How to use the lab.B.transpose function in lab

def compare(a):
        allclose(B.transpose(a), to_np(a).T)

def test_shorthands():
    a = Dense(np.random.randn(4, 4))
    allclose(a.T, B.transpose(a))
    allclose(a.__matmul__(a), B.matmul(a, a))

def matmul(a, b, tr_a=False, tr_b=False):
    a = B.transpose(a) if tr_a else a
    b = B.transpose(b) if tr_b else b

    # Get shape of `b`.
    b_rows, b_cols = B.shape(b)

    # If `b` is square, don't do complicated things.
    if b_rows == b_cols and b_rows is not None:
        return dense(a) * B.diag(b)[None, :]

    # Compute the core part.
    cols = B.minimum(B.shape(a)[1], b_cols)
    core = dense(a)[:, :cols] * B.diag(b)[None, :cols]

    # Compute extra zeros to be appended.
    extra_cols = b_cols - cols
    extra_zeros = B.zeros(B.dtype(b), B.shape(a)[0], extra_cols)

def transpose(a): return Woodbury(diag=B.transpose(a.diag),
                                  lr=B.transpose(a.lr),
                                  lr_pd=a.lr_pd)

def transpose(a): return matrix(B.transpose(dense(a)))