docstr: Try several cases to get lines:
  
  0) There are headers on row 0 and row 1 and their
  total summed lengths equals the length of the next line.
  Treat row 0 as columns and row 1 as indices
  1) Look for implicit index: there are more columns
  on row 1 than row 0. If this is true, assume that row
  1 lists index columns and row 0 lists normal columns.
  2) Get index from the columns if it was listed.
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code: def _get_index_name(
      self,
  ) -> tuple[Sequence[Hashable] | None, list[Hashable], list[Hashable]]:
      """
      Try several cases to get lines:
  
      0) There are headers on row 0 and row 1 and their
      total summed lengths equals the length of the next line.
      Treat row 0 as columns and row 1 as indices
      1) Look for implicit index: there are more columns
      on row 1 than row 0. If this is true, assume that row
      1 lists index columns and row 0 lists normal columns.
      2) Get index from the columns if it was listed.
      """
      columns: Sequence[Hashable] = self.orig_names
      orig_names = list(columns)
      columns = list(columns)
  
      line: list[Scalar] | None
      if self._header_line is not None:
          line = self._header_line
      else:
          try:
              line = self._next_line()
          except StopIteration:
              line = None
  
      next_line: list[Scalar] | None
      try:
          next_line = self._next_line()
      except StopIteration:
          next_line = None
  
      # implicitly index_col=0 b/c 1 fewer column names
      implicit_first_cols = 0
      if line is not None:
          # leave it 0, #2442
          # Case 1
          # error: Cannot determine type of 'index_col'
          index_col = self.index_col  # type: ignore[has-type]
          if index_col is not False:
              implicit_first_cols = (
                  len(line) - self.num_original_columns
              )
  
          # Case 0
          if (
              next_line is not None
              and self.header is not None
              and index_col is not False
          ):
              if (
                  len(next_line)
                  == len(line) + self.num_original_columns
              ):
                  # column and index names on diff rows
                  self.index_col = list(range(len(line)))
                  self.buf = self.buf[1:]
  
                  for c in reversed(line):
                      columns.insert(0, c)
  
                  # Update list of original names to include all indices.
                  orig_names = list(columns)
                  self.num_original_columns = len(columns)
                  return line, orig_names, columns
  
      if implicit_first_cols > 0:
          # Case 1
          self._implicit_index = True
          if self.index_col is None:
              self.index_col = list(range(implicit_first_cols))
  
          index_name = None
  
      else:
          # Case 2
          (index_name, _, self.index_col) = self._clean_index_names(
              columns, self.index_col
          )
  
      return index_name, orig_names, columns
  
imports: from __future__ import annotations
  from collections import abc, defaultdict
  import csv
  from io import StringIO
  import re
  from typing import IO, TYPE_CHECKING, DefaultDict, Literal, cast
  import warnings
  import numpy
  from pandas._libs import lib
  from pandas.errors import EmptyDataError, ParserError, ParserWarning
  from pandas.util._decorators import cache_readonly
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import is_bool_dtype, is_integer, is_numeric_dtype
  from pandas.core.dtypes.inference import is_dict_like
  from pandas.io.common import dedup_names, is_potential_multi_index
  from pandas.io.parsers.base_parser import ParserBase, parser_defaults
label_desc: Use image node_id 24 for calling the PythonParser obj's underlying member method code with example usage: obj._get_index_name() and returns: index_name,  orig_names,  columns, line,  orig_names,  columns