code: def test_invalid_images(invalid_value):
      dataset = PetFinderDataset()
      train_df = dataset.train_df
      invalid_num = int(0.5 * len(train_df))
      train_df.loc[0:invalid_num, "Images"] = invalid_value
  
      predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
      )
      hyperparameters = {
          "model.names": [
              "categorical_mlp",
              "numerical_mlp",
              "timm_image",
              "hf_text",
              "fusion_mlp",
          ],
          "model.hf_text.checkpoint_name": "google/electra-small-discriminator",
          "model.timm_image.checkpoint_name": "swin_small_patch4_window7_224",
          "env.num_workers": 0,
          "env.num_workers_evaluation": 0,
      }
  
      with tempfile.TemporaryDirectory() as save_path:
          if os.path.isdir(save_path):
              shutil.rmtree(save_path)
  
          predictor.fit(
              train_data=train_df,
              time_limit=10,
              save_path=save_path,
              hyperparameters=hyperparameters,
          )
  
          score = predictor.evaluate(dataset.test_df)
          verify_predictor_save_load(predictor, dataset.test_df)
  
file_path: autogluon/multimodal/tests/unittests/others_2/test_images.py
imports: import copy
  import os
  import pickle
  import shutil
  import tempfile
  import pytest
  from autogluon.multimodal import MultiModalPredictor
  from ..predictor.test_predictor import verify_predictor_save_load
  from ..utils.unittest_datasets import PetFinderDataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_invalid_images(invalid_value) without return types