Remove inplace argument when calling nn.reshape() (#27376)

* remove inplace argument

* fix sample code

* fix sample code

python/paddle/fluid/dygraph/parallel.py

@@ -416,7 +416,7 @@ class DataParallel(layers.Layer):
                 g_var_shapes.append(g_var.shape)
                 flattened_vars.append(
                     nn.reshape(
-                        x=g_var, shape=[np.prod(g_var.shape)], inplace=True))
+                        x=g_var, shape=[np.prod(g_var.shape)]))
             coalesced_grad = nn.concat(flattened_vars)
             coalesced_grads_and_grad_vars.append(
                 [coalesced_grad, grad_vars, g_var_shapes])

python/paddle/fluid/layers/loss.py

@@ -1755,7 +1755,7 @@ def npair_loss(anchor, positive, labels, l2_reg=0.002):
     Beta = 0.25
     batch_size = labels.shape[0]
 
-    labels = nn.reshape(labels, shape=[batch_size, 1], inplace=True)
+    labels = nn.reshape(labels, shape=[batch_size, 1])
     labels = nn.expand(labels, expand_times=[1, batch_size])
 
     labels = equal(labels, nn.transpose(labels, perm=[1, 0])).astype('float32')

python/paddle/fluid/layers/nn.py

@@ -6102,14 +6102,16 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
     Examples:
         .. code-block:: python
             
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
             
             # example 1:
             # attr shape is a list which doesn't contain Tensors.
             data_1 = fluid.data(
               name='data_1', shape=[2, 4, 6], dtype='float32')
             reshaped_1 = fluid.layers.reshape(
-              x=data_1, shape=[-1, 0, 3, 2], inplace=True)
+              x=data_1, shape=[-1, 0, 3, 2])
             # the shape of reshaped_1 is [2,4,3,2].
 
             # example 2:

python/paddle/tensor/manipulation.py

@@ -363,7 +363,7 @@ def roll(x, shifts, axis=None, name=None):
         outputs={'Out': out},
         attrs={'axis': axis,
                'shifts': shifts})
-    out = layers.reshape(out, shape=origin_shape, inplace=True)
+    out = layers.reshape(out, shape=origin_shape)
     return out