class_docstr: Accessor object for datetimelike properties of the Series values.
  
  Returns a Series indexed like the original Series.
  Raises TypeError if the Series does not contain datetimelike values.
  
  Examples
  --------
  >>> seconds_series = pd.Series(
  ...     pd.timedelta_range(start="1 second", periods=3, freq="S")
  ... )
  >>> seconds_series
  0   0 days 00:00:01
  1   0 days 00:00:02
  2   0 days 00:00:03
  dtype: timedelta64[ns]
  >>> seconds_series.dt.seconds
  0    1
  1    2
  2    3
  dtype: int32
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docstr: Return an array of native :class:`datetime.timedelta` objects.
  
  Python's standard `datetime` library uses a different representation
  timedelta's. This method converts a Series of pandas Timedeltas
  to `datetime.timedelta` format with the same length as the original
  Series.
  
  Returns
  -------
  numpy.ndarray
      Array of 1D containing data with `datetime.timedelta` type.
  
  See Also
  --------
  datetime.timedelta : A duration expressing the difference
      between two date, time, or datetime.
  
  Examples
  --------
  >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
  >>> s
  0   0 days
  1   1 days
  2   2 days
  3   3 days
  4   4 days
  dtype: timedelta64[ns]
  
  >>> s.dt.to_pytimedelta()
  array([datetime.timedelta(0), datetime.timedelta(days=1),
  datetime.timedelta(days=2), datetime.timedelta(days=3),
  datetime.timedelta(days=4)], dtype=object)
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code: def to_pytimedelta(self) -> np.ndarray:
      """
      Return an array of native :class:`datetime.timedelta` objects.
  
      Python's standard `datetime` library uses a different representation
      timedelta's. This method converts a Series of pandas Timedeltas
      to `datetime.timedelta` format with the same length as the original
      Series.
  
      Returns
      -------
      numpy.ndarray
          Array of 1D containing data with `datetime.timedelta` type.
  
      See Also
      --------
      datetime.timedelta : A duration expressing the difference
          between two date, time, or datetime.
  
      Examples
      --------
      >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
      >>> s
      0   0 days
      1   1 days
      2   2 days
      3   3 days
      4   4 days
      dtype: timedelta64[ns]
  
      >>> s.dt.to_pytimedelta()
      array([datetime.timedelta(0), datetime.timedelta(days=1),
      datetime.timedelta(days=2), datetime.timedelta(days=3),
      datetime.timedelta(days=4)], dtype=object)
      """
      return self._get_values().to_pytimedelta()
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, cast
  import numpy
  from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_integer_dtype, is_list_like, is_period_dtype, is_timedelta64_dtype
  from pandas.core.dtypes.generic import ABCSeries
  from pandas.core.accessor import PandasDelegate, delegate_names
  from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray
  from pandas.core.arrays.arrow.array import ArrowExtensionArray
  from pandas.core.arrays.arrow.dtype import ArrowDtype
  from pandas.core.base import NoNewAttributesMixin, PandasObject
  from pandas.core.indexes.datetimes import DatetimeIndex
  from pandas.core.indexes.timedeltas import TimedeltaIndex
label_desc: Use image node_id 1 for calling the TimedeltaProperties obj's underlying member method code with example usage: obj.to_pytimedelta() and returns: self