parameters: (a, axis=None, dtype=None)
return_statement: return a.cumsum(axis, dtype)
docstring: Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs)
      as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are
      replaced by zeros.
  
      Zeros are returned for slices that are all-NaN or empty.
  
      Note:
          If ``a.dtype`` is :class:`int8`, :class:`int16` or :class:`bool`, the result
          `dtype` will be elevated to :class:`int32`.
  
      Args:
          a (Tensor): Input tensor.
          axis (int, optional): Axis along which the cumulative sum is computed. The
              default (None) is to compute the cumsum over the flattened array.
          dtype (:class:`mindspore.dtype`, optional): If not specified, stay the same as `a`,
              unless `a` has an integer dtype with a precision less than that of the
              default platform integer. In that case, the default platform integer
              is used.
  
      Returns:
          Tensor.
  
      Raises:
          TypeError: If input arguments have types not specified above.
          ValueError: If axis is out of range.
  
      Supported Platforms:
          ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> a = np.array([[1, 2], [3, np.nan]])
          >>> output = np.nancumsum(a)
          >>> print(output)
          [1. 3. 6. 6.]
          >>> output = np.nancumsum(a, axis=0)
          >>> print(output)
          [[1. 2.]
          [4. 2.]]
          >>> output = np.nancumsum(a, axis=1)
          >>> print(output)
          [[1. 3.]
          [3. 3.]]
docstring_summary: Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs)
      as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are
      replaced by zeros.
docstring_tokens: Return the cumulative sum of array elements over a given axis treating Not a Numbers ( NaNs ) as zero . The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros .
function: def nancumsum(a, axis=None, dtype=None):
      """
      Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs)
      as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are
      replaced by zeros.
  
      Zeros are returned for slices that are all-NaN or empty.
  
      Note:
          If ``a.dtype`` is :class:`int8`, :class:`int16` or :class:`bool`, the result
          `dtype` will be elevated to :class:`int32`.
  
      Args:
          a (Tensor): Input tensor.
          axis (int, optional): Axis along which the cumulative sum is computed. The
              default (None) is to compute the cumsum over the flattened array.
          dtype (:class:`mindspore.dtype`, optional): If not specified, stay the same as `a`,
              unless `a` has an integer dtype with a precision less than that of the
              default platform integer. In that case, the default platform integer
              is used.
  
      Returns:
          Tensor.
  
      Raises:
          TypeError: If input arguments have types not specified above.
          ValueError: If axis is out of range.
  
      Supported Platforms:
          ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> a = np.array([[1, 2], [3, np.nan]])
          >>> output = np.nancumsum(a)
          >>> print(output)
          [1. 3. 6. 6.]
          >>> output = np.nancumsum(a, axis=0)
          >>> print(output)
          [[1. 2.]
          [4. 2.]]
          >>> output = np.nancumsum(a, axis=1)
          >>> print(output)
          [[1. 3.]
          [3. 3.]]
      """
      a = F.select(_isnan(a), zeros(F.shape(a), F.dtype(a)), a)
      return a.cumsum(axis, dtype)
function_tokens: def nancumsum ( a , axis = None , dtype = None ) : a = F . select ( _isnan ( a ) , zeros ( F . shape ( a ) , F . dtype ( a ) ) , a ) return a . cumsum ( axis , dtype )