fix nll_loss doc;test=document_fix; (#29247) (#29311)

fix nll_loss doc;test=document_fix

python/paddle/nn/functional/loss.py

@@ -767,23 +767,20 @@ def nll_loss(input,
 
     Examples:
         .. code-block:: python
+
                 import paddle
-                import numpy as np
                 from paddle.nn.functional import nll_loss
                 log_softmax = paddle.nn.LogSoftmax(axis=1)
 
-                input_np = np.array([[0.88103855, 0.9908683 , 0.6226845 ],
-                                     [0.53331435, 0.07999352, 0.8549948 ],
-                                     [0.25879037, 0.39530203, 0.698465  ],
-                                     [0.73427284, 0.63575995, 0.18827209],
-                                     [0.05689114, 0.0862954 , 0.6325046 ]]).astype(np.float32)
-                label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
-
-                input = paddle.to_tensor(input_np)
+                input = paddle.to_tensor([[0.88103855, 0.9908683 , 0.6226845 ],
+                          [0.53331435, 0.07999352, 0.8549948 ],
+                          [0.25879037, 0.39530203, 0.698465  ],
+                          [0.73427284, 0.63575995, 0.18827209],
+                          [0.05689114, 0.0862954 , 0.6325046 ]], "float32")
                 log_out = log_softmax(input)
-                label = paddle.to_tensor(label_np)
+                label = paddle.to_tensor([0, 2, 1, 1, 0], "int64")
                 result = nll_loss(log_out, label)
-                print(result) # [1.0720209]
+                print(result) # Tensor(shape=[1], dtype=float32, place=CPUPlace, stop_gradient=True, [1.07202101])
     """
     if reduction not in ['sum', 'mean', 'none']:
         raise ValueError(

python/paddle/nn/layer/loss.py

@@ -690,25 +690,19 @@ class NLLLoss(fluid.dygraph.Layer):
         .. code-block:: python
 
                 import paddle
-                import numpy as np
 
-                nll_loss = paddle.nn.layer.NLLLoss()
+                nll_loss = paddle.nn.loss.NLLLoss()
                 log_softmax = paddle.nn.LogSoftmax(axis=1)
 
-                input_np = np.array([[0.88103855, 0.9908683 , 0.6226845 ],
-                                 [0.53331435, 0.07999352, 0.8549948 ],
-                                 [0.25879037, 0.39530203, 0.698465  ],
-                                 [0.73427284, 0.63575995, 0.18827209],
-                                 [0.05689114, 0.0862954 , 0.6325046 ]]).astype(np.float32)
-                label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
-
-                place = paddle.CPUPlace()
-                paddle.disable_static(place)
-                input = paddle.to_tensor(input_np)
+                input = paddle.to_tensor([[0.88103855, 0.9908683 , 0.6226845 ],
+                                          [0.53331435, 0.07999352, 0.8549948 ],
+                                          [0.25879037, 0.39530203, 0.698465  ],
+                                          [0.73427284, 0.63575995, 0.18827209],
+                                          [0.05689114, 0.0862954 , 0.6325046 ]], "float32")
                 log_out = log_softmax(input)
-                label = paddle.to_tensor(label_np)
+                label = paddle.to_tensor([0, 2, 1, 1, 0], "int64")
                 result = nll_loss(log_out, label)
-                print(result.numpy()) # [1.0720209]
+                print(result) # Tensor(shape=[1], dtype=float32, place=CPUPlace, stop_gradient=True, [1.07202101])
 
     """