def METHOD_NAME(mode, name, image_resize, is_training, auto_augment, batch_size, aug_splits):
    """
    test transform_list API(distribute)
    command: pytest -s test_transforms.py::test_transforms_standalone_imagenet
    API Args:
        dataset_name='',
        image_resize=224,
        is_training=False,
        auto_augment=None
        **kwargs
    """
    ms.set_context(mode=mode)
    dataset_url = (
        "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/intermediate/Canidae_data.zip"
    )
    root_dir = os.path.join(get_dataset_download_root(), "Canidae")
    data_dir = os.path.join(root_dir, "data", "Canidae")  # Canidae has prefix path "data/Canidae" in unzipped file.
    if not os.path.exists(data_dir):
        DownLoad().download_and_extract_archive(dataset_url, root_dir)
    dataset = create_dataset(
        name=name,
        root=data_dir,
        split="train",
        shuffle=True,
        num_samples=None,
        num_parallel_workers=2,
        download=False,
    )
    num_aug_splits = 0
    if aug_splits > 0 and auto_augment is not None:
        assert aug_splits == 3, "Currently, only support 3 splits of augmentation"
        num_aug_splits = aug_splits
    # create transforms
    transform_list = create_transforms(
        dataset_name=name,
        image_resize=image_resize,
        is_training=is_training,
        auto_augment=auto_augment,
        separate=num_aug_splits > 0,
    )
    # load dataset
    loader = create_loader(
        dataset=dataset,
        batch_size=batch_size,
        drop_remainder=True,
        is_training=is_training,
        transform=transform_list,
        num_parallel_workers=2,
        separate=num_aug_splits > 0,
    )
    output_shape = loader.output_shapes()
    assert output_shape[0][2] == image_resize, "image_resize error !"
    if num_aug_splits == 3 and is_training:
        assert output_shape[0][0] == 3 * batch_size and output_shape[1][0] == 3 * batch_size, "augment splits error!"