code: def test_image_augmentation(test_image, augmentation_pipeline_ops):
      # define augmentation pipeline
      feature = ImageInputFeatureConfig.from_dict(
          {
              "name": "foo",
              "type": "image",
              "augmentation": augmentation_pipeline_ops,
          }
      )
      augmentation_pipeline = ImageAugmentation(feature.augmentation)
      # apply augmentation pipeline to batch of test images
      augmentation_pipeline(test_image)
  
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 5 for calling a global function with example usage: test_image_augmentation(test_image, augmentation_pipeline_ops) without return types