code: def _na_for_min_count(values, axis):
      """Return the missing value for `values`
  
      Parameters
      ----------
      values : ndarray
      axis : int or None
          axis for the reduction
  
      Returns
      -------
      result : scalar or ndarray
          For 1-D values, returns a scalar of the correct missing type.
          For 2-D values, returns a 1-D array where each element is missing.
      """
      # we either return np.nan or pd.NaT
      if is_numeric_dtype(values):
          values = values.astype("float64")
      fill_value = na_value_for_dtype(values.dtype)
  
      if values.ndim == 1:
          return fill_value
      else:
          result_shape = values.shape[:axis] + values.shape[axis + 1 :]
          result = np.empty(result_shape, dtype=values.dtype)
          result.fill(fill_value)
          return result
  
imports: from distutils.version import LooseVersion
  import functools
  import itertools
  import operator
  import warnings
  import numpy
  from pandas._libs import iNaT, lib, tslibs
  import pandas.compat
  from pandas.core.dtypes.cast import _int64_max, maybe_upcast_putmask
  from pandas.core.dtypes.common import _get_dtype, is_any_int_dtype, is_bool_dtype, is_complex, is_complex_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_datetime_or_timedelta_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype, is_numeric_dtype, is_object_dtype, is_scalar, is_timedelta64_dtype, pandas_dtype
  from pandas.core.dtypes.dtypes import DatetimeTZDtype
  from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
  import pandas.core.common
  from pandas.core.config import get_option
label_desc: Use image node_id 9 for calling a global function with example usage: _na_for_min_count(values, axis) and returns: fill_value, result