def constant_timeseries(
    value: float = 1,
    start: Optional[Union[pd.Timestamp, int]] = pd.Timestamp("2000-01-01"),
    end: Optional[Union[pd.Timestamp, int]] = None,
    length: Optional[int] = None,
    freq: Union[str, int] = None,
    column_name: Optional[str] = "constant",
    dtype: np.dtype = np.float64,
) -> TimeSeries:
    """
    Creates a constant univariate TimeSeries with the given value, length (or end date), start date and frequency.

    Parameters
    ----------
    value
        The constant value that the TimeSeries object will assume at every index.
    start
        The start of the returned TimeSeries' index. If a pandas Timestamp is passed, the TimeSeries will have a pandas
        DatetimeIndex. If an integer is passed, the TimeSeries will have a pandas RangeIndex index. Works only with
        either `length` or `end`.
    end
        Optionally, the end of the returned index. Works only with either `start` or `length`. If `start` is
        set, `end` must be of same type as `start`. Else, it can be either a pandas Timestamp or an integer.
    length
        Optionally, the length of the returned index. Works only with either `start` or `end`.
    freq
        The time difference between two adjacent entries in the returned index. In case `start` is a timestamp,
        a DateOffset alias is expected; see
        `docs <https://pandas.pydata.org/pandas-docs/stable/user_guide/TimeSeries.html#dateoffset-objects>`_.
        By default, "D" (daily) is used.
        If `start` is an integer, `freq` will be interpreted as the step size in the underlying RangeIndex.
        The freq is optional for generating an integer index (if not specified, 1 is used).
    column_name
        Optionally, the name of the value column for the returned TimeSeries
    dtype
        The desired NumPy dtype (np.float32 or np.float64) for the resulting series

    Returns
    -------
    TimeSeries
        A constant TimeSeries with value 'value'.
    """

    index = generate_index(start=start, end=end, freq=freq, length=length)
    values = np.full(len(index), value, dtype=dtype)

    return TimeSeries.from_times_and_values(
        index, values, freq=freq, columns=pd.Index([column_name])
    )

    Creates a constant univariate TimeSeries with the given value, length (or end date), start date and frequency.

    Parameters
    ----------
    value
        The constant value that the TimeSeries object will assume at every index.
    start
        The start of the returned TimeSeries' index. If a pandas Timestamp is passed, the TimeSeries will have a pandas
        DatetimeIndex. If an integer is passed, the TimeSeries will have a pandas RangeIndex index. Works only with
        either `length` or `end`.
    end
        Optionally, the end of the returned index. Works only with either `start` or `length`. If `start` is
        set, `end` must be of same type as `start`. Else, it can be either a pandas Timestamp or an integer.
    length
        Optionally, the length of the returned index. Works only with either `start` or `end`.
    freq
        The time difference between two adjacent entries in the returned index. In case `start` is a timestamp,
        a DateOffset alias is expected; see
        `docs <https://pandas.pydata.org/pandas-docs/stable/user_guide/TimeSeries.html#dateoffset-objects>`_.
        By default, "D" (daily) is used.
        If `start` is an integer, `freq` will be interpreted as the step size in the underlying RangeIndex.
        The freq is optional for generating an integer index (if not specified, 1 is used).
    column_name
        Optionally, the name of the value column for the returned TimeSeries
    dtype
        The desired NumPy dtype (np.float32 or np.float64) for the resulting series

    Returns
    -------
    TimeSeries
        A constant TimeSeries with value 'value'.
    
darts/utils/timeseries_generation.py