code: def _preprocess_for_cut(x):
      """
      handles preprocessing for cut where we convert passed
      input to array, strip the index information and store it
      separately
      """
      # Check that the passed array is a Pandas or Numpy object
      # We don't want to strip away a Pandas data-type here (e.g. datetimetz)
      ndim = getattr(x, "ndim", None)
      if ndim is None:
          x = np.asarray(x)
      if x.ndim != 1:
          raise ValueError("Input array must be 1 dimensional")
  
      return x
  
imports: from __future__ import annotations
  from typing import Any, Callable, Literal
  import numpy
  from pandas._libs import Timedelta, Timestamp
  from pandas._libs.lib import infer_dtype
  from pandas._typing import IntervalLeftRight
  from pandas.core.dtypes.common import DT64NS_DTYPE, ensure_platform_int, is_bool_dtype, is_categorical_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_datetime_or_timedelta_dtype, is_extension_array_dtype, is_integer, is_list_like, is_numeric_dtype, is_scalar, is_timedelta64_dtype
  from pandas.core.dtypes.generic import ABCSeries
  from pandas.core.dtypes.missing import isna
  from pandas import Categorical, Index, IntervalIndex, to_datetime, to_timedelta
  from pandas.core import nanops
  import pandas.core.algorithms
label_desc: Use image node_id 8 for calling a global function with example usage: _preprocess_for_cut(x) and returns: x