code: def check_below_min_count(
      shape: tuple[int, ...],
      mask: npt.NDArray[np.bool_] | None,
      min_count: int,
  ) -> bool:
      """
      Check for the `min_count` keyword. Returns True if below `min_count` (when
      missing value should be returned from the reduction).
  
      Parameters
      ----------
      shape : tuple
          The shape of the values (`values.shape`).
      mask : ndarray[bool] or None
          Boolean numpy array (typically of same shape as `shape`) or None.
      min_count : int
          Keyword passed through from sum/prod call.
  
      Returns
      -------
      bool
      """
      if min_count > 0:
          if mask is None:
              # no missing values, only check size
              non_nulls = np.prod(shape)
          else:
              non_nulls = mask.size - mask.sum()
          if non_nulls < min_count:
              return True
      return False
  
imports: from __future__ import annotations
  import functools
  import itertools
  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_complex, is_float, is_float_dtype, is_integer, is_numeric_dtype, is_object_dtype, needs_i8_conversion, pandas_dtype
  from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
label_desc: Use image node_id 33 for calling a global function with example usage: check_below_min_count(shape, mask, min_count) and returns: False, True