add linear and xwplusb

mindspore/ops/composite/clip_ops.py

@@ -124,13 +124,13 @@ def clip_by_global_norm(x, clip_norm=1.0, use_norm=None):
         >>> x2 = np.array([[1., 4.],[3., 1.]]).astype(np.float32)
         >>> input_x = (Tensor(x1), Tensor(x2))
         >>> out = clip_by_global_norm(input_x, 1.0)
+        >>> print(out)
         ([[ 2.98142403e-01,  4.47213590e-01],
          [ 1.49071202e-01,  2.98142403e-01]],
-
         [[ 1.49071202e-01,  5.96284807e-01],
          [ 4.47213590e-01,  1.49071202e-01]])
     """
 
     clip_norm = _check_value(clip_norm)
-    out = _ClipByGlobalNorm(clip_norm, use_norm)(x)
-    return out
+    clip_val = _ClipByGlobalNorm(clip_norm, use_norm)(x)
+    return clip_val

mindspore/ops/composite/math_ops.py

@@ -41,22 +41,22 @@ def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
     Count number of nonzero elements across axis of input tensor
 
     Args:
-        - **x** (Tensor[Number]) - Input data is used to count non-zero numbers.
-        - **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Only constant value is allowed.
-                                                         Default: (), reduce all dimensions.
-
-        - **keep_dims** (bool) - If true, keep these reduced dimensions and the length is 1.
-                                 If false, don't keep these dimensions. Default: False.
-        - **dtype** (Union[Number, mstype.bool_]) - The data type of the output tensor. Only constant value is allowed.
-                                                    Default: mstype.int32
+        x (Union(tuple[Tensor], list[Tensor])): Input data is used to count non-zero numbers.
+        axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Only constant value is allowed.
+                                                  Default: (), reduce all dimensions.
+        keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
+                          If false, don't keep these dimensions. Default: False.
+        dtype (Union[Number, mstype.bool_]): The data type of the output tensor. Only constant value is allowed.
+                                             Default: mstype.int32
 
     Returns:
           Tensor, number of nonzero element. The data type is dtype.
 
     Examples:
-        >>> input_tensor = Tensor(np.array([[0, 1, 0], [1, 1, 0]]).astype(np.float32))
+        >>> input_x = Tensor(np.array([[0, 1, 0], [1, 1, 0]]).astype(np.float32))
         >>> nonzero_num = count_nonzero(x=input_x, axis=[0, 1], keep_dims=True, dtype=mstype.int32)
-        nonzero_num: [[3]]
+        >>> print(nonzero_num)
+        [[3]]
     """
 
     const_utils.check_valid_type(F.dtype(x), mstype.number_type, 'input x')