbaeyUYA+O7VIxzH8sL8d5NVXmPec2D0DI/2qUnm12o1kaBJAd955J375y18qptm2bRvGjh1rKFOsWb58OZYuXRr9u6OjA6NGjUJLS4vhGygigUAAZWVl2LNnj21m12jF6WV0WvlWrFiBJ554IulvY8aMAUC2QwScVu8G4vTyAc4vY2dnJ0aOHImCggLD59IkgJYtW4aFCxcqpqmoULfXUnFxMT799NO479ra2qK/yR3T3t4e911fXx+OHDkiewwA+Hw++HyJQ0J+v9+RFSRCXl6eo8sHOL+MTinfz3/+c/zoRz+K++7YsWM499xz8dlnnyEnJ4dsh0A4pd7J4fTyAc4vo5fB7F5NAqiwsBCFhYWGLwoA06ZNw0MPPYT29vaoa/qdd95BXl4exo0bJ3tMR0cHNm3ahEmTJgEA3nvvPYTDYUydOpVJvgiCYE8y2xEI9O/NduaZZ2oy1GQ7CIJgAbepIS0tLdiyZQtaWloQCoWwZcsWbNmyBceOHQMAXHzxxRg3bhy+973v4YsvvsDf/vY33HXXXVi8eHH0jevTTz/F2LFjsW/fPgBAVVUVZs+ejUWLFuHTTz/FP//5T9x666247rrrVM/iIAhCbMh2EARhChInFixYIAFI+Lz//vvRNM3NzdIll1wiZWVlScOGDZOWLVsm9fb2Rn9///33JQDSrl27ot8dPnxYuv7666WcnBwpLy9PuvHGG6Wuri5Neevp6ZFWrFgh9fT0GC2mkDi9fJLk/DI6vXySJF9Gsh3WQeWzP04vI8vyuXIrDIIgCIIg3A2/1dEIgiAIgiAEhQQQQRAEQRCugwQQQRAEQRCugwQQQRAEQRCugwQQQRAEQRCuw3UC6KGHHsL06dORnZ2N/Pz8pGlaWlpw2WWXITs7G0VFRfjJT36Cvj69W1xaT3l5OTweT9xn5cqVqQ8UlKeffhrl5eXIzMzE1KlTE1YFtjP33ntvwrMSbXsILfz973/HFVdcgdLSUng8Hqxduzbud0mScM8996CkpARZWVmYOXMmtm/fbk1mFSC7YX+7ATjXdjjNbgDm2A7XCaCTJ0/immuuwS233JL091AohMsuuwwnT57E+vXr8cc//hGrV6/GPffcY3JO2XL//ffjwIED0c+SJUuszpIuXn75ZSxduhQrVqzA5s2bMX78eMyaNSthmwM7c9ZZZ8U9q48++sjqLOmmu7sb48ePx9NPP53094cffhhPPfUUnn32WXzyyScYPHgwZs2ahZ4e9TvemwHZDXvbDcD5tsNJdgMwyXYYXknIpjz//POS3+9P+P7tt9+WvF6v1NraGv3umWeekfLy8qRgMGhiDtkxatQo6fHHH7c6G0yYMmWKtHjx4ujfoVBIKi0tlWpray3MFTtWrFghjR8/3upscAGA9Prrr0f/DofDUnFxsfTII49Ev+vo6JB8Pp/05z//2YIcpobshn1xsu1wst2QJH62w3UeoFRs2LABZ599NoYPHx79btasWQgEAti6dauFOTPGypUrMXToUEyYMAGPPPKILV3zJ0+exKZNmzBz5szod16vFzNnzsSGDRsszBlbtm/fjtLSUlRUVGDevHloaWmxOktc2LVrF1pbW+Oep9/vx9SpU233PMluiI0bbIdb7AbAznZo2gzVDbS2tsYZMQDRv1tbW63IkmFuu+02TJw4EQUFBVi/fj2WL1+OAwcO4LHHHrM6a5o4dOgQQqFQ0ufz1VdfWZQrtkydOhWrV69GZWUlDhw4gPvuuw/nn38+GhoakJuba3X2mBJpT8mep93aGtkNsXG67XCT3QDY2Q5HeIDuvPPOhACwgR8nVPJYtJR56dKlmDFjBmpqanDzzTfj0UcfxapVqxAMBi0uBTGQSy65BNdccw1qamowa9YsvP322+jo6MArr7xiddYcB9kNshtOgeyGPhzhAVq2bBkWLlyomKaiokLVuYqLixNmBrS1tUV/EwUjZZ46dSr6+vrQ3NyMyspKDrnjw7Bhw5CWlhZ9HhHa2tqEejYsyc/Px5lnnokdO3ZYnRXmRJ5ZW1sbSkpKot+3tbXhnHPO4X59shvJcZrdANxnO5xsNwB2tsMRAqiwsBCFhYVMzjVt2jQ89NBDaG9vR1FREQDgnXfeQV5eHsaNG8fkGiwwUuYtW7bA6/VGy2cXMjIyMGnSJKxbtw5z5swBAITDYaxbtw633nqrtZnjxLFjx7Bz505873vfszorzBk9ejSKi4uxbt26qNEKBAL45JNPZGdbsYTshjbsajcA99kOJ9sNgKHtYBmpbQd2794tff7559J9990n5eTkSJ9//rn0+eefS11dXZIkSVJfX59UXV0tXXzxxdKWLVukuro6qbCwUFq+fLnFOdfH+vXrpccff1zasmWLtHPnTmnNmjVSYWGhNH/+fKuzpouXXnpJ8vl80urVq6V//etf0g9/+EMpPz8/bvaNnVm2bJn0wQcfSLt27ZL++c9/SjNnzpSGDRsmtbe3W501XXR1dUXbGADpsccekz7//HNp9+7dkiRJ0sqVK6X8/HzpjTfekOrr66Urr7xSGj16tHTixAmLcx4P2Q172w1JcrbtcJrdkCRzbIfrBNCCBQskAAmf999/P5qmublZuuSSS6SsrCxp2LBh0rJly6Te3l7rMm2ATZs2SVOnTpX8fr+UmZkpVVVVSb/4xS+knp4eq7Omm1WrVkkjR46UMjIypClTpkgff/yx1Vlixty5c6WSkhIpIyNDGjFihDR37lxpx44dVmdLN++//37S9rZgwQJJkvqns959993S8OHDJZ/PJ1100UVSY2OjtZlOAtkN+9sNSXKu7XCa3ZAkc2yHR5Ikyag7iiAIgiAIwk44YhYYQRAEQRCEFkgAEQRBEAThOkgAEQRBEAThOkgAEQRBEAThOkgAEQRBEAThOkgAEQRBEAThOkgAEQRBEAThOkgAEQRBEAThOkgAEQRBEAThOkgAEQRBEAThOkgAEQRBEAThOv5/de49ohOx5UwAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -415,14 +415,14 @@
       "-------------------------------------------------\n",
       "| \u001b[39m1        \u001b[39m | \u001b[39m-0.2361  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9943696\u001b[39m |\n",
       "| \u001b[39m2        \u001b[39m | \u001b[39m-0.2864  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m-0.999771\u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m-0.2149  \u001b[39m | \u001b[35mrbf      \u001b[39m | \u001b[35m1.0      \u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.236   \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9997250\u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.2532  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.9998403\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m-0.2532  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.2788  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.3175170\u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.2229  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m0.7279032\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.2928  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m-1.0     \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.295   \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m-0.508421\u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-0.2625  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.7449728\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m-0.2361  \u001b[39m | \u001b[35mpoly2    \u001b[39m | \u001b[35m0.9944598\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.298   \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m-0.999625\u001b[39m |\n",
+      "| \u001b[35m6        \u001b[39m | \u001b[35m-0.2361  \u001b[39m | \u001b[35mpoly2    \u001b[39m | \u001b[35m0.9945010\u001b[39m |\n",
+      "| \u001b[35m7        \u001b[39m | \u001b[35m-0.2152  \u001b[39m | \u001b[35mrbf      \u001b[39m | \u001b[35m0.9928960\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.2153  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m0.9917667\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.2362  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9897298\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.2362  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9874217\u001b[39m |\n",
       "=================================================\n"
      ]
     }
diff --git a/ruff.toml b/ruff.toml
index bb9ac4a9f..9c08e69ce 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -126,3 +126,6 @@ split-on-trailing-comma = false
 
 [lint.pydocstyle]
 convention = "numpy"
+
+[lint.flake8-pytest-style]
+fixture-parentheses = false
diff --git a/tests/test_acceptance.py b/tests/test_acceptance.py
deleted file mode 100644
index 56d1bbf92..000000000
--- a/tests/test_acceptance.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# import numpy as np
-
-# from bayes_opt import BayesianOptimization
-# from bayes_opt.util import ensure_rng
-
-
-# def test_simple_optimization():
-#     """
-#     ...
-#     """
-#     def f(x, y):
-#         return -x ** 2 - (y - 1) ** 2 + 1
-
-
-#     optimizer = BayesianOptimization(
-#         f=f,
-#         pbounds={"x": (-3, 3), "y": (-3, 3)},
-#         random_state=12356,
-#         verbose=0,
-#     )
-
-#     optimizer.maximize(init_points=0, n_iter=25)
-
-#     max_target = optimizer.max["target"]
-#     max_x = optimizer.max["params"]["x"]
-#     max_y = optimizer.max["params"]["y"]
-
-#     assert (1 - max_target) < 1e-3
-#     assert np.abs(max_x - 0) < 1e-1
-#     assert np.abs(max_y - 1) < 1e-1
-
-
-# def test_intermediate_optimization():
-#     """
-#     ...
-#     """
-#     def f(x, y, z):
-#         x_factor = np.exp(-(x - 2) ** 2) + (1 / (x ** 2 + 1))
-#         y_factor = np.exp(-(y - 6) ** 2 / 10)
-#         z_factor = (1 + 0.2 * np.cos(z)) / (1 + z ** 2)
-#         return (x_factor + y_factor) * z_factor
-
-#     optimizer = BayesianOptimization(
-#         f=f,
-#         pbounds={"x": (-7, 7), "y": (-7, 7), "z": (-7, 7)},
-#         random_state=56,
-#         verbose=0,
-#     )
-
-#     optimizer.maximize(init_points=0, n_iter=150)
-
-#     max_target = optimizer.max["target"]
-#     max_x = optimizer.max["params"]["x"]
-#     max_y = optimizer.max["params"]["y"]
-#     max_z = optimizer.max["params"]["z"]
-
-#     assert (2.640 - max_target) < 0
-#     assert np.abs(2 - max_x) < 1e-1
-#     assert np.abs(6 - max_y) < 1e-1
-#     assert np.abs(0 - max_z) < 1e-1
-
-
-# if __name__ == '__main__':
-#     r"""
-#     CommandLine:
-#         python tests/test_bayesian_optimization.py
-#     """
-#     import pytest
-#     pytest.main([__file__])
diff --git a/tests/test_bayesian_optimization.py b/tests/test_bayesian_optimization.py
index 5c13a6703..48e1af115 100644
--- a/tests/test_bayesian_optimization.py
+++ b/tests/test_bayesian_optimization.py
@@ -333,11 +333,3 @@ def test_duplicate_points():
     optimizer.register(params=next_point_to_probe, target=target)
     # and again (should throw warning)
     optimizer.register(params=next_point_to_probe, target=target)
-
-
-if __name__ == "__main__":
-    r"""
-    CommandLine:
-        python tests/test_bayesian_optimization.py
-    """
-    pytest.main([__file__])
diff --git a/tests/test_observer.py b/tests/test_observer.py
index 8c8d54eb2..24b3e723f 100644
--- a/tests/test_observer.py
+++ b/tests/test_observer.py
@@ -114,13 +114,3 @@ def max(self):
     assert start_time == tracker._start_time
     if "win" not in sys.platform:
         assert previous_time < tracker._previous_time
-
-
-if __name__ == "__main__":
-    r"""
-    CommandLine:
-        python tests/test_observer.py
-    """
-    import pytest
-
-    pytest.main([__file__])
diff --git a/tests/test_parameter.py b/tests/test_parameter.py
index 3fe5eecae..b2394a454 100644
--- a/tests/test_parameter.py
+++ b/tests/test_parameter.py
@@ -2,9 +2,11 @@
 
 import numpy as np
 import pytest
+from scipy.optimize import NonlinearConstraint
+from sklearn.gaussian_process import GaussianProcessRegressor, kernels
 
 from bayes_opt import BayesianOptimization
-from bayes_opt.parameter import CategoricalParameter, FloatParameter, IntParameter
+from bayes_opt.parameter import CategoricalParameter, FloatParameter, IntParameter, wrap_kernel
 from bayes_opt.target_space import TargetSpace
 
 
@@ -168,6 +170,22 @@ def test_to_string():
     assert space._params_config["fruit"].to_string("strawberry", 10) == "strawberry"
 
 
+def test_preconstructed_parameter():
+    pbounds = {"p1": (0, 1), "p2": (1, 2), "p3": IntParameter("p3", (-1, 3))}
+
+    def target_func(p1, p2, p3):
+        return p1 + p2 + p3
+
+    optimizer1 = BayesianOptimization(target_func, pbounds)
+
+    pbounds = {"p1": (0, 1), "p2": (1, 2), "p3": (-1, 3, int)}
+    optimizer2 = BayesianOptimization(target_func, pbounds)
+
+    assert optimizer1.space.keys == optimizer2.space.keys
+    assert (optimizer1.space.bounds == optimizer2.space.bounds).all()
+    assert optimizer1.space._params_config["p3"].to_float(2) == 2.0
+
+
 def test_integration_mixed_optimization():
     fruit_ratings = {"apple": 1.0, "banana": 2.0, "mango": 5.0, "honeydew melon": -10.0, "strawberry": np.pi}
 
@@ -183,3 +201,46 @@ def target_func(p1, p2, p3, fruit):
 
     optimizer = BayesianOptimization(target_func, pbounds)
     optimizer.maximize(init_points=2, n_iter=10)
+
+
+def test_integration_mixed_optimization_with_constraints():
+    fruit_ratings = {"apple": 1.0, "banana": 2.0, "mango": 5.0, "honeydew melon": -10.0, "strawberry": np.pi}
+
+    pbounds = {
+        "p1": (0, 1),
+        "p2": (1, 2),
+        "p3": (-1, 3, int),
+        "fruit": ("apple", "banana", "mango", "honeydew melon", "strawberry"),
+    }
+
+    def target_func(p1, p2, p3, fruit):
+        return p1 + p2 + p3 + fruit_ratings[fruit]
+
+    def constraint_func(p1, p2, p3, fruit):
+        return (p1 + p2 + p3 - fruit_ratings[fruit]) ** 2
+
+    constraint = NonlinearConstraint(constraint_func, 0, 4.0)
+
+    optimizer = BayesianOptimization(target_func, pbounds, constraint=constraint)
+    init_points = [
+        {"p1": 0.5, "p2": 1.5, "p3": 1, "fruit": "banana"},
+        {"p1": 0.5, "p2": 1.5, "p3": 2, "fruit": "mango"},
+    ]
+    for p in init_points:
+        optimizer.register(p, target=target_func(**p), constraint_value=constraint_func(**p))
+    optimizer.maximize(init_points=0, n_iter=2)
+
+
+def test_wrapped_kernel_fit():
+    pbounds = {"p1": (0, 1), "p2": (1, 10, int)}
+    space = TargetSpace(None, pbounds)
+
+    space.register(space.random_sample(0), 1.0)
+    space.register(space.random_sample(1), 5.0)
+
+    kernel = wrap_kernel(kernels.Matern(nu=2.5, length_scale=1e5), space.kernel_transform)
+    gp = GaussianProcessRegressor(kernel=kernel, alpha=1e-6, n_restarts_optimizer=5)
+
+    gp.fit(space.params, space.target)
+
+    assert gp.kernel_.length_scale != 1e5
diff --git a/tests/test_seq_domain_red.py b/tests/test_seq_domain_red.py
index 82c7c87bc..c22dd0d1b 100644
--- a/tests/test_seq_domain_red.py
+++ b/tests/test_seq_domain_red.py
@@ -185,11 +185,3 @@ def test_mixed_parameters():
     target_space = TargetSpace(target_func=black_box_function, pbounds=pbounds)
     with pytest.raises(ValueError):
         _ = SequentialDomainReductionTransformer().initialize(target_space)
-
-
-if __name__ == "__main__":
-    r"""
-    CommandLine:
-        python tests/test_seq_domain_red.py
-    """
-    pytest.main([__file__])
diff --git a/tests/test_target_space.py b/tests/test_target_space.py
index b2e1af801..c269569da 100644
--- a/tests/test_target_space.py
+++ b/tests/test_target_space.py
@@ -294,9 +294,18 @@ def test_no_target_func():
         target_space.probe({"p1": 1, "p2": 2})
 
 
-if __name__ == "__main__":
-    r"""
-    CommandLine:
-        python tests/test_target_space.py
-    """
-    pytest.main([__file__])
+def test_change_typed_bounds():
+    pbounds = {
+        "p1": (0, 1),
+        "p2": (1, 2),
+        "p3": (-1, 3, int),
+        "fruit": ("apple", "banana", "mango", "honeydew melon", "strawberry"),
+    }
+
+    space = TargetSpace(None, pbounds)
+
+    with pytest.raises(ValueError):
+        space.set_bounds({"fruit": ("apple", "banana", "mango", "honeydew melon")})
+
+    with pytest.raises(ValueError):
+        space.set_bounds({"p3": (-1, 2, float)})
diff --git a/tests/test_util.py b/tests/test_util.py
index 37bc52020..9a88262dc 100644
--- a/tests/test_util.py
+++ b/tests/test_util.py
@@ -98,11 +98,3 @@ def c(x, y):
 
     print(optimizer.space)
     assert len(optimizer.space) == 12
-
-
-if __name__ == "__main__":
-    r"""
-    CommandLine:
-        python tests/test_target_space.py
-    """
-    pytest.main([__file__])

From 7c84390b017b7ebcd970ae35da811644755ca787 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Fri, 1 Nov 2024 12:47:00 +0100
Subject: [PATCH 15/21] Remove `tqdm` dependency, use EI acq

---
 examples/typed_hyperparameter_tuning.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/typed_hyperparameter_tuning.py b/examples/typed_hyperparameter_tuning.py
index 592c82b82..1267a29e8 100644
--- a/examples/typed_hyperparameter_tuning.py
+++ b/examples/typed_hyperparameter_tuning.py
@@ -5,7 +5,6 @@
 from sklearn.model_selection import KFold
 from sklearn.metrics import log_loss
 import matplotlib.pyplot as plt
-from tqdm import tqdm
 
 N_FOLDS = 10
 N_START = 2
@@ -34,7 +33,8 @@
 
 METRIC_SIGN = -1
 
-for i, (train_idx, test_idx) in enumerate(tqdm(kfold.split(data.data), total=N_FOLDS)):
+for i, (train_idx, test_idx) in enumerate(kfold.split(data.data)):
+    print(f'Fold {i + 1}/{N_FOLDS}')
     def gboost(log_learning_rate, max_depth, min_samples_split):
         clf = GradientBoostingClassifier(
             n_estimators=10,
@@ -50,6 +50,7 @@ def gboost(log_learning_rate, max_depth, min_samples_split):
     continuous_optimizer = BayesianOptimization(
         f=gboost,
         pbounds=continuous_pbounds,
+        acquisition_function=acquisition.ExpectedImprovement(xi=1e-2, random_state=42),
         verbose=0,
         random_state=42,
     )
@@ -57,6 +58,7 @@ def gboost(log_learning_rate, max_depth, min_samples_split):
     discrete_optimizer = BayesianOptimization(
         f=gboost,
         pbounds=discrete_pbounds,
+        acquisition_function=acquisition.ExpectedImprovement(xi=1e-2, random_state=42),
         verbose=0,
         random_state=42,
     )

From f1e4493aa74e5d99bcf5e2c03d413500bd39166f Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Fri, 1 Nov 2024 12:47:12 +0100
Subject: [PATCH 16/21] Add more text to typed optimization notebook.

---
 examples/parameter_types.ipynb | 137 ++++++++++++++++++++-------------
 1 file changed, 85 insertions(+), 52 deletions(-)

diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
index 29aa62feb..59751afd6 100644
--- a/examples/parameter_types.ipynb
+++ b/examples/parameter_types.ipynb
@@ -1,5 +1,14 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimizing over Parameters\n",
+    "\n",
+    "Sometimes, you need to optimize a target that is not just a function of floating-point values, but relies on integer or categorical parameters. This notebook shows how such problems are handled by following an approach from \"Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes\" by Garrido-Merchán and Hernández-Lobato. One simple way of handling an integer-valued parameter is to run the optimization as normal, but then round to the nearest integer after a point has been suggested. This method is similar, except that the rounding is performed in the _kernel_. Why does this matter? It means that the kernel is aware that two parameters, that map the to same point but are potentially distinct before this transformation are the same."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -14,6 +23,13 @@
     "from sklearn.gaussian_process.kernels import Matern"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at a simple, one-dimensional, integer-valued target function and compare a typed optimizer and a continuous optimizer."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -21,7 +37,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -37,6 +53,7 @@
     "c_pbounds = {'x': (-10, 10)}\n",
     "bo_cont = BayesianOptimization(target_function_1d, c_pbounds, verbose=0)\n",
     "\n",
+    "# one way of constructing an integer-valued parameter is to add a third element to the tuple\n",
     "d_pbounds = {'x': (-10, 10, int)}\n",
     "bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0)\n",
     "\n",
@@ -68,21 +85,23 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see, that the discrete optimizer is aware that the function is discrete and does not try to predict values between the integers. The continuous optimizer tries to predict values between the integers, despite the fact that these are known.\n",
+    "We can also see that the discrete optimizer predicts blocky mean and standard deviations, which is a result of the discrete nature of the function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# We can see, that the discrete optimizer is aware that the function is discrete\n",
-    "# and does not try to predict values between the integers. The continuous optimizer\n",
-    "# tries to predict values between the integers, despite the fact that these are known.\n",
-    "# We can also see that the discrete optimizer predicts blocky mean and standard deviations,\n",
-    "# which is a result of the discrete nature of the function."
+    "Let's look at a mixed-parameter optimization problem!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,13 +131,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "continuous_optimizer = BayesianOptimization(\n",
     "    f=discretized_function,\n",
-    "    #acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
+    "    acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
     "    pbounds=c_pbounds,\n",
     "    verbose=2,\n",
     "    random_state=1,\n",
@@ -129,7 +148,7 @@
     "d_pbounds = {'x': (-5, 5), 'y': (-5, 5, int)}\n",
     "discrete_optimizer = BayesianOptimization(\n",
     "    f=discretized_function,\n",
-    "    #acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
+    "    acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
     "    pbounds=d_pbounds,\n",
     "    verbose=2,\n",
     "    random_state=1,\n",
@@ -140,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -153,21 +172,21 @@
       "-------------------------------------------------\n",
       "| \u001b[39m1        \u001b[39m | \u001b[39m0.03061  \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m2.2032449\u001b[39m |\n",
       "| \u001b[39m2        \u001b[39m | \u001b[39m-0.6535  \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m-1.976674\u001b[39m |\n",
-      "| \u001b[35m3        \u001b[39m | \u001b[35m0.504    \u001b[39m | \u001b[35m-4.726124\u001b[39m | \u001b[35m2.5029536\u001b[39m |\n",
-      "| \u001b[35m4        \u001b[39m | \u001b[35m0.8909   \u001b[39m | \u001b[35m-4.025056\u001b[39m | \u001b[35m2.8839939\u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m0.1895   \u001b[39m | \u001b[39m-4.768219\u001b[39m | \u001b[39m3.8258626\u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m-1.884   \u001b[39m | \u001b[39m-0.278106\u001b[39m | \u001b[39m-4.999266\u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.1732  \u001b[39m | \u001b[39m-2.319678\u001b[39m | \u001b[39m4.9916277\u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m0.1496   \u001b[39m | \u001b[39m-2.960408\u001b[39m | \u001b[39m1.2794799\u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m0.2145   \u001b[39m | \u001b[39m5.0      \u001b[39m | \u001b[39m5.0      \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m0.7503   \u001b[39m | \u001b[39m4.9913061\u001b[39m | \u001b[39m2.2368164\u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m-1.75    \u001b[39m | \u001b[39m4.9955782\u001b[39m | \u001b[39m0.1215197\u001b[39m |\n",
-      "| \u001b[35m12       \u001b[39m | \u001b[35m1.448    \u001b[39m | \u001b[35m4.8295217\u001b[39m | \u001b[35m3.1123113\u001b[39m |\n",
-      "| \u001b[39m13       \u001b[39m | \u001b[39m0.5424   \u001b[39m | \u001b[39m2.6035879\u001b[39m | \u001b[39m3.5449777\u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m1.139    \u001b[39m | \u001b[39m4.9690678\u001b[39m | \u001b[39m3.7619280\u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m-0.7848  \u001b[39m | \u001b[39m4.9731235\u001b[39m | \u001b[39m-4.953273\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.8025   \u001b[39m | \u001b[35m-0.829779\u001b[39m | \u001b[35m2.6549696\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.9203   \u001b[39m | \u001b[35m-0.981065\u001b[39m | \u001b[35m2.6644394\u001b[39m |\n",
+      "| \u001b[35m5        \u001b[39m | \u001b[35m1.008    \u001b[39m | \u001b[35m-1.652553\u001b[39m | \u001b[35m2.7133425\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.9926   \u001b[39m | \u001b[39m-1.119714\u001b[39m | \u001b[39m2.8358733\u001b[39m |\n",
+      "| \u001b[35m7        \u001b[39m | \u001b[35m1.322    \u001b[39m | \u001b[35m-2.418942\u001b[39m | \u001b[35m3.4600371\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.5063  \u001b[39m | \u001b[39m-3.092074\u001b[39m | \u001b[39m3.7368226\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.6432  \u001b[39m | \u001b[39m-4.089558\u001b[39m | \u001b[39m-0.560384\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m1.267    \u001b[39m | \u001b[39m-2.360726\u001b[39m | \u001b[39m3.3725022\u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m0.4649   \u001b[39m | \u001b[39m-2.247113\u001b[39m | \u001b[39m3.7419056\u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m-1.740988\u001b[39m | \u001b[39m3.4854116\u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m0.986    \u001b[39m | \u001b[39m1.2164322\u001b[39m | \u001b[39m4.4938459\u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m-2.27    \u001b[39m | \u001b[39m-2.213867\u001b[39m | \u001b[39m0.3585570\u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-1.853   \u001b[39m | \u001b[39m1.7935035\u001b[39m | \u001b[39m-0.377351\u001b[39m |\n",
       "=================================================\n",
-      "Max: 1.4481057894148166\n",
+      "Max: 1.321554535694256\n",
       "\n",
       "\n",
       "====================  Typed Optimizer  ====================\n",
@@ -176,21 +195,21 @@
       "-------------------------------------------------\n",
       "| \u001b[39m1        \u001b[39m | \u001b[39m0.8025   \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m3        \u001b[39m |\n",
       "| \u001b[39m2        \u001b[39m | \u001b[39m-2.75    \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m0        \u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m0.7987   \u001b[39m | \u001b[39m-0.825462\u001b[39m | \u001b[39m3        \u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m0.43     \u001b[39m | \u001b[39m-4.993422\u001b[39m | \u001b[39m3        \u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m0.7154   \u001b[39m | \u001b[39m-1.047005\u001b[39m | \u001b[39m4        \u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m-0.7853  \u001b[39m | \u001b[39m4.9917202\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m-0.6984  \u001b[39m | \u001b[39m-4.564365\u001b[39m | \u001b[39m5        \u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m0.3414   \u001b[39m | \u001b[39m3.9775494\u001b[39m | \u001b[39m5        \u001b[39m |\n",
-      "| \u001b[35m9        \u001b[39m | \u001b[35m1.428    \u001b[39m | \u001b[35m4.9979954\u001b[39m | \u001b[35m3        \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m1.138    \u001b[39m | \u001b[39m4.9849806\u001b[39m | \u001b[39m4        \u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.1651  \u001b[39m | \u001b[39m-4.981477\u001b[39m | \u001b[39m-3       \u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m-0.4769  \u001b[39m | \u001b[39m4.9926394\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[35m13       \u001b[39m | \u001b[35m2.413    \u001b[39m | \u001b[35m3.1997928\u001b[39m | \u001b[35m3        \u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m0.2625   \u001b[39m | \u001b[39m2.3496062\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m0.678    \u001b[39m | \u001b[39m2.4925340\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m0.8007   \u001b[39m | \u001b[39m-0.827713\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.749   \u001b[39m | \u001b[39m2.2682240\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m0.3718   \u001b[39m | \u001b[39m-2.339072\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.2146   \u001b[39m | \u001b[39m4.9971028\u001b[39m | \u001b[39m5        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m0.7473   \u001b[39m | \u001b[39m4.9970839\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m8        \u001b[39m | \u001b[35m0.8275   \u001b[39m | \u001b[35m4.9986856\u001b[39m | \u001b[35m-3       \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m0.3464   \u001b[39m | \u001b[39m4.9987136\u001b[39m | \u001b[39m-2       \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.7852  \u001b[39m | \u001b[39m4.9892216\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.6627  \u001b[39m | \u001b[39m-4.999635\u001b[39m | \u001b[39m-4       \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-0.1697  \u001b[39m | \u001b[39m-4.992664\u001b[39m | \u001b[39m-3       \u001b[39m |\n",
+      "| \u001b[35m13       \u001b[39m | \u001b[35m1.428    \u001b[39m | \u001b[35m4.9950290\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m1.137    \u001b[39m | \u001b[39m4.9970984\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[35m15       \u001b[39m | \u001b[35m1.641    \u001b[39m | \u001b[35m4.0889271\u001b[39m | \u001b[35m3        \u001b[39m |\n",
       "=================================================\n",
-      "Max: 2.4125141680884403\n",
+      "Max: 1.6407143853831352\n",
       "\n",
       "\n"
      ]
@@ -208,12 +227,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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v6xfHG2+8gR9//BFHjx5Fhw4dlB7/559/cOvWLYwfP17psRs3bij8Ok9LS4NUKlU4Wdrco9z/888/mD59OqZMmYK3335b6fGQkBCcP38eXbt2NXustmDkyJGYOnUqsrKysHnzZvTu3Vtl3ty4cUNp2fXr11GzZk35cCMuLi6QSCRKJ/9XFRAQgEuXLoExpvA/VpV3qlCO8CtHbP5Q7OPHj7Fz50688cYbGDRokNJt0qRJKCkpwd69ewEAAwcOxPnz5+Vd5ZXJqv5atWoBgMoCLiQkBMePH0dFRYV82e+//y4/nCQj+2VR+ZfEiRMnkJKSYtgLVhEPABw5ckS+7NGjRwq9LLoydB9V1atXL0gkEnzzzTcKy1esWAGBQIDXX39d71jJC7rmgjbCwsLQrVs3+a3yOUHaEAqF6N69O/bs2aNwaCYnJwebN29Ghw4d5D0AQqFQ6Zf3tm3bcO/ePYVlurz3ACA+Ph5OTk4YP348Hjx4oPDYw4cPMWHCBNSsWRPx8fFKz61aIH/99dcAoPCerVWrltaxcC0rKwtDhgxBhw4dsHTpUpVthgwZgnv37uHHH39Ueuzx48d49OiRscO0KcOGDYNAIMAHH3yAmzdvqh3nLiUlReHc0bt372LPnj3o3r27fHy3gQMHYseOHSp/aOfl5cn/7tWrF+7fv4/t27fLl5WVlWH16tVaxUw5wq8csfkeO9mJqn379lX5eLt27SAWi7Fp0yYMHToU8fHx2L59OwYPHowxY8YgPDwcDx8+xN69e7Fq1So0b94cISEhcHd3x6pVq+Di4oJatWohIiICQUFBeOedd7B9+3b07NkTQ4YMQXp6OjZu3Kh0+fYbb7yBnTt3YsCAAejduzcyMjKwatUqhIWFobS0lLPX3717d9SrVw+xsbGIj4+HUCjE2rVrIRaLcefOHb3Waeg+qqpPnz7o0qULPv74Y9y6dQvNmzfH33//jT179mDKlCnVXvpOtKNrLpjKZ599hv3796NDhw547733UKNGDfzwww8oLy/HkiVL5O3eeOMNLFiwAKNHj0ZUVBQuXryITZs2KQ2Iqst7D3h+DtD69evx9ttvo2nTpoiNjUVQUBBu3bqFNWvWID8/H7/88ovK92FGRgb69u2Lnj17IiUlBRs3bsTw4cPRvHlzeZvw8HAcOHAAy5cvh5+fH4KCgow2jVtVkydPRl5eHmbMmKF06L1Zs2Zo1qwZoqOjsXXrVkyYMAFJSUlo3749JBIJrl69iq1btyIhIQGtW7c2Sby2QCwWo2fPnti2bRvc3d3Ru3dvle2aNGmCHj16KAx3AgDz58+Xt1m8eDGSkpIQERGBsWPHIiwsDA8fPsTZs2dx4MAB+TmvY8eOxTfffIORI0fizJkz8PX1xYYNG1CzZk2tYqYc4VmOGO16WwvRp08f5ujoyB49eqS2zahRo5i9vb38kvEHDx6wSZMmMX9/f/lAhDExMQqXlO/Zs0c++CCqDK3w5ZdfMn9/fyYSiVj79u3Z6dOnlYY7kUqlbOHChSwgIICJRCLWsmVL9vvvv6scvBcGDHfCGGNnzpxhERERzMHBgdWrV48tX75c48CPVVWN3dB9pOo1lpSUsLi4OObn58fs7e1ZgwYNNA5QXJW6YWbIC/rkQtX3njGGO2Hs+QDFPXr0YM7OzqxmzZqsS5cuCsMZMPZ8uJNp06YxX19f5uTkxNq3b89SUlJUvj815ac6Fy5cYMOGDWO+vr7M3t6e+fj4sGHDhrGLFy8qtZUNmXD58mU2aNAg5uLiwjw8PNikSZOUpiu6evUq69SpE3NyctJ68FVV+6zq+142EPTSpUuV4pLp3LmzVoOvVlRUsC+++II1btyYiUQi5uHhwcLDw9n8+fNZUVGRxjiIalWHO6ls69atDAAbN26cysdl+3njxo2sQYMG8u8IVcOS5OTksIkTJ7K6devK37ddu3Zlq1evVmh3+/Zt1rdvX1azZk3m6enJPvjgA60HKJahHOFHjgj+2xAhhBCOzJs3D/Pnz0deXp7C+X2EaGPPnj3o378/jhw5onLQX4FAgIkTJyqdnmJJKEeMx+bPsSOEEEL45Mcff0RwcLDKCxEIqY7Nn2NHCCGE8MGWLVtw4cIF7Nu3DytXruTFFZbE8lBhRwghhPDAsGHD4OzsjNjYWPl8poToymLOsZMdj6+sYcOG8rHMCCGaUQ4Roj/KH2IpLKrHrnHjxjhw4ID8fo0aFhU+IWZHOUSI/ih/iCWwqHdljRo14OPjY+4wCLFYlEOE6I/yh1gCiyrsbty4AT8/Pzg6OiIyMhKLFi1SOeG7THl5OcrLy+X3pVIpHj58iNq1a9NJqQSMMZSUlMDPzw92drZxgbguOUT5Q6pjazlE+UO4ZLT8MeooeRz6448/2NatW9n58+fZX3/9xSIjI1m9evVYcXGx2ufIBhqkG9003e7evWvCd7L56JpDlD900/ZmCzlE+UM3Y924zh+LuXiiqsLCQgQEBGD58uWIjY1V2abqL6aioiLUq1cPfx/5F7WcXVQ+RxsBBWerb6TBbY9WBj1fH+fuiTU+3sI/T+PjgOGvWxPB2aM6tWetDB/fqeRRGRr1HYXCwkK4ubkZvD5LU10OqcufQ/+cgLOzsylDBQCcPnUSE8aNrrbdqtXr0LpNWxNExC1xaYa5Q6jW8X8vYujkWUrLbTGH9M2fJStXY8YH46pd/9erN6JV63acxlyZMT/PrV3KuUsYPG1Bte22fTkHkS2aqH3cWN9BFnUotjJ3d3eEhoYiLS1NbRuRSASRSKS0vJazC5wNKOxcn2o3f546hmxbX061XDU+7uz8pNp1GPq6NRE4Kf+fNGHO3MViq4dFqsshdfnj7OwMZxfTv4c7dn4FPj6+yMnJhqrfowKBAN4+PujY+RUIhUKTx6cv75L053841zJvIFroGtUGft5iZOXmofK/wBZzSN/8aRfVGd4+vsjV8D728vFF+46vGvV9bMzPc2vXLTIcfl6eyMrNh6qeMQEAP29PdIsM1+p/yHX+WOxJEaWlpUhPT4evr6+5QyHEIllaDgmFQnz0yTwAyh+EsvsfzZ5nMUWdd0n6i6LOQgiFQiyOnwQAsMFaToG++SMUCjHj488BqH8fz/joM4t5H9sioVCIJVOf97pWTQPZ/S/ixpntf2gxhd306dNx+PBh3Lp1C8nJyRgwYACEQiGGDRtm7tAIsQjWkEPde7yOld+sgre34pWJ3j4+WPnNKnTv8bqZItOeJRZ0lfXp2gnrl86Hr5fm0zusDZf5061Hbyz7ag28qryPvXx8seyrNejWozdXYRMj6dslChsWfwRfL8V5bv28PbFh8Ufo2yXKTJFZ0KHYzMxMDBs2DA8ePIBYLEaHDh1w/PhxiMW29eFCiL6sJYe693gdXbt1x+lTJ5GXlwux2Aut27TlfQ+HJRdzVfXp2gm9XmmPxORTKs+5s0Zc50+3Hr3RpVtPnD19HPl5OfAUe6NV63a8fx+TF/p2iULvThFIPpeK7PwC+Hh6IKpFY7P/Dy2msNuyZQtn6wooOKv3+QUZHm0M3n5QwSmD16HzNmupjl0eS4H65wpOHeY8HtamM2fr0jc+wePy6htZES5zyFBcFDl9G3sB8Hp+p+yWTs/NcQkxePvasIZizvGe+pkVXvHR7dxYS2aM/BEKhWgT0R7Af5/FxXRBQ3WM8X1kiBoAOgHASwCkBYAOFwIa6zvIYgo7PtG3uDNHQWcpuCz0CL/wsbipLiZDCj8+vl5taSriCCGWgQo7QginLLmwkbGG16ANKuSIviQSCe8OQWpLIpUiOS0T2cWP4ONaC1H160Bo4gG2JVIpUtIyjbJuKuwIIQazlULI0lEhR7iwNykZM5avxv3cfPkyPy9PLJk6zqwXDWhjz7nr+HB7Iu4VlsqX+bs744tBXdGvRajZYuCSxVwVSwjhF9nVnVTU8Z/jvatU1BFO7E1KRvTMhQpFHQBk5eYjeuZC7E1KNlNk1dtz7jqif9qjVFDdLyxF9E97sOfcdbPFwCUq7AghOqldlIYbSTuw/c9EHD19DhKJxNwhETWMVdBJJFIcuXAdO4/Syf62RCKRYMby1SoH5ZUt+3DFal5+JkikUny4PVFz7NsPQiKVmiUGLtGhWEKITiIHj0F23gP5fT9vMRbHT0Kfrp3MGBWpzJi9c7uT/8X01VtxL7/QaNsg/JR8LlWpp64yBuBeTj6Sz6WiY3gz0wWmheS0TI29ZAzAvcISJKdlomNoPbPEwBXqsSOE6KRyUQcAWbl5iImfi98Sj5gpIiJj7EOuu5P/xfCFq6mos1HZ+RrGxdKjnSllFz/itJ0xYzAU9dgRQgzC2PPppWYt/Qa9XmlvMVfGWTqJRIKUfy8iO+8BfMS10cXLARAa57f607QbkEilmPbdZqMfRiL85ePpwWk7U/Jx1W4uZm3bGTMGQ1FhRwgxGGPAvZw8pPx7ER1at9DqObr2LD3xf1mPyKzTb4lHMHPpN7ifkydf5u/pjmXjhqB/VEuD1/807YbSsuT0e7hvgsNIhL+iWjSGn5cnsnLzVRb4AjyfUiuqRWNTh1atqPp14O/ujPuFpepjd3dBVP06ZouBK3QolhDCmaqHaWVkhwgr33TFxTqswW+JRxATP1ehqAOA+/mFGL5wNXYn/6vXep+m3ZDfVDHVYSTCX0KhEEumjgPwYrJ7Gdn9L+LG8bLXXmhnhy8GdQWgIfZBrxp1PDtNMXCJCjtCCGd8xLXlf5uiALO1Ak8ikWDm0m/AVPzcly2KX70NEon2V/ZpKuYqM9VhJMJvfbtEYcPij+Dr5amw3M/bExsWf8Trcez6tQjFhnf6wc/dWWG5n7sLNrzTzyTj2KmLgUt0KJYQYjCBAPDzEqOLlwOEZii0ZMWdtR+uTfn3olJPXWUMQGZ+AY6lpqFTs+q/pLQp6GSiQvzh5+6MLCMfRiL817dLFHp3irDImSf6tQjFG83qm3XmCVkMB1IzMOiHnZyvnwo7DVRNNhwMxWXpvx3Xfn192hkcU3U0zbmq61y1XE+2rLCvqtlvmYk5CvfrdPXWejshJtjP5AWBAAADlsUOgNBIJ+9ry9oLPHWHuqvKKijS+LguBZ2M0M4OSwZ0QfS63/Dfv5zoIaDgLFyf1uR0nVx/VmujBoBOAPASAGkBcPYoL+f8Vvcd7fffDVlFuHXtvso2Vb+HZHT5PlIlpE87CO3sEGmk8/noUCyxOroU20R3lQ+3AoB/bQ9s/mgcJyftc8VaD89W3ffq+Hq4qX1Mn6JOpnfTEMzqGQn3mo56r4MQW2fs7yjqsSOE6CRl21pcunETD65fhK+HG9o3rm/2njpVHO9dtbqeu8iWTeHnLUZWTp7aK/v8PT3QvnF9zre99/wNzNiVpHBlrJuTA4oeV3C+LUKI/vj3aazG999/j2bNmsHV1RWurq6IjIzEn3/+ae6wCLEYXOWQnVCIbr6OGNq5DTo1C+VlUWethEIhFsdPAqD+yr6l4war/Z/o21u39/wNRK/7TWm4E1sq6rjKn5Rzl3g55ZYhKp49w7cHT2P60lX49pfdqKiw/PeFhDFckJbhsLQYF6RlkKi6YsmQ9UulSEnL5HSdMhbTY1enTh0sXrwYDRo0AGMM69evR79+/fDvv/+icWP+jZlDCN9wlUOOWdeBmk7VtpNIpDiWmoasgiKz9exZY6/d4Je9YP/ROKVpvfw9PbB03GDOD4lLpFLM2JVk8+fUcZU/g6ctgJ+XJ5ZMHcfrK0i1NXvXIXx98DSklQqfj79ai/eH98en748xY2T6S8zJx5Ir6ciRvihQPVED4+zEiLJz4WT9y+f8YLTpxSymsOvTp4/C/c8//xzff/89jh8/ToUdIVowZQ6pmk+UywF0bV3/qJboE9HcJIUzDUz8HJf5k5Wbj+iZC3k/PEh1Zu86hJWJyhflSaVSrNz4/GpPSyvuEnPyMf3cFaUfMvl4hoXSLHwEYAj0v3hC3fq5ZJHHUCQSCbZs2YJHjx4hMjLS3OEQYnGMmUPq5hM1dABdokgotEOnZqFGPyROAxMrMzR/ZF/qH65YbbGHZSuePcPXB09rbPPNZss6LCthDEuupGssulZL8/Q+LKvN+rlgUYXdxYsX4ezsDJFIhAkTJmDXrl0ICwtT2768vBzFxcUKN0JsmS45pE/+SCRSTF+9VeUHl74D6BJFpj60TAMTv8Bl/jAA93LykXwu1QSRc+/HI+cUDr+qIpFK8eOOP0wUkeHOFhQhp1xzIZqPZzhbzXBChqyfCxZV2DVs2BDnzp3DiRMn8O677yImJgaXL19W237RokVwc3OT3+rWrWvCaAnhH11ySJ/8OZaaptRTV1nlAXSJZZANTGzMKZAshTHyJzu/wJghG81NDXleWUZmtnED4VC+lkWXtu24ep6uLKqwc3BwQP369REeHo5FixahefPmWLlypdr2s2bNQlFRkfx29+5dE0ZLCP/okkOa8kcikeLIhev49fApHLlwXd4DV93AuDLatiPcsq/fQOfnyAYmBow7v6Ul4Cp/KvPx9DBmyEYT7OmuVbugOj7GDYRDniIHTttx9TxdWczFE6pIpVKUl5erfVwkEkEkEpkwIkIsi6YcUpc/v5+4gDnrd6u8MELTwLiVadvOUNZ2RazME/+X9R6E2b5+A52HPenbvAE2jO6jNI6dr5szsops98IKffJHRoDn86tGtbDMi//GdmqBj3Yd0ng4Vmhnh7EDe5kwKsO08nCDt8gBueUVas+D80QNtNLz80ub9XPBYgq7WbNm4fXXX0e9evVQUlKCzZs349ChQ0hISDB3aIRYBK5yKPbLn5WWyS6M2DjzHfh7uuN+fqHJB9C1NeYo7no3DUFy+j35HJtN/D0R8NH3esVgabj8DpL1fH4RN84i5ldVxaFGDbz/amuVV8XKTBreHw4Opuml4oJQIMCMRiGYfu6K2mnzxtmJIRTo13etzfq5YDGFXW5uLkaOHImsrCy4ubmhWbNmSEhIwGuvvWbu0AixCMbMIYbnX1Yf/rQDS94ZjBGLf1T64NJmAF0uWWtvXWWGFneAboMWC+3s0LHBi3PFip+oP2JibbjMHz9vT3wRZ/nj2H024BUAUBrHTmhnh0kWOo5dV29PLGvR6Pk4duXcj2MnW//yW3dpHLs1a9aYOwRCLJqxc0h2YYSnqzM2m3AAXVV0KeokEglS/r2I7LwH8BHXRmTLptX2ouS4hGi1bu+SdK3j0JchxR2gX4EH/Ddyfvo9vbdrabjKn21fzkG3yHCL7amr6rMBr2BOnw748cg53LSrhaA6Phg7sJdF9dRV1dXbE6941UbCgVsowDN4oAYaC5z07qlTtf7YMb1wIDUDg37Yyck6K7OYwo5Uj7XpbO4QeCGkTztzh2DTsgqKMLRzG5MNoGuI3xKPYObSb3A/J0++zM9bjMXxk9Cnayel9toWdKraG7PIM7S4A5QvrNBU6KmaN5ZoJ7JFE6sp6mQcatTAxFdbm+Q7iLXpDMGpw0bfjlAgQDO7mkZZt+w7KrJ+HaOsX8AYxxOg8VhxcTHc3Nxw7GwanJ0NnxZE5kyml9Ky8Dq5Wj8/qED9OQq6yvBow8l6uIxJG7rErU9sqj4Iihq1QZ2uQ1BUVARXV1ed12lrZPlTnYSFcejULNQEEammbW/db4lHEBM/F1U/AWU/ytcvnS8v7nQt6DQxdi+eoQVedWQDUFfebZRD1ZPlT2biVrg6G6dgIJaluLTMKN9B1GNHCOGEuS+M0PXw68yl3ygVdQDA2PPibtbSbxDeZyTnvSuyItFYBZ5sPxijwNM0ADUhfCeRSJB8LhXZ+QXw8fRAVIvGVtd7ClBhRwjRg0AAhaLI1BdGVKXrhRIp/15UOPxaFWPAvZw8nD51EhHtjDNtoakKPIC7Iq+6AagJ4au9ScmYsXw17ufmy5f5eXliyVTLv4ilKn6d8EII4b0fPpsFXy+xwjI/bzHWL5tvkgsjqtLn6tfsvAdatcvL0/6UCn3luITIb8byxP9lhZu+aGBpYon2JiUjeuZChaIOALJy8xE9cyH2JiWbKTLjoB47QohOenZuj4E9u6q8kvTJf22MfZ4XYNhwJj7i2lq1E4uVz581JmP34smo2nfa/M9MNbA0IVyRSCSYsXy12vmrBQA+XLEavTtFWM1hWSrsCCE6EwqF6NC6hdrHjXmeFxfj00W2bAo/bzGycvNUnmcnEAjg7eOD1m3aGrwtfZjqatrKtNmv4T4N4Ldyo9r9RgjfJJ9LVeqpq4wBuJeTj+RzqegY3sx0gRkRHYolhBgNF4f/uFyPjFAoxOL4SQBeXAUrI/hvwUez5/HiF3zlQ7XGPFyrDU37jRA+ys4v4LSdJaAeO0KISfBtJog+XTth/dL5mLFsFbKzs+TLvX188NHseeje43UzRqde1eLOVD16MrL9VnX8P0L4yMfTg9N2loAKO0KIzWrbfzQS+4zE6VMnkZeXC7HYC63btOVFT5221PXiGbPg69O1E3q90h6JyacwdPIso22HEENFtWgMPy9PZOXmq52/2s/bE1EtGps6NKOhwo4QYnMqF0NCodBoQ5qYky6HbfUpAoVCIdq1bKrz8wgxJaFQiCVTxyF65kK181d/ETfOon7MVYcKO0IIr+gzd6suzH2eGh/pu09KUcJxJIRwr2+XKGxY/JHyOHbenvgizvrGsaPCjhDCG7rO3aorKuoIsU19u0Shd6cImnmCaCabszSolooHq7nAhqs5Xa2FqeemJfyjbu7WrNw8xMTPVZi7VR9U1BFi24RCodUMaaKJTRZ25+6J4VRLecLd8DrGH2VeFV2KvDOZ1QyY+sjAYGTbQW+Vy7XdR3wr1G52n660rLSUDiPxhbZzt/Z6pb1ev7CpqCN8saesB5wE3Ez4bq7vLHWq+9ynDg1FpfbG+Q7SubArLy/HiRMncPv2bZSVlUEsFqNly5YICgoyRnyEWBXKH9W0nbs15d+LGgdGrooKOsPcfaT4Q7JuLfMXEpRDxBpIJBKcPX3cKOvWurA7duwYVq5cid9++w1Pnz6Fm5sbnJyc8PDhQ5SXlyM4OBjjxo3DhAkT4OLiYpRgCbFUlD+aaTt3q7btAOsu6qoWXOba7qMyR5Ntm3KIWIsDCfuw5POPkVNp/EwuaTXzRN++fTF06FAEBgbi77//RklJCR48eIDMzEyUlZXhxo0bmD17NhITExEaGor9+/cbJVhCLBHlT/W0nbtVm3Z8mKHBUHcfeWm82RrKIWItDiTsw/TJsUYr6gAte+x69+6NHTt2wN7eXuXjwcHBCA4ORkxMDC5fvoysLOMFTIilsbb8yXMOgiu4PSRX/dytgJ+XGJEaxk2z1GLOFgs1XVlbDhHbJJFIsOTzj8GMPNGyVoXd+PHjtV5hWFgYwsLC9A6IEGtjjfkjK6K4mt1ANgdpTPxcCARQKO5kc5Iuip+k8sIJSyvoqJDTnTXmELE9Z08fN2pPnYxWh2IrCw4OxoMHyue5FBYWIjg4mJOgCLFW1pY/XE5OL5uD1NdLrLDcz0uscqgTSzvkasmHUW/liVTe7uSLTB6LteUQsR35eTkm2Y7OV8XeunULEolEaXl5eTnu3bvHSVCEWCtrzh8uJqeXzUGqauYJSyriKuNzMXcrz/SFmaGsOYeIdfMUe5tkO1oXdnv37pX/nZCQADc3N/l9iUSCxMREBAYGchocIdbCFvNH38nphUIhGnQZiAb/3c/X2Jrf+FTUWWIRV5kt5hCxLq1at4O3jy9yc7KNep6d1oVd//79AQACgQAxMTEKj9nb2yMwMBBffvklp8ERYi0of16w1J43XfChoLP0Qq4qyiFi6YRCIWZ8/DmmT46FQCAwWnGndWEnlUoBAEFBQTh16hQ8PT2NEhAh1ojyx3aYu6iztoJOhnKIWINuPXpj2VdrjDqOnc7n2GVkZBgjDkJsAuWPdTNXUWetxZwqlEPE0nXr0RtduvXEsX8O4v1xIzhfv15zxSYmJiIxMRG5ubnyX1Eya9eu5SQwQqwV5Y91oqLOdCiHLJM55xDn2zy1QqEQrVq3M8q6dS7s5s+fjwULFqB169bw9fWFQDbIlAX5v2+OooZ9LaXla3Rek7PKpU07Ndd5TdorNOK6q3fytIOWLdsbNQ4uVDxRPdipMVlD/hB+MHZBd/NWucbHpVIJbl5MNGoMqlh6Dqn7/qlM2++Qk6cd0La1u84xhNcxzpy/1RVP5izsbInOhd2qVavw888/Izo62hjxEGLVKH+sk6l764xV1FVXzMlcO7Mb+7dMQ0mB6YcXsfQcKn5wAR7ebSEQKA+2TQgXdB6guKKiAlFRUcaIRSvffvstAgMD4ejoiIiICJw8edJssRCiK8of62MNRd3NW+U6FXU7vx9mlqIOsPwcunJ6Fk4nvoUHWUeMFCGxdToXdu+88w42b95sjFiq9euvv2Lq1KmYO3cuzp49i+bNm6NHjx7IzTVOtzIhXKP8IYbguqjTpaADnh9+3b9lGgDjznWpiTXkUMWTPFw9M4eKO2IUOh+KffLkCVavXo0DBw6gWbNmSpMyL1++nLPgqlq+fDnGjh2L0aNHA3jeJb9v3z6sXbsWM2fONNp2CeEK5Y91MffQJobQpaCTuXv9qNl66mSsKYdupn6Dl3za02FZwimdC7sLFy6gRYsWAIBLly4pPGbMk1grKipw5swZzJo1S77Mzs4O3bp1Q0pKisrnlJeXo7z8xYdXcXGx0eIjRBuUP0RfXPbW6VPUAUBpUTZnMejLUnJIm/ypeJKL4gcX4ObZ0jhBE5ukc2GXlJRkjDiqlZ+fD4lEAm9vxbnWvL29cfXqVZXPWbRoEebPn2+K8AjRCuUPsWTObj7mDsFickjb/Kkof8hZjIQAepxjZ0lmzZqFoqIi+e3u3bvmDokQi0H5o5kpD8PyZay6uqEd4OLhD8CyhhgxB23zx0H0kokjI9ZOqx67N998Ez///DNcXV3x5ptvamy7c+dOTgKrytPTE0KhEDk5OQrLc3Jy4OOj+lekSCSCSMSPD0Riuyh/iLWwsxPitbe+xM7vh+F5cWeaiygsMYe0yR8HRy+41m7GaZyEaNVj5+bmJj93wc3NTePNWBwcHBAeHo7ExBcDYkqlUiQmJiIyMtJo2yXEUJQ/1seSL5owVMPw/njz3V/g4uFnsm1aaw4FN55EF04QzmnVY7du3TqVf5va1KlTERMTg9atW6Nt27b43//+h0ePHsmvUCKEjyh/CN8EB4r0voACeF7cNWjZBzcvJmDb15p70LhgbTnk4OiF4MaTUNu3k5EiJbZMr7lizWXo0KHIy8vDnDlzkJ2djRYtWuCvv/5SOpmVEKKM8sdyBYrLeXOenYydnRB1QzuYOwyT4iKHGrVeRDNPEKMSMMaqPUmiZ8+emDdvHtq10zxhbUlJCb777js4Oztj4sSJnAXJleLiYri5uWHMgttwcHSttr0+c/CZwsnThSbbFtf7wFhzFFZ1JrP6Q2WPHxVjYh83FBUVwdW1+veDvqwtf07/mwpnFxdzh2M25joMa6wZJwxR/rgYy9/3ohzSgq7fP1UZ8llsqs/dqqx9btjq5satTmlpCdq3qs95/mjVYzd48GAMHDgQbm5u6NOnD1q3bg0/Pz84OjqioKAAly9fxtGjR/HHH3+gd+/eWLp0KWcBGkN4S3c41VK/E18kAT9G5K9apOia4NUVgvp+YGj7YaGQ3AV6bUpnQf/Nsa0p8UpLS0wSi7XlD7EewYHPi0VDCzxjs6Ycqu77x6B1m6mAA6y/iFPF0NdcXFrGUSSKtCrsYmNjMWLECGzbtg2//vorVq9ejaKiIgDPB4QMCwtDjx49cOrUKTRq1MgogRJiqSh/CBeMeTiW7wUe5RAh2tP6HDuRSIQRI0ZgxIgRAICioiI8fvwYtWvXVprShRCiiPLHOpj7alhjn2snK/AA/hV5lEOEaEfviyeMfWk5IdaM8ofwXeUiD+BfoUc5RIhqFnVVLCGE2DpzXSFbtdADgMeP+HWlLiHEyqcUI4QQrpj7MGxlgWJ+9Z4RQviDCjtCCKkGn4o6mUBxORV4hBAlVNgRQogFo+KOEFKZzoVdTEwMjhw5YoxYCLF6lD+Wh4+9dVXZUnFHOUSIZjpfPFFUVIRu3bohICAAo0ePRkxMDPz9/Y0RGyFWxxryJ7NMjFp2hg+wWrcWPwYAV8cSCrrKZMUd36Ye45o15BAhxqRzj93u3btx7949vPvuu/j1118RGBiI119/Hdu3b8fTp0+NESMhVoPy54W7j7xU3viAL3How9rPvaMcIkQzvc6xE4vFmDp1Ks6fP48TJ06gfv36iI6Ohp+fH+Li4nDjxg2u4yTEalD+aKau4DN2scW34tJQsgLPGos8yiFC1DPo4omsrCzs378f+/fvh1AoRK9evXDx4kWEhYVhxYoVXMVIiFWi/NGdpqJPl4KMrz2FxmKtRR7lECHKdD7H7unTp9i7dy/WrVuHv//+G82aNcOUKVMwfPhwuLo+P+9m165dGDNmDOLi4jgPmAst/PPg7PzE3GFozdCJncPrvPj7TKZXpeWy9Wq3fqUJjwsMCsskNE3SbKwJmDWxhvzhM2sv0LigqrizpPPyLD2H+Pr9Y+iE9oQ/dC7sfH19IZVKMWzYMJw8eRItWrRQatOlSxe4u7tzEB4/VC6GDGVokWbpMjzaGG3dlvDBZIv5Q/hP3568R06m7wGkHCJEM50LuxUrVmDw4MFwdHRU28bd3R0ZGRkGBUaINbKG/Ll09ihat+8BoVBo7lCIDbKGHCLEmHQ+xy46OlpjQhFC1LOG/Jn9/psY8Xoo/jmw29yhEBtkDTlEiDHRzBOEEJ3l597HgunDqLgjhBCeocKOEKI7xgAA3y+ZDolEYuZgCCGEyFBhRwjRD2PIy8nEpbNHzR0JIYSQ/1BhRwgxyIP8bHOHQAgh5D9U2BFCDFLb08fcIRBCCPmPzsOdEEIIAEAggNjLH01adTB3JIQQQv5DPXaEEN0JBACAd2cso/HsCCGER6iwI4ToTOzljznLfkHHbv3NHQohhJBK6FAsIUQnn329k2aeIIQQnqLCrhJ1c40G1VK8Lzh1WP+NXAZYm876P19L6uZkrW6uWi7nW1W1n4Khet+l/3Zcp3WH9GmnV0yVCR6bfp5La9CkVQcq6gjhIUuYL5sYHx2KJYQQQgixElTYEUIIISay/6+9OHXiGM3YQozGYgq7vn37ol69enB0dISvry+io6Nx//59c4dFiEWg/CHEMFzl0LyPpuKd6DfxepdwHEjYZ4RIia2zmMKuS5cu2Lp1K65du4YdO3YgPT0dgwYNMndYhFgEyh/Ctbq1clGnZp65wzAZrnMoNycb0yfHUnFHOGcxF0/ExcXJ/w4ICMDMmTPRv39/PH36FPb29maMjBD+o/whXKhbS/PFV9aM6xxijEEgEGDJwtno0q0nXZBEOGMxhV1lDx8+xKZNmxAVFaUxocrLy1Fe/uLKx+LiYlOERwivUf4QXdlyQaeKNjmkTf4wxpCTdR9nTx9Hm4j2RouX2BaLORQLAB9++CFq1aqF2rVr486dO9izZ4/G9osWLYKbm5v8VrduXRNFSgj/UP4QfVBR94IuOaRL/uTn5RgjXGKjzFrYzZw5EwKBQOPt6tWr8vbx8fH4999/8ffff0MoFGLkyJFgjKld/6xZs1BUVCS/3b171xQvixCToPwhxmbtRZ0xc0iX/PEUe3P+2ojtMuuh2GnTpmHUqFEa2wQHB8v/9vT0hKenJ0JDQ9GoUSPUrVsXx48fR2RkpMrnikQiiEQiLkMmhDcof4gxWXtRBxg3h7TJH4FAAC8fX7RqbfiA64TImLWwE4vFEIvFej1XKpUCgMI5DITYEsof3d3KM26hGii2jv1pC0UdYN4cEggEAIAZH31GF04QTlnExRMnTpzAqVOn0KFDB3h4eCA9PR2ffPIJQkJC1PY2EEKes9X8MXYRp+02raXYs2XGyCEvH1/M+OgzdOvRm+Noia2ziMKuZs2a2LlzJ+bOnYtHjx7B19cXPXv2xOzZs+lQESHVsJX8MUchp42qcVGhZ3m4zKF5C5ejTt1AtGrdjnrqiFFYRGHXtGlTHDx40NxhEGKRrDl/+FrMaVI5Zj4WebZyGFYXXObQOxF14OrsABSf5WR9hFRlEYWdqWR4tKm2zZlMLyBMv67z8Drm/cA8k+mltKxqTKr2QVDBKb22x9p0VvtY1e0Iuuu27puV/tY7vtIyAF/p9VxiXpZY0Kkiex18LPAIsXbafOcbU6l9iVHWa1Hj2BFCiLUUdZXdyhNZ5esihJge9dgRQiyCLRQ+1INHCDEU9dgRQnjPFoq6ymzt9RJCuEOFHSGE12y1yLHV100IMQwVdoQQ3rL14obOvSOE6IoKO0IIL1FB8wLtC0KItujiCUII75ijkLl5S78LFoIDTRPrrTyRSS6quPvIi8ayI8SCUWFHCOEVUxV1+hZy1a3HmIWeqYo7QojlosKOEGIzuCrmtNmGqXryCCGkMjrHjhDCG8bsrTNFUVd1e8bYJp1vRwjRhHrsCCFWzdQFnbrtc9mDR4dkCSHqUI8dIcRqmbuoq4xPsRBCrBf12BkgvA5dOca1oIJTGh8396TNxHi4PsTIx0Lq5q1yOveOEGJUNlnYrfndHg6ODhrbtG3tzvl2z2R6cb7O6pw8XVjpXqGKx1/sB3Wv+Qx6cxKLsQphfYu9UvsSjiMhfMHHok6Gq+LOnIdjJRIJTp86ibt3b5tl+4Row1Y7AmyysCOEEG1JpRLcvX4UpUXZcHbzQd3QDrCzExq0Tkvuufs74U8s/HQesrOzzB0KIRZLIpHg7OnjRlm3xZxjFxgYCIFAoHBbvHixucMixCLYUv5w2Vt37cxufPdhKDYv64G9P8Zg87Ie+O7DUFw7s5uzbViSvxP+xAeTJthkUWdLOUSM60DCPrzeJRzvjxthlPVbVI/dggULMHbsWPl9FxcXM0ZDiGWh/NHNtTO7sfP7YQCYwvKSgvvY+f0wvPnuL2gY3t8ssRmTulknJBIJFn46D4wxlY/bAsohYqgDCfswfXKsUfPIogo7FxcX+Pj4mDsMQiwS5Y/2pFIJ9m+ZhqpF3XMMgAD7t0xHg5Z99D4sa2mHY0+fOmmTPXWVUQ4RQ0gkEiz5/GOj/ziyqMJu8eLF+PTTT1GvXj0MHz4ccXFxqFFD/UsoLy9HefmLwzJFRUUAgIon1Z80//hR9UepS0t1O/n+8SNHndpzoeJJsdZttXnNhtBmfxWXlmleB4cXPDz6Lx5b6YHgKn/KHhnnohOxE3An3/BCp/yx4Ydi714/ipKCexpaMJQUZOLmxQTUDe2g93YePzLs9T5y4v7iiVKp6v+vpgslKIeUqcufkkeaP+MId/h2gdzZ08eRo+LHEef5wyzEl19+yZKSktj58+fZ999/z9zd3VlcXJzG58ydO5fh+c9rutFN7S09Pd1E72LzofyhmzFvlEPKKH/opu2N6/wRMGa+n1ozZ87EF198obHNlStX8PLLLystX7t2LcaPH4/S0lKIRKp/9Vb9xVRYWIiAgADcuXMHbm5uhgWvp+LiYtStWxd3796Fq6srxWDGGIqKilCvXj0UFBTA3d3dLDEYgvLHdt+7fImDckh9DvExfwB+vG8ohueMlT9mPRQ7bdo0jBo1SmOb4OBglcsjIiLw7Nkz3Lp1Cw0bNlTZRiQSqUw4Nzc3s34gA4CrqyvFwJMY7Ows5uJwBZQ/FANf4qAcUs4hPucPwI/3DcXwHNf5Y9bCTiwWQywW6/Xcc+fOwc7ODl5eph/0lxA+oPwhxDCUQ8QaWcTFEykpKThx4gS6dOkCFxcXpKSkIC4uDiNGjICHh4e5wyOE1yh/CDEM5RCxJBZR2IlEImzZsgXz5s1DeXk5goKCEBcXh6lTp+q8nrlz56o9p8gUKAaKwdQof6wvBr7EwYcYTIGLHOLLvuJDHBSDcWMw68UThBBCCCGEO5Z5xishhBBCCFFChR0hhBBCiJWgwo4QQgghxEpQYUcIIYQQYiWsurALDAyEQCBQuC1evFjjc548eYKJEyeidu3acHZ2xsCBA5GTk6N3DLdu3UJsbCyCgoLg5OSEkJAQzJ07FxUVFRqf98orryjFPmHCBK23++233yIwMBCOjo6IiIjAyZMnNbbftm0bXn75ZTg6OqJp06b4448/tN5WVYsWLUKbNm3g4uICLy8v9O/fH9euXdP4nJ9//lnp9To6Gja37rx585TWqWoE+cq43A/WwNw5ZIv5A/Ajhyh/DGer+QPQd5BZ84fTCcp4JiAggC1YsIBlZWXJb6WlpRqfM2HCBFa3bl2WmJjITp8+zdq1a8eioqL0juHPP/9ko0aNYgkJCSw9PZ3t2bOHeXl5sWnTpml8XufOndnYsWMVYi8qKtJqm1u2bGEODg5s7dq1LDU1lY0dO5a5u7uznJwcle2PHTvGhEIhW7JkCbt8+TKbPXs2s7e3ZxcvXtT59TLGWI8ePdi6devYpUuX2Llz51ivXr1YvXr1NO77devWMVdXV4XXm52drdf2ZebOncsaN26ssM68vDy17bneD9bA3Dlki/nDGD9yiPLHcLaYP4yZP4dsPX+svrBbsWKF1u0LCwuZvb0927Ztm3zZlStXGACWkpLCWVxLlixhQUFBGtt07tyZffDBB3qtv23btmzixIny+xKJhPn5+bFFixapbD9kyBDWu3dvhWURERFs/Pjxem2/qtzcXAaAHT58WG2bdevWMTc3N062JzN37lzWvHlzrdsbez9YIj7mkK3lD2PmySHKH8PZYv4wxr8csrX8sepDsQCwePFi1K5dGy1btsTSpUvx7NkztW3PnDmDp0+folu3bvJlL7/8MurVq4eUlBTOYioqKsJLL71UbbtNmzbB09MTTZo0waxZs1BWVlbtcyoqKnDmzBmF12BnZ4du3bqpfQ0pKSkK7QGgR48enL3moqIiAKj2NZeWliIgIAB169ZFv379kJqaavC2b9y4AT8/PwQHB+Ptt9/GnTt31LY19n6wVHzLIVvLH8B8OUT5Yzhbyh+Anzlka/ljETNP6Gvy5Mlo1aoVXnrpJSQnJ2PWrFnIysrC8uXLVbbPzs6Gg4MD3N3dFZZ7e3sjOzubk5jS0tLw9ddfY9myZRrbDR8+HAEBAfDz88OFCxfw4Ycf4tq1a9i5c6fG5+Xn50MikcDb21thube3N65evaryOdnZ2Srbc/GapVIppkyZgvbt26NJkyZq2zVs2BBr165Fs2bNUFRUhGXLliEqKgqpqamoU6eOXtuOiIjAzz//jIYNGyIrKwvz589Hx44dcenSJbi4uCi1N+Z+sFR8yyFbyx/AfDlE+WM4W8sfgH85ZJP5o3Mfn5l9+OGHDIDG25UrV1Q+d82aNaxGjRrsyZMnKh/ftGkTc3BwUFrepk0bNmPGDIPjyMzMZCEhISw2Nlbn152YmMgAsLS0NI3t7t27xwCw5ORkheXx8fGsbdu2Kp9jb2/PNm/erLDs22+/ZV5eXjrHWdWECRNYQEAAu3v3rk7Pq6ioYCEhIWz27NkGxyBTUFDAXF1d2U8//aTycWPuBz7hQw5R/miPLzlE+fMc5Y9mfMshW8wfi+uxmzZtGkaNGqWxTXBwsMrlERERePbsGW7duoWGDRsqPe7j44OKigoUFhYq/GLKycmBj4+PQXHcv38fXbp0QVRUFFavXq3xeepiB57/4goJCVHbztPTE0KhUOkqKlWvQcbHx0en9tqaNGkSfv/9dxw5ckTnXzz29vZo2bIl0tLSDIqhMnd3d4SGhqpdp7H2A9/wIYcof7TDpxyi/HmO8kd9/gD8yiGbzR+9Sk8LtXHjRmZnZ8cePnyo8nHZiavbt2+XL7t69arBJ65mZmayBg0asLfeeos9e/ZMr3UcPXqUAWDnz5+vtm3btm3ZpEmT5PclEgnz9/fXeOLqG2+8obAsMjJS7xNXpVIpmzhxIvPz82PXr1/Xax3Pnj1jDRs2ZHFxcXo9X5WSkhLm4eHBVq5cqfJxrveDNTJHDtla/jDGzxyi/DGcLeQPY+bPIVvPH6st7JKTk9mKFSvYuXPnWHp6Otu4cSMTi8Vs5MiR8jaZmZmsYcOG7MSJE/JlEyZMYPXq1WMHDx5kp0+fZpGRkSwyMlLvODIzM1n9+vVZ165dWWZmpsKlz+riSEtLYwsWLGCnT59mGRkZbM+ePSw4OJh16tRJq21u2bKFiUQi9vPPP7PLly+zcePGMXd3d/ml29HR0WzmzJny9seOHWM1atRgy5YtY1euXGFz58416FLzd999l7m5ubFDhw4pvN6ysjJ5m6oxzJ8/X35J/pkzZ9hbb73FHB0dWWpqql4xMMbYtGnT2KFDh1hGRgY7duwY69atG/P09GS5ubkqY+B6P1g6PuSQLeYPY/zIIcofw9hq/jBm/hyy9fyx2sLuzJkzLCIigrm5uTFHR0fWqFEjtnDhQoVzGzIyMhgAlpSUJF/2+PFj9t577zEPDw9Ws2ZNNmDAAIUk0NW6devUngOhLo47d+6wTp06sZdeeomJRCJWv359Fh8fr9M4Ql9//TWrV68ec3BwYG3btmXHjx+XP9a5c2cWExOj0H7r1q0sNDSUOTg4sMaNG7N9+/bp/ZrVvd5169apjWHKlCnyeL29vVmvXr3Y2bNn9Y6BMcaGDh3KfH19mYODA/P392dDhw5VOEfE2PvB0vEhh2wxfxjjRw5R/hjGlvOHMfoOMmf+CBhjTPcDuIQQQgghhG+sfhw7QgghhBBbQYUdIYQQQoiVoMKOEEIIIcRKUGFHCCGEEGIlqLAjhBBCCLESVNgRQgghhFgJKuwIIYQQQqwEFXaEEEIIIVaCCjsrsWbNGnTv3l1h2bx58+Dt7Q2BQIDdu3dj1KhR6N+/v9Fjyc/Ph5eXFzIzM42+LUK4QPlDiP4of/iFCjsr8OTJE3zyySeYO3eufNmVK1cwf/58/PDDD8jKysLrr79ulG2rSlZPT0+MHDlSIR5C+IryhxD9Uf7wDxV2VmD79u1wdXVF+/bt5cvS09MBAP369YOPjw9EIpFJYxo9ejQ2bdqEhw8fmnS7hOiK8ocQ/VH+8A8VdjySl5cHHx8fLFy4UL4sOTkZDg4OSExMVPu8LVu2oE+fPvL78+bNk9+3s7ODQCBQ+bzy8nJMnjwZXl5ecHR0RIcOHXDq1Cn54xKJBLGxsQgKCoKTkxMaNmyIlStXKmxn/fr12LNnDwQCAQQCAQ4dOgQAaNy4Mfz8/LBr1y699gUhuqL8IUR/lD9WhBFe2bdvH7O3t2enTp1ixcXFLDg4mMXFxWl8jpubG9uyZYv8fklJCVu3bh0DwLKyslhWVhZjjLGYmBjWr18/ebvJkyczPz8/9scff7DU1FQWExPDPDw82IMHDxhjjFVUVLA5c+awU6dOsZs3b7KNGzeymjVrsl9//VW+nSFDhrCePXvKt1NeXi5f/9ChQ1lMTAxHe4aQ6lH+EKI/yh/rQIUdD7333nssNDSUDR8+nDVt2pQ9efJEbduCggIGgB05ckRh+a5du1jVur1yYpWWljJ7e3u2adMm+eMVFRXMz8+PLVmyRO32Jk6cyAYOHKhynVXFxcWxV155Re26CDEGyh9C9Ef5Y/lqmK2rkKi1bNkyNGnSBNu2bcOZM2c0np/w+PFjAICjo6NO20hPT8fTp08Vzouwt7dH27ZtceXKFfmyb7/9FmvXrsWdO3fw+PFjVFRUoEWLFlptw8nJCWVlZTrFRYihKH8I0R/lj+Wjc+x4KD09Hffv34dUKsWtW7c0tq1duzYEAgEKCgo4j2PLli2YPn06YmNj8ffff+PcuXMYPXo0KioqtHr+w4cPIRaLOY+LEE0ofwjRH+WP5aPCjmcqKiowYsQIDB06FJ9++ineeecd5Obmqm3v4OCAsLAwXL58WafthISEwMHBAceOHZMve/r0KU6dOoWwsDAAwLFjxxAVFYX33nsPLVu2RP369eVXO1XevkQiUbmNS5cuoWXLljrFRYghKH8I0R/lj3Wgwo5nPv74YxQVFeGrr77Chx9+iNDQUIwZM0bjc3r06IGjR4/qtJ1atWrh3XffRXx8PP766y9cvnwZY8eORVlZGWJjYwEADRo0wOnTp5GQkIDr16/jk08+UbhqCQACAwNx4cIFXLt2Dfn5+Xj69CkAoKysDGfOnFEatJIQY6L8IUR/lD9Wwtwn+ZEXkpKSWI0aNdg///wjX5aRkcFcXV3Zd999p/Z5qampzMnJiRUWFsqXVXfyKmOMPX78mL3//vvM09OTiUQi1r59e3by5En540+ePGGjRo1ibm5uzN3dnb377rts5syZrHnz5vI2ubm57LXXXmPOzs4MAEtKSmKMMbZ582bWsGFDPfcEIbqj/CFEf5Q/1kPAGGPmLCwJNwYPHoxWrVph1qxZ5g4FANCuXTtMnjwZw4cPN3cohFSL8ocQ/VH+8AsdirUSS5cuhbOzs7nDAPB8rr4333wTw4YNM3cohGiF8ocQ/VH+8Av12BFCCCGEWAnqsSOEEEIIsRJU2BFCCCGEWAkq7AghhBBCrAQVdoQQQgghVoIKO0IIIYQQK0GFHSGEEEKIlaDCjhBCCCHESlBhRwghhBBiJaiwI4QQQgixEv8Px0QdeWyD1dEAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -273,9 +292,16 @@
     "## 3. Categorical variables\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also handle categorical variables! This is done under-the-hood by constructing parameters in a one-hot-encoding representation, with a transformation in the kernel rounding to the nearest one-hot representation. If you want to use this, you can specify a collection of strings as options."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -296,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -349,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -360,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -402,9 +428,16 @@
     "## 4. Use in ML"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A typical usecase for integer and categorical parameters is optimizing the hyperparameters of a machine learning model. Below you can find an example where the hyperparameters of an SVM are optimized."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -430,7 +463,7 @@
    "source": [
     "from sklearn.datasets import load_breast_cancer\n",
     "from sklearn.svm import SVC\n",
-    "from sklearn.metrics import f1_score, log_loss\n",
+    "from sklearn.metrics import log_loss\n",
     "from sklearn.model_selection import train_test_split\n",
     "from bayes_opt import BayesianOptimization\n",
     "\n",

