code: def _maybe_null_out(
      result: np.ndarray | float | NaTType,
      axis: AxisInt | None,
      mask: npt.NDArray[np.bool_] | None,
      shape: tuple[int, ...],
      min_count: int = 1,
  ) -> np.ndarray | float | NaTType:
      """
      Returns
      -------
      Dtype
          The product of all elements on a given axis. ( NaNs are treated as 1)
      """
      if mask is None and min_count == 0:
          # nothing to check; short-circuit
          return result
  
      if axis is not None and isinstance(result, np.ndarray):
          if mask is not None:
              null_mask = (
                  mask.shape[axis] - mask.sum(axis) - min_count
              ) < 0
          else:
              # we have no nulls, kept mask=None in _maybe_get_mask
              below_count = shape[axis] - min_count < 0
              new_shape = shape[:axis] + shape[axis + 1 :]
              null_mask = np.broadcast_to(below_count, new_shape)
  
          if np.any(null_mask):
              if is_numeric_dtype(result):
                  if np.iscomplexobj(result):
                      result = result.astype("c16")
                  elif not is_float_dtype(result):
                      result = result.astype("f8", copy=False)
                  result[null_mask] = np.nan
              else:
                  # GH12941, use None to auto cast null
                  result[null_mask] = None
      elif result is not NaT:
          if check_below_min_count(shape, mask, min_count):
              result_dtype = getattr(result, "dtype", None)
              if is_float_dtype(result_dtype):
                  # error: Item "None" of "Optional[Any]" has no attribute "type"
                  result = result_dtype.type("nan")  # type: ignore[union-attr]
              else:
                  result = np.nan
  
      return result
  
imports: from __future__ import annotations
  import functools
  import itertools
  import operator
  from typing import Any, Callable, cast
  import warnings
  import numpy
  from pandas._config import get_option
  from pandas._libs import NaT, NaTType, iNaT, lib
  from pandas._typing import ArrayLike, AxisInt, CorrelationMethod, Dtype, DtypeObj, F, Scalar, Shape, npt
  from pandas.compat._optional import import_optional_dependency
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import is_any_int_dtype, is_bool_dtype, is_complex, is_datetime64_any_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype, is_numeric_dtype, is_object_dtype, is_scalar, is_timedelta64_dtype, needs_i8_conversion, pandas_dtype
  from pandas.core.dtypes.dtypes import PeriodDtype
  from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
  from pandas.core.construction import extract_array
label_desc: Use image node_id 31 for calling a global function with example usage: _maybe_null_out(result, axis, mask, shape, min_count) and returns: result, result