code: def test_customizing_predictor_image_aug(
      train_transforms, val_transforms
  ):
      download_dir = "./"
      train_data, test_data = shopee_dataset(download_dir)
      predictor = MultiModalPredictor(label="label", verbosity=4)
      predictor.fit(
          train_data=train_data,
          hyperparameters={
              "model.timm_image.train_transforms": train_transforms,
              "model.timm_image.val_transforms": val_transforms,
          },
          time_limit=10,  # seconds
      )
  
      assert str(train_transforms) == str(
          predictor._learner._data_processors["image"][
              0
          ].train_transforms
      )
      assert str(val_transforms) == str(
          predictor._learner._data_processors["image"][0].val_transforms
      )
      assert (
          len(
              predictor._learner._data_processors["image"][
                  0
              ].train_processor.transforms
          )
          == len(train_transforms) + 2
      )
      assert (
          len(
              predictor._learner._data_processors["image"][
                  0
              ].val_processor.transforms
          )
          == len(val_transforms) + 2
      )
  
file_path: autogluon/multimodal/tests/unittests/others_2/test_data_augmentation.py
imports: import copy
  import os
  import pickle
  import shutil
  import tempfile
  import pytest
  from torchvision import transforms
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.constants import BEST, BINARY, BIT_FIT, DATA, DISTILLER, ENVIRONMENT, GREEDY_SOUP, LORA, LORA_BIAS, LORA_NORM, MODEL, MULTICLASS, NORM_FIT, OPTIMIZATION, UNIFORM_SOUP
  from autogluon.multimodal.utils.misc import shopee_dataset
  from ..predictor.test_predictor import verify_predictor_save_load
  from ..utils.unittest_datasets import AEDataset, HatefulMeMesDataset, IDChangeDetectionDataset, PetFinderDataset
label_desc: Use image node_id 4 for calling a global function with example usage: test_customizing_predictor_image_aug(train_transforms, val_transforms) without return types