code: def maybe_cast_to_datetime(
      value: np.ndarray | list, dtype: np.dtype
  ) -> ExtensionArray | np.ndarray:
      """
      try to cast the array/value to a datetimelike dtype, converting float
      nan to iNaT
  
      Caller is responsible for handling ExtensionDtype cases and non dt64/td64
      cases.
      """
      from pandas.core.arrays.datetimes import DatetimeArray
      from pandas.core.arrays.timedeltas import TimedeltaArray
  
      assert dtype.kind in ["m", "M"]
      if not is_list_like(value):
          raise TypeError("value must be listlike")
  
      # TODO: _from_sequence would raise ValueError in cases where
      #  _ensure_nanosecond_dtype raises TypeError
      _ensure_nanosecond_dtype(dtype)
  
      if is_timedelta64_dtype(dtype):
          res = TimedeltaArray._from_sequence(value, dtype=dtype)
          return res
      else:
          try:
              dta = DatetimeArray._from_sequence(value, dtype=dtype)
          except ValueError as err:
              # We can give a Series-specific exception message.
              if (
                  "cannot supply both a tz and a timezone-naive dtype"
                  in str(err)
              ):
                  raise ValueError(
                      "Cannot convert timezone-aware data to "
                      "timezone-naive dtype. Use "
                      "pd.Series(values).dt.tz_localize(None) instead."
                  ) from err
              raise
  
          return dta
  
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 32 for calling a global function with example usage: maybe_cast_to_datetime(value, dtype) and returns: res, dta