--------
    svd : Singular value decomposition of a matrix
    orth : Matrix range

    Examples
    --------
    1-D null space:

    >>> import numpy as np
    >>> from scipy.linalg import null_space
    >>> A = np.array([[1, 1], [1, 1]])
    >>> ns = null_space(A)
    >>> ns * np.sign(ns[0,0])  # Remove the sign ambiguity of the vector
    array([[ 0.70710678],
           [-0.70710678]])

    2-D null space:

    >>> from numpy.random import default_rng
    >>> rng = default_rng()
    >>> B = rng.random((3, 5))
    >>> Z = null_space(B)
    >>> Z.shape
    (5, 2)
    >>> np.allclose(B.dot(Z), 0)
    True

    The basis vectors are orthonormal (up to rounding error):

    >>> Z.T.dot(Z)
    array([[  1.00000000e+00,   6.92087741e-17],
           [  6.92087741e-17,   1.00000000e+00]])

    TrG