def test_do_not_check_for_valid(art_warning, fix_get_synthetic_data, fix_make_dummy_model):
    """
    No checking for valid data. Expect a mixed adversarial/normal data to be returned.
    """
    try:
        l0_budget = 1250
        param_dic = {"maxlen": 2**20, "input_dim": 257, "embedding_size": 8}
        attack = MalwareGDTensorFlow(
            classifier=fix_make_dummy_model[0],
            embedding_weights=fix_make_dummy_model[1],
            l_0=l0_budget,
            param_dic=param_dic,
        )
        x = np.copy(fix_get_synthetic_data[0])
        y = np.copy(fix_get_synthetic_data[1])
        size_of_files = np.copy(fix_get_synthetic_data[2])

        attack.l_0 = l0_budget

        adv_x = attack.generate(x, y, size_of_files, verify_input_data=False)
        assert len(adv_x) == 5

        # We expect 2 files to have been made adversarial the following cannot be converted to valid adv samples:
        #   2nd datapoint (file too large to support any modifications)
        #   4th datapoint (file too large to support append attacks)
        #   5th datapoint (benign file)
        for i, size in enumerate(size_of_files):
            if i in [0, 2]:
                assert np.array_equal(adv_x[i, :size], fix_get_synthetic_data[0][i, :size])
                assert not np.array_equal(
                    adv_x[i, size : size + l0_budget], fix_get_synthetic_data[0][i, size : size + l0_budget]
                )
                assert np.array_equal(adv_x[i, size + l0_budget :], fix_get_synthetic_data[0][i, size + l0_budget :])
            else:
                assert np.array_equal(adv_x[i], fix_get_synthetic_data[0][i])
    except ARTTestException as e:
        art_warning(e)

    No checking for valid data. Expect a mixed adversarial/normal data to be returned.
    
tests/attacks/evasion/test_pe_malware_attack.py