def METHOD_NAME(self):
    """ test end to end adaround with only weight quantized """
    AimetLogger.set_level_for_all_areas(logging.DEBUG)
    torch.cuda.empty_cache()
    seed_all(1000)
    model = models.resnet18().eval()
    model = model.to(torch.device('cuda'))
    input_shape = (1, 3, 224, 224)
    dummy_input = create_rand_tensors_given_shapes(input_shape, torch.device('cuda'))
    orig_output = dummy_forward_pass(model, input_shape)
    # create fake data loader with image size (3, 224, 224)
    data_loader = create_fake_data_loader(dataset_size=64, batch_size=16, image_size=input_shape[1:])
    params = AdaroundParameters(data_loader=data_loader, num_batches=4, default_num_iterations=5,
                                default_reg_param=0.01, default_beta_range=(20, 2))
    adarounded_model = Adaround.apply_adaround(model, dummy_input, params, path='./', filename_prefix='resnet18',
                                               default_param_bw=4,
                                               default_quant_scheme=QuantScheme.post_training_tf_enhanced)
    ada_output = dummy_forward_pass(adarounded_model, input_shape)
    self.assertFalse(torch.all(torch.eq(orig_output, ada_output)))
    # Test exported encodings JSON file
    with open('./resnet18.encodings') as json_file:
        encoding_data = json.load(json_file)
        print(encoding_data)
    self.assertTrue(isinstance(encoding_data["conv1.weight"], list))
    # Delete encodings JSON file
    if os.path.exists("./resnet18.encodings"):
        os.remove("./resnet18.encodings")