From 187fd08b1e2930fb29560060084aa18dd421a3a7 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Fri, 15 Nov 2024 11:09:47 +0100
Subject: [PATCH 17/21] Save files while moving device

---
 bayes_opt/__init__.py          |   2 -
 bayes_opt/parameter.py         |   6 +-
 bayes_opt/target_space.py      |  11 +++
 docsrc/index.rst               |   8 +-
 docsrc/reference/parameter.rst |   5 +
 examples/advanced-tour.ipynb   | 161 ++++++---------------------------
 examples/parameter_types.ipynb | 133 +++++++++++++++++++--------
 7 files changed, 150 insertions(+), 176 deletions(-)
 create mode 100644 docsrc/reference/parameter.rst

diff --git a/bayes_opt/__init__.py b/bayes_opt/__init__.py
index 121983f29..7ed07ed46 100644
--- a/bayes_opt/__init__.py
+++ b/bayes_opt/__init__.py
@@ -9,7 +9,6 @@
 from bayes_opt.constraint import ConstraintModel
 from bayes_opt.domain_reduction import SequentialDomainReductionTransformer
 from bayes_opt.logger import JSONLogger, ScreenLogger
-from bayes_opt.parameter import BayesParameter
 from bayes_opt.target_space import TargetSpace
 
 __version__ = importlib.metadata.version("bayesian-optimization")
@@ -24,5 +23,4 @@
     "ScreenLogger",
     "JSONLogger",
     "SequentialDomainReductionTransformer",
-    "BayesParameter",
 ]
diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index 0f69dc785..90fea618b 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -477,7 +477,7 @@ def wrap_kernel(kernel: kernels.Kernel, transform: Callable[[Any], Any]) -> kern
     kernel_type = type(kernel)
 
     class WrappedKernel(kernel_type):
-        @copy_signature(getattr(kernel_type.__init__, "deprecated_original", kernel_type.__init__))
+        @_copy_signature(getattr(kernel_type.__init__, "deprecated_original", kernel_type.__init__))
         def __init__(self, **kwargs: Any) -> None:
             super().__init__(**kwargs)
 
@@ -492,8 +492,8 @@ def __reduce__(self) -> str | tuple[Any, ...]:
     return WrappedKernel(**kernel.get_params())
 
 
-def copy_signature(source_fct: Callable[..., Any]) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
-    """Clones a signature from a source function to a target function.
+def _copy_signature(source_fct: Callable[..., Any]) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
+    """Clone a signature from a source function to a target function.
 
     via
     https://stackoverflow.com/a/58989918/
diff --git a/bayes_opt/target_space.py b/bayes_opt/target_space.py
index f101ae397..39d1f9926 100644
--- a/bayes_opt/target_space.py
+++ b/bayes_opt/target_space.py
@@ -242,22 +242,33 @@ def make_params(self, pbounds: BoundsMapping) -> dict[str, BayesParameter]:
             A dictionary with the parameter names as keys and the corresponding
             parameter objects as values.
         """
+        any_is_not_float = False  # TODO: remove in an upcoming release
         params: dict[str, BayesParameter] = {}
         for key in pbounds:
             pbound = pbounds[key]
 
             if isinstance(pbound, BayesParameter):
                 res = pbound
+                if not isinstance(pbound, FloatParameter):
+                    any_is_not_float = True
             elif (len(pbound) == 2 and is_numeric(pbound[0]) and is_numeric(pbound[1])) or (
                 len(pbound) == 3 and pbound[-1] is float
             ):
                 res = FloatParameter(name=key, bounds=(float(pbound[0]), float(pbound[1])))
             elif len(pbound) == 3 and pbound[-1] is int:
                 res = IntParameter(name=key, bounds=(int(pbound[0]), int(pbound[1])))
+                any_is_not_float = True
             else:
                 # assume categorical variable with pbound as list of possible values
                 res = CategoricalParameter(name=key, categories=pbound)
+                any_is_not_float = True
             params[key] = res
+        if any_is_not_float:
+            msg = (
+                "Non-float parameters are experimental and may not work as expected."
+                " Exercise caution when using them and please report any issues you encounter."
+            )
+            warn(msg, stacklevel=4)
         return params
 
     def make_masks(self) -> dict[str, NDArray[np.bool_]]:
diff --git a/docsrc/index.rst b/docsrc/index.rst
index ac664a582..e2a169432 100644
--- a/docsrc/index.rst
+++ b/docsrc/index.rst
@@ -11,6 +11,7 @@
    Basic Tour </basic-tour>
    Advanced Tour </advanced-tour>
    Constrained Bayesian Optimization </constraints>
+   Parameter Types </parameter_types>
    Sequential Domain Reduction </domain_reduction>
    Acquisition Functions </acquisition_functions>
    Exploration vs. Exploitation </exploitation_vs_exploration>
@@ -26,6 +27,7 @@
    reference/constraint
    reference/domain_reduction
    reference/target_space
+   reference/parameter
    reference/exception
    reference/other
 
@@ -121,11 +123,13 @@ section. We suggest that you:
    to learn how to use the package's most important features.
 -  Take a look at the `advanced tour
    notebook <advanced-tour.html>`__
-   to learn how to make the package more flexible, how to deal with
-   categorical parameters, how to use observers, and more.
+   to learn how to make the package more flexible or how to use observers.
 -  To learn more about acquisition functions, a central building block
    of bayesian optimization, see the `acquisition functions
    notebook <acquisition_functions.html>`__
+-  If you want to optimize over integer-valued or categorical
+   parameters, see the `parameter types
+   notebook <parameter_types.html>`__.
 -  Check out this
    `notebook <visualization.html>`__
    with a step by step visualization of how this method works.
diff --git a/docsrc/reference/parameter.rst b/docsrc/reference/parameter.rst
new file mode 100644
index 000000000..91b8f2e9a
--- /dev/null
+++ b/docsrc/reference/parameter.rst
@@ -0,0 +1,5 @@
+:py:mod:`bayes_opt.parameter`
+--------------------------------
+
+.. automodule:: bayes_opt.parameter
+   :members: 
diff --git a/examples/advanced-tour.ipynb b/examples/advanced-tour.ipynb
index dc72e40ed..9e93d09d7 100644
--- a/examples/advanced-tour.ipynb
+++ b/examples/advanced-tour.ipynb
@@ -96,7 +96,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Next point to probe is: {'x': -0.331911981189704, 'y': 1.3219469606529486}\n"
+      "Next point to probe is: {'x': np.float64(-0.331911981189704), 'y': np.float64(1.3219469606529486)}\n"
      ]
     }
    ],
@@ -167,12 +167,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-18.503835804889988 {'x': 1.953072105336, 'y': -2.9609778030491904}\n",
-      "-1.0819533157901717 {'x': 0.22703572807626315, 'y': 2.4249238905875123}\n",
-      "-6.50219704520679 {'x': -1.9991881984624875, 'y': 2.872282989383577}\n",
-      "-5.747604713731052 {'x': -1.994467585936897, 'y': -0.664242699361514}\n",
-      "-2.9682431497650823 {'x': 1.9737252084307952, 'y': 1.269540259274744}\n",
-      "{'target': 0.7861845912690544, 'params': {'x': -0.331911981189704, 'y': 1.3219469606529486}}\n"
+      "-18.707136686093495 {'x': np.float64(1.9261486197444082), 'y': np.float64(-2.9996360060323246)}\n",
+      "0.750594563473972 {'x': np.float64(-0.3763326769822668), 'y': np.float64(1.328297354179696)}\n",
+      "-6.559031075654336 {'x': np.float64(1.979183535803597), 'y': np.float64(2.9083667381450318)}\n",
+      "-6.915481333972961 {'x': np.float64(-1.9686133847781613), 'y': np.float64(-1.009985740060171)}\n",
+      "-6.8600832617014085 {'x': np.float64(-1.9763198875239296), 'y': np.float64(2.9885278383464513)}\n",
+      "{'target': np.float64(0.7861845912690544), 'params': {'x': np.float64(-0.331911981189704), 'y': np.float64(1.3219469606529486)}}\n"
      ]
     }
    ],
@@ -190,112 +190,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Dealing with discrete parameters\n",
-    "\n",
-    "**There is no principled way of dealing with discrete parameters using this package.**\n",
-    "\n",
-    "Ok, now that we got that out of the way, how do you do it? You're bound to be in a situation where some of your function's parameters may only take on discrete values. Unfortunately, the nature of bayesian optimization with gaussian processes doesn't allow for an easy/intuitive way of dealing with discrete parameters - but that doesn't mean it is impossible. The example below showcases a simple, yet reasonably adequate, way to dealing with discrete parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func_with_discrete_params(x, y, d):\n",
-    "    # Simulate necessity of having d being discrete.\n",
-    "    assert type(d) == int\n",
-    "    \n",
-    "    return ((x + y + d) // (1 + d)) / (1 + (x + y) ** 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def function_to_be_optimized(x, y, w):\n",
-    "    d = int(w)\n",
-    "    return func_with_discrete_params(x, y, d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer = BayesianOptimization(\n",
-    "    f=function_to_be_optimized,\n",
-    "    pbounds={'x': (-10, 10), 'y': (-10, 10), 'w': (0, 5)},\n",
-    "    verbose=2,\n",
-    "    random_state=1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|   iter    |  target   |     w     |     x     |     y     |\n",
-      "-------------------------------------------------------------\n",
-      "| \u001b[30m1         | \u001b[30m-0.06199  | \u001b[30m2.085     | \u001b[30m4.406     | \u001b[30m-9.998    |\n",
-      "| \u001b[35m2         | \u001b[35m-0.0344   | \u001b[35m1.512     | \u001b[35m-7.065    | \u001b[35m-8.153    |\n",
-      "| \u001b[30m3         | \u001b[30m-0.2177   | \u001b[30m0.9313    | \u001b[30m-3.089    | \u001b[30m-2.065    |\n",
-      "| \u001b[35m4         | \u001b[35m0.1865    | \u001b[35m2.694     | \u001b[35m-1.616    | \u001b[35m3.704     |\n",
-      "| \u001b[30m5         | \u001b[30m-0.2187   | \u001b[30m1.022     | \u001b[30m7.562     | \u001b[30m-9.452    |\n",
-      "| \u001b[35m6         | \u001b[35m0.2488    | \u001b[35m2.684     | \u001b[35m-2.188    | \u001b[35m3.925     |\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "| \u001b[35m7         | \u001b[35m0.2948    | \u001b[35m2.683     | \u001b[35m-2.534    | \u001b[35m4.08      |\n",
-      "| \u001b[35m8         | \u001b[35m0.3202    | \u001b[35m2.514     | \u001b[35m-3.83     | \u001b[35m5.287     |\n",
-      "| \u001b[30m9         | \u001b[30m0.0       | \u001b[30m4.057     | \u001b[30m-4.458    | \u001b[30m3.928     |\n",
-      "| \u001b[35m10        | \u001b[35m0.4802    | \u001b[35m2.296     | \u001b[35m-3.518    | \u001b[35m4.558     |\n",
-      "| \u001b[30m11        | \u001b[30m0.0       | \u001b[30m1.084     | \u001b[30m-3.737    | \u001b[30m4.472     |\n",
-      "| \u001b[30m12        | \u001b[30m0.0       | \u001b[30m2.649     | \u001b[30m-3.861    | \u001b[30m4.353     |\n",
-      "| \u001b[30m13        | \u001b[30m0.0       | \u001b[30m2.442     | \u001b[30m-3.658    | \u001b[30m4.599     |\n",
-      "| \u001b[30m14        | \u001b[30m-0.05801  | \u001b[30m1.935     | \u001b[30m-0.4758   | \u001b[30m-8.755    |\n",
-      "| \u001b[30m15        | \u001b[30m0.0       | \u001b[30m2.337     | \u001b[30m7.973     | \u001b[30m-8.96     |\n",
-      "| \u001b[30m16        | \u001b[30m0.07699   | \u001b[30m0.6926    | \u001b[30m5.59      | \u001b[30m6.854     |\n",
-      "| \u001b[30m17        | \u001b[30m-0.02025  | \u001b[30m3.534     | \u001b[30m-8.943    | \u001b[30m1.987     |\n",
-      "| \u001b[30m18        | \u001b[30m0.0       | \u001b[30m2.59      | \u001b[30m-7.339    | \u001b[30m5.941     |\n",
-      "| \u001b[30m19        | \u001b[30m0.0929    | \u001b[30m2.237     | \u001b[30m-4.535    | \u001b[30m9.065     |\n",
-      "| \u001b[30m20        | \u001b[30m0.1538    | \u001b[30m0.477     | \u001b[30m2.931     | \u001b[30m2.683     |\n",
-      "| \u001b[30m21        | \u001b[30m0.0       | \u001b[30m0.9999    | \u001b[30m4.397     | \u001b[30m-3.971    |\n",
-      "| \u001b[30m22        | \u001b[30m-0.01894  | \u001b[30m3.764     | \u001b[30m-7.043    | \u001b[30m-3.184    |\n",
-      "| \u001b[30m23        | \u001b[30m0.03683   | \u001b[30m1.851     | \u001b[30m5.783     | \u001b[30m7.966     |\n",
-      "| \u001b[30m24        | \u001b[30m-0.04359  | \u001b[30m1.615     | \u001b[30m-5.133    | \u001b[30m-6.556    |\n",
-      "| \u001b[30m25        | \u001b[30m0.02617   | \u001b[30m3.863     | \u001b[30m0.1052    | \u001b[30m8.579     |\n",
-      "| \u001b[30m26        | \u001b[30m-0.1071   | \u001b[30m0.8131    | \u001b[30m-0.7949   | \u001b[30m-9.292    |\n",
-      "| \u001b[30m27        | \u001b[30m0.0       | \u001b[30m4.969     | \u001b[30m8.778     | \u001b[30m-8.467    |\n",
-      "| \u001b[30m28        | \u001b[30m-0.1372   | \u001b[30m0.9475    | \u001b[30m-1.019    | \u001b[30m-7.018    |\n",
-      "| \u001b[30m29        | \u001b[30m0.08078   | \u001b[30m1.917     | \u001b[30m-0.2606   | \u001b[30m6.272     |\n",
-      "| \u001b[30m30        | \u001b[30m0.02003   | \u001b[30m4.278     | \u001b[30m3.8       | \u001b[30m8.398     |\n",
-      "=============================================================\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer.set_gp_params(alpha=1e-3)\n",
-    "optimizer.maximize()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Tuning the underlying Gaussian Process\n",
+    "## 2. Tuning the underlying Gaussian Process\n",
     "\n",
     "The bayesian optimization algorithm works by performing a gaussian process regression of the observed combination of parameters and their associated target values. The predicted parameter $\\rightarrow$ target hyper-surface (and its uncertainty) is then used to guide the next best point to probe."
    ]
@@ -304,14 +199,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.1 Passing parameter to the GP\n",
+    "### 2.1 Passing parameter to the GP\n",
     "\n",
     "Depending on the problem it could be beneficial to change the default parameters of the underlying GP. You can use the `optimizer.set_gp_params` method to do this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -320,12 +215,12 @@
      "text": [
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[30m1         | \u001b[30m0.7862    | \u001b[30m-0.3319   | \u001b[30m1.322     |\n",
-      "| \u001b[30m2         | \u001b[30m-18.19    | \u001b[30m1.957     | \u001b[30m-2.919    |\n",
-      "| \u001b[30m3         | \u001b[30m-12.05    | \u001b[30m-1.969    | \u001b[30m-2.029    |\n",
-      "| \u001b[30m4         | \u001b[30m-7.463    | \u001b[30m0.6032    | \u001b[30m-1.846    |\n",
-      "| \u001b[30m5         | \u001b[30m-1.093    | \u001b[30m1.444     | \u001b[30m1.096     |\n",
-      "| \u001b[35m6         | \u001b[35m0.8586    | \u001b[35m-0.2165   | \u001b[35m1.307     |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.7862   \u001b[39m | \u001b[39m-0.331911\u001b[39m | \u001b[39m1.3219469\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-18.34   \u001b[39m | \u001b[39m1.9021640\u001b[39m | \u001b[39m-2.965222\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.8731   \u001b[39m | \u001b[35m-0.298167\u001b[39m | \u001b[35m1.1948749\u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-6.497   \u001b[39m | \u001b[39m1.9876938\u001b[39m | \u001b[39m2.8830942\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-4.286   \u001b[39m | \u001b[39m-1.995643\u001b[39m | \u001b[39m-0.141769\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-6.781   \u001b[39m | \u001b[39m-1.953302\u001b[39m | \u001b[39m2.9913127\u001b[39m |\n",
       "=================================================\n"
      ]
     }
@@ -348,7 +243,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.2 Tuning the `alpha` parameter\n",
+    "### 2.2 Tuning the `alpha` parameter\n",
     "\n",
     "When dealing with functions with discrete parameters,or particularly erratic target space it might be beneficial to increase the value of the `alpha` parameter. This parameters controls how much noise the GP can handle, so increase it whenever you think that extra flexibility is needed."
    ]
@@ -358,7 +253,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.3 Changing kernels\n",
+    "### 2.3 Changing kernels\n",
     "\n",
     "By default this package uses the Matern 2.5 kernel. Depending on your use case you may find that tuning the GP kernel could be beneficial. You're on your own here since these are very specific solutions to very specific problems. You should start with the [scikit learn docs](https://scikit-learn.org/stable/modules/gaussian_process.html#kernels-for-gaussian-processes)."
    ]
@@ -376,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -433,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -476,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -485,7 +380,7 @@
        "['optimization:start', 'optimization:step', 'optimization:end']"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -497,7 +392,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "bayesian-optimization-t6LLJ9me-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -511,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.1.undefined"
+   "version": "3.10.13"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
index 59751afd6..f2333cd36 100644
--- a/examples/parameter_types.ipynb
+++ b/examples/parameter_types.ipynb
@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Optimizing over Parameters\n",
+    "# Optimizing over non-float Parameters\n",
     "\n",
-    "Sometimes, you need to optimize a target that is not just a function of floating-point values, but relies on integer or categorical parameters. This notebook shows how such problems are handled by following an approach from \"Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes\" by Garrido-Merchán and Hernández-Lobato. One simple way of handling an integer-valued parameter is to run the optimization as normal, but then round to the nearest integer after a point has been suggested. This method is similar, except that the rounding is performed in the _kernel_. Why does this matter? It means that the kernel is aware that two parameters, that map the to same point but are potentially distinct before this transformation are the same."
+    "Sometimes, you need to optimize a target that is not just a function of floating-point values, but relies on integer or categorical parameters. This notebook shows how such problems are handled by following an approach from [\"Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes\" by Garrido-Merchán and Hernández-Lobato](https://arxiv.org/abs/1805.03463). One simple way of handling an integer-valued parameter is to run the optimization as normal, but then round to the nearest integer after a point has been suggested. This method is similar, except that the rounding is performed in the _kernel_. Why does this matter? It means that the kernel is aware that two parameters, that map the to same point but are potentially distinct before this transformation are the same."
    ]
   },
   {
@@ -27,6 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## 1. Simple integer-valued function\n",
     "Let's look at a simple, one-dimensional, integer-valued target function and compare a typed optimizer and a continuous optimizer."
    ]
   },
@@ -35,9 +36,17 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/r0/12hxq7zs2mx5kr76ks1_n9k80000gn/T/ipykernel_63433/3876025054.py:9: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "  bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -96,7 +105,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at a mixed-parameter optimization problem!"
+    "## 2. Mixed-parameter optimization"
    ]
   },
   {
@@ -133,7 +142,16 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/r0/12hxq7zs2mx5kr76ks1_n9k80000gn/T/ipykernel_63433/1961947877.py:12: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "  discrete_optimizer = BayesianOptimization(\n"
+     ]
+    }
+   ],
    "source": [
     "continuous_optimizer = BayesianOptimization(\n",
     "    f=discretized_function,\n",
@@ -232,7 +250,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -276,7 +294,7 @@
     "    ax.set_xlabel('x (float)')\n",
     "    ax.set_xticks([-5.0, -2.5, 0., 2.5, 5.0])\n",
     "    ax.set_ylabel('y (int)')\n",
-    "    ax.set_yticks([-5, -3, 0, 3, 5])\n",
+    "    ax.set_yticks([-4, -2, 0, 2, 4])\n",
     "\n",
     "for ax in axs:\n",
     "    make_plot_fancy(ax)\n",
@@ -296,7 +314,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also handle categorical variables! This is done under-the-hood by constructing parameters in a one-hot-encoding representation, with a transformation in the kernel rounding to the nearest one-hot representation. If you want to use this, you can specify a collection of strings as options."
+    "We can also handle categorical variables! This is done under-the-hood by constructing parameters in a one-hot-encoding representation, with a transformation in the kernel rounding to the nearest one-hot representation. If you want to use this, you can specify a collection of strings as options.\n",
+    "\n",
+    "NB: As internally, the categorical variables are within a range of `[0, 1]` and the GP used for BO is by default isotropic, you might want to ensure your other features are similarly scaled to a range of `[0, 1]` or use an anisotropic GP."
    ]
   },
   {
@@ -315,9 +335,9 @@
     "    \"\"\"cf Ladislav-Luksan\n",
     "    \"\"\"\n",
     "    if k=='1':\n",
-    "        return f1(x1, x2)\n",
+    "        return f1(10 * x1, 10 * x2)\n",
     "    elif k=='2':\n",
-    "        return f2(x1, x2)\n"
+    "        return f2(10 * x1, 10 * x2)\n"
    ]
   },
   {
@@ -325,38 +345,46 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/r0/12hxq7zs2mx5kr76ks1_n9k80000gn/T/ipykernel_63433/2996397825.py:3: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "  categorical_optimizer = BayesianOptimization(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "|   iter    |  target   |    x1     |    x2     |     k     |\n",
       "-------------------------------------------------------------\n",
-      "| \u001b[39m1        \u001b[39m | \u001b[39m-2.052   \u001b[39m | \u001b[39m-1.659559\u001b[39m | \u001b[39m4.4064898\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[35m2        \u001b[39m | \u001b[35m13.49    \u001b[39m | \u001b[35m-7.437511\u001b[39m | \u001b[35m9.9808103\u001b[39m | \u001b[35m1        \u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m6.822    \u001b[39m | \u001b[39m-1.616109\u001b[39m | \u001b[39m-4.455463\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-16.13   \u001b[39m | \u001b[39m-7.462442\u001b[39m | \u001b[39m9.9962686\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m3.259    \u001b[39m | \u001b[39m-1.232832\u001b[39m | \u001b[39m-5.747412\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m-7.048   \u001b[39m | \u001b[39m3.9207862\u001b[39m | \u001b[39m6.4592598\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m-3.913   \u001b[39m | \u001b[39m4.5863779\u001b[39m | \u001b[39m-3.245964\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m5.802    \u001b[39m | \u001b[39m-6.913901\u001b[39m | \u001b[39m2.0971273\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m1.222    \u001b[39m | \u001b[39m-8.335282\u001b[39m | \u001b[39m0.8594578\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m4.208    \u001b[39m | \u001b[39m-2.902313\u001b[39m | \u001b[39m-1.968247\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m-4.159   \u001b[39m | \u001b[39m-6.153418\u001b[39m | \u001b[39m8.7915989\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m-4.333   \u001b[39m | \u001b[39m-6.356472\u001b[39m | \u001b[39m1.4090214\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m13       \u001b[39m | \u001b[39m3.66     \u001b[39m | \u001b[39m0.6606721\u001b[39m | \u001b[39m-9.219624\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m1.083    \u001b[39m | \u001b[39m5.0543932\u001b[39m | \u001b[39m0.7205085\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m9.608    \u001b[39m | \u001b[39m5.0760338\u001b[39m | \u001b[39m-6.436109\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m16       \u001b[39m | \u001b[39m-10.34   \u001b[39m | \u001b[39m6.9559950\u001b[39m | \u001b[39m-3.097946\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m17       \u001b[39m | \u001b[39m4.211    \u001b[39m | \u001b[39m-8.886123\u001b[39m | \u001b[39m-8.283778\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m18       \u001b[39m | \u001b[39m7.692    \u001b[39m | \u001b[39m-7.058465\u001b[39m | \u001b[39m7.2905639\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m19       \u001b[39m | \u001b[39m-2.327   \u001b[39m | \u001b[39m3.3476177\u001b[39m | \u001b[39m4.5905557\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m20       \u001b[39m | \u001b[39m4.103    \u001b[39m | \u001b[39m-5.313351\u001b[39m | \u001b[39m4.9166311\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-2.052   \u001b[39m | \u001b[39m-0.165955\u001b[39m | \u001b[39m0.4406489\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m13.49    \u001b[39m | \u001b[35m-0.743751\u001b[39m | \u001b[35m0.9980810\u001b[39m | \u001b[35m1        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m10.26    \u001b[39m | \u001b[39m-0.734149\u001b[39m | \u001b[39m0.9535175\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-16.13   \u001b[39m | \u001b[39m-0.746244\u001b[39m | \u001b[39m0.9996268\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m3.259    \u001b[39m | \u001b[39m-0.123283\u001b[39m | \u001b[39m-0.574741\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-7.048   \u001b[39m | \u001b[39m0.3920786\u001b[39m | \u001b[39m0.6459259\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m-3.913   \u001b[39m | \u001b[39m0.4586377\u001b[39m | \u001b[39m-0.324596\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m5.802    \u001b[39m | \u001b[39m-0.691390\u001b[39m | \u001b[39m0.2097127\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m1.222    \u001b[39m | \u001b[39m-0.833528\u001b[39m | \u001b[39m0.0859457\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m4.208    \u001b[39m | \u001b[39m-0.290231\u001b[39m | \u001b[39m-0.196824\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-4.159   \u001b[39m | \u001b[39m-0.615341\u001b[39m | \u001b[39m0.8791598\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-4.333   \u001b[39m | \u001b[39m-0.635647\u001b[39m | \u001b[39m0.1409021\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m3.66     \u001b[39m | \u001b[39m0.0660672\u001b[39m | \u001b[39m-0.921962\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m1.083    \u001b[39m | \u001b[39m0.5054393\u001b[39m | \u001b[39m0.0720508\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m9.608    \u001b[39m | \u001b[39m0.5076033\u001b[39m | \u001b[39m-0.643610\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m16       \u001b[39m | \u001b[39m-10.34   \u001b[39m | \u001b[39m0.6955995\u001b[39m | \u001b[39m-0.309794\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m17       \u001b[39m | \u001b[39m4.211    \u001b[39m | \u001b[39m-0.888612\u001b[39m | \u001b[39m-0.828377\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m18       \u001b[39m | \u001b[39m7.692    \u001b[39m | \u001b[39m-0.705846\u001b[39m | \u001b[39m0.7290563\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m19       \u001b[39m | \u001b[39m-2.327   \u001b[39m | \u001b[39m0.3347617\u001b[39m | \u001b[39m0.4590555\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m20       \u001b[39m | \u001b[39m4.103    \u001b[39m | \u001b[39m-0.531335\u001b[39m | \u001b[39m0.4916631\u001b[39m | \u001b[39m1        \u001b[39m |\n",
       "=============================================================\n"
      ]
     }
    ],
    "source": [
-    "pbounds = {'x1': (-10, 10), 'x2': (-10, 10), 'k': ('1', '2')}\n",
+    "pbounds = {'x1': (-1, 1), 'x2': (-1, 1), 'k': ('1', '2')}\n",
     "\n",
     "categorical_optimizer = BayesianOptimization(\n",
     "    f=SPIRAL,\n",
@@ -386,12 +414,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -418,7 +446,10 @@
     "axs[0].scatter(k1[:,0], k1[:,1], c='k')\n",
     "axs[1].contourf(X1, X2, Z2, vmin=vmin, vmax=vmax)\n",
     "axs[1].scatter(k2[:,0], k2[:,1], c='k')\n",
-    "axs[1].set_aspect(\"equal\")\n"
+    "axs[1].set_aspect(\"equal\")\n",
+    "axs[0].set_title('k=1')\n",
+    "axs[1].set_title('k=2')\n",
+    "fig.tight_layout()\n"
    ]
   },
   {
@@ -440,6 +471,14 @@
    "execution_count": 13,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/r0/12hxq7zs2mx5kr76ks1_n9k80000gn/T/ipykernel_63433/3228056642.py:37: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "  optimizer = BayesianOptimization(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -500,7 +539,6 @@
     "optimizer = BayesianOptimization(\n",
     "    f_target,\n",
     "    params_svm,\n",
-    "    #acquisition_function=acquisition.ExpectedImprovement(1e-2, random_state=1),\n",
     "    random_state=1,\n",
     "    verbose=2\n",
     ")\n",
@@ -510,12 +548,35 @@
     "optimizer.maximize(init_points=2, n_iter=8)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Defining your own Parameter\n",
+    "\n",
+    "Maybe you want to optimize over another form of parameters, which does not align with `float`, `int` or categorical. For this purpose, you can create your parameter.\n",
+    "\n",
+    "As an example, consider a parameter that is discrete, but still admits a distance representation (like an integer) while not being uniformly spaced."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from bayes_opt.parameter import BayesParameter\n",
+    "\n",
+    "\n",
+    "class MyParameter(BayesParameter):\n",
+    "    def __init__(self, name: str, bounds) -> None:\n",
+    "        super().__init__(name, bounds)\n",
+    "\n",
+    "    def is_continuous(self):\n",
+    "        return False\n",
+    "    \n",
+    "        "
+   ]
   },
   {
    "cell_type": "code",

From 31223a96fbfaf7e1edbe09508a615560a63079f8 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Tue, 10 Dec 2024 18:52:52 +0100
Subject: [PATCH 18/21] Update with custom parameter type example

---
 examples/parameter_types.ipynb | 242 ++++++++++++++++++++++++++++++---
 1 file changed, 225 insertions(+), 17 deletions(-)

diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
index f2333cd36..b89acefc3 100644
--- a/examples/parameter_types.ipynb
+++ b/examples/parameter_types.ipynb
@@ -40,13 +40,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/r0/12hxq7zs2mx5kr76ks1_n9k80000gn/T/ipykernel_63433/3876025054.py:9: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/3876025054.py:9: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
       "  bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -147,7 +147,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/r0/12hxq7zs2mx5kr76ks1_n9k80000gn/T/ipykernel_63433/1961947877.py:12: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/1961947877.py:12: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
       "  discrete_optimizer = BayesianOptimization(\n"
      ]
     }
@@ -250,7 +250,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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dqtTuyZMn8PLyUjzX9YujU6dO+PHHH3H06FG0aNFC5fUjR47g9u3biI6OVnntxo0bSr/O4+LikJeXp3SytLlHuT9y5Ag++ugjjB49Gv369VN5PSgoCOfPn8ebb75p9lrtwcCBAzF27FgkJCRg7dq16Nixo9rc3LhxQ2Xa33//DVdXV8VwI+7u7pDL5Son/xdUuXJlXLp0CUIIpX9jdblThxmxrIzY/aHY7OxsbNmyBZ06dUKPHj1UHiNGjEBmZiZ27NgBAOjevTvOnz+v2FX+uvxef8mSJQFAbQcuKCgIf/75J3JzcxXTdu7cqTiclC//l8XrvyROnjyJEydOGPaG1dQDAIcPH1ZMe/bsmdJeFl0Zuo0KevvttyGXy/Htt98qTV+wYAEkEgn+9a9/6V0r/UPXLGgjODgY7dq1UzxePydIG1KpFO3bt8f27duVDs0kJSVh7dq1aNGihWIPgFQqVfnlvXHjRjx48EBpmi6fPQAYP348XFxcEB0djUePHim9lpaWhmHDhsHV1RXjx49XmbdgB3nhwoUAoPSZLVmypNa1GFtCQgJ69eqFFi1aYO7cuWrb9OrVCw8ePMCPP/6o8lp2djaePXtm6jLtSp8+fSCRSDBq1CjcvHmz0HHuTpw4oXTu6L1797B9+3a0b99eMb5b9+7dsXnzZrU/tFNSUhT//fbbb+Phw4fYtGmTYlpWVhaWLFmiVc3MiGVlxO732OWfqNqlSxe1rzdt2hTe3t5Ys2YNevfujfHjx2PTpk3o2bMnhgwZgkaNGiEtLQ07duzAokWLUK9ePQQFBaFUqVJYtGgR3N3dUbJkSYSGhqJKlSp4//33sWnTJkRERKBXr16Ij4/H6tWrVS7f7tSpE7Zs2YJ33nkHHTt2xK1bt7Bo0SIEBwfj6dOnRnv/7du3R6VKlRAVFYXx48dDKpVi2bJl8Pb2xt27d/VapqHbqKDOnTujTZs2+Pjjj3H79m3Uq1cPe/bswfbt2zF69OgiL30n7eiaheIyc+ZM7N27Fy1atMDw4cNRokQJLF68GDk5Ofjiiy8U7Tp16oQZM2Zg8ODBCAsLw8WLF7FmzRqVAVF1+ewBr84BWrlyJfr164c6deogKioKVapUwe3bt7F06VKkpqbil19+Ufs5vHXrFrp06YKIiAicOHECq1evRt++fVGvXj1Fm0aNGuGPP/7A/Pnz4e/vjypVqpjsNm4FjRw5EikpKZgwYYLKofe6deuibt26GDBgADZs2IBhw4bhwIEDaN68OeRyOa5du4YNGzZg9+7daNy4cbHUaw+8vb0RERGBjRs3olSpUujYsaPadiEhIejQoYPScCcAMH36dEWbOXPm4MCBAwgNDcXQoUMRHByMtLQ0nD17Fn/88YfinNehQ4fi22+/xcCBAxEbGws/Pz+sWrUKrq6uWtXMjFhYRkx2va2V6Ny5s3B2dhbPnj0rtM2gQYOEo6Oj4pLxR48eiREjRojy5csrBiKMjIxUuqR8+/btisEHUWBohS+//FKUL19eyGQy0bx5c3HmzBmV4U7y8vLErFmzROXKlYVMJhMNGjQQO3fuVDt4LwwY7kQIIWJjY0VoaKhwcnISlSpVEvPnz9c48GNBBWs3dBupe4+ZmZlizJgxwt/fXzg6Oopq1appHKC4oMKGmaF/6JOFgp89Uwx3IsSrAYo7dOgg3NzchKurq2jTpo3ScAZCvBruZNy4ccLPz0+4uLiI5s2bixMnTqj9fGrKZ2EuXLgg+vTpI/z8/ISjo6MoV66c6NOnj7h48aJK2/whE65cuSJ69Ogh3N3dRenSpcWIESNUbld07do10apVK+Hi4qL14KvqtlnBz33+QNBz585VqStfeHi4VoOv5ubmis8//1zUrl1byGQyUbp0adGoUSMxffp0kZ6errEOUq/gcCev27BhgwAgPvjgA7Wv52/n1atXi2rVqim+I9QNS5KUlCRiYmJExYoVFZ/bN998UyxZskSp3Z07d0SXLl2Eq6ur8PLyEqNGjdJ6gOJ8zIhlZETy/ysiIiIjmTZtGqZPn46UlBSl8/uItLF9+3Z069YNhw8fVjvor0QiQUxMjMrpKdaEGTEduz/HjoiIyJL8+OOPCAwMVHshAlFR7P4cOyIiIkuwbt06XLhwAbt27cLXX39tEVdYkvVhx46IiMgC9OnTB25uboiKilLcz5RIVzzHjoiIiMhG8Bw7IiIiIhvBjh0RERGRjbCrc+zy8vLw8OFDuLu786RUghACmZmZ8Pf3h4MDf+MUhfmhgpgh7TE/VJCp8mNXHbuHDx+q3IyX6N69e6hQoYK5y7B4zA8VhhkqGvNDhTF2fuyqY5d/A/k9h/9CSTf3IloXLuDxGYPquF26+G+/c+6Bt8bX65dP0fg6YPj71ujscd3aNwwzeJWZz7JQs8sQxeeCNMvfTld2rYJ7Se1uNUS2LfNZFoI7DmCGtJC/jfYfOQ03NzczV2O5/DKumbsEkznx1yX0Gv2JynRj58euOnb5u79LurnDzYCOnUeuYV9qhqxbXy4lPTS+7ub2vMhlGPq+NXKR6dbeiB0LHhbRTv52ci/pCg+3kmauxnrJ5XIc/+sSklLT4OtVBmENQiCVSs1dlkGYoaLlbyM3Nze4sSNcKI882/3b0i6sMfx9vJCQnIrXhyMxdn7sqmNHRGROO/YfxcR5i/AwOVUxzd/HC59/NAxd2vIuA0S2TCqV4vPxwzFw/AxIAJhqrDme7UpEVAx27D+KgRNmKnXqACAhORUDJ8zEjv1HzVQZERWXLm1b4Oe5n8LPx3T3x2XHjojIxORyOSbOW6T2F3r+tElfLoZcLi/OsojIDLq0bYFLO1dhw1efmWT5dnkoNuDxGb3PF7tZJtTg9QemnTR4GTqv01V97Ypa0jTMfOaI8Qtq3NJ4y9K3vuwc49VApMHxvy6p7Kl7nQDwICkFx/+6hJaN6xVfYVTs/DKu2fR5ZIVxir9g7hIsTngp05ybapcdO0Pp27kzR4fOahizo0dkYZJSNf1y0r0dEVFh2LEjIjIxX68yRm1HZAhbvDLb2sjz8nDs2k2TLNtqz7GbM2cOJBIJRo8ebe5SiKwO81O8whqEwN/HC4UdeJEAKO/rjbAGIcVZFhnAWjO0Y/9RhHQagE7R4xH18Wx0ih6PkE4DePFOMdp28iJqxPwX785ZZpLlW2XH7vTp01i8eDHq1q1r7lKIrA7zU/ykUik+/2gYAKh07vKfzxkXzb0mVsJaM7Rj/1EMHD9D/ZXZ42ewc1cMtp28iL5frsSDR+kmW4fVdeyePn2Kfv364ccff0Tp0qXNXQ6RVWF+zKdL2xb4+YspKsMc+Pt64+cvpnAcOythrRmSy+WYOPd7zVdmz/uBV2abkDwvDx+t2Gay8evyWd05djExMejYsSPatWuHmTNnamybk5ODnJx/rnzMyMgwdXlEFs2S86PreT/WeJ5Ql7Yt0DG8mdXVTf/QNkOW9v3DK7PN79jVmybdU5fPqjp269atw9mzZ3H69Gmt2s+ePRvTp083cVVE1sGS86PrHRms+Q4OUqmUX5xWSpcMWdr3D6/MNr+Ex5nFsh6rORR77949jBo1CmvWrIGzs7NW80yePBnp6emKx71790xcJZFlsuT86HpHBt7BgcxB1wxZ2vcPr8w2P7/SxXOPYKvZYxcbG4vk5GQ0bNhQMU0ul+Pw4cP49ttvkZOTo3I4QyaTQSbT8ebyRDbIUvNT1B0ZJHh1R4aO4c0glUqRm5uLMbMWamw/cd4ieLqVREraEx7qJKPRNUOW9v2Tf2V2wRvQ55Pg1fmevDLbdJrXCkT5sp54+CjdpOfZWU3H7s0338TFixeVpg0ePBg1a9bExIkT+YebSANLzY8u5/08zsjE6Fnf4NGTws9VEgAeJqeiy/DJimnWcoiWLJulZkhbmm5AL/n//5nz0YcW/z6smdTBAfMGdUPfL1eq/BsYk9V07Nzd3RESovxLomTJkihbtqzKdCJSZqn50fZ8nl8PncAPv+h3NVn+IVpeeUqGsNQM6SL/BvQT536vfH6qrzfmfPQh81EMuoXWwdpxkfhoxTaTXUhhNR07IrI92p7Ps/63/Xr/ulV3SJfIXvHKbPPrFloHnd+ojT3nrplkkGKr7tgdPHjQtCtQc3P5QChPi//fn9ovr3NTQysqmoZ7rup8r1o1798QStuqiO12f1+S0vMKb/pqvZ6g4tjONsDk+dGCNuf9lC3tidTHhv2y5VAOZAqWkCFdOMVfUPz3m6UlQOmyr57cvmymiswr49zFohuZUN0XuSZZrtVcFUukLZ0622RW2tyRoVdEG6Otj0M5EJGtY8eOiMyqqDsyvB3ezGjr4lAORGTrrPpQLBHZBk3n/cjlcpTycMeTDP0H9+RQDkSmk/vyJRbvPoabiWkILFcG0R2aw6mEfXUv5Hl5+PNeMpKeZsHXzRVNK/pA6lD4vrP89qZgX1ueiCxWYXdkkEqlGN6nK2YtXq3VctQO5QBgzrhoniBOZGT/Wb0TX+88hLy8f1I3adVOjOoUjln9O5mxsuKz69odfLz3FBIysxTT/Nxd8d+3mqBjzcpatTcmHoolIov30ZA+KONZ+KjtEgDlfb2xcs5/Cj2kmz+Ug1wux5Ez57Hp9wM4cuY8b3pOpKf/rN6JBTsOKnXqACAvT2DBjoP4z+qdZqqs+Oy6dgdRWw6qdNISM7MQteUgdl27o1V7Y+IeOyKyeFKpFF9/PAoDJ8xUuXr29T1yXdq2QOc2zXE09gKOxF4ABNCicV20bFQXgHXfY5bIkuS+fImvdx7S2OabnYcw7b0Imz0sK8/Lw8d7T2m8E86UP04honpFSB0cNLY3Jtvc2kRkc/IvslDpmPl6Kzp1ALDr0AmlNnOX/QJ/Hy/06NAaC1dtUvmj+jA5FQMmzMSH73VFx9ZhHNOLSAuLdx9T2VNXkDxPYPHuY/h3x/Biqqp4/XkvWeOeNwHgYUYW/ryXjOaVyxXZ3ljYsSMiq1HU4Ko79h9Vu1fvYXIqvlm1SeOyf1i3HT+s2849eERauJmo3dBB2razRklPteuk5bfTtr2h2LEjIqtS2EUWcrkcE+ctMvgwx0PegoyoSIHltBs6SNt21sjXzVWndtq2NxQvniAim3D8r0tKh2gNIfDqFmS8sIJIvegOzeHgUHBYcWVSBwmiOzQvpoqKX9OKPvBzd1UZXD2fBIC/x6uhT7Rpbyzs2BGRTTD2XSXyb0FGRKqcSpTAqE6az50b2SncZi+cAACpgwP++1YTAIXfOWdmuyaK8ew0tTcmduyIyCaY4q4SvAUZUeFm9e+EMV1aq+y5kzpIMKZLa7sYx65jzcpY+m5rlHNXPszq5+GKpe+2VhnHrrD2xmS7XWkisithDULg7+OFhORUow0nwFuQEWk2q38nTHsvwq7vPNGxZmVEVK+o9Z0n8tvvv/kQ/TfsM3o99rPl7UHjluauwCIEdW5q7hLIDKRSKT7/aBgGTphZ6N0nRvR/F9+t3Ya8vLwil1eetyAj0opTiRI2O6SJtqQODmheuZxO7fPPvTM2u+zY3S7dGG5uhY9ir9A+tMgmkvZA7H3Vf5xGFVTvAXezkGUEpp0suhYt3SxTdM3aCEw7afSOYpCG5RWsu6KG5ei1vc4cUZ3WMAzAN7oviyyWNmPdNQ6pichJs4pcVvf24RzPjowuwaMmMt21+P4xhYbBiv8sn37FPDVYEOegulq1c4q/YJoCsp6bZLF22bEjIttV1Fh33dq1wso5wOD/zNG4527znkOYNmIwO3dEZHTyvDwcu1bY7h7DWM3FE7Nnz8Ybb7wBd3d3+Pj4oFu3brh+/bq5yyKyGvaUofyx7npEtEHLxvVUOmdlS3kWeTiWV8XS6+wpP2Ra205eRI2Y/+LdOctMsnyr6dgdOnQIMTEx+PPPP7F37168ePEC7du3x7Nnz8xdGpFVYIb+oe3VrrwqlvIxP2QM205eRN8vV+LBo3STrcNqDsX+/vvvSs9XrFgBHx8fxMbGolWrVmaqish6MEP/0PZqV14VS/mYHzKUPC8PH63YZrSr9gtjNR27gtLTX/V2y5Qp/A9vTk4OcnJyFM8zMjJMXheRtSgqQ7acn6KGRpHg1QUXvCqWCmPP+SH9HLt606R76vJZzaHY1+Xl5WH06NFo3rw5QkIK/8M7e/ZseHp6Kh4VK2q61pLIfmiTIVvOT/7QKEDhI8bPGRfNCydILXvPD+kn4XFmsazHKjt2MTExuHTpEtatW6ex3eTJk5Genq543Lt3r5gqJLJs2mTI1vOTPzSKn4+X0nR/X2/8/MUUdGnbwkyVkaVjfkgffqWLZ5gbqzsUO2LECOzcuROHDx9GhQoVNLaVyWSQyWTFVBmRddA2Q/aQn6KGRiEqiPkhfTWvFYjyZT3x8FG6Sc+zs5qOnRAC//73v7F161YcPHgQVapUMXdJRFaFGVIvf2gUIk2YHzKU1MEB8wZ1Q98vV6rcHceYrOZQbExMDFavXo21a9fC3d0diYmJSExMRHZ2trlLI7IKzBCR/pgfMoZuoXWwdlwk/Mt6mmwdVtOx++GHH5Ceno7WrVvDz89P8Vi/fr25SyOyCswQkf6YHzKWbqF1cP27j7Fl0hCTLN+qDsVamvx7lga6qnmxiHFNjXVPV1thzPvlknqWmCEia8H8kDFJHRzQvGagSZZtNR07Yzr3wBsuJT1UpjeqkGyGanTr5MXe99HcIMvAYvLXg85qp2u7jSyto3az/Ucq054+LZ5Lz4mI8v12sRycXVW/f4qfn7kLKFStAM23+yt2gW1MstispxkAphh9uTp37HJycnDy5EncuXMHWVlZ8Pb2RoMGDXgiKZEWmB8iwzBDRJpp3bE7duwYvv76a/zvf//Dixcv4OnpCRcXF6SlpSEnJweBgYH44IMPMGzYMLi7F89YLUTWgvkhMgwzRKQdrS6e6NKlC3r37o2AgADs2bMHmZmZePToEe7fv4+srCzcuHEDU6ZMwb59+1C9enXs3bvX1HUTWQ3mh8gwzBCR9rTaY9exY0ds3rwZjo6Oal8PDAxEYGAgIiMjceXKFSQkJBi1SCJrxvwQGYYZItKeVh276OhorRcYHByM4OBgvQsisjXMD5FhmCEi7ek8jl1gYCAePXqkMv3JkycIDDTNpbtEtoL5ITIMM0Skmc4du9u3b0Mul6tMz8nJwYMHD4xSFJGtYn6IDMMMEWmm9VWxO3bsUPz37t274en5z+0w5HI59u3bh4CAAKMWR2QrmB8iwzBDRNrRumPXrVs3AIBEIkFkZKTSa46OjggICMCXX35p1OKIbAXzQ2QYZohIO1p37PLyXo0EXaVKFZw+fRpeXl4mK4rI1jA/xieXy3H8r0tISk2Dr1cZhDUIgVQqNXdZZCLMEJF2dL7zxK1bt0xRB5FdYH6MY8f+o5g4bxEeJqcqpvn7eOHzj4ahS9sWZqyMTI0ZItJMr3vF7tu3D/v27UNycrLiV1S+ZcuWGaUwIlvF/Bhmx/6jGDhhJgrekj0hORUDJ8zEz19MYefOxjFDRIXTuWM3ffp0zJgxA40bN4afnx8kEokp6jKpn789ihKOJVWmL9V5Sepv5FynVT2dl6S9JyZcdtFOnXHSsmVLk9ZhDLnP1Q92akq2kB9zksvlmDhvkUqnDgAEAAmASV8uRsfwZjwsa6OsPUO/rz+p9vvH2lVrWMNoy7p+xWiLMooawWVNstznWToPTKIVnTt2ixYtwooVKzBgwABT1ENk05gfwxz/65LS4deCBIAHSSk4/tcltGxsyh9YZC7WnqH01PMoUy4UEgl/eJBp6NxdzM3NRVhYmClq0cp3332HgIAAODs7IzQ0FKdOnTJbLUS6Yn4Mk5SaZtR2ZH2sPUNXTk7Eqd09kfrwkIkqJHunc8fu/fffx9q1a01RS5HWr1+PsWPHYurUqTh79izq1auHDh06IDk52Sz1EOmK+TGMr1cZo7Yj62MLGcp9noKrp6awc0cmofOh2OfPn2PJkiX4448/ULduXZWbMs+fP99oxRU0f/58DB06FIMHDwbwapf8rl27sGzZMkyaNMlk6yUyFubHMGENQuDv44WE5FS159lJAPj7eiOsQUhxl0bFxJYydPPiNyjr14KHZcmodO7YXbhwAfXr1wcAXLp0Sek1U57Empubi9jYWEyePFkxzcHBAe3atcOJEyfUzpOTk4OcnBzF84yMDJPVR6QN5scwUqkUn380DAMnzIQEUOrc5W+9OeOieeGEDbOWDGmTn5zsZKSnXkAp7wamKZrsks4duwMHDpiijiKlpqZCLpfD19dXabqvry+uXbumdp7Zs2dj+vTpxVEekVaYH8N1adsCP38xRXUcO19vzBkXbdVDnXDQ5aJZS4a0zU9uziOj1UgE6DmOnbWYPHkyxo4dq3iekZGBihUrmrEiIuthyfnp0rYFOoY3s6lOEAddti3a5sdJZpqhNMh+adWxe/fdd7FixQp4eHjg3Xff1dh2y5YtRimsIC8vL0ilUiQlJSlNT0pKQrly5dTOI5PJIJPJTFIPkbaYH9OQSqU2M6QJB13WzBozpE1+ZC4+8PSqa9Q6ibS6KtbT01Nx7oKnp6fGh6k4OTmhUaNG2Ldvn2JaXl4e9u3bh2bNmplsvUSGssf8yOVyHDlzHpt+P4AjZ85DLpcbfR22oqhBl4FXgy7b8za01QwF1hnJCyfI6LTaY7d8+XK1/13cxo4di8jISDRu3BhNmjTBV199hWfPnimuUCKyRPaWHx5S1A0HXS6arWVI5uKDwDoj4eUfbqJKyZ5Z1Tl2vXv3RkpKCj799FMkJiaifv36+P3331VOZiUiVcWRHx5S1B0HXbYexshQcOjnvPMEmZRWHbuIiAhMmzYNTZs21dguMzMT33//Pdzc3BATE2OUAgsaMWIERowYYdAyajcPgZOz+vu8vq5J41IGrcdUTp15UmzrMvY2aFSheAbDjb3vU2Sb7GcOKI7bhdtafgrD+7jqh4MuF82WMtTwzR5wcnY3YkW2yVT3Z7UHWnXsevbsie7du8PT0xOdO3dG48aN4e/vD2dnZzx+/BhXrlzB0aNH8euvv6Jjx46YO3euqes2SKMGpeBSsvCO3T+dD8sYkb9gJ0XXzlZRHUF9O2/adtIC007+86SYdjoEur76/5tlQgtt8/RpZrHUYmv5KQwPKeqHgy4XzV4ypC92gvRXKyDPbOvOemqadWvVsYuKikL//v2xceNGrF+/HkuWLEF6ejqAVwNCBgcHo0OHDjh9+jRq1aplkkKJrJW95IeHFPXDQZeLZi8ZIjIGrc+xk8lk6N+/P/r37w8ASE9PR3Z2NsqWLatySxciUmYP+eEhRf3Z8qDLxmIPGSIyBr0vnjD1peVEtswW88NDioaxxUGXTckWM0RkDFZ1VSwRWS4eUjScLQ26TETmodUAxURE2sg/pOjn46U03d/Xm0OdEBEVA+6xIyKj4iFFIiLzYceOiIyOhxSJiMxD50OxkZGROHz4sClqIbJ5zA+RYZghIs107tilp6ejXbt2qFatGmbNmoUHDx6Yoi4im8T8EBmGGSLSTOeO3bZt2/DgwQN8+OGHWL9+PQICAvCvf/0LmzZtwosXL0xRI5HNYH6IDMMMEWmm11Wx3t7eGDt2LM6fP4+TJ0+iatWqGDBgAPz9/TFmzBjcuHHD2HUS2Qzmh8gwzBBR4Qwa7iQhIQF79+7F3r17IZVK8fbbb+PixYsIDg7GggULjFUjkU1ifogMwwwRqZIIIdQNEl+oFy9eYMeOHVi+fDn27NmDunXr4v3330ffvn3h4eEBANi6dSuGDBmCx48fm6RofWVkZMDT0xPHzsbBzc3d3OWYRex9H8V/N6qQrNO8gWknjV2OWWU8y0L5N99Denq64rNraraQn3sHN8PDraS5yyELkPH0GSq27s4MaSE/P6f+ugo3d/v8/jFE+fQr5i7B6DKePkPF8HeMnh+dhzvx8/NDXl4e+vTpg1OnTqF+/foqbdq0aYNSpUoZoTzL8HpnyFC6dqZszc0yoSZbtjV0PO0xP0TGxAwRaaZzx27BggXo2bMnnJ2dC21TqlQp3Lp1y6DCiGyRLeTnxF+X0C6sMQccJrOwhQwRyeVynPjrkkmWrfM5dgMGDNAYKFO4ffs2oqKiUKVKFbi4uCAoKAhTp05Fbm5usdZBZChbyE+vMVMR0jkSO/YfNXKlREWzhQyRfdux/yhCOg1Ar9GfmGT5VnHniWvXriEvLw+LFy9G1apVcenSJQwdOhTPnj3DvHnzzF0ekUUzRX4SklMxcMJM3v+V7AK/g8hYduw/ioHjZ0Cnixt0ZBUdu4iICERERCieBwYG4vr16/jhhx8YKqIimCI/AoAEwKQvF6NjeDMeliWbxu8gMga5XI6Jc783aacOsJKOnTrp6ekoU6aMxjY5OTnIyclRPM/IyDB1WURWwRj5EQAeJKXg+F+XeF9YsjtFZYjfP1TQ8b8u4WFyqsnXY9A4duYSFxeHhQsXIjo6WmO72bNnw9PTU/GoWLFiMVVIZLmMnZ+k1DRTlElksbTJEL9/qKDi+ltp1o7dpEmTIJFIND6uXbumNM+DBw8QERGBnj17YujQoRqXP3nyZKSnpyse9+7dM+XbISpWlpIfXy/Ne/6ILJUpM8TvHyqouP5WmvVQ7Lhx4zBo0CCNbQIDAxX//fDhQ7Rp0wZhYWFYsmRJkcuXyWSQyWSGlklkkcydHwkAf19vhDUI0bZkIotiygzx+4cKCmsQAn8fLyQkp9ruxRPe3t7w9vbWqu2DBw/Qpk0bNGrUCMuXL4eDg1UeRSYyGnPmR/L//z9nXDQvnCCrxe8gKk5SqRSfjx+OgeNnQAKYrHNnFZ/MBw8eoHXr1qhUqRLmzZuHlJQUJCYmIjEx0dylEVk8U+TH39ebQ52Q3eB3EBlLl7Yt8PPcT+Hn42WydVjFVbF79+5FXFwc4uLiUKFCBaXXdLzVLZHdMXZ+NiyYzjtPkF3hdxAZU5e2LdAxvBn+OH7GJIMUS4QdfSrzb8J87Gwc3NxUb8Ks9b1GzxwxrJDGLQ2bXwv63pPVqPdb1WE7xf/vT50WHdS5qa7VqMjIzkH58d8U6w3MrVl+fu4d3AwPt5LmLocsQMbTZ6jYujszpIX8/Jz66yrc3FW/fwxRPv2KUZdni5ziL5i7BBUZWc/hO2iK0fNjFYdiiYiIiKho7NgREREVk9jTJyGXy81dBpmZPC8Px67dNMmy2bEjIiIqJh9+MAjtwpti7+5fzV0Kmcm2kxdRI+a/eHfOMpMsnx07IiKiYpSclIjRI6LZubND205eRN8vV+LBo3STrYMdOyIiomKUf83i7JnTeFjWjsjz8vDRim0mHZwYYMeOiIio2AkhkJjwELGnjTgSAVm0Y1dvmnRPXT527IiIiMwkJSXZ3CVQMUl4nFks62HHjoiIyEy8vX3MXQIVE7/Sxh2/sDDs2BERERUziUSCcn7+aPSGfoPJk/VpXisQ5ct6Ku61bSrs2BERERUjieTVV/vkKdN4az47InVwwLxB3QDApJ07duyIiIiKkW85P3z17WK81eFtc5dCxaxbaB2sHRcJ/7KeJltHCZMtmYiIiJT8sGQFWoS34Z46O9YttA46v1Ebe85dM8kgxdxjR0REVEwavRHKTh1B6uCA5jUDTbJs7rF7zc0yRZ/EGnvfBwjurNfyG1Uw72XtsfdVr74qWJO6bRCYpuc4S41bFvpSwfVI2uu26NfvsKd3fc+yAHyj37xERHq4me4DV7mHXvNWLZWkdvoDz2BDSrIPDS1vGz3NzAQwxejL5R47IiKiYnL5r6O82wSZlNV17HJyclC/fn1IJBKcO3fO3OUQWRXmh8gwhmboszFd8X7nqji+f6vxiyOCFXbsJkyYAH9/f3OXQWSVmB8iwxgjQ4+SH2DOhN7s3JFJWFXH7rfffsOePXswb948c5dCZHWYHyLDGC9Dr24D/9OXY3lYlozOai6eSEpKwtChQ7Ft2za4urpqNU9OTg5ycnIUzzMyMkxVHpFFY36IDKNrhorOj0Bq0n1c+eso6jQON3K1ZM+sYo+dEAKDBg3CsGHD0LhxY63nmz17Njw9PRWPihUrmrBKIsvE/BAZRp8MaZuftNQEY5ZKZN6O3aRJkyCRSDQ+rl27hoULFyIzMxOTJ0/WafmTJ09Genq64nHv3j0TvROi4sf8EBnGlBnSNj9lvPyM9XaIAJj5UOy4ceMwaNAgjW0CAwOxf/9+nDhxAjKZTOm1xo0bo1+/fli5cqXaeWUymco8RLaC+SEyjCkzVHR+JPDyLY/gBi30qJyocGbt2Hl7e8Pb27vIdt988w1mzpypeP7w4UN06NAB69evR2ho0YMKE9ki5ofIMObL0KtbwL8/bj7vQkFGZxUXT1SqVEnpuZubGwAgKCgIFSpUMEdJRFaD+SEyjLEz5OVbHu+Pm4+wtu8YpT6i11lFx46IiMgWfLJgOxqGdeCeOjIZq+zYBQQEQAhh7jKIrBLzQ2QYQzJUu0ELdurIpKyyY2cpGlVINncJNicw7aTG12+W4TlhREREhbHLjt3SnY5wcnbS2KZJ41JGX2/sfR+jL7Mop848ee3ZEzWv/7MdCnvPsehslFpM1RHWt7P31CnTyJUQEZlO3BNfc5dARpT11MUky7XLjh0RERHZD7lcjit/HUVaagLKePkh2MyHxOVyOS7/ddQky2bHjoiIiGzW8f1b8eO8sXiUfF8xraxPBQz9yDxXJqurx5is4pZiRERERLo6vn8r5kzordKJepT8AHMm9Mbx/Vstoh5jYseOiIiIbI5cLseP88YCUHcF86tpP305FnK53ALqMR67OhSbf3l67vOiT5rPflZ0n/fpU91Ovs9+5qxTe2PIfZ6hdVtt3rMhtNleGc+yNC/DiBc8PPv/ejj0h3byt1NmEf9GZD/yPwvMUNHyt1HWM+3/JpNhLv91tIg9YwKpSfdx9vhu1C6GW7sVVo+x8yMRdpTImzdvIigoyNxlkIWJj49HYGCgucuweMwPFYYZKhrzQ4Uxdn7sao9dmTJlAAB3796Fp6enWWrIyMhAxYoVce/ePXh4eLAGM9aQnp6OSpUqKT4XpBnzYzk1WEodzJD2LCE/gGV8bljDK6bKj1117BwcXh1q9PT0NOsfZADw8PBgDRZSQ/7ngjRjfiyvBkupgxkqmiXlB7CMzw1reMXY+WEaiYiIiGwEO3ZERERENsKuOnYymQxTp06FTCZjDazBImqwJpawvViDZdVhCTVYC0vZVpZQB2swbQ12dVUsERERkS2zqz12RERERLaMHTsiIiIiG8GOHREREZGNYMeOiIiIyEbYdMcuICAAEolE6TFnzhyN8zx//hwxMTEoW7Ys3Nzc0L17dyQlJeldw+3btxEVFYUqVarAxcUFQUFBmDp1KnJzczXO17p1a5Xahw0bpvV6v/vuOwQEBMDZ2RmhoaE4deqUxvYbN25EzZo14ezsjDp16uDXX3/Vel0FzZ49G2+88Qbc3d3h4+ODbt264fr16xrnWbFihcr7dXY27N6606ZNU1lmzZo1Nc5jzO1gC8ydIXvMD2AZGWJ+DGev+QH4HWTW/AgbVrlyZTFjxgyRkJCgeDx9+lTjPMOGDRMVK1YU+/btE2fOnBFNmzYVYWFhetfw22+/iUGDBondu3eL+Ph4sX37duHj4yPGjRuncb7w8HAxdOhQpdrT09O1Wue6deuEk5OTWLZsmbh8+bIYOnSoKFWqlEhKSlLb/tixY0IqlYovvvhCXLlyRUyZMkU4OjqKixcv6vx+hRCiQ4cOYvny5eLSpUvi3Llz4u233xaVKlXSuO2XL18uPDw8lN5vYmKiXuvPN3XqVFG7dm2lZaakpBTa3tjbwRaYO0P2mB8hLCNDzI/h7DE/Qpg/Q/aeH5vv2C1YsEDr9k+ePBGOjo5i48aNimlXr14VAMSJEyeMVtcXX3whqlSporFNeHi4GDVqlF7Lb9KkiYiJiVE8l8vlwt/fX8yePVtt+169eomOHTsqTQsNDRXR0dF6rb+g5ORkAUAcOnSo0DbLly8Xnp6eRllfvqlTp4p69epp3d7U28EaWWKG7C0/QpgnQ8yP4ewxP0JYXobsLT82fSgWAObMmYOyZcuiQYMGmDt3Ll6+fFlo29jYWLx48QLt2rVTTKtZsyYqVaqEEydOGK2m9PR0rW76u2bNGnh5eSEkJASTJ09GVlZWkfPk5uYiNjZW6T04ODigXbt2hb6HEydOKLUHgA4dOhjtPaenpwNAke/56dOnqFy5MipWrIiuXbvi8uXLBq/7xo0b8Pf3R2BgIPr164e7d+8W2tbU28FaWVqG7C0/gPkyxPwYzp7yA1hmhuwtPyV0nsOKjBw5Eg0bNkSZMmVw/PhxTJ48GQkJCZg/f77a9omJiXByckKpUqWUpvv6+iIxMdEoNcXFxWHhwoWYN2+exnZ9+/ZF5cqV4e/vjwsXLmDixIm4fv06tmzZonG+1NRUyOVy+Pr6Kk339fXFtWvX1M6TmJiotr0x3nNeXh5Gjx6N5s2bIyQkpNB2NWrUwLJly1C3bl2kp6dj3rx5CAsLw+XLl1GhQgW91h0aGooVK1agRo0aSEhIwPTp09GyZUtcunQJ7u7uKu1NuR2slaVlyN7yA5gvQ8yP4ewtP4DlZcgu86PzPj4zmzhxogCg8XH16lW18y5dulSUKFFCPH/+XO3ra9asEU5OTirT33jjDTFhwgSD67h//74ICgoSUVFROr/vffv2CQAiLi5OY7sHDx4IAOL48eNK08ePHy+aNGmidh5HR0exdu1apWnfffed8PHx0bnOgoYNGyYqV64s7t27p9N8ubm5IigoSEyZMsXgGvI9fvxYeHh4iJ9++knt66bcDpbEEjLE/GjPUjLE/LzC/GhmaRmyx/xY3R67cePGYdCgQRrbBAYGqp0eGhqKly9f4vbt26hRo4bK6+XKlUNubi6ePHmi9IspKSkJ5cqVM6iOhw8fok2bNggLC8OSJUs0zldY7cCrX1xBQUGFtvPy8oJUKlW5ikrde8hXrlw5ndpra8SIEdi5cycOHz6s8y8eR0dHNGjQAHFxcQbV8LpSpUqhevXqhS7TVNvB0lhChpgf7VhShpifV5ifwvMDWFaG7DY/enU9rdTq1auFg4ODSEtLU/t6/omrmzZtUky7du2awSeu3r9/X1SrVk2899574uXLl3ot4+jRowKAOH/+fJFtmzRpIkaMGKF4LpfLRfny5TWeuNqpUyelac2aNdP7xNW8vDwRExMj/P39xd9//63XMl6+fClq1KghxowZo9f86mRmZorSpUuLr7/+Wu3rxt4OtsgcGbK3/AhhmRlifgxnD/kRwvwZsvf82GzH7vjx42LBggXi3LlzIj4+XqxevVp4e3uLgQMHKtrcv39f1KhRQ5w8eVIxbdiwYaJSpUpi//794syZM6JZs2aiWbNmetdx//59UbVqVfHmm2+K+/fvK136XFgdcXFxYsaMGeLMmTPi1q1bYvv27SIwMFC0atVKq3WuW7dOyGQysWLFCnHlyhXxwQcfiFKlSiku3R4wYICYNGmSov2xY8dEiRIlxLx588TVq1fF1KlTDbrU/MMPPxSenp7i4MGDSu83KytL0aZgDdOnT1dckh8bGyvee+894ezsLC5fvqxXDUIIMW7cOHHw4EFx69YtcezYMdGuXTvh5eUlkpOT1dZg7O1g7SwhQ/aYHyEsI0PMj2HsNT9CmD9D9p4fm+3YxcbGitDQUOHp6SmcnZ1FrVq1xKxZs5TObbh165YAIA4cOKCYlp2dLYYPHy5Kly4tXF1dxTvvvKMUAl0tX7680HMgCqvj7t27olWrVqJMmTJCJpOJqlWrivHjx+s0jtDChQtFpUqVhJOTk2jSpIn4888/Fa+Fh4eLyMhIpfYbNmwQ1atXF05OTqJ27dpi165der/nwt7v8uXLC61h9OjRinp9fX3F22+/Lc6ePat3DUII0bt3b+Hn5yecnJxE+fLlRe/evZXOETH1drB2lpAhe8yPEJaRIebHMPacHyH4HWTO/EiEEEL3A7hEREREZGlsfhw7IiIiInvBjh0RERGRjWDHjoiIiMhGsGNHREREZCPYsSMiIiKyEezYEREREdkIduyIiIiIbAQ7dkREREQ2gh07G7F06VK0b99eadq0adPg6+sLiUSCbdu2YdCgQejWrZvJa0lNTYWPjw/u379v8nURGQPzQ6Q/5seysGNnA54/f45PPvkEU6dOVUy7evUqpk+fjsWLFyMhIQH/+te/TLJudWH18vLCwIEDleohslTMD5H+mB/Lw46dDdi0aRM8PDzQvHlzxbT4+HgAQNeuXVGuXDnIZLJirWnw4MFYs2YN0tLSinW9RLpifoj0x/xYHnbsLEhKSgrKlSuHWbNmKaYdP34cTk5O2LdvX6HzrVu3Dp07d1Y8nzZtmuK5g4MDJBKJ2vlycnIwcuRI+Pj4wNnZGS1atMDp06cVr8vlckRFRaFKlSpwcXFBjRo18PXXXyutZ+XKldi+fTskEgkkEgkOHjwIAKhduzb8/f2xdetWvbYFka6YHyL9MT82RJBF2bVrl3B0dBSnT58WGRkZIjAwUIwZM0bjPJ6enmLdunWK55mZmWL58uUCgEhISBAJCQlCCCEiIyNF165dFe1Gjhwp/P39xa+//iouX74sIiMjRenSpcWjR4+EEELk5uaKTz/9VJw+fVrcvHlTrF69Wri6uor169cr1tOrVy8RERGhWE9OTo5i+b179xaRkZFG2jJERWN+iPTH/NgGduws0PDhw0X16tVF3759RZ06dcTz588Lbfv48WMBQBw+fFhp+tatW0XBfvvrwXr69KlwdHQUa9asUbyem5sr/P39xRdffFHo+mJiYkT37t3VLrOgMWPGiNatWxe6LCJTYH6I9Mf8WL8SZttVSIWaN28eQkJCsHHjRsTGxmo8PyE7OxsA4OzsrNM64uPj8eLFC6XzIhwdHdGkSRNcvXpVMe27777DsmXLcPfuXWRnZyM3Nxf169fXah0uLi7IysrSqS4iQzE/RPpjfqwfz7GzQPHx8Xj48CHy8vJw+/ZtjW3Lli0LiUSCx48fG72OdevW4aOPPkJUVBT27NmDc+fOYfDgwcjNzdVq/rS0NHh7exu9LiJNmB8i/TE/1o8dOwuTm5uL/v37o3fv3vjss8/w/vvvIzk5udD2Tk5OCA4OxpUrV3RaT1BQEJycnHDs2DHFtBcvXuD06dMIDg4GABw7dgxhYWEYPnw4GjRogKpVqyqudnp9/XK5XO06Ll26hAYNGuhUF5EhmB8i/TE/toEdOwvz8ccfIz09Hd988w0mTpyI6tWrY8iQIRrn6dChA44eParTekqWLIkPP/wQ48ePx++//44rV65g6NChyMrKQlRUFACgWrVqOHPmDHbv3o2///4bn3zyidJVSwAQEBCACxcu4Pr160hNTcWLFy8AAFlZWYiNjVUZtJLIlJgfIv0xPzbC3Cf50T8OHDggSpQoIY4cOaKYduvWLeHh4SG+//77Que7fPmycHFxEU+ePFFMK+rkVSGEyM7OFv/+97+Fl5eXkMlkonnz5uLUqVOK158/fy4GDRokPD09RalSpcSHH34oJk2aJOrVq6dok5ycLN566y3h5uYmAIgDBw4IIYRYu3atqFGjhp5bgkh3zA+R/pgf2yERQghzdizJOHr27ImGDRti8uTJ5i4FANC0aVOMHDkSffv2NXcpREVifoj0x/xYFh6KtRFz586Fm5ubucsA8Opefe+++y769Olj7lKItML8EOmP+bEs3GNHREREZCO4x46IiIjIRrBjR0RERGQj2LEjIiIishHs2BERERHZCHbsiIiIiGwEO3ZERERENoIdOyIiIiIbwY4dERERkY1gx46IiIjIRvwf6k1OaCtLhb8AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -349,7 +349,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/r0/12hxq7zs2mx5kr76ks1_n9k80000gn/T/ipykernel_63433/2996397825.py:3: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/2996397825.py:3: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
       "  categorical_optimizer = BayesianOptimization(\n"
      ]
     },
@@ -414,12 +414,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -475,7 +475,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/r0/12hxq7zs2mx5kr76ks1_n9k80000gn/T/ipykernel_63433/3228056642.py:37: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/30298674.py:37: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
       "  optimizer = BayesianOptimization(\n"
      ]
     },
@@ -554,28 +554,236 @@
    "source": [
     "## 5. Defining your own Parameter\n",
     "\n",
-    "Maybe you want to optimize over another form of parameters, which does not align with `float`, `int` or categorical. For this purpose, you can create your parameter.\n",
+    "Maybe you want to optimize over another form of parameters, which does not align with `float`, `int` or categorical. For this purpose, you can create your own, custom parameter. A simple example is a parameter that is discrete, but still admits a distance representation (like an integer) while not being uniformly spaced.\n",
+    "\n",
+    "However, you can go further even and encode constraints and even symmetries in your parameter. Let's consider the problem of finding a triangle which maximizes an area given its sides $a, b, c$ with a constraint that the perimeter is fixed, i.e. $a + b + c=s$.\n",
+    "\n",
+    "We will create a parameter that encodes such a triangle, and via it's kernel transform ensures that the sides sum to the required length $s$. As you might expect, the solution to this problem is an equilateral triangle, i.e. $a=b=c=s/3$.\n",
     "\n",
-    "As an example, consider a parameter that is discrete, but still admits a distance representation (like an integer) while not being uniformly spaced."
+    "To define the parameter, we need to subclass `BayesParameter` and define a few important functions/properties.\n",
+    "\n",
+    "- `is_continuous` is a property which denotes whether a parameter is continuous. When optimizing the acquisition function, non-continuous parameters will not be optimized using gradient-based methods, but only via random sampling.\n",
+    "- `random_sample` is a function that samples randomly from the space of the parameter.\n",
+    "- `to_float` transforms the canonical representation of a parameter into float values for the target space to store. There is a one-to-one correspondence between valid float representations produced by this function and canonical representations of the parameter. This function is most important when working with parameters that use a non-numeric canonical representation, such as categorical parameters.\n",
+    "- `to_param` performs the inverse of `to_float`: Given a float-based representation, it creates a canonical representation. This function should perform binning whenever appropriate, e.g. in the case of the `IntParameter`, this function would round any float values supplied to it.\n",
+    "- `kernel_transform` is the most important function of the Parameter and defines how to represent a value in the kernel space. In contrast to `to_float`, this function expects both the input, as well as the output to be float-representations of the value.\n",
+    "- `dim` is a property which defines the dimensionality of the parameter. In most cases, this will be 1, but e.g. for categorical parameters it is equivalent to the cardinality of the category space.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from bayes_opt.logger import ScreenLogger\n",
     "from bayes_opt.parameter import BayesParameter\n",
+    "from bayes_opt.event import Events\n",
+    "from bayes_opt.util import ensure_rng\n",
     "\n",
     "\n",
-    "class MyParameter(BayesParameter):\n",
-    "    def __init__(self, name: str, bounds) -> None:\n",
+    "class FixedPerimeterTriangleParameter(BayesParameter):\n",
+    "    def __init__(self, name: str, bounds, perimeter) -> None:\n",
     "        super().__init__(name, bounds)\n",
+    "        self.perimeter = perimeter\n",
     "\n",
+    "    @property\n",
     "    def is_continuous(self):\n",
-    "        return False\n",
+    "        return True\n",
+    "    \n",
+    "    def random_sample(self, n_samples: int, random_state):\n",
+    "        random_state = ensure_rng(random_state)\n",
+    "        samples = []\n",
+    "        while len(samples) < n_samples:\n",
+    "            samples_ = random_state.dirichlet(np.ones(3), n_samples)\n",
+    "            samples_ = samples_ * self.perimeter # scale samples by perimeter\n",
+    "\n",
+    "            samples_ = samples_[np.all((self.bounds[:, 0] <= samples_) & (samples_ <= self.bounds[:, 1]), axis=-1)]\n",
+    "            samples.extend(np.atleast_2d(samples_))\n",
+    "        samples = np.array(samples[:n_samples])\n",
+    "        return samples\n",
     "    \n",
-    "        "
+    "    def to_float(self, value):\n",
+    "        return value\n",
+    "    \n",
+    "    def to_param(self, value):\n",
+    "        return value * self.perimeter / sum(value)\n",
+    "\n",
+    "    def kernel_transform(self, value):\n",
+    "        return value * self.perimeter / np.sum(value, axis=-1, keepdims=True)\n",
+    "\n",
+    "    def to_string(self, value, str_len: int) -> str:\n",
+    "        len_each = (str_len - 2) // 3\n",
+    "        str_ = '|'.join([f\"{float(np.round(value[i], 4))}\"[:len_each] for i in range(3)])\n",
+    "        return str_.ljust(str_len)\n",
+    "\n",
+    "    @property\n",
+    "    def dim(self):\n",
+    "        return 3 # as we have three float values, each representing the length of one side.\n",
+    "\n",
+    "def area_of_triangle(sides):\n",
+    "    a, b, c = sides\n",
+    "    s = np.sum(sides, axis=-1) # perimeter\n",
+    "    A = np.sqrt(s * (s-a) * (s-b) * (s-c))\n",
+    "    return A\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|      iter       |     target      |      sides      |\n",
+      "-------------------------------------------------------\n",
+      "| \u001b[39m1              \u001b[39m | \u001b[39m0.4572         \u001b[39m | \u001b[39m0.29|0.70|0.00 \u001b[39m |\n",
+      "| \u001b[35m2              \u001b[39m | \u001b[35m0.5096         \u001b[39m | \u001b[35m0.58|0.25|0.15 \u001b[39m |\n",
+      "| \u001b[39m3              \u001b[39m | \u001b[39m0.5081         \u001b[39m | \u001b[39m0.58|0.25|0.15 \u001b[39m |\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/626783072.py:8: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "  optimizer = BayesianOptimization(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "| \u001b[35m4              \u001b[39m | \u001b[35m0.5386         \u001b[39m | \u001b[35m0.44|0.28|0.26 \u001b[39m |\n",
+      "| \u001b[39m5              \u001b[39m | \u001b[39m0.5279         \u001b[39m | \u001b[39m0.38|0.14|0.47 \u001b[39m |\n",
+      "| \u001b[39m6              \u001b[39m | \u001b[39m0.5328         \u001b[39m | \u001b[39m0.18|0.36|0.45 \u001b[39m |\n",
+      "| \u001b[39m7              \u001b[39m | \u001b[39m0.4366         \u001b[39m | \u001b[39m0.02|0.22|0.74 \u001b[39m |\n",
+      "| \u001b[39m8              \u001b[39m | \u001b[39m0.4868         \u001b[39m | \u001b[39m0.00|0.61|0.37 \u001b[39m |\n",
+      "| \u001b[39m9              \u001b[39m | \u001b[39m0.4977         \u001b[39m | \u001b[39m0.56|0.01|0.42 \u001b[39m |\n",
+      "| \u001b[35m10             \u001b[39m | \u001b[35m0.5418         \u001b[39m | \u001b[35m0.29|0.40|0.30 \u001b[39m |\n",
+      "| \u001b[39m11             \u001b[39m | \u001b[39m0.3361         \u001b[39m | \u001b[39m0.06|0.87|0.06 \u001b[39m |\n",
+      "| \u001b[39m12             \u001b[39m | \u001b[39m0.06468        \u001b[39m | \u001b[39m0.99|0.00|0.00 \u001b[39m |\n",
+      "| \u001b[39m13             \u001b[39m | \u001b[39m0.01589        \u001b[39m | \u001b[39m0.0|0.00|0.99  \u001b[39m |\n",
+      "| \u001b[39m14             \u001b[39m | \u001b[39m0.4999         \u001b[39m | \u001b[39m0.21|0.16|0.61 \u001b[39m |\n",
+      "| \u001b[39m15             \u001b[39m | \u001b[39m0.499          \u001b[39m | \u001b[39m0.53|0.46|0.00 \u001b[39m |\n",
+      "| \u001b[39m16             \u001b[39m | \u001b[39m0.4937         \u001b[39m | \u001b[39m0.00|0.41|0.58 \u001b[39m |\n",
+      "| \u001b[39m17             \u001b[39m | \u001b[39m0.5233         \u001b[39m | \u001b[39m0.33|0.51|0.14 \u001b[39m |\n",
+      "| \u001b[39m18             \u001b[39m | \u001b[39m0.5204         \u001b[39m | \u001b[39m0.17|0.54|0.28 \u001b[39m |\n",
+      "| \u001b[39m19             \u001b[39m | \u001b[39m0.5235         \u001b[39m | \u001b[39m0.51|0.15|0.32 \u001b[39m |\n",
+      "| \u001b[39m20             \u001b[39m | \u001b[39m0.5412         \u001b[39m | \u001b[39m0.31|0.27|0.41 \u001b[39m |\n",
+      "| \u001b[39m21             \u001b[39m | \u001b[39m0.4946         \u001b[39m | \u001b[39m0.41|0.00|0.57 \u001b[39m |\n",
+      "| \u001b[39m22             \u001b[39m | \u001b[39m0.5355         \u001b[39m | \u001b[39m0.41|0.39|0.19 \u001b[39m |\n",
+      "| \u001b[35m23             \u001b[39m | \u001b[35m0.5442         \u001b[39m | \u001b[35m0.35|0.32|0.32 \u001b[39m |\n",
+      "| \u001b[39m24             \u001b[39m | \u001b[39m0.5192         \u001b[39m | \u001b[39m0.16|0.28|0.54 \u001b[39m |\n",
+      "| \u001b[39m25             \u001b[39m | \u001b[39m0.5401         \u001b[39m | \u001b[39m0.39|0.23|0.36 \u001b[39m |\n",
+      "=======================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "param = FixedPerimeterTriangleParameter(\n",
+    "    name='sides',\n",
+    "    bounds=np.array([[0., 1.], [0., 1.], [0., 1.]]),\n",
+    "    perimeter=1.\n",
+    ")\n",
+    "\n",
+    "pbounds = {'sides': param}\n",
+    "optimizer = BayesianOptimization(\n",
+    "    area_of_triangle,\n",
+    "    pbounds,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "logger = ScreenLogger(verbose=2, is_constrained=False)\n",
+    "logger._default_cell_size = 15\n",
+    "\n",
+    "for e in [Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP, Events.OPTIMIZATION_END]:\n",
+    "    optimizer.subscribe(e, logger)\n",
+    "\n",
+    "optimizer.maximize(init_points=2, n_iter=23)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This seems to work decently well, but we can improve it significantly if we consider the symmetries inherent in the problem: This problem is permutation invariant, i.e. we do not care which side specifically is denoted as $a$, $b$ or $c$. Instead, we can, without loss of generality, decide that the shortest side will always be denoted as $a$, and the longest always as $c$. If we enhance our kernel transform with this symmetry, the performance improves significantly. This can be easily done by sub-classing the previously created triangle parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|      iter       |     target      |      sides      |\n",
+      "-------------------------------------------------------\n",
+      "| \u001b[39m1              \u001b[39m | \u001b[39m0.4572         \u001b[39m | \u001b[39m0.00|0.29|0.70 \u001b[39m |\n",
+      "| \u001b[35m2              \u001b[39m | \u001b[35m0.5096         \u001b[39m | \u001b[35m0.15|0.25|0.58 \u001b[39m |\n",
+      "| \u001b[39m3              \u001b[39m | \u001b[39m0.498          \u001b[39m | \u001b[39m0.06|0.33|0.60 \u001b[39m |\n",
+      "| \u001b[35m4              \u001b[39m | \u001b[35m0.5097         \u001b[39m | \u001b[35m0.13|0.27|0.58 \u001b[39m |\n",
+      "| \u001b[35m5              \u001b[39m | \u001b[35m0.5358         \u001b[39m | \u001b[35m0.19|0.36|0.43 \u001b[39m |\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/2335620304.py:20: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
+      "  optimizer = BayesianOptimization(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "| \u001b[35m6              \u001b[39m | \u001b[35m0.5443         \u001b[39m | \u001b[35m0.33|0.33|0.33 \u001b[39m |\n",
+      "| \u001b[39m7              \u001b[39m | \u001b[39m0.5405         \u001b[39m | \u001b[39m0.28|0.28|0.42 \u001b[39m |\n",
+      "| \u001b[39m8              \u001b[39m | \u001b[39m0.5034         \u001b[39m | \u001b[39m0.01|0.49|0.49 \u001b[39m |\n",
+      "| \u001b[39m9              \u001b[39m | \u001b[39m0.4977         \u001b[39m | \u001b[39m0.01|0.42|0.56 \u001b[39m |\n",
+      "| \u001b[39m10             \u001b[39m | \u001b[39m0.5427         \u001b[39m | \u001b[39m0.27|0.36|0.36 \u001b[39m |\n",
+      "=======================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "class SortingFixedPerimeterTriangleParameter(FixedPerimeterTriangleParameter):\n",
+    "    def __init__(self, name: str, bounds, perimeter) -> None:\n",
+    "        super().__init__(name, bounds, perimeter)\n",
+    "\n",
+    "    def to_param(self, value):\n",
+    "        value = np.sort(value, axis=-1)\n",
+    "        return super().to_param(value)\n",
+    "\n",
+    "    def kernel_transform(self, value):\n",
+    "        value = np.sort(value, axis=-1)\n",
+    "        return super().kernel_transform(value)\n",
+    "\n",
+    "param = SortingFixedPerimeterTriangleParameter(\n",
+    "    name='sides',\n",
+    "    bounds=np.array([[0., 1.], [0., 1.], [0., 1.]]),\n",
+    "    perimeter=1.\n",
+    ")\n",
+    "\n",
+    "pbounds = {'sides': param}\n",
+    "optimizer = BayesianOptimization(\n",
+    "    area_of_triangle,\n",
+    "    pbounds,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "logger = ScreenLogger(verbose=2, is_constrained=False)\n",
+    "logger._default_cell_size = 15\n",
+    "\n",
+    "for e in [Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP, Events.OPTIMIZATION_END]:\n",
+    "    optimizer.subscribe(e, logger)\n",
+    "\n",
+    "optimizer.maximize(init_points=2, n_iter=8)"
    ]
   },
   {
@@ -595,7 +803,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "bayesian-optimization-t6LLJ9me-py3.10",
+   "display_name": "bayesian-optimization-tb9vsVm6-py3.9",
    "language": "python",
    "name": "python3"
   },
@@ -609,7 +817,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

From 9b1fbc1568139f10cca7b4bc532f6201dae2868a Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Wed, 18 Dec 2024 14:50:51 +0100
Subject: [PATCH 19/21] Mention that parameters are not sorted

---
 examples/basic-tour.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/basic-tour.ipynb b/examples/basic-tour.ipynb
index 3cbcbd407..4ecd83296 100644
--- a/examples/basic-tour.ipynb
+++ b/examples/basic-tour.ipynb
@@ -252,7 +252,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or as an iterable. Beware that the order has to be alphabetical. You can usee `optimizer.space.keys` for guidance"
+    "Or as an iterable. Beware that the order has to match the order of the initial `pbounds` dictionary. You can usee `optimizer.space.keys` for guidance"
    ]
   },
   {

From 1a54e1bfde8bbdc32c0fbc6222889708c54194c9 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Wed, 18 Dec 2024 15:09:26 +0100
Subject: [PATCH 20/21] Change array reg warning

---
 bayes_opt/bayesian_optimization.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/bayes_opt/bayesian_optimization.py b/bayes_opt/bayesian_optimization.py
index d91199f03..d7f2e4035 100644
--- a/bayes_opt/bayesian_optimization.py
+++ b/bayes_opt/bayesian_optimization.py
@@ -224,10 +224,10 @@ def register(
         # TODO: remove in future version
         if isinstance(params, np.ndarray) and not self._sorting_warning_already_shown:
             msg = (
-                "You're attempting to register an np.ndarray. Currently, the optimizer internally sorts"
-                " parameters by key and expects any registered array to respect this order. In future"
-                " versions this behaviour will change and the order as given by the pbounds dictionary"
-                " will be used. If you wish to retain sorted parameters, please manually sort your pbounds"
+                "You're attempting to register an np.ndarray. In previous versions, the optimizer internally"
+                " sorted parameters by key and expected any registered array to respect this order."
+                " In the current and any future version the order as given by the pbounds dictionary will be"
+                " used. If you wish to retain sorted parameters, please manually sort your pbounds"
                 " dictionary before constructing the optimizer."
             )
             warn(msg, stacklevel=1)
@@ -252,10 +252,10 @@ def probe(self, params: ParamsType, lazy: bool = True) -> None:
         # TODO: remove in future version
         if isinstance(params, np.ndarray) and not self._sorting_warning_already_shown:
             msg = (
-                "You're attempting to register an np.ndarray. Currently, the optimizer internally sorts"
-                " parameters by key and expects any registered array to respect this order. In future"
-                " versions this behaviour will change and the order as given by the pbounds dictionary"
-                " will be used. If you wish to retain sorted parameters, please manually sort your pbounds"
+                "You're attempting to register an np.ndarray. In previous versions, the optimizer internally"
+                " sorted parameters by key and expected any registered array to respect this order."
+                " In the current and any future version the order as given by the pbounds dictionary will be"
+                " used. If you wish to retain sorted parameters, please manually sort your pbounds"
                 " dictionary before constructing the optimizer."
             )
             warn(msg, stacklevel=1)

From 05fbbcd18fcea2966ceeb872cf1886d116062fd9 Mon Sep 17 00:00:00 2001
From: till-m <musertill@gmail.com>
Date: Wed, 25 Dec 2024 12:18:13 +0100
Subject: [PATCH 21/21] Update Citations, parameter notebook

