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 int2ip(self) -> "cudf.core.column.StringColumn":
      if self.dtype != cudf.dtype("int64"):
          raise TypeError("Only int64 type can be converted to ip")
  
      return libcudf.string_casting.int2ip(self)
  
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 12 for calling the NumericalColumn obj's underlying member method code with example usage: obj.int2ip() and returns: libcudf