code: def maybe_infer_to_datetimelike(
      value: npt.NDArray[np.object_],
  ) -> (
      np.ndarray
      | DatetimeArray
      | TimedeltaArray
      | PeriodArray
      | IntervalArray
  ):
      """
      we might have a array (or single object) that is datetime like,
      and no dtype is passed don't change the value unless we find a
      datetime/timedelta set
  
      this is pretty strict in that a datetime/timedelta is REQUIRED
      in addition to possible nulls/string likes
  
      Parameters
      ----------
      value : np.ndarray[object]
  
      Returns
      -------
      np.ndarray, DatetimeArray, TimedeltaArray, PeriodArray, or IntervalArray
  
      """
      if not isinstance(value, np.ndarray) or value.dtype != object:
          # Caller is responsible for passing only ndarray[object]
          raise TypeError(type(value))  # pragma: no cover
      if value.ndim != 1:
          # Caller is responsible
          raise ValueError(value.ndim)  # pragma: no cover
  
      if not len(value):
          return value
  
      # error: Incompatible return value type (got "Union[ExtensionArray,
      # ndarray[Any, Any]]", expected "Union[ndarray[Any, Any], DatetimeArray,
      # TimedeltaArray, PeriodArray, IntervalArray]")
      return lib.maybe_convert_objects(  # type: ignore[return-value]
          value,
          # Here we do not convert numeric dtypes, as if we wanted that,
          #  numpy would have done it for us.
          convert_numeric=False,
          convert_period=True,
          convert_interval=True,
          convert_timedelta=True,
          convert_datetime=True,
          dtype_if_all_nat=np.dtype("M8[ns]"),
      )
  
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 31 for calling a global function with example usage: maybe_infer_to_datetimelike(value) and returns: lib, value