code: def sequence_to_td64ns(
      data,
      copy: bool = False,
      unit=None,
      errors: DateTimeErrorChoices = "raise",
  ) -> tuple[np.ndarray, Tick | None]:
      """
      Parameters
      ----------
      data : list-like
      copy : bool, default False
      unit : str, optional
          The timedelta unit to treat integers as multiples of. For numeric
          data this defaults to ``'ns'``.
          Must be un-specified if the data contains a str and ``errors=="raise"``.
      errors : {"raise", "coerce", "ignore"}, default "raise"
          How to handle elements that cannot be converted to timedelta64[ns].
          See ``pandas.to_timedelta`` for details.
  
      Returns
      -------
      converted : numpy.ndarray
          The sequence converted to a numpy array with dtype ``timedelta64[ns]``.
      inferred_freq : Tick or None
          The inferred frequency of the sequence.
  
      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.
      """
      assert unit not in [
          "Y",
          "y",
          "M",
      ]  # caller is responsible for checking
  
      inferred_freq = None
      if unit is not None:
          unit = parse_timedelta_unit(unit)
  
      data, copy = dtl.ensure_arraylike_for_datetimelike(
          data, copy, cls_name="TimedeltaArray"
      )
  
      if isinstance(data, TimedeltaArray):
          inferred_freq = data.freq
  
      # Convert whatever we have into timedelta64[ns] dtype
      if is_object_dtype(data.dtype) or is_string_dtype(data.dtype):
          # no need to make a copy, need to convert if string-dtyped
          data = _objects_to_td64ns(data, unit=unit, errors=errors)
          copy = False
  
      elif is_integer_dtype(data.dtype):
          # treat as multiples of the given unit
          data, copy_made = _ints_to_td64ns(data, unit=unit)
          copy = copy and not copy_made
  
      elif is_float_dtype(data.dtype):
          # cast the unit, multiply base/frac separately
          # to avoid precision issues from float -> int
          if is_extension_array_dtype(data):
              mask = data._mask
              data = data._data
          else:
              mask = np.isnan(data)
          # The next few lines are effectively a vectorized 'cast_from_unit'
          m, p = precision_from_unit(unit or "ns")
          with warnings.catch_warnings():
              # Suppress RuntimeWarning about All-NaN slice
              warnings.filterwarnings(
                  "ignore",
                  "invalid value encountered in cast",
                  RuntimeWarning,
              )
              base = data.astype(np.int64)
          frac = data - base
          if p:
              frac = np.round(frac, p)
          with warnings.catch_warnings():
              warnings.filterwarnings(
                  "ignore",
                  "invalid value encountered in cast",
                  RuntimeWarning,
              )
              data = (base * m + (frac * m).astype(np.int64)).view(
                  "timedelta64[ns]"
              )
          data[mask] = iNaT
          copy = False
  
      elif is_timedelta64_dtype(data.dtype):
          data_unit = get_unit_from_dtype(data.dtype)
          if not is_supported_unit(data_unit):
              # cast to closest supported unit, i.e. s or ns
              new_reso = get_supported_reso(data_unit)
              new_unit = npy_unit_to_abbrev(new_reso)
              new_dtype = np.dtype(f"m8[{new_unit}]")
              data = astype_overflowsafe(
                  data, dtype=new_dtype, copy=False
              )
              copy = False
  
      else:
          # This includes datetime64-dtype, see GH#23539, GH#29794
          raise TypeError(
              f"dtype {data.dtype} cannot be converted to timedelta64[ns]"
          )
  
      data = np.array(data, copy=copy)
  
      assert data.dtype.kind == "m"
      assert data.dtype != "m8"  # i.e. not unit-less
  
      return data, inferred_freq
  
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 2 for calling a global function with example usage: sequence_to_td64ns(data, copy, unit, errors) and returns: data,  inferred_freq