 spaced time points (where the difference between any
    two adjacent points is specified by the given frequency) such that they all
    satisfy `start <[=] x <[=] end`, where the first one and the last one are, resp.,
    the first and last time points in that range that fall on the boundary of ``freq``
    (if given as a frequency string) or that are valid for ``freq`` (if given as a
    :class:`pandas.tseries.offsets.DateOffset`). (If exactly one of ``start``,
    ``end``, or ``freq`` is *not* specified, this missing parameter can be computed
    given ``periods``, the number of timesteps in the range. See the note below.)

    Parameters
    ----------
    start : str or datetime-like, optional
        Left bound for generating dates.
    end : str or datetime-like, optional
        Right bound for generating dates.
    periods : int, optional
        Number of periods to generate.
    freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'D'
        Frequency strings can have multiples, e.g. '5H'. See
        :ref:`here <timeseries.offset_aliases>` for a list of
        frequency aliases.
    tz : str or tzinfo, optional
        Time zone name for returning localized DatetimeIndex, for example
        'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is
        timezone-naive unless timezone-aware datetime-likes are passed.
    normalize : bool, default False
        Normalize start/end dates to midnight before generating date range.
    name : str, default None
        Name of the resulting DatetimeIndex.
    inclusive : {"both", "neither", "left", "right"}, default "both"
        Include boundaries; Whether to set each bound as closed or open.

        .. versionadded:: 1.4.0
    unit : str, default None
        Specify the desired resolution of the result.

        .. versionadded:: 2.0.0
    **kwargs
        For compatibility. Has no effect on the result.

    Returns
    -------
    DatetimeIndex

    See Also
    --------
    DatetimeIndex : An immutable container for datetimes.
    timedelta_range : Return a fixed frequency TimedeltaIndex.
    period_range : Return a fixed frequency PeriodIndex.
    interval_range : Return a fixed frequency IntervalIndex.

    Notes
    -----
    Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
    exactly three must be specified. If ``freq`` is omitted, the resulting
    ``DatetimeIndex`` will have ``periods`` linearly spaced elements between
    ``start`` and ``end`` (closed on both sides).

    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
    --------
    **Specifying the values**

    The next four examples generate the same `DatetimeIndex`, but vary
    the combination of `start`, `end` and `periods`.

    Specify `start` and `end`, with the default daily frequency.

    >>> pd.date_range(start='1/1/2018', end='1/08/2018')
    DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                   '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
                  dtype='datetime64[ns]', freq='D')

    Specify timezone-aware `start` and `end`, with the default daily frequency.

    >>> pd.date_range(
    ...     start=pd.to_datetime("1/1/2018").tz_localize("Europe/Berlin"),
    ...     end=pd.to_datetime("1/08/2018").tz_localize("Europe/Berlin"),
    ... )
    DatetimeIndex(['2018-01-01 00:00:00+01:00', '2018-01-02 00:00:00+01:00',
                   '2018-01-03 00:00:00+01:00', '2018-01-04 00:00:00+01:00',
                   '2018-01-05 00:00:00+01:00', '2018-01-06 00:00:00+01:00',
                   '2018-01-07 00:00:00+01:00', '2018-01-08 00:00:00+01:00'],
                  dtype='datetime64[ns, Europe/Berlin]', freq='D')

    Specify `start` and `periods`, the number of periods (days).

    >>> pd.date_range(start='1/1/2018', periods=8)
    DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                   '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
                  dtype='datetime64[ns]', freq='D')

    Specify `end` and `periods`, the number of periods (days).

    >>> pd.date_range(end='1/1/2018', periods=8)
    DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',
                   '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],
                  dtype='datetime64[ns]', freq='D')

    Specify `start`, `end`, and `periods`; the frequency is generated
    automatically (linearly spaced).

    >>> pd.date_range(start='2018-04-24', end='2018-04-27', periods=3)
    DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
                   '2018-04-27 00:00:00'],
                  dtype='datetime64[ns]', freq=None)

    **Other Parameters**

    Changed the `freq` (frequency) to ``'M'`` (month end frequency).

    >>> pd.date_range(start='1/1/2018', periods=5, freq='M')
    DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
                   '2018-05-31'],
                  dtype='datetime64[ns]', freq='M')

    Multiples are allowed

    >>> pd.date_range(start='1/1/2018', periods=5, freq='3M')
    DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
                   '2019-01-31'],
                  dtype='datetime64[ns]', freq='3M')

    `freq` can also be specified as an Offset object.

    >>> pd.date_range(start='1/1/2018', periods=5, freq=pd.offsets.MonthEnd(3))
    DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
                   '2019-01-31'],
                  dtype='datetime64[ns]', freq='3M')

    Specify `tz` to set the timezone.

    >>> pd.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo')
    DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00',
                   '2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00',
                   '2018-01-05 00:00:00+09:00'],
                  dtype='datetime64[ns, Asia/Tokyo]', freq='D')

    `inclusive` controls whether to include `start` and `end` that are on the
    boundary. The default, "both", includes boundary points on either end.

    >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive="both")
    DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
                  dtype='datetime64[ns]', freq='D')

    Use ``inclusive='left'`` to exclude `end` if it falls on the boundary.

    >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive='left')
    DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'],
                  dtype='datetime64[ns]', freq='D')

    Use ``inclusive='right'`` to exclude `start` if it falls on the boundary, and
    similarly ``inclusive='neither'`` will exclude both `start` and `end`.

    >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive='right')
    DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'],
                  dtype='datetime64[ns]', freq='D')

    **Specify a unit**

    >>> pd.date_range(start="2017-01-01", periods=10, freq="100AS", unit="s")
    DatetimeIndex(['2017-01-01', '2117-01-01', '2217-01-01', '2317-01-01',
                   '2417-01-01', '2517-01-01', '2617-01-01', '2717-01-01',
                   '2817-01-01', '2917-01-01'],
                  dtype='datetime64[s]', freq='100AS-JAN')
    N