---
 README.md                      |  13 ++++
 docsrc/index.rst               |  14 ++++
 examples/parameter_types.ipynb | 135 ++++++++-------------------------
 3 files changed, 60 insertions(+), 102 deletions(-)

diff --git a/README.md b/README.md
index 37e7e5b4d..c485d11ca 100644
--- a/README.md
+++ b/README.md
@@ -185,3 +185,16 @@ For constrained optimization:
     year={2014}
 }
 ```
+
+For optimization over non-float parameters:
+```
+@article{garrido2020dealing,
+  title={Dealing with categorical and integer-valued variables in bayesian optimization with gaussian processes},
+  author={Garrido-Merch{\'a}n, Eduardo C and Hern{\'a}ndez-Lobato, Daniel},
+  journal={Neurocomputing},
+  volume={380},
+  pages={20--35},
+  year={2020},
+  publisher={Elsevier}
+}
+```
diff --git a/docsrc/index.rst b/docsrc/index.rst
index e2a169432..5c198c6f2 100644
--- a/docsrc/index.rst
+++ b/docsrc/index.rst
@@ -199,6 +199,20 @@ For constrained optimization:
        year={2014}
    }
 
+For optimization over non-float parameters:
+
+::
+
+   @article{garrido2020dealing,
+       title={Dealing with categorical and integer-valued variables in bayesian optimization with gaussian processes},
+       author={Garrido-Merch{\'a}n, Eduardo C and Hern{\'a}ndez-Lobato, Daniel},
+       journal={Neurocomputing},
+       volume={380},
+       pages={20--35},
+       year={2020},
+       publisher={Elsevier}
+   }
+
 .. |tests| image:: https://github.com/bayesian-optimization/BayesianOptimization/actions/workflows/run_tests.yml/badge.svg
 .. |Codecov| image:: https://codecov.io/github/bayesian-optimization/BayesianOptimization/badge.svg?branch=master&service=github
    :target: https://codecov.io/github/bayesian-optimization/BayesianOptimization?branch=master
diff --git a/examples/parameter_types.ipynb b/examples/parameter_types.ipynb
index b89acefc3..3d668300a 100644
--- a/examples/parameter_types.ipynb
+++ b/examples/parameter_types.ipynb
@@ -15,12 +15,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import warnings\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from bayes_opt import BayesianOptimization\n",
     "from bayes_opt import acquisition\n",
     "\n",
-    "from sklearn.gaussian_process.kernels import Matern"
+    "from sklearn.gaussian_process.kernels import Matern\n",
+    "\n",
+    "# suppress warnings about this being an experimental feature\n",
+    "warnings.filterwarnings(action=\"ignore\")"
    ]
   },
   {
@@ -36,17 +40,9 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/3876025054.py:9: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
-      "  bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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K6RnRaLUK8RFGTj8kjXt/Np3rFo1lbGI4voDKyl013P7eT3ywqQy3z9+7fnSj2upmzd46yixNA7pdIYQQQ8ugj6y/+eabLF26lGeeeYZ58+bx2GOPceKJJ7Jr1y6SkpI6fUxUVBS7du1q/V3WZwkhhBAH149FDbj9nX8f+wMq/9tczic/VaACaTEmLp+fzdjECDLiwpiUEtWuvfX448kFjtPr+eKllyA1FY46quej4G2dfXawPNu330JFRf+21YvtJUeZiDDq2Fxiwenxo1EUZo2JZdaYWHZWWnlnQxkFtQ4+3FLBmr11XDx3DDMyYvrWp074/So7yq3U2txMTo0a0LXyQgghhoZBD9YfffRRrr76aq644goAnnnmGT7++GNeeOEFbr311k4foygKKSkpg901IYQQQnRBVVUKax0AuDx+FG3H04Ymj5+nv85ne4UVgIUTErnwsEz0Wg1j4s1MSI7s8JiIiAj+7+abgxfjL7qo/x3VamHRov5vp5Pteb1eykpKaGpqYvLkye2ahRt1HJYTx+YSCxant/X2SSlR3H5yJOuLGnhrfSm1dg9PfJnH3Ow4Lpk3hnDjwJ1+1djcWF11TE+PJlwvgxtCCDGSDGqw7vF4WL9+PbfddlvrbRqNhuOPP541a9aEfJzdbicrK4tAIMChhx7Kvffey9SpUwezq0IIIYRow+cPsLXcSo3FEbJNg9PD4yv2UNrQhEGn4fLDs5iXGw9AWkxYp4E6QHx8PA8//PCg9Hugffrpp5x55plA5zP93n33Xc4440x+Kmvkjddf44Fbr293v6I3EX3ExUTOOYN1hfXsqbZx1ZG51G5bxT03/iLk895090Occt7PAVi/+mtu++WFIdtec8s9eJf8kty4sL68RCGEEEPUoAbrtbW1+P1+kpOT292enJzMzp07O33MxIkTeeGFF5gxYwaNjY08/PDDLFiwgG3btpGRkdGhvdvtxu12t/5utQav7Hu9Xrxeb4f2Q0lL/4Z6P4Xsq+FE9tXwIPtpaHN7/WwubcTp9qEGguutW/5vUdHo4m9f5tPg9BJl0vGbRblkxZtR/T4SIo2MSzCNiP07a9YsYmJisFgsqJ1kiff5fPj9PiYnm4kyajq0UT1NNKx8HseObxh/xYM0OOHhL3YxLTwMVdFAoPP17GrAj+r37fu5s4z3rW0D+L0+dldYgOBgiRja5Bg4fMi+Gh6G037qTR8Vtaujfz+Vl5eTnp7O6tWrmd+mXuott9zC119/zdq1a7vdhtfrZfLkyVx00UX8+c9/7nD/H//4R+65554Ot7/22muttViFEEIIMXAqnfDkdi02r0KSSeWayX7ie5EL1uPx0NjYiF6vJyYmZtD6OVA8Hg9Op7PT+8xmMwaDAQgOIDQ1hU76pjGE8XFFGN9XB9eX55g9nJduI1zX8VQsLCwMozFYos3r9eJwhJ7hYDKZ2LFjB08++SS5ubn84Q9/6PFrE0IIcWA5nU4uvvhiGhsbiYqK6rLtoI6sJyQkoNVqqaqqand7VVVVj9ek6/V6Zs2aRV5eXqf333bbbSxdurT1d6vVSmZmJosXL+72xR9sXq+XZcuWccIJJ6DXD1xpFzHwZF8NH7KvhgfZT0OTze1lS0kjXl+g9TY14KepcCNh2bNQNFrKG108tTEPm9dHRoyJpceNI9IUPJ0w6jXMzorFoOs6udvq1as5//zzGTduHNu3bx/U13SgqarKtubEb525chrMLLbw7zXFFDgNPF+eyvULc8iI7XoKe3Q3z6utcVFXV0dcXBxh2bOIMBs5JCO6230hDg45Bg4fsq+Gh+G0n1pmgvfEoAbrBoOB2bNns2LFCs466ywAAoEAK1as4Prrr+/6wc38fj8//fQTp5xySqf3G43G1ivPben1+iG/o1oMp76OdrKvhg/ZV8OD7Kehw+bysqXMjk/VoGg7ZhZXNFpqnX4eXZGP1eUjMzaMm0+YSERzoK7RwCFZcYT3oK54IBC8GGAwGEbk/j9kTDwbSyw0ODqfjj4nJ4HUmHCeXJlHjd3NfV/s4aojczh0TGyfn1Pb/Hf0+/0oGi1OL2wqs3PomFjCDBKwD1VyDBw+ZF8ND8NhP/Wmf4Ne52Pp0qU899xzvPTSS+zYsYNf//rXOByO1uzwl112WbsEdH/605/44osv2Lt3Lxs2bODnP/85RUVFXHXVVYPdVSGEEGJUcrh9bCi24POHXhlnc/l4bPluGpu8pMe0D9QBJiRHEt2DQB32ralumT4+0mg0CodkRLf7++wvPTaMP5w6mcmpkXh8AZ7+Kp+vd9f0+Tm1zZn6Wy6EQDBT/49F9Tg9vj5vVwghxMEz6KXbLrjgAmpqarjrrruorKxk5syZfPbZZ61J54qLi9Fo9l0zaGho4Oqrr6ayspLY2Fhmz57N6tWrmTJlymB3VQghhBh1XF4/G4st7aa+78/th398tZcqm5v4cAP/d/z4doFoSrSJjNie54lpSa4z1Ec/+kOn1TAzM4YfCutxezv/20YYddx03AReXVvEN3tq+c/3RVibvJw2I7XTzPNd0TbXgW8brAO4vQHWFzVw6JjYAS0ZJ4QQYvAdkKP29ddfH3La+1dffdXu97/97W/87W9/OwC9EkIIIUY3nz/AxmILLm/nGckBAqrKf/ZoKGxwEmHU8X/HTyDGvG9E3GzQMiml8xJtoYz0kfUWJr2WGRkxbChqwB/ofNaCVqNw6eFZRIXp+WhLBR9sLsfq8nLR3DFoehGwa3XBUzq/v+O+dHsDbChuYHZWLGaDBOxCCDFcDPo0eCGEEEIMPaqqsqWsEYe76ynSH/5UyU8NGnQahRuOHUdK9L6074oCU9Oi0XWyxr0rLcH6SB5ZbxEdpmdyatcJbxVF4ayZ6Vw8dwwKsHJXDS+vKSLQi4I9Gk1wZL2zYB2aA/airi/MCCGEGFokWBdCCCFGod1VdurtXdfjXl/UwEc/BSu6XDovk7GJEe3uz04IJ9rc+4B7tIyst0iJNpEV3/0ygWMnJXHVUTkoCqzKq+1VwB5mDid73ETS09NDtnF5/WwoasDtk4BdCCGGA5kLJYQQQowyZZYmSuo7rxveoqTByQvfFQCwKDXAgty4dvdHmnTkJoT36fnHjx/Pr371KyZPntynxw9H45IisLq8NDi8XbablxOPgsJzq/ayKq8WVVW5fEF2t1PicydO4dl3V+Dc+2OX7ZweP5uKLczOiu31jAghhBAHlgTrQgghxCjS2ORlV2XXNV6dHh//WJmP2xdgckoEZ2RZ2t2v0cCUtKheJ0FrMX/+fObPn9+nxw5XiqIwLT2adQWhE861mJsTvDDy3Kq9fJdf17quva9/7/3ZXD62lDUyMyMGjWZgtimEEGLgySVVIYQQYpTw+gNsLWsk0EWsqKoqL68posbuJiHCwK+OzEa7XzyXFR9OpGnkrzcfaEadlmlp0fQk5p6bE8fVR+aiKPDNnlre2VA2oH2pt3vY0c1FGyGEEAeXBOtCCCHEKLG93EqTp+v1yt/m1fJjUQNaReGXR+V2KPcVbtSRE9+36e8tHA4HdXV1NDU19Ws7w1FsuIGcHi4fmJsTx6WHZwHw2bZKPt1aEbJtRWkRvzhjIUuXLu1xXyosLgpqHT1uL4QQ4sCSYF0IIYQYBUrqndTY3F22KbM08ca6EgB+Niud3P0SygFMTo3s99TpJ554goSEhJBlXUe6nIRwYsN7NjPh6PGJnHNoMGncOxvK+GZPTaftAv4ApYX5VFSEDug7k19tp9rq6tVjhBBCHBgSrAshhBAjnM3lZU+1rcs2Hl+Af36Tj8cfYGpaFIunJndokxYT1q7Gel+Ntmzw+1MUpbnkXc8uepw8LZWTpqYA8J/vi9hSaunQRqvrunRbV7aVW7G5uk58J4QQ4sCTYF0IIYQYwQIBlW3l1i7XqQO8u7GUcouL6DA9Vx6R0yH7uF6nYXxyx5H2vvB6g4HhaA3WAUx6LZNSuq6/3tY5h6ZzxNh4VBX++c1eivfL5q/VBpcrBLrb0Z3wB1Q2lzTi8fX+sUIIIQaPBOtCCCHECJZfY8fu8nXZZmelleU7qgG4YkE2UWEdp2jnJkagH6BSX6N9ZL1FSrSJlGhTj9oqSjAj/KSUSNy+AE+s2EO9w9N6f3+CdQjWYP+prBG1h3XdhRBCDD4J1oUQQogRyuL0dBiB3Z/L6+fF1YUAHD0+gWnp0Z22S+1hUNkTLcG6Xi8Z5SemRGLU9+x0TKfVcO2isaRGm7A0efn7l3tweYPT3jXNF1ICgUCfA/YGh4e8anufHiuEEGLgSbAuhBBCjED+gMr2civdDZS+9WMJtXYPCREGzp+T2eH+ASrt3Y6MrO+j12qYktrz6fBmg44bjxtPpElHSUMT//xmL/6A2jqyDhDow7r1FkV1Tkk4J4QQQ4QE60IIIcQItLfGjrObMm1byxr5Zk8tAEsWZGPSazu0SY4OG/C+yZr19uIjjKTF9PzvnBBh5IZjx6HXKvxU1sg7G0rR6fSkZmSRlpZGQO3f2vNtFVacnq6XTgghhBh8uu6bCCGEEGI4sbq8PZr+/vKaIgCOm5TUabIzrVYhN8HM3gHu3+GHH47L5WL69OkDvOXha0JyBPUOT+u09u7kJkRw5RE5PPPNXr7YXsWYODMvffIdzr0/YjAY+9UXv19lS2kjc7Pj+l2mTwghRN/JyLoQQggxgqhqz6a/v7+pjHpncPr72c11vPeXEx+OQddxtL2/rrzySv7zn/9w+umnD/i2hyudVsOk1MhePWZOdhynTA+WdHtpTSFF3Vyg6Q27y8fubsr9CSGEGFwSrAshhBAjSHG9s9vs7wW1DlY0Z3+/9PAsjJ0E5GEGLWPizIPSR9G5hAhjj7PDtzjrkHSmp0fj9av84+sC7ANYLr20volqm6xfF0KIg0WCdSGEEGKEcHn97K1xdNnGFwjw8ppCVGBeThxT0zrP/j4uKWLQpkA3NTXhcrn6nLV8JJuQHIlB1/PTM41G4eqjckiONFLv9PKHj/dSV1s7YP3ZXm7t8dR8IYQQA0uCdSGEEGKE2FVpwx/oev778u3VlDQ0EW7QckEn2d8Bos16kqMGrlTb/n72s58RFhbGK6+8MmjPMVwZdBomJPduOrzZoOO6Y8YRcDsheQIfbq8bsP74/CrbyqX+uhBCHAwSrAshhBAjQI3NTY3N3W2b/20uB+C8OZlEhXVe53x8UsSA968tyQbftZRoE/ERvfvbpMWEYfn8CQC+K2lifVHDgPWnweGlqG7g1sMLIYTomQMSrD/11FNkZ2djMpmYN28e69at67L9f//7XyZNmoTJZGL69Ol88sknB6KbQgghxLAUCKjsruo6GZiqqryytgiPP8DE5EiOGBvfabvESCMx5sENolvqrOv1nV8sEDApJQptL5ch+Io20rj2HQBeXF04oOvN99basbkGcEG8EEKIbg16sP7mm2+ydOlS7r77bjZs2MAhhxzCiSeeSHV1daftV69ezUUXXcSVV17Jxo0bOeusszjrrLPYunXrYHdVCCGEGJYK6xw0dVNTfUOxhW3lVnQahUsPz0JROgaCihJcqz7YWoJ1GVkPLcygJSchvFeP0Wp1WL55mcxILU1eP898vRevf2DyAgQCsLXMSqCbZRZCCCEGzqAH648++ihXX301V1xxBVOmTOGZZ57BbDbzwgsvdNr+8ccf56STTuJ3v/sdkydP5s9//jOHHnooTz755GB3VQgxHPj98NVX8Prrwf/9nQcoVVY3u6tsbCqx8ENhPesK6tlQ3MD2civFdc6uR4h6+BxCDAUur7/bKcpun583fywB4KSpKSEzjqfFhBFu1A14H/fXMg1eRta7lhVvJsLU8/2h0Wog4OescXoijDqK65288UPJgPXH4faRX2MfsO0JIYTo2qB+I3s8HtavX89tt93WeptGo+H4449nzZo1nT5mzZo1LF26tN1tJ554Iu+///5gdlUIMRy8+y7ceCOUlu67LSMDHn8czj6bapuL/KpGyhywZ10xje4ADrcPleAUYKNOS4RJR5RJR3KUibSYMNJiTGTEmjHptT16DiGGmj1V9takch63i+2bfsTf5gKTKczMHiWNeoeHuHAD2YFy1q/O67AdjaKgjE1icurC1tvWrl1LfX09mzZtwmAwoNPtO20wGAwsXLiv7Q8//IDFYum0j1qtlmOPPbb1940bN7ZuQ4SmKAqTUiL5sbBn68+12uD+idAFuPqoHB5bvoevd9cwPimCw3M7X/bQW8X1zgOyVEIIIcQgB+u1tbX4/X6Sk5Pb3Z6cnMzOnTs7fUxlZWWn7SsrKztt73a7cbv3JdSxWq1A8Kp9y5X7oaqlf0O9n0L21VCgvPce2gsvBFWl7eRdtawMzj2Xf15/P69nzKHM4sIX0AEF3W7ToFVIjwljXGI4s7NiOLd4HfFXXBbyOfxvvIH6s58N9EsbleQzNTAamzxUNuwb6Xz0rqWs+Ojddm2yZsxHd+odAJx/aBpP3HAWJQUdg3WAnJwcdu3a1fr7tddey4YNGzptm5SURGmbi1pLly5l1apVnbYNDw+noaFjwKnVauU90I1wvUJypJ5KS1O3beMSEtEQAFVlSnI4p01P5sOfqnh5TRGZMUbSelnDvTMqsLWknjnZcb1eUy/2kWPg8CH7angYTvupN30c/Llug+y+++7jnnvu6XD7F198gdlsPgg96r1ly5Yd7C6IHpJ9dZD4/Sy+9lq0+wXRAIqqEgDOePEhHrzmeQIaLWFalXgTRBtUwnWgUUAB3H6w+6DRo1DrAo8fCuqcFNQ5WbG9kjOfualDoN7yHCrgue46lul0oNUeiFc9KshnamCV5W0HgoF0y3eg4bBzcQdUJkQHmOTNIy0pHq3q6/TxcXFx7ZK6hoeHk52d3WnbyMjIdm2NRmPItkajsV3bk08+GYvFQk1NjSSRHUCPPHBv809NOPf+yLHhsCtaw+5GeHrFDm6e7scwAIcvJ/B552MuopfkGDh8yL4aHobDfnI6e15dY1CD9YSEBLRaLVVVVe1ur6qqIiUlpdPHpKSk9Kr9bbfd1m7avNVqJTMzk8WLFxMVFdXPVzC4vF4vy5Yt44QTTpB1e0Oc7KuBt2nTJn73u99ht3dc/3j99ddzySWXAPDTTz/xz4su4sy60HWDNUCarZbb9Xt5aNkHeBtKqTOF0/YRKnDimedx5kVXEFBVKsrK+NNdt6HGZBCIzWKuy02qLfRzKIC5tpYNjz/OtsTEdvcde+yxXH311QA4HA6uuuqqkNtZsGABN9xwAwA+n49LL700ZNs5c+Zw8803t/7+85//vN305ramTZvGH/7wh5DbGmrkM9V/lY0udlZY292m6oIjp9fd8QDzF53AT2VWnvhqL1oFLjlqCuHRJv783DsdtpWbGMGY+I4XuE855ZQe76tTTjmlx33vTVsRVNHoYtd++3t/asBPU+FGwrJnoWiCUfmv0r386eNdVDb5+LghhUvmZgxYn2aOiZHp8H0kx8DhQ/bV8DCc9lPLTPCeGNRg3WAwMHv2bFasWMFZZ50FQCAQYMWKFVx//fWdPmb+/PmsWLGCm266qfW2ZcuWMX/+/E7bG41GjEZjh9v1ev2Q31EthlNfRzvZVwPn9ddf5+uvv+70vrq6uta/s8fjwbp7d4+2uTjKzdU/fBXy/rNOPYljp6YBkGdqIm/dl633zethv3d/8w37hzpR0TFce+2+98U773QMhloYDIZ276Gu2vp8vnZt33vvvZBTpxobG4fle1M+U33jD6gUNrhQtO2/xsPCIwiPjMJgCsOHhjfXlwFw7ORk0uM6z/Ju0GnITuq+TJjsq4NrTIKeKruXRmf30ycVjbb1vREdruMXR+bwt+V7+GpPLdMyYpiZGTMgfdpT08S8XLNMh+8H+VwNH7KvhofhsJ96079Bnwa/dOlSLr/8cubMmcPcuXN57LHHcDgcXHHFFQBcdtllpKenc9999wFw4403snDhQh555BFOPfVU3njjDX788UeeffbZwe6qEOIA8vmC03Avuuii1lH0FpMnT0ZVVRqcXprMSWRfeQ88f3e324yfNo3333+fH3/8kTlz5rRLhgUwduzY1p9TU1P56KOPWn+P27IFbr+92+eIuegPXJSTSLyybwpT1tgJfLmzijC9DgN+/nj/I4TptZj02g4nsePHj2/9WaPRdFnpIicnp93vjz/+OIFA52WYMjIGbrRMDH2FdQ7c3o7vhYdeeLv150+3VlBlcxNl0nHGjLSQ28pJCJdga5iYmBLJDwX1qCGqpz34h5so2f0TN9zzNyZOP7T19qlp0SyekswX26t4cXUhd58+hdgBGBF3evzsrbEzPjmy39sSQgjR0aAH6xdccAE1NTXcddddVFZWMnPmTD777LPWJHLFxcVoNPsqyC1YsIDXXnuNO+64g9tvv53x48fz/vvvM23atMHuqhDiAHriiSd44oknUFW1Xb1njy9AmaWJ7/Lq2FDUwAvfFWCNm8kNkQmk2mo7rCcHgsWhMzIIP+kkTmkOZk855ZQur1yGh4dz6qmn7rvhpJPgH/+AsjI6OxMOAJWRCXyeMZeAX8vE5EhOPySVSSnB5TaBQLCskQM44vRLWrsVYdQRH2EgIcJIdJi+3WvVarVcd911Pf6b/frXv+7y/muuuYbCwkIeeughpk+f3uPtiuHF5fVT3E2pNovTw0dbKgA4d3YGYSEWKocZtKTHhA14H8XgiDLpSY8No7S+82Rze3dtY+/uXditjR3uO3tWOjsrbRTXO3l+VQFLj5+AZgAu0hTXO0mKMhEdNrRHsoQQYjga9DrrEFx/WlRUhNvtZu3atcybt2/C6VdffcWLL77Yrv15553Hrl27cLvdbN26Vda2CTGCtQSvXn+AvGob3+XVsrPCystrCnl0+W4sTV4SY8xU/en+YFtF2X8Dwf8fe6x/id+02mB5trbbbPMciqJQ8+cHOHJiMlqNwq4qGw9/sZsHP9/JzsrO1x6pKthcPgprnfxY2MC3e2rZWWmlsWlwMpV+8803fP7559TW1g7K9sXQsLfG0VqqLZR3N5bh9gXITQjvsmRXTkL4gARs4sAZmxiBXtf56Zumeeq739cxgaBOq+GXR+di1GnYWWnjs22dV9npLVWFHRVW1FDD/UIIIfrsgATrQggRiqqqlNQ7+S6vlsJaJ3V2Nw99vosvtgcTTR4zMZEPrj+SWTddCW+/Denp7TeQkRG8fSBqoJ99dsjnUN5+m0Nu/AV//dl07vvZNI6ZmIhOo7C7ys7DX+zm8RV7KOumtJLHF6C0vokfCur5fm8dZZYmAt0EXb3RMpNgOJQtEX1jd/uoaAz9Pnvgtuu5+aYbWJ0fTJZ4wWGZaPa/+NTMbNSSOgClvMSBpddqGJfUef4BbfMFy1CJKFOiTFw0dwwAH2wqZ29txwSffWF3+SjqZraHEEKI3hv2pduEEMPTo48+ytffrmLhaeczY/4xAOytsfPUV/k0NnkxG7RceWQOVx6Zsy/b8Nlnw5lnwrffQkUFpKbCUUcNbCm1bp4jM86MUZ9EYqSJk6el8unWCr7ZXctPZY1sLW/kqHEJnDkzvdspoXaXjx3lVvKr7YyJM5MZ1/8kTQZD8O/k8Xj6tR0xdOVV20OuVwbYumEd6pHXYALmZscxNrHzoA4gNyGi3bIMMXykx4RR1tCEdb9ZOvuC9c5L8wEcMTaebeWN/FDYwHPfFHDXaVNCLpPojb21dpKijJgNcmophBADRY6oQoiDYuW3q/no/ffInDKbGfOPYX1RA899uxdfQCUt2sT1x45j8dSUjmWBtFpYtGhwO9fNcyRFmjCO0bKxpIFL5mVx/ORk3tlQyoZiC9/sqWVtQT0nTUvhxCkpGEJMV23h8QXIq7ZTXO8kJyGcjNiwPgdQMrI+slmcHmpt7q4bpU7DlDUDnQLnHJoeslmESUeKjKoPay3J5tpqmQYfCDGyDsGlR5censXeGgc1djevrSvmyiNzQrbvqUAAdlbaOHRMbL+3JYQQIkimwQshDih/QOWn0kYsdhcAWq2Ob3bX8Mw3+fgCKodkRHP7KZNZNDGJhIiOZRmHimiznjnZcRj1GpKjTFy7aBy/P3EiOQnhuH0BPthUzt3/28aWUkuPtufxBdhVaeP7vfXUO/o2Mi7B+siWV931lGWfP4B+dnA5yGEpWuK7+PzkJoYPaN/EgRcdpidtv+SA3U2Db2E26LjqqBwUBdbsrePHovou2/dUvd1DZaNrQLYlhBBCgnUhxAHk8vr5obCeKqurdZrmHjWZl78vQlXh6PEJXLdoHJPTojqchA5FEUYdh2XHYW6eQjo+OZLbT57EL4/KJdasp8bu5okv83hyZR619m5GRJs53D42FDWwrbwRr7/zMm2hSLA+clXbXFi6qa+9clcNmugU/I4GFqSHLssVadKRFCmj6iPB2KRwdNp9M3HM4RGYzeZ2VXZCGZ8UycnTUgD4z5oiLM6BWT6zu8rW62OXEEKIzkmwLoQ4IGwuLz8U1mN3BYN0n99HzKIr2O5NBODU6alcengWqTFhXa6zHWpMei2zs2OJMAWnnyqKwtycOP585jROnJqMVlHYVGLhrg+28dGWcnw9PImtsLj4fm9dj4N82Bes+zrJBC2GL1VVya92dNnG7vbx4ZZyACzfvtLluuGxIZKTieHHqNOSm7Bvf97zxAu89tprHL34tB49/owZaYyJM+Pw+HlxTeGAZHRvWdojhBCi/yRYF0IMOovTw/qiBtzeYKCqqiq2jAVEzzsHgAvmZPKzWelEmw1MTYs6mF3tE6NOy+ysWKLaJJUz6bWcNzuTu0+fwsTkSDz+AO9vKudPH29nb03PTmTd3gCbii3srrL1KGv8Rx99RCAQ4PLLL+/zaxFDT6XVhcPd9QWYj7aU4/T48dUVY9+yDE2IpIvRZv2QXl4iei8zLgyzsW8J4nRaDVcdmYNOo7C1zMrXu2sGpE9lDU00djMTRAghRPckWBdCDKp6h4eNxRZ8/n3B5geby2nKnAfAYeENnDAlGYNOw4yM6GFb81mv1XDomBhiw9tngU+LCeO3iydw9ZE5RJp0lFtc3PfpTt74oRi3t+t1pS2K65z8WNSAq5v2Wq1WsnuPMIGAyt6arkfVK60uVu4MBllNa15Ho1HQaDoP3obTrBXRM4qiMDE5ss+PT4sJ45xDMwB4a30pVdaBWXO+s1JqrwshRH9JsC6EGDQNDg+bSyz424wKf7SlnI+2VABQv/xZJke40GhgRkY0Jv0AlmA7CHRaDTMzY4mPaL9eWFEU5uXG8+czpjE/Nx4VWL6jmrs/3Ma28sYebdva5GVtQT0NfUw+J4anMksTTZ6uL9K8vb4Uv6oyPT2aN9/6L59vKSM9q2N279hwPXHhodeyi+ErPsJIYqSR1559grvvvpvVKz/v1eOPm5zEpJRIPL4Az68qaHfM7iuby0dpQ1O/tyOEEKOZBOtCiEHR2ORlU2n7QH3lzmre3xRcV3v2zFRef/ZxjjjuZMYnRXYs0TZMaTUKh2TEkBjZcapxhEnHlUfmcNNx44kLN1Br9/C35Xt44buCbqc5A3h9ATaWNFBm6fwE+Omnn+bcc8/lvffe6/frEAefP6BSWNf1qPrOSiubSixoFDhvdkaXbduubRYjz/jkCArzdrJ582aqK8p69ViNonDFgmzC9Fr21jr4dGvFgPQpv8aO29ezGURCCCE6kmBdCDHgnB4fm0os+NtMfV9f1MBr64oBOOOQNE6ZkY7RFEZ6fASZceaD1dVBodEozMiIDlnHelp6NH86YyrHTUpCAVbn1/HHHo6yBwKwo9zaaQKn9evX884777Bjx47+vgQxBJQ2OFvzPHQmEFB584cSABZOSOyygkJchIFYGVUf0cwGHZHm4EXC7kq3dSY+wsgl88YA8OHmCgpru75Q1BM+vyrJ5oQQoh8kWBdCDCi3z8/GYgte374gY3eVjee+3YtKsDzb6TNSgeBI8+TU4ZdQricURWFqWhTpsZ0HUCa9lovmjuH3J00iOdJIg9PL35bv4dW1RT1ay15Y62B7efs1oVK6beTw+QMU1jm7bLN6bx0lDU2E6bWccUgaqqpy57WXcvcNS3DYbe3ajpVR9VEhKix4Qcbfx4oQ83LimJMVi19V+dd3BXh8/S/BVmFxDVhZOCGEGG0kWBdCDJhAQGVLaWO7NbZlliaeXJmHL6AyMyOGS+ZloSgKzz/2F56460Z27Ry5o8CKojA5NYqs+NAzB8YlRXDX6VM4dlISEKyVfc9H23s0GlVuaeKnssbWTPESrI8cxfXOdhe89uf2+nlvY3Cq82kzUok06Qn4/Xz/9TJWf/lZu2AtPsJAtFkfalNiBGk5BvRlZB2Cx6yfz8siOkxPZaOLdzaUDki/dlbaJNmcEEL0gQTrQogBs73C2q5cj8Xp4bHlu3F6/IxNDOfqo3PQNmd73/DNcl5/9VVqagamVNBQNj45ssva1kadlovnjuHmEyYQZzZQbXPzwOc7eXt9Kd5u6rJXW91sLrUQCKgYDMFRNY9HRrGGM68/QFF916Pqn2+vorHJS0KEofVCjz+wL0DT6vbVWZe66qOHrnm/67V9D4wjTDquWJANwIqd1Wwvt/a7X3ZJNieEEH0iwboQYkAU1jqobNxX8sfrD/DUV/k0OL2kRJu44ZjxGHXBbO+ZcWbU5sBC1yaoGMlyEsKZlBpJV5XVJqdG8cczprBgbDyqCp9tq+QvH++grJuT3Dq7h02llta/pYysD29FdY52+R72Z3F6+GxbJQDnHJqBXhv8Km87mq7RBG9LjDQSZZJR9dGi5RgQ3ce66y2mpUezaEIiAP9eXYDT07dp9W3l19gHZFq9EEKMJhKsCyH6rc7uJr9m37RtVVV5dW0xBbUOzAYtvzl2HBGm4ElkVJie8UkRrQHlaAnWATJizUxPj0bTxZHXbNDxiyNyuG7RWCJNOsosTfzlk+2s3Fnd5TTSeruH+qbgBRAJ1ocvt89PSX3XF2fe31SOxxcgNyGcOVmxrbcHAvsCoZaR9dzE8MHpqBiS9Ho9Wq0Wo05DakznCS576rzZGSQ159N4fV1Jv/vm86vsrZVkc0II0RsSrAsh+sXl9bO13ErbOPKrXTWsyqtFUeBXR+eSFBk8adRpleZgVcHXPArYssZytEiKMjEzMxadtoshdmDWmFjuOX0q09Oj8fpVXl1XzFMr87G5QgfinkDwkC7T4IevojpnlzWuS+qdfJdXC8D5czJR2kzVaDuyrtVoSY4yESmj6qPKI488wjvvvMNdd93FuKQItN0cZ7pi1Gu58sgcFAXW7K1jfVFDv/tX1tDU5TFMCCFEexKsCyH6LBBQ+amssUPm9zeay0mdMyuDqWnRrfdNSYsizBCcntkSrI+mkfUWceEGZmfFYtR3fQiOCtPzm2PHceFhmeg0CptKLdzz4XZ2VHS+hvT8K6/jwx/3cv0d9w9Gt8Ugc3n9lDaEXquuqipvrS9BBeZkxTJuv7XogXZr1rUyqj7KGXVachP69x4YmxjByVNTAPjP90U0NvUv0FbV4HeEEEKInpFgXQjRZ/k19nYJ5eodHp7+Oh+/qjI3O44Tpya33jcm3tw6wg6jO1gHiDTpOSw7rnV5QCiKonD85GT+cMpkUqJNWJq8PLpsN2+vL8W3X/I5g8GIKcxMtd1LXrWcEA83hXUOAl0s6d1abmVHhQ2dRuGcQzM63N+SAVyj0ZASHUa4cXR+tsQ+mbFmzIb+rV8/45A0MmPDsLt9vLymsN9Z3RscXqqsru4bCiGEGNxgvb6+nksuuYSoqChiYmK48sorsdu7Xq+0aNEiFEVp9++aa64ZzG4KIfqg1u6mqE0daK8/wD++ysPm8pEZG8bl87Nap+hGhekZl9h+FHC0B+sQrLU+JyuWhEhjt20z48zceepkjh6fgEow+dz9n+0MedJbWOukpJuM4mLocHn9lFtCr1X3B1T++2Nwxsqxk5JI7OQ9E5eQxGdbyvh4Q6GMqo9Sb775Jvfeey/PPvssABqNwvjkyH5tU6fV8Isjc9BpFDaXNvJdfl2/+7mnyt5aclIIIURog3qWfMkll1BRUcGyZcvwer1cccUV/PKXv+S1117r8nFXX301f/rTn1p/N5tD1ygWQhx4bp+/XTkfVVX5z/dFFNY5CTdouXbROIz64GhO23XqbRUUFODz+YiJiTmQXR9ydFoNh2REk19jp7C26+DaqNNy2fxspqZF89KaQgrrnPz54+1cPj+bw7Lj2PzDaj5/93XGTprGOZf/it1VNox6TbsZDWJoKqjtelR9VV4t5Y0uwg1aTp2e2mkbRVHQarWkxUdgNozei2Cj2Z49e1i3bh02m41t27a13l5rc3PBNTcTHRsPwKrln7Bh9dcht3PxNf9HQlJw+vvab5az9qtlJERNpDJuBi+v2sP3bz2FwRc8Xp33i2tJzcgCYMOab1i17OOQ2/3ZpVeTmTMOl9dPYZ2D3EQpKyiEEF0ZtG/zHTt28Nlnn/HDDz8wZ84cAP7+979zyimn8PDDD5OWlhbysWazmZSUlMHqmhCin3ZU2NqV4PlyZzWr8+uaE8qNbTfqNyV13zr1tkZ7kN6WoiiMS4okwqhnR4W1ywRjALOzYslJCOe5b/eyp9rOP7/Zy+4qG9ElRSz733+xNVo45/JfoaqwrcyKMUtLdJgkGhuqmjx+KhpDj6o3efy8v6kMgNMPSetyertGIxngR7O4uDggeA62Y8eOdvedcdmvWoP1HZvX8+GbL4XczukXLWkN1ndv3Rxsq2hIvvg+TBlT2RQYQ9WbtwMqJ5x1QWuwnr9za5fbPWrx6WTmjOOn9d/zwa5tnL34KI45+qj+vGQhhBjRBi1YX7NmDTExMa2BOsDxxx+PRqNh7dq1/OxnPwv52FdffZVXXnmFlJQUTj/9dO68804ZXRdiiChtcFJrc7f+vqvSxpvN03PPm53BlLSo1vsy48wkRcmobk+lRJsIN2r5qbQRp8ffZdu4cAO/XTyRDzaV8cnWSlbuqiFWOwZddHLrEgMITp/eUmrhsOw4TPr+rV0Vg2Nvrb3LUfXPtlVic/lIijS21r7uTG1VBf95/M+8mZzAP//5z0HoqRjqLrvsMn766SfS0tLQatt/3ieNScHSfFiZvWAhprCwkNuJiUto/XnGYfO57LrfAtCk2PhR9WMaM51jb/obGd7S1qAeYMrMw1rbdiYlIxOAb7/4mPdeeQ5X41IJ1oUQoguDFqxXVlaSlJTU/sl0OuLi4qisrAz5uIsvvpisrCzS0tLYsmULv//979m1axfvvvtup+3dbjdu977AwWoNTs31er1DvtZwS/+Gej+F7KsWLo+f3eUW1OaR3zqHh2e+ziegwrzsWI6fEI/qDwaKkWF6smONnf7NVFXl2muvRafTcd999xERMXBTIYf7vjJpYWZGJLurbNRY3V221QA/OySFcQlmnl9dRIPHSOqSx3Hs/qx1PwC4/LCxsJZZY2LRavpeymkgDff9NFCaPD4q6u2EytlV7/Dwxfbgd+Y5s1LREkD1dx7ZO60NfPLBu8THx/Pkk08OWB9lXw0fRqOR008/nRNOOKFDWUyfP8C6gno8vgCz5i5g1twFXW6r5Rgy49C5zDh0buvtE/bU8sq6UorCxnPZOaeREG1qbTtlxiymzJjV7XZblkVZHU3U2ZxEjcISg/K5Gj5kXw0Pw2k/9aaPvQ7Wb731Vh544IEu2+w/9ao3fvnLX7b+PH36dFJTUznuuOPIz89n7NixHdrfd9993HPPPR1u/+KLL4bNaPyyZcsOdhdED8m+2sfjhye3abG5FdLNKucm1dBUUNN6vxP4bFvnj/X7/Tz//PMAHHXUUQMarLcYTftqLPC7afD39Q7qTNE4Z5zLK19u4IwxAXTNaUSdwOfbD2YvOzea9lNfvL1Hg9evYWykykRPHs69odvaCguB4Ofrk08+GfC+yL4aPgZzX83RwPoYDTssGv61cgf/N82PtpfpigO24HeFu76cVV+O7veVfK6GD9lXw8Nw2E9OZ88TAPc6WL/55ptZsmRJl21yc3NJSUmhurq63e0+n4/6+vperUefN28eAHl5eZ0G67fddhtLly5t/d1qtZKZmcnixYuJiorq0H4o8Xq9LFu2rNMr4GJokX0FpfVNreXAVFXl9TXFlDoaiDBqueGEicREGFrbTs+MIT7cEGpTuFz7MpiffPLJREb2L1txWyNtXzk8PnaUW7G7fF22MwOnV3zB4x+8S/Tcs/m6QkOxJ4JfHZXdbl/kJkYwJv7gX8gcafupL5weHz8U1IccVS+qc/JD7W4ALjhiIuFd7DedVuGw5OB3q9ls5pRTThmwfsq+Gj56sq82FDdgdfZv5OkXqV7u/ngnJQ4/K53pnDGj86SHoRjjlgOgiUjAnDuHyWnRJEd1XxVjJJHPVWgeXwCnx4fT46fJ48fl8+P2BfD4Anj9Afz+0HldtBoFnVZBr9Vg1Gsx6bSE6bWYjVrCDdrW5Le9IftqeBhO+6llJnhP9DpYT0xMJDEx9Jq5FvPnz8disbB+/Xpmz54NwJdffkkgEGgNwHti06ZNAKSmdv5FYDQaMRo7HuD1ev2Q31EthlNfR7vRuq+cHh+FDS4UbfCQsWJHFd8XNKBR4JqFY0mI3hdEZCeEkxLTdYKrtktXwsLCBuVvOlL2VYxez7yxJgrqHBR1U4fbYDJiWfkCcaod41FXUFDn5C+f7uZXR+cyOTV48bKwwUVMhIn4iKFxYjxS9lNfFFc5QaOjs4UJqqry1sZyAOblxJGb1PXF55ykCGocwSR0Op1OPlOjXFf7alJaLD8Whr5I1BOxkTp+Pi+LZ7/dy8dbq5iRGUdOQs8TG2p1wb4FVBVFq6Ow3kVabHiHqiGjwWj/XAUCKjaXjwanh8YmL1aXF7c31BedAmhRuoi3A4AnEPzn8PqB9vlf9DoNUSYd0WF6Ys0GosP0PX7fjfZ9NVwMh/3Um/4NWp31yZMnc9JJJ3H11Vezbt06vvvuO66//nouvPDC1kzwZWVlTJo0iXXr1gGQn5/Pn//8Z9avX09hYSH/+9//uOyyyzj66KOZMWPGYHVVCNGNthnKd1Zaeas1oVwmk1L2BRFxEQbG9iATddu1OqO5znpPaTQKYxMjmJsTT7Q59AFe13ICXLaFu06bwpg4M3a3j0eX7+bzbZWoqoqqwk9ljTR1k8BODC6720eV1RXy/k0lFnZX2dFpFM6eld7ltox6DZlxZvz+4D7dP7GYEG1Fh+lJjQ6dXK6n5ubEcVh2LAEVnl9V0K5CSHc0zfPmA83vWZfXT1F9z6eFiuHN5fVTUu9kU4mFr/fU8ENhPXnVdmps7i4C9YHh9QWos3vYW+NgfVEDX++uYWNxAyX1Tlxe+V4UQ8+gniW/+uqrXH/99Rx33HFoNBrOOeccnnjiidb7vV4vu3btap23bzAYWL58OY899hgOh4PMzEzOOecc7rjjjsHsphCiC6UNThocweC6zu7mma/3ElBhfm48x0/el0QyzKBlWlo0itL9Feq22colsOi5CKOOw7LjKLM0kVdtx7vfyfG02fP477db0esNhEcYufWkSbyytojV+XX8d30phXUOlszPBrStGeJH40jWULC3xh7yPq8/wFvrSwE4YUpyt7MgchLC0WqU1s+VXAAT3RmXFEG1zYWvi+nEPXHJ3Cx2V9mptLp4d2MpFx42pkeP0zbP0gq0mSpUWOcgLcaEUSffCSNRk8dPpdVFtdWFrZtlXQeSP6BSZ/dQZ/ewq9JGVJielCgTSVFGqaAihoRB/UaPi4vjtddeC3l/dnY2apt5WJmZmXz99deD2SUhRC+4vH72VAeDCrfPz1Nf5WN3+8iKN3Pp4VmtgblWozAjIxqDrmeTdVqCCo1Gg0YzaBN8Rqz0mDCSIo0U1DoobXC2To03GIwY4vYFdgadhisWZJMdH86bP5TwQ2ED5RYX1y4aC5jYWWlrV2pPHBg2l5fqLjL9L99RRY3NTXSYnlOnd70W2GzUkh4THCWVkXXRUwadhrGJEeyqtPVrOxEmHUsWZPP4ij0s31HNzMyYdrOtQll08lmMnzqDpNR9s0b8fpW9NY7WJTti+PP5A1TZ3FRYmrD0M0/CgWJt8mJt8rKn2kZcuIH02DBijO3PU95++23Kyso6fbxer+faa69t/f2DDz6gsDn5Z2duvPHG1p8//vhj8vLyQra99tprW6dPf/HFF50m9M7IyODss8/u0cCJGB7k8rsQIqRdlTb8fhVVVXl5TRHF9U4iTTquXTi2XWA+OTWKyF6U3pERwP7TazVMSI4kM9ZMfo2dKqur0zWoiqJw7KQkMmPDeOabvZRZmvjrJzu46sgcAGLMetJi+j8lVvRcfo0j5H0Wp4ePtlQAcM6h6d2O7IxLjGg9KTvyyCOxWq3tRiuFCCUjNowyS1O3ySu7Mz09moUTEvl6dw0vfFfIH0+fgtnQ9bE9PSuH9KycDreXW5rIjDMTYZTvhuHM5vJS2tBEpdXVZTK4oUxVaR1x1wQ8QHDWk14Pf//73/nmm286fVx4eHi7YP2f//wnn376acjnaRus//vf/+add94J2fbqq69uDdZfffVVXn755U7bffvttxx55JGhX5wYVuRoKIToVLXNRY0tOPq3bEcVawvq0Sjwq6Nz203LzU4IJyXa1Kttp6amUlpa2m46vOibMIOWaenR5CSEs3ZrHo8/8gAGo4lf/e6P7dqNT47kzlMn8/TX+eTXOPj7l3mcMTMNRYGoML2cHB8gjU4vtbbQo+rvbizD7QuQkxDO4bnxXW4r2qwnKWrfZ0+r1Q5oZQUxsimKwqSUSH4sbOj3ts6bncH2cis1djdv/FDCL47oGIj3hKrC7iobh46J7XefxIFXa3dTVOekweE52F0ZUNdfchalBXu489FnOfP0U1m46JjW/Fv72z/p9cKFC4mOju7R8xxxxBFdJh5rO2tq3rx5eDzt/84rVqygpqaGysrKHj2fGB7k7EwI0YHPH2idHrmjwsp/m9fPXjCnfUK5xEgj45J6XyNdp9ORnt510izRO+FGHSlhAf73+otEx8Zy7a33dBjRiDEb+N3iibz+Qwlf767hg03llDY0oSiwcEISWlm/Pujya0OvVS+odbA6vw6Aiw7LRNPNNMbxffjsCdFWjNlASrSJysbQyQ57wqTX8osjsnnw812szq9jVmYMs7oIuIvydrF983qS0zM59PCj2t1Xb/dQZ3cPmYoVomuqqlJtc1NQ6+j3LI2hqqaqApvNRlh4JMV1ThZd9GsujgkjOz6829lPv//973v8PP/3f//X47bXXnttuxF8gHfeeYfKykpmzpzZ4+2IoU8WiwohOsivceD2BqixufnnN3tRVVgwNp5jJ+1LKBdp0jEtvWdXi8WBYTAE66kHfD6OGpfAxJRIwvcbMddpNVx6eBZL5mej1SisL2rgj//bzqo9NcEGHg889hjccEPwf8/IGiE5mCxOD/X2zv+eqqry+rpiIJi8MTex60A8MdJIjNnQ7raNGzdy5ZVXcv/99w9Mh8WoMD45Ap22/xfqxidHcuLUFABe/r4Ia1PoNcob167i0buW8vFbnU/j3V1lb5fTSAxNVVYXa/bW8VNp44gN1P1+P/W11QAkJAXf34EAlNY3sSa/jrxqG17/0Fh6dM4553Ddddcxbty4g90VMYAkWBdCtGN1eSltcOL2+fnHV3nY3T6y90soZ9JrOSQzps8jseXl5dx000386U9/Gsiuj3ot0+e8Xi86bbCc1/yx8czJjiUtJqzdCfmR4xP47QkTiDDqKK538ps3NlH4i2vBbIb/+z948sng/2Yz3HLLwXpJI0pedehR9e8L6tlb68Co03DOoV3POtFoggHW/goLC3nhhRf48MMP+91XMXoYdVrGdnNxqKfOnJlGekwYNpeP/6wtChlwt0znDYQIchxuH+X9HO0Xg6fO7ub75iDd6R7Z5c4a6moI+P1oNBpi4hPb3ecPqBTWOlmdX0dJvVMuMIlBIcG6EKKVqqrsrLARCKi8uLqQkoamYEK5RePQN9fF1WkVZo6J6VdJk6qqKh5//HGeffbZgeq6oH2w3laM2cCUtCiOHp/IjIxokqKMaDUK45MjuePUyaTHhPHrT/5J1r+fRvXvd+Ll98NDD0nA3k91dnfIbMgur593mpeanDo9tcOI+f7SY8ydJvBqyQYviRtFb2XEhhFh6v/7Rq/VcOWROWg1ChuLLazZW9dpu5bSbX5/6NHY/Go7viEyYimCbC4vG4ob2FhsGbEj6furrSwHIDY2NmSlDa8vuHRwbUE9FufBm422Y8cOVq5cSWlp6UHrgxh4EqwLIVqVNjRhbfLyydZKfihsQKso/HrhWOLCg8GDVqMwMzOm38nIJBv84GgJ1v1+f6cZwTUahaQoEzMyYjh6QiLTM6KZmhbNHxaP5eof3gcg5FyJRx+VKfH90FUG+E9+qsDS5CUxwsgJU5K73I5Oq5CbGN7pfS2fKyndJnpLURQm96DkWk+MiTNzxiHB5Fuvryuhzt4xoaKm+eJvIBB6VNbjC1BY5xyQPon+8foD7Ky0sq6gPuRSnpGqtjqYrC0hIaHbtnaXjx8LG9hebj0oU+Pvuusujj32WP73v/8d8OcWg0fOlMWQFgioePwBvP4A/oCKP6ASUEFFRUFBowQDSK1GQa/VoNdqJElWH7l9fvJr7GwqsfDexmD90IvnjWFCcjC7tEYD0zOiux316wkJ1gdH2yyyXq8Xo9GIz+fjt7/9bcjHTJ8+nSsarWjUbk4s/H5WXXwxR779dutNt956Ky5X51NVc3Jy2pWkueuuu7Bara2/BwIB4uPjOeWUU7p7WcNetc0Vcv1uuaWJz7dXAXD+nIzWGSyh5CZEhGwjI+uiP6KbyziWW5r6va2TpqawucTC3loH/15dyNITJrRLmNgysh7YfybPfkrqnWTEhvVrJpfonzJLE3nVdry+gQ0+PR43X378LrZGS7vbDQYjZ178i9bfv/7sA6qbR7f3p9XqOPvSq1t/X7X8EypKi0I+57mXX9O6nO/7r76gpDA/ZNuzLr4SvcFAbVU5YeZw4uLievKygOBxvc7hZmJKJEmRvauW0x8tx/79Z9eJ4U2+0cVBp6oqDo8fu8uH3e3D6fHR5PHj8gX69OWg1SoYdRrC9FrCDFrCDTrMBi3hRp184Xchr9pOUZ2T577dC8AxExNZOCG4PktRYFpaNAkDlJ1XgvXBERYWhslk4sgjj0SjaRm5CvD444+HfMxZZ53FlRkZPdp+7dq17X5/+umn2wXgbR1xxBHtgvV//etfVFRUtGsTHR3N7bff3qPnHq5UVSW/uvNRdVVVeW1dMf6AyoyMaGZmxnS5LbNRS0ZsWMj7ZWRd9Nf45Ahq7O5+B2ZajcKVR+Zwz0fb2Vlp48ud1Rw/ed+sEU3ze9TfTbDuD6jk19iZmibJTA80u9vHzgpryOU7/fXVJ+/zyB0ds59HxcS1C9Y/fOMlNv+wutNtGIymdsH6Z+++xtqvl4d8znMvv6b15+Ufvs3Xn4UegT7tvMvQGwwsOOYkdm/djFnp3YwCtzfAlpJGUqKDQXt3F2IHQqilcGJ4kzNlccAFAioNTg8NTi+NTR6sTT78gYFLyuH3qzj9/k6Tnuh1GiJNOqJMeqLD9MSY9QfkADrUWZwe9lTaefLLPNy+AJNSIrngsEygOVBPj25Xz7m/Wr5IJFgfWEajkffeew+r1dr6pa3RaLjttttCPmbKlClQW9uj7acccUS735cuXYrb3XnN8KysrHa/33DDDdhswXKANpuNJ598Eocj9NTwkaLS6sLh7nxt59qCenZW2jBoNVx02JjWEZ9QJiRHouli5pCMrIv+0ms1TEiOYFtZ5xfheiM5ysR5h2bw6rpi3tlQytS0KFKjgxebNJqWBHPdJyerbHQxJs5MpCl0/WkxcAIBlcI6B4V1DjpZTTVgGuqCFUhSM7OYMvOw1tvDzOZ27Q5dsJCElM5rmu9/rDtk7hFERMX06PmnHToPnT70TEGtLvgeTUrLYPb8oxmb0LdzoMpGFxanl6lpUcSG939mYldavvdbLtyKkUG+0cUB4fL6qbG5qbW7aXB6BvULoCteX4B6e/vySREmHbFmA7HhemLNhlEXvKuqytbyRp75Jp8au5uECAO/OjoXnUaDRhMcUR/IQB1kZH0wnXTSSe1+1+l03HvvvV0/yOOB3/42mEyuEyqgajTo//wY5ZYmkiKN6LQa7r777h73q+0Fg5qaGp588kl8Pt+Izp4bCKjsDbFW3enx8daPJQCcOiOVxMiuZ63ERxi6ndkiI+tiIKRGB6fCNzj6Pzq3aGIiG0ssbK+w8vyqAm47eTJajcLUWYdx56PPEZuQ2O02VDVYym12Vui67WJgWF1etpdbD1jyOK1Ox8y5R7L0T4+EbHPxL28Med/+zlvy6x63PeuSK3vc9thTf4Zz7489br8/l9fPhuIGshPCyU0I7/bCbF/JNPiRSc6UxaDx+gNUNrqosroGbRrVQLC7fNhdPkrqg6PI0WF64iOMxEcYiBoFV/KL65w8/20BOyttGHUarj9mHJEmPVqtwoz0aOIHaOp7Wy1BRds11uIgMhhg6dJg1vf9tITS/zrsLGp3NzDXq7BLq5AcaSIjLqxPn5GoqCieeeYZduzYMaKD9TJLE02ezi+AvL+xHKvLR0qUicXdJJXTaGBiSmS3z/fzn/+c008/HYNhcEdvxMg3KSWKtQV1/b6wrigKSxZkc/f/tlFY5+STrRWcPiONpNR0klK7LlHYVoPDQ63dPWBLsUR7qqpSUDv4o+ltXXDl9Vxw5fUj+jugLVWFghoHFqeHaenRGHUDf1FVpsGPTBKsiwFX7/BQ1tBEjd110EbQ+0pVweL0YnF6ya8Go15DYqSRWNPIHKly+/w8/XU+K3fVoABXHZlDRqwZg07DzDExg3axYuHChezatUuC9aHkwQeD/z/6aPsRdq2WD469gHsPvRi+3Uudw81JU1MotzRRbmkixqxnTJyZxEhjj0cLjEYjv/jFL/jkk09a19aPND5/gL21nY+qF9Y5WLm7GoBL5o3pdjbPmLjwTku17c9sNmPebwqpEH0RbtSRHR8ecmZIb8SFG7hk3hj+taqAjzZXMD09muz4zisadGVPlZ34cMOgjUqOVk6Pj61l1pBJMAfbaNufDQ4va/fWM2OAEva2JdPgRyYJ1sWA8AdUyi1NlDQ4O10rPly5vQFK65soaa4Fu6PCSkpsOAnhxi7Xjg4XL35XyJs/BKfinjcng1ljYokw6ZiZ2b866t0JDw9nwoQJg7Z90UcPPgh/+Qv84x+Qnw9jx6Jcey3H+BWOf2sTy3dU886GMursHi6eOwaNRmm+uNWI2aAlKyGc1CjTiPhs9FdRvbPTJF3+gMor3xehqjAvJ47JqV2XyzLpteQk9D6wEaK/suPDqbS6BuQ7fV5OHBtLLKwvauD5VQVcNy+BnRu/JzwiisOOOrZH23C4fZRZmsiIlQtSA6Xc0sSuStuA5g0S3fP4AqwvamBCciSZcQP3fj7ttNNITU3l8MMPH7BtioNPgnXRL15/gJJ6JyUNTQNe1mMoqmp0UW33odUqJEYYSY4yER9uGB7Bid8P334LFRWQmsq3yZN4+ItdqAQzv58wOZmUaBOTU6Ok/N1oZjDATTe1uykauPv0qSREGHnzhxK+2l2D1eXl6qNyW0eFnR4/O8qtFNQ4yEkMJy3a1OWIyfLly1m3bh3HHHPMiJth4fb5Ka7vvD708h1VFNY5CdNrOW9291n4J6RE9PjzuHz5cj744AMOP/xwLrnkkl71WYj9aTQKU1Kj+LGwod/bUhSFn88bQ161nYpGF6+u2ctnd1zD2EnTehysA+ytcZASZUI3ynLLDDSfP8COChtV1s5Lbx4In7z9Kmu/XsbRJ57Ocaedc9D6cbCoKuyqtGFz+ZiU0nXy0J467rjjOO644wagd2IokaOd6BOvP0B+jZ1VebXsrXGMikC9Lb9fpbLRxeYSC9/sqWFbeSN1dvfQXXv17ruQnQ3HHAMXXwzHHMP4w6dz7PbvmJERzSXzspiSHs209OgDEqhv3LiRO+64g5dffnnQn0sMjMw4Mxcclsk1C8ei0yhsKLbwt+W7cXraT7dzeYNB+5r8Oqq7OBE855xzuPfee6mqqhrsrh9wBbUO/P6Ox4Iqq4v3N5UBcMGczG6nQCZGGntVo3f9+vU8+eSTLF8eunSREL0RYzaQ3kW5wN6INOm58ogcFGCHzYB5wgICgd6N2nt8AQrrOr8QJnrG6vKytqD+oAbqAHt3bWP1l59RsjfvoPbjYCu3NLGhuAHPKDuPFj0nwbroFX9ApbDWwXd5tRTUdH5COtr4/CoVFhcbiy18s6eWHRVW6h2eoRO4v/sunHsulJa2uznJWssz79/L/exhwbh40mMG5oSsJ7Zs2cJf//pXXn/99QP2nKL/pqRFcfjYeG46fjwmvYbdVXYe+nwXFmfH+rNOj58tpY38WFiP1dVxLaTRGEwU5fH0rnbtUOdw+yhraOpwe0BVeWlNIV6/yuTUSI4YF9/ldrRapUdJ5dqSbPBiMIxPisCoH5jTxSlpUZw4NQWAuJN/Q8DQu/c4QEm9E5d35Cy3O5BK6p38WFgfMvHlgdR6vJKqMFicXn4srO9w8bu3KisrWbduHfn5+QPUMzEUSLAueqyisYk1+XXkVdvxSZDeKa8vQFlDExuKGvh2Ty07Kw9y4O73w403Budb7UcDoChMefBuzNoDO+1dSrcNT0adlimpUUxKieKWxZOIMukoaWji/s92hhylsTi9/FBQz44KK17/vpGDlozloeq0D1d51fbOPm58u6eW3VV2DDoNlx2e3W1SpXGJEb3OGyF11sVg0Gk1TErpOrdCb5w1K43kMBWtKQJ17iW9Xi/tD6jk19gHrD+jgT+gsrWskV2VtiGT+NfvC17E1Y2wZVB95fT4+aGwgcZ+VE968cUXmTdvHn/9618HsGfiYJNvdNGtxiYvu6ts/TqAjEYeXzA5XWl9E3qdhsQII4mRxkFd426327nuuusoKyvjvvvu4zCHo8OIeluKqkJJCVueeooZv/kNACtXruzyQP/73/+eE044AYA1a9Zw5513hmx74403cvrppwOwYcMGbrnlFgDKyoJTgSWoGH4SI41kxAVnYdx68iT+tnwPNTY393+2kxuPG99plmdVJVghwuZmQnIkKdGm1mB9JJWYaXB4qLF1vPhQ7/Dw3/XBRI5nz0rvtqZ6tFlPRh+mHsvIuhgsiZFGUqJNVDb2f+q0TqPhlEx4/icnmsSxfPxTBWccktarbVQ2uhgTZyZyFJRX7S+nx8fmkkYc7qGVIdzXEqzLeUArry/AhuIGpqZH9WoJVAsp3TYyySdEhOT1B8irtlNuaep0pEj0nNcXaC11pdUoxIUbiI8wkBBhHNCs619++WXrOvC6ujpo6FlioKaCgtafq6qqWLFiRci2l19+eevPtbW1XbY999xzW39uaGjo0DYjo/sEW2LomZAUSYPDSxImbj1pEo+v2ENxvZOHPt/FdYvGMSWt81E4jy/A1rJGKhqbWoP1kTQNfneVrcNtAVXlhe8KcHkDjE0M59iJSV1uQ6OBKalRfSpnJCPrYjBNSI6k3uEZkLW1cWYtdZ8/ReIZv+PDLeVMSolkQnLPp8SrKuyptnPomNh+92Ukq7G52VbeOCRnQ/q8LRcX5XjVlj+g8lNpI5NTVdJ6uTxRgvWRadA+IX/961/5+OOP2bRpEwaDAYvF0u1jVFXl7rvv5rnnnsNisXDEEUfw9NNPM378+MHqpgihyupiV6VNEl4MAn9Apcbmbh6Bs2E2aIkNNxBrNhBj1vcreHe5gqMekydPZsaMGfh27OzRhzxn/vzWn+fPn89rr70Wsm3bkiCHHnpol23nzJnT+vPUqVPbtTUajSxevLgHvRNDjUajMC09ih8K64kO0/O7xRP5x1d57Ki08fiXe7jyiBzm5sSFfHyd3YOP4Pt8pEyDL7c0YXN1HLlatr2KnZU2DDoNvzgip9tZNbkJEYQb+/bV3BKsy8i6GAwGnYZJKZFsKW3s97Y0Gg3OHV/jm3IEunEL+Ne3Bdx1+hQievHer7d7qLW7SYjoeqbKaFVQ6yC/euguF2iZBi9r1jtSVdhebsXnVxkT3/PSbi0XaqXO+sgyaJ8Qj8fDeeedx/z583n++ed79JgHH3yQJ554gpdeeomcnBzuvPNOTjzxRLZv347J1PvpIKL33D4/OytsnU7lFIPD6fHj9DS1JqUy6DREhemJMOqIMOoIM2gx6TUYdV2fgPsDKg5XcJQyOj6JMo+JnxInMDsmkThLTecJKhQFMjJIOmdf2ZSsrCyysrJ61Pf09HQuuuiiHrVNSUnpcVsx9EWa9IxPimRXpY0wg5bfHDee51cV8GNRA899uxeby8txk5NDPr5lneKu8gaO8vm7fX8PZaHW0JY2OHlv477s78lRXX+PxZj1ZPXixGx/kgtCDLakKBMp0e5+T4dPzRjD7/76OLqwSFY4jVTZ3Ly0ppBrF47t1aySPVV24sMNfZqJMlL5Ayrbyhuptg7t87h9M4FkKUMou6ts+FWVnISOy8s6IyPrI9OgfaPfc889QDDZQU+oqspjjz3GHXfcwZlnngnAyy+/THJyMu+//z4XXnjhYHVVNKtsdLGz0jokp0uNJh5fgFqbm9r9LphoNKDXatBqFLTNJyYqEAioePwBfH6VXRXBEQ9PAOrsbp5bVUzCoqt5+v17URUluEa9RcvJzWOPgYzEiT7IjDNT5/BQa3Oj12r45dG5RK0r4ctd1bz+QwlWl4+zZqZ1eiJ9wS+uo37vFmJSsvh+bz2TUyP7tEZvKCiodeD2tp+F5PUHeO7bAnwBlRkZ0Rw9PqHLbWi1ClPS+jb9vcXtt9/Or3/9a6KiBi4ZmBD7G4jp8FExcSw+6wIAcusc3PfpTjYWW1i+o5oTpoS+yLc/h9tHmaWJjNi+X+QaSVxeP5tKLNg7meUz1Nz9+Av4fF60Gjn/6Ep+tR1/QGVcUkS3bSVYH5mGzOX3goICKisrOf7441tvi46OZt68eaxZsyZksO52u9tNo7RarUDwjTrU36wt/TvY/fT6A+ypsndZE3m0U5trwaq9rAk7kPx+8HfzVvF7gyPrGq2OZ77KZ1NpI7rJR7Bq3j846sm/QnNiNwA1PR3/I4+gnn46DPHPSm8Mlc/VaDE+IQyrowm3N4ACXDg7lSiThvc3V/LxTxU0Ot38fG4m2v2mfy868TSaCtMJS07B4/awubCO1NgwxiVGdGg7lLk8fopqGlH3i1veWV9GmaWJSKOOy+dmQMBPV5dBxyZFoVfUfr1vIyMjiYwMrvsdyPe/fKaGjwOxrxRgfKKZraWWAdleVoyRCw5N47Ufy3h7fQnZcSbGJfZsJBEgv7KR+DAtOu3wKnA00PuqscnDtjLrsFm+qAD65oEC1T+0Ly4c7HPAgqpG/D4vuYndB+wwPGKgwTCcvqt600dFHeSaUi+++CI33XRTt2vWV69ezRFHHEF5eTmpqamtt59//vkoisKbb77Z6eP++Mc/to7it/Xaa69hNsuVVjG6LFu2jH88+zxjL78fb/w4dIrKlRMDTIlVwe8nfvt2TA0NuGJjqZsyRUbUxaBZXaXw1l4NKgpTYgIsmRDAOErebpvrFF7YHXyxV0/0My1OZisJ0ZbL5WLLli1oNBrmzJmDqsLLezRsqNMQbVC5ZYafCJkdLUSvFBYWsmbNGlJSUjjmmGMOdndEF5xOJxdffDGNjY3dzobr1cj6rbfeygMPPNBlmx07djBp0qTebLZfbrvtNpYuXdr6u9VqJTMzk8WLFw/5qYBer5dly5ZxwgkntE5dOVBUVaWg1klJvUMyvfeAGvDTVLiRsOxZKEN4ytbYI81MMx+FVROBQavhuoXZ/Hx+FlEt5W2ay6iNZAfzczWa7a1xUFznaP39+FxIyGjkue8K2W7R8FReOL9ZlEtUWHCfFOzeQdX2NYyddyKJqenttqXRwLikyF5nwj0QCgsL+eCDDwBwePxUWJra3T9m2uG8XqACAY4aY6Zkx9eUhNjWtFmHccihs5mdFUt9bQ1vvPFGyOedO3cu85sTQdbX1/Of//ynQ5v33nuPcePGcemll7Jw4cI+vb7OyGdq+DiQ+8rnD/BjUQMuT+9HGy0lhdx774WEmcP54PtdAFwxxk/5Z7uptLp5tSyWGxfl9rjMqUYD83LiMQ5gdZXBNlD7av9j73Dx0lMPUV5SxDmX/ZIJU2Yc7O50aSidA46JDye3i5kn11577QHszdAynL6rWmaC90SvgvWbb76ZJUuWdNkmNze3N5tslZKSAgTLRrUdWa+qqmLmzJkhH2c0GjEaO2YC1ev1Q35HtTjQfXV5/Wwtb8Ti9IJGx/CZcHrwKRotyhAtM1Jjc/NGvoJVE0GEUcdvjh3HURMSiY8cnTNMhtMxYCSYkBqNzRMIHleazcqO5+ZwI3//Mo+i+ibu/yKPG48fT0qUiRefepg1Kz/nN3caOP3CJe22pQJ7apqwulUmp0YOqemt+fn5/O53v+v0PkVnYNrNr9JEcDr/IREWfvdQx5lfLa5a+gcu+9lizCY9O6urQ24X4I477uDoo48GgmUQQ7VdvXo1M2fObLekbKDIZ2r4OBD7Sq+HGZnx/FhU3+uL/lp9sHRjIBBo/U4N0+r49aJx/PWTHWyvsPHx9poe119XgcIGN9PSo3vXkSGgr/sqEFDZVm6lyuoesuclXfnxu6/ZtXUTx5569rDp/1A4ByyxuDEY9D1OOjcaDYfvqt70r1fvuMTERBITE3vdoZ7IyckhJSWFFStWtAbnVquVtWvX8utf/3pQnnM0qrW72VZuxTtM1jSJnsmvsfPUyjysLh/x4Qb+7/gJZMabe5SQRIiBoCgK09KjWVtQ3+74MjYxgttOnsRjy/dQY3dz/6c7ueHYca1fVD5v6DrrVVYXNpeX6RnRRJoO3hfv5s2bqampYeLEiaSmpnLJJZdgc/lwuNuvs6xOOwIrYUSadPxqYS62yiKOO+2cEFuFI+bMbJ31EhsbyyWXXBKy7SGHHNL6c1RUVMi23W1HiIEUbdaTnRBOQU3vRnZbamsH/O1H5dNjwrh0XhbPf1fAh5vLGZsYztS0ngXglY0uMuPMRIcN7ZP0geDxBdhcaqHROfTX5obSUr1CK9ngey2/2o5WUTqUdXM4HBQWFqLX65kwYcJB6p0YaIN2eai4uJj6+nqKi4vx+/1s2rQJgHHjxhEREQwgJk2axH333cfPfvYzFEXhpptu4i9/+Qvjx49vLd2WlpbGWWedNVjdHDVUVWVvraPXX6hi6FuVV8sr3xfhC6ikRuo5Y4wX1VrF+MnT0Q+hEUkx8pn0WqamRbGp2NLu9uQoE7edPIknvtxDYZ2Th7/YRXz8OAB83SRZcXr8/FBYz4TkyIOW8flvf/sbL730Evfffz+///3v+efzL7K2oI5Am2ueX+6s5rV1xSjAL4/KJdZsIDZ3PLc+8FSn20yPDWNy6r6lWjk5Obzyyis96k9aWlqP2wox2HITwmlweNrNqumOpjlfir+TxGLzx8azp9rGN3tqee7bAu44dXKPa6nvqbIxJzuux/0YjhxuH5tKLDT1YfnBUCJ11vtnd5UNrVYhvc1ysdWrV7N48WJmzJjB5s2bD2LvxEAatDP5u+66i1mzZnH33Xdjt9uZNWsWs2bN4scff2xts2vXLhobG1t/v+WWW7jhhhv45S9/yWGHHYbdbuezzz6TGuv95PUH2FRikUB9hPEHVN78oYQXVxfiC6gcOiaGKY3fc/tlp/La0w8NyfW+YuRLiDCS3cn0vKgwPb9bPJEZGdF4/SqVmccReehpeD2hR9ZbBAKws8LG1rJGfP4DPyvIZrMBtGZa31lpbReoby1r5PUfigE4+9D0dkF4Z2LD9UxMjhyczgpxgLXMqtFpe76oTqMJnn4GAgE6y3N80dwxZMWbsbt9PLUyD7evZ4GpxemlagRXtmlwePihsH7YB+rQts66BOt9tbPCSrVt3/tdSreNTIMWrL/44ouoqtrh36JFi1rbqKrabg28oij86U9/orKyEpfLxfLly2UaRz/ZXF7WFdRTZ+/+hFgMH/UODw9/sYtlO6oAOH1GKtcsHIumubZbXKQE6uLgGZsYTmy4ocPtRr2W6xaNC9YcVxTiTriG7docfIGeBeCVjS7WFdRjcx3YE5GWRDBRUVGUNjhpcOx7/nJLE//8Zi+qCkeMjeekqSldbsts0DI9PabHibOEGA5Mei1T0nqe1FfbZt1voJPPv16r4dqFY4k06ShpaOLF1YWdBvWdyau2EwiMvMy5lY0uNpY04POPjNfmax5Z18k0+D5T1eDF4gZH8By/dXmZb2iXwhO9I3NkR7Bqq4sfCxtGxBVYsc+mEgv3fLiNPdV2THoNv144ljNnpqNRlNaTHpNBvvzEwRMcaYvCqO/4FaPVKFx6eBZpjVtR1QCVxgz+tmxPjwPwlmnxpQ3Oge52SC0j68awcPZU21tvr7O7+dvy3TR5/UxIjuDSw7NQlNBBuEGnYdaYWAw6+eoVI09SpInMuJ4tVdG2KRva2VR4gPgII79eOBatovBDYQOfbavs0babPH6K6g/c8eFAKKh1sLWskR5e1xwWfN6WNesyst4fgQBsKrVgc3lbZynIyPrIImcMI1R+jZ0tpY34R+DV5dHK5fXz6toinlyZh8PjJzvezF2nTWF2VmxrG40a/CbXSv10cZAZdVpmZMSg6eRbRlEUMt1F1LzzFzQBH7uqbPz1kx2UNTR1bNyJlmnxW0oteA/AtPiWkfUGrxZ/86iWtcnLo8t30+D0khJt4tcLx3aZtV6nVZg1JoYwg3w2xcg1PimitTxjV4ymMH5z5/3cdPdDaLsohTUhOZKL5mYC8O6GMraUWnrUj8I6R4+nzg9lqqqyvdxKfpuLhCOFX0bWB4zfr7KpxEJACX4HSbA+ssjlrBHGH1DZVt5ItdV9sLsiBtCOCisvrSmktnk5wwmTkznn0PQOwUGsOXjSI8G6GAqiw/RMTIliR3nHeqJzjzoWs9pESo6X5XXh1Njd3PvpDi6fn83cnJ4liKq2urE21TMtPYoYc8dp9wOlZWTdqwkmuXK4fTy2Yg9VVjdx4QaWHj+hy2z1Wq3CrMzYg5rRXogDQaNRmJHRsSrE/nR6fYeSjaEsmphEcb2zNeHc7adMIjW666Vefr9KfrWjV1PzhxqfP8BPZY0jdhnji5+swev1EB4h+TsGgtsboKg2uH5dpsGPLBKsjyAur5/NJRZsLvmQjhR2t493N5TyzZ5aAOLCDVw+P6vTUjYp0SaMzQl+JGGLGCrSY8Kwu3yU7Dctdcac+YyL02POncM8LzzzTT47K208++1e9lTbOH9OZo+qGbi8ftYXNZAVH05uQvigrAVvGVkPj4jE2uTlb8t3U9LQRKRJx9ITJhDXyfr8FrrmQD3aLIG6GB1Mei3T0qLYVGLpdf31UC6eO4Zyi4u8GjuPr9jD7SdP7nYEv6KxiYy4sNbyiMOJy+tnU4kF+wg+nwsLDycMqRU+kNzNk0lkZH1kkTP6EaKxycuWUgtu7wha0DSK+QMq3+yu4f1NZTiacw4cMzGRcw7NwKTvOGqu0yqMT45oza4qI+tiKJmQHIHD46M+xAhRhEnH/x0/gQ82l/HJT5Ws3FXD3loHvzo6l6TI7quBqCoU1jqotbuZmhY1oCPYqqpy/W1/orauHk14HA9+sYvKRhdRJh03nzCRlKjQ/QuuUY+REXUx6sRHGMlNjOhy+vamtavw+/3MmDMfvaHrmTE6rYbrjhnLvZ/spMbu5smVefx28cQu8z+oKuyuHH6l3OxuH5uKLbi8w38avziwomPjOHfJNSTFxxzsrogBJMH6CFBtdbGt3Crr00cAVVXZVm7lv+tLKbME1++mx4Rx8dwxTEwJPVVsQnIkRp2W448/HqPRyLx58w5Ul4XolqIoTE+P5sfCBhzu4EiRpb6Owt27iVdiGTNuIlqNwtmzMhifFMnzqwooqnPyxw+3c97sDBZNSOwycVsLu8vHD4X1ZMWHkxM/MKPs+TV2jjnjQkrqnTy2Mo96h4dYs56bF3cdqIcbdczMlDXqYvTKSQjH5vKGXJZ3y5Xnoaoqb339E7EJid1uL9Kk58bjxnPvpzvYW+vg+VUF/GphLpoujg0Wp5fKRhcp0cOjBHC9w8OWUsuIyfjelUfvuhlFUbj65juJiOo4W1D0XkxcAr/63R+BYFLCnE7KqIrhRxLMDXOFtQ5JJDdC7Kiw8sBnu3hsxR7KLE2YDVounjuGu06b0mWgHhdhaK2pfuyxx3L33Xdz0kknHahuC9Ejem1wlLklQ/y3yz7mlltu4d9P3N+u3fT06OB7PjkSjy/Aq2uL+dvyPdQ7erZuMxCAghoH3xfUtZaz6atau5vCWicbixu4/7Od1Ds8JEca+f1Jk7oM1BMjjRyWHSuBuhj1pqZFE2HqfFxI0zwDLFQ2+M6kRJu4/phx6DQK64sbeGd9abeP2VNtGxbnSOWWJjaNoNJsXVFVlU/feZVP3n4Fr2dkrsk/2PKr7VRbXd03FEOejKwPU6qqsqPCRrmlZ9mTxdAUUFW2lDbyxfZKdlcFpwvqNAqLJiZy2vS0kCc5LbQahckpwzeBjhhdTHotMzNj+LGoAV1zPdjO1tbFhRu4efEEvtxZzTsbStleYeXOD7Zy2oxUTpic3GXW9RZOd3Ate0q0iXFJEZ0uH2nl98O330JFBaSmwlFH0eSHjcUNvPF9ASt21wEKk1MiuWbhWMKNIYIPDYxPiuxx+SohRjqtRmFmZgzrCurx7JdwTqPR4sdHwN+76d4TkiNZsiCbf60q4PPtVcRHGDl2UlLI9m5vgIJaB+OSIvr0Gg6E/Bo7BTWOg92NA6btPpfSbQPH7/dTW1WO3+cnNTOLbeVWTAbtsMzbIPaRT8gw5G3OEBpq/acY+lxeP2vy61i+o4oqW3CKoE6jcNT4BE6dntrjzNbjkiLajd6VlZXR0NBAcnIyiYndTysU4kCLNOmZmRHD54bgyYPP13kiHI2icPzkZKalRfPv1QXk1zh4Z0MZq/JqufCwMUxP79m0ycpGF9U2F5mxZrLiwzuucX33XbjxRijdN0KnZmSw8YY7+ZM6jpKGJkDBu3MlN/78ZnSd1aIDYsP1TEqJChnICzFamfRaDsmMYUNRQ7sRbq1WgxfwB3q/Nvvw3Hhq7W7e31TOa+uKCTNomZ8bH7J9cb2D9JiwITnbZUeFlWr7yE0k15m2x/2WC7ei/5x2Gz8/4TAAPt1UgqLo2VxiYW5OHEbd0Hvvi56RafDDjMvr58fCBgnUhyFVVdlVaeOF7wpY+t/NvLqumCqbmzC9lhOnJnPvz6ZzybysHgfqseH6DiN4f/3rX5k+fTpPPfXUYLwEIQZEbLiBnORYALyerstMpkSb+P1Jk7hiQTZRJh1VVjePr9jDg5/vZEeFFbUH6aYDASiqc/Jdfi17qmz7Eje9+y6ce267QB2A0jIO//2vmPL9Ckwalep3/4Jh20edBupmg5bpGdHMzoqTQF2IEKLD9ExNj6Lt8nKNNvh5Cfj7lhj31OmprSPq//6ugE0llpBtAwHYXWXr0/MMlpaZBlWNo2+qctvSYlK9ZuC0rVnfckHE7Q2wpbSRwDBYCiI6J5+QYcTq8rK5RDK+DyeBgEp+jZ0NxRY2FDdQ12YNbUqUiWMnJbFgbHzXU3Q7odUoTE7tOP1dssGL4SI+KnihqaGultKivWRk5QLQ5HDw04bvO7TXA2cnw05fIj9WeNldZeeRZbtJMvqYHOlhbLiX/QfNk1LTyR43CQCvx8O6td8CoADRRi2X//Y6zKrK/umpFFRU4E9fPsuNKT527fmesENmt2sTG64nI9ZMUqSxR8nvhBjtkiJNTEgOsKsyGDRrNb1fs96WoihceFgmTR4/a/bW8czX+dx0/HgmhVgaVmNzU2t3kxBh7NsLGEAOt48NxQ0HuxsHjb/NyLpWJyPrA0XbZvTc32apQaPTy85KG1PSZNnkcCTB+jBRY3OztUwSyQ0HNlfwoLijwsqmEgvWNnVSjToNc7PjOHJ8ArkJ4X0+yR+XFIHZ0PHjK8G6GC7CwoJJEUsK8vjyo7e57LpbAKiuKOUP11wS8nHnXfFr7v31bXy2rZJvdldT7dZR7daxstSOM28dTXlraSrYiOpxcvoFl/Obux4AwGG3ttvuQuDaLvqnAZKttVQ+8yAAkVFRJEUZiTUbSIw09voCmxACMuPMePwBCmocaJpzTwT6MA2+hUZRWLIgm6bmuuR//zKPmxdPIDeh8/XpuyttxOUaBqRSRF/V2d38VNaI1zN6S7P5vMHzIkVR5HxlALW98OHfb4lZuaWJSJNOcqoMQxKsDwMl9U52V9nowWxPcYCpqkqt3UNhnYP8Gjs7K22UNrRP+hfWnFTr0DExTEmL6ve6odhwQ8iDrQTrYrg48sgjOfzww/F4PBwyMQetRsEfUDEYTYyfOiPk4xKS04gLN3Dx3DEsSNNx31Mv4B8zF014HBHTjiVi2rGgBsBRR1WYwn/XlxBh1IHXTe7p16HqDGCOY0ZlEaz4V7f9XJCdgzslmTtv/S0zMmIG8C8gxOg0NjECn1/lsut+h9fjITa+f/lVtBqFXx2dy+Mr9rCz0sZjy/fwf8dP6LRsldPjp6jeedBKWhXXOdlTLedzLVO0dTKqPqC0Wi2KoqCqausFkbb2VNuINOl6vNxSDA0SrA9xe6psFNU5D3Y3BOD2Q02dkwqbl/LGJkoanBTWOrG7Ox4Q02PCmJwaybS0aCalRPYoe3VP6LQKU7uYxtSyDkzWgImhzmw2c+utt3LKKaeg1+tpbAou80nNzOIfb33Ro21kZ6Txz/vuIKCq7Kmys7nUwuZSC1VWN0QkUgV8vq1q3wOmnNz6Y3WgZxe07v33C7BoUS9emRCiOxNTIrnm19cOWEUbvVbD9ceM42/Ld5Nf4+DRZbu56fjxjE3sOMJeWOsgNdp0QGfHBAIqOyqtVFhG3/r0zsQnpfDmV1v6vARChKbV6vD5vJ3+bQMB2FLayNycOJkdNozIGf0Q5Q+obCtvpNradfIlMXA8vgCNTV7qHG7qHB7q7R7qHB7q7G6qbS7qHDpgd4fHaTUKGbFh5MSHMyE5kkkpkUSFDc7V4kkpUV0eYGVkXQxX0WF65ubEsanEgt3VuxM4jaIwMSWSiSmRnD8nE2uTl5IGJyX1TTS6vNhdPtw+P0adFoNOQ1y4gYyjc3Cu+DthNZUonQ1zKQpkZMBRRw3QKxRCtNWyfnagAnaTXsv/HT+Bx1fsYU+1nb8t382Nx41nfFJku3b+QDDZ6yGZMQPyvN1xef1sKW3E2tR55YvRSKvVEpcYutye6DudPhish6q04vEF2FrWyKFjYg/qchDRcxKsD0FuX/DA3uiUA3tveXwBnB4fDo8fpzv4v8Pjw+n277u9+XeHx4fT48fp8eNw+/D1IB9ApElHarSJtOgw0mLCyI43kxlnRj9AI+ddSYk2kRJt6rKNBOtiODPptRyWHceOCiuV/ciQHBWmZ2pYNFPTQpd3UxRwPPgI5isuCf7SNmBvySXx2GMgnyUhBsXmzZtxOBxEJ4yh0TcwF7hNei03HTeev6/Ma50Sf+2isR2OBTU2NzU2N4mRg5tsrsHh4aeyxg415oUYLKeedxl+v48wczib1q5i7TfLO20XadLzh9/eyNixYwH47rvveO+990Ju98orr2Ty5MkA/PDDD7z55psh21566aUccsghAGzatIlXXnml03YLFizg7LPP7tHrGs0kWD9Y/H6Ur78m/ZtvUMLD4ZhjQKvF7vaxucRC0yhOPOL1B3C4fTjaBNQDGXB3RadRiA83EBdhID7cSHyEgfhwAwlmHTENO0mcOBNFe+A/Nia9lokpkd22O+OMM8jKyuLQQw89AL0SYuBpNQrT0qOJjzCwq9KGzz/wizsNOg3T0qOJm3wRRBo71FknIyMYqMtJhBCD5rzzzmPPnj2sWrWKrHEzBmzJn1Gv5YZjx/GPlflsq7DyxIo8lhyR3aEO++4qG3HhBrSDNLpYWBvMZTPa16d3prq8lDdfeIro2Dguu+53qKqK2xfA7vZhd/twuH14/SpefwBf8/8AGo2CVlHQaIJLH8L0WswGLWaDjnCjljC9dtRX57jm9/e0/rxr6ybefvGZkG1PO/W01mB906ZNPPLIIyHbHnvssa3B+rZt27psO2/evNZgfffu3SHbPv744zQ0NBAR0XlCSBEkwfrB8O67cOON6EpLmQPw6KOQkYHt/odZP+eYQTk5PVhaDsA2lw+bOzgd1dbyz+3F5goemO1tbnP1szSdRgGzQdd8ANcSbtBhNjYfzJsP6mZj8Pbw/W436TWdHuhVvw+nvV/d6jNFgWnpUT0avb/kkku45JLQmbSFGC5So8OINRvYXWUb0OVAyVEmJqZEYmip83b22XDmmfDtt1BRAampwanvMqIuxKBqmQHm8/kYnxz8TO6pGpgvWqMuGLC/8F0h6wrreX5VAY1OLydOTW79jm/y+CmotTMuqfsL4b3h8QXYXmGl1ibLGFsEVJU6u4dKq4s6u5vdBZWsdiQSFpbBlv9uxj4Agy0QvBAba9YTazYQF24gKdJIanQYqdEmkiKNA5Y/aLiYNH0W5//iupD3O40xONw+wo06Zs2axS233BKybW5ubuvP06ZN67LtxIkT2/28f1tVVXnooYcYP348Xq/MIu6OBOsH2rvvwrnnsv+lVrWsjIhLLyL2b/+i5oRTD1LnuqeqKk6PH5vbh83VJvhu+d29Lxi3Nwff3j5cfOhJwN0aaPcw4B6uchLCJXOnGJVMei0zMmJocHjIr7Fj6cfSoAiTjvFJEcR3VmNZq5UkckIcYC3Besvyraz4cIw6LdsrGgkMwKxxnVbDVUflEGPW88X2Kt7eUEqdw80Fh2Wi0wSDtuJ6JynRYcGKEQOgweFha3kj7n4OOgxnNpeXojonxfVOKhpdlFmaqGx04fG3/5uETz4aAEubtfw6jUKkSYfZoMOo06DTKug1wf8VFPyqSiCg4ldVPL4ATd7gzMomrx+PL4DHF6DK6g4mGd2PRgkuJ8yJDycnIfgvPTas9b0wEh0y9wgOmXtEl21aEs4tWLCABQsW9Gi7c+bMYc6cOT3rwyGHtI6yt/Xggw/26PFiEIP1v/71r3z88cds2rQJg8GAxWLp9jFLlizhpZdeanfbiSeeyGeffTZIvTzA/P7gdMtO5kQpqooKZN75fzxbVoKq0RCXmMSik89qbfPJf1/B1dT5NLHouHiOO+2cXnfJ0zztyNFm6lHLaHfb0e+2I+D+Pszp0msVIo16Ikw6Ik06IozB/yNNeiKbf45o87vZIFOZIFimrTclZioqKnC5XCQmJsq0IjFixIYbmBMeh8XpobShiRqbG38PRmEUpbnUYax50NemCiF6p6VqSUuwDsFgKkyvZXOpZUDWeWsUhfPnZBJj1vPWj6Ws3FVDmaWJa44eS1SYnkAAdlRYOSw7rl/PEwio7K21U1TnHFXT3t1ePwV1DorqnBTUOiisc1Br93TaVqdRSI4ykRhhJOCo5at3XiTGpOGuvzzUel5o0PV9sMXjC9Dg9DT/81Lv8FDZ6KKisYlKqwuXN0C5xUW5xcV3+XUAGLQaxiVFMDk1kskpUYyJM4+6pGsOt48dFVampYfO8SIOrkEL1j0eD+eddx7z58/n+eef7/HjTjrpJP7973+3/m40jqATrG+/bb8ucj8KEGezUvlTPd/FJBNeaaU4ppBwo44wvZbXv9qE3VKH6veB34vq94KiAUVDUnompkkLCagqATWYpM7tDeBq/t/tC/7s8vibg/Lg//tf6ewpk16zL/g26lqD8EijvjkIb76t+XdjPw7Ao1VwXW1Ur/5uS5Ys4YsvvuDll1/m0ksvHcTeCXHgxZgNxJgNBAIqDU4PliYvDrcPty+AP6CiEPzchBt1RJn0xIUb9k13F0IMKW2nwbcVbQ5Whtha1tiv2TRtLZ6SQlKkiX+t2svuKjt/+XgH1x4zluz4cBqdXkrqnWTGmfu0bZvLy7Zya6+rWAyWQCCAv83fVKfXt55H+P1+Av7QOZG0Oh2a5pHmzto2ef3k1zjYU+Mgr8ZJUZ2z0wGcpEgDY2LNpEWbSI02khEXTlJUGFqNgt/vZ/3qIj784X0SJkwme4Bq3ht0GpKjTCRHdUzEq6oqDU4vxfVOCmsdFNQ6KKhz4PT42V5hZXuFFSjDbNAyPT2aWZkxTEuPHjXlzSobXcSGG0iPCTvYXRGdGLRg/Z57ggkOXnzxxV49zmg0kpKSMgg9GgIqKnrULF1vxJR1CH7gmz21rbcb511IqEsXKvDv1YV96pZGgXBj8Kpmy7/9g+2WQLwlKD8Q2c9Hs+A69WiMut59UUg2eDEaaDQK8RHGzqe0CyGGhf2nwbdl0muZnRVLfo2dwtqBSTw3MzOGP5wymSdX5lFldXP/pzs5d3YGx01KIq/GTmKksVfBWSCgNo8qOwZk2v5AcDrsXHPOcVSUFLXe9q8PviZrXHAN8avPPMp//hE6MdiTb3zKxOmzAHj7xad5/omHMI6ZRljWTIyZ0zAk56Jo2v+NYs16jE217Fj5Hp7KPbgr8yhyO/ihTZu/PP0KqUcfD8Dy//2Xh++4CQCdbnDK3O5PURTiwoPr2Gc2l+xTVZXyRhc7KqzsrLCxq8qG0+NnbUE9awvq0WkUJqdGMTsrlkMzBjavwVC0u9JGdJh+wJaEdGfevHnYbDa++OILMjIyDshzDldDbs36V199RVJSErGxsRx77LH85S9/IT4+PmR7t9uN271vbYrVagXA6/UOuaQFSmJij/7gRx05laTJWa2lx+xuH06vH59fxRdQm/8PZshUlOA0L42i0FBXTXnRXqJjYpk8ZSpGnQaTXoNRp8Go07b+HmHUBYNzg7Z51L63o94B1D6OyA9XasDf7v/Blp0YQaRB6fV7uGWEQlXVIff+P1BaXvdoff3Dheyn4UP21fAxnPZVS7DucrlC9jcr1kSUUcOuShuuAaiSkxKh5/YTJ/DC6iI2l1l544cStpZaWDJ/DNvLNExPiUBZtao12aR65JGdJpusd3jYU2XrV+WewTivKNy1rV2g3rJ91d98btDNVYWA309pnY1tFTbWayeReePrKLr2OXO8DRW4S7Zy5glHc9zhM0kIN/Dhmy+xdu3bXW24tQ+o+/owa94R+24/CNIi9aRFxnPchHj8AZWCWgebSq1sLLVQbQuW3fuprJFXtQrTYzQcYbAwNS160CoIHEw+P2wuqmN2VuwBeX07duzAZrNhs9kG7Hg1nI5/vemjoqqDu7rmxRdf5KabburRmvU33ngDs9lMTk4O+fn53H777URERLBmzZqQI4V//OMfW0fx23rttdcwm/s2pWnQ+P0s/uUvMdXV0dnHQAWaEhJY9s9/9ikT8eeff87TTz/NvHnzuO222/rdXTH83HbbbezYsYNbbrmlx4lChBBCiANtxYoV1NXVccQRR5Cenn5An1tV4dtKhf8VafCqChE6lTvrv+OMt54jrK6utV1TfDw/XXUVFfPnH9D+9dXWrVu54447SEtL46GHHgLAZDK1nkN7PJ4OQYLTp5Bn17HHpiPPrqfR2/4MNVofYHykj9xwHznhPqINaofter1ePJ7O16pDcNZsS46ClrYajYawsKE57VpVoaoJNtcrrK/VUNW0728SpVeZn6yyIClAjEzu6rNLLrkEh8PBU089dcA//0OB0+nk4osvprGxkaioqC7b9mpk/dZbb+WBBx7oss2OHTuYNGlSbzbb6sILL2z9efr06cyYMYOxY8fy1Vdfcdxxx3X6mNtuu42lS5e2/m61WsnMzGTx4sXdvviDQfnHP+DCC1EJJpVroTaPbO/5w72Yx8/r07bDUvKCPxgjMOf2LEuj6Bk14KepcCNh2bM6TAEbSGFGLbPHxPa5vMj9998PBDN1nnLKKQPZtWHD6/WybNkyTjjhBPT6AzPFTvSe7KfhQ/bV8DGc9lVfvqPsbi/5NQ4aQiQx642TxsL0qU3867sipq79kvPev7/DQIqpvp7DHnwQ16uvsfeoxVQ2Ng3YlPfBOK/QVARL35kiY0icvrDD/Waap+/XO9lWbmVbhY2C/ZLi6bUKE5IimJYWydTUKFKijKMy51Bu87+zVJXCWjurtuxhQ4Meq9vP56UKy8o0zMyIZtGEBCYlR4yov9HktGiSowb3SoTJZMLhcLBgwQKmTp06INscTse/lpngPdGrYP3mm29myZIlXbZpW4evv3Jzc0lISCAvLy9ksG40GjtNQqfX64fmjjr/fNDpglnh2ySbUzIy4LHHiD/mJGoqbH3atM4Q/Dv4fH4U7ZBb4TAiKBrtoP1tdVqFQ7PjCDP0ffuB5rMIk8k0NN//B9CQPQaIdmQ/DR+yr4aP4bKvNmzYwI4dO0Lef+qppxITEwPAli1b+OmnnwCwu33U2Fw43PumkB925DFExQSzuhfl7SJv59aQ2z308KOJTUgkIz6SJZN0nHnfMwAdgvVgpR4FZenNVHzxA2h1KAN8vX4gzyu8vuDfw2A0tdumxelha7mVrWWNbK+w4txv+n5atImpadFMS49ifFKkJOZsQwFyEiNJzglw0aKpbCq3s3JXNbur7GwoaWRDSSOZsWGcPC31gE0hH2x7ap3ERZow9+N8tDstszI0Gs2AH6uGw/GvN/3r1V5ITEwkMTGx1x3qq9LSUurq6khNTT1gz3lAnH02nHkmvpUr2fTpp8w8+WR0xxwDWi0ZQCAAu6t6H7C3JOrw+4b+Wg3RnqLA9PTofh8YJcGcEEKI4eL111/n4YcfDnn/1q1bW4P1d999t9Nljy2efOPT1mB97TfLee6RP4ds+/C/3yE2IXg+63jnFRKt9SHbKqiYKsuJXf89Dd3UrD7YcidO5v/++DDmqBh2VlrZWmZla3kjpQ1N7dqZDVomp0YxNS2KaWnRxIUbQmxRtKXTajgsO47DsuMoa2hi5a5qVu+to6ShiWe/3UvCRgMnTknhiHEJw/qCh9+vsrXMypys2EErZddZ6UbRuUG7ZFJcXEx9fT3FxcX4/X42bdrE/7N33+FRVekfwL93eibJpPdK6ChNlKYIiBRRFym6LpaFH4vr7uqquLvCWkFX111WsWNHVNaKu2tBiTRRwQIq0iKhp5I+qVPv74/JDInJTCZkJvfeme/neXiYcubOm5xMee855z0A0K9fP8/+z4MGDcJDDz2E2bNno6GhAcuXL8fcuXORmpqKw4cP4y9/+Qv69euH6dOnBytM6ajVECdORHFjI4ZPnNhujXp2ghFOUUThqYZuHdL9h//zbVBI/gakRAeksvVVV12FMWPGICcnJwBRERERBc+AAQMwdepUr/dHRp7e1qtv375e24qiiOF90xEbY0BVoxUp6Zk4Z3zHaeBuUaZYz+VMP0e17cUlfrWTgruyeUG9AYcTxuBgWT0sG3/y3C8AyE2M9CTnfRIjQ2IEWEoZcRG4dmwOrhiRgS0Fp7Dp4ClUNljx+tcn8OGPpbh0aBou6J+o2N2TzM02HK5oQP+U4FTC97UbBLUXtGT9nnvuwSuvvOK5PnKkayuILVu2YNKkSQCAgoIC1NXVAXB12p49e/DKK6+gtrYW6enpmDZtGu6///7Q2mvdT7mJkXCKIo5UNPr9GLXnLBWTdSXJijee8f6uP/eXv/wlIMchIiIKtsWLF2Px4sV+tb3uuutw3XXX+dX2vJv+Dzf95no0WuxosTlhdzrhcLoWZqtVArRqFQxaNSJ1asRcMw94ZXWXx3xsfwPsCUdwQb9EDEyNljTZ9STnZa4tx34qr0f9z/Z5Nxk0nqntQ9JMiDbIe1qwUkUZNLh8eDqmnZWCLwqr8PG+MlQ3upL2j/eV4bJhaRjXNwEalfKS9uNVTYiP1AVlm9TMzEwIgsCZoH4IWrK+Zs2aLvdYb1uIPiIiAp988kmwwlGkvCTXDAR/E3ZjZBRSM7IQn5QSzLAogJKi9RiQEiV1GERERCHDqNP4v6zsoklAZiZQXAx0skGSE0CpMRY7M4bAeawaXx+rRpReg5FZsRiZHYv+ydGI0AU34TA323C0qhHHKhtxtPVf48/Wnes0KmRFq5EoNGBYVhzOGzYYqhAqeiZ3eo0aFw1KxoT+idh+qBIf/liKqkYrXtlxHB/vK8OVo7IwPDNGcYXo9pWYMTYvIeDT+r/88suAHi+UsQqZzOUluSpMHvZjSvyIMRfg1Y3f9EJUFAgxRi3OzgjsG/epU6cAAHFxcbIvrkFERCQ5tRp47DFg3jxXAZmf7dQjiCJubqrFBP0xiLljsPtELRosdmwvrMT2wkoIApAdb0T/5ChkxRuRZjIgNab7xblsDidqm2yoabKitK4FpXXNKK1tQUldM2qaOtYi0qlV6JsciYEp0RiYGo0+CZF4b+1qPLdyBap/MQ9jhj/Z418NdZ9WrcJFg5JxQb9EbP3pFD76sQzlZgue3FKIwanRuOq8LGTFyWxraR+sdif2l5oxIitW6lDCFpN1BeiTGAm1IJxR0TmSJ6NejRFZsQGfRjd+/HgcPnwYX3zxBfdZJyIi8secOcA773S6U88/s7Lx3pdf4EZrLeaOy8U1Y0T8VF6Pb4/XYF9JHSobrDhe1YTjVU3tDhml1yDKoEF06/8alQCV4PrncIposdnRVK+C5WABapttHaaxtyUASIsxIDcxEn0SItEnMRKZcREdtnl173Wu1Ybf8lG50WlUmDYkFRf0S8RHP5bh0wPlOFBWjxUf7MeEfomYNSIDMRHKGFSprLfgZHVTwJZsUvcwWVeI7AQjVCqgoKy+s1lapCAGrRrnZMcFpegIq8ETERGdgdaderB9O1BaCqSlARMm4OANNwBffgGLpQWAa8374DQTBqeZAADVjVYcOlWPw6caUVLXjNK6FtQ129BgsaPBYkeZzydVAThdqV2jEhBn1CHFpEdabATSYgxIj4lARmyEX1PtbRYLAEAXhrWe5Mqo02DeqExMHJCEd3YXYdfxGnx2qBLfHKvBnJEZmDggKWgV1wPp0Kl6xEXqEKUPTOp43XXX4eDBg3jyyScxZsyYgBwzVDFZV5DMOCM0KhX2l9ahdTvtdo4XFuAff/0jYhMS8bdnXu/9AKlLeq0K5+TEwqANTjLNZJ2IiOgMqdVAaxFkt4iICNddYufb4sZH6jCmTwLG9Enw3NZktaOm0YZ6i2vEvMFih8MpwimKcDhFaNUq6FSAUH0MMRn9ERdlQKxRiyi9pkdL42xWV7Ku1XErNrlJitbjdxP74qfyerzxzUmcqG7C61+fwJdHqnDd2Bxky3zU2ukE9hbXYXRufEBOLuzduxfff/89amtrex5ciGOyrjCpMQZo1QL2FNV5Kpu6Wa0W/LTvBySmhNi+9CFCp1HhnOy4Hu+l7ot72z73Nn5ERER05tzJepTaCYNWjRZb11tNnS5wF+G1jeiwo+nIURgzTBD83D6uK9bWZF2nMwTkeBR4A1KicdfMwdj6UwXWf1eEo5WNeODD/ZgyOAWzhqcHbTAnEBpa7CisaMCAAGznxq3b/Ke8fQQICVF6nJMT16Eyo0bjWvtit3d+9peko9OocE5OHCIDNH3IG46sExERBY47WbdaWjA0IwZy3oHL5l6zzpF1WVOpBFw0KBkPzDob5+bEwSkC+fvLcc9/92FfSZ3U4fl0oqoJ1Y3WHh+Hybr/ZPyWQ77ERGhxXm48jPrTSZlnn3Ub91mXE71WhVE5cQFb5+MLk3UiIqLAycnJwejRo5GdnY0Yoxb9k3s+qhgs1tZ19VyzrgyxRh1unNgXt0zpj8QoHaqbrHj000NYu+OYXzM4pLKvpA42RyfrcbuBybr/OFdWwSJ0apyXG489RXWoabRyZF2GDFo1zsmJDerU97aYrBMREQXOokWLsGjRIs/1rHgj6pptKKtrkTCqzk25fB76DjobZ49iwS4lGZoRg+WXn4V3vyvG5oOn8NmhSuwrMWPB+FxPIUM5sdicOFhaj6GZMWd8DCbr/mOyrnBatQojs2JRUF6P8hL+4ctJpF6DkdnBKybXmWuuuQYNDQ2IjY3tteckIiIKJ0PSTGi02H1utyaFUeMnYtT4iVKHQWdAr1Vj/uhsnJMdizVfHkNlgxX/yv8JkwYk4cpRmdDLbC17ubkFiXU6pMV4r8vgi7u2krvWEnnHZD0EqFq3EWmsiQPAkXU5iDVqMTwrNijbs/ny5JNP9urzERERhRuVSsDwrFh8fbQaVnvPpgMTtTUo1YT7Lj8L7+wqwtafKrD1pwocLKvHDRPykJ0gr4rxB8vqEWfUndGgVGxsLBISEqDVKmOveSlxzXoIyU2OQUJiIuISEiFyM3bJpJgMQdtHnYiIiHrPxx9/jOzsbMyaNavd7QatGsOzYmVVcK7wwI/Y9903MNfWSB0K9YBBq8a1Y3Ow5OIBiI3Qoszcggc3HMDG/WVwyuj7vcMhYl9J3RnlHO+99x4qKysxZ86cIEQWWmT0FkM9lZiYiMqKCpQUlyAhmsVFpNAnKRJDM2MCsgflmTCbzWhoaODJGiIiogCw2+04efIkSkpKOtwXE6HFWelnvm430B6/fyluvfZy7N39ldShUAAMSTfh3suHYERWLOxOEW99W4THNh1CXbN8ZtDWNNpwvKpJ6jBCGpP1EOTez7tfchQEaXLGsKNWCxiWGYO+SVGSxSCKImJiYhAdHY1Tp05JFgcREVGocG/d1tzc3On9KSYD+qdI99nfltXi2mddq+XWbaEi2qDFHyb1xbVjsqFVC9hXYsZ97+/DnqJaqUPzOFLZgPoW+ZxACDVM1kNYbmIkzs2Nh1Enr6IUocaod1XlTzYZJI3D6Ty9bo7V4ImIiHquq2QdAHISImWxnthqdSXr3LottAiCgEkDk3H3pUOQGReB+hY7Ht9ciLd3nYTDKf1MSqcT2FtshrMbsdx7772YNGkS/ve//wUxstDAZD2EiKKISZMm4fzzz0dtbS0A1xStMXkJyIw/s2qN5FtqjAGjc+N7ZQ/1rrTdBYDJOhERUc8Zja4k3FeyDgADUqKRGiPtSXtba7Ku1UsbBwVHemwE7pw5GFMGJQMAPtlXjpUbC1DbZJU4MqDRYkdhRYPf7ffu3Ytt27ahqKgoiFGFBibrIUQQBGzfvh1ffvlluw8VtUrAoFQTzsmJQwRH2QNCrRZwVoYJZ2fEQCOTQnJtk3X3lhhERER05vwZWXc7K92EJAlrBrmnwet0nAYfqrRqFX41Ohu/m9gXBq0Kh041YPkH+3GwzCx1aDhR1YTqRv9OHHCfdf/JI8uggHFvgWCzdVw7Eh+pw9i8BOQmGmVVvVRp4iJ1GJeXcMZ7SwYLR9aJiIgCqzvJuiAIGJoRg4QoaZJlm9WVKGl1nAYf6kblxOGuNtPi/5X/Ez78sVTyavH7Supgc3S9naF7UInJeteYsoUYX8k64Bpl75ccjdF9EhAXyTOv3TUgNRqjcuLOaE/JYGOyTkREFFhRUVEYPHgwzj77bL92WlGpBAzPjJUkYbdZWwBwzXq4SDUZsOySQRjfNwGiCLz3XTGe3FyIBotdspgsNicKyuq7bMeRdf9xrmyIcZ+pstt9v1Cj9BqMyonDqfoWFJY3oMnKF4svKTEGHIVrvZBcMVknIiIKrPj4eOzfv79bj3En7D8W16Gi3hKkyDq6/qa/wNLcDFNsfK89J0lLr1Hj/87vgwHJ0Xj96+PYU1yH+z/Yjz9M6idZ0cOyuhYkRul91nBgsu6/oCXrx44dw/3334/NmzejrKwM6enpuPbaa3HnnXf6XEvT0tKC22+/HW+88QYsFgumT5+Op59+GikpKcEKNaR0NbL+c8nRBiRG6lFc24yjlY2w2rueuhJOogwaDEyJRpROwNHvpI7GN61Wi1/+8pdwOBxM1omIiAKouroaFov3xDstLc1zuaamBi0tLUhSi6huqcepnyXs8YnJnssN5jpYfQywxMYnej7TG+vNaGnxPh1/zrWLoWbNmrB0Qf9EZCcY8cy2w6iot+DvHx/Er8fnYEyfBEniOVhmRqxR63UmKpN1/wXtFX3w4EE4nU48++yz6NevH/bu3YvFixejsbERK1eu9Pq42267DR9++CHefvttxMTE4KabbsKcOXPwxRdfBCvUkNLdZB1wnQHOijciPTYCJ6ubcLy6CbYwT9oNWjXykiKRFmOAIAjd+n1KJTo6Gm+88YbUYRAREYWca6+9Fhs2bPB6f9sp8jfccAPeeecdr23f//YI9K0DV8/84z7k/+9tr23f3r4XsfGJAIAXH/0b3n/zFa9tX934NVIzsr3eT6EtO96Iuy8djOe2H8HeYjOe334UJ6qaMOecTKhVQq/GYneI2FdixqicuE7vNxgMMBgMULGIVpeClqzPmDEDM2bM8FzPy8tDQUEBnnnmGa/Jel1dHV588UWsW7cOF110EQDg5ZdfxuDBg7Fz506MHTs2WOGGDJPJhIaGBr/WVf2cWiUgNzESWfFGlNQ243hVE1ps4XXGy6BVIyfBiIzYCKh6+Y2NiIiI5EmlUvk9a81b286+malUKqj8PK7QjbYUnow6Df44uT/+80MxPvqxDJ/sL8fJmmbccGFer28zXNNoxYmqpk6n4z/11FN46qmnejUeperVXqurq0N8vPd1NLt27YLNZsPFF1/suW3QoEHIzs7Gjh07Ok3WLRZLu2lJZrNr6wKbzSb70VB3fIGMc8+ePR2OfyZSo7VIiTKhosGK4pom1DXJ+3fZU0a9BtnxRiRH66FSCXA47Gg7MycYfRVooih6psALQvieaFBCXxH7SUnYV8rBvgqe9957z+f9bX/nr732Gl577bUObSobrDhQWgeHQ4TodH3JWHLfP3D7in/5PLbocE2Tv2nZ/bhp2f1+taXAcfeV+3+5EwDMHpaKrBgDXt5xAvtLzXjgw/34w4V9kBnXu7WXDpXVIFoPROm1QX8uJb3/dSdGQTyTIdgzUFhYiFGjRmHlypVYvHhxp23WrVuHhQsXdlgTNHr0aEyePBkPP/xwh8fcd999WL58eafHMhqlKaxAJIXS0lL87ne/g8Fg4HR4IiIiojBX0gi8UKBGlUWATiVifj8nRiZIu70bAU1NTZg/fz7q6upgMpl8tu32yPrSpUs7TZrbOnDgAAYNGuS5XlxcjBkzZuDKK6/0mqifqWXLlmHJkiWe62azGVlZWZg2bVqXP7zUbDYb8vPzMXXqVM9ac7lzOkVUNVlRYbagqsECh1N5L/hIgwapJgNSTHroNP5NJ1NCX/30008AAL1ej5kzZ0ocjXSU0FfEflIS9pVysK+UweZw4mBJDU7++BUickdCUHFqu5yJTgeaj32nyL7qB+DuAXY89/lx7C+rx5qf1Cgfkowrhqf16nLPzHgj+iVHea6vWbMG7733HmbPno0FCxYE5DmU9P7nngnuj24n67fffnuXv9S8vDzP5ZKSEkyePBnjx4/Hc8895/NxqampsFqtqK2tRWxsrOf28vJypKamdvoYvV4PfSf7SWq1Wtl3lFsgY7311luxf/9+3H///RgzZkxAjvlz6Xod0uOi4HCKqGq0oKLegqoGq6wryZsitEiK1iM5Wo/IM1izU19fj6NHj+LUqVPIzc0F4HpT8LWdS3x8PLKysgC4ql3u3bvXa9vY2Fjk5OQAcE1nb7ucocPPYjKhT58+nut79uyBKIo4evQoAFeFTaX87QeTkt4Dwhn7STnYV8rBvpI3rRYYmpWAkz8Cao0aosAK7kogqNQQ1MrrqyijBrdcPADrvyvCJ/vKsWH/KZysa8ENE/Jg1PXOz1NcZ0VyjBMJUa6c7dChQ9iwYQOGDBkS8PcqJbz/dSe+bvdQUlISkpKS/GpbXFyMyZMnY9SoUXj55Ze7rPg3atQoaLVabNq0CXPnzgUAFBQU4MSJExg3blx3Qw1LO3fuxFdffYWbbrop6M+lVglIjjYgOdq1j2J9iw01jTbUNFlR22yTtKK8UadGrFGH+Egd4iK10Ps5gt4Zq9WKoUOHoqSkBIcPH8YTTzwBAKisrMSIESO8Pm7RokV44YUXAAANDQ0+21599dX497//DcCV2Ptq+4tf/AL//e9/PdfPO+88WK1Wz3UNt20hIiJShFG58ThY3oSGFq41p+BRqwRcOSoLOfGRWPPlMewtNuNvHx7AHyb3Q3ps76xj319qxti8BGjVKm7d1g1B+1ZfXFyMSZMmIScnBytXrkRFRYXnPvcoeXFxMaZMmYK1a9di9OjRiImJwaJFi7BkyRLEx8fDZDLh5ptvxrhx41gJ3k9nsnVboEQbtIg2aD1VH5utDphbbKhvsaPR4vrXbHMgkFUSVCpXBfcovQZReg2iDVqYIjQ9Ss5/rqqqCiUlJQBc26Odfm6V1xkfABATE+O5LAiCz7ZtZ5IA8Nk2Lq79NhjuGSluixYt8vpYIiIiko9InQajc+NxrKoRx6oa4ZTvJEUKAaP7xCPVZMCTWwtRXm/BgxsOYNH5fTAyu/Mt1gLJYnPiQKkZwzJjmax3Q9CS9fz8fBQWFqKwsBCZmZnt7nPXtLPZbCgoKEBTU5PnvkcffRQqlQpz586FxWLB9OnT8fTTTwcrzJDjHlW126U/QxuhUyNCp0ZKm9IBoiiixeZEi80Bi90Jq90Jq8MJu9MJu0OEKALONtm8ShAgCIBGLUCjUkGnVkGnUUGvUSFCp4Zeowp65XN3wUOdTteumGFKSgpKS0v9OobJZPK7rUaj8bstABw/ftzvtkRERCQvKpWAvKQopJgMOFhmRk2j/KtZk3JlJ7j2Y1+97QgKyuvx1NbDmDU8HZcOS4MqyN+pT5ktKK5t9iTrcshX5C5oyfqCBQu6XNuem5vbYT9wg8HAvfd6QMqRdX8IguBJ4pXCnaxzejkREREFS6Reg1E58Sg3t6DwVAOarRx1pOCINmhx29T+eOvbImw+eAr//aEEJ2qasOj8PjBog/sd/aeyejhE10kBjqx3zfciclIcuSfrSuRO1uVerIKIiIiUL8VkwLi8BAxIiYZWw6/qFBwalQrzR2djwbhcaFQCvjtRiwc3HMCp+pagPq/DKaKydfYIk/Wu8R0gxDBZDzz3enAm60RERNQbVCoB2QlGnN83Af2So6Bj0k5BckH/RPx5+kDERGhRUtuCBz48gH0ldUF9TruTI+v+4is/xGi1WqjVajhZoSRgHA4HIiMjYTAYpA6FiIiIwohGrUJuYiQu6JeIwemmM9p+lqgrfZOicPelg5GXGIkmqwOrNh3CJ/vKOixXDpSrFv0Bn/xYgn8+sTooxw8lfMWHmLfeeivoBdfCzZgxY1BTU4OPPvpI6lCIiIgoDKlUAjJiI5ARG4GaRiuKa5tRUW+BwxmcZIrCT6xRhz9PH4jXvzqBzwsr8fauIpyobsKvx+UGfGaHezvvfSVmjOkTH/R18krGkfUQw0SdiIiIKHTFRepwdkYMJvRPxJB0E+KjdODXPwoErVqFX4/LwfzR2VAJwFdHq/H3jw+iqsESlOez2Z3YV2IO2gh+KODIeghas2YN/vWvf3m9/4UXXsCYMWMAAG+++SYeeOABr22ffPJJTJw4EQDwv//9D3feeafXtv/85z8xY8YMAMDGjRtx++23e227YsUKzJ492+fPQURERESd06hVSI+NQHpsBGwOJ6oarKhssKCq0Qqbncsh6cwIgoCLBiUjPdaA1duO4ER1Ex746AB+N7EvBqREB+Q5dn25DR+98xoGDR2JKxf+HkcrG5GXFBWQY4caJushqLKyEnv37vV6f2Njo+dydXW1z7b19fWey7W1tT7b1tWdLkZhNpt9tq2pqfF6n9xs3boVf//732EymTBz5kypwyEiIiJqR6tWITXGgNQYV32d+hYbapta/zVbYbExeafuGZRqwt2XDsaTWwpxsqYZ/9r4E64+LwuTBib1eCZvadFxfPbJ+3DY7bhyIXC0shGxRh3iI3UBij50MFkPQVdddRXOOeccr/ePGDHCc/nyyy/HwIEDvbYdNmyY5/LUqVOxadMmr23POussz+ULL7zQZ9tBgwZ5vU9uTp48iU8++aTd742IiIhIrqINWkQbtMiKd1232B2ob7GjvsWORosdDRY7mq0OrnknnxKi9Fh6ySC88uVxfH2sGq9/fQLHq5twzZhsaNVnvppapXKtUXdXgxdFYG9xHcbkxUOv4fr1tpish6Ds7GxkZ2f71TYzMxOZmZl+tU1LS0NaWppfbZOTk3HRRRd5vf/zzz/H+vXrMWrUKIwbN86vY0qF+6wTERGRkuk1auij1EiM0re7vcXmQIvNgWabAy02J1psDljsTljd/xwOcIOh8KbXqLF4Qh9kxUdg/e5ifF5YieNVjbhxYl+kmM5spyRVa6LvdJ7eus1qd2JvsRnnZMeyBlcbTNZJEm+99RaeeOIJ3HnnnUzWiYiIiCRg0Kph0KoR66ONwynC5nDC5nDC7hBhczrhcIqwO0TYnSIcTmfr/65/dqcI58/+d4giHA6O4iuVIAi45Ow0ZMcb8fz2ozhZ04z7P9yPX4/LxXm58d0+nrp1ZN3paH8mqKbRisMVjeiXzPXrbkzWSRIREREAgObmZokj6Zo7Wddo+HIhIiKi8KJWCVCr1AHZXsvuOJ3Yu5J913XPiQCHE7bW/62O0yP8nK4vD2elx+Dey4fguc+O4NCpBjz72RH8VF6Pq87N6ta0eJW6NVlvM7LudqyyETERWiRF6zvcF46YfZAkjEYjAKCpqUniSLrGkXUiIiKintOoVTiTJcl2hxMWu+ufe+p+i82JZpsDzVYHLHYHuPtX74gz6vCnaQPx3++L8dHeMmwpqMDhikbcODEPydH+TYtXeUbWOybrALCvpA6j+8TDqGOqyt8ASYLJOhERERH5w5XkqxDpZbDV6RTRZHOgyWpHo8XhKaLXZLVzzX0QqFUC5pyTif4p0Xjx86M4Ud2EFR/sx/zR2RiXl9DlmnP3mnVHJyPrAGB3iNhTVIfzcuOhVoX3+nUm6yQJJU2Dt9vtADgNnoiIiEiOVCoBUXoNovQaoM1W4KIooqE1ca9vscPcbEN9i53T6gNkaEYM7rlsCJ7f7poW/9IXx/DDyTpcNzYHUQbv35vPv+gS/O/rw1BrvbdpaLFjf4kZQzNjghG6Ypx5zX2iHlDSyPoDDzwAi8WChQsXSh0KEREREflJEAREG7RIi4nAgJRonJsbj0kDkzA6Lx4DU6ORGmOAXst0qCfiI3X487SBmD0yA2pBwK4TNbj3/X3YW1zn9TEarRYRkZHQ6XyvSy83t+BoZWOgQ1YUDhWSJNwj60pI1gHXm71azX0fiYiIiJRMEASYDFqYDFpktd7WZLWjpsmGmkYrqhutsNo5d747VCoBlw5Nw9npJrzw+VGU1rVg1aZDOL9vAq48N8s14+EMHT7VgEi92u/18KGGyTpJYuLEifjggw+QmpoqdShEREREFMaMOg2MOg0yYl2DSfUtNlQ1WFHVaEFNvV3i6JQjJyESd186BO/sLsLmg6fwxeEq7Cmuw6/Oy8Z5uXGetexHDx3AO2tWIyk1HQtuvqPL4+4rMSMiR41oQ/jVj+K8D5JEeno6Lr30UowaNUrqULq0atUqXH311fj222+lDoWIiIiIgizaoEVuYiRG5cRjfL9EAEBKjAFqdXgXO/OHTqPC/NHZuGPGQKTHGFDfYsdz24/g8c2FqKh3FW2urjiFjf95Ezs2f+LXMR0OET+crEOLrfOCdKGMyTpRF3bu3In169ejrKxM6lCIiIiIqBe59w8fnGbCxP5JGJEdi/TYCGg1TKN86Z8cjbsvG4JZw9OhUQn4sbgOd/93L97dXQS76Fpa6nD4P2uhxebADydrw644YND+yo4dO4ZFixahT58+iIiIQN++fXHvvffCarX6fNykSZMgCEK7fzfeeGOwwiSJ1NTUYM2aNXjllVekDqVL3LqNiIiIiFQqAYlRegxJN+HC/okYkR2LtFiOuHujVatw+fB03Hv5EAxOjYbdKWLD3jK8dkyPqOEz0N28u77Fjj1FtRDF8EnYg7Zm/eDBg3A6nXj22WfRr18/7N27F4sXL0ZjYyNWrlzp87GLFy/GihUrPNfdlcMpdJSVlWHhwoWIi4vDr3/9a6nD8cmdrHPrNiIiIiICXIXqEqP0SIzSY7BTREWDBWV1LahqtHBv959Ji4nAkqkD8ENRHd7+9iTK6y1ImHETxIYqbD9UgXF9E6BR+TeGXNVgxf5SM85KD48t3YKWfcyYMQMzZszwXM/Ly0NBQQGeeeaZLpN1o9HIwmMhzn0CRgn7rHNknYiIiIi8UakEpJgMSDEZYLU7UW5uQXFtMxpaWJzOTRAEjMiKxdnpJryxZTc2/VQDdVQCXtlxHB/+WIopg1Jwfr8EGHVdp6eltS3QqVXonxLtusHhgLBtGzI++wxCZCQweTIQIrs49epii7q6OsTHx3fZ7vXXX0diYiLOPvtsLFu2TDHbe5H/3Ml6S0sLnDI//ciRdSIiIiLyh06jQla8EWPzEjA6Lx6Z8RHQcJq8h0atwnkpahQ/+xtYv3kLJoMGlQ1WvPntSfzpnT1Yu+MYjlU2djnV/XhVk2sP9vXrgdxcaKZOxbmPPALN1KlAbq7r9hDQa9lHYWEhnnjiiS5H1efPn4+cnBykp6djz549uOOOO1BQUID1Xn7hFovFk0wBgNlsBgDYbDbYbLbA/QBB4I5P7nEGQ9vEt76+XtZLHVpaWgC4RtbDsa+UJpxfV0rCflIO9pVysK+Ug32lHD3pqwg10DchArlxBlTUW1BS1wxzE/tcACDaLbDt3YgHVz6AnUerseWnShTXtuCzQ5X47FAlkqJ0ODc7FqOyY5EVHwGV0PGEh/nVdRBv/y0gimh7r1hcDMybB8cbb0CcPbvXfi5/dedvSRC7uUJ/6dKlePjhh322OXDgAAYNGuS5XlxcjIkTJ2LSpEl44YUXuvN02Lx5M6ZMmYLCwkL07du3w/333Xcfli9f3uH2devWyToBDHcOhwNz584FAKxduxYmk0niiLy79dZbcezYMdx3330YMWKE1OEQERERESmW3W5HQ0MD1Go1oqNdU9lFEThcD3xepsLeGgE25+n026gW0dckok+0iHQjkGYUYVI7MOO3N8BQVYXO5i2IAJoTE5H/7LOymxLf1NSE+fPno66ursscqNvJekVFBaqqqny2ycvLg06nAwCUlJRg0qRJGDt2LNasWQOVn8UD3BobGxEVFYWPP/4Y06dP73B/ZyPrWVlZqKyslHUCCLjOquTn52Pq1KlhuR46KioKVqsVhYWFyM7Oljocr2w2GxoaGrB9+3ZccsklYdlXShLuryulYD8pB/tKOdhXysG+Uo5g9ZXd4US52YKi2iY0W8Jv/3C32xfORfPPljyLai3E5IGIGHgBbPF9YLF3XDI79vgPeOONO7s8vj0/H+LEiQGLNxDMZjMSExP9Sta7PQ0+KSkJSUlJfrUtLi7G5MmTMWrUKLz88svdTtQB4PvvvwcApKWldXq/Xq+HXq/vcLtWq1XMm5+SYg0ko9EIq9UKu90u65/f3T8ajSZs+0qJ2FfKwH5SDvaVcrCvlIN9pRyB7iutFsg16JGbbEJ1oxUnq5tQ2WBBGO1KBgA48tMBNNabO7lnN4ZUH8Jjr/4PJ6qaUFBejzfeeQ+iKQXa+EwkN9b6dXxNRYXrly0j3fk7Ctqa9eLiYkyaNAk5OTlYuXIlKioqPPe5K70XFxdjypQpWLt2LUaPHo3Dhw9j3