modified the example of diag_embed english doc, test=develop (#24012)

python/paddle/nn/functional/extension.py

@@ -46,27 +46,68 @@ def diag_embed(input, offset=0, dim1=-2, dim2=-1):
     This OP creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2) 
     are filled by ``input``. By default, a 2D plane formed by the last two dimensions 
     of the returned tensor will be selected.
+
     The argument ``offset`` determines which diagonal is generated:
+
     - If offset = 0, it is the main diagonal.
     - If offset > 0, it is above the main diagonal.
     - If offset < 0, it is below the main diagonal.
+
     Args:
         input(Variable|numpy.ndarray): The input tensor. Must be at least 1-dimensional. The input data type should be float32, float64, int32, int64.
         offset(int, optional): Which diagonal to consider. Default: 0 (main diagonal).
         dim1(int, optional): The first dimension with respect to which to take diagonal. Default: -2.
         dim2(int, optional): The second dimension with respect to which to take diagonal. Default: -1.
+    
     Returns:
         Variable, the output data type is the same as input data type.
+    
     Examples:
         .. code-block:: python
+
             import paddle.nn.functional as F
             import paddle.fluid.dygraph as dg
             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  ]]
             with dg.guard():
                 data1 = F.diag_embed(diag_embed)
-                data2 = F.diag_embed(diag_embed, offset=1, dim1=0, dim2=2)
+                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.        ]]]
     """
     inputs = {'Input': [input]}
     attrs = {'offset': offset, 'dim1': dim1, 'dim2': dim2}
@@ -80,26 +121,24 @@ def diag_embed(input, offset=0, dim1=-2, dim2=-1):
                     'diag_embed')
 
         input_shape = list(input.shape)
-        assert (len(input_shape) >= 1,                     \
+        assert len(input_shape) >= 1,                     \
                 "Input must be at least 1-dimensional, "   \
                 "But received Input's dimensional: %s.\n" %  \
-                len(input_shape))
+                len(input_shape)
 
-        assert (
-            np.abs(dim1) <= len(input_shape),
-            "Dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n"
-            % (-(len(input_shape) + 1), len(input_shape), dim1))
+        assert np.abs(dim1) <= len(input_shape),    \
+            "Dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n"  \
+            % (-(len(input_shape) + 1), len(input_shape), dim1)
 
-        assert (
-            np.abs(dim2) <= len(input_shape),
-            "Dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n"
-            % (-(len(input_shape) + 1), len(input_shape), dim2))
+        assert np.abs(dim2) <= len(input_shape),      \
+            "Dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n"  \
+            % (-(len(input_shape) + 1), len(input_shape), dim2)
 
         dim1_ = dim1 if dim1 >= 0 else len(input_shape) + dim1 + 1
         dim2_ = dim2 if dim2 >= 0 else len(input_shape) + dim2 + 1
-        assert ( dim1_ != dim2_,
+        assert dim1_ != dim2_,       \
                "dim1 and dim2 cannot be the same dimension." \
-                "But received dim1 = %d, dim2 = %d\n"%(dim1, dim2))
+                "But received dim1 = %d, dim2 = %d\n"%(dim1, dim2)
 
     if not in_dygraph_mode():
         __check_input(input, offset, dim1, dim2)