code: def maybe_downcast_to_dtype(
      result: ArrayLike, dtype: str | np.dtype
  ) -> ArrayLike:
      """
      try to cast to the specified dtype (e.g. convert back to bool/int
      or could be an astype of float64->float32
      """
      do_round = False
  
      if isinstance(dtype, str):
          if dtype == "infer":
              inferred_type = lib.infer_dtype(result, skipna=False)
              if inferred_type == "boolean":
                  dtype = "bool"
              elif inferred_type == "integer":
                  dtype = "int64"
              elif inferred_type == "datetime64":
                  dtype = "datetime64[ns]"
              elif inferred_type in ["timedelta", "timedelta64"]:
                  dtype = "timedelta64[ns]"
  
              # try to upcast here
              elif inferred_type == "floating":
                  dtype = "int64"
                  if issubclass(result.dtype.type, np.number):
                      do_round = True
  
              else:
                  # TODO: complex?  what if result is already non-object?
                  dtype = "object"
  
          dtype = np.dtype(dtype)
  
      if not isinstance(dtype, np.dtype):
          # enforce our signature annotation
          raise TypeError(dtype)  # pragma: no cover
  
      converted = maybe_downcast_numeric(result, dtype, do_round)
      if converted is not result:
          return converted
  
      # a datetimelike
      # GH12821, iNaT is cast to float
      if dtype.kind in ["M", "m"] and result.dtype.kind in ["i", "f"]:
          result = result.astype(dtype)
  
      elif dtype.kind == "m" and result.dtype == _dtype_obj:
          # test_where_downcast_to_td64
          result = cast(np.ndarray, result)
          result = array_to_timedelta64(result)
  
      elif dtype == np.dtype("M8[ns]") and result.dtype == _dtype_obj:
          result = cast(np.ndarray, result)
          return np.asarray(maybe_cast_to_datetime(result, dtype=dtype))
  
      return result
  
imports: from __future__ import annotations
  import datetime
  import functools
  from typing import TYPE_CHECKING, Any, Literal, Sized, TypeVar, cast, overload
  import warnings
  import numpy
  from pandas._libs import lib
  from pandas._libs.missing import NA, NAType, checknull
  from pandas._libs.tslibs import NaT, OutOfBoundsDatetime, OutOfBoundsTimedelta, Timedelta, Timestamp, get_unit_from_dtype, is_supported_unit
  from pandas._libs.tslibs.timedeltas import array_to_timedelta64
  from pandas._typing import ArrayLike, Dtype, DtypeObj, NumpyIndexT, Scalar, npt
  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_bool_dtype, is_complex, is_complex_dtype, is_datetime64_dtype, is_extension_array_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype, is_numeric_dtype, is_object_dtype, is_scalar, is_signed_integer_dtype, is_string_dtype, is_timedelta64_dtype, is_unsigned_integer_dtype, pandas_dtype
  from pandas.core.dtypes.dtypes import 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 9 for calling a global function with example usage: maybe_downcast_to_dtype(result, dtype) and returns: result, converted, np