code: def test_load_model_with_augmentation_pipeline(
      train_data_rgb,
  ):
      augmentation_pipeline_ops = [
          {"type": "random_blur"},
          {"type": "random_rotate"},
      ]
      preprocessing = {
          "standardize_image": None,
          "width": 300,
          "height": 300,
      }
      encoder = {
          "type": "alexnet",
          "use_pretrained": False,
          "model_cache_dir": os.path.join(os.getcwd(), "tv_cache"),
      }
  
      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
      )
  
      with tempfile.TemporaryDirectory() as tmp_dir:
          model.save(tmp_dir)
          LudwigModel.load(tmp_dir)
  
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 10 for calling a global function with example usage: test_load_model_with_augmentation_pipeline(train_data_rgb) without return types