class_docstr: A Column object for Numeric types.
  
  Parameters
  ----------
  data : Buffer
  dtype : np.dtype
      The dtype associated with the data Buffer
  mask : Buffer, optional
class_docstr_tok: ["A","Column","object","for","Numeric","types",".","Parameters","--","--","--","--","--","data",":","Buffer","dtype",":","np.dtype","The","dtype","associated","with","the","data","Buffer","mask",":","Buffer",",","optional"]
code: def _process_for_reduction(
      self, skipna: Optional[bool] = None, min_count: int = 0
  ) -> Union[NumericalColumn, ScalarLike]:
      skipna = True if skipna is None else skipna
  
      if self._can_return_nan(skipna=skipna):
          return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
  
      col = self.nans_to_nulls() if skipna else self
      return super(NumericalColumn, col)._process_for_reduction(
          skipna=skipna, min_count=min_count
      )
  
imports: from __future__ import annotations
  from typing import Any, Callable, Mapping, Optional, Sequence, Tuple, Union, cast
  import cupy
  import numpy
  import pandas
  from typing_extensions import Self
  import cudf
  from cudf import _lib
  from cudf._lib.types import size_type_dtype
  from cudf._typing import ColumnBinaryOperand, ColumnLike, Dtype, DtypeObj, ScalarLike
  from cudf.api.types import is_bool_dtype, is_float_dtype, is_integer, is_integer_dtype, is_scalar
  from cudf.core.buffer import Buffer, cuda_array_interface_wrapper
  from cudf.core.column import ColumnBase, as_column, build_column, column, full, string
  from cudf.core.dtypes import CategoricalDtype
  from cudf.core.mixins import BinaryOperand
  from cudf.utils.dtypes import NUMERIC_TYPES, min_column_type, min_signed_type, np_dtypes_to_pandas_dtypes, numeric_normalize_types
  from .numerical_base import NumericalBaseColumn
label_desc: Use image node_id 23 for calling the NumericalColumn obj's underlying member method code with example usage: obj._process_for_reduction(skipna, min_count) and returns: super, cudf