code: def convert_dtypes(
      input_array: ArrayLike,
      convert_string: bool = True,
      convert_integer: bool = True,
      convert_boolean: bool = True,
      convert_floating: bool = True,
      infer_objects: bool = False,
      dtype_backend: Literal[
          "numpy_nullable", "pyarrow"
      ] = "numpy_nullable",
  ) -> DtypeObj:
      """
      Convert objects to best possible type, and optionally,
      to types supporting ``pd.NA``.
  
      Parameters
      ----------
      input_array : ExtensionArray or np.ndarray
      convert_string : bool, default True
          Whether object dtypes should be converted to ``StringDtype()``.
      convert_integer : bool, default True
          Whether, if possible, conversion can be done to integer extension types.
      convert_boolean : bool, defaults True
          Whether object dtypes should be converted to ``BooleanDtypes()``.
      convert_floating : bool, defaults True
          Whether, if possible, conversion can be done to floating extension types.
          If `convert_integer` is also True, preference will be give to integer
          dtypes if the floats can be faithfully casted to integers.
      infer_objects : bool, defaults False
          Whether to also infer objects to float/int if possible. Is only hit if the
          object array contains pd.NA.
      dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
          Back-end data type applied to the resultant :class:`DataFrame`
          (still experimental). Behaviour is as follows:
  
          * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
            (default).
          * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
            DataFrame.
  
          .. versionadded:: 2.0
  
      Returns
      -------
      np.dtype, or ExtensionDtype
      """
      inferred_dtype: str | DtypeObj
  
      if (
          convert_string
          or convert_integer
          or convert_boolean
          or convert_floating
      ) and isinstance(input_array, np.ndarray):
          if input_array.dtype == object:
              inferred_dtype = lib.infer_dtype(input_array)
          else:
              inferred_dtype = input_array.dtype
  
          if is_string_dtype(inferred_dtype):
              if not convert_string or inferred_dtype == "bytes":
                  inferred_dtype = input_array.dtype
              else:
                  inferred_dtype = pandas_dtype_func("string")
  
          if convert_integer:
              target_int_dtype = pandas_dtype_func("Int64")
  
              if input_array.dtype.kind in "iu":
                  from pandas.core.arrays.integer import (
                      NUMPY_INT_TO_DTYPE,
                  )
  
                  inferred_dtype = NUMPY_INT_TO_DTYPE.get(
                      input_array.dtype, target_int_dtype
                  )
              elif input_array.dtype.kind in "fcb":
                  # TODO: de-dup with maybe_cast_to_integer_array?
                  arr = input_array[notna(input_array)]
                  if (arr.astype(int) == arr).all():
                      inferred_dtype = target_int_dtype
                  else:
                      inferred_dtype = input_array.dtype
              elif (
                  infer_objects
                  and input_array.dtype == object
                  and (
                      isinstance(inferred_dtype, str)
                      and inferred_dtype == "integer"
                  )
              ):
                  inferred_dtype = target_int_dtype
  
          if convert_floating:
              if input_array.dtype.kind in "fcb":
                  # i.e. numeric but not integer
                  from pandas.core.arrays.floating import (
                      NUMPY_FLOAT_TO_DTYPE,
                  )
  
                  inferred_float_dtype: DtypeObj = (
                      NUMPY_FLOAT_TO_DTYPE.get(
                          input_array.dtype,
                          pandas_dtype_func("Float64"),
                      )
                  )
                  # if we could also convert to integer, check if all floats
                  # are actually integers
                  if convert_integer:
                      # TODO: de-dup with maybe_cast_to_integer_array?
                      arr = input_array[notna(input_array)]
                      if (arr.astype(int) == arr).all():
                          inferred_dtype = pandas_dtype_func("Int64")
                      else:
                          inferred_dtype = inferred_float_dtype
                  else:
                      inferred_dtype = inferred_float_dtype
              elif (
                  infer_objects
                  and input_array.dtype == object
                  and (
                      isinstance(inferred_dtype, str)
                      and inferred_dtype == "mixed-integer-float"
                  )
              ):
                  inferred_dtype = pandas_dtype_func("Float64")
  
