code: def test_eval_step(self, trainer):
      start_model_state_dict = deepcopy(trainer.model.state_dict())
  
      step_1_loss = trainer.eval_step(epoch=1)
  
      step_1_model_state_dict = deepcopy(trainer.model.state_dict())
  
      # check that weights were not updated
      assert 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.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 10 for calling the Test_Main_Training obj's underlying member method code with example usage: obj.test_eval_step(trainer) without return types