code: def _convert_to_ndarrays(
      self,
      dct: Mapping,
      na_values,
      na_fvalues,
      verbose: bool = False,
      converters=None,
      dtypes=None,
  ):
      result = {}
      for c, values in dct.items():
          conv_f = (
              None if converters is None else converters.get(c, None)
          )
          if isinstance(dtypes, dict):
              cast_type = dtypes.get(c, None)
          else:
              # single dtype or None
              cast_type = dtypes
  
          if self.na_filter:
              col_na_values, col_na_fvalues = _get_na_values(
                  c, na_values, na_fvalues, self.keep_default_na
              )
          else:
              col_na_values, col_na_fvalues = set(), set()
  
          if c in self._parse_date_cols:
              # GH#26203 Do not convert columns which get converted to dates
              # but replace nans to ensure to_datetime works
              mask = algorithms.isin(
                  values, set(col_na_values) | col_na_fvalues
              )
              np.putmask(values, mask, np.nan)
              result[c] = values
              continue
  
          if conv_f is not None:
              # conv_f applied to data before inference
              if cast_type is not None:
                  warnings.warn(
                      (
                          "Both a converter and dtype were specified "
                          f"for column {c} - only the converter will be used."
                      ),
                      ParserWarning,
                      stacklevel=find_stack_level(),
                  )
  
              try:
                  values = lib.map_infer(values, conv_f)
              except ValueError:
                  mask = algorithms.isin(values, list(na_values)).view(
                      np.uint8
                  )
                  values = lib.map_infer_mask(values, conv_f, mask)
  
              cvals, na_count = self._infer_types(
                  values,
                  set(col_na_values) | col_na_fvalues,
                  cast_type is None,
                  try_num_bool=False,
              )
          else:
              is_ea = is_extension_array_dtype(cast_type)
              is_str_or_ea_dtype = is_ea or is_string_dtype(cast_type)
              # skip inference if specified dtype is object
              # or casting to an EA
              try_num_bool = not (cast_type and is_str_or_ea_dtype)
  
              # general type inference and conversion
              cvals, na_count = self._infer_types(
                  values,
                  set(col_na_values) | col_na_fvalues,
                  cast_type is None,
                  try_num_bool,
              )
  
              # type specified in dtype param or cast_type is an EA
              if cast_type is not None:
                  cast_type = pandas_dtype(cast_type)
              if cast_type and (cvals.dtype != cast_type or is_ea):
                  if not is_ea and na_count > 0:
                      if is_bool_dtype(cast_type):
                          raise ValueError(
                              f"Bool column has NA values in column {c}"
                          )
                  cvals = self._cast_types(cvals, cast_type, c)
  
          result[c] = cvals
          if verbose and na_count:
              print(f"Filled {na_count} NA values in column {c!s}")
      return result
  
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 13 for calling the ParserBase obj's underlying member method code with example usage: obj._convert_to_ndarrays(dct, na_values, na_fvalues, verbose, converters, dtypes) and returns: result