code: def read(
      self,
      nrows: int | None = None,
  ) -> tuple[
      Index | MultiIndex | None,
      Sequence[Hashable] | MultiIndex,
      Mapping[Hashable, ArrayLike],
  ]:
      index: Index | MultiIndex | None
      column_names: Sequence[Hashable] | MultiIndex
      try:
          if self.low_memory:
              chunks = self._reader.read_low_memory(nrows)
              # destructive to chunks
              data = _concatenate_chunks(chunks)
  
          else:
              data = self._reader.read(nrows)
      except StopIteration:
          if self._first_chunk:
              self._first_chunk = False
              names = dedup_names(
                  self.orig_names,
                  is_potential_multi_index(
                      self.orig_names, self.index_col
                  ),
              )
              index, columns, col_dict = self._get_empty_meta(
                  names,
                  self.index_col,
                  self.index_names,
                  dtype=self.kwds.get("dtype"),
              )
              columns = self._maybe_make_multi_index_columns(
                  columns, self.col_names
              )
  
              if self.usecols is not None:
                  columns = self._filter_usecols(columns)
  
              col_dict = {
                  k: v for k, v in col_dict.items() if k in columns
              }
  
              return index, columns, col_dict
  
          else:
              self.close()
              raise
  
      # Done with first read, next time raise StopIteration
      self._first_chunk = False
  
      # error: Cannot determine type of 'names'
      names = self.names  # type: ignore[has-type]
  
      if self._reader.leading_cols:
          if self._has_complex_date_col:
              raise NotImplementedError(
                  "file structure not yet supported"
              )
  
          # implicit index, no index names
          arrays = []
  
          if self.index_col and self._reader.leading_cols != len(
              self.index_col
          ):
              raise ParserError(
                  "Could not construct index. Requested to use "
                  f"{len(self.index_col)} number of columns, but "
                  f"{self._reader.leading_cols} left to parse."
              )
  
          for i in range(self._reader.leading_cols):
              if self.index_col is None:
                  values = data.pop(i)
              else:
                  values = data.pop(self.index_col[i])
  
              values = self._maybe_parse_dates(
                  values, i, try_parse_dates=True
              )
              arrays.append(values)
  
          index = ensure_index_from_sequences(arrays)
  
          if self.usecols is not None:
              names = self._filter_usecols(names)
  
          names = dedup_names(
              names, is_potential_multi_index(names, self.index_col)
          )
  
          # rename dict keys
          data_tups = sorted(data.items())
          data = {k: v for k, (i, v) in zip(names, data_tups)}
  
          column_names, date_data = self._do_date_conversions(
              names, data
          )
  
          # maybe create a mi on the columns
          column_names = self._maybe_make_multi_index_columns(
              column_names, self.col_names
          )
  
      else:
          # rename dict keys
          data_tups = sorted(data.items())
  
          # ugh, mutation
  
          # assert for mypy, orig_names is List or None, None would error in list(...)
          assert self.orig_names is not None
          names = list(self.orig_names)
          names = dedup_names(
              names, is_potential_multi_index(names, self.index_col)
          )
  
          if self.usecols is not None:
              names = self._filter_usecols(names)
  
          # columns as list
          alldata = [x[1] for x in data_tups]
          if self.usecols is None:
              self._check_data_length(names, alldata)
  
          data = {k: v for k, (i, v) in zip(names, data_tups)}
  
          names, date_data = self._do_date_conversions(names, data)
          index, column_names = self._make_index(
              date_data, alldata, names
          )
  
      return index, column_names, date_data
  
imports: from __future__ import annotations
  from collections import defaultdict
  from typing import TYPE_CHECKING, Hashable, Mapping, Sequence
  import warnings
  import numpy
  from pandas._libs import lib, parsers
  from pandas._typing import ArrayLike, DtypeArg, DtypeObj, ReadCsvBuffer
  from pandas.compat._optional import import_optional_dependency
  from pandas.errors import DtypeWarning
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import is_categorical_dtype, pandas_dtype
  from pandas.core.dtypes.concat import concat_compat, union_categoricals
  from pandas.core.indexes.api import ensure_index_from_sequences
  from pandas.io.common import dedup_names, is_potential_multi_index
  from pandas.io.parsers.base_parser import ParserBase, ParserError, is_index_col
label_desc: Use image node_id 4 for calling the CParserWrapper obj's underlying member method code with example usage: obj.read(nrows) and returns: index,  column_names,  date_data, index,  columns,  col_dict