code: def _objects_to_td64ns(
      data, unit=None, errors: DateTimeErrorChoices = "raise"
  ):
      """
      Convert a object-dtyped or string-dtyped array into an
      timedelta64[ns]-dtyped array.
  
      Parameters
      ----------
      data : ndarray or Index
      unit : str, default "ns"
          The timedelta unit to treat integers as multiples of.
          Must not be specified if the data contains a str.
      errors : {"raise", "coerce", "ignore"}, default "raise"
          How to handle elements that cannot be converted to timedelta64[ns].
          See ``pandas.to_timedelta`` for details.
  
      Returns
      -------
      numpy.ndarray : timedelta64[ns] array converted from data
  
      Raises
      ------
      ValueError : Data cannot be converted to timedelta64[ns].
  
      Notes
      -----
      Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause
      errors to be ignored; they are caught and subsequently ignored at a
      higher level.
      """
      # coerce Index to np.ndarray, converting string-dtype if necessary
      values = np.array(data, dtype=np.object_, copy=False)
  
      result = array_to_timedelta64(values, unit=unit, errors=errors)
      return result.view("timedelta64[ns]")
  
imports: from __future__ import annotations
  from datetime import timedelta
  import operator
  from typing import TYPE_CHECKING, Iterator, cast
  import warnings
  import numpy
  from pandas._libs import lib, tslibs
  from pandas._libs.tslibs import BaseOffset, NaT, NaTType, Tick, Timedelta, astype_overflowsafe, get_supported_reso, get_unit_from_dtype, iNaT, is_supported_unit, npy_unit_to_abbrev, periods_per_second, to_offset
  from pandas._libs.tslibs.conversion import precision_from_unit
  from pandas._libs.tslibs.fields import get_timedelta_days, get_timedelta_field
  from pandas._libs.tslibs.timedeltas import array_to_timedelta64, floordiv_object_array, ints_to_pytimedelta, parse_timedelta_unit, truediv_object_array
  from pandas._typing import AxisInt, DateTimeErrorChoices, DtypeObj, NpDtype, npt
  from pandas.compat.numpy import function
  from pandas.util._validators import validate_endpoints
  from pandas.core.dtypes.common import TD64NS_DTYPE, is_dtype_equal, is_extension_array_dtype, is_float_dtype, is_integer_dtype, is_object_dtype, is_scalar, is_string_dtype, is_timedelta64_dtype, pandas_dtype
  from pandas.core.dtypes.missing import isna
  from pandas.core import nanops
  from pandas.core.array_algos import datetimelike_accumulations
  from pandas.core.arrays import datetimelike
  from pandas.core.arrays._ranges import generate_regular_range
  import pandas.core.common
  from pandas.core.ops import roperator
  from pandas.core.ops.common import unpack_zerodim_and_defer
label_desc: Use image node_id 4 for calling a global function with example usage: _objects_to_td64ns(data, unit, errors) and returns: result