def isneginf(x, y=None):
if y is None:<EOL><INDENT>x = nx.asarray(x)<EOL>y = nx.empty(x.shape, dtype=nx.bool_)<EOL><DEDENT>nx.logical_and(nx.isinf(x), nx.signbit(x), y)<EOL>return y<EOL>
Test element-wise for negative infinity, return result as bool array.

Parameters
----------
x : array_like
    The input array.
y : array_like, optional
    A boolean array with the same shape and type as `x` to store the
    result.

Returns
-------
y : 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 `y` 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, or if first and second arguments have different shapes.

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], dtype=bool)

>>> 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])