bp1mDlzJhISErBnzx7cdtttuPDCCzFs2LBghUoScc+0SE1NRU1Njdd2Op0OkZGRAACn04m6ujq/2oqiiNraWq9ttVotoqKiPNd9xcAlFUREREQUDPGROsRH6tBsdaCopgnFtc2wO8Ija7/n0RfgsHdeNT/SFAONSoW8pCjkJUUhrXEYbFYrnGIjUi0G/57Ay4CvUgQtWc/Pz0dhYSEKCwuRmZnZ7j73zHubzYaCggJPtXedTodPP/0Uq1atQmNjI7KysjB37lzcddddwQqTJDRr1iwAwPHjx5Gbm+u13Y033ohnnnkGAFBdXe1zZsf111+PV155BYCrMJyv3Qfmzp2Ld955x3PdV9vrrrsOkydP9no/EREREVFPROjU6J8SjbykKJTWNeNEdROaQnyK/DnjLvS77YgxF5y+cv5EtLz5AvSnyiB0Nh1BEIDMTGDChABEKZ2gJesLFizAggULfLbJzc1tV5Y/KysL27ZtC1ZIRGds165d+MUvfiF1GEREREQU4tQqAZlxRmTGGVHVYMGJ6iZUNVilDkte1GoU3fcQ+v5+oSsxb5uwuyvHr1olu+Jy3cWNo0ly2dnZPrcwENps1ZCQkOB3W4PB4HdbwPc2Ck6nExs2bPB6PxERERFRoCVE6ZEQpUeT1Y6T1c0oqWuGI0ymyPuSbNKjzw3XQ0iOBm65BSgqOn1nZqYrUZ8zR7L4AoXJOklOEIR2+65L0RaAz7bcB5WIiIiIpGLUaTAwNRp5SZEorW3ByZomNFtDe4q8N7mJRvRLdm35hjlzgFmzYN+yBd9v2IARl1wCzeTJih9Rd2OyTkREREREpABatQrZCUZkxUegssGKkzVNqA6TKfJqlYDBaSakxvysuJxaDXHiRBQ3NmL4xIkhk6gDTNaJiIiIiIgURRAEJEXrkRStR6PFjqKa0J4iH6nXYGhmDKL04ZW+htdPS0REREREFEIi9a4p8n2TIlFmbkFRTTMaWjrfDk2JMuMj0D85GmqV0HXjEMNknYiIiIiISOE0apWninxdsw3FNc0or29R7Gh7hE6NwWkmxEfqpA5FMkzWiYiIiIiIQkhMhBYxEVoMcEThVL0FJbXNqG1SRsFklQrISYhEbkJkWI6mt8VknYiIiIiIKARp1Cqkx0YgPTYCzVYHyswtKK1rRpNFfpXkBQFIjTGgb1IUDNrQKRLXE0zWiYiIiIiIQlyETo0+iZHokxiJ+hYbTtVbcMpsQaNF2vXtapWA1BgDchKMMOqYnrbF3wYREREREVEYiTZoEW3Qom9SFJqsdlTWW1HZaEFdkw0OZ++scY8yaJAeE4G0WAO0alWvPKfSMFknIiIiIiIKU0adBtkJGmQnGOF0iqhrtqGmyYq6Zhvqmm2wB6hAnUrlWkufEOnaci4yzLZhOxP8DRERERERERFUKgFxkTrEtanA3mJzoL7FjiarHY0WB1rsDlhsTtgcTjicYruReJUK0KhU0KpV0GtVMOrUiNRpYDJoEW3QQBXmBeO6i8k6ERERERERdcqgVbcWfNN7bSOKIgSBiXigcXEAERERERERnTEm6sHBZJ2IiIiIiIhIZpisExEREREREckMk3UiIiIiIiIimWGyTkRERERERCQzTNaJiIiIiIiIZCaoyfovfvELZGdnw2AwIC0tDddddx1KSkp8PqalpQV/+MMfkJCQgKioKMydOxfl5eXBDJOIiIiIiIhIVoKarE+ePBlvvfUWCgoK8O677+Lw4cOYN2+ez8fcdttteP/99/H2229j27ZtKCkpwZw5c4IZJhEREREREZGsaIJ58Ntuu81zOScnB0uXLsUVV1wBm80GrVbboX1dXR1efPFFrFu3DhdddBEA4OWXX8bgwYOxc+dOjB07NpjhEhEREREREclCr61Zr66uxuuvv47x48d3mqgDwK5du2Cz2XDxxRd7bhs0aBCys7OxY8eO3gqViIiIiIiISFJBHVkHgDvuuANPPvkkmpqaMHbsWHzwwQde25aVlUGn0yE2Nrbd7SkpKSgrK+v0MRaLBRaLxXPdbDYDAGw2G2w2W89/gCByxyf3OIl9pSTsK2VgPykH+0o52FfKwb5SDvaVMiipn7oToyCKotidgy9duhQPP/ywzzYHDhzAoEGDAACVlZWorq7G8ePHsXz5csTExOCDDz6AIAgdHrdu3TosXLiwXfINAKNHj8bkyZM7fd777rsPy5cv7/RYRqOxOz8aERERERERUdA0NTVh/vz5qKurg8lk8tm228l6RUUFqqqqfLbJy8uDTqfrcHtRURGysrLw5ZdfYty4cR3u37x5M6ZMmYKampp2o+s5OTm49dZb262Bd+tsZD0rKwuVlZVd/vBSs9lsyM/Px9SpU70uDSB5YF8pB/tKGdhPysG+Ug72lXKwr5SDfaUMSuons9mMxMREv5L1bk+DT0pKQlJS0hkF5nQ6AaDDyLnbqFGjoNVqsWnTJsydOxcAUFBQgBMnTnSa3AOAXq+HXq/vcLtWq5V9R7kpKdZwx75SDvaVMrCflIN9pRzsK+VgXykH+0oZlNBP3YkvaGvWv/rqK3zzzTe44IILEBcXh8OHD+Puu+9G3759PYl3cXExpkyZgrVr12L06NGIiYnBokWLsGTJEsTHx8NkMuHmm2/GuHHjWAmeiIiIiIiIwkbQknWj0Yj169fj3nvvRWNjI9LS0jBjxgzcddddnpFwm82GgoICNDU1eR736KOPQqVSYe7cubBYLJg+fTqefvrpYIVJREREREREJDtBS9aHDh2KzZs3+2yTm5uLny+ZNxgMeOqpp/DUU08FKzQiIiIiIiIiWQv61m29zZ38u7dwkzObzYampiaYzWbZr60Id+wr5WBfKQP7STnYV8rBvlIO9pVysK+UQUn95M5T/anzHnLJen19PQAgKytL4kiIiIiIiIiIOqqvr0dMTIzPNt3euk3unE4nSkpKEB0d3ele7nLi3mbu5MmTst9mLtyxr5SDfaUM7CflYF8pB/tKOdhXysG+UgYl9ZMoiqivr0d6ejpUKpXPtiE3sq5SqZCZmSl1GN1iMplk/0dFLuwr5WBfKQP7STnYV8rBvlIO9pVysK+UQSn91NWIupvvVJ6IiIiIiIiIeh2TdSIiIiIiIiKZYbIuIb1ej3vvvdez7zzJF/tKOdhXysB+Ug72lXKwr5SDfaUc7CtlCNV+CrkCc0RERERERERKx5F1IiIiIiIiIplhsk5EREREREQkM0zWiYiIiIiIiGSGyToRERERERGRzDBZJyIiIiIiIpIZJutEREREREREMsNknYiIiIiIiEhmmKwTERERERERyQyTdSIiIiIiIiKZYbJOREREREREJDNM1omIiIiIiIhkhsk6ERERERERkcwwWSciIiIiIiKSGSbrREREIeS+++6DIAhSh0FEREQ9xGSdiIhIxtasWQNBEDz/DAYD0tPTMX36dDz++OOor6+XOkS/7d+/H/fddx+OHTsmdShERESyx2SdiIhIAVasWIFXX30VzzzzDG6++WYAwK233oqhQ4diz549nnZ33XUXmpubpQrTp/3792P58uVM1omIiPygkToAIiIi6toll1yCc88913N92bJl2Lx5My677DL84he/wIEDBxAREQGNRgONpnc+3u12O5xOJ3Q6Xa88HxERUTjhyDoREZFCXXTRRbj77rtx/PhxvPbaawA6X7Oen5+PCy64ALGxsYiKisLAgQPx17/+tV2blpYW3HfffRgwYAAMBgPS0tIwZ84cHD58GABw7NgxCIKAlStXYtWqVejbty/0ej32798PADh48CDmzZuH+Ph4GAwGnHvuufjf//7nOf6aNWtw5ZVXAgAmT57smda/detWT5sNGzZgwoQJiIyMRHR0NC699FLs27cv4L83IiIiJeDIOhERkYJdd911+Otf/4qNGzdi8eLFHe7ft28fLrvsMgwbNgwrVqyAXq9HYWEhvvjiC08bh8OByy67DJs2bcLVV1+NW265BfX19cjPz8fevXvRt29fT9uXX34ZLS0tuOGGG6DX6xEfH499+/bh/PPPR0ZGBpYuXYrIyEi89dZbuOKKK/Duu+9i9uzZuPDCC/HHP/4Rjz/+OP76179i8ODBAOD5/9VXX8Wvf/1rTJ8+HQ8//DCamprwzDPP4IILLsB3332H3Nzc4P4iiYiIZIbJOhERkYJlZmYiJibGMwL+c/n5+bBardiwYQMSExM7bbN27Vps2rQJjzzyCG677TbP7UuXLoUoiu3aFhUVobCwEElJSZ7bLr74YmRnZ+Obb76BXq8HAPz+97/HBRdcgDvuuAOzZ89GXl4eJkyYgMcffxxTp07FpEmTPI9vaGjAH//4R/zmN7/Bc88957n917/+NQYOHIgHH3yw3e1EREThgNPgiYiIFC4qKsprVfjY2FgAwH//+184nc5O27z77rtITEz0FK5r6+dT6ufOndsuUa+ursbmzZtx1VVXob6+HpWVlaisrERVVRWmT5+OQ4cOobi42Gf8+fn5qK2txa9+9SvP4ysrK6FWqzFmzBhs2bLF5+OJiIhCEUfWiYiIFK6hoQHJycmd3vfLX/4SL7zwAn7zm99g6dKlmDJlCubMmYN58+ZBpXKdsz98+DAGDhzoV2G6Pn36tLteWFgIURRx99134+677+70MadOnUJGRobXYx46dAiAaw1+Z0wmU5dxERERhRom60RERApWVFSEuro69OvXr9P7IyIi8Nlnn2HLli348MMP8fHHH+PNN9/ERRddhI0bN0KtVnfr+SIiItpdd4/W/+lPf8L06dM7fYy32H5+jFdffRWpqakd7u+t6vZERERywk8/IiIiBXv11VcBwGuiDAAqlQpTpkzBlClT8Mgjj+DBBx/EnXfeiS1btuDiiy9G37598dVXX8Fms0Gr1Xbr+fPy8gAAWq0WF198sc+2P59S7+YuYJecnNzlMYiIiMIF16wTEREp1ObNm3H//fejT58+uOaaazptU11d3eG2ESNGAAAsFgsA1zr0yspKPPnkkx3a/rzA3M8lJydj0qRJePbZZ1FaWtrh/oqKCs/lyMhIAEBtbW27NtOnT4fJZMKDDz4Im83m8xhEREThgiPrRERECrBhwwYcPHgQdrsd5eXl2Lx5M/Lz85GTk4P//e9/MBgMnT5uxYoV+Oyzz3DppZciJycHp06dwtNPP43MzExccMEFAIDrr78ea9euxZIlS/D1119jwoQJaGxsxKefforf//73mDVrls/YnnrqKVxwwQUYOnQoFi9ejLy8PJSXl2PHjh0oKirCDz/8AMB1kkCtVuPhhx9GXV0d9Ho9LrroIiQnJ+OZZ57Bddddh3POOQdXX301kpKScOLECXz44Yc4//zzOz2RQEREFMqYrBMRESnAPffcAwDQ6XSIj4/H0KFDsWrVKixcuBDR0dFeH/eLX/wCx44dw0svvYTKykokJiZi4sSJWL58OWJiYgAAarUaH330Ef72t79h3bp1ePfdd5GQkOBJwLsyZMgQfPvtt1i+fDnWrFmDqqoqJCcnY+TIkZ64ASA1NRWrV6/GQw89hEWLFsHhcGDLli1ITk7G/PnzkZ6ejr///e/45z//CYvFgoyMDEyYMAELFy7s4W+PiIhIeQSxq/ltRERERERERNSruGadiIiIiIiISGaYrBMRERERERHJDJN1IiIiIiIiIplhsk5EREREREQkM0zWiYiIiIiIiGSGyToRERERERGRzITcPutOpxMlJSWIjo6GIAhSh0NEREREREQEABBFEfX19UhPT4dK5XvsPOSS9ZKSEmRlZUkdBhEREREREVGnTp48iczMTJ9tQi5Zj46OBuD64U0mk8TR+Gaz2bBx40ZMmzYNWq1W6nDIB/aVcrCvlIH9pBzsK+VgXykH+0o52FfKoKR+MpvNyMrK8uStvoRcsu6e+m4ymRSRrBuNRphMJtn/UYU79pVysK+Ugf2kHOwr5WBfKQf7SjnYV8qgxH7yZ8k2C8wRERERERERyQyTdSIiIiIiIiKZYbJOREREREREJDNM1omIiIiIiIhkhsk6ERERERERkcwwWSciIiIiIiKSmZDbuo2IiIjCkMMBbN8OlJYCaWnAhAmAWi11VERERGeMyToREREp2/r1wC23AEVFp2/LzAQeewyYM0e6uIiIiHqA0+CJiIhIudavB+bNa5+oA0Bxsev29euliYuIiKiHmKwTERGRMjkcrhF1Uex4n/u2W291tSMiIlIYToMnIiKiXnfhkqdx3G5CfFw8dHodAKCpqRlmc53Xx8TFxkFv0AMAmptbMHjfDrz58xH1tkQROHkSixb+A5/GZnltZjLFwGiMAABYLFbU1FS3uz9bXYftj/7B3x+NiIgoIJisExERUa+y2x04ocuBoANqLCJgsbTeo4JgjPP6uForAKu7rYAUu82v54usrYGQPszr/fV2oN5s8Vz/eQwnEYcacwPiTFF+PR8REVEgMFknIiKiXtXUcjoxfmZuP2RnpAIAamtrUV5e7vVxGRkZiIpyJczmOjNsH58A3u/6+W6cdx4mZSV5vT81JRUxsTEAgMbGRhS1Ga2/7YOTELQG7Dl0AhNHDen6yYiIiAKEyToRERH1qrbJ+vhBmYiJjnRdSY8BhuT4d5D0GGDADcAjD7qKyXW2bl0QgMxMDLlmDob4vY1bDEb3T/dcu/3dfRC1Bhw8XsZknYiIehULzBEREVGvamyTrEe0rkE/I2q1a3s2wJWYt+W+vmpVj/ZbT45yxWfVGs/4GB04HMDWrcC//+36nwXwiIioE72SrD/11FPIzc2FwWDAmDFj8PXXX3ttu2bNGgiC0O6fwWDojTCJiIioFzS1WAEAotMBnbaHk/zmzAHeeQfIyGh/e2am6/Ye7rN+/kjXaLrW5H0afbesXw/k5gKTJwPz57v+z83lFnNERNRB0KfBv/nmm1iyZAlWr16NMWPGYNWqVZg+fToKCgqQnJzc6WNMJhMKCgo814Wfny0nIiIixWqxuJJ1OO2BOeCcOcCsWcD27UBpKZCWBkyY0KMRdbeUGNeAQVldS4+P5dkT/udT9t17wgfg5AIREYWOoI+sP/LII1i8eDEWLlyIIUOGYPXq1TAajXjppZe8PkYQBKSmpnr+paSkBDtMIiIi6iVqrWtquUYVwK8hajUwaRLwq1+5/g9Aog4AqSZXrEfLanp2IO4JT0RE3RTUkXWr1Ypdu3Zh2bJlnttUKhUuvvhi7Nixw+vjGhoakJOTA6fTiXPOOQcPPvggzjrrrGCGSkRERL0kOiYWAGCKCuA68CA5WfAjADW2HqpCzq1vdLg/KioaOp0WAGCxWtHY0NDpccYW7fdrT/hf/nI5dmYOQWRkJPR614kCm82G+vp6rw81Go0wGAxQqwRMSRYw0/8fj4iIZCyoyXplZSUcDkeHkfGUlBQcPHiw08cMHDgQL730EoYNG4a6ujqsXLkS48ePx759+5CZmdmhvcVigcVyulCN2WwG4Ppgs9n8239VKu745B4nsa+UhH2lDOwn5QhGXzVbXMfSqlWy/xuYdu4gPL9vLwSNHlBHd7i/0Q40evZ7FyAYOrYBgBSrf9PoU6wtEAzRaHIATU2nfzfejgsAzU6gubXtt4L8f6fE90AlYV8pg5L6qTsxym7rtnHjxmHcuHGe6+PHj8fgwYPx7LPP4v777+/Q/qGHHsLy5cs73L5x40YYjfI/Yw8A+fn5UodAfmJfKQf7ShnYT8oRyL46WucEoIPV0oyPPvooYMcNlruHtaC4qqLT++Li4jwj4M3Nzairq+u0XU69f+vzz8myIyGlCCaTyfM9xmKxoKbG+zT86OhofHPCjB3WLBQVF/N1pSDsK+VgXymDEvqpqanJ77ZBTdYTExOhVqtRXl7e7vby8nKkpqb6dQytVouRI0eisLCw0/uXLVuGJUuWeK6bzWZkZWVh2rRpMJlMZx58L7DZbMjPz8fUqVOh1WqlDod8YF8pB/tKGdhPyhGMvlrz4XYAzaitrsLMmdcE5Jiy53BA/O9bQEkJhE7WrYuCAGRk4Jp/PXhG6+3//uqH2PETYLU7+LpSAL4HKgf7ShmU1E/umeD+CGqyrtPpMGrUKGzatAlXXHEFAMDpdGLTpk246aab/DqGw+HAjz/+iJkzO1+BpdfrPWe029JqtbLvKDclxRru2FfKwb5SBvaTcgSyr+wOJwBAEB3h0/9aLfD4466q74LQvtCcIEAAgMceg/YMt6s16HQARIgQ+LpSEPaVcrCvlEEJ/dSd+IJeDX7JkiV4/vnn8corr+DAgQP43e9+h8bGRixcuBAAcP3117crQLdixQps3LgRR44cwe7du3Httdfi+PHj+M1vfhPsUImIiKgXtFhdU8I7G2EOaUHcE16naR2NF4L+1Y6IiHpJ0Nes//KXv0RFRQXuuecelJWVYcSIEfj44489RedOnDgBVZutW2pqarB48WKUlZUhLi4Oo0aNwpdffokhQ4YEO1QiIiLqBS02OwABKjilDqX3BWlPeK1GDcAOkck6EVHI6JUCczfddJPXae9bt25td/3RRx/Fo48+2gtRERERkRQsVjsALVRiGCbrwOk94QNIo2lN0pmsExGFDL6jExERUa+y2h0AAJUQZtPgg0inaR1/EQRpAyEiooBhsk5ERES9ymJzrVlXgcl6oEQYXMV2VWp5F1YiIiL/MVknIiKiXhUTFw8ASIiLlTaQEHLW4EEAgJi4OIkjISKiQGGyTkRERL0qr98AAMCQQQMkjiR0qFqnvzs5WYGIKGQwWSciIqJeZWvdZ12r5teQQNGomawTEYUafkoSERFRr3IXmNMxWQ+YU2VlAIDGpmaJIyEiokDhpyQRERH1qk2btwIAdu74UtpAQojTYQMAOCSOg4iIAofJOhEREfUqW+tcbTW3bgsYbevWbYKgljgSIiIKFCbrRERE1Ktsdteadfc6a+o5vc69zzq/2hERhQq+oxMREVGvco+sa1RM1gNF1zqyDhW/2hERhQq+oxMREVGvsjtcybqWyXrA6LWt0+BVajhaq+0TEZGyMVknIiKiXmVvHVnn1m2Bo9NpPZetdruEkRARUaDwU5KIiIh6FZP1wHOPrAOA1cZknYgoFPBTkoiIiHpVTFwCACA5KUHiSEJHbIzJc1mj1UkYCRERBQqTdSIiIupVfQcMBACMGjlC2kBCiLrN+n+Hk1viERGFAibrRERE1KvsrQXQdNy6LWDUwunfpZ3JOhFRSGCyTkRERL3K1pqsc8164KhUAiC6kvTKqmqJoyEiokDgpyQRERH1qp1ffwsA+PabryWOJLSITldhuabmFokjISKiQGCyTkRERL2qdZt1qAVO1w4op2vGAqvBExGFhl5J1p966ink5ubCYDBgzJgx+Ppr32fS3377bQwaNAgGgwFDhw7FRx991BthEhERUVsOB4Rt25Dx2WcQtm0DHI7AHFZ0ra82tNlujAJAdCXrFibrREQhIejJ+ptvvoklS5bg3nvvxe7duzF8+HBMnz4dp06d6rT9l19+iV/96ldYtGgRvvvuO1xxxRW44oorsHfv3mCHSkRERG7r1wO5udBMnYpzH3kEmqlTgdxc1+09JAqurx96JusBJbYm6zY7k3UiolAQ9GT9kUceweLFi7Fw4UIMGTIEq1evhtFoxEsvvdRp+8ceewwzZszAn//8ZwwePBj3338/zjnnHDz55JPBDpWIlC5Io4AB53AAW7cC//6363+5xknha/16YN48oKio/e3Fxa7be5iwO+EaWWeyHliC0/VeYrXzPYWIKBQE9VPSarVi165dWLZsmec2lUqFiy++GDt27Oj0MTt27MCSJUva3TZ9+nT85z//CWaoRKR069cDt9wCTVERzgWARx4BMjOBxx4D5syROrrTWuNslwTJMU5SJKvNjo+++K5dsqbT6dA3r6/n+uEjh2G1Wjt9vFajRb8+fTDwppuhFUV02FhNFCECaL7xd9hgSseAQYM9dx0/fgxNzc2dHlclCBg4cJDnusUpQAPAoGOyHlAi16wTEYWSoH5KVlZWwuFwICUlpd3tKSkpOHjwYKePKSsr67R9WVlZp+0tFgssFovnutlsBgDYbDbYbLaehB907vjkHiexr+ROeO89qK++GvhZciG2jgI63ngD4uzZksXnppQ4ewNfU8Ex6U/PoiSiTyf3lPp9jLEnNuKN0hKv9wsAjBWn8PZLW7Azu8r/4PI/91zUxKYBALQaFf8GAkiAq2CfxSr/70Dhju+BysG+UgYl9VN3YlT8Ke2HHnoIy5cv73D7xo0bYTQaJYio+/Lz86UOgfzEvpIhhwPTfv97qDsZBRRaRwGtf/gD8jUaQK2WIkIXpcTZy/iaCqyyJgARgLO5HnC4Rs/VajViYmI8berq6uDwsvRCrVIht8W/PbqTq08itt9Qz/V6s9nrWmlBEBAXF3e6bX09UF+BSEsqi8gGUHxcLKotQGV1NX+vCsH3QOVgXymDEvqpqanJ77ZBTdYTExOhVqtRXl7e7vby8nKkpqZ2+pjU1NRutV+2bFm7afNmsxlZWVmYNm0aTCZTD3+C4LLZbMjPz8fUqVOh1WqlDod8YF8FXv5XP2LZv7+ETexYOiMjI8Mzw6axoREFPxV4Pc6MlirMqvI+uicAMFZWYv0rn+HEOROQkZEBwDUrZ9++fV4fl5SUhKysLACARiXg0J5vPaNWP5eSkor+A/oDAOx2O3Z2ssxnZNEhv+L8dN02fJfpOlZCQgIGDxniafPF5194Ckj9XGxsLM4eOhR9kyLx67HZEIQOE5hlha+p4Lj945fgBHDDcAPuuG7uGR1D2JYKvLeyy3aP/GkOxIkTz+g5KDhWHtyOakszzj13NM7LS5Q6HPKB74HKwb5SBiX1k3smuD+CmqzrdDqMGjUKmzZtwhVXXAEAcDqd2LRpE2666aZOHzNu3Dhs2rQJt956q+e2/Px8jBs3rtP2er0eer2+w+1arVb2HeWmpFjDHfsqcB7931eoi+5sui5wqB44VN8msTXleD2OveiYX88XI+pwtNmAo4X+Hfe4BTjetq0h12vbojpg1zdt1qAbOv5c51hP+BWnYNWjqPXxRY3AD22Pq/ceb1ELsLe17eRBqeiXHOXX80mNr6kAc1dZ1/Xg9zp5squOQnExIHZygkoQgMxMaCZPDqtZIEqgVbeepFOp+bpSCL4HKgf7ShmU0E/diS/o0+CXLFmCX//61zj33HMxevRorFq1Co2NjVi4cCEA4Prrr0dGRgYeeughAMAtt9yCiRMn4l//+hcuvfRSvPHGG/j222/x3HPPBTtUIupFVrsI6ICY+mOYOjip3X25ubnIyEgHAJjN9fjxxx+9HmdcdC7wftfPN7hPBPqMiUVurivhbW5uwe7du722T0tLQ16eK2kuq6hG/vavfLYdOHAgANfI+ueff96hTUxM5yPinbU7R+taL5yYmIizzz7bc9+2bdsgdpY8AYiLi8PX5mjUt9jRYGFxqXDVb8BAHCyrx5jR5535QdRqV8HDefNciXnbvzn3jI1Vq5ioy9Cp8nJAHY2fDhVifP9kqcMhIqIeCnqy/stf/hIVFRW45557UFZWhhEjRuDjjz/2THE9ceIEVKrT02DHjx+PdevW4a677sJf//pX9O/fH//5z3/afWElIuUbO248ju8qxrWXTcKfLznLd+OJg73f53AAqx/qchTwd4/c0yG5mD++n5/RZuJ304b52RZYMr2TeB0OYOubXcZ5/5qHvSZBf5zS3+fznn3HO4AQgcNHjmJE1ki/46XQYXe6/ra0PU2k58wB3nmn850LVq3izgUy1dTYAJiiUVNbJ3UoREQUAL1SYO6mm27yOu1969atHW678sorceWVVwY5KiKSktB6ki5Cr+vZgZQyCtgLcTY2mIHoCJyq7EaFbgopjtZkXaMOQM2COXOAWbNg37IF32/YgBGXXMKp7zInwDWDx+algCARESlLx8pORES9wD0CqFYF4G3IPQrYWjzOIzPTdbtcRgGDHKfQWnzOwj2Ww1b5qQoAQPHJk4E5oFoNceJEFF94oauYHBN1WRNaTwLa7EzWiYhCgeK3biMiZTpwsABAJI4cLgQm9e35AZUyCtgaJ7ZvB0pLgbQ0YMKEgMTprlZvtfGLerhqam4BIo1oqPe/0iyFDvd7gM3hX40MIiKSNybrRCSJsvJTQFQfVJwqC9xB3aOAjY0YLudRQLUamDQp4IdVQYQDgJUj62FLFFQQAOi0Mv3bp6Byz1Oycxo8EVFI4DR4IpJE6yx4aAIxDZ4AuJJ1gNPgw1rr60mrYbIejjwj63aOrBMRhQJ+SyYiSXiSdTXfhgLFnaxbuV41fAmuJF2v5cS5cORO1u2cBk9EFBL4LZmIJOH+KslkPXBUApP1sCe4Xk86jqyHpXNHubZsHDN2nMSREBFRIPBbMhFJwj2yzum6gXPOiOEAgNFjxkocCUnGnaxzZD0suU9+unfbICIiZWOyTkSScMK1DzRH1gNHr3PtWS8KPAEStlSuvufIenhy1wBxMFknIgoJ/JZMRJIQ3SPrcq3YrkBatesEiN3J9arhSqV2jaj3yc2ROBKSwtEjhwEAP+7bJ3EkREQUCEzWiUgSZw0dBgAYdc5IiSMJHUUnjgMA9uzdL3EkJAWnU4R7PFWn4TT4cFRdVQkAOHWqQuJIiIgoEJisE5EkVK0j6kaDXuJIQkfFqXIAwMmiYokjISm0Xaesbp1lQeHFs886p8ETEYUEJutEJAn3l0m1iklFoGha39Ft3LYpLLXdrqu5sVHCSEgq7rdTbt1GRBQamKwTkSSKikoAAOVlpRJHEjrUgnvNOkfVwpHFZvdcFh12Hy0pVLnrdbLAHBFRaGCyTkSSqKyuBgDU1lRLHEnocM9S4KhaeLLYbJ7Lep1WwkhIKp6RdSbrREQhgck6EUnDvR80t5gKGI2aI+vhzGI9PZqu5esqLLlLFXBknYgoNDBZJyJJiO591plUBIzWM7LOL+rhyGp3Jeuiww6tliPr4UjVuhTGwe0biYhCApN1IpKE2PqlkltMBY6mdcEqR9bDk3tkXXQ6oFbzJFg4mnDB+QCAsePPlzgSIiIKBCbrRCQN9zR4LZOKQBk35jwAwNhx4yWOhKRgax1ZB5P1sOWpVSAE6OudwwFs3Qr8+9+u/x2OwByXiIj8wiEtIpKEKKggANBxum7AREZEAABUGv5Ow5GnGrzohCBwS8RwpFG5p8EHYHbN+vXALbcARUWnb8vMBB57DJgzp+fHJyKiLnFknYgkwgJzgeaeBm/jmvWwFBsXDwBIiIuVNhCSzOHCQwCAfQcO9uxA69cD8+a1T9QBoLjYdfv69T07PhER+SWoI+vV1dW4+eab8f7770OlUmHu3Ll47LHHEBUV5fUxkyZNwrZt29rd9tvf/harV68OZqhE1MsSkpJQ0+xAVmaG1KGEjOKTxwEAB3/6CcBwaYOhXucUXaOqrAQfvk6VlwHIxO4Ttbh02bPt7hszZgwiIgwAgMOHj+DkyZOdHkPldOK1F++DSXSXAW1DFOEEULXgN/i/r8rhVKlwzjnnwGSKBgCcPHESh48c8Rrf8OHDEdd6MilKr8X8MdmIieBMICIib4KarF9zzTUoLS1Ffn4+bDYbFi5ciBtuuAHr1q3z+bjFixdjxYoVnutGozGYYRKRBASVGoADBr1O6lBCRkV5GYAYFBWXSh0KScA99dk9FZrCj8mgBRoBhykd+342wWbfzuKftc7s9BhjT+5BTNUpr8+hApBUX4PIk7XYmT0M+3adAtC2fefHBYB931cBqPJcLy0pxor5F3ptT0QU7oKWrB84cAAff/wxvvnmG5x77rkAgCeeeAIzZ87EypUrkZ6e7vWxRqMRqampwQqNiGTA7nBtLaRmYhEw7iUFnAUfnsorKgEAdbU1EkdCUrn31zPw6wfXQBcZ06FuwejRo2EwuOpaHD3qfWT97LpDfj3X2XWHcMoeg5EjRiLaZAIAFBWdxBEfI+vDhg1HbGws3vt8D+pUJny7Zz/AZJ2IyKugJes7duxAbGysJ1EHgIsvvhgqlQpfffUVZs+e7fWxr7/+Ol577TWkpqbi8ssvx9133+11dN1iscBisXium81mAIDNZoPNZgvQTxMc7vjkHiexrwJNFEU0NbcAggZNDfWwxeoDduxw7itV64kPpyj/nz+c+ylYKipdyXq9uS6gv1f2lXKkJ8biL5echalTp0Lrs3jnAK/3CNsGAB+93OVzLb1tPu6YOLGT407p8rGff7YNdSoT7E4xbP+u+LpSDvaVMiipn7oTY9CS9bKyMiQnJ7d/Mo0G8fHxKCsr8/q4+fPnIycnB+np6dizZw/uuOMOFBQUYL2XYiYPPfQQli9f3uH2jRs3Kmb6fH5+vtQhkJ/YV4HhcDhgtYtQaTXYtnUrjsQH/rUajn1VWlwECAPRYrPjo48+kjocv4RjPwXL7gLXemXR4QhK/7OvlKNHfeVwYFpCAgxVVR3XrAMQATQnJiLfbAbO8O/MXFcLJOSgprZOMe9VwcLXlXKwr5RBCf3U1NTkd9tuJ+tLly7Fww8/7LPNgQMHuntYjxtuuMFzeejQoUhLS8OUKVNw+PBh9O3bt0P7ZcuWYcmSJZ7rZrMZWVlZmDZtGkyt07LkymazIT8/348z4CQ19lVgtbS0QNixCQAw5aJJ6JeeGLBjh3NffV3+Ab4+Ami0OsycOVPqcHwK534Klkr1V8DndVAJYkD7n32lHIHqK+Hpp4Grr4YIQBBPr6sRW6fW6556CjMvv/yMj7/6yxKcAhBlMsn+vSpY+LpSDvaVMiipn9wzwf3R7WT99ttvx4IFC3y2ycvLQ2pqKk6dal+gxG63o7q6ulvr0ceMGQMAKCws7DRZ1+v10Os7TqHVarWy7yg3JcUa7thXgWGxWACVa321MSIiKL/TcOyriNZifU4IivnZw7GfgsWztbbo5GsqzPW4r666CtBoOuyzLmRmAqtWQdPDfda1ahXgBEQRYf83xdeVcrCvlEEJ/dSd+LqdrCclJSEpKanLduPGjUNtbS127dqFUaNGAQA2b94Mp9PpScD98f333wMA0tLSuhsqEcmU1WaDILj2BNfr5P2GqiR6rQaAHXYnsPlgudTh+KSBU+oQQo6ttWhj25FQojM2Zw4waxawfTtQWgqkpQETJgDqnm8NqFYBcLIYJhFRV4K2Zn3w4MGYMWMGFi9ejNWrV8Nms+Gmm27C1Vdf7akEX1xcjClTpmDt2rUYPXo0Dh8+jHXr1mHmzJlISEjAnj17cNttt+HCCy/EsGHDghUqEfUyq/V0YQ1XgkmBcNaQwcCeH5GR2x9OmefCDvBbeqB5knWeCKFAUauBSZMCf1jhdDFMIiLyLqjfkl9//XXcdNNNmDJlClQqFebOnYvHH3/cc7/NZkNBQYFnkb1Op8Onn36KVatWobGxEVlZWZg7dy7uuuuuYIZJRL2suU2yrlGrJIwktEQaXdsyqTTyPwHCwd/As9kcADiyTvJ39lln4dvva9G3v/eq9ERy9cknn3RY6uum0Wjwq1/9ynN906ZNKCkp8Xqs6667znN527ZtOHHihNe2v/rVr6Bp/Xz/4osvOt0mMSMjA5MnT+6wdSMpV1C/0cXHx2PdunVe78/NzYXY5ktFVlYWtm3bFsyQiEgG2o6sa1RM1gNF27p1m0MBw1UiR9YDrk/fvsD3+zBi+FCpQyHyKTMjDfi+FvHxCVKHQtRtDz74ID777LNO74uMjGyXrP/rX//Chg0bvB6rbbL+xBNP4N133/Xadu7cuZ5k/bnnnsPatWs7bbdjxw6MHTvW589AyiH/4RciCjkJiafrXqhVPPsbKOa6WgBATU21tIH4Qe7T9JXIPaPCPcOCSK7c7/t2BZxYJJo+fToOHjyIV199FVOmTMHo0aMREdH5+6zBYGh3fdSoUXD6+YE3YsQINDQ0eL1f1WZwY+jQoZg+fXq7+7/66ivU1taiuLjYr+cjZWCyTkS9TqU5XVSOyXrgNDW4tgJpaGiUOBKSgr21WhdfUyR3tdVVAICKyiqJIyHqWmFhIUpLSz2J+D//+U+/H3v//ff73bY7y37/9Kc/4U9/+lO729asWYOioiIMHjzY7+OQ/DFZJ6Je13aaNvOKwPFU1ufSgoArrm1GUXWT1GH4tGPPIQDAyePHAYyWNhgiH/bv2wcgEQWHCgFMlTocIq+cTidKS0sBwFMgW6662lqblInJOhH1urLWwiwqiCyCEkAGd7IuMFkPtBabA/UtdqnD8Kn01CkA0SguOil1KEQ+uWd/sBYiyd2pU6dgt9uhUqmQmpoqdTgUhpisE1GvKy0tAwA4HPJOfpTm9Mh6z/dBpvZEBWQVjtat21Tcuo1kTtu6C4gTPFlL8lZUVAQAiI2N9RR3k6uTJ0+iuroaGRkZSExMlDocChAOvxBRr7O6t5hilbGAitDrAACCSuN3QRvyjxLqYLmXl/CDneRO40nWieTNXawtIUH+OxfcdtttGDFiBN566y2pQ6EAkvcpIiIKSVZ764i66JA2kBBjaDOybrfboNPp4XA4sO7ZVV4fk9WnLyZdcoXn+mvPPOJ1FDktKwcXXz7Pc/2NF56AzWrttG1SWjpmzD69fc07a1ajualN4TvRibNykjFz5syufzAZUMDAuidZFwQFBEthzTMNniPrYctut+PDDz9EdXX73Uv0ej3mz5/vub5hwwaUlZV1egy1Wo3rr7/ecz0/P98zEt6ZBQsWeJbebdmyBceOHfPa9tprr4VWq0VRURH0er0iknWt1vUdwGazddGSlITJOhH1OqvNPf2dSUUgRRldlWpVai2E1i/BTocDa5/yXrn2/CmXtEvWX33mX3A6Oj+Jcs74ie2S9X8//ziaGuo7bXvWyNHtkvW31zyD6orydm3i4+Nx2223+f6hZMKpgGzdPZuCI+skd1qNa6mOKDJZD1dvvfUWrrnmmg63JyQktEvW//GPf2Dr1q2dHsNgMLRL1h977DF8+OGHXp+zbQG21atX+xyBvvLKK6HVajF16lRcddVVqKmp8fHTyIM7WbfbucQwlDBZJ6JeZ7W3ToMXOQkykDz7a6tU0LR+aKtUKlz2y+u9PiZv4Fntrl965XUQvfRLdt6AdtdnzP4VrNaWTtumZ+W2uz71F1eisXVruZbmJnz6v3dgNpu9xiU3CsjV4WgNUsWRdZI5jYpr1sOde3p5RkYGhg8f7rndZDK1azdmzBgYjcZOj6HT6dpdP/fcc/2uLzJy5Eife5qr1a4TSgMGDMD48eMVsbTMvaaeI+uhhck6UYhxOEU02+Q9vby+uXXqtBIyIAXRtNkH73h1k2eq6Zyb7/P5uJM1p7ckm33TPX63vey3S/1uO33hEs9lc20Ntu34FrbqIkUUbgOUMbLeWl8OauY/JHMDB/QHjhUhVeZbYVHwuBPKGTNm4IUXXvDa7u9//7vfx7zvvvv8brt06VIsXer7M8xt0aJF+Oijj/w+tlQ4DT40MVknCjE1TVZ8f6JW6jB8OlRWByAaAssLBZS7wjIAPPDhAQkj6Vr6/z0Jk61aMcm6EsLMyuuPgz/VYOKFF0odCpFPGRlpAIoQ/bNRVAoff/7zn3HzzTdDpeLCnUDhNPjQxGSdKMQoIanI7T8YKC1CkgIKtihJpF6Dy4en4bOfKqUOxSenKKK+xY4mXZxivqgpYWRdrdUDAOJimACRvLlnAdkd8n9dUXBotVpPckmBwWnwoYnJOlGIERVQtE0fEQkAiDDoJY4k9Dw4eyi+OlLddUMJ1TXbcPvbP8AuCsoZWZc6AD+4TyioOQ+eZK6x3lWvor6xsYuWROSviy++GBEREZg4caLUoVAAMVknol7n3rNarWJSEWjubWnkrO3a+sbmlg5FguRICSPrp8pKAahw5NAh4JLBUodD5FXhoUMAgNKyUxJHQlJZt24dPv30U1x22WWYM2eO1OGEhMsuuwyXXXaZ1GFQgClj/iER+U/+OQVKi04AgNdtv+jMyT9VBzRtRn5PFne+f67cKCBXR2WFK/E59FOBxJEQ+aZpra/BfdbD144dO/Dyyy9j9+7dUodCJGscWScKMQrIKXDi2BEAaaiurpI6lJCjgIF1aNusU29o7nzrNzlxOkUcLDWjot4idSg+NcE1Q4ETVkjuPMUwlfCGRUHhXlfNdeuBU1tbi8rKSkRFRSE1NVXqcChAmKwThRgljAA6WvcrVSni1IKyCAoYqVKpBIhOOwSVBk0tVqnD6dK/vzmBO/+zV+owuqaOBwBoOGeOZE6rce1hLQr8Yw1X7orlTNYDZ/Xq1Vi2bBkWLlyIl156SepwKECYrBOFkObmZty99M8oOHIc82+4Bf0GDwUA7N65HR+8+YrXx81b8DsMGT4KALB399dY/+pzXtvOmv9/GH7eeABAwd7v8eaLT3ptO3PetTj3/EkAgKM/HcCrz/wLAFBiMwKjr1dAWqk8ihmoctgBlQbNVvlXrT1W6SqCFW3QIM4o3/X1VRXlqDx5BOmJNVKHQuSTrjVZB5P1sMWR9cDjPuuhick6UQjZvHkzXlj9FADgkjnzPbeXF5/E9o0feH3c5Euu8FyuKCvx2Xbc5Omey9UVp3y2HTl2gudyTXWlp21E/3FIHg3otHwLCltO16hKY7O8p5YDgMXumgkycUASrhiRIXE03r302H/x7zcfQ8wf/yh1KEQ+adzJOk/Zhi0m64Hn3rqN+6yHlqB9U/7b3/6GDz/8EN9//z10Oh1qa2u7fIwoirj33nvx/PPPo7a2Fueffz6eeeYZ9O/fP1hhEoWUxtZtcNKz+yC77+nXzZDho3DTnQ96fVzfQWd7Lvc/a5jPtoOGjvRczu0/0Gfbs88Z7bmcmZPnaVvijMEuJ5CXm+Pjp6EzoZiRdacDABQxst5ic8WqU8t7FNDpcMWpVqu7aEkkLc80eBX/VsMVk/XA48h6aApasm61WnHllVdi3LhxePHFF/16zD/+8Q88/vjjeOWVV9CnTx/cfffdmD59Ovbv3w+DwRCsUIlChqP1y3pyWgZS0rM8t+f0G4icfgP9OkZmTh4yc/L8apuWmYNZ8//Pr7bJaRmetl8drcKu7Udh0Mt3SrFSqRSSrQutyboS1qy32Fwj61q5J+utv1P36AqRXGVlpAOogl7P73bhisl64DFZD01B+0Rfvnw5AGDNmjV+tRdFEatWrcJdd92FWbNmAQDWrl2LlJQU/Oc//8HVV18drFCJQoY7WVfJfGSN+6xTVFQkGkQgOTVd6lC65B5Z16rl/fc6+9rFuOqqqzB6iH8n24ikkpSYCAAQ1DyxFK7Wrl2LpqYmREdHSx1KyOA0+NAkm3fJo0ePoqysDBdffLHntpiYGIwZMwY7duzwmqxbLBZYLKfXPJrNZgCus0pyP7Pkjk/ucZJy+sr9WlAJAkSHfN+sHa0fJCoE/neqlL4KFodTlHXfu8XGxKChtgWxCUmy76tma2vVYhVk/btNTEpGdkIu0pKiAvo7DffXlJIopa/E1lkgDqco+1iDRSl9FSwRERGIiIgAIP/fgVL6SmidWWe1WmUfazAopZ+A7sUom2S9rKwMAJCSktLu9pSUFM99nXnooYc8o/htbdy4EUajMbBBBkl+fr7UIZCf5N5X3333neuCpQFNR76VNhgfWk4JANSorDyFjz76KCjPIfe+CncqmxqAgK+/3Y2mI/Lewq/0lCtWsfIomnBE6nB8OngEOBikY/M1pRxy76vKBisAI+xOZ9A+A5RC7n1Fp8m9r6qqqjBz5kxkZmaG9etK7v0EAE1NTX637VayvnTpUjz88MM+2xw4cACDBg3qzmF7ZNmyZViyZInnutlsRlZWFqZNmwaTydRrcZwJm82G/Px8TJ06lWt2ZE4pfRWXmIwfDWfDro/DmqIEqcPxyvVFrRnpqamYOXNEQI+tlL4KFlEUsa2gQuowuiTs3wvAjsycXMycMFjqcHx68eROwGxGVHo/NGmbsHvndq9tB549Ajl9BwAAaqoq8M3nW7y27T94KPoMcP3s5toa7Nzm/QtGn/6D0X+IayvGxnozvtj8cYc233y+BXm5OVh03dUYPXp0h/vPVLi/ppREKX21e99PwI/HAAiYMeMSqMJwSZRS+ipYHnnkEZw4cQKLFi3C0KFDpQ7HJyX11R/DeDcQJfWTeya4P7qVrN9+++1YsGCBzzZ5eWe2Vi41NRUAUF5ejrS0NM/t5eXlGDFihNfH6fV66PX6DrdrtVrZd5SbkmINd3LvK1t8Ho5rqgEHUHyyTupwupRsMgTt9yn3vgqm5NhIqUPoUvnJo4ApC9/s+g6/vmiY1OH4ZLW7Rv61Wg2OHT6ElXcv8dr298seQO6AIQCA4pPHfbZddOudyBvs+pJ6qrzUZ9trfnsbBrTuxFBdXeW17TYAQ/rl4Pzzz/f5M52JcH5NKY3c+8oYcbqwnKDWQKuRd/HGYJJ7XwXL+vXr8fXXX2PGjBk455xzpA7HL+HaV0qjhH7qTnzdStaTkpKQlJTU7YD80adPH6SmpmLTpk2e5NxsNuOrr77C7373u6A8J1GoaW4thJVi0mPakFSJo/Et2qDBby/sK3UYIWl4VqzUIXRJpwZaAFha/2blrL6pBQBgbaqHKS4e5024yGvblPRMz+UoU4zPtmlZ2Z7Lxshon20zcvp4LhsijF7bpiUlsiAryZ5Oc7oIqlOU9zIYCg5Wgw88q9WKqqoqqFSqDsuKSbmCtmb9xIkTqK6uxokTJ+BwOPD9998DAPr164eoqCgAwKBBg/DQQw9h9uzZEAQBt956Kx544AH079/fs3Vbeno6rrjiimCFSRRSzPUNAIBorYCJA4JzYi1QYoxaxBj5IR2uNILrC7rFLv9kvbKmDlAb8c3WjbjhN/+HB1ev8+txffoP9rttRk4fv9smp2V4bds3OQqZifKfWUHhTac7/d5vdzJZD0dM1gNv27ZtmDZtGoYNG4YffvhB6nAoQIKWrN9zzz145ZVXPNdHjnRN39uyZQsmTZoEACgoKEBd3empun/5y1/Q2NiIG264AbW1tbjgggvw8ccfc491Ij9t2boNQDpKjhUCOFvqcHwKvxWK1Jam9Q/AandKG4gf7HBN0TVGdFxyJTd8XZES6NuMrNsd8n8PoMBjsh547t8lt24LLUFL1tesWdPlHuviz6Y+CYKAFStWYMWKFcEKiyikuUcoBAV8Y1dCjBQ87iWqFgUk6064EotIBewwwtcVKYFWe/rrp1UBs2so8JisB557n3UlbF1G/gvfih5EIcjhTtahhGmFzCrCmba1+rNN5qNqoihCVLm+AEVFKiBZ5+uKFECn1UIUXa99q5WJRThish547t8lk/XQIpt91omo5xxO15cfJXxd5whgeHOPrNtkvl7V5hA9f6xDc5IwNDNG4oh8izbwY53kLyIiAmoBcAJwKuITiwKNyXrgcRp8aOKnOlEIcY+sh+GWtaQwfbIzcbgYyMjKkToUn1raTNHtk5aAFBNrqBD1lE6ng06rQYvNCUGl7voBFHK+/vprmBuaYEpKxan6FqnD8UmvkvdJZTdOgw9NTNaJQojd4Z4GL39KiJGCZ0C/vvi0+Ij8k/XWreVE0YmEWJPE0RCFDnXrjJWjlY2wynw5TEZsBAxanlQIpKysLJyoakJBeT1cG3nKV0q0Mkb/OQ0+NDFZJwohDlE5I+sC58GHNV3rPHi5f0m32FzxaSAiNTVV4miIQkjrmvUj5XWy3xUi3qhjsh4EDlEZI9buk7ZyFx8fj4ULF8Jk4onlUMJknSiEZOfk4rtjQFxCotShdImpengT7VYAQI25QeJIfHN/SYsyGhAbGyttMEQhxFxXC7UxBvVmM5AQLXU4PikjpVSWv/zlL6hrtmPmdb9HZJS8+7/ZqoxkPSUlBS+99JLUYVCAsRo8UQgZMHAQACAtI0viSLrGgfXwtuf73QCAr3d9J3Ekvrm3ltNr+HFJFFBOVwJkc8g/Efr5VsPUM6Io4p///Ceee/JRWFvkPQUe4PaCJC2OrBOFEHeBObUC5sFzi6nwpm+dUhrQWfAOB7B9O1BaCqSlARMmAOqeTV2trW8EAGgEeU/TJVKc1gTYoYBESOabViiOo80JGrVG/qmIUs7ViKKI+vp62O12xMXFcblhiJD/K4SI/NbQ2AQAcCpg2w5+hoQ3vaY1WQ/UBK/164FbbgGKik7flpkJPPYYMGfOGR/2p8NHAQDFJ471MEAiakd0JWx2p/yTdZET4QOqbQE0DbduC5ja2lrEx8cDAKxWK7fFCxFM1olCyKYtWwB9fxw+uAe4oK/U4RB55R5ZD8gey+vXA/PmdRz+KC523f7OO2ecsNc1uE6AqUX5JxREitJaYM4u8+JygHJGVpWiXbKugJF1t6+PVkOllm+8jQ31nst2u53JeoiQ718cEXWbe6qeRiVAr5X3Glsd1wCHtQid6+PHKujw8TcHkZySAgCwtLSgsLDQ6+MSEhKRmuaqym6z2nDowH7M/u2NMIpix7RfFCEKAmw3/RHfDx2PgkOHvB43JjYWmZmZAACnw4kDB/YDAHYeLAKQDLXAb+tEAdWarDucTNbDTdtkXa1RTkLZZLFDkPGmAFbn6U9Bm82GiIgICaOhQGGyThRC3N95Yo06TOifJG0wRD5E6F1f0JymNNz47mEAh/18pBnAEc+1sSf24NrKCq+tBVGErrQYj9z7MnZmD+viuCc6uT0ZAKBlsk4UUEJrBmxzyH/ZFqfBB5Y7WRcEAeoe1hWh09qe+OBe66GDyTpRCHG0fp9QQoE5Cm+/ung0nv/sTTgMMTDFxCAmxrUvrM1mR1lpqdfHRUdHIzYuFoCrMFVyZWcJdkcDnfXY2VDp9f7IyEjEJ7jW+olOEUVt1r4LohNzR6b59TxE5J/EhHhUOwGjUf57QrPAXGC5E0muVw8sler0jEW7AmoXkX+YrBOFEGfrSIWa1dtI5tKT4vHI5TmYOXNmz9bVjdcC+au7bLb8xmlYPmnSmT8PEQVUeloKqovNMJpipA6lS9y6LbBSU1Oxb98+7Drm/QQqdZ8gCNBqtbDZbBxZDyFcNEoUQtwr/9R8ZVO4mDDBVfXd2wkqQQCyslztiEg21K2jgA4FDFszVw8srVaLIUOGoE//IVKHEnLcsxWYrIcOjqwThRD3ntUaFbN1ChNqtWt7tnnzXIl522/V7gR+1aoe77dORIFlt1oAABZLi8SRdI3JenA4+IsNuMtmzYFOJcJoNGLHjh3YsmWL17bXXHMNcnJyAAC7du3CJ5984rXtVVddhX79+gEA9uzZgw8++MBr29mzZ2Pw4MEAgAMHDuC9997rtN2YMWMwZcqULn+mcMdkXSoOB4Rt25Dx2WcQIiOByZP5ZZJ6LD0jAxX1QGpqstShEPWeOXNc27N1ts/6qlU92mediIJj/969QFJflJw8AfRLlTocn1hgLrBKSkqwevVqVNm0mH3dDVKHE1L++cRq5CREAgBeeeUV3HnnnV7bjh8/3pOs79y502fbESNGeJL13bt3+2zbv39/T7L+448/em2r1+tRXV0No9Ho+4cKc0zWpbB+PXDLLdAUFeFcAHjkEdeXysceC8iXylPmFphb5F9YIsWkR7SBxUUCqW//AfhhdzEGtL6hEoWNOXOAWbOA7duB0lIgLc019Z0nQYlkSWhNgZvtTtS3yHvKbotN/tvLKUlRURHuv/9+JKdlMFkPsLarSoYNG4bf/OY3XtumpZ0unDpkyBCfbbOysjyX+/fv77Ntnz592l3+eVun04mXXnoJsbGxaG5uZrLeBSbrvW39etd0zZ9P/Skudt3+zjs9TtgLKxrQZHH06Bi9IUKnZrIeYM7Wd2lWg6ewpFYDLCJHpAhCa5WVzaUabH7rB4mj8S1Kr8GnSyYiNcYgdSghwVMNXkF7rCuFs01+MWPGDMyYMcOvx02ePBmTJ0/2q+3555+P888/36+25513Hs4777wOt7/44ot+PZ6CmKz/7W9/w4cffojvv/8eOp0OtbW1XT5mwYIFeOWVV9rdNn36dHz88cdBirKXORyuaZqdrdERXWeYaxfdgAdOtEBUqRAdbcKwYaf3Bf76m69hs1o7PbQxMhIjR4yESgC0ahUMWvmPJjkVUFRGaVqsraMTIkcBiIhIvrQVBbDH50FQy3/cqMFix4EyM5P1AHFvK6bm1m0Bx50LQk/Q3iGtViuuvPJKjBs3rltnT2bMmIGXX37Zc12v1wcjPGls395+PeXPCADiaqtQ/PVx7MweBkAECtqebda3/uvca4dcbacPScGV52Z5bScXTr6hBNznX3wJxPXH99/tBi7kVHgiIpKnyOJvUPD+s/jb6tcxeoJ8i0w9tOEADlc0wmrnSfBAOT2yLv8TNUrDcbDQE7SS0cuXL8dtt92GoUOHdutxer0eqampnn9xcXFBilACpaV+NcuqOIRI83GkOCsxcUCS519cSwkizcc7/ZdkP4V+yVEAgKrGzkff5UYJ27UojQjX9Hc1q8ETEZGMqVvrSTgd8l62515WZnPIP1lvbm7GqFGjEBUV5fl34MABz/0PPvhgu/t+/m/Xrl2eto8++qjPttu3b/e0fe6553y2bVtl/LXXXsNll10GgNPgg0EpA2EzZszABRdcgJKSEqlDkT3ZndLaunUrkpOTERcXh4suuggPPPAAEhISvLa3WCywWCye62azGYDrrJ3c9hgUkpL8+oU/dMd8PDhxYif3jPT5uGXPv49C6HH4pwKIF+ScUYy9SY595I07TrnH6/4qoYIo+1iDRSl9Fe7YT8rBvlIOJfWVO1l32G0QHfItiqtpTdabLYH9zhKMvtq9ezd2797d7jar1ep5jubmZjQ2Nnp9fNu2LS0tPttaLJYzatv2e/vAs4bLuu/dRKej3f9yZrPZFfH6//LLL1FfX4+6ujokJSUF5JhKev/rToyCGOTFDWvWrMGtt97q15r1N954A0ajEX369MHhw4fx17/+FVFRUdixY4fnTf3n7rvvPixfvrzD7evWrZNfdUGHA9NuuAGGqip0Vv5LBNCcmIj8Z589owrGL23eix8iRkBXfQT/vDS7x+GS8iz5TyEcKYMwWizANeP7Sh0OERFRp95//31UVFTgoosuQm5urtThePX8QRX21qhwdZ4D41LkPWq5d+9e3HXXXUhJScGKFSsAAPHx8dC2rg1vaGjwmVR3p21sbKxnqWpTUxPq6+v9atvc3Ayz2QyVSoXExEQIAgvihqNrrrkGjY2NeOqpp5CRkSF1OL2uqakJ8+fPR11dHUwmk8+23RpZX7p0KR5++GGfbQ4cOIBBgwZ157AeV199tefy0KFDMWzYMPTt2xdbt27FlCmdr2datmwZlixZ4rluNpuRlZWFadOmdfnDS0F4+mng6qshAhDanCcRW9+sdE89hZmXX35Gx/7siBk/lAOiRg9j3rmBCDeoUmMjMCg1Wuow/GKz2ZCfn4+pU6d6Psjk6M8froYDQF6fXMycOVPqcCShlL4Kd+wn5WBfKYeS+mrmzJnYdbwG9c3yHgXTFx8FauowcMhZmDkmcAMhwegr93EiYxPQZ9ylHe7vzhBWd9smdqOt9/my8iQ6HWg+9h0ickdCUMm7gHNKjAGD0+SX//ycwWBAY2Mjzj//fAwZMiQgx1TS+597Jrg/upWs33777ViwYIHPNnl5ed05ZJfHSkxMRGFhoddkXa/Xd1qETqvVyrOjrroK0GhcVeHbFJsTMjOBVaug6cG2bUa96+d1CmpFVFcVVGp59pEPsv27cms96aPXyTzOXiD7viIA7CclYV8ph1L66uTxIzhUeMTr/WefMwbGSFc9nqLjR1By4qjXtkOGn4soUwwAoPTkcZw8Vui17aChI2GKjQcAlJecxPHDP3lt67SnAwAcohCU32kg+8pdZV2nNyjie6DSCCoFfL9WyHdr94xpQQj860oJ73/dia9bf3FJSUkBW1fgj6KiIlRVVSEtLa3XnrNXzJkDzJoF+5Yt+H7DBoy45BJoJk8+o6nvbbm3axNVMn8jacUCc4HnbF1goWGBOSIikrn/vfk6Xnv+Sa/3P//frcjt55qtufmDd/Hq0//y2vbJNzZg4FBXbZ/t+R/g+X/d77XtypffxfDRrn2id27Nx5N/+6vXtlNXvAtAD4sCqsEPHjwY9674GxpVkVKHQhJRyldrT80KmReYlIOgZXUnTpxAdXU1Tpw4AYfDge+//x4A0K9fP0RFuc6SDho0CA899BBmz56NhoYGLF++HHPnzkVqaioOHz6Mv/zlL+jXrx+mT58erDClo1ZDnDgRxY2NGD5xYo8TdQAwaF0JmlKSdaVUrFSSuLh4VABISoyXOhQiIiKfktPS0f+sYV7v1+lO72uekJTqs60+IsJzOS4hyWfbiNbRegCIjU/wfVydFmhURjX4/v3746bbbsf3J2qlDoUkopTv1kzW/Re0rO6ee+7BK6+84rk+cqTrbOeWLVswadIkAEBBQQHq6uoAuDptz549eOWVV1BbW4v09HRMmzYN999/f2jttR5EETpXd4oyX0/jppQ3FCXJyMpGxcla9A3gchQiIqJgmP9/v8Ulv/w/v9peetV1uPSq6/xqO3XWVZg66yq/2k6cMQsTZ8zyev8b35zAjzWnFLPPOr9bhbcg1w0PmOjoaERHK6NuldSClqyvWbMGa9as8dmm7R9UREREu30YqfuiIvQAHIBa3us03BRwklpx3EsL3FvNEBERyZf8P6u+3/kZEDMIew8cBGacWQHl3lJaWoofCo6g2m5Acnqm1OGQBJQyDX7//v1Sh6AYXNgaQqZe5NqbXa2L6KKlPHDNeuDZW8+AcCcUIiKSOyV8VlkaXVuSNTRZJI6ka+vWrcMlky/AS4//XepQSCJOfrcOOUzWQ4hB07r+wykq4sXKqVqBd6CgAADw08EDEkdCRETkmwJydc9MNatd/mtrW1paAAA6HZePhisFfP2nbmKyHkLc1eABwKqAOeYcWQ+G1mrwar60iYhI3gQFDK1rWz9PlVAN3mJxjf5rdTqJIyGpKGXN+h/+8AdMnz4du3fvljoU2eM3+hBy7Mghz2UlFELhyHrgiYLrJa3VKKPIIBERhS/5p+qnT34roRq8Z2SdhZnDllLGwb788kts3LgRFRUVUocie8rY44v80tzUBNFuhaDRKeJDhcl6ELiT9QBsBUhERBRMGXERiI+U9yhwbKQOJVDGjMXTI+tM1sOVUr5bc+s2/zFZDyFarRZOuxVqjU4RHypOp2u6jhKmwSmFKAgQAGi1TNaJiEjeEqPkn1TGGvWAA7A55J8Ecc06MVkPPZwGH0K0Wi1Em+usqs2ujBcr160HmOB689NxZJ2IiKjH4mNMAAC1Vv4JMNesk0JydSbr3cCR9RCi0Wgg2q0AlDFdCwAcosg/wkBqnaWg4Zp1IiKiHrts5nR88NpuZGbnSh1Kl2bNmoWIuGQMHHGe1KGQREQoI1tnsu4/5kkhRKvVepJ1JaxZB1xT4SlwdHoD7ABMUZFSh0JERKR47mrwShgEmT17NgaMuQiltS1Sh0ISUcr3aibr/uM0+BDiStZdU6CU8KECuEbWKXCMkVEAgNTUFIkjISIiUj6dpjVZV8AuO4BykjUKHqcClpiq1WqoVCo4+QfbJY6shxCtVgvYbQCU86FSXNPs+SCUs9QoZbxU3DUANCoW7SMiIuqpvT98DwA4frJY2kD8UFhYiMMnq6GOSkREJGfYhSunKEIl840RP/30UxaY9pMyMhDyS1JSEqZMnojPCysVMw3+ZHWT1CH4JT7CJHUIfnHPVFDxDZCIiKjHHDYLAAEWu/yn61533XXYuXMn7nv8ZZw/5RKpwyGJKGBgnYl6N8h/SJO6xaB1dakSthhRErsC3vlEUURzi2sZRE11lcTREBERKV9khKsKvCjIv3Cruxo8t24Lb0rZvo38w5H1EGNo3V/7lLkFJ6rkPWodZdAgPlIZ24soYYs5p9MJqFq3buM+60RERD0WFWEAYFVEsu7eZ51bt4W3PUV1kPtqyFeffQInDv6AG2/8LaZPny51OLLGZD2EiKKIHdu3AjED8cn+cnyyv1zqkLr0l+kDMSAlWuowuqSEkXWbzQ5BcM2s0Gr40iYiIuqpSKMBgBmiSjnJuk5vkDgSkpK52SZ1CF3a/e03+PzTDzF16sVShyJ7/EYfQgRBwIGPX0Xc1N8hIS0barV8Vzk0WOywOUQU1TQrIllXQmVNq93uuaznPutEREQ9Fm2McF1Qyf8rs2cavJ7T4EneVK05Crdu65r833moW5ylB1Dy/G/xz/xvkZyeKXU4Xr34+VHsOFKlmKr1dgXUALC1KX6j02kljISIiCg0mCJdybqg1sDpFKEKxPxihwPCtm3I+OwzCJGRwOTJgLrnJ9k90+C1nAZP8qZScZ91f8l36JXOiKZ1+rPc//g9+5YqpGq9EqbBW22nR9Z1nAZPRETUYzHRp7dAswaiIvz69UBuLjRTp+LcRx6BZupUIDfXdXsPnR5Z5zR4kje1QvIVOeA3+hCj1bpGVO12ea9X8STrChlZV0KBubbT4LVavrSJiIh6Ki0lyXPZ6hTRozR4/Xpg3jzg59W6i4tdt7/zDjBnzhkf/pZbbsFPxZWIMsX0JEqioFOpOA3eX0EbWT927BgWLVqEPn36ICIiAn379sW9994Lq9Xq83EtLS34wx/+gISEBERFRWHu3LkoL5d/oTS5UEqyrlcrK1m3O5UQ5+mXs57JOhERUY/p2tT/qTE3knpY2QAAJ09JREFUob6xudN/FrvD86+h2dKxjbkBzj/+EWJn22qJIkQAzltuQb25od3jWmx238dt8+/u+5bjxjuWIzomttd+P0RnQq3mNHh/Be0b/cGDB+F0OvHss8+iX79+2Lt3LxYvXozGxkasXLnS6+Nuu+02fPjhh3j77bcRExODm266CXPmzMEXX3wRrFBDijtZd7QZZZUjpU2DV8LIemT06UJ9arnv2UFERKQAgiBAqxZgc4iY+MjnZ3ycsSf24I3iYu/PI4oQioqw+MYnsDN72Bk/T9+kSNwxYxBUgry/B9Q2WfHWt0Votsk/WdOpBUyPB/pIHUgI4Zp1/wUtWZ8xYwZmzJjhuZ6Xl4eCggI888wzXpP1uro6vPjii1i3bh0uuugiAMDLL7+MwYMHY+fOnRg7dmywwg0ZnpF1m7xH1jkNPvDcMapVAgSZf0gTEREpxYX9k7Dp4KkeHSO5oSag7bw5XNGIktpmZMYZe3ScYNtccApfH6uWOgy/RVpV6HOW1FGEjj/c+Tesef5pxEfL++9UDnp1rmxdXR3i4+O93r9r1y7YbDZcfPHpPfcGDRqE7Oxs7Nixo9Nk3WKxeApqAIDZbAYA2Gw22GSesLrjC2Sc+/btwzfH62BziBAd8h1d17bOKrPY7bKO081qc51UCFhfORwQPv8cKC0F0tIgXnBBjyvBtlhcS0zUKkH2f/vBFIzXFQUe+0k52FfKwb4KjmfmD0dFbT3sPmYtRkebPJebm5s6tNV/aQHe7/q5/nr9WNw+/lzP9ajIKAit63tbWpp99u3SD45gx5Fq/FRmRoZJ3hXhD5S4vq9PHpCInPgIiaPx7tCpRnxxpBrVFkB0chQ4ULRqNVRqNZxOJ5wBWmqqpPe/7sTYa8l6YWEhnnjiCZ9T4MvKyqDT6RAbG9vu9pSUFJSVlXX6mIceegjLly/vcPvGjRthNCrjbE1+fr7UIfS+KgGAGs31tWg68q3U0XSpqfX/QPRV2o4dGPrCC4ioqvLc1pyQgB9/8xuUjht3xsctKKoEkApLczM++uijHsepdGH5ulIg9pNysK+Ug30lQypgWkICDFVV6GzumwigOTERX6kAfLPzjJ4izub6brV9/0nUlR3vQbDBJQI4VqUCIGBSVBliZTwZUKsT8AXUqLUKaD72ndThhJTPjwTnuEp4/2tqauq6UatuJ+tLly7Fww8/7LPNgQMHMGjQIM/14uJizJgxA1deeSUWL17c3af0admyZViyZInnutlsRlZWFqZNmwaTyeTjkdKz2WzIz8/H1KlTPdPXA+GrI1Votsr77F+UthYoPAaHNhrGvP5Sh9MlvUZEzU+7etxXwnvvQf2Pf3SoBGuorsZ5//gHHG+8AXH27DM6dsS3+4CTxYDTgZkzLz/jGJUuWK8rCiz2k3Kwr5SDfSVvwtNPA1dfDRGuNepuYuvSNd1TT2Hm5Wf++Z10rAYfvfgNTjYKONnY833bgy3NpEf64BFSh+FTek0zcLAANRYgInckBJX8f69KsGXDf3Fo13b84rKZuPrqqwNyTCW9/7lngvuj28n67bffjgULFvhsk5eX57lcUlKCyZMnY/z48Xjuued8Pi41NRVWqxW1tbXtRtfLy8uRmpra6WP0ej30en2H27Varew7yi2Qsd5zzz34cvePmLfwD+g3eGhAjhkM+taf1+pwQlDLv3J5c0sTCkuqkFdahYSERACuohi+diowGo2ev2On04my4mIM++MtUItih7Pqgii6PqyX3I5TF12C0lPe18bp9XokJCR4rpeUlAAACosrXTeITsX87QeTkt4Dwhn7STnYV8rBvpKpq64CNBrglluAoiLPzUJmJrBqFTQ92LYNAMb1S8Ivz81CSW1zTyMNOkEAxvdNlP13wPho1xT9RrsAq1OAgbvtBMThg/vwzltvICcrA9ddd11Aj62E97/uxNftv7ikpCQkJSV13RCuEfXJkydj1KhRePnllz176nkzatQoaLVabNq0CXPnzgUAFBQU4MSJExjXg+nB4WTjxo346quvcOGM2fJO1hVUYM5qteF3L2yDEJ2CJ57fc8bHGXtiD94o73w5B+BK2DXFRbj9j0/2qBJsh/1biYiISB7mzAFmzYJ9yxZ8v2EDRlxyCTSTJ/e4bg3gqlx/+fB0tCigwrpSGHVq6DUqWOxO1DRZkaaXdy0ApVBx6za/Be30UHFxMSZNmoScnBysXLkSFRUVnvvco+TFxcWYMmUK1q5di9GjRyMmJgaLFi3CkiVLEB8fD5PJhJtvvhnjxo1jJXg/aTSuLnXIvGibkrZuO1ZyCkK06wSVSnRA4z4bJortihv+nFqtbtc2qa7Ca9u20pprIdqtXu9XqVTQ6k5/WFhaWtrdn6dRTnVVIiKisKNWQ5w4EcWNjRg+cWJAEnU37twaWIIgIN6oRanZgj3FZtS2yPt7a5xRh9QYg9RhdIlbt/kvaMl6fn4+CgsLUVhYiMzMzHb3ia0jfzabDQUFBe0W2T/66KNQqVSYO3cuLBYLpk+fjqeffjpYYYac0/usy7sSojtZtyhgZL220ZUM2+vKcfTx6858as1WI/DRo102e/SWS/DopEln9hxEREQUtrh1a+DFR+pQarbg7d0lUofil3suHYLsBHkX2VapXXkAk/WuBS1ZX7BgQZdr23Nzcz2Ju5vBYMBTTz2Fp556KlihhTTPPus+theRA51aOdPg65pdo9xic13PDjRhApCZCRQXdz5VXRBc90+Y0LPnISIiorDEkfXAu2hgIurMZjg1Eei0lL9MVDVYYbE7UWZukX+y3jqyLvd8RQ5YJSHEnE7WlTGybneKcDpFqGT86WJubv1dNtf37EBqNfDYY8C8ea7EvG3C7j4TvmpVQKfDERERUfiQ8/cppRqWEYN+FgeMeYNkXRDvic2H8ENRHVrs8h+t5pp1//mu+EaK45kGb5P3mSp3sg7If926uaX1d2npYbIOuArLvPMOkJHR/vbMTNftPawES0REROGLuXr40mtcCbDFJu/v1QCg5pp1v8n39BCdEaUUmNOq2yTrdicMWvmOJjdYHAAECNaGwBywtRIstm8HSkuBtDTX1HeOqBMREVEPcM16+DJo3fWg5J8AX3HtItxy02+RlxovdSiyx2Q9xKxZswa3rngEDTZ5T5pQCQJ0ahWsDqfsR9Y1UXFAdS3mzJwauIOq1QCLyBEREVEAqZishy33yHqLAkbWDRFGxMZFIzJS3mvr5UDeGR11W1RUFKKjTae3DJMxnUL2WndPg4+L4LktIiIiki+m6uFLr6CRdaDzWsvUEbOPEPTx/97F62te8Hr/7+5Ygf5DhgEAvti0Ae+ufdZr20W33omzRp4HAPjm8y349/OPeW173e/+hJFjLwAA/PD1F3jlqX96bfvLRTdBp04GAGw+eAqxRvmeXCgzu7Zui5ZviEREREQcWQ9j+tZBMCWMrO/5dgde3vw+Jow9DzfeeKPU4cgak/UQVFFWih+/3en1/gaz2XO5uqLcZ9v6uhrP5dqqCp9t62oq21yu9tl22qyrEKlPQ3UTsPWnCq/t5OTrrR8Dv7pE6jCIiIiIOsVcPXy56z8pYWT9+OGfsO6Vl9BsrmGy3gUm6yFo4tQZMManer0/t/9Az+VR4yfh7kee99q2/1nDPZeHnTfOZ9tBw87xXB48fFQXxx2GPG08vjpaDVHm82CKjh/BNx+8jkNisdShEBEREXnFkfXwZVBSNXi1uyC2/E8sSI3JegjK69cfEYlZfrVNz85FenauX21T0rOQku7fcZNS05GUmu71/kP79+DkkW8xru9A9Bt8tl/HlMpHxV9gy3cfQnPuuVKHQkREROSVitWowpZ7zboy9ll3xWq3y3v3KjngSzoECQooL/LJe2/g73f8AZ/nfyh1KF2yWS0ATu9hT0RERCRHHFkPX0pas8591v3HZD0EKeGsqt4QAQBoaWmSOJKu2axWAEzWiYiISN5UzNXDlpLWrKvUTNb9pYC0jrpLCSPreoMBAGBtaZE4kq7ZbTYATNaJiIhI3gSOrIct98i6ReZbIgOAiiPrfuOa9RCUk2BEaoxB6jB8+jI9AQDQ0tIscSRds9lc0+A1Gr5ciIiISL44DT58eUbWFTAN3r1mncl615h9hCCDVu15wcpVfEw0AMCigGTdymnwREREpACcBh++3CPrVocTTqcIlYz/GEZPmIL8r3/E8NwUqUORPU6DJ0lERLjWrCthGvx1v78dO348jGuuuUbqUIiIiIi84sh6+Go7UCf3qfARxkikpmchKSlJ6lBkjyPrJAmj0QhAGdPgdTo94mJNKG89wUBEREQkR8zVw5dGJUAlAE4R2Fdahyi9vNM8nYZjxv6Qdy9SyBo3bhweefoFOCPipA7FLzKeSUREREQEgAXmwpkgCDBo1WiyOrB62xGpw+mSxmnFosTDWPaXP0kdiqwxWSdJ5ObmYt4vf4WCsnqpQ+nSR++8jtJDP2Jw/z6YOXOm1OEQERERdUrNZD2sXXJ2KnYcqZI6jC6V1jbDrtLh9fc+ZLLeBSbrJBk5F75o67udn2Hrhv9i0aJFUodCRERE5BVz9fB2ydlpuOTsNKnD6NJt675GvV2AQ2OUOhTZC9pigWPHjmHRokXo06cPIiIi0LdvX9x7772eytreTJo0CYIgtPt34403BitMkojZbManGz7El5s/ljqULtlYDZ6IiIgUgMk6KYGxdbjYoWWy3pWgjawfPHgQTqcTzz77LPr164e9e/di8eLFaGxsxMqVK30+dvHixVixYoXnursYGYWO4uJiLJh/JaJj4rD+yxlSh+OTzcZknYiIiOSP1eBJCSK1AtAC2Dmy3qWgJeszZszAjBmnk7C8vDwUFBTgmWee6TJZNxqNSE1NDVZoJAPurduUsM+6e2Rdo+GqESIiIpIvJuukBJFa19+pqIsKzAEdDgjbtiHjs88gREYCkycDanXXj1OAXq2ZX1dXh/j4+C7bvf7660hMTMTZZ5+NZcuWoampqReio97kni1htbTA6ZT3XpCcBk9ERERKoJByQBTmolqTdac+AMn6+vVAbi40U6fi3EcegWbqVCA313V7COi1ocLCwkI88cQTXY6qz58/Hzk5OUhPT8eePXtwxx13oKCgAOu9/MItFgssFovnutlsBgDYbDbYbLbA/QBB4I5P7nEGQ9vE19LUCIOM9zC3WVsAuGIOx75SmnB+XSkJ+0k52FfKwb5SjmD1lcNhh+iwB/SY4U50Otr9Tz0X2ZoGiPqoHr0GhPfeg/rqqwFRRNvzVGJxMTBvHhxvvAFx9uyeBRsE3fmZBVEUxe4cfOnSpXj44Yd9tjlw4AAGDRrkuV5cXIyJEydi0qRJeOGFF7rzdNi8eTOmTJmCwsJC9O3bt8P99913H5YvX97h9nXr1nGtu4w5HA7MnTsXALB27VqYTCaJI/Lu1ltvxbFjx3DfffdhxIgRUodDRERERKRY35Q78NoRPTL1Fvz5nDOcru5wYNoNN8BQVYXOJpSIAJoTE5H/7LOymxLf1NSE+fPno66ursscqNvJekVFBaqqfO/fl5eXB51OBwAoKSnBpEmTMHbsWKxZswYqVfdm3jc2NiIqKgoff/wxpk+f3uH+zkbWs7KyUFlZKesEEHCdVcnPz8fUqVPDcop1VFQUrFYrXtv4NZJT06UOxytzbQ2yTQKOFuzDZZddFpZ9pSTh/rpSCvaTcrCvlIN9pRzB6iur3YGiGvnXA7LYnSiva5E6DL+ITgeaj32HiNyREFTySvqU6qfyBvzz00IYdWoI1cfhdHQ+ayEyMhIDBgzwXN+7d69nVPq84p+w9v3Hunwue34+xIkTAxN4gJjNZiQmJvqVrHd7GnxSUhKSkpL8altcXIzJkydj1KhRePnll7udqAPA999/DwBIS+t8z0C9Xg+9Xt/hdq1Wq5gPKiXFGkgRERGwWq2wWq0Q1PIt3haTkIScjCiUHCsM275SIvaVMrCflIN9pRzsK+UIdF9ptVoMjDAE7HjBYm6x4VSDsqbrCyq1rL+vKklKrBFqQUCT1QFEZXpt1wyg8kj16RuMpwf3YouP+fVcmooKQGbvh915zQftL664uBiTJk1CTk4OVq5ciYqKCs997krvxcXFmDJlCtauXYvRo0fj8OHDWLduHWbOnImEhATs2bMHt912Gy688EIMGzYsWKGSRFatWoX9JWbEJfh38kdKLNhCREREFBjaMxjAo9ARa9RhxRVnIUqvwVdffQW7vfOR9ejoaAwbNtRz/ZtvvoW1tfBzXrOfy529DPgqRdCS9fz8fBQWFqKwsBCZme3PmLhn3ttsNhQUFHiqvet0Onz66adYtWoVGhsbkZWVhblz5+Kuu+4KVpgkoQULFmDzwXKUFp3E7HEDvba77JfX45Z7/gEAqKupwrwLzvLaduovrsRfHnoCgGtbuMtG9fHadsLUS3HPqhdPP/Ys79sF/vb3f8C40ed5vZ+IiIiI/KPmKEjY65cUhTF5CZg1Yo7fj5k1IuP0FcccYP2LQHEx0NmqbkEAMjOBCRMCEK10gpasL1iwAAsWLPDZJjc3F22XzGdlZWHbtm3BColkSCn7gX7wv//iookXSh0GERERkeJp1cr4/kfB0+MTNmo18NhjwLx5rsS8bcLuzi9WrZJdcbnu4sILkpRaJSAxJR1vffaj1zZ6w+lt3aJj4ny21ekN7S77bKtrX+vAV9vJQ7PxxbYtXu8nIiIiIv8IggC1SoDD2a061xRCVIGYXTFnDvDOO8AttwBFRadvz8x0Jepz/B+1lysm6yQptUqAWq32e926SqXyu60gCN1aD++rrV6r8/s4REREROSbRs1kPZwFbHbtnDnArFmwb9mC7zdswIhLLoFm8mTFj6i7MVknSaXFRKDF1nlRCTlRc7oWERERUcBw3Xp4UwdyKaxaDXHiRBQ3NmL4xIkhk6gDTNZJYn0SI6UOwS/uPR2JiIiIqOe0ahUA+Q/YUHBwQwD/8NdERERERES9iiPr4U0pRaalxmSdiIiIiIh6FfdaD288WeMfvkqIiIiIiKhXMVkLbxxZ9w+TdSIiIiIi6lXcaz288WSNf5isExERERFRr9KomYaEs4BWgw9hrAZPRERERES9SqMSFFER3OmUOoLQxFzdP0zWiYiIiIioV2XFG5EVb5Q6jC4dLTdjz2Gpowg9nAbvHwWczyIiIiIiIup9Oi3TpWBgsu4f/vURERERERF1Qq9hUhkMnAbvHybrREREREREndCxEF5Q/H979x4UVf3+AfzNJguSsojcCxA0wbxllARdTKHQnJQ0M3TUjNQKzQs1XiZFbMrr5ExGZTOGNWmmM5alloMoOgqSIkxJxAiDEAiSGhclYIHn+0c/9tfK7sKaeznt+zWzM+45n8/h2X14PDx79pzDC8z1DH/7iIiIiIiIDFD3usvWIfwn8WvwPcNmnYiIiIiIyAA2lZah4vvaI2zWiYiIiIiIyGr4NfieYbNOREREREREVqNis94jvM86ERERERGRCYP9+uKuXvbdOtXUN6OuSWvrMHpExUPGPWLR37hJkyahoKAAtbW16NevH2JjY7Fx40YEBAQYndPc3Izk5GTs2bMHLS0tiIuLw0cffQRfX19LhkpERERERGRQgEdvODs72zoMk+5W90Je+Z+2DqNH+DX4nrFosz527FisWrUK/v7+qKqqwptvvonnn38e2dnZRucsXboUhw4dwr59+6DRaLBw4UJMmTIFp0+ftmSoREREREREitXvbjVGBGoAsXUk3eOF+3rGos360qVLdf8ODg7GihUrEB8fD61Wa/CTqfr6euzYsQO7d+/GuHHjAADp6ekYMmQIzpw5g0ceecSS4RIRERERESmWT19XW4dAd5DVzha4fv06du3ahejoaKNfIcnLy4NWq0VsbKxuWXh4OIKCgpCTk2OtUImIiIiIiIhsyuJXSVi+fDk+/PBDNDU14ZFHHsHBgweNjq2pqYFarYaHh4fecl9fX9TU1Bic09LSgpaWFt3zhoYGAIBWq4VWa98XWOiMz97jJOZKSZgrZWCelIO5Ug7mSjmYK+VgrpRBSXkyJ0YnETHrrIYVK1Zg48aNJscUFRUhPDwcAHD16lVcv34d5eXlSE1NhUajwcGDB+Fk4KICu3fvxty5c/WabwAYPXo0xo4da/Dnrl27FqmpqQa35ebmZs5LIyIiIiIiIrKYpqYmzJgxA/X19XB3dzc51uxm/Y8//sC1a9dMjgkNDYVare6yvLKyEoGBgcjOzkZUVFSX9ceOHUNMTAz+/PNPvaPrwcHBWLJkid458J0MHVkPDAzE1atXu33xtqbVapGRkYGnnnrK7q8u6eiYK+VgrpSBeVIO5ko5mCvlYK6Ug7lSBiXlqaGhAV5eXj1q1s3+Gry3tze8vb1vK7COjg4A6HLkvFNERAScnZ2RmZmJqVOnAgCKi4tRUVFhsLkHABcXF7i4uHRZ7uzsbPeJ6qSkWB0dc6UczJUyME/KwVwpB3OlHMyVcjBXyqCEPJkTn8XOWc/NzcXZs2fx2GOPoV+/figtLcXq1asxcOBAXeNdVVWFmJgYfPHFFxg9ejQ0Gg0SExOxbNkyeHp6wt3dHYsWLUJUVBSvBE9EREREREQOw2LNupubG/bv34+UlBTcvHkT/v7+GD9+PN5++23dkXCtVovi4mI0NTXp5m3duhUqlQpTp05FS0sL4uLi8NFHH1kqTCIiIiIiIiK7Y7Fmffjw4Th27JjJMQMGDMCtp8y7uroiLS0NaWlplgqNiIiIiIiIyK5Z/NZt1tbZ/Hfews2eabVaNDU1oaGhwe7PrXB0zJVyMFfKwDwpB3OlHMyVcjBXysFcKYOS8tTZp/bkOu//uWa9sbERABAYGGjjSIiIiIiIiIi6amxshEajMTnG7Fu32buOjg5cvnwZffv2NXgvd3vSeZu533//3e5vM+fomCvlYK6UgXlSDuZKOZgr5WCulIO5UgYl5UlE0NjYiICAAKhUKpNj/3NH1lUqFe69915bh2EWd3d3u/+lor8xV8rBXCkD86QczJVyMFfKwVwpB3OlDErJU3dH1DuZbuWJiIiIiIiIyOrYrBMRERERERHZGTbrNuTi4oKUlBTdfefJfjFXysFcKQPzpBzMlXIwV8rBXCkHc6UM/9U8/ecuMEdERERERESkdDyyTkRERERERGRn2KwTERERERER2Rk260RERERERER2hs06ERERERERkZ1hs25B7777LqKjo+Hm5gYPDw+DYyoqKjBx4kS4ubnBx8cHb731Ftra2kxu9/r165g5cybc3d3h4eGBxMRE3LhxwwKvwHFlZWXBycnJ4OPs2bNG5z355JNdxr/66qtWjNzxDBgwoMt7vmHDBpNzmpubkZSUhP79+6NPnz6YOnUqrly5YqWIHdOlS5eQmJiIkJAQ9O7dGwMHDkRKSgpaW1tNzmNNWUdaWhoGDBgAV1dXREZG4qeffjI5ft++fQgPD4erqyuGDx+Ow4cPWylSx7V+/Xo8/PDD6Nu3L3x8fBAfH4/i4mKTc3bu3NmlflxdXa0UseNau3Ztl/c9PDzc5BzWlG0Y+hvCyckJSUlJBsezpqzn5MmTePbZZxEQEAAnJyd8++23eutFBGvWrIG/vz969+6N2NhYXLx4sdvtmru/szU26xbU2tqKadOm4bXXXjO4vr29HRMnTkRrayuys7Px+eefY+fOnVizZo3J7c6cOROFhYXIyMjAwYMHcfLkScyfP98SL8FhRUdHo7q6Wu/xyiuvICQkBA899JDJufPmzdObt2nTJitF7bjWrVun954vWrTI5PilS5fi+++/x759+3DixAlcvnwZU6ZMsVK0jum3335DR0cHtm/fjsLCQmzduhWffPIJVq1a1e1c1pRlff3111i2bBlSUlJw/vx5jBw5EnFxcaitrTU4Pjs7GwkJCUhMTER+fj7i4+MRHx+PCxcuWDlyx3LixAkkJSXhzJkzyMjIgFarxdNPP42bN2+anOfu7q5XP+Xl5VaK2LENHTpU730/deqU0bGsKds5e/asXp4yMjIAANOmTTM6hzVlHTdv3sTIkSORlpZmcP2mTZvwwQcf4JNPPkFubi7uvvtuxMXFobm52eg2zd3f2QUhi0tPTxeNRtNl+eHDh0WlUklNTY1u2ccffyzu7u7S0tJicFu//vqrAJCzZ8/qlv3www/i5OQkVVVVdzx2+ltra6t4e3vLunXrTI4bM2aMLF682DpBkYiIBAcHy9atW3s8vq6uTpydnWXfvn26ZUVFRQJAcnJyLBAhGbNp0yYJCQkxOYY1ZXmjR4+WpKQk3fP29nYJCAiQ9evXGxz/wgsvyMSJE/WWRUZGyoIFCywaJ+mrra0VAHLixAmjY4z9/UGWlZKSIiNHjuzxeNaU/Vi8eLEMHDhQOjo6DK5nTdkGAPnmm290zzs6OsTPz082b96sW1ZXVycuLi7y1VdfGd2Oufs7e8Aj6zaUk5OD4cOHw9fXV7csLi4ODQ0NKCwsNDrHw8ND7+hubGwsVCoVcnNzLR6zo/ruu+9w7do1zJ07t9uxu3btgpeXF4YNG4aVK1eiqanJChE6tg0bNqB///4YNWoUNm/ebPJUkry8PGi1WsTGxuqWhYeHIygoCDk5OdYIl/5PfX09PD09ux3HmrKc1tZW5OXl6dWDSqVCbGys0XrIycnRGw/8ve9i/VhXfX09AHRbQzdu3EBwcDACAwMxefJko39f0J118eJFBAQEIDQ0FDNnzkRFRYXRsawp+9Da2oovv/wSL7/8MpycnIyOY03ZXllZGWpqavTqRqPRIDIy0mjd3M7+zh70snUAjqympkavUQege15TU2N0jo+Pj96yXr16wdPT0+gc+vd27NiBuLg43HvvvSbHzZgxA8HBwQgICMDPP/+M5cuXo7i4GPv377dSpI7njTfewIMPPghPT09kZ2dj5cqVqK6uxvvvv29wfE1NDdRqdZfrSPj6+rKGrKikpATbtm3Dli1bTI5jTVnW1atX0d7ebnBf9NtvvxmcY2zfxfqxno6ODixZsgSPPvoohg0bZnRcWFgYPvvsM4wYMQL19fXYsmULoqOjUVhY2O3+jG5fZGQkdu7cibCwMFRXVyM1NRWPP/44Lly4gL59+3YZz5qyD99++y3q6urw0ksvGR3DmrIPnbVhTt3czv7OHrBZN9OKFSuwceNGk2OKioq6vZAI2cbt5K+yshJHjhzB3r17u93+P68dMHz4cPj7+yMmJgalpaUYOHDg7QfuYMzJ07Jly3TLRowYAbVajQULFmD9+vVwcXGxdKgO73ZqqqqqCuPHj8e0adMwb948k3NZU0RdJSUl4cKFCybPgwaAqKgoREVF6Z5HR0djyJAh2L59O9555x1Lh+mwJkyYoPv3iBEjEBkZieDgYOzduxeJiYk2jIxM2bFjByZMmICAgACjY1hTZG1s1s2UnJxs8hM3AAgNDe3Rtvz8/LpcgbDzitR+fn5G59x6EYS2tjZcv37d6Bz6f7eTv/T0dPTv3x+TJk0y++dFRkYC+PsoIhuLnvs3dRYZGYm2tjZcunQJYWFhXdb7+fmhtbUVdXV1ekfXr1y5whq6Debm6vLlyxg7diyio6Px6aefmv3zWFN3lpeXF+66664ud0MwVQ9+fn5mjac7a+HChbqLy5p7JM/Z2RmjRo1CSUmJhaIjQzw8PDB48GCj7ztryvbKy8tx9OhRs7+1xZqyjc7auHLlCvz9/XXLr1y5ggceeMDgnNvZ39kDNutm8vb2hre39x3ZVlRUFN59913U1tbqvtqekZEBd3d33H///Ubn1NXVIS8vDxEREQCAY8eOoaOjQ/dHLBlnbv5EBOnp6Zg9ezacnZ3N/nkFBQUAoPcfCXXv39RZQUEBVCpVl9NFOkVERMDZ2RmZmZmYOnUqAKC4uBgVFRV6n5ZTz5iTq6qqKowdOxYRERFIT0+HSmX+ZVNYU3eWWq1GREQEMjMzER8fD+Dvr1hnZmZi4cKFBudERUUhMzMTS5Ys0S3LyMhg/ViYiGDRokX45ptvkJWVhZCQELO30d7ejl9++QXPPPOMBSIkY27cuIHS0lLMmjXL4HrWlO2lp6fDx8cHEydONGsea8o2QkJC4Ofnh8zMTF1z3tDQgNzcXKN34bqd/Z1dsPUV7v7LysvLJT8/X1JTU6VPnz6Sn58v+fn50tjYKCIibW1tMmzYMHn66aeloKBAfvzxR/H29paVK1fqtpGbmythYWFSWVmpWzZ+/HgZNWqU5ObmyqlTp+S+++6ThIQEq78+R3D06FEBIEVFRV3WVVZWSlhYmOTm5oqISElJiaxbt07OnTsnZWVlcuDAAQkNDZUnnnjC2mE7jOzsbNm6dasUFBRIaWmpfPnll+Lt7S2zZ8/Wjbk1TyIir776qgQFBcmxY8fk3LlzEhUVJVFRUbZ4CQ6jsrJSBg0aJDExMVJZWSnV1dW6xz/HsKasb8+ePeLi4iI7d+6UX3/9VebPny8eHh66O5XMmjVLVqxYoRt/+vRp6dWrl2zZskWKiookJSVFnJ2d5ZdffrHVS3AIr732mmg0GsnKytKrn6amJt2YW3OVmpoqR44ckdLSUsnLy5MXX3xRXF1dpbCw0BYvwWEkJydLVlaWlJWVyenTpyU2Nla8vLyktrZWRFhT9qa9vV2CgoJk+fLlXdaxpmynsbFR1zsBkPfff1/y8/OlvLxcREQ2bNggHh4ecuDAAfn5559l8uTJEhISIn/99ZduG+PGjZNt27bpnne3v7NHbNYtaM6cOQKgy+P48eO6MZcuXZIJEyZI7969xcvLS5KTk0Wr1erWHz9+XABIWVmZbtm1a9ckISFB+vTpI+7u7jJ37lzdBwB0ZyUkJEh0dLTBdWVlZXr5rKiokCeeeEI8PT3FxcVFBg0aJG+99ZbU19dbMWLHkpeXJ5GRkaLRaMTV1VWGDBki7733njQ3N+vG3JonEZG//vpLXn/9denXr5+4ubnJc889p9c00p2Xnp5u8P/Df35mzJqynW3btklQUJCo1WoZPXq0nDlzRrduzJgxMmfOHL3xe/fulcGDB4tarZahQ4fKoUOHrByx4zFWP+np6boxt+ZqyZIlurz6+vrKM888I+fPn7d+8A5m+vTp4u/vL2q1Wu655x6ZPn26lJSU6NazpuzLkSNHBIAUFxd3Wceasp3OHujWR2c+Ojo6ZPXq1eLr6ysuLi4SExPTJYfBwcGSkpKit8zU/s4eOYmIWOUQPhERERERERH1CO+zTkRERERERGRn2KwTERERERER2Rk260RERERERER2hs06ERERERERkZ1hs05ERERERERkZ9isExEREREREdkZNutEREREREREdobNOhEREREREZGdYbNOREREREREZGfYrBMRERERERHZGTbrRERERERERHaGzToRERERERGRnfkfdWCCkabhcmcAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -60,11 +56,11 @@
     "    return np.sin(np.round(x)) - np.abs(np.round(x) / 5)\n",
     "\n",
     "c_pbounds = {'x': (-10, 10)}\n",
-    "bo_cont = BayesianOptimization(target_function_1d, c_pbounds, verbose=0)\n",
+    "bo_cont = BayesianOptimization(target_function_1d, c_pbounds, verbose=0, random_state=1)\n",
     "\n",
     "# one way of constructing an integer-valued parameter is to add a third element to the tuple\n",
     "d_pbounds = {'x': (-10, 10, int)}\n",
-    "bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0)\n",
+    "bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0, random_state=1)\n",
     "\n",
     "fig, axs = plt.subplots(2, 1, figsize=(10, 6), sharex=True, sharey=True)\n",
     "\n",
@@ -142,16 +138,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/1961947877.py:12: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
-      "  discrete_optimizer = BayesianOptimization(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "continuous_optimizer = BayesianOptimization(\n",
     "    f=discretized_function,\n",
@@ -345,14 +332,6 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/2996397825.py:3: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
-      "  categorical_optimizer = BayesianOptimization(\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -361,24 +340,24 @@
       "-------------------------------------------------------------\n",
       "| \u001b[39m1        \u001b[39m | \u001b[39m-2.052   \u001b[39m | \u001b[39m-0.165955\u001b[39m | \u001b[39m0.4406489\u001b[39m | \u001b[39m2        \u001b[39m |\n",
       "| \u001b[35m2        \u001b[39m | \u001b[35m13.49    \u001b[39m | \u001b[35m-0.743751\u001b[39m | \u001b[35m0.9980810\u001b[39m | \u001b[35m1        \u001b[39m |\n",
-      "| \u001b[39m3        \u001b[39m | \u001b[39m10.26    \u001b[39m | \u001b[39m-0.734149\u001b[39m | \u001b[39m0.9535175\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m4        \u001b[39m | \u001b[39m-16.13   \u001b[39m | \u001b[39m-0.746244\u001b[39m | \u001b[39m0.9996268\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m5        \u001b[39m | \u001b[39m3.259    \u001b[39m | \u001b[39m-0.123283\u001b[39m | \u001b[39m-0.574741\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m6        \u001b[39m | \u001b[39m-7.048   \u001b[39m | \u001b[39m0.3920786\u001b[39m | \u001b[39m0.6459259\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m7        \u001b[39m | \u001b[39m-3.913   \u001b[39m | \u001b[39m0.4586377\u001b[39m | \u001b[39m-0.324596\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m8        \u001b[39m | \u001b[39m5.802    \u001b[39m | \u001b[39m-0.691390\u001b[39m | \u001b[39m0.2097127\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m9        \u001b[39m | \u001b[39m1.222    \u001b[39m | \u001b[39m-0.833528\u001b[39m | \u001b[39m0.0859457\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m10       \u001b[39m | \u001b[39m4.208    \u001b[39m | \u001b[39m-0.290231\u001b[39m | \u001b[39m-0.196824\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m11       \u001b[39m | \u001b[39m-4.159   \u001b[39m | \u001b[39m-0.615341\u001b[39m | \u001b[39m0.8791598\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m12       \u001b[39m | \u001b[39m-4.333   \u001b[39m | \u001b[39m-0.635647\u001b[39m | \u001b[39m0.1409021\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m13       \u001b[39m | \u001b[39m3.66     \u001b[39m | \u001b[39m0.0660672\u001b[39m | \u001b[39m-0.921962\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m14       \u001b[39m | \u001b[39m1.083    \u001b[39m | \u001b[39m0.5054393\u001b[39m | \u001b[39m0.0720508\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m15       \u001b[39m | \u001b[39m9.608    \u001b[39m | \u001b[39m0.5076033\u001b[39m | \u001b[39m-0.643610\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m16       \u001b[39m | \u001b[39m-10.34   \u001b[39m | \u001b[39m0.6955995\u001b[39m | \u001b[39m-0.309794\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m17       \u001b[39m | \u001b[39m4.211    \u001b[39m | \u001b[39m-0.888612\u001b[39m | \u001b[39m-0.828377\u001b[39m | \u001b[39m2        \u001b[39m |\n",
-      "| \u001b[39m18       \u001b[39m | \u001b[39m7.692    \u001b[39m | \u001b[39m-0.705846\u001b[39m | \u001b[39m0.7290563\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m19       \u001b[39m | \u001b[39m-2.327   \u001b[39m | \u001b[39m0.3347617\u001b[39m | \u001b[39m0.4590555\u001b[39m | \u001b[39m1        \u001b[39m |\n",
-      "| \u001b[39m20       \u001b[39m | \u001b[39m4.103    \u001b[39m | \u001b[39m-0.531335\u001b[39m | \u001b[39m0.4916631\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-14.49   \u001b[39m | \u001b[39m-0.743433\u001b[39m | \u001b[39m0.9709879\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-13.33   \u001b[39m | \u001b[39m0.9950794\u001b[39m | \u001b[39m-0.352913\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m9.674    \u001b[39m | \u001b[39m0.5436849\u001b[39m | \u001b[39m-0.574376\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m9.498    \u001b[39m | \u001b[39m-0.218693\u001b[39m | \u001b[39m-0.709177\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m11.43    \u001b[39m | \u001b[39m-0.918642\u001b[39m | \u001b[39m-0.648372\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.4882   \u001b[39m | \u001b[39m-0.218182\u001b[39m | \u001b[39m-0.012177\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m7.542    \u001b[39m | \u001b[39m-0.787692\u001b[39m | \u001b[39m0.3452580\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-2.161   \u001b[39m | \u001b[39m0.1392349\u001b[39m | \u001b[39m-0.125728\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.8336  \u001b[39m | \u001b[39m0.1206357\u001b[39m | \u001b[39m-0.543264\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-8.413   \u001b[39m | \u001b[39m0.4981209\u001b[39m | \u001b[39m0.6434939\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m6.372    \u001b[39m | \u001b[39m0.0587256\u001b[39m | \u001b[39m-0.892371\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m-12.71   \u001b[39m | \u001b[39m0.7529885\u001b[39m | \u001b[39m-0.780621\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-1.521   \u001b[39m | \u001b[39m0.4118274\u001b[39m | \u001b[39m-0.517960\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m16       \u001b[39m | \u001b[39m11.88    \u001b[39m | \u001b[39m-0.755390\u001b[39m | \u001b[39m-0.533137\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m17       \u001b[39m | \u001b[39m0.6373   \u001b[39m | \u001b[39m0.2249733\u001b[39m | \u001b[39m-0.053787\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m18       \u001b[39m | \u001b[39m2.154    \u001b[39m | \u001b[39m0.0583506\u001b[39m | \u001b[39m0.6550869\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[35m19       \u001b[39m | \u001b[35m13.69    \u001b[39m | \u001b[35m-0.741717\u001b[39m | \u001b[35m-0.820073\u001b[39m | \u001b[35m2        \u001b[39m |\n",
+      "| \u001b[39m20       \u001b[39m | \u001b[39m1.615    \u001b[39m | \u001b[39m-0.663312\u001b[39m | \u001b[39m-0.905925\u001b[39m | \u001b[39m1        \u001b[39m |\n",
       "=============================================================\n"
      ]
     }
@@ -388,7 +367,7 @@
     "\n",
     "categorical_optimizer = BayesianOptimization(\n",
     "    f=SPIRAL,\n",
-    "    #acquisition_function=acquisition.ExpectedImprovement(1e-2),\n",
+    "    acquisition_function=acquisition.ExpectedImprovement(1e-2),\n",
     "    pbounds=pbounds,\n",
     "    verbose=2,\n",
     "    random_state=1,\n",
@@ -419,7 +398,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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VSdSOpbAL7C/i94yFnafKx2xfAUjUQZ6gixel01vQiSzmeAo5PcWbUht4iD0e3bEshF1gP1HL6Lx2LMEPEnTtMbOIi0ekuhl1vpVE7fRItRRqi9FtMBq2F3VmE3R2EnMiCDglRLKXhdBTKuwAdjO7YhHvTVqLsKu72onOX6q1jGCFaILOqAeplUWcHELrb8Q1MCpqZ8buWFuLup90PgPApWkfJOjY1tNsQi4ebeujVuTxEHYA/+5YLW/P9X0o0mcksa4ryyisqILO7kIuGkYJPKVRO7sKO1uLOjkoWTkiFJZCx+pizmpCLhasRJ5cRBB2AHXHmg27CjoScsowQuDpvaSh2YQdiboYqO12tbqgY1E/Owm5WOgh8vQSdoH9RC1Dws4U2FHQ6SnmjLoHeE8+0lPgGSHsAvuL+L1Awo5EXRSMFnRWFHMk5OLD6xzpMYEC4DvOjuCPSIKO90OSp5ATsY1Hs4mH2NNjMoHewg6IP4FCBGFHok4FdhN0JOasgx5dCSTszIldBB0PMWfm9hzJdmbrOnOO3pGwaw+JugjEitLFmhihFS1ibnTWlrC/WdmpRdCZScypdWJmdOYk7Ii22EHQsRRzVm+7bevHZEkuTtE7vdepBsQWdrq0zMWLFyMnJweJiYkoKirC5s2bo5b93ve+B4fD0e4zcuTIYJl777233fdlZWVMbFUr6LRGs9QIutFZW4If1mhZkzXxgNs0gk7rGoKB34d+zACr9Q5jEc+xmeVcxcNM/i0SVhd0ddekam7PgTVC5awVakVY1p3F9YiE3utUxzoXRk644R6pW7VqFaZPn44lS5agqKgIzz33HEpLS7F3715kZGS0K7969WpcuHAh+Pfp06cxePBg3HHHHWHlysrK8Oqrrwb/druNExF6CzoeIi4ULWLObPBYvaDt/kR9CFDETjtm929WFnQshBzRntDzosV38ojc6T0TX8SIHXdR9+yzz2LSpEmYMGECAGDJkiV49913sXTpUsycObNd+bS0tLC/33jjDSQnJ7dzem63G1lZWUxtVROl0yLoeIk5tV2vdhJzociZRKAFkUWeHktyWVnYmcm/tSXaNWF9LfQWdFrEnFnboVGwEHisxZ3dhR3XFnzhwgVUVlaipKTk8gGdTpSUlGDjxo2y9vHKK69gzJgx6NSpU9j2DRs2ICMjA/369cP999+P06dPR91Hc3MzfD5f2KctJOiU18VM3axy0KtrRbRuWj26EazYFSuKfwPk+bhQrCjotHTr2bVblSVau2hZdsvauSuWays+deoUWltbkZmZGbY9MzMTXq837u83b96MXbt24Wc/+1nY9rKyMqxYsQIVFRV46qmn8PHHH2PEiBFobW2NuJ+FCxciNTU1+OnRo4fsOvCYGKFE0CkZM6fGVrVj56wk5tqit7gTQdCQsFOOKP4NUObj9HrI6CXo1IoBO4+R440I4s6uwk7o1vzKK69g4MCBGDp0aNj2MWPG4Ec/+hEGDhyIUaNGYe3atdiyZQs2bNgQcT+zZs1CfX198HP06NGw79WsGqE2SqdU0PGEonOx0dPhiyDuSNjpCyv/BsT3cQGsNIZOq5gj+KNFOJOwUwfXMXXdunWDy+VCdXV12Pbq6uq440XOnj2LN954A48//njc4+Tm5qJbt27Yt28fhg8f3u57t9sddaCxnt2ucgWdGjGnJEpn17FzauE95i6U0GMY8eDRY3yIksW5RUYU/wbE9nEBrCboFP9GQCHH06eIVl81fpTFeDu9/Y0e45RjwfWqJyQkoKCgABUVFcFtfr8fFRUVKC4ujvnbt956C83Nzbj77rvjHufYsWM4ffo0unfvrtnmACwF3eDcYyToLIDeb/giRO/aokfETrQ6R8NM/s0qgk5NdE6UyJze6Y9ETbek5npo9Tt69xBEq58e4pL77Nfp06dj/PjxKCwsxNChQ/Hcc8/h7Nmzwdli48aNw5VXXomFCxeG/e6VV17BqFGjcMUVV4Rtb2xsxLx583D77bcjKysL+/fvxyOPPIK+ffuitLRUkW1vN3ZFcpf221mOo+Mp5gBzCjoe65vqKTr1jNyFHkevB5MIC1h7vvLjdE+uh2CCyP4tgF6CTg5aBZ2i8oIIOZEQaSa+Uj/Ke+kx1rPwo/lR3v6Tu6gbPXo0Tp48iTlz5sDr9WLIkCEoLy8PDi4+cuQInM7wE7l3717885//xPvvv99ufy6XCzt27MDy5ctRV1eH7Oxs3HzzzZg/fz73XHVKRZGdBR0P4abmeDzFnpXFnQipTlL3i/VAjISZ/ZveM13VPsjMJOZEE3HxMHq4R+C4enTJ6p3qBDBG2DkkSZK47FlgfD4fUlNT8fK2AiR3cYV9x6rb1S6CTm/xphTJ70fz3kNorfPB5UmBu18Okg4lcTmWng5dzwkcUb9jmIoiEi0Xm1D51mzU19cjJSWFybHsQsDHFdyxAB06Jrb7ngQdW8wm5uJhlvOopl3JaVN63R+dd9SgYtcipj6O1n4NgVW3qyiCjse6raKLuFDObd2F2tfWovXM5Rvf1TUVaXf/AMmF+QDYRvKMmFDB2/nSxAnrYUQuOqWYQcxZTciFoveQjwBqumR5ROz0oi4vBdjFdp/GDzowAUrEkRUEHXBJvEX6mIVzW3fh5IsrwwQdALSeqcfJF1fi3NZLdxKPuvF0hJLfD1/1Ppw69Dl81fuQureF+8NFhFQnBBvMkFxYSZsyYhKESJMOeGPUJAsl11TN5BlRJk7wgCJ138Ci29Uqgs7sSH4/al9bG7NM7cq1SLpuABwh451ChZ3WCB6PqF3t0Z04XPkOLpy77JASklPRq2AU0jBQiIHhWhDpDZpgA29Bpyd2EXLRMGLCVuhx45ZnPE5Nr/F1rDH3U0AgeAs6udhd0AG4NIbuTOybu7W2Hs17D0X9nlX0jpVTqD26E1/9Y3mYoAOAC+fq8dU/lqP26E6ub9QiJNUktCHyODol0RZKLWQsep8PpVE7ueidmBjQRxCTqIP2KJ0cQadkua9IyInSkaC7RGtd7HUvlZRjIe60PoQkvx+HK9+JWeZw5V8g+S85IDMLO+qG5YPI4+hEjc6RmIuN3sJO7rVX8oJghL/h3YZtL+q0To6QK+i0QIJOGS6PvFlEcssB7MSdGhpOHmgXoWvLhXN1aDh5IPg3CTsigCGTCGReQ5Gjc0R8rBC1s9rShbYXddFgJZJI0OmPu18OXF1j39CutFS4++Uo3rdWcafm4XThfIOqcmaONJCwY0N9H/YunlW3qxJBpxdmvmeMRM/zxqs7NhZm6oa1tahbXXNdxO0su121QIJOHQ6nE2l3/yBmmbSxPwibJKEUFuJOLglJEZY9UVBOT4dE4+usDYu2pHT8nB6QmGODnsJOSXdsPKzUDWtrUacFPbpd4yGqoAusdRvtowfJhflInzq2XcTOlZaK9Kljg3nqtKKHsOuSnouE5NiOKSHZgy7puVG/N6Ow81TJGxtJiEO8h6OI4+dIzLFFxKgdC2HHo048IumU0qQNcoSSCOPoRBJ0SoXa4NxjutifXJiPpOsGtFtRQkuELhJNuc2qU6DImbbvcDrRq2AUvvrH8qhlehXcGrdeZp2iT4iB1m5X0aJzAAk6nrD2N9GQ64dYrB2rV520QKJOISIIOqPRK9rGAofTicS86BEsVgQidlrEXSzHlNZjIK7+zvgIeeo86FVwK9J6DJR1HN1WouC8aDWhL1rFj2iCjsScPogm7IDYvskKuTJJ1IUQL3okiqAzIkpnJiFnJFqjdvGEXdcrr7k0G/Z8AxKSuqBLeq6qyCMrZ6vHMmKE+MS6ziTo7I2eSxqGHi9mWQ2+SfRonbiWmRDeY+gA/QUdj3FwInUd84DnODuH04mUzL7olnMtUjL7aupKpocbIQct7YQEHRHALOPszP4iSqLuG7RG6VgIOpHG0fGa1GB1QRdAr5mxWmEym5Fmw9qaSA9BUWe4EsZiFmEXD5HbEok6GejR9SiKoOM5Q9Uugi4ACTvC7KhpGzTDlYiFGYSdmaN15IkRW2yIMI5ODzHEW8zZTdCxwGzCLhoUrbMPJOgIOZhB2MVD1HZFEyU0osc4Op7wjkIaLebUTloAtEXaQvehxQY904ZoHQCs1dbWHVVwDcpT/XsiNkpnaMu5lqERDdEEnagPXeISos2MVZPyREkdtGZIkIvtRZ2WKJ3Zx9FZSdDxuFHi7ZOF6JODmYRdNOTMNnMNyiNhxwE92ikJOu2wOC9mq7dowg647Kt4pTfR+qIfD13i34sXL0ZOTg4SExNRVFSEzZs3Ry27bNkyOByOsE9iYmJYGUmSMGfOHHTv3h1JSUkoKSnBV199xdRmEVJ48BJFvFd20KO7NfGAO+xjBG1tiPZhgVm6YllMmmjdUaX6+EYgsn9rzlEn6JQ8AEUTdKITWOKq7YfnvkU+76J1xQLKXlLU2M/zRYv7lV61ahWmT5+OuXPnYtu2bRg8eDBKS0tRU1MT9TcpKSk4ceJE8HP48OGw7xctWoQXXngBS5YswaZNm9CpUyeUlpaiqalJkW27DmWrqhOgT5SOB2aOzhkt4ozGCsJOLmYRdiL7N9Gw4xg6kcSVKHZEwuzCTg28hB33K/vss89i0qRJmDBhAgYMGIAlS5YgOTkZS5cujfobh8OBrKys4CczMzP4nSRJeO655zB79mzceuutGDRoEFasWIHjx4/jnXfeYWKzFbtd9Vh3lYegs7uQa4tozlgp8Ryl2bpezejf4sHjAWsnQSeqcGqLaCJPRGEnF7W2q42kx4Lrlbxw4QIqKytRUlJy+YBOJ0pKSrBx48aov2tsbESvXr3Qo0cP3Hrrrdi9e3fwu4MHD8Lr9YbtMzU1FUVFRVH32dzcDJ/PF/aJhhW7XfWoE2ubSchFxwwPSCbRul17Ne+DJ6L4N0C+jzPinjJDe9WKSOJILSLUQQRRbna4Xr1Tp06htbU17E0UADIzM+H1eiP+pl+/fli6dCn+8pe/4LXXXoPf78cNN9yAY8cuCZPA75Tsc+HChUhNTQ1+evToobpOZut21SM6x1LQkZiThxrHqyb3EpeojUVSnIji3wB2Po719bayoBNBBPHCyHrpcS2teM0CCFez4uJijBs3DkOGDMGwYcOwevVqpKen449//KPqfc6aNQv19fXBz9GjRyOWs1K3q9m6W0nMKUetsFMq7tQ6WbWO02xdsErg4d8A+T7Oiugt6Kwq5CJhdeHKknjtUK/nG9cr1a1bN7hcLlRXV4dtr66uRlZWlqx9dOzYEddeey327dsHAMHfKdmn2+1GSkpK2KctVup2NVN3K4k5/QiNkOkl7OTYYlZE8W+APB8XDzNG6fQUdErEjeT349yhffDt3IZzh/ZB8pu/W1FPcWfm8XVGw7VGCQkJKCgoQEVFRXCb3+9HRUUFiouLZe2jtbUVO3fuRPfu3QEAvXv3RlZWVtg+fT4fNm3aJHufajBLt6vZBB2hDS1OycxL4YiA2fybnveblR6WSsVMQ9UOHHh+Po4u/wNOrH4NR5f/AQeen4+Gqh0crdQPvcSdGYWdCGMCuV+Z6dOn4+WXX8by5ctRVVWF+++/H2fPnsWECRMAAOPGjcOsWbOC5R9//HG8//77OHDgALZt24a7774bhw8fxs9+9jMAl2aOTZs2DQsWLMBf//pX7Ny5E+PGjUN2djZGjRqlykY9ul3jwUIomUXQUXSOLVqm6SvNnq4UtXnrzNIFawb/JgeWDyOrRHPUiJeGqh04/uYytPjC76sWXz2Ov7nMMsIO0EfciSCSWKLHc4/7ihKjR4/GyZMnMWfOHHi9XgwZMgTl5eXBgcBHjhyB03m5YZw5cwaTJk2C1+tF165dUVBQgM8++wwDBgwIlnnkkUdw9uxZTJ48GXV1dbjxxhtRXl7eLomnSPCO0plJ0BHs0bLqhJLM6XplgDcL5N/CsYKgU1sHye9HTfmamGVqyt9B5375cDitcw/pueINL1jWwWgf6ZAkSTLs6Abh8/kuzRBbMhfXXnMqZllWUbpYok6rWCJBRwDKHnSRInQ8VwaIZlusSGFLazMqdi1CfX29qjFidibUxzmTEuPee6weaGYXdVrsP3doH44u/0Pccj3GP4DknL6qjyMyIl4XJehxH4QmHfafb8LR++Yx9XHWeV1QQX7OcV2OwzNKR4KOCKDV8Rkxxs4KEybMDgk6Nl2JLQ3R85+qKWdGeHXJmnF8nVGYvwYcEX0sHQk6oi1ynVI0MSVX2Cl1slZwlkR0zCroWIqQDl3kRVrkljMzdr/fY7VT3s9De595HeDV7WoWQUdYF2YRHorWGYbZx0JpgbXwSOqZiw4psdtyhxQPknrmMj2uqLCO2lG0Th7mtp4jekTpRIbSlpgXvaJ1hDngfQ+aMUrHw2aH04mMsttilskoG2WpSRJyMKNIMqPNAbjPfrUzZo7SsaIpt7ndQyV0oKhaSCzyRe6MWCUzvawwS44Ix2wPP972dskbhOw770VN+ZqwtCYdUjzIKBuFLnmDuB5fVFjd+0bPLFVCLFsTD7iZPAcjQaIuAiJH6czY7cqj8Ubap9FCr63TMtL5sHCiSlKdaKXumlSKEOqM3gI70gueXFjYqtf92CVvEDr3y8f5IwfQ0uBDhy4pSOqZa7sIXVuMfqlT2v6Mtlct