code: def test_invalid_augmentation_parameters(
      train_data_rgb,
      augmentation_pipeline_ops,
  ):
      with pytest.raises(ConfigValidationError):
          run_augmentation_training(
              train_data=train_data_rgb,
              backend="local",
              encoder={
                  "type": "alexnet",
                  "model_cache_dir": os.path.join(
                      os.getcwd(), "tv_cache"
                  ),
              },
              preprocessing={},
              augmentation_pipeline_ops=augmentation_pipeline_ops,
          )
  
imports: import copy
  import logging
  import os
  import tempfile
  from typing import Dict, List
  import pytest
  import torch
  from ludwig.api import LudwigModel
  from ludwig.constants import IMAGENET1K
  from ludwig.data.dataset_synthesizer import cli_synthesize_dataset
  from ludwig.error import ConfigValidationError
  from ludwig.features.image_feature import ImageAugmentation
  from ludwig.schema.features.image_feature import ImageInputFeatureConfig
label_desc: Use image node_id 9 for calling a global function with example usage: test_invalid_augmentation_parameters(train_data_rgb, augmentation_pipeline_ops) without return types