def METHOD_NAME(data):
    _, x, sigma = data
    sigma = np.eye(len(x))
    efficient = blocked_inner_prod(x, sigma)
    nobs = x[0].shape[0]
    omega = np.kron(sigma, np.eye(nobs))
    k = len(x)
    bigx = []
    for i in range(k):
        row = []
        for j in range(k):
            if i == j:
                row.append(x[i])
            else:
                row.append(np.zeros((nobs, x[j].shape[1])))
        bigx.append(np.hstack(row))
    bigx = np.vstack(bigx)
    expected = bigx.T @ omega @ bigx
    assert_allclose(efficient, expected)