From d6e259bbe99f82be49685197f7d12fd57132daee Mon Sep 17 00:00:00 2001
From: gouzil <66515297+gouzil@users.noreply.github.com>
Date: Thu, 6 Jul 2023 20:04:52 +0800
Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9COPY-FROM=20No.=203=20autogra?=
 =?UTF-8?q?d=20(#54921)?=
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* [autograd] add copy-from; test=document_fix

* [autograd] add copy-from; test=document_fix

* fix
---
 python/paddle/autograd/py_layer.py | 48 ++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/python/paddle/autograd/py_layer.py b/python/paddle/autograd/py_layer.py
index 08c4fb9ac8d..d2dd31f08dc 100644
--- a/python/paddle/autograd/py_layer.py
+++ b/python/paddle/autograd/py_layer.py
@@ -29,6 +29,27 @@ def with_mateclass(meta, *bases):
 class PyLayerContext:
     """
     ``PyLayerContext`` can assist the :ref:`api_paddle_autograd_PyLayer` in implementing certain functionalities.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            from paddle.autograd import PyLayer
+
+            class cus_tanh(PyLayer):
+                @staticmethod
+                def forward(ctx, x):
+                    # ctx is a object of PyLayerContext.
+                    y = paddle.tanh(x)
+                    ctx.save_for_backward(y)
+                    return y
+
+                @staticmethod
+                def backward(ctx, dy):
+                    # ctx is a object of PyLayerContext.
+                    y, = ctx.saved_tensor()
+                    grad = dy * (1 - paddle.square(y))
+                    return grad
     """
 
     def save_for_backward(self, *tensors):
@@ -266,6 +287,33 @@ class PyLayer(with_mateclass(PyLayerMeta, core.eager.PyLayer, PyLayerContext)):
 
     After building the custom operator, apply it by running the ``apply`` method.
 
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            from paddle.autograd import PyLayer
+
+            class cus_tanh(PyLayer):
+                @staticmethod
+                def forward(ctx, x):
+                    y = paddle.tanh(x)
+                    # Pass tensors to backward.
+                    ctx.save_for_backward(y)
+                    return y
+
+                @staticmethod
+                def backward(ctx, dy):
+                    # Get the tensors passed by forward.
+                    y, = ctx.saved_tensor()
+                    grad = dy * (1 - paddle.square(y))
+                    return grad
+
+            data = paddle.randn([2, 3], dtype="float64")
+            data.stop_gradient = False
+            z = cus_tanh.apply(data)
+            z.mean().backward()
+
+            print(data.grad)
     """
 
     @staticmethod
-- 
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