code: def _get_empty_meta(
      self,
      columns,
      index_col,
      index_names,
      dtype: DtypeArg | None = None,
  ):
      columns = list(columns)
  
      # Convert `dtype` to a defaultdict of some kind.
      # This will enable us to write `dtype[col_name]`
      # without worrying about KeyError issues later on.
      dtype_dict: defaultdict[Hashable, Any]
      if not is_dict_like(dtype):
          # if dtype == None, default will be object.
          default_dtype = dtype or object
          dtype_dict = defaultdict(lambda: default_dtype)
      else:
          dtype = cast(dict, dtype)
          dtype_dict = defaultdict(
              lambda: object,
              {
                  columns[k] if is_integer(k) else k: v
                  for k, v in dtype.items()
              },
          )
  
      # Even though we have no data, the "index" of the empty DataFrame
      # could for example still be an empty MultiIndex. Thus, we need to
      # check whether we have any index columns specified, via either:
      #
      # 1) index_col (column indices)
      # 2) index_names (column names)
      #
      # Both must be non-null to ensure a successful construction. Otherwise,
      # we have to create a generic empty Index.
      index: Index
      if (
          index_col is None or index_col is False
      ) or index_names is None:
          index = default_index(0)
      else:
          data = [
              Series([], dtype=dtype_dict[name]) for name in index_names
          ]
          index = ensure_index_from_sequences(data, names=index_names)
          index_col.sort()
  
          for i, n in enumerate(index_col):
              columns.pop(n - i)
  
      col_dict = {
          col_name: Series([], dtype=dtype_dict[col_name])
          for col_name in columns
      }
  
      return index, columns, col_dict
  
imports: from __future__ import annotations
  from collections import defaultdict
  from copy import copy
  import csv
  import datetime
  from enum import Enum
  import itertools
  from typing import TYPE_CHECKING, Any, Callable, Hashable, Iterable, List, Mapping, Sequence, Tuple, cast, final, overload
  import warnings
  import numpy
  from pandas._libs import lib, parsers
  import pandas._libs.ops
  from pandas._libs.parsers import STR_NA_VALUES
  from pandas._libs.tslibs import parsing
  from pandas._typing import ArrayLike, DtypeArg, DtypeObj, Scalar
  from pandas.compat._optional import import_optional_dependency
  from pandas.errors import ParserError, ParserWarning
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.astype import astype_array
  from pandas.core.dtypes.common import ensure_object, is_bool_dtype, is_dict_like, is_dtype_equal, is_extension_array_dtype, is_float_dtype, is_integer, is_integer_dtype, is_list_like, is_object_dtype, is_scalar, is_string_dtype, pandas_dtype
  from pandas.core.dtypes.dtypes import CategoricalDtype, ExtensionDtype
  from pandas.core.dtypes.missing import isna
  from pandas import ArrowDtype, DatetimeIndex, StringDtype
  from pandas.core import algorithms
  from pandas.core.arrays import ArrowExtensionArray, BooleanArray, Categorical, ExtensionArray, FloatingArray, IntegerArray
  from pandas.core.arrays.boolean import BooleanDtype
  from pandas.core.indexes.api import Index, MultiIndex, default_index, ensure_index_from_sequences
  from pandas.core.series import Series
  from pandas.core.tools import datetimes
  from pandas.io.common import is_potential_multi_index
label_desc: Use image node_id 27 for calling the ParserBase obj's underlying member method code with example usage: obj._get_empty_meta(columns, index_col, index_names, dtype) and returns: index,  columns,  col_dict