def METHOD_NAME(x, out=None):
    """
    Test element-wise for negative infinity, return result as bool array.
    Parameters
    ----------
    x : array_like
        The input array.
    out : array_like, optional
        A boolean array with the same shape and type as `x` to store the
        result.
    Returns
    -------
    out : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a numpy boolean array is
        returned with values True where the corresponding element of the
        input is negative infinity and values False where the element of
        the input is not negative infinity.
        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as
        zeros and ones, if the type is boolean then as False and True. The
        return value `out` is then a reference to that array.
    See Also
    --------
    isinf, isposinf, isnan, isfinite
    Notes
    -----
    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).
    Errors result if the second argument is also supplied when x is a scalar
    input, if first and second arguments have different shapes, or if the
    first argument has complex values.
    Examples
    --------
    >>> np.isneginf(np.NINF)
    array(True, dtype=bool)
    >>> np.isneginf(np.inf)
    array(False, dtype=bool)
    >>> np.isneginf(np.PINF)
    array(False, dtype=bool)
    >>> np.isneginf([-np.inf, 0., np.inf])
    array([ True, False, False])
    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isneginf(x, y)
    array([1, 0, 0])
    >>> y
    array([1, 0, 0])
    """
    is_inf = nx.isinf(x)
    try:
        signbit = nx.signbit(x)
    except TypeError:
        raise TypeError('This operation is not supported for complex values '
                        'because it would be ambiguous.')
    else:
        return nx.logical_and(is_inf, signbit, out)