code: def test_data_process_image(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
      )
  
file_path: autogluon/multimodal/tests/unittests/others/test_process_image.py
imports: import PIL
  import pytest
  import torch
  from autogluon.multimodal.data.process_image import ImageProcessor
  from autogluon.multimodal.models.timm_image import TimmAutoModelForImagePrediction
  from autogluon.multimodal.utils.misc import shopee_dataset
label_desc: Use image node_id 2 for calling a global function with example usage: test_data_process_image(augmentations) without return types