def METHOD_NAME(X, alpha_s, beta_s, p):
    m, d = np.shape(X)
    # print alpha_s
    # print beta_s
    # for debugging LCC Enc & Dec
    # beta_s = np.concatenate((alpha_s, beta_s))
    # print beta_s
    U = gen_Lagrange_coeffs(beta_s, alpha_s, p).astype("int")
    # print U
    X_LCC = np.zeros((len(beta_s), d), dtype="int")
    for i in range(len(beta_s)):
        X_LCC[i, :] = np.dot(np.reshape(U[i, :], (1, len(alpha_s))), X)
    # print X
    # print np.mod(X_LCC, p)
    return np.mod(X_LCC, p)