code: def test_factor_ae_train_step(self, trainer):
      start_model_state_dict = deepcopy(trainer.model.state_dict())
  
      step_1_loss = trainer.train_step(epoch=1)
  
      step_1_model_state_dict = deepcopy(trainer.model.state_dict())
  
      # check that weights were updated
      assert not all(
          [
              torch.equal(
                  start_model_state_dict[key],
                  step_1_model_state_dict[key],
              )
              for key in start_model_state_dict.keys()
          ]
      )
  
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 5 for calling the Test_FactorVAE_Training obj's underlying member method code with example usage: obj.test_factor_ae_train_step(trainer) without return types