code: def test_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
      for key in start_model_state_dict.keys():
          if "encoder" in key:
              assert not torch.equal(
                  step_1_model_state_dict[key],
                  start_model_state_dict[key],
              ), key
  
          if "decoder" in key:
              assert not torch.equal(
                  step_1_model_state_dict[key],
                  start_model_state_dict[key],
              )
  
imports: import os
  from copy import deepcopy
  import pytest
  import torch
  from pythae.models import RAE_L2, RAE_L2_Config
  from pythae.trainers import CoupledOptimizerTrainer, CoupledOptimizerTrainerConfig
  from tests.data.custom_architectures import *
label_desc: Use image node_id 11 for calling the Test_Main_Training obj's underlying member method code with example usage: obj.test_main_train_loop(trainer) without return types