code: def _infer_columns(
      self,
  ) -> tuple[list[list[Scalar | None]], int, set[Scalar | None]]:
      names = self.names
      num_original_columns = 0
      clear_buffer = True
      unnamed_cols: set[Scalar | None] = set()
      self._header_line = None
  
      if self.header is not None:
          header = self.header
  
          if isinstance(header, (list, tuple, np.ndarray)):
              have_mi_columns = len(header) > 1
              # we have a mi columns, so read an extra line
              if have_mi_columns:
                  header = list(header) + [header[-1] + 1]
          else:
              have_mi_columns = False
              header = [header]
  
          columns: list[list[Scalar | None]] = []
          for level, hr in enumerate(header):
              try:
                  line = self._buffered_line()
  
                  while self.line_pos <= hr:
                      line = self._next_line()
  
              except StopIteration as err:
                  if 0 < self.line_pos <= hr and (
                      not have_mi_columns or hr != header[-1]
                  ):
                      # If no rows we want to raise a different message and if
                      # we have mi columns, the last line is not part of the header
                      joi = list(
                          map(
                              str,
                              header[:-1]
                              if have_mi_columns
                              else header,
                          )
                      )
                      msg = f"[{','.join(joi)}], len of {len(joi)}, "
                      raise ValueError(
                          f"Passed header={msg}"
                          f"but only {self.line_pos} lines in file"
                      ) from err
  
                  # We have an empty file, so check
                  # if columns are provided. That will
                  # serve as the 'line' for parsing
                  if have_mi_columns and hr > 0:
                      if clear_buffer:
                          self._clear_buffer()
                      columns.append([None] * len(columns[-1]))
                      return columns, num_original_columns, unnamed_cols
  
                  if not self.names:
                      raise EmptyDataError(
                          "No columns to parse from file"
                      ) from err
  
                  line = self.names[:]
  
              this_columns: list[Scalar | None] = []
              this_unnamed_cols = []
  
              for i, c in enumerate(line):
                  if c == "":
                      if have_mi_columns:
                          col_name = f"Unnamed: {i}_level_{level}"
                      else:
                          col_name = f"Unnamed: {i}"
  
                      this_unnamed_cols.append(i)
                      this_columns.append(col_name)
                  else:
                      this_columns.append(c)
  
              if not have_mi_columns:
                  counts: DefaultDict = defaultdict(int)
                  # Ensure that regular columns are used before unnamed ones
                  # to keep given names and mangle unnamed columns
                  col_loop_order = [
                      i
                      for i in range(len(this_columns))
                      if i not in this_unnamed_cols
                  ] + this_unnamed_cols
  
                  # TODO: Use pandas.io.common.dedup_names instead (see #50371)
                  for i in col_loop_order:
                      col = this_columns[i]
                      old_col = col
                      cur_count = counts[col]
  
                      if cur_count > 0:
                          while cur_count > 0:
                              counts[old_col] = cur_count + 1
                              col = f"{old_col}.{cur_count}"
                              if col in this_columns:
                                  cur_count += 1
                              else:
                                  cur_count = counts[col]
  
                          if (
                              self.dtype is not None
                              and is_dict_like(self.dtype)
                              and self.dtype.get(old_col) is not None
                              and self.dtype.get(col) is None
                          ):
                              self.dtype.update(
                                  {col: self.dtype.get(old_col)}
                              )
                      this_columns[i] = col
                      counts[col] = cur_count + 1
              elif have_mi_columns:
                  # if we have grabbed an extra line, but its not in our
                  # format so save in the buffer, and create an blank extra
                  # line for the rest of the parsing code
                  if hr == header[-1]:
                      lc = len(this_columns)
                      # error: Cannot determine type of 'index_col'
                      sic = self.index_col  # type: ignore[has-type]
                      ic = len(sic) if sic is not None else 0
                      unnamed_count = len(this_unnamed_cols)
  
                      # if wrong number of blanks or no index, not our format
                      if (
                          lc != unnamed_count
                          and lc - ic > unnamed_count
                      ) or ic == 0:
                          clear_buffer = False
                          this_columns = [None] * lc
                          self.buf = [self.buf[-1]]
  
              columns.append(this_columns)
              unnamed_cols.update(
                  {this_columns[i] for i in this_unnamed_cols}
              )
  
              if len(columns) == 1:
                  num_original_columns = len(this_columns)
  
          if clear_buffer:
              self._clear_buffer()
  
          first_line: list[Scalar] | None
          if names is not None:
              # Read first row after header to check if data are longer
              try:
                  first_line = self._next_line()
              except StopIteration:
                  first_line = None
  
              len_first_data_row = (
                  0 if first_line is None else len(first_line)
              )
  
              if (
                  len(names) > len(columns[0])
                  and len(names) > len_first_data_row
              ):
                  raise ValueError(
                      "Number of passed names did not match "
                      "number of header fields in the file"
                  )
              if len(columns) > 1:
                  raise TypeError(
                      "Cannot pass names with multi-index columns"
                  )
  
              if self.usecols is not None:
                  # Set _use_cols. We don't store columns because they are
                  # overwritten.
                  self._handle_usecols(
                      columns, names, num_original_columns
                  )
              else:
                  num_original_columns = len(names)
              if self._col_indices is not None and len(names) != len(
                  self._col_indices
              ):
                  columns = [
                      [names[i] for i in sorted(self._col_indices)]
                  ]
              else:
                  columns = [names]
          else:
              columns = self._handle_usecols(
                  columns, columns[0], num_original_columns
              )
      else:
          try:
              line = self._buffered_line()
  
          except StopIteration as err:
              if not names:
                  raise EmptyDataError(
                      "No columns to parse from file"
                  ) from err
  
              line = names[:]
  
          # Store line, otherwise it is lost for guessing the index
          self._header_line = line
          ncols = len(line)
          num_original_columns = ncols
  
          if not names:
              columns = [list(range(ncols))]
              columns = self._handle_usecols(
                  columns, columns[0], num_original_columns
              )
          else:
              if (
                  self.usecols is None
                  or len(names) >= num_original_columns
              ):
                  columns = self._handle_usecols(
                      [names], names, num_original_columns
                  )
                  num_original_columns = len(names)
              else:
                  if not callable(self.usecols) and len(names) != len(
                      self.usecols
                  ):
                      raise ValueError(
                          "Number of passed names did not match number of "
                          "header fields in the file"
                      )
                  # Ignore output but set used columns.
                  self._handle_usecols([names], names, ncols)
                  columns = [names]
                  num_original_columns = ncols
  
      return columns, num_original_columns, unnamed_cols
  
imports: from __future__ import annotations
  from collections import abc, defaultdict
  import csv
  from io import StringIO
  import re
  import sys
  from typing import IO, TYPE_CHECKING, DefaultDict, Hashable, Iterator, List, Literal, Mapping, Sequence, cast
  import numpy
  from pandas._libs import lib
  from pandas._typing import ArrayLike, ReadCsvBuffer, Scalar
  from pandas.errors import EmptyDataError, ParserError
  from pandas.core.dtypes.common import is_integer
  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 7 for calling the PythonParser obj's underlying member method code with example usage: obj._infer_columns() and returns: columns,  num_original_columns,  unnamed_cols, columns,  num_original_columns,  unnamed_cols