fix api error format showing on website.

mindspore/nn/wrap/cell_wrapper.py

@@ -119,7 +119,7 @@ class WithGradCell(Cell):
 
     Wraps the network with backward cell to compute gradients. A network with a loss function is necessary
     as argument. If loss function in None, the network must be a wrapper of network and loss function. This
-    Cell accepts '*inputs' as inputs and returns gradients for each trainable parameter.
+    Cell accepts '\*inputs' as inputs and returns gradients for each trainable parameter.
 
     Note:
         Run in PyNative mode.
@@ -132,7 +132,7 @@ class WithGradCell(Cell):
             output value. Default: None.
 
     Inputs:
-        - **(*inputs)** (Tuple(Tensor)) - Tuple of input tensors with shape :math:`(N, \ldots)`.
+        - **(\*inputs)** (Tuple(Tensor)) - Tuple of input tensors with shape :math:`(N, \ldots)`.
 
     Outputs:
         list, a list of Tensors with identical shapes as trainable weights.
@@ -178,7 +178,7 @@ class TrainOneStepCell(Cell):
     r"""
     Network training package class.
 
-    Wraps the network with an optimizer. The resulting Cell is trained with input *inputs.
+    Wraps the network with an optimizer. The resulting Cell is trained with input '\*inputs'.
     The backward graph will be created in the construct function to update the parameter. Different
     parallel modes are available for training.
 
@@ -188,7 +188,7 @@ class TrainOneStepCell(Cell):
         sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
 
     Inputs:
-        - **(*inputs)** (Tuple(Tensor)) - Tuple of input tensors with shape :math:`(N, \ldots)`.
+        - **(\*inputs)** (Tuple(Tensor)) - Tuple of input tensors with shape :math:`(N, \ldots)`.
 
     Outputs:
         Tensor, a scalar Tensor with shape :math:`()`.

mindspore/ops/composite/math_ops.py

@@ -38,7 +38,7 @@ def _check_validate_keepdims(keep_dims, name):
 
 
 def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
-    """
+    r"""
     Count number of nonzero elements across axis of input tensor
 
     Args:
@@ -47,7 +47,7 @@ def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
                                                   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.
+        dtype (Union[Number, mstype.bool\_]): The data type of the output tensor. Only constant value is allowed.
                                              Default: mstype.int32
 
     Returns: