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.

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