修改了英文API文档

python/paddle/device/cuda/__init__.py

@@ -355,8 +355,8 @@ def _set_current_stream(stream):
 @signature_safe_contextmanager
 def stream_guard(stream):
     '''
-    **Notes**:
-        **This API only supports dygraph mode currently.**
+    Notes:
+        This API only supports dygraph mode currently.
 
     A context manager that specifies the current stream context by the given stream.
 

python/paddle/fluid/framework.py

@@ -786,7 +786,8 @@ def disable_signal_handler():
 
     Make sure you called paddle.disable_signal_handler() before using above mentioned frameworks.
 
-    Returns: None
+    Returns:
+        None
 
     Examples:
         .. code-block:: python

python/paddle/fluid/layers/rnn.py

@@ -1824,7 +1824,6 @@ def dynamic_decode(
 
         .. code-block:: python
 
-            import numpy as np
             import paddle
             from paddle.nn import BeamSearchDecoder, dynamic_decode
             from paddle.nn import GRUCell, Linear, Embedding

python/paddle/framework/framework.py

@@ -90,6 +90,9 @@ def set_grad_enabled(mode):
     Args:
         mode(bool): whether to enable (`True`), or disable (`False`) grad.
 
+    Returns:
+        None.
+
     Examples:
         .. code-block:: python
 

python/paddle/incubate/tensor/math.py

@@ -28,7 +28,7 @@
     reason="paddle.incubate.segment_sum will be removed in future",
 )
 def segment_sum(data, segment_ids, name=None):
-    r"""
+    """
     Segment Sum Operator.
 
     This operator sums the elements of input `data` which with
@@ -45,7 +45,7 @@ def segment_sum(data, segment_ids, name=None):
                             For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-       output (Tensor): the reduced result.
+       Tensor: the reduced result.
 
     Examples:
 
@@ -92,7 +92,7 @@ def segment_sum(data, segment_ids, name=None):
     reason="paddle.incubate.segment_mean will be removed in future",
 )
 def segment_mean(data, segment_ids, name=None):
-    r"""
+    """
     Segment mean Operator.
 
     Ihis operator calculate the mean value of input `data` which
@@ -110,7 +110,7 @@ def segment_mean(data, segment_ids, name=None):
                             For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-       output (Tensor): the reduced result.
+       Tensor: the reduced result.
 
     Examples:
 
@@ -158,7 +158,7 @@ def segment_mean(data, segment_ids, name=None):
     reason="paddle.incubate.segment_min will be removed in future",
 )
 def segment_min(data, segment_ids, name=None):
-    r"""
+    """
     Segment min operator.
 
     This operator calculate the minimum elements of input `data` which with
@@ -175,7 +175,7 @@ def segment_min(data, segment_ids, name=None):
                             For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-       output (Tensor): the reduced result.
+       Tensor: the reduced result.
 
     Examples:
 
@@ -224,7 +224,7 @@ def segment_min(data, segment_ids, name=None):
     reason="paddle.incubate.segment_max will be removed in future",
 )
 def segment_max(data, segment_ids, name=None):
-    r"""
+    """
     Segment max operator.
 
     This operator calculate the maximum elements of input `data` which with
@@ -241,7 +241,7 @@ def segment_max(data, segment_ids, name=None):
                             For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-       output (Tensor): the reduced result.
+       Tensor: the reduced result.
 
     Examples:
 

