docstr: Set the columns that should not undergo dtype conversions.
  
  Currently, any column that is involved with date parsing will not
  undergo such conversions. If usecols is specified, the positions of the columns
  not to cast is relative to the usecols not to all columns.
  
  Parameters
  ----------
  col_indices: The indices specifying order and positions of the columns
  names: The column names which order is corresponding with the order
         of col_indices
  
  Returns
  -------
  A set of integers containing the positions of the columns not to convert.
code: def _set_noconvert_dtype_columns(
      self, col_indices: list[int], names: Sequence[Hashable]
  ) -> set[int]:
      """
      Set the columns that should not undergo dtype conversions.
  
      Currently, any column that is involved with date parsing will not
      undergo such conversions. If usecols is specified, the positions of the columns
      not to cast is relative to the usecols not to all columns.
  
      Parameters
      ----------
      col_indices: The indices specifying order and positions of the columns
      names: The column names which order is corresponding with the order
             of col_indices
  
      Returns
      -------
      A set of integers containing the positions of the columns not to convert.
      """
      usecols: list[int] | list[str] | None
      noconvert_columns = set()
      if self.usecols_dtype == "integer":
          # A set of integers will be converted to a list in
          # the correct order every single time.
          usecols = sorted(self.usecols)
      elif callable(self.usecols) or self.usecols_dtype not in (
          "empty",
          None,
      ):
          # The names attribute should have the correct columns
          # in the proper order for indexing with parse_dates.
          usecols = col_indices
      else:
          # Usecols is empty.
          usecols = None
  
      def _set(x) -> int:
          if usecols is not None and is_integer(x):
              x = usecols[x]
  
          if not is_integer(x):
              x = col_indices[names.index(x)]
  
          return x
  
      if isinstance(self.parse_dates, list):
          for val in self.parse_dates:
              if isinstance(val, list):
                  for k in val:
                      noconvert_columns.add(_set(k))
              else:
                  noconvert_columns.add(_set(val))
  
      elif isinstance(self.parse_dates, dict):
          for val in self.parse_dates.values():
              if isinstance(val, list):
                  for k in val:
                      noconvert_columns.add(_set(k))
              else:
                  noconvert_columns.add(_set(val))
  
      elif self.parse_dates:
          if isinstance(self.index_col, list):
              for k in self.index_col:
                  noconvert_columns.add(_set(k))
          elif self.index_col is not None:
              noconvert_columns.add(_set(self.index_col))
  
      return noconvert_columns
  
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, 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.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_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, DataFrame, DatetimeIndex, StringDtype, concat
  from pandas.core import algorithms
  from pandas.core.arrays import ArrowExtensionArray, BaseMaskedArray, 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 14 for calling the ParserBase obj's underlying member method code with example usage: obj._set_noconvert_dtype_columns(col_indices, names) and returns: noconvert_columns, x