class_docstr: Immutable Index of timedelta64 data.
  
  Represented internally as int64, and scalars returned Timedelta objects.
  
  Parameters
  ----------
  data : array-like (1-dimensional), optional
      Optional timedelta-like data to construct index with.
  unit : {'D', 'h', 'm', 's', 'ms', 'us', 'ns'}, optional
      The unit of ``data``.
  
      .. deprecated:: 2.2.0
       Use ``pd.to_timedelta`` instead.
  
  freq : str or pandas offset object, optional
      One of pandas date offset strings or corresponding objects. The string
      ``'infer'`` can be passed in order to set the frequency of the index as
      the inferred frequency upon creation.
  dtype : numpy.dtype or str, default None
      Valid ``numpy`` dtypes are ``timedelta64[ns]``, ``timedelta64[us]``,
      ``timedelta64[ms]``, and ``timedelta64[s]``.
  copy : bool
      Make a copy of input array.
  name : object
      Name to be stored in the index.
  
  Attributes
  ----------
  days
  seconds
  microseconds
  nanoseconds
  components
  inferred_freq
  
  Methods
  -------
  to_pytimedelta
  to_series
  round
  floor
  ceil
  to_frame
  mean
  
  See Also
  --------
  Index : The base pandas Index type.
  Timedelta : Represents a duration between two dates or times.
  DatetimeIndex : Index of datetime64 data.
  PeriodIndex : Index of Period data.
  timedelta_range : Create a fixed-frequency TimedeltaIndex.
  
  Notes
  -----
  To learn more about the frequency strings, please see `this link
  <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
  
  Examples
  --------
  >>> pd.TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'])
  TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
                 dtype='timedelta64[ns]', freq=None)
  
  We can also let pandas infer the frequency when possible.
  
  >>> pd.TimedeltaIndex(np.arange(5) * 24 * 3600 * 1e9, freq='infer')
  TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
                 dtype='timedelta64[ns]', freq='D')
docstr: Can we compare values of the given dtype to our own?
docstr_tok: ["Can","we","compare","values","of","the","given","dtype","to","our","own","?"]
code: def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
      """
      Can we compare values of the given dtype to our own?
      """
      return lib.is_np_dtype(dtype, "m")
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING
  import warnings
  from pandas._libs import index, lib
  from pandas._libs.tslibs import Resolution, Timedelta, to_offset
  from pandas._libs.tslibs.timedeltas import disallow_ambiguous_unit
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import is_scalar, pandas_dtype
  from pandas.core.dtypes.generic import ABCSeries
  from pandas.core.arrays.timedeltas import TimedeltaArray
  import pandas.core.common
  from pandas.core.indexes.base import Index, maybe_extract_name
  from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin
  from pandas.core.indexes.extension import inherit_names
label_desc: Use image node_id 4 for calling the TimedeltaIndex obj's underlying member method code with example usage: obj._is_comparable_dtype(dtype) and returns: lib