python/paddle/nn/functional/extension.py

@@ -57,48 +57,48 @@ def diag_embed(input, offset=0, dim1=-2, dim2=-1):
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.nn.functional as F
-            import numpy as np
-
-            diag_embed = np.random.randn(2, 3).astype('float32')
-            # [[ 0.7545889 , -0.25074545,  0.5929117 ],
-            #  [-0.6097662 , -0.01753256,  0.619769  ]]
-
-            data1 = F.diag_embed(diag_embed)
-            data1.numpy()
-            # [[[ 0.7545889 ,  0.        ,  0.        ],
-            #  [ 0.        , -0.25074545,  0.        ],
-            #   [ 0.        ,  0.        ,  0.5929117 ]],
-
-            # [[-0.6097662 ,  0.        ,  0.        ],
-            #  [ 0.        , -0.01753256,  0.        ],
-            #  [ 0.        ,  0.        ,  0.619769  ]]]
-
-            data2 = F.diag_embed(diag_embed, offset=-1, dim1=0, dim2=2)
-            data2.numpy()
-            # [[[ 0.        ,  0.        ,  0.        ,  0.        ],
-            #   [ 0.7545889 ,  0.        ,  0.        ,  0.        ],
-            #   [ 0.        , -0.25074545,  0.        ,  0.        ],
-            #   [ 0.        ,  0.        ,  0.5929117 ,  0.        ]],
-            #
-            #  [[ 0.        ,  0.        ,  0.        ,  0.        ],
-            #   [-0.6097662 ,  0.        ,  0.        ,  0.        ],
-            #   [ 0.        , -0.01753256,  0.        ,  0.        ],
-            #   [ 0.        ,  0.        ,  0.619769  ,  0.        ]]]
-
-            data3 = F.diag_embed(diag_embed, offset=1, dim1=0, dim2=2)
-            data3.numpy()
-            # [[[ 0.        ,  0.7545889 ,  0.        ,  0.        ],
-            #   [ 0.        , -0.6097662 ,  0.        ,  0.        ]],
-            #
-            #  [[ 0.        ,  0.        , -0.25074545,  0.        ],
-            #   [ 0.        ,  0.        , -0.01753256,  0.        ]],
-            #
-            #  [[ 0.        ,  0.        ,  0.        ,  0.5929117 ],
-            #   [ 0.        ,  0.        ,  0.        ,  0.619769  ]],
-            #
-            #  [[ 0.        ,  0.        ,  0.        ,  0.        ],
-            #   [ 0.        ,  0.        ,  0.        ,  0.        ]]]
+
+            diag_embed_input = paddle.arange(6)
+
+            diag_embed_output1 = F.diag_embed(diag_embed_input)
+            print(diag_embed_output1)
+            # Tensor(shape=[6, 6], dtype=int64, place=Place(cpu), stop_gradient=True,
+            #        [[0, 0, 0, 0, 0, 0],
+            #         [0, 1, 0, 0, 0, 0],
+            #         [0, 0, 2, 0, 0, 0],
+            #         [0, 0, 0, 3, 0, 0],
+            #         [0, 0, 0, 0, 4, 0],
+            #         [0, 0, 0, 0, 0, 5]])
+
+            diag_embed_output2 = F.diag_embed(diag_embed_input, offset=-1, dim1=0,dim2=1 )
+            print(diag_embed_output2)
+            # Tensor(shape=[7, 7], dtype=int64, place=Place(cpu), stop_gradient=True,
+            #        [[0, 0, 0, 0, 0, 0, 0],
+            #         [0, 0, 0, 0, 0, 0, 0],
+            #         [0, 1, 0, 0, 0, 0, 0],
+            #         [0, 0, 2, 0, 0, 0, 0],
+            #         [0, 0, 0, 3, 0, 0, 0],
+            #         [0, 0, 0, 0, 4, 0, 0],
+            #         [0, 0, 0, 0, 0, 5, 0]])
+
+            diag_embed_input_2dim = paddle.reshape(diag_embed_input,[2,3])
+            print(diag_embed_input_2dim)
+            # Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
+            #        [[0, 1, 2],
+            #         [3, 4, 5]])
+            diag_embed_output3 = F.diag_embed(diag_embed_input_2dim,offset= 0, dim1=0, dim2=2 )
+            print(diag_embed_output3)
+            # Tensor(shape=[3, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
+            #        [[[0, 0, 0],
+            #          [3, 0, 0]],
+
+            #         [[0, 1, 0],
+            #          [0, 4, 0]],
+
+            #         [[0, 0, 2],
+            #          [0, 0, 5]]])
     """
     if not isinstance(input, Variable):
         input = assign(input)

python/paddle/tensor/einsum.py

@@ -863,7 +863,7 @@ def gen_einsum_op(equation, *operands):
 
 
 def einsum(equation, *operands):
-    r"""
+    """
 
     einsum(equation, *operands)
 

python/paddle/tensor/linalg.py

@@ -2025,7 +2025,7 @@ def slogdet(x, name=None):
 
 
 def svd(x, full_matrices=False, name=None):
-    r"""
+    """
     Computes the singular value decomposition of one matrix or a batch of regular matrices.
 
     Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:
@@ -2047,7 +2047,7 @@ def svd(x, full_matrices=False, name=None):
             For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`
+        Tuple of 3 tensors(U, S, VH): VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`
 
     Examples:
         .. code-block:: python

python/paddle/tensor/manipulation.py

@@ -2275,7 +2275,7 @@ def unique_consecutive(
     dtype="int64",
     name=None,
 ):
-    r"""
+    """
     Eliminates all but the first element from every consecutive group of equivalent elements.
 
     Note:
@@ -2296,7 +2296,7 @@ def unique_consecutive(
             :ref:`api_guide_Name`. Default is None.
 
     Returns:
-        tuple (out, inverse, counts). `out` is the unique consecutive tensor for `x`. `inverse` is provided only if `return_inverse` is True. `counts` is provided only if `return_counts` is True.
+        tuple(out, inverse, counts): `out` is the unique consecutive tensor for `x`. `inverse` is provided only if `return_inverse` is True. `counts` is provided only if `return_counts` is True.
 
     Example:
         .. code-block:: python

python/paddle/tensor/math.py

@@ -3437,7 +3437,7 @@ def cumprod(x, dim=None, dtype=None, name=None):
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        Tensor, the result of cumprod operator.
+        Tensor: the result of cumprod operator.
 
     Examples:
         .. code-block:: python