code: def training_pipeline(self, model, train_dataset):
      vae_config = VAEConfig(
          input_dim=tuple(train_dataset.data[0].shape), latent_dim=2
      )
      vae = VAE(vae_config)
      pipe = TrainingPipeline(model=model)
      return pipe
  
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 7 for calling the Test_Pipeline_Standalone obj's underlying member method code with example usage: obj.training_pipeline(model, train_dataset) and returns: pipe