9m5lHOEx49WMgk5PmnKbwz5G4/nKH/wYgZyHGAvRpqR+ZnnLJuIj91qG3o9G3Zd6tzuH04nknL5IGXgdknP62l7QBWAxzk6LP1X6bDCjvzKfxZxhEaXzt0o49fnXOPbBv3Hq868htYY3QjXCiQSdckQUeGaD12zYSNCECfa4D/GJXisRdFrR2rbM+GC2OkZfE72eCUb4fOp+ZcyWdbVYseAIar2XxWFieifkP/QdZA/rY6BlsbGaoGtL4Cbm2UUrJ1yv95gQOTbF6/rUsxuWsA4ivEwZLR6I6GhaBYeBH5XbHcurGzbxgBvnujcx3y+1+BC0Rum2rKvFc1P3odZ7IWx708mz2Dq7HMc/3i9slM4u8I7cyXE0Zo3axUNunehBazxa2p+c68fqHuNtJ2EsRl8jEV48WEOtnhH+VgkrFhwBYiy6tuuFf0LyK3NS1O3KBxFuZpHyLsWLxOk1iYEiggQLjBYLhHzUXitW/lPOs0BLe9L7BZ5aPiP2bG1oF6FrS1NNI5r3HtLHIJnYUdAF4BW1U+IArBaxs1p9iHDMEKUjQWc+jL5mvIWdnpjDSh3Q2vVaV3NRVrnWOvnr/lG3qz6IIOx4iyEWDolltM4sDtKK8Gxr8e4lqyRCJtij5tqxbMsi9N6wgO4ARngyOsoq5/LIW/ePul31xWhhBxgf5WLV9al5tiJ1wQpJvPZs9EORBJ35Ef0asraPx+x0sc+gTrBIY9K/sAsS0zvFLONKS4W7X47mYxF8MPqhBPAVdqJF6wjzoPfDVul9ILoYIOTD61rKiRLzegbo+cJOdwIjnC4H8h/6TswyaWN/ICsJJUXpjIP1TW10l4JSRInWEebCyBciEnTWw8hxyfHasujtTWzrTEb2sD4oXFDWLmKXmNEZ6VPHIrkw3yDLwiFBFxurR+y0wipaJ7pzJC4hcrcrtSGCCMf2yYdZrfMaWBYse1gfdL+xN07vOIGm02eReEUnHOtyvexlYmhyhPVQm7ySR6JiPdcz1GJ/3TWp6LyjhrFFhJEvC3InSVC+QwIwdu3VeImJRV4Xlu4KDjhcTnS79kpcVfIf6HbtlUKt+0dROmMw0wNIThcsja2zB6JG6cx0PxHqkXud5Qos3rOvRUCXO2Px4sXIyclBYmIiioqKsHnz5qhlX375ZXznO99B165d0bVrV5SUlLQrf++998LhcIR9ysrKeFeDOxSlI9rC422QHohsIf9GEPwwyl+xHlunV2SP+9latWoVpk+fjrlz52Lbtm0YPHgwSktLUVMTuWtlw4YNuOuuu/DRRx9h48aN6NGjB26++WZ8/fXXYeXKyspw4sSJ4OfPf/6zYtt+nLFNVZ3aEuh6jYRIkTGRbLEjRmdOZ42caF3cdWdNLjBF9m+8oSgdoRd0zeXD/Uw9++yzmDRpEiZMmIABAwZgyZIlSE5OxtKlSyOWX7lyJR544AEMGTIE/fv3x5/+9Cf4/X5UVFSElXO73cjKygp+unbtyrsqXKEonTiIGKLXW9iJkCuuLk9eTkcjsbJ/Y/kgZTWejh7uhJ6IMGlOKVzvkAsXLqCyshIlJSWXD+h0oqSkBBs3bpS1j3PnzuHixYtIS0sL275hwwZkZGSgX79+uP/++3H69Omo+2hubobP5wv72A2K0oWTeMAd9cMTUR5KothhZkTxb4D+Ps6Ihx21WXsj4vUX0SauFp06dQqtra3IzMwM256ZmQmv1ytrH48++iiys7PDHGdZWRlWrFiBiooKPPXUU/j4448xYsQItLa2RtzHwoULkZqaGvz06NFDfaUUIFdIUZSOH0YIN16I2A3LogvWrIji3wB5Pk7JdRDxYUUQRrRLs0XrhE5p8uSTT+KNN97Ahg0bkJiYGNw+ZsyY4P8HDhyIQYMGoU+fPtiwYQOGDx/ebj+zZs3C9OnTg3/7fD5mwi7WeDrCGEQXbVqmw/NIc2I0IqcH4Akr/wbw9XFtMVuUrq29ovsHqxJ6HbRcg2j+Qq5vTDzgZtqGRfNfXJ8O3bp1g8vlQnV1ddj26upqZGVlxfztM888gyeffBLvv/8+Bg0aFLNsbm4uunXrhn379kX83u12IyUlJexjJ+zQ9Wr2KJwRxHOAcsfV2TW9iSj+DWDr40R7aWAp6AhjaHsdmnKbTXVtzGQr17s3ISEBBQUFYYOAA4OCi4uLo/5u0aJFmD9/PsrLy1FYWBj3OMeOHcPp06fRvXt3JnbrCXW9aseMQk7Lg0qkt0K5mNHmeJB/4w8JOmuj9hqZ9cVDDz/I/cxMnz4dL7/8MpYvX46qqircf//9OHv2LCZMmAAAGDduHGbNmhUs/9RTT+Gxxx7D0qVLkZOTA6/XC6/Xi8bGRgBAY2MjHn74YfzrX//CoUOHUFFRgVtvvRV9+/ZFaWkp7+oQAkGROcJo7ObfzCKUzGInYR1hJwrcx9SNHj0aJ0+exJw5c+D1ejFkyBCUl5cHBxcfOXIEzpAVF/7nf/4HFy5cwE9+8pOw/cydOxe//e1v4XK5sGPHDixfvhx1dXXIzs7GzTffjPnz58Pt1vcBb4b8dKLYwRoriDkaW2d+rObfeLQptfeqWltI0JmPeMtyRSPUh/L2h2pt1BuHJEmS0Ubojc/nQ2pqKl7eVoDkLi7V+9Eq6vToerWiqDPDjSUXLeF4Vk4sng1yx8zJGYMXzeZINrRcbELlW7NRX19vu3GwWgn4uL4zfweX+9IkDDltLVabUiuWlN6vWl5Y5NhoJf9hFuS2HbXXRmmb4dWWld5jrc1N2Pfkr5n6OHrVJ0yF1Ryy3cbWRYOijkQAitARStHLf5ihjZEntTBWi9JZTdARhEiQsCaMRi/RZOVnCd3FBkGzXgkWiBSts2tqEzsgeoRCdPvsjhIRZeZrKcKLkfEWmBQzTJKwElZ+szLaEeh5fJFEKGENzCwCCPMhensjUWdRSFgSrJCbhJgwL0a/WBBEKKILJ5GhO5kgTA5FvwieiPyAFdk2wp4Y/YJEos4AaDydcsh5EwRB2AeRfb7ItpGoI0yD2dYLVILRb3esoMkS5sOsbc+qvoAgtGDOu5mwNeTM22OmLlgz2UoQhHGY1dcb+aJEoo4xIkxQEMEG3lg5amcEIkRrRLCBCEfUe0xUuwjCaMiLEqaGxB1BqIeENCE6ovp3Ue2iO1oFsXLUEcYg6g1mFSitCUEQhHyMemHqYMhRbQzNfOVHU26zqZMU113t1DTeTMtC6ARhFsz0AtfWViP9U6TzZmZ/aUb08M/0BCAIgrAh8R4wZhJPohFtWIhR5zTacc0yfMUMNooCiTrCUtDNbzyU1kRMKIqrD/F8kN4+Ss7xyG/ywYh7ju5ygiB0h9KaEGoQXXzItU/EevC0ScT6skDEepGosxh2SGdCEIQ2KGrHHhEf8EptErEOAUS2TQ563XN0ZxMEQdgEEnN8UCM4RBUpotplVvS+53Q52uLFi5GTk4PExEQUFRVh8+bNMcu/9dZb6N+/PxITEzFw4EC89957Yd9LkoQ5c+age/fuSEpKQklJCb766iueVSAIgoiI2fybnIeMlR/srOsm6rkS1S47oqew436kVatWYfr06Zg7dy62bduGwYMHo7S0FDU1NRHLf/bZZ7jrrrswceJEfP755xg1ahRGjRqFXbt2BcssWrQIL7zwApYsWYJNmzahU6dOKC0tRVNTE+/qEARBBCH/ph8iihTJ70dT1QGc3bgdTVUHIPmVjRUVsU6AuHaxwsr1c0iSJPE8QFFREa6//nr8/ve/BwD4/X706NEDU6dOxcyZM9uVHz16NM6ePYu1a9cGt33rW9/CkCFDsGTJEkiShOzsbMyYMQO/+tWvAAD19fXIzMzEsmXLMGbMmLg2+Xw+pKam4uVtBUju4lJcp1jJh+ONaeOdp47G1Jk795LWCQRa3gjjHVvJrFY5yYoj2RpqQ8vFJlS+NRv19fVISUmRfWw9EdG/AZd9XN+Zv4PLnai4XiwfeqzuR9Fsaqjager1a9B65vJ94eqairS7f4Dkwnzd7QmFxbliaROra2e1ttTa3IR9T/6aqY/jGqm7cOECKisrUVJScvmATidKSkqwcePGiL/ZuHFjWHkAKC0tDZY/ePAgvF5vWJnU1FQUFRVF3WdzczN8Pl/Yh7AmZhZ0LKBZpfohin8DyMfpTUPVDhx/c1mYoAOA1jP1OPniSpzbuivKLyPDUmSw2pdo+2G9L1aIZhNXUXfq1Cm0trYiMzMzbHtmZia8Xm/E33i93pjlA/8q2efChQuRmpoa/PTo0UNVfQiCIAKI4t8AsX2caA89rUh+P2rK18QsU7tyLdOu2ECS4FjJgs2SSJjgiy2mQs2aNQv19fXBz9GjR402ieCE3Z0azW60J+TjlKHFT5w/cgAtvthDEVpr69G895DifbcVb7FWppAj9JTSdozg+ZzzTPZL6AfXtV+7desGl8uF6urqsO3V1dXIysqK+JusrKyY5QP/VldXo3v37mFlhgwZEnGfbrcbbre9u+UIgmCLKP4NsL6PE+llraVBXtd2a525usDPbd2F2tfWthsjmPmft6FL3iADLSOUwPW1PiEhAQUFBaioqAhu8/v9qKioQHFxccTfFBcXh5UHgPXr1wfL9+7dG1lZWWFlfD4fNm3aFHWfBEEQrCH/Zk86dJE3oN3lEXNyTyTObd2Fky+ujDhG8Piby9BQtUPVfkUS43aBa6QOAKZPn47x48ejsLAQQ4cOxXPPPYezZ89iwoQJAIBx48bhyiuvxMKFCwEADz30EIYNG4b//u//xsiRI/HGG29g69ateOmllwAADocD06ZNw4IFC3D11Vejd+/eeOyxx5CdnY1Ro0bxrg5BEEQQ8m/mpSm3WdXEqqSeuXB1TW0ngEJxpaXC3S9Hg3X6Ifn9qH1tbcwyNeXvoHO/fDic5h/eYXWhyV3UjR49GidPnsScOXPg9XoxZMgQlJeXBwcCHzlyBM6QhnLDDTfg9ddfx+zZs/HrX/8aV199Nd555x3k51+eIv7II4/g7NmzmDx5Murq6nDjjTeivLwciYnKp+4TBEGohfyb/XA4nUi7+wc4+eLKqGXSxv7ANAKoee+hmAIVAFp8dTh/5ACSc/rK3i8v8aRWjNsF7nnqRMTKeerk2GB1zHzDU546c+WpExWR8tQF0HJf8oyuqLGrKbc58hi0tFSkjVWep85Izm7cjlNLVsUt1/3HdyNl4HWy9yvaNQsgUtvmkaeOe6SOIAiiLTRLV1ys3j3FiuTCfCRdN+BSpKvOB5cnBe5+OaaJ0AWQO/ZP7lhCgNqQkZCoIwiCKXKidAQhCkq780IFi8PpRGJeLg+zdMPdLyfuGMEOKR4k9TR3PQF7iE1zvVIQRBzM3PVKEAShN4ExgrHIKBslOwJpB+EkMiTqCMugRdBJfj/OHdoH385tOHdon+Js8CwwcjwdQfBG5Ie9yLbpQXJhPtKnjoWra3iU3ZWWivSpYylPnYmg7led+eLAVbpMlrAbWgRdQ9UO1JSvCcsS3yElFRlllHQzgJJJEgRBmI+YYwQPyNuH3cWxCNCrvQpGZ20x2gQiBK2C7viby9ot+9Pi05Z0kyCszuDcY8GPFSBBcnmMYKfiIUjMyw12uco5N6KfPyX2mbltk6hjjAiNQAQb9CDxgFtzl2u8hblryt8xpCtWb7R2/VrFBoIvVnrwE9bE7M9PEnWE6dAq5gLIWZg7kHRTdGg8HaEnbR98Zn8QhkLCTjl0zsSBngSEaWAl5gLIXZhbbjktWCVKpTbpMGF+SNjZEzOcK7k2RmrDPNt1cw77c0fe1QDsvuKDKMhNpqkk6SZBELExgwgAIttpFtv1gs6HeJCoI0wBj/xzST1z0SEldmTJKkk3CUIuWu41K0XrABItoYSei6bcZtOcGy1ROjNCos6iWKWB8sThdCKj7LaYZZQk3TQK3t2ZlM6EYI1ZBAFwWcCYyWZe0HkQH7GfVgJDaU30hZcj6ZI3CNl33tsuYtchxYPsO+/VJU+d0ePp9Dw+jacjCEILPHptjAiC8HqmUfJhDgzOPRZ33BwlIRaHLnmD0Llf/qXZsA0+dOiSgqSeucJH6ACxRBKt+SoezTnNSD6RqOsx5fg/gtADO0YVSdQRpkHpwttKcDidSM7py2XfBEG0h+f9TBBysVpwRZzXfII5VmusgPXevLR0fYoUpdOK0V3QBFus6HsIcyHys4KnbdZ5KpgQ6qJQh8g3q9mIJ6ZYTpKwkgg1EyLfL3Jto4gewQMrvnyQl9UATZYwDpEfVHKxUpSOxtMRbbHiA5MwB1Z4PqiF65OhtrYWY8eORUpKCjweDyZOnIjGxsaY5adOnYp+/fohKSkJPXv2xIMPPoj6+vBogcPhaPd54403eFZFMaI4NFHs4IGdb1zCeOzs31hC9zFhBKyejUqjyLzbO9eJEmPHjsWJEyewfv16XLx4ERMmTMDkyZPx+uuvRyx//PhxHD9+HM888wwGDBiAw4cP47777sPx48fx9ttvh5V99dVXUVZWFvzb4/HwrAo3aBasNgI3iJ26Z0SL0tkVM/k3oyYlyJ0JS5MmCFbY/SWBm6irqqpCeXk5tmzZgsLCQgDAiy++iFtuuQXPPPMMsrOz2/0mPz8f//d//xf8u0+fPnjiiSdw9913o6WlBR06XDbX4/EgKyuLl/mWwg4pBswm7kSYGMBqPJ0d13sl/0YQ+hPPv9tt9YhIcPO0GzduhMfjCTo8ACgpKYHT6cSmTZtk76e+vh4pKSlhDg8AfvGLX6Bbt24YOnQoli5dCkmSou6jubkZPp8v7MMKGlcnFlbP/m41cQSIIXCVIpJ/A9j7OJYvR3IfoFa9ZwlzwzIgokcb5xap83q9yMjICD9Yhw5IS0uD1+uVtY9Tp05h/vz5mDx5ctj2xx9/HDfddBOSk5Px/vvv44EHHkBjYyMefPDBiPtZuHAh5s2bp64iGpAbIaMuWD5Eu4GMjuaZUcQQ4Yjk3wB5Ps4MXZxmsJEQE4rSXULxa//MmTMjDuQN/ezZs0ezYT6fDyNHjsSAAQPw29/+Nuy7xx57DN/+9rdx7bXX4tFHH8UjjzyCp59+Ouq+Zs2ahfr6+uDn6NGjmu0zG1ZvyEoIjeZF+ogKyygdK2Fpta5XM/o3QHwfx8L/sBB7g3OPkS/UGT3Ouch+W28UR+pmzJiBe++9N2aZ3NxcZGVloaamJmx7S0sLamtr444VaWhoQFlZGbp06YI1a9agY8eOMcsXFRVh/vz5aG5uhtvd/sZ3u90Rt4sERevEgWe0QK2Y0lsYscxPp9qGKnbDJORiRv8G6O/j1PgrkSZNBGy3+lhjI2nbPkQY223kM1Yv4alY1KWnpyM9PT1uueLiYtTV1aGyshIFBQUAgA8//BB+vx9FRUVRf+fz+VBaWgq3242//vWvSEyMv27h9u3b0bVrV8VObXXNdbi7yxeKfhOJ0VlbsMp7fcTvRGjIAUSyxY6YRdDJhUWUTrSuaCv5t0iYpXuTh52RHugk7vigp3gysttVxHuJ29MiLy8PZWVlmDRpEjZv3oxPP/0UU6ZMwZgxY4Izw77++mv0798fmzdvBnDJ4d188804e/YsXnnlFfh8Pni9Xni9XrS2tgIA/va3v+FPf/oTdu3ahX379uF//ud/8Lvf/Q5Tp07lVRXdIMdC6IFoQsqMkH9Th5IHq55datRLwga9u7fN0u2qp51c89StXLkSU6ZMwfDhw+F0OnH77bfjhRdeCH5/8eJF7N27F+fOnQMAbNu2LThzrG/f8MXVDx48iJycHHTs2BGLFy/GL3/5S0iShL59++LZZ5/FpEmTeFbFMlC0zhjMFKWT0/VKK0iY17/JjYIlHnAb/tBkGbGL12VMUTtt8BZzbduBkrap1jbebSE/5zhYj351SPHmylsQn8+H1NRUjCifhI6dEpilJYnWBQsoaxy8bw5yWvFhHVZXI+p4CToW+elYTZCIZotndz1aWptRsWtRMO0HIZ+Aj+uxZC6cSe27eOW271gPTi1+SqkPCtirVWTKtZl8pHyUtAMt5zW0zSptB7xEnZz7KJatAzIP4O9lLzP1cWIO1iG4Ql0NsbGzoNOTWLZIkh9nzh7W0Rp7wSICp+UBrdQH6T0znWbJykMvQQeoz0Eq6nXkZReJOgGht0TjsLKgk4OeUbpo1B7diY+rXsC2g6tU74O4RH7OcaNNMDWiCgKjMYvo5Wmj1igdL0jUIXa3qRJideOKdgOIZo8IiDCTiaegEylKF43aozvx1T+Wo/lig9GmWB6jx8sZ4YPUvDCbRcDohV3OhVmDKyTqBEWPBmWXm1MOPASdUhFldOoSo6N0kt+Pw5XvqPotwQe98sWZAbuLOy31N0IgGX2teI1HjQeJum9gFa1jiVnfFMyGHQQdiygdyxmvkexpOHkAF84Zn/TYasR6gGiN1pnRR2m12WixoDdmFLNms5clJOoYY6YuWEBMm/Qi8YDb8C7XuqudhkfoWKKlLhfOU5erHTGjDzKj0FEKqzqaUfjHI95zw6goHUCiTnioG5YPPMWc3KiYXmJOjj3xul71yEuXkNSF+zHsCs9oHQv09kGs/GpA+FjJh5q9PnYXoiTqQhB1woSZG5ho8I7OiSbo9ERunaKdoy7puXB3JGEnGvHuF5YCSU9Y+1UriCHW9uv97BLh/BsZpQNI1BHfIMLNwBMRuloB/btbzRKlAwCH04m87FJdjmVHRI/WWQUziTuekUY7CjoRIFHXBhEnTADUDauGgJDTS8zFElBmHTsnV9CxqJtndz0yPXkY0usnFLEzABGEndmjdaGI2jUrql1aYFkXLatIGB2lA0jUcYPHhAkSdvHRW8jFw0gxJ2JeOjk2ZXryMCzvQVzXe7QOFtkLXvc3S99kJWEXIFRI6V0/I45txyFDIrwUAUAHow0QkVXe65mtB8uaeItS2wlRhFsAM0biYnW96hmla4vD4UTXTr2Y75eITVNuc8T7KvGAW/dluvQUBnr71UjHYlFfEZ4NZu525WW7nteFRB1HRmdtidqdq7fTUgJL20QTXnZBa5ROb0EnJ/ExwYZ493c0YRcP1sJIZB/JAxEEmVbMLOi0IEqUDqDu16iIOrYOoG5YIjZyBZ3eQkrE7mC7Ypb7W0877SQgeWAHQRfpZSeeoNPbThJ1nOGVjNgswk6kNxhCHhSlI0S6b0nYiY8VBJ2aOogm6AASdTEROVoHkLAj2qM1SsdL0FGUTjzi3dtt71s5XbJWEEVWqIOeWEHQWQlbi7pdh7J1OQ7PpcNI2BEB9BpHxxKK0omNKPetFWfEWgGrCDqrROkAm4s6OegRrSNhR+hJJCGlRNDpFaVr3bVX1e8I+ci5r5Xet1aYQQhcqgeJu+hYRdCpgcWzbHTWFvw4YxsDa8KxvajTq2HyTpFCws7eaInS8RR0hLHIeWjwEHa8MOLBTsKuPVYSdErq0pTbLOteMFKAcvXQtbW1GDt2LFJSUuDxeDBx4kQ0NjbG/M33vvc9OByOsM99990XVubIkSMYOXIkkpOTkZGRgYcffhgtLS2q7Yx3Uc0QrQNI2NkVJYKubZTOiC7XALG6Xlt3VOloiTrM4t9YvVAquW/1WKlBT0jYXcZKgo4HcuzlGeThKurGjh2L3bt3Y/369Vi7di0++eQTTJ48Oe7vJk2ahBMnTgQ/ixYtCn7X2tqKkSNH4sKFC/jss8+wfPlyLFu2DHPmzOFZFSbCTo+ExmYSdiTuLiP5/Th3aB98O7fh3KF9kPwyJzzoFKED1EXprDxBwkz+LZ7vMduDE6BxdnpjRHc072tsxDXlrQO4JR+uqqpCeXk5tmzZgsLCQgDAiy++iFtuuQXPPPMMsrOjT1JITk5GVlZWxO/ef/99fPnll/jggw+QmZmJIUOGYP78+Xj00Ufx29/+FgkJCarsFWGlBlbJNvWoCytbA8LOzkmKG6p2oKZ8DVp8lyNXHVJSkVF2G7rkDWJ2nNDImB6CTq4tZsRs/s0IzOSH5BI4ltHPCr0xQvyY8RzHs1mPwA63SN3GjRvh8XiCDg8ASkpK4HQ6sWnTppi/XblyJbp164b8/HzMmjUL586dC9vvwIEDkZmZGdxWWloKn8+H3bt3R9xfc3MzfD5f2CcSvLph/a0Svtzkw2d/O42BhysgtUaPXrBqyHq8VbG86ewatWuo2oHjby4LE3QA0OKrx/E3l6GhakfU36qNgukl6FRPkDBB16tI/g2Q5+OsGK0DaJwdT4yaLKLHNWVdLxEEHcAxUuf1epGRkRF+sA4dkJaWBq/XG/V3P/3pT9GrVy9kZ2djx44dePTRR7F3716sXr06uN9Qhwcg+He0/S5cuBDz5s3TUh3VbFlXixULjqDWeyG4LS3rAHKn3ITsYX24H5/32zLLN2W9o3aS34/zRw6gpcGHDl1SkNQzFw6nfhMBJL8fNeVrYpapKX8Hnfvlt7NLjWBSM36Ox8QIs0fpALH8GyDfx8VauhBgH/nSqwfEiCXFrBy1M0q0WvFc6o1ijz1z5sx2A33bfvbs2aPaoMmTJ6O0tBQDBw7E2LFjsWLFCqxZswb79+9Xvc9Zs2ahvr4++Dl69GjUsiyjdVvW1eK5qfvCBB0A1FZfwNbZ5Tj+ceQ6sW7YekTsWEfteI+5a6jagQPPz8fR5X/AidWv4ejyP+DA8/NjRsZYc/7IgXYRura0+Opw/siBsG1mGKfGwkZXfj8GlijDjP4NUObj9IoYBNBLIBgxgQKwXuoTOwg6q0bpABWRuhkzZuDee++NWSY3NxdZWVmoqakJ297S0oLa2tqo40kiUVRUBADYt28f+vTpg6ysLGzevDmsTHV1NQBE3a/b7YbbzS76s8p7fdyL5G+VsGLBEUCK8KUEwAHseuGf6H5jbzhc/KNDZh3fwiN6F+jybEugyzP7znuZjmWLRktD5GEAscrpKeiMiNIZ3fVqRv8GKPdxsSJ2RkS9WGKU/WaO3Bl9vUnQsUOxqEtPT0d6enrccsXFxairq0NlZSUKCgoAAB9++CH8fn/Qkclh+/btAIDu3bsH9/vEE0+gpqYm2P2xfv16pKSkYMCAAYrqkp9zHF9W57bbzkIA7dna0C5CF4YENNU04vSOE+h27ZXtvubhmPRwOrwcamjUTovA09LlyZoOXVIUlTOLoGMSpRuUh5ZW/cdZWsm/acGs3bABjBSmoccVXeCZRcxJrX6c3nECTafPIvGKTrhiUHddgiFa0VvQARwnSuTl5aGsrAyTJk3C5s2b8emnn2LKlCkYM2ZMcGbY119/jf79+wffTPfv34/58+ejsrIShw4dwl//+leMGzcO3/3udzFo0KXIyc0334wBAwbgnnvuwRdffIF169Zh9uzZ+MUvfsE0Gqe1G7au5qKs4zSdPhv1O5GWRFEC726Q0O5ZpV20ars85doS6RONpJ656JASe5xbhxQPknrmmkbQxULuWDrXoDwux2eJ2f0bYN2JEwGM6o4NRcSu2YBNRtsl99oc/3g/1t+xAp89+A62zVuPzx58B+vvWBF1+FIs9IzSGSHoAM556lauXIn+/ftj+PDhuOWWW3DjjTfipZdeCn5/8eJF7N27Nzj7KyEhAR988AFuvvlm9O/fHzNmzMDtt9+Ov/3tb8HfuFwurF27Fi6XC8XFxbj77rsxbtw4PP7446psjHVRtAg7T0ZHWccfcXX0sS8AX2FnptmxsVAiptR0eaoRbHJwOJ3IKLstZpmMslHoqm24lSK0Cjot4rN1R5UpBF0AM/i3eOj54LHDWK1ohAopo7qGRRBygDKxffzj/dg6uxxNJ8ODH00nz8Ycl64HLNrV6prrGFgSjkOSpEijviyNz+dDamoqRpRPQsdOCTEbutrs0P5WCQ997wvUVl+IPK7OAaRlJeD5jwbjrZNDY+7fLAmFoyGCIwnQVHUA1U++HLdc5sxJSMxr3zWvhnjdxZHz1HmQUTYKPTrkM7FBDjwFnZoZry2tzajYtQj19fVISZHXVU1cIuDjXt5WgOQurrjlY72g6j0GiSci+aJQWJ4TUesIKKun1OrH+jtWtBN0oSRmdMZ/vnmPrK5YlueF1Ti61/YPxt/LXmbq47ilNDETscZfqB0L4nQ5MG52Tzw3dR/gQLiwc1z6Z9xvesLpcuieZiASPMe8BPYrgrNx98uBq2sqWs9EFxmutFS4++UwOZ6c8X9d8gahc7/8dulVzBShi4UVUpjYGbNPnAhF1LqIaBNL1DxbTu84EVPQAbHHpYei5/k1qts1gPgjDQVAbTfs9aVpmPZiX6RlhmeBT8tKwLQX++L60rTgNhHGt/AOz4vQDeJwOpF29w9ilkkb+wMmkySUTOhwOJ1IzumLlIHXITmnr+kEnRnSrBDR0dP/GC1gRBhrZxe0nOtY482VlBN1HB2vNeVJ1H1DvIanRdg9v2EwfvNaf/zi2T74zWv98fxHg8MEHSsbWcFz/IUIzjS5MB/pU8fC1TV8koIrLRXpU8ciuVB7l6eWGbpWmBQRgKJ0xqJkzI6dhB1A4o43Ws9t4hWdmJZjgYgTI9pC3a8MiZa/zulyYEBR/P7yeN2wgDXWOhShCyS5MB9J1w1A895DaK3zweVJgbtfju4RuraYUdBRlM46yPFBrBBhvW1ArOEhVoDVNb1iUHckpneKO6buikHdo36v5zg6JfC8xyhSF4LWaB2g/WKJovbbItLsKVY4nE4k5uWiU/EQJOaxWSKMBF3IdxSlEwKlPimWDxJBhPGCInfaYH3+HC4n8h/6Tswy+Q/eGHWShIgTI/SAInUKkfN2KWfFiViIMHEiFmZKrqkndhN0sSBBJzb+Vgl7tjagruYiPBkd0b+wC5wuR/B7vVac0Bqta+tnWURAKHInH9b+v931HHY9CheUYdfz/wiL2CVmdEb+gzdGXT9dZEHHOxJOoq4NchyWCN0GRgu7AG1tkHNeRLCbJVqXMNNL0LEWc9Ttah5CXzS3rKvFigVHwla8SctKwLjZPdtN3jKDsAslUEeW4g6wns/SCsvnXzxRlD2sD7rf2NuQFSXMFKELQKJOJfEcEe9oHSCOsAtFNHt4Y9fonB7drnV5KcAuJrsivmHLutpLaZba5M6srb6A56buazcrPxaiCjuArbgDKHoH8I/KxcLhcsZNWxJAr2uk5vmux3hVW4+p+3HGtojbWTVePcbXGR0xtCuJB9y2FXSxoG5XcXnj6wKsWHAkcjL0b7ateOII/K2XC4g+Izaejx2dtYVpNCUwbsxOfpd1feVeEzXPT726XUWM0AWwtaiLBatuRBJ21kKrmAPML+j0sL/umthr4hLKOb3jRFiXazskoPbEBezZ2hC2WYQcmlphLe4A6wq80HoZIeYAawo6vWaV217Uab3RSdjZA1ZiTs/xc3oLOmbdriTouCA3mWtdzcV22/QSdjyidaEEhAVPgWdGX8zTdqXn24qCTk9sL+piIbeBk7CzLizEHGD+6BxAgs7syE3S6snoGHG7VYRdAB7iLkBbkSeSb9bLNp7nNxQzCDq9onQATZQAwCbhph6pTuQg4uQJM8JCyAHWEHOENYibzNVxaRZs/8IuUfehV7olNRMn1PpXLZMqpFa/7FmZ0erDy18bISS1Pt+UXgMzCLpY7DqUzXyfDkmSIg2btTQ+nw+pqal4eVsBkru4gtujNSglDUfujcS7X56EnXJYCbkAVhJ0RkTpWi42ofKt2aivr0dKSvwVWYjLBHzciPJJ6Njp8trTxz/ej62zywEHwidMfJOiTu7s13h+iJX/USNMWDyA5fjZ4x/vb58/Lb0T8h/6TtT8aVZFr3MeilkEXax6fb67G47eN4+pj6NXexkocSxyG5rayKDcBqZ53b1vuh1DP1aER/2sMHYuFOp2NS9tIwHZw/qgcEEZEruFd8WmZSUoSmdita7YtsTrOgyI47ZRz6aTZ7F1djmOf7xfsw2iw2p84irv9YYJunjdzzx71ngFXihSFxKpA9hE6wBrROzkCJ2m3GbV+zcKXgJV72S8uqwMYaCgo0idegI+rseSubj2mlPtvg90G17bsjPiihJysXrELpRAXaVWP9bfsSLumqT/+eY9uiTI1RNe51QJerUpntHHQB3855soUsebaBdSqVMRKWLHc2yFGSJ6vO3TMzIH6BOdAyh1iVWI5IsCyVxv+OEVGFCUokrQAeJH7FgOUA9EpQYd+yimoAOApppGnN5xgtmxjYTXjGG7CjrekKhTgFmFHaDOuaqNwkUSenqIPb2Pa1UxB8QXdHokGa7vQ+6JN6y6KmPB6sVS7QOd9cM1UsqXSMhNISMivIRcAKsLuljwHu/O1WvW1tZi7NixSElJgcfjwcSJE9HY2Bi1/KFDh+BwOCJ+3nrrrWC5SN+/8cYbiu37SeczEbezvKhKhJ3aaflyUSvsWHWxRhNdrD56YWUxJwcaR3cJ0f1bKLH8kB7CDmATtfviwFWGjbMLEC3lS1vkppAxmlABxzsNidrnnNkEnVFROoDzmLoRI0bgxIkT+OMf/4iLFy9iwoQJuP766/H6669HLN/a2oqTJ0+GbXvppZfw9NNP48SJE+jcufMlox0OvPrqqygrKwuW83g8SExMlGVXYLzJmX/n4j1Ht6jlWI2vA5Q5NN5ryqm9QUTsWtUTK46Zi4QIEyPqrnaitbkJ+578tbBj6kT1b0D4mDpn0uXfRfNDej7MjBxnB2ivq79VwkPf+wK11RciL7n2TWqY5z8aHNatbeSDPoCRyXPV1t9Kgq5tXXiMqeOWp66qqgrl5eXYsmULCgsLAQAvvvgibrnlFjzzzDPIzm6fn8XlciErKyts25o1a3DnnXcGHV4Aj8fTrqwaxnQ+gzcauyr6jZpcTIHychwR73x2ahenbspttqWws4uYA8QRdKJjFv8mF1Y+R07ONyNz2QHa6+p0OTBudk88N3Vf1NQw437Ts904RV7rnSo9hhEYKejktBGzJUmOBbdI3dKlSzFjxgycOXO5i7OlpQWJiYl46623cNttt8XdR2VlJQoLC/Hpp5/ihhtuuGy0w4Hs7Gw0NzcjNzcX9913HyZMmACHI/Jg3+bmZjQ3X+5C9Pl86NGjB878Oxcp38x+jSbslKhuufCK2ukZ1raDuLNSnjk5xKpv6q4zOHP2CJovNsLdsTO6duoJh0O5zUoEnciROpH8GxDdx7WN1AH6JVnVK2qnpVtXS323rKvFigVHwtbTTeuegHG/6Sk7NYzVET06B+jX5iPVyVSROq/Xi4yMjPCDdeiAtLQ0eL1eWft45ZVXkJeXF+bwAODxxx/HTTfdhOTkZLz//vt44IEH0NjYiAcffDDifhYuXIh58+apqgeL1SbaouQNU8lbpRpb1b41WzlqZzcxB8Suc/On/8LHx9eh+eLlhd7dHbsgL7sUmZ482cewQoQugEj+DVDm49RGuJQixx+xiNppqY+WqN31pWkoKOmKPVsbUFdzUVNqGKuh5ZlpF0HHC8VedObMmVEH+wY+e/bs0WzY+fPn8frrr2PixIntvnvsscfw7W9/G9deey0effRRPPLII3j66aej7mvWrFmor68Pfo4ePdquzJgokyYAdmlOQlFykZUMLlXTSNXOTmM5iUIE9JoAEZj4IIqIiVXn2qM7sf3w22GCDgCaLzZg++G3UV1XJesYZhF0ZvRvgDwfJwfWL7CiT6AAtKU+cbocGFCUojk1jFXQmkaGVeRWzvg5vbqq9V7dSXGkbsaMGbj33ntjlsnNzUVWVhZqamrCtre0tKC2tlbWWJG3334b586dw7hx4+KWLSoqwvz589Hc3Ay3u330yO12R9zeFr3G1wVQ+oYZuFniNUa10UU7R+14izlRREtbYtVb8vtx5F+rY/5+z/H3kZHaL2ZXrFkEHWBO/wbI93EBYvke1mN65UbsAnZpQWvUDhB3XJroGC3mAOPGz4kwCSaAYlGXnp6O9PT0uOWKi4tRV1eHyspKFBQUAAA+/PBD+P1+FBUVxf39K6+8gh/96EeyjrV9+3Z07dpVkVNTSizHpFXYBfYhF57OR8skCsB8Y+14iTmRhEo04tVd2rKzXYSuLU0XfThz9gjSOudE/N5Mgg6wl39jLez8rVLUrkg5EygAdt2xgX2pgcSdMrQKGj3HVeot6IxYg53bmLq8vDyUlZVh0qRJWLJkCS5evIgpU6ZgzJgxwZlhX3/9NYYPH44VK1Zg6NChwd/u27cPn3zyCd577712+/3b3/6G6upqfOtb30JiYiLWr1+P3/3ud/jVr37FxO5Y0Toe4+sCqHnD5C3u1EbtAPOIu4Co0CLuRBMmcpCTXPjExeg510JpjlLObIJOCWb1b21hNb4u4qSBrASMmx0+acAsUTuAxF0sWDwH9U5tYwdBB3AUdQCwcuVKTJkyBcOHD4fT6cTtt9+OF154Ifj9xYsXsXfvXpw7dy7sd0uXLsVVV12Fm2++ud0+O3bsiMWLF+OXv/wlJElC37598eyzz2LSpEnM7FYj7Iwc8MtLaGpxrmYVd3ZA7moR7o6dY5YLEKmclQVdALP6N7nIjdZtWVd7Kb1HmzwKtdUX8NzUfZj2Yl/Fwg4QI2oHhPtXuws8Vs8as0fnADEFHcA5+bCohCYfDqQ0aUu8sXUsExNHQo/ZaUrRWjezCDwro2T5L0ny4+OqF2J2wSZ2TMF386aGjaljJehETmkiOtGSD0dDbZqTYCLekAhdGFES8QaQKxKMTn3SFjuJO5ZBAytE5wKwEHU8UpqY/3WZE7FmwwJ8ZsSGYqTSj4bWNRytNluWF4Hz1PajFaXruTocTuRll8b8Tf/sm7kIOkJf1C4jtmdrQ3RBBwASUHviAvZsjfxiIPehy2L9WC0zZNsSmOUp0gB5lvCon14zWwH+s1tFjdIBnLtfzY6a2bAA24zpgf2JhNYxL6EChaJ34C505YwXjLZaRKYnD0PwE1S1yVOX2DEF/bNvDstTR4LO3KiZOCF3cftY5eROogDYjLdj7Vet0j3Lc7y4Vngvs6kEkQUdQKJOE7xmxLZFr2ShSmHhYO0o8PSMVmoRdAEyPXnISO0XdUWJeGIOIEFnBSIJO7mL28spp2Qimijj7drS1n6RRR7vKKPVxBwgvqADbC7q3m7siv/q4otZJl60Tk9hF9inaLCardZW7FhF5LEQcWrOBQtBF8DhcEZMW8JT0KXu13fNXUL5C2T/wi5Iy0qIu7h9/8Iusvand9Qu8HtefjVaPfQWe3p2Exsx/tGsgi4/5zjUpQmPjq0nSry8rQDJXVxxx88B6idOAHwUvIjiLgCvNxYziDweUTijBV00eHe3dv7yHCrfmk0TJVSgdKJEW5RMnAjOfgUiLm7fdvarXJQKEatNUpMrVEQZ12dVMQfEP8dq6j449xgunr2Av5e9zNTHkaj7ZvarGYUdII4DioRe4WgjxJ5eXahK66aHmAP4CzrPV360XGwiUaeSgI8bUT4JX1bnqtqHUmHHa3F7u4u7eEitfpzecQJNp88i8YpOuGJQdzhc+g13MGpmstkFHQASdayIJOqA+MJOzqQJrcJO8vvRvPcQWut8cHlS4O6XA4cz9g1qBucjyniDAKFiSdQZuSIKOj3GzwXqQaJOPaGirmOnBNX3nxJhF2tFCRaoiUiJlgqFNcc/3o9dz/8DTSfPBrclpndC/kPfQfawPlyPbXUxB/AVdACJOmZEE3WAscLu3NZdqH1tLVrPXH7wurqmIu3uHyC5MD/ucUV2PgFEE3ciYtbuVoCdoANI1GmhragD1N97anPY8UBtV6MVo3fHP96PrbPLo35fuKCMubAz8jyK1ta0CjqARB0zYom6ANHEndwUJ0qF3bmtu3DyxZVRf5M+dawsYQeI5XhiQQKvPTyic4D5BB1Aok4LkUQdYA1hBxi/eLzRPlZq9WP9HSvCInRtSczojP988x7NXbFGC2IR2xcLQQfwEXWUZyAK0cSbnLF3QOyG2PbiSn4/al9bG3N/tSvXQvLLe4CzTLLJk0AiSaMdpCiIKOjqrkmVNX6OUpaYAx73mhED9bUkl2WZyNgoP3t6x4mYgg4AmmoacXrHCVX7Z1U/LT6edwLhaOgl6Hhh65Qm8XijsWtEESc3KbHcdCfNew+FdblGorW2Hs17DyExT/6g59DGJ7pwCrXPDIKUJbzEHKBd0MUtw0jMKakToQ01qZbipf2Qu04sa5SkQGkLK5/T9rd6+Nqm07EFndJygPERuQBG5vbTW9D9OGMb/q54j7GxtahbXXMd7u7yRcwyAfEmN0LXFjnCrrUudq68AHLLRULkPHdtsYvA4zV2DuAv5gD+gs5Tpb69E5f4ccY2/O3st9ptV5PjTVRhB2gTdwBbn6OHyEu8opPmcix9K4s6Gp2oWW9BNzprC85FX1ZbNbYWdYB8R6RmubAA8YTdpip5fekuj/Y+dzNF7wDrCjyKzl0iqqDbXY8WZkexNywTpIss7ADt4g5o7xdZi7xIx1DKFYO6IzG9U9wxdVcM6h7VBq2wen4YLeYAYwQdL2w9USJ0ELGRGamlVj/eu+31mF2wrrRUXPnfj8RNb6IGM4i7tphV4PGMzgHqBZ3e0TkgtqADgJbWZlTsWkQTJVQQaTIYyzyachdVFwHWY/709D2xznO82a9KJtdpsUMtZmkfPAXduYZWTLqukma/aiXazDAjhZ2eN2gszCbwzCLueIs5QJ2gkyvmAH0EHUCijgXRZvjbVdgBfCd0GOWHIqbBSktF2lh5abCiwes5IFJ7AIyP0JGoY0Q0UQcYL+zaJpJkcYNqQXSRJ7qoIzEXmXhROoBEnRZipW2ys7ALoNeMXT38k5qE9YC+vt2MbYDlvRCt/iTqGBFL1AHGd8We3nEC//7SregG1QvRRJ6ook4PMQcoF3RKxBxgnKADSNRpIV4uTjMJu7aT1LSMb26LldZNDSCKj2b5HGXdBkQQdABwi3QKXf/jAIk6rcQTdYCxwi6Amhtd7+WvjHQgogk6tWvQqk3noUTQiSDmAPmCDiBRpwU5CdZZ+h+ewi5W5gErCjwzw1PIhaL2usu5xnoJujGdz8DX0Mpc1HELAT3xxBO44YYbkJycDI/HI+s3kiRhzpw56N69O5KSklBSUoKvvvoqrExtbS3Gjh2LlJQUeDweTJw4EY2Njaps3HUoO+p3q7zXc7/J4yVX1CqYEg+4wz48CE1SyTshpx7HUIPa8+v5yq86OidX0MlJHhxWnmMiYSWCDgBad+3lYgcrzODjftL5TNSHI0v/I+d+VOtTYz3Ax3xTP7Upp0IJnA8RuwpFhvV5Y3U92yKaoOMFt0jd3Llz4fF4cOzYMbzyyiuoq6uL+5unnnoKCxcuxPLly9G7d2889thj2LlzJ7788kskJiYCAEaMGIETJ07gj3/8Iy5evIgJEybg+uuvx+uvvy7btsBbbI8lc+FMSozrvMwWtZMjMERdxN4saBHJWhLtKhFzSuG5KoRiQbejCi3SRWzAX4SN1JnBx535dy5SvonUxRJHZuiOVfIgZBnBAyiK1xbWz0S511aUCJ0WzRBaVx6ROu7dr8uWLcO0adPiOjxJkpCdnY0ZM2bgV7/6FQCgvr4emZmZWLZsGcaMGYOqqioMGDAAW7ZsQWFhIQCgvLwct9xyC44dO4bs7OiRt1DaijpA20ViBevuWCXCg0RefLRGO7WumiBH0Ikm5gB1gg6A8KIugMg+LlTUAeqFHSBGd6yaCAdrgQfYT+TxeP4pvZZqrqPc62SEoAP4iDphkg8fPHgQXq8XJSUlwW2pqakoKirCxo0bMWbMGGzcuBEejyfo7ACgpKQETqcTmzZtwm233RZx383NzWhuvixa6usvPUz85y9v+3x3NwBAfs7xiPt4bf9g/Dhjm/oKyuCHnf6F1TXXRf1+QOaBmF3GbTnXvQnuQ/KESMeq8L+bc0jkAQg7f61oUrWP1P2XRI2WZLqeKl/U39flhTiDi/JtrO/zjZjjeKlT9/sj2h2tPqFdri24COCSGLICRvg4X2O4oL4Fp/B2lIcjS//z+e5uUX1pgNf2DwYARX51acOltv4TBYLgFpwCgKj1VsMPO/2r3bZY585MRLoerFY++ElYlEr+7y5du1ZFx7p0PS7ELXepTcf3m4H2fDFKvufAeYt0rgL1blvnwP3J0scJI+q8Xi8AIDMzM2x7ZmZm8Duv14uMjIyw7zt06IC0tLRgmUgsXLgQ8+bNa7f9618+2W7b0Rg2sl6jLTKVuhyFsBC7jDaAL6dPn0ZqqvIIpGgY4eN6XXdIoZXs/E8sXxqKGr86ScVv+GMN383zOafvdWN7PeK151jnLV69Wfo4RaJu5syZeOqpp2KWqaqqQv/+/TUZxZpZs2Zh+vTpwb/r6urQq1cvHDlyxBIPi1j4fD706NEDR48eFboLSyt2qSdgn7rW19ejZ8+eSEtL0+2Y5OPMhV3uBcA+dbVLPQE+Pk6RqJsxYwbuvffemGVyc3NVGZKVlQUAqK6uRvfu3YPbq6urMWTIkGCZmpqasN+1tLSgtrY2+PtIuN1uuN3tuyFTU1Mt32gCpKSk2KKudqknYJ+6OnXM00g+zpzY5V4A7FNXu9QTYOvjFIm69PR0pKenMzt4KL1790ZWVhYqKiqCDs7n82HTpk24//77AQDFxcWoq6tDZWUlCgoKAAAffvgh/H4/ioqKuNhFEIR9IB9HEISZ4fYKfOTIEWzfvh1HjhxBa2srtm/fju3bt4flW+rfvz/WrFkDAHA4HJg2bRoWLFiAv/71r9i5cyfGjRuH7OxsjBo1CgCQl5eHsrIyTJo0CZs3b8ann36KKVOmYMyYMbJnhREEQbCAfBxBEMIhcWL8+PESgHafjz76KFgGgPTqq68G//b7/dJjjz0mZWZmSm63Wxo+fLi0d+/esP2ePn1auuuuu6TOnTtLKSkp0oQJE6SGhgZFtjU1NUlz586VmpqatFTRFNilrnappyTZp66i15N8nPHYpZ6SZJ+62qWeksSnrrZcJowgCIIgCMJqiLNSPEEQBEEQBKEaEnUEQRAEQRAWgEQdQRAEQRCEBSBRRxAEQRAEYQFsI+qeeOIJ3HDDDUhOTobH45H1G0mSMGfOHHTv3h1JSUkoKSnBV199xddQjdTW1mLs2LFISUmBx+PBxIkTw1IsROJ73/seHA5H2Oe+++7TyWL5LF68GDk5OUhMTERRURE2b94cs/xbb72F/v37IzExEQMHDsR7772nk6XaUVLXZcuWtbt+iYmJOlqrjk8++QQ//OEPkZ2dDYfDgXfeeSfubzZs2IDrrrsObrcbffv2xbJly7jbaQbs4t8A8nGhmNXH2cG/Acb4ONuIugsXLuCOO+4IJvmUw6JFi/DCCy9gyZIl2LRpEzp16oTS0lI0Nalb2F0Pxo4di927d2P9+vVYu3YtPvnkE0yePDnu7yZNmoQTJ04EP4sWLdLBWvmsWrUK06dPx9y5c7Ft2zYMHjwYpaWl7bLvB/jss89w1113YeLEifj8888xatQojBo1Crt2ib9QqtK6Apeyr4dev8OHD+tosTrOnj2LwYMHY/HixbLKHzx4ECNHjsT3v/99bN++HdOmTcPPfvYzrFu3jrOl4mMX/waQjwtgVh9nF/8GGOTjmCVHMQmvvvqqlJqaGrec3++XsrKypKeffjq4ra6uTnK73dKf//xnjhaq58svv5QASFu2bAlu+/vf/y45HA7p66+/jvq7YcOGSQ899JAOFqpn6NCh0i9+8Yvg362trVJ2dra0cOHCiOXvvPNOaeTIkWHbioqKpJ///Odc7WSB0rrKbdMiA0Bas2ZNzDKPPPKIdM0114RtGz16tFRaWsrRMnNhZf8mSeTjQjGrj7Ojf5Mk/XycbSJ1Sjl48CC8Xi9KSkqC21JTU1FUVISNGzcaaFl0Nm7cCI/Hg8LCwuC2kpISOJ1ObNq0KeZvV65ciW7duiE/Px+zZs3CuXPneJsrmwsXLqCysjLsWjidTpSUlES9Fhs3bgwrDwClpaXCXrsAauoKAI2NjejVqxd69OiBW2+9Fbt379bDXF0x6zUVETP6N4B8XChmvB/Iv8WGxTVVtParnfB6vQCAzMzMsO2ZmZnB70TD6/UiIyMjbFuHDh2QlpYW0+af/vSn6NWrF7Kzs7Fjxw48+uij2Lt3L1avXs3bZFmcOnUKra2tEa/Fnj17Iv7G6/Wa6toFUFPXfv36YenSpRg0aBDq6+vxzDPP4IYbbsDu3btx1VVX6WG2LkS7pj6fD+fPn0dSUpJBlpkPM/o3gHxcKGb0ceTfYsPCx5k6Ujdz5sx2AyjbfqI1FDPBu56TJ09GaWkpBg4ciLFjx2LFihVYs2YN9u/fz7AWBC+Ki4sxbtw4DBkyBMOGDcPq1auRnp6OP/7xj0abRmjALv4NIB9HRIf8mzJMHambMWMG7r333phlcnNzVe07KysLAFBdXY3u3bsHt1dXV2PIkCGq9qkWufXMyspqN9i0paUFtbW1wfrIoaioCACwb98+9OnTR7G9rOnWrRtcLheqq6vDtldXV0etV1ZWlqLyoqCmrm3p2LEjrr32Wuzbt4+HiYYR7ZqmpKRYMkpnF/8GkI+zi48j/xYbFj7O1KIuPT0d6enpXPbdu3dvZGVloaKiIujkfD4fNm3apGiGGQvk1rO4uBh1dXWorKxEQUEBAODDDz+E3+8POjE5bN++HQDCnL2RJCQkoKCgABUVFRg1ahQAwO/3o6KiAlOmTIn4m+LiYlRUVGDatGnBbevXr0dxcbEOFqtHTV3b0traip07d+KWW27haKn+FBcXt0vZYIZrqha7+DeAfJxdfBz5t9gw8XFqZnGYkcOHD0uff/65NG/ePKlz587S559/Ln3++edSQ0NDsEy/fv2k1atXB/9+8sknJY/HI/3lL3+RduzYId16661S7969pfPnzxtRBVmUlZVJ1157rbRp0ybpn//8p3T11VdLd911V/D7Y8eOSf369ZM2bdokSZIk7du3T3r88celrVu3SgcPHpT+8pe/SLm5udJ3v/tdo6oQkTfeeENyu93SsmXLpC+//FKaPHmy5PF4JK/XK0mSJN1zzz3SzJkzg+U//fRTqUOHDtIzzzwjVVVVSXPnzpU6duwo7dy506gqyEZpXefNmyetW7dO2r9/v1RZWSmNGTNGSkxMlHbv3m1UFWTR0NAQvA8BSM8++6z0+eefS4cPH5YkSZJmzpwp3XPPPcHyBw4ckJKTk6WHH35YqqqqkhYvXiy5XC6pvLzcqCoIg138mySRjwtgVh9nF/8mScb4ONuIuvHjx0sA2n0++uijYBkA0quvvhr82+/3S4899piUmZkpud1uafjw4dLevXv1N14Bp0+flu666y6pc+fOUkpKijRhwoQwx37w4MGweh85ckT67ne/K6WlpUlut1vq27ev9PDDD0v19fUG1SA6L774otSzZ08pISFBGjp0qPSvf/0r+N2wYcOk8ePHh5V/8803pf/4j/+QEhISpGuuuUZ69913dbZYPUrqOm3atGDZzMxM6ZZbbpG2bdtmgNXK+OijjyLek4G6jR8/Xho2bFi73wwZMkRKSEiQcnNzw+5XO2MX/yZJ5ONCMauPs4N/kyRjfJxDkiRJVZyQIAiCIAiCEAZTz34lCIIgCIIgLkGijiAIgiAIwgKQqCMIgiAIgrAAJOoIgiAIgiAsAIk6giAIgiAIC0CijiAIgiAIwgKQqCMIgiAIgrAAJOoIgiAIgiAsAIk6giAIgiAIC0CijiAIgiAIwgKQqCMIgiAIgrAAJOoIgiAIgiAswP8H5wZhcmjWkQYAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -471,14 +450,6 @@
    "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/30298674.py:37: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
-      "  optimizer = BayesianOptimization(\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -567,6 +538,7 @@
     "- `to_float` transforms the canonical representation of a parameter into float values for the target space to store. There is a one-to-one correspondence between valid float representations produced by this function and canonical representations of the parameter. This function is most important when working with parameters that use a non-numeric canonical representation, such as categorical parameters.\n",
     "- `to_param` performs the inverse of `to_float`: Given a float-based representation, it creates a canonical representation. This function should perform binning whenever appropriate, e.g. in the case of the `IntParameter`, this function would round any float values supplied to it.\n",
     "- `kernel_transform` is the most important function of the Parameter and defines how to represent a value in the kernel space. In contrast to `to_float`, this function expects both the input, as well as the output to be float-representations of the value.\n",
+    "- `to_string` produces a stringified version of the parameter, which allows users to define custom pretty-print rules for ththe ScreenLogger use.\n",
     "- `dim` is a property which defines the dimensionality of the parameter. In most cases, this will be 1, but e.g. for categorical parameters it is equivalent to the cardinality of the category space.  "
    ]
   },
@@ -641,21 +613,7 @@
       "-------------------------------------------------------\n",
       "| \u001b[39m1              \u001b[39m | \u001b[39m0.4572         \u001b[39m | \u001b[39m0.29|0.70|0.00 \u001b[39m |\n",
       "| \u001b[35m2              \u001b[39m | \u001b[35m0.5096         \u001b[39m | \u001b[35m0.58|0.25|0.15 \u001b[39m |\n",
-      "| \u001b[39m3              \u001b[39m | \u001b[39m0.5081         \u001b[39m | \u001b[39m0.58|0.25|0.15 \u001b[39m |\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/626783072.py:8: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
-      "  optimizer = BayesianOptimization(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "| \u001b[39m3              \u001b[39m | \u001b[39m0.5081         \u001b[39m | \u001b[39m0.58|0.25|0.15 \u001b[39m |\n",
       "| \u001b[35m4              \u001b[39m | \u001b[35m0.5386         \u001b[39m | \u001b[35m0.44|0.28|0.26 \u001b[39m |\n",
       "| \u001b[39m5              \u001b[39m | \u001b[39m0.5279         \u001b[39m | \u001b[39m0.38|0.14|0.47 \u001b[39m |\n",
       "| \u001b[39m6              \u001b[39m | \u001b[39m0.5328         \u001b[39m | \u001b[39m0.18|0.36|0.45 \u001b[39m |\n",
@@ -696,6 +654,7 @@
     "    random_state=1,\n",
     ")\n",
     "\n",
+    "# Increase the cell size to accommodate the three float values\n",
     "logger = ScreenLogger(verbose=2, is_constrained=False)\n",
     "logger._default_cell_size = 15\n",
     "\n",
@@ -727,21 +686,7 @@
       "| \u001b[35m2              \u001b[39m | \u001b[35m0.5096         \u001b[39m | \u001b[35m0.15|0.25|0.58 \u001b[39m |\n",
       "| \u001b[39m3              \u001b[39m | \u001b[39m0.498          \u001b[39m | \u001b[39m0.06|0.33|0.60 \u001b[39m |\n",
       "| \u001b[35m4              \u001b[39m | \u001b[35m0.5097         \u001b[39m | \u001b[35m0.13|0.27|0.58 \u001b[39m |\n",
-      "| \u001b[35m5              \u001b[39m | \u001b[35m0.5358         \u001b[39m | \u001b[35m0.19|0.36|0.43 \u001b[39m |\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/lh/5r0ljfq55b72g4z_69q8svb40000gq/T/ipykernel_23039/2335620304.py:20: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.\n",
-      "  optimizer = BayesianOptimization(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "| \u001b[35m5              \u001b[39m | \u001b[35m0.5358         \u001b[39m | \u001b[35m0.19|0.36|0.43 \u001b[39m |\n",
       "| \u001b[35m6              \u001b[39m | \u001b[35m0.5443         \u001b[39m | \u001b[35m0.33|0.33|0.33 \u001b[39m |\n",
       "| \u001b[39m7              \u001b[39m | \u001b[39m0.5405         \u001b[39m | \u001b[39m0.28|0.28|0.42 \u001b[39m |\n",
       "| \u001b[39m8              \u001b[39m | \u001b[39m0.5034         \u001b[39m | \u001b[39m0.01|0.49|0.49 \u001b[39m |\n",
@@ -785,20 +730,6 @@
     "\n",
     "optimizer.maximize(init_points=2, n_iter=8)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {