How to use the lab.B function in lab

def test_uprank():
    allclose(uprank(0), [[0]])
    allclose(uprank(np.array([0])), [[0]])
    allclose(uprank(np.array([[0]])), [[0]])
    assert type(uprank(Component('test')(0))) == Component('test')

    k = OneKernel()

    assert B.shape(k(0, 0)) == (1, 1)
    assert B.shape(k(0, np.ones(5))) == (1, 5)
    assert B.shape(k(0, np.ones((5, 2)))) == (1, 5)

    assert B.shape(k(np.ones(5), 0)) == (5, 1)
    assert B.shape(k(np.ones(5), np.ones(5))) == (5, 5)
    assert B.shape(k(np.ones(5), np.ones((5, 2)))) == (5, 5)

    assert B.shape(k(np.ones((5, 2)), 0)) == (5, 1)
    assert B.shape(k(np.ones((5, 2)), np.ones(5))) == (5, 5)
    assert B.shape(k(np.ones((5, 2)), np.ones((5, 2)))) == (5, 5)

    with pytest.raises(ValueError):
        k(0, np.ones((5, 2, 1)))
    with pytest.raises(ValueError):
        k(np.ones((5, 2, 1)))

    m = OneMean()

def __init__(self, constant, rows, cols=None):
        self.constant = constant
        self.rows = rows
        self.cols = rows if cols is None else cols

        # Construct and initialise the low-rank representation.
        left = B.ones(B.dtype(self.constant), self.rows, 1)
        if self.rows is self.cols:
            right = left
        else:
            right = B.ones(B.dtype(self.constant), self.cols, 1)
        middle = B.expand_dims(B.expand_dims(self.constant, axis=0), axis=0)
        LowRank.__init__(self, left=left, right=right, middle=middle)

    @_dispatch(B.Numeric, B.Numeric)
    @uprank
    def __call__(self, x, y):
        return Dense(B.exp(-B.pw_dists(x, y)))

def __call__(self, x):
        return B.ones(B.dtype(x), B.shape(x)[0], 1)

@B.schur.extend(Woodbury)
def schur(a):
    if a.schur is None:
        prod = B.matmul(a.lr.right, B.inverse(a.diag), a.lr.left, tr_a=True)
        if a.lr_pd:
            a.schur = B.inverse(a.lr.middle) + prod
        else:
            a.schur = B.inverse(-a.lr.middle) - prod
    return a.schur

@B.subtract.extend(Dense, Dense)
def subtract(a, b): return B.add(a, -b)

def uprank(x):
    """Ensure that the rank of `x` is 2.

    Args:
        x (tensor): Tensor to uprank.

    Returns:
        tensor: `x` with rank at least 2.
    """
    # Simply return non-numerical inputs.
    if not isinstance(x, B.Numeric):
        return x

    # Now check the rank of `x` and act accordingly.
    rank = B.rank(x)
    if rank > 2:
        raise ValueError('Input must be at most rank 2.')
    elif rank == 2:
        return x
    elif rank == 1:
        return B.expand_dims(x, axis=1)
    else:
        # Rank must be 0.
        return B.expand_dims(B.expand_dims(x, axis=0), axis=1)

    @_dispatch(B.DType, [object])
    def __init__(self, dtype, rows, cols=None):
        Constant.__init__(self, B.cast(dtype, 0), rows=rows, cols=cols)