Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c89cdaa8",
+   "id": "1712a456",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79064ab7",
+   "id": "5de82c94",
    "metadata": {},
    "source": [
     "\n",

_downloads/36007a4d548729b824a1364eb165d070/polynomial_custom_function.ipynb

@@ -60,8 +60,11 @@
     "        \"\"\"\n",
     "        In the forward pass we receive a Tensor containing the input and return\n",
     "        a Tensor containing the output. ctx is a context object that can be used\n",
-    "        to stash information for backward computation. You can cache arbitrary\n",
-    "        objects for use in the backward pass using the ctx.save_for_backward method.\n",
+    "        to stash information for backward computation. You can cache tensors for\n",
+    "        use in the backward pass using the ``ctx.save_for_backward`` method. Other\n",
+    "        objects can be stored directly as attributes on the ctx object, such as\n",
+    "        ``ctx.my_object = my_object``. Check out `Extending torch.autograd <https://docs.pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd>`_\n",
+    "        for further details.\n",
     "        \"\"\"\n",
     "        ctx.save_for_backward(input)\n",
     "        return 0.5 * (5 * input ** 3 - 3 * input)\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3eef1456",
+   "id": "a868b114",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94777eff",
+   "id": "5d646d60",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0c5ea646",
+   "id": "c095ce67",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "261289fd",
+   "id": "98194279",
    "metadata": {},
    "source": [
     "\n",

_downloads/71d2368d801179561137f31cc6d0a837/polynomial_custom_function.py

@@ -33,8 +33,11 @@ def forward(ctx, input):
         """
         In the forward pass we receive a Tensor containing the input and return
         a Tensor containing the output. ctx is a context object that can be used
-        to stash information for backward computation. You can cache arbitrary
-        objects for use in the backward pass using the ctx.save_for_backward method.
+        to stash information for backward computation. You can cache tensors for
+        use in the backward pass using the ``ctx.save_for_backward`` method. Other
+        objects can be stored directly as attributes on the ctx object, such as
+        ``ctx.my_object = my_object``. Check out `Extending torch.autograd <https://docs.pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd>`_
+        for further details.
         """
         ctx.save_for_backward(input)
         return 0.5 * (5 * input ** 3 - 3 * input)

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bf2bba0d",
+   "id": "c47d3657",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efa41e29",
+   "id": "f780c92a",
    "metadata": {},
    "source": [
     "\n",

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8fc086ce",
+   "id": "ee972872",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f40d97f4",
+   "id": "c5392049",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9ae2705b",
+   "id": "1d6bd52b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8abb154",
+   "id": "45cfaab1",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bc7f6850",
+   "id": "dd60e2ba",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e400474",
+   "id": "382015c9",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "416cb5eb",
+   "id": "33ec1b84",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78da22be",
+   "id": "98dfdbbd",
    "metadata": {},
    "source": [
     "\n",