I found the test code, but how do I run it as a standalone Python script?

[QingXu51820]

def test(test_set, teacher, student, autoencoder, teacher_mean, teacher_std,
         q_st_start, q_st_end, q_ae_start, q_ae_end, test_output_dir=None,
         desc='Running inference'):
    y_true = []
    y_score = []
    for image, target, path in tqdm(test_set, desc=desc):
        orig_width = image.width
        orig_height = image.height
        image = default_transform(image)
        image = image[None]
        if on_gpu:
            image = image.cuda()
        map_combined, map_st, map_ae = predict(
            image=image, teacher=teacher, student=student,
            autoencoder=autoencoder, teacher_mean=teacher_mean,
            teacher_std=teacher_std, q_st_start=q_st_start, q_st_end=q_st_end,
            q_ae_start=q_ae_start, q_ae_end=q_ae_end)
        map_combined = torch.nn.functional.pad(map_combined, (4, 4, 4, 4))
        map_combined = torch.nn.functional.interpolate(
            map_combined, (orig_height, orig_width), mode='bilinear')
        map_combined = map_combined[0, 0].cpu().numpy()

        defect_class = os.path.basename(os.path.dirname(path))
        if test_output_dir is not None:
            img_nm = os.path.split(path)[1].split('.')[0]
            if not os.path.exists(os.path.join(test_output_dir, defect_class)):
                os.makedirs(os.path.join(test_output_dir, defect_class))
            file = os.path.join(test_output_dir, defect_class, img_nm + '.tiff')
            tifffile.imwrite(file, map_combined)

        y_true_image = 0 if defect_class == 'good' else 1
        y_score_image = np.max(map_combined)
        y_true.append(y_true_image)
        y_score.append(y_score_image)
    auc = roc_auc_score(y_true=y_true, y_score=y_score)
    return auc * 100

efficientad.py line 262-297