code: def test_model_train_output(self, factor_ae, demo_data):
      # model_configs.input_dim = demo_data['data'][0].shape[-1]
  
      # factor_ae = FactorVAE(model_configs)
  
      with pytest.raises(ArithmeticError):
          factor_ae({"data": demo_data["data"][0]})
  
      factor_ae.train()
  
      out = factor_ae(demo_data)
  
      assert isinstance(out, ModelOutput)
  
      assert set(
          [
              "loss",
              "recon_loss",
              "autoencoder_loss",
              "discriminator_loss",
              "recon_x",
              "recon_x_indices",
              "z",
              "z_bis_permuted",
          ]
      ) == set(out.keys())
  
      assert out.z.shape[0] == int(
          demo_data["data"].shape[0] / 2
      ) + 1 * (demo_data["data"].shape[0] % 2 != 0)
      assert out.z_bis_permuted.shape[0] == int(
          demo_data["data"].shape[0] / 2
      )
      assert out.recon_x.shape == (
          int(demo_data["data"].shape[0] / 2)
          + 1 * (demo_data["data"].shape[0] % 2 != 0),
      ) + (demo_data["data"].shape[1:])
      assert out.recon_x_indices.shape[0] == int(
          demo_data["data"].shape[0] / 2
      ) + 1 * (demo_data["data"].shape[0] % 2 != 0)
  
      assert not torch.equal(out.z, out.z_bis_permuted)
  
imports: import os
  from copy import deepcopy
  import pytest
  import torch
  from pythae.customexception import BadInheritanceError
  from pythae.data.preprocessors import DataProcessor
  from pythae.models import AutoModel, FactorVAE, FactorVAEConfig
  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 AdversarialTrainer, AdversarialTrainerConfig, 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(factor_ae, demo_data) without return types