code: def test_training_pipeline(
      self, tmpdir, training_pipeline, train_dataset, model
  ):
      with pytest.raises(AssertionError):
          pipeline = TrainingPipeline(
              model=VAE(VAEConfig(input_dim=(1, 2, 3))),
              training_config=Pipeline(),
          )
  
      tmpdir.mkdir("dummy_folder")
      dir_path = os.path.join(tmpdir, "dummy_folder")
      training_pipeline.training_config.output_dir = dir_path
      training_pipeline.training_config.num_epochs = 1
      training_pipeline(train_dataset.data)
      assert isinstance(training_pipeline.model, model.__class__)
  
      if model.__class__ == RAE_L2:
          assert (
              training_pipeline.trainer.decoder_optimizer.state_dict()[
                  "param_groups"
              ][0]["weight_decay"]
              == model.model_config.reg_weight
          )
  
imports: import os
  import pytest
  import torch
  from torch.utils.data import Dataset, DataLoader
  from pythae.customexception import DatasetError
  from pythae.data.datasets import DatasetOutput
  from pythae.models import VAE, VAEConfig, Adversarial_AE, Adversarial_AE_Config, RAE_L2, RAE_L2_Config, VAEGAN, VAEGANConfig
  from pythae.pipelines import *
  from pythae.samplers import NormalSampler, NormalSamplerConfig
  from pythae.trainers import BaseTrainerConfig
label_desc: Use image node_id 9 for calling the Test_Pipeline_Standalone obj's underlying member method code with example usage: obj.test_training_pipeline(tmpdir, training_pipeline, train_dataset, model) without return types