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 normalize_binop_value(
      self, other: ScalarLike
  ) -> Union[ColumnBase, cudf.Scalar]:
      if isinstance(other, ColumnBase):
          if not isinstance(other, NumericalColumn):
              return NotImplemented
          return other
      if isinstance(other, cudf.Scalar):
          if self.dtype == other.dtype:
              return other
          # expensive device-host transfer just to
          # adjust the dtype
          other = other.value
      # Try and match pandas and hence numpy. Deduce the common
      # dtype via the _value_ of other, and the dtype of self. TODO:
      # When NEP50 is accepted, this might want changed or
      # simplified.
      # This is not at all simple:
      # np.result_type(np.int64(0), np.uint8)
      #   => np.uint8
      # np.result_type(np.asarray([0], dtype=np.int64), np.uint8)
      #   => np.int64
      # np.promote_types(np.int64(0), np.uint8)
      #   => np.int64
      # np.promote_types(np.asarray([0], dtype=np.int64).dtype, np.uint8)
      #   => np.int64
      common_dtype = np.result_type(self.dtype, other)
      if common_dtype.kind in {"b", "i", "u", "f"}:
          if self.dtype.kind == "b":
              common_dtype = min_signed_type(other)
          return cudf.Scalar(other, dtype=common_dtype)
      else:
          return NotImplemented
  
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 11 for calling the NumericalColumn obj's underlying member method code with example usage: obj.normalize_binop_value(other) and returns: other, cudf, NotImplemented, NotImplemented, other