code: def test_model_train_output(self, vae, demo_data):
      vae.train()
  
      out = vae(demo_data)
  
      assert isinstance(out, ModelOutput)
  
      assert set(
          ["recon_loss", "reg_loss", "loss", "recon_x", "z"]
      ) == set(out.keys())
  
      assert out.z.shape[0] == demo_data["data"].shape[0]
      assert out.recon_x.shape == demo_data["data"].shape
  
imports: import os
  from copy import deepcopy
  import pytest
  import torch
  from pydantic import ValidationError
  from pythae.customexception import BadInheritanceError
  from pythae.models import AutoModel, VAE_LinNF, VAE_LinNF_Config
  from pythae.models.base.base_utils import ModelOutput
  from pythae.pipelines import GenerationPipeline, TrainingPipeline
  from pythae.samplers import GaussianMixtureSamplerConfig, IAFSamplerConfig, MAFSamplerConfig, NormalSamplerConfig, TwoStageVAESamplerConfig
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
  from tests.data.custom_architectures import Decoder_AE_Conv, Encoder_VAE_Conv, NetBadInheritance
label_desc: Use image node_id 3 for calling the Test_Model_forward obj's underlying member method code with example usage: obj.test_model_train_output(vae, demo_data) without return types