code: def test_local_model_training_with_augmentation_pipeline(
      train_data_rgb,
      encoder,
      preprocessing,
      augmentation_pipeline_ops,
  ):
      model = run_augmentation_training(
          train_data=train_data_rgb,
          backend="local",
          encoder=encoder,  # Ludwig encoder
          preprocessing=preprocessing,  # Ludwig image preprocessing
          augmentation_pipeline_ops=augmentation_pipeline_ops,  # Ludwig image augmentation
      )
  
      if augmentation_pipeline_ops is not False:
          assert model.config_obj.input_features[0].has_augmentation()
      else:
          assert not model.config_obj.input_features[
              0
          ].has_augmentation()
  
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 6 for calling a global function with example usage: test_local_model_training_with_augmentation_pipeline(train_data_rgb, encoder, preprocessing, augmentation_pipeline_ops) without return types