code: def test_model_train_output(self, vaegan, demo_data):
      # model_configs.input_dim = demo_data['data'][0].shape[-1]
  
      # vaegan = VAEGAN(model_configs)
  
      vaegan.train()
  
      out = vaegan(demo_data)
  
      assert isinstance(out, ModelOutput)
  
      assert set(
          [
              "loss",
              "recon_loss",
              "encoder_loss",
              "decoder_loss",
              "discriminator_loss",
              "recon_x",
              "z",
              "update_discriminator",
              "update_encoder",
              "update_decoder",
          ]
      ) == 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 numpy
  import pytest
  import torch
  from pythae.customexception import BadInheritanceError
  from pythae.models import VAEGAN, AutoModel, VAEGANConfig
  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 BaseTrainerConfig, CoupledOptimizerAdversarialTrainer, CoupledOptimizerAdversarialTrainerConfig
  from tests.data.custom_architectures import Decoder_AE_Conv, Discriminator_MLP_Custom, 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(vaegan, demo_data) without return types