def METHOD_NAME(augmentations):
    download_dir = "./ag_automm_tutorial_imgcls"
    train_df, test_df = shopee_dataset(download_dir)
    image = PIL.Image.open(train_df["image"][0]).convert("RGB")
    model = TimmAutoModelForImagePrediction(prefix="timm_image", checkpoint_name="swin_tiny_patch4_window7_224")
    image_processor = ImageProcessor(
        model=model,
        train_transforms=augmentations["model.timm_image.train_transforms"],
        val_transforms=augmentations["model.timm_image.val_transforms"],
        size=224,
        norm_type="imagenet",
    )
    transformed_image = image_processor.train_processor(image)
    assert (
        len(image_processor.train_processor.transforms) == len(augmentations["model.timm_image.train_transforms"]) + 2
    )