code: def test_vaegan_main_train_loop(self, trainer):
      start_model_state_dict = deepcopy(trainer.model.state_dict())
  
      trainer.train()
  
      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 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 8 for calling the Test_VAEGAN_Training obj's underlying member method code with example usage: obj.test_vaegan_main_train_loop(trainer) without return types