update the demo of paddle.grad.

python/paddle/fluid/dygraph/base.py

@@ -375,47 +375,46 @@ def grad(outputs,
     Examples 1:
         .. code-block:: python
 
-            import paddle.fluid as fluid
+            import paddle
+            paddle.disable_static()
 
             def test_dygraph_grad(create_graph):
-                with fluid.dygraph.guard(): 
-                    x = fluid.layers.ones(shape=[1], dtype='float32') 
-                    x.stop_gradient = False
-                    y = x * x
-
-                    # Since y = x * x, dx = 2 * x 
-                    dx = fluid.dygraph.grad(
-                            outputs=[y],
-                            inputs=[x], 
-                            create_graph=create_graph, 
-                            retain_graph=True)[0]
-
-                    z = y + dx
-
-                    # If create_graph = False, the gradient of dx
-                    # would not be backpropagated. Therefore,
-                    # z = x * x + dx, and x.gradient() = 2 * x = 2.0
-                    
-                    # If create_graph = True, the gradient of dx
-                    # would be backpropagated. Therefore, 
-                    # z = x * x + dx = x * x + 2 * x, and
-                    # x.gradient() = 2 * x + 2 = 4.0 
-
-                    z.backward()
-                    return x.gradient() 
-
-            print(test_dygraph_grad(create_graph=False)) # [2.] 
+                x = paddle.ones(shape=[1], dtype='float32')
+                x.stop_gradient = False
+                y = x * x
+
+                # Since y = x * x, dx = 2 * x
+                dx = paddle.grad(
+                        outputs=[y],
+                        inputs=[x],
+                        create_graph=create_graph,
+                        retain_graph=True)[0]
+
+                z = y + dx
+
+                # If create_graph = False, the gradient of dx
+                # would not be backpropagated. Therefore,
+                # z = x * x + dx, and x.gradient() = 2 * x = 2.0
+
+                # If create_graph = True, the gradient of dx
+                # would be backpropagated. Therefore,
+                # z = x * x + dx = x * x + 2 * x, and
+                # x.gradient() = 2 * x + 2 = 4.0
+
+                z.backward()
+                return x.gradient()
+
+            print(test_dygraph_grad(create_graph=False)) # [2.]
             print(test_dygraph_grad(create_graph=True)) # [4.]
 
     Examples 2:
         .. code-block:: python
 
-            import paddle.fluid as fluid
-
-            fluid.enable_dygraph()
+            import paddle
+            paddle.disable_static()
 
             def test_dygraph_grad(grad_outputs=None):
-                x = fluid.layers.fill_constant(shape=[1], value=2.0, dtype='float32')
+                x = paddle.fill_constant(shape=[1], value=2.0, dtype='float32')
                 x.stop_gradient = False
 
                 y1 = x * x
@@ -431,15 +430,15 @@ def test_dygraph_grad(grad_outputs=None):
                 # Therefore, the final result would be:
                 # dx = 2 * x * dy1 + 3 * dy2 = 4 * dy1 + 3 * dy2.
 
-                dx = fluid.dygraph.grad(
+                dx = paddle.grad(
                     outputs=[y1, y2], 
                     inputs=[x],
                     grad_outputs=grad_outputs)[0]
 
                 return dx.numpy()
 
-            THREE = fluid.layers.fill_constant(shape=[1], value=3.0, dtype='float32')
-            FOUR = fluid.layers.fill_constant(shape=[1], value=4.0, dtype='float32')
+            THREE = paddle.fill_constant(shape=[1], value=3.0, dtype='float32')
+            FOUR = paddle.fill_constant(shape=[1], value=4.0, dtype='float32')
 
             # dy1 = [1], dy2 = [1]
             print(test_dygraph_grad(None)) # [7.]