parameters: (
      values: np.ndarray,
      *,
      axis: int | None = None,
      skipna: bool = True,
      mask: np.ndarray | None = None,
  )
return_statement: return result
docstring: Parameters
      ----------
      values : ndarray
      axis : int, optional
      skipna : bool, default True
      mask : ndarray[bool], optional
          nan-mask if known
  
      Returns
      -------
      result : int or ndarray[int]
          The index/indices  of max value in specified axis or -1 in the NA case
  
      Examples
      --------
      >>> import pandas.core.nanops as nanops
      >>> arr = np.array([1, 2, 3, np.nan, 4])
      >>> nanops.nanargmax(arr)
      4
  
      >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
      >>> arr[2:, 2] = np.nan
      >>> arr
      array([[ 0.,  1.,  2.],
             [ 3.,  4.,  5.],
             [ 6.,  7., nan],
             [ 9., 10., nan]])
      >>> nanops.nanargmax(arr, axis=1)
      array([2, 2, 1, 1])
docstring_summary: Parameters
      ----------
      values : ndarray
      axis : int, optional
      skipna : bool, default True
      mask : ndarray[bool], optional
          nan-mask if known
docstring_tokens: Parameters ---------- values : ndarray axis : int optional skipna : bool default True mask : ndarray [ bool ] optional nan - mask if known
function: def nanargmax(
      values: np.ndarray,
      *,
      axis: int | None = None,
      skipna: bool = True,
      mask: np.ndarray | None = None,
  ) -> int | np.ndarray:
      """
      Parameters
      ----------
      values : ndarray
      axis : int, optional
      skipna : bool, default True
      mask : ndarray[bool], optional
          nan-mask if known
  
      Returns
      -------
      result : int or ndarray[int]
          The index/indices  of max value in specified axis or -1 in the NA case
  
      Examples
      --------
      >>> import pandas.core.nanops as nanops
      >>> arr = np.array([1, 2, 3, np.nan, 4])
      >>> nanops.nanargmax(arr)
      4
  
      >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
      >>> arr[2:, 2] = np.nan
      >>> arr
      array([[ 0.,  1.,  2.],
             [ 3.,  4.,  5.],
             [ 6.,  7., nan],
             [ 9., 10., nan]])
      >>> nanops.nanargmax(arr, axis=1)
      array([2, 2, 1, 1])
      """
      values, mask, _, _, _ = _get_values(values, True, fill_value_typ="-inf", mask=mask)
      # error: Need type annotation for 'result'
      result = values.argmax(axis)  # type: ignore[var-annotated]
      result = _maybe_arg_null_out(result, axis, mask, skipna)
      return result
function_tokens: def nanargmax ( values : np . ndarray , * , axis : int | None = None , skipna : bool = True , mask : np . ndarray | None = None , ) -> int | np . ndarray : values , mask , _ , _ , _ = _get_values ( values , True , fill_value_typ = "-inf" , mask = mask ) # error: Need type annotation for 'result' result = values . argmax ( axis ) # type: ignore[var-annotated] result = _maybe_arg_null_out ( result , axis , mask , skipna ) return result