code: def test_trainer_callback_calls(
      self,
      trainer_cls,
      configs_and_models,
      train_dataset,
      dummy_callback,
  ):
      trainer = trainer_cls(
          model=configs_and_models[0],
          train_dataset=train_dataset,
          eval_dataset=train_dataset,
          training_config=configs_and_models[1],
          callbacks=[dummy_callback],
      )
  
      trainer.prepare_training()
  
      assert "on_train_step_begin" not in dummy_callback.step_list
      assert "on_train_step_end" not in dummy_callback.step_list
      trainer.train_step(epoch=1)
      assert "on_train_step_begin" in dummy_callback.step_list
      assert "on_train_step_end" in dummy_callback.step_list
  
      assert "on_eval_step_begin" not in dummy_callback.step_list
      assert "on_eval_step_end" not in dummy_callback.step_list
      trainer.eval_step(epoch=1)
      assert "on_eval_step_begin" in dummy_callback.step_list
      assert "on_eval_step_end" in dummy_callback.step_list
  
      assert "on_save" not in dummy_callback.step_list
      trainer.save_model(
          configs_and_models[0],
          os.path.join(configs_and_models[1].output_dir, "final_model"),
      )
      assert "on_save" in dummy_callback.step_list
  
      assert "on_train_begin" not in dummy_callback.step_list
      assert "on_train_end" not in dummy_callback.step_list
      assert "on_epoch_begin" not in dummy_callback.step_list
      assert "on_epoch_end" not in dummy_callback.step_list
      assert "on_prediction_step" not in dummy_callback.step_list
      assert "on_log" not in dummy_callback.step_list
      trainer.train()
      assert "on_train_begin" in dummy_callback.