docstr: Extract and return the names, index_names, col_names if the column
  names are a MultiIndex.
  
  Parameters
  ----------
  header: list of lists
      The header rows
  index_names: list, optional
      The names of the future index
  passed_names: bool, default False
      A flag specifying if names where passed
docstr_tok: ["Extract","and","return","the","names",",","index_names",",","col_names","if","the","column","names","are","a","MultiIndex",".","Parameters","--","--","--","--","--","header",":","list","of","lists","The","header","rows","index_names",":","list",",","optional","The","names","of","the","future","index","passed_names",":","bool",",","default","False","A","flag","specifying","if","names","where","passed"]
code: def _extract_multi_indexer_columns(
      self,
      header,
      index_names: Sequence[Hashable] | None,
      passed_names: bool = False,
  ) -> tuple[
      Sequence[Hashable],
      Sequence[Hashable] | None,
      Sequence[Hashable] | None,
      bool,
  ]:
      """
      Extract and return the names, index_names, col_names if the column
      names are a MultiIndex.
  
      Parameters
      ----------
      header: list of lists
          The header rows
      index_names: list, optional
          The names of the future index
      passed_names: bool, default False
          A flag specifying if names where passed
  
      """
      if len(header) < 2:
          return header[0], index_names, None, passed_names
  
      # the names are the tuples of the header that are not the index cols
      # 0 is the name of the index, assuming index_col is a list of column
      # numbers
      ic = self.index_col
      if ic is None:
          ic = []
  
      if not isinstance(ic, (list, tuple, np.ndarray)):
          ic = [ic]
      sic = set(ic)
  
      # clean the index_names
      index_names = header.pop(-1)
      index_names, _, _ = self._clean_index_names(
          index_names, self.index_col
      )
  
      # extract the columns
      field_count = len(header[0])
  
      # check if header lengths are equal
      if not all(
          len(header_iter) == field_count for header_iter in header[1:]
      ):
          raise ParserError(
              "Header rows must have an equal number of columns."
          )
  
      def extract(r):
          return tuple(r[i] for i in range(field_count) if i not in sic)
  
      columns = list(zip(*(extract(r) for r in header)))
      names = columns.copy()
      for single_ic in sorted(ic):
          names.insert(single_ic, single_ic)
  
      # Clean the column names (if we have an index_col).
      if len(ic):
          col_names = [
              r[ic[0]]
              if (
                  (r[ic[0]] is not None)
                  and r[ic[0]] not in self.unnamed_cols
              )
              else None
              for r in header
          ]
      else:
          col_names = [None] * len(header)
  
      passed_names = True
  
      return names, index_names, col_names, passed_names
  
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 6 for calling the ParserBase obj's underlying member method code with example usage: obj._extract_multi_indexer_columns(header, index_names, passed_names) and returns: names,  index_names,  col_names,  passed_names, header,  index_names,  None,  passed_names, tuple