parameters: (a, axis=None, dtype=None, keepdims=False)
return_statement: return F.tensor_div(sum_a, _count_nonnan(a, axis, keepdims))
docstring: Computes the arithmetic mean along the specified axis, ignoring NaNs.
  
      Returns the average of the array elements. The average is taken over the flattened
      array by default, otherwise over the specified axis. float32 intermediate and
      return values are used for integer inputs.
  
      Note:
          Numpy arguments `out` is not supported.
  
      Args:
          a (Union[int, float, list, tuple, Tensor]): Array containing numbers
              whose mean is desired. If `a` is not an array, a conversion is attempted.
          axis (Union[int, tuple of int, None], optional): Axis or axes along which the mean is
              computed. The default is to compute the mean of the flattened array.
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
          keepdims (boolean, optional): Defaults to False. If this is set to True, the axes which
              are reduced are left in the result as dimensions with size one. With this option,
              the result will broadcast correctly against the original `a`.
  
      Returns:
          Tensor.
  
      Raises:
          ValueError: If axes are out of the range of ``[-a.ndim, a.ndim)``, or
              if the axes contain duplicates.
  
      Supported Platforms:
          ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> a = np.array([[1, np.nan], [3, 4]])
          >>> output = np.nanmean(a)
          >>> print(output)
          2.6666667
          >>> output = np.nanmean(a, axis=0)
          >>> print(output)
          [2. 4.]
          >>> output = np.nanmean(a, axis=1)
          >>> print(output)
          [1.  3.5]
docstring_summary: Computes the arithmetic mean along the specified axis, ignoring NaNs.
docstring_tokens: Computes the arithmetic mean along the specified axis ignoring NaNs .
function: def nanmean(a, axis=None, dtype=None, keepdims=False):
      """
      Computes the arithmetic mean along the specified axis, ignoring NaNs.
  
      Returns the average of the array elements. The average is taken over the flattened
      array by default, otherwise over the specified axis. float32 intermediate and
      return values are used for integer inputs.
  
      Note:
          Numpy arguments `out` is not supported.
  
      Args:
          a (Union[int, float, list, tuple, Tensor]): Array containing numbers
              whose mean is desired. If `a` is not an array, a conversion is attempted.
          axis (Union[int, tuple of int, None], optional): Axis or axes along which the mean is
              computed. The default is to compute the mean of the flattened array.
          dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
              output Tensor.
          keepdims (boolean, optional): Defaults to False. If this is set to True, the axes which
              are reduced are left in the result as dimensions with size one. With this option,
              the result will broadcast correctly against the original `a`.
  
      Returns:
          Tensor.
  
      Raises:
          ValueError: If axes are out of the range of ``[-a.ndim, a.ndim)``, or
              if the axes contain duplicates.
  
      Supported Platforms:
          ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> a = np.array([[1, np.nan], [3, 4]])
          >>> output = np.nanmean(a)
          >>> print(output)
          2.6666667
          >>> output = np.nanmean(a, axis=0)
          >>> print(output)
          [2. 4.]
          >>> output = np.nanmean(a, axis=1)
          >>> print(output)
          [1.  3.5]
      """
      if dtype is None:
          dtype = mstype.float32
      a = _to_tensor(a)
      axis = _check_axis_valid(axis, F.rank(a))
      sum_a = nansum(a, axis=axis, dtype=dtype, keepdims=keepdims)
      return F.tensor_div(sum_a, _count_nonnan(a, axis, keepdims))
function_tokens: def nanmean ( a , axis = None , dtype = None , keepdims = False ) : if dtype is None : dtype = mstype . float32 a = _to_tensor ( a ) axis = _check_axis_valid ( axis , F . rank ( a ) ) sum_a = nansum ( a , axis = axis , dtype = dtype , keepdims = keepdims ) return F . tensor_div ( sum_a , _count_nonnan ( a , axis , keepdims ) )