          if convert_boolean:
              if input_array.dtype.kind == "b":
                  inferred_dtype = pandas_dtype_func("boolean")
              elif (
                  isinstance(inferred_dtype, str)
                  and inferred_dtype == "boolean"
              ):
                  inferred_dtype = pandas_dtype_func("boolean")
  
          if isinstance(inferred_dtype, str):
              # If we couldn't do anything else, then we retain the dtype
              inferred_dtype = input_array.dtype
  
      else:
          inferred_dtype = input_array.dtype
  
      if dtype_backend == "pyarrow":
          from pandas.core.arrays.arrow.array import to_pyarrow_type
          from pandas.core.arrays.string_ import StringDtype
  
          assert not isinstance(inferred_dtype, str)
  
          if (
              (convert_integer and inferred_dtype.kind in "iu")
              or (convert_floating and inferred_dtype.kind in "fc")
              or (convert_boolean and inferred_dtype.kind == "b")
              or (
                  convert_string
                  and isinstance(inferred_dtype, StringDtype)
              )
              or (
                  inferred_dtype.kind not in "iufcb"
                  and not isinstance(inferred_dtype, StringDtype)
              )
          ):
              if isinstance(
                  inferred_dtype, PandasExtensionDtype
              ) and not isinstance(inferred_dtype, DatetimeTZDtype):
                  base_dtype = inferred_dtype.base
              elif isinstance(
                  inferred_dtype, (BaseMaskedDtype, ArrowDtype)
              ):
                  base_dtype = inferred_dtype.numpy_dtype
              elif isinstance(inferred_dtype, StringDtype):
                  base_dtype = np.dtype(str)
              else:
                  base_dtype = inferred_dtype
              if (
                  base_dtype.kind == "O"  # type: ignore[union-attr]
                  and input_array.size > 0
                  and isna(input_array).all()
              ):
                  import pyarrow as pa
  
                  pa_type = pa.null()
              else:
                  pa_type = to_pyarrow_type(base_dtype)
              if pa_type is not None:
                  inferred_dtype = ArrowDtype(pa_type)
      elif dtype_backend == "numpy_nullable" and isinstance(
          inferred_dtype, ArrowDtype
      ):
          # GH 53648
          inferred_dtype = _arrow_dtype_mapping()[
              inferred_dtype.pyarrow_dtype
          ]
  
      # error: Incompatible return value type (got "Union[str, Union[dtype[Any],
      # ExtensionDtype]]", expected "Union[dtype[Any], ExtensionDtype]")
      return inferred_dtype
  
imports: from __future__ import annotations
  import datetime
  import functools
  from typing import TYPE_CHECKING, Any, Literal, TypeVar, cast, overload
  import warnings
  import numpy
  from pandas._config import using_pyarrow_string_dtype
  from pandas._libs import Interval, Period, lib
  from pandas._libs.missing import NA, NAType, checknull
  from pandas._libs.tslibs import NaT, OutOfBoundsDatetime, OutOfBoundsTimedelta, Timedelta, Timestamp, is_supported_dtype
  from pandas._libs.tslibs.timedeltas import array_to_timedelta64
  from pandas.compat.numpy import np_version_gt2
  from pandas.errors import IntCastingNaNError, LossySetitemError
  from pandas.core.dtypes.common import ensure_int8, ensure_int16, ensure_int32, ensure_int64, ensure_object, ensure_str, is_bool, is_complex, is_float, is_integer, is_object_dtype, is_scalar, is_string_dtype, pandas_dtype
  from pandas.core.dtypes.dtypes import ArrowDtype, BaseMaskedDtype, CategoricalDtype, DatetimeTZDtype, ExtensionDtype, IntervalDtype, PandasExtensionDtype, PeriodDtype
  from pandas.core.dtypes.generic import ABCExtensionArray, ABCIndex, ABCSeries
  from pandas.core.dtypes.inference import is_list_like
  from pandas.core.dtypes.missing import is_valid_na_for_dtype, isna, na_value_for_dtype, notna
  from pandas.io._util import _arrow_dtype_mapping
label_desc: Use image node_id 30 for calling a global function with example usage: convert_dtypes(input_array, convert_string, convert_integer, convert_boolean, convert_floating, infer_objects, dtype_backend) and returns: inferred_dtype