!28446 correct code examples etc.

Merge pull request !28446 from chentangyu/code_docs_cty_master_I4NUSK_I4NWQW_I4OTOQ

docs/api/api_python/nn/mindspore.nn.HSwish.rst

@@ -36,5 +36,5 @@ mindspore.nn.HSwish
     >>> hswish = nn.HSwish()
     >>> result = hswish(x)
     >>> print(result)
-    [-0.3333  -0.3333  0  1.666  0.6665]
+    [-0.3333 -0.3333  0.      1.667   0.6665]
     

mindspore/python/mindspore/nn/layer/activation.py

@@ -735,7 +735,7 @@ class HSwish(Cell):
         >>> hswish = nn.HSwish()
         >>> result = hswish(x)
         >>> print(result)
-        [-0.3333  -0.3333  0  1.666  0.6665]
+        [-0.3333 -0.3333  0.      1.667   0.6665]
     """
 
     def __init__(self):

mindspore/python/mindspore/nn/sparse/sparse.py

@@ -114,9 +114,9 @@ class SparseTensorDenseMatmul(Cell):
         >>> sparse_dense_matmul = nn.SparseTensorDenseMatmul()
         >>> out = sparse_dense_matmul(indices, values, sparse_shape, dense)
         >>> print(out)
-        [[2 2]
-         [6 6]
-         [0 0]]
+        [[2. 2.]
+         [6. 6.]
+         [0. 0.]]
     """
 
     def __init__(self, adjoint_st=False, adjoint_dt=False):

mindspore/python/mindspore/ops/op_info_register.py

@@ -676,7 +676,7 @@ class CustomRegOp(RegOp):
     Class used for generating the registration information for the `func` parameter of :class:`mindspore.ops.Custom`.
 
     Args:
-        op_name (str): kernel name. No need to set this value as `Custom` operator will generate a unique name
+        op_name (str): kernel name. No need to set this value as `Custom`, operator will generate a unique name
             automatically. Default: "Custom".
 
     Examples:

mindspore/python/mindspore/ops/operations/math_ops.py

@@ -748,8 +748,8 @@ class ReduceSum(_Reduce):
 
 class ReduceAll(_Reduce):
     """
-    Reduces a dimension of a tensor by the "logicalAND" of all elements in the dimension, by Default. And also can
-    reduces a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
+    Reduces a dimension of a tensor by the "logicalAND" of all elements in the dimension, by default. And also can
+    reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
     controlling `keep_dims`.
 
     Args:
@@ -807,8 +807,8 @@ class ReduceAll(_Reduce):
 
 class ReduceAny(_Reduce):
     """
-    Reduces a dimension of a tensor by the "logical OR" of all elements in the dimension, by Default. And also can
-    reduces a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
+    Reduces a dimension of a tensor by the "logical OR" of all elements in the dimension, by default. And also can
+    reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
     controlling `keep_dims`.
 
     Args:
@@ -866,8 +866,8 @@ class ReduceAny(_Reduce):
 
 class ReduceMax(_Reduce):
     """
-    Reduces a dimension of a tensor by the maximum value in this dimension, by Default. And also can
-    reduces a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
+    Reduces a dimension of a tensor by the maximum value in this dimension, by default. And also can
+    reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
     controlling `keep_dims`.
 
     Args:
@@ -953,8 +953,8 @@ class ReduceMax(_Reduce):
 
 class ReduceMin(_Reduce):
     """
-    Reduces a dimension of a tensor by the minimum value in the dimension, by Default. And also can
-    reduces a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
+    Reduces a dimension of a tensor by the minimum value in the dimension, by default. And also can
+    reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
     controlling `keep_dims`.
 
     Args:
@@ -1031,8 +1031,8 @@ class ReduceMin(_Reduce):
 
 class ReduceProd(_Reduce):
     """
-    Reduces a dimension of a tensor by multiplying all elements in the dimension, by Default. And also can
-    reduces a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
+    Reduces a dimension of a tensor by multiplying all elements in the dimension, by default. And also can
+    reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
     controlling `keep_dims`.
 
     Args:

mindspore/python/mindspore/ops/operations/sparse_ops.py

@@ -52,9 +52,9 @@ class SparseToDense(PrimitiveWithInfer):
         >>> sparse_to_dense = ops.SparseToDense()
         >>> out = sparse_to_dense(indices, values, sparse_shape)
         >>> print(out)
-        [[0 1 0 0]
-         [0 0 2 0]
-         [0 0 0 0]]
+        [[0. 1. 0. 0.]
+         [0. 0. 2. 0.]
+         [0. 0. 0. 0.]]
     """
 
     @prim_attr_register
@@ -133,9 +133,9 @@ class SparseTensorDenseMatmul(PrimitiveWithInfer):
         >>> sparse_dense_matmul = ops.SparseTensorDenseMatmul()
         >>> out = sparse_dense_matmul(indices, values, sparse_shape, dense)
         >>> print(out)
-        [[2 2]
-         [6 6]
-         [0 0]]
+        [[2. 2.]
+         [6. 6.]
+         [0. 0.]]
     """
 
     @prim_attr_register