def METHOD_NAME():
    N = 128
    k = te.reduce_axis((0, N), "k")
    A = te.placeholder((N, N), name="A")
    B = te.placeholder((N, N), name="B")
    C = te.compute(A.shape, lambda y, x: te.sum(A[y, k] * B[k, x], axis=k), name="C")
    s = te.create_schedule(C.op)
    feas = feature.get_buffer_curve_sample_flatten(s, [A, B, C], sample_n=30)
    # sample_n * #buffers * #curves * 2 numbers per curve
    assert len(feas) == 30 * 3 * 4 * 2