docstr: Infer types of values, possibly casting
  
  Parameters
  ----------
  values : ndarray
  na_values : set
  no_dtype_specified: Specifies if we want to cast explicitly
  try_num_bool : bool, default try
     try to cast values to numeric (first preference) or boolean
  
  Returns
  -------
  converted : ndarray or ExtensionArray
  na_count : int
docstr_tok: ["Infer","types","of","values",",","possibly","casting","Parameters","--","--","--","--","--","values",":","ndarray","na_values",":","set","no_dtype_specified",":","Specifies","if","we","want","to","cast","explicitly","try_num_bool",":","bool",",","default","try","try","to","cast","values","to","numeric","(","first","preference",")","or","boolean","Returns","--","--","--","-","converted",":","ndarray","or","ExtensionArray","na_count",":","int"]
code: def _infer_types(
      self,
      values,
      na_values,
      no_dtype_specified,
      try_num_bool: bool = True,
  ) -> tuple[ArrayLike, int]:
      """
      Infer types of values, possibly casting
  
      Parameters
      ----------
      values : ndarray
      na_values : set
      no_dtype_specified: Specifies if we want to cast explicitly
      try_num_bool : bool, default try
         try to cast values to numeric (first preference) or boolean
  
      Returns
      -------
      converted : ndarray or ExtensionArray
      na_count : int
      """
      na_count = 0
      if issubclass(values.dtype.type, (np.number, np.bool_)):
          # If our array has numeric dtype, we don't have to check for strings in isin
          na_values = np.array(
              [val for val in na_values if not isinstance(val, str)]
          )
          mask = algorithms.isin(values, na_values)
          na_count = mask.astype("uint8", copy=False).sum()
          if na_count > 0:
              if is_integer_dtype(values):
                  values = values.astype(np.float64)
              np.putmask(values, mask, np.nan)
          return values, na_count
  
      dtype_backend = self.dtype_backend
      non_default_dtype_backend = (
          no_dtype_specified and dtype_backend is not lib.no_default
      )
      result: ArrayLike
  
      if try_num_bool and is_object_dtype(values.dtype):
          # exclude e.g DatetimeIndex here
          try:
              result, result_mask = lib.maybe_convert_numeric(
                  values,
                  na_values,
                  False,
                  convert_to_masked_nullable=non_default_dtype_backend,  # type: ignore[arg-type]  # noqa
              )
          except (ValueError, TypeError):
              # e.g. encountering datetime string gets ValueError
              #  TypeError can be raised in floatify
              na_count = parsers.sanitize_objects(values, na_values)
              result = values
          else:
              if non_default_dtype_backend:
                  if result_mask is None:
                      result_mask = np.zeros(
                          result.shape, dtype=np.bool_
                      )
  
                  if result_mask.all():
                      result = IntegerArray(
                          np.ones(result_mask.shape, dtype=np.int64),
                          result_mask,
                      )
                  elif is_integer_dtype(result):
                      result = IntegerArray(result, result_mask)
                  elif is_bool_dtype(result):
                      result = BooleanArray(result, result_mask)
                  elif is_float_dtype(result):
                      result = FloatingArray(result, result_mask)
  
                  na_count = result_mask.sum()
              else:
                  na_count = isna(result).sum()
      else:
          result = values
          if values.dtype == np.object_:
              na_count = parsers.sanitize_objects(values, na_values)
  
      if result.dtype == np.object_ and try_num_bool:
          result, bool_mask = libops.maybe_convert_bool(
              np.asarray(values),
              true_values=self.true_values,
              false_values=self.false_values,
              convert_to_masked_nullable=non_default_dtype_backend,  # type: ignore[arg-type]  # noqa
          )
          if result.dtype == np.bool_ and non_default_dtype_backend:
              if bool_mask is None:
                  bool_mask = np.zeros(result.shape, dtype=np.bool_)
              result = BooleanArray(result, bool_mask)
          elif result.dtype == np.object_ and non_default_dtype_backend:
              # read_excel sends array of datetime objects
              inferred_type = lib.infer_dtype(result)
              if inferred_type != "datetime":
                  result = (
                      StringDtype()
                      .construct_array_type()
                      ._from_sequence(values)
                  )
  
      if dtype_backend == "pyarrow":
          pa = import_optional_dependency("pyarrow")
          if isinstance(result, np.ndarray):
              result = ArrowExtensionArray(
                  pa.array(result, from_pandas=True)
              )
          else:
              # ExtensionArray
              result = ArrowExtensionArray(
                  pa.array(result.to_numpy(), from_pandas=True)
              )
  
      return result, na_count
  
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, Hashable, Iterable, List, Mapping, Sequence, Tuple, 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._typing import ArrayLike, DtypeArg, DtypeObj, Scalar
  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_dtype_equal, 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, DatetimeIndex, StringDtype
  from pandas.core import algorithms
  from pandas.core.arrays import ArrowExtensionArray, 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 15 for calling the ParserBase obj's underlying member method code with example usage: obj._infer_types(values, na_values, no_dtype_specified, try_num_bool) and returns: result,  na_count, values,  na_count