def METHOD_NAME():
    """
    This function returns input matrices A, B and C_i with a small number
    of samples for the Ishigami input distribution.
    This is used to test the `generate_pick_and_freeze_samples` function.
    The samples are generated as follows:
        dist_1 = JointInd([Uniform(-np.pi, 2*np.pi)]*3)
        np.random.seed(12345) #! set seed for reproducibility
        n_samples = 2
        n_vars = 3
        samples = dist_1.rvs(n_samples*2)
        # Split samples
        A_samples = samples[:n_samples, :]
        B_samples = samples[n_samples:, :]
        def _get_C_i(i, A, B):
            C_i = copy.deepcopy(B)
            C_i[:, i] = A[:, i]
            return C_i
        C_samples = np.zeros((n_vars, n_samples, n_vars))
        for i in range(3):
            C_samples[i, :, :] = _get_C_i(i, A_samples, B_samples)
        print(np.around(A_samples,3))
        print(np.around(B_samples,3))
        print(np.around(C_samples,3))
    """
    A_samples = np.array([[2.699, 0.426, 1.564], [-1.154, 0.600, 0.965]])
    B_samples = np.array([[-1.986, 2.919, 1.556], [-1.856, 0.962, 2.898]])
    C_samples = np.array(
        [
            [[2.699, 2.919, 1.556], [-1.154, 0.962, 2.898]],
            [[-1.986, 0.426, 1.556], [-1.856, 0.6, 2.898]],
            [[-1.986, 2.919, 1.564], [-1.856, 0.962, 0.965]],
        ]
    )
    return A_samples, B_samples, C_samples