code: def model(self, request, train_dataset):
      model = request.param[0](
          request.param[1](
              input_dim=tuple(train_dataset.data[0].shape), latent_dim=2
          )
      )
  
      return model
  
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 5 for calling the Test_Pipeline_Standalone obj's underlying member method code with example usage: obj.model(request, train_dataset) and returns: model