code: def run_augmentation_training(
      train_data: str = "",
      backend: str = "local",
      encoder: Dict = None,
      preprocessing: Dict = None,
      augmentation_pipeline_ops: List[Dict] = None,
  ):
      # unpack training data
      train_fp, input_features, output_features = train_data
  
      # add encoder and preprocessing specification to input feature
      input_features[0].update(
          {
              "encoder": encoder,
              "preprocessing": preprocessing,
          }
      )
  
      # add augmentation pipeline to input feature if specified
      test_input_features = copy.deepcopy(input_features)
      test_input_features[0].update(
          {"augmentation": augmentation_pipeline_ops}
      )
  
      config = {
          "input_features": test_input_features,
          "output_features": output_features,
          "trainer": {
              "epochs": 2,
              "batch_size": 8,
          },
          "backend": {
              "type": backend,
          },
      }
  
      with tempfile.TemporaryDirectory() as tmpdir:
          model = LudwigModel(config, logging_level=logging.INFO)
          model.experiment(
              dataset=train_fp,
              skip_save_processed_input=True,
              skip_save_model=True,
              output_directory=os.path.join(tmpdir, "output"),
          )
      return model
  
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 4 for calling a global function with example usage: run_augmentation_training(train_data, backend, encoder, preprocessing, augmentation_pipeline_ops) and returns: model