parameters: (a, axis=None)
return_statement: return a.shape[axis]
docstring: Returns the number of elements along a given axis.
  
      Args:
          a (Union[int, float, bool, list, tuple, Tensor]): Input data.
          axis (int): Axis along which the elements are counted. Default: None.
              If None, give the total number of elements.
  
      Returns:
          Number of elements along the specified axis.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Raises:
          TypeError: If input is not array_like or `axis` is not int.
          ValueError: If any axis is out of range or duplicate axes exist.
  
      Examples:
          >>> import mindspore.numpy as np
          >>> x = np.arange(10).reshape(2, 5).astype('float32')
          >>> print(np.size(x))
          10
          >>> print(np.size(x, axis=1))
          5
docstring_summary: Returns the number of elements along a given axis.
docstring_tokens: Returns the number of elements along a given axis .
function: def size(a, axis=None):
      """
      Returns the number of elements along a given axis.
  
      Args:
          a (Union[int, float, bool, list, tuple, Tensor]): Input data.
          axis (int): Axis along which the elements are counted. Default: None.
              If None, give the total number of elements.
  
      Returns:
          Number of elements along the specified axis.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Raises:
          TypeError: If input is not array_like or `axis` is not int.
          ValueError: If any axis is out of range or duplicate axes exist.
  
      Examples:
          >>> import mindspore.numpy as np
          >>> x = np.arange(10).reshape(2, 5).astype('float32')
          >>> print(np.size(x))
          10
          >>> print(np.size(x, axis=1))
          5
      """
      a = _to_tensor(a)
      if axis is None:
          return a.size
      if not isinstance(axis, int):
          _raise_type_error("axis argument should be integer.")
      axis = _canonicalize_axis(axis, a.ndim)
      return a.shape[axis]
function_tokens: def size ( a , axis = None ) : a = _to_tensor ( a ) if axis is None : return a . size if not isinstance ( axis , int ) : _raise_type_error ( "axis argument should be integer." ) axis = _canonicalize_axis ( axis , a . ndim ) return a . shape [ axis ]