code: def test_generation_pipeline(self, tmpdir, train_dataset):
      with pytest.raises(NotImplementedError):
          pipe = GenerationPipeline(
              model=VAE(VAEConfig(input_dim=(1, 2, 3))),
              sampler_config=BaseTrainerConfig(),
          )
  
      tmpdir.mkdir("dummy_folder")
      dir_path = os.path.join(tmpdir, "dummy_folder")
      pipe = GenerationPipeline(
          model=VAE(VAEConfig(input_dim=(1, 2, 3)))
      )
      assert isinstance(pipe.sampler, NormalSampler)
      assert pipe.sampler.sampler_config == NormalSamplerConfig()
  
      gen_data = pipe(
          num_samples=1,
          batch_size=10,
          output_dir=dir_path,
          return_gen=True,
          save_sampler_config=True,
          train_data=train_dataset.data,
          eval_data=None,
      )
  
      assert tuple(gen_data.shape) == (1,) + (1, 2, 3)
      assert len(os.listdir(dir_path)) == 1 + 1
      assert "sampler_config.json" in os.listdir(dir_path)
  
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 13 for calling the Test_Pipeline_Standalone obj's underlying member method code with example usage: obj.test_generation_pipeline(tmpdir, train_dataset) without return types