code: def sequence_to_td64ns(data, copy=False, unit="ns", errors="raise"):
      """
      Parameters
      ----------
      array : list-like
      copy : bool, default False
      unit : str, default "ns"
          The timedelta unit to treat integers as multiples of.
      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.
      """
      inferred_freq = None
      unit = parse_timedelta_unit(unit)
  
      # Unwrap whatever we have into a np.ndarray
      if not hasattr(data, "dtype"):
          # e.g. list, tuple
          if np.ndim(data) == 0:
              # i.e. generator
              data = list(data)
          data = np.array(data, copy=False)
      elif isinstance(data, ABCSeries):
          data = data._values
      elif isinstance(data, (ABCTimedeltaIndex, TimedeltaArray)):
          inferred_freq = data.freq
          data = data._data
  
      # 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/frace separately
          # to avoid precision issues from float -> int
          mask = np.isnan(data)
          m, p = precision_from_unit(unit)
          base = data.astype(np.int64)
          frac = data - base
          if p:
              frac = np.round(frac, p)
          data = (base * m + (frac * m).astype(np.int64)).view(
              "timedelta64[ns]"
          )
          data[mask] = iNaT
          copy = False
  
      elif is_timedelta64_dtype(data.dtype):
          if data.dtype != _TD_DTYPE:
              # non-nano unit
              # TODO: watch out for overflows
              data = data.astype(_TD_DTYPE)
              copy = False
  
      elif is_datetime64_dtype(data):
          # GH#23539
          warnings.warn(
              "Passing datetime64-dtype data to TimedeltaIndex is "
              "deprecated, will raise a TypeError in a future "
              "version",
              FutureWarning,
              stacklevel=4,
          )
          data = ensure_int64(data).view(_TD_DTYPE)
  
      else:
          raise TypeError(
              "dtype {dtype} cannot be converted to timedelta64[ns]".format(
                  dtype=data.dtype
              )
          )
  
      data = np.array(data, copy=copy)
      if data.ndim != 1:
          raise ValueError(
              "Only 1-dimensional input arrays are supported."
          )
  
      assert data.dtype == "m8[ns]", data
      return data, inferred_freq
  
imports: from __future__ import division
  from datetime import timedelta
  import textwrap
  import warnings
  import numpy
  from pandas._libs import lib, tslibs
  from pandas._libs.tslibs import NaT, Timedelta, Timestamp, iNaT
  from pandas._libs.tslibs.fields import get_timedelta_field
  from pandas._libs.tslibs.timedeltas import array_to_timedelta64, parse_timedelta_unit, precision_from_unit
  import pandas.compat
  from pandas.util._decorators import Appender
  from pandas.core.dtypes.common import _NS_DTYPE, _TD_DTYPE, ensure_int64, is_datetime64_dtype, is_dtype_equal, is_float_dtype, is_integer_dtype, is_list_like, is_object_dtype, is_scalar, is_string_dtype, is_timedelta64_dtype, is_timedelta64_ns_dtype, pandas_dtype
  from pandas.core.dtypes.dtypes import DatetimeTZDtype
  from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries, ABCTimedeltaIndex
  from pandas.core.dtypes.missing import isna
  from pandas.core import ops
  from pandas.core.algorithms import checked_add_with_arr
  import pandas.core.common
  from pandas.tseries.frequencies import to_offset
  from pandas.tseries.offsets import Tick
  from .None import datetimelike
label_desc: Use image node_id 4 for calling a global function with example usage: sequence_to_td64ns(data, copy, unit, errors) and returns: data,  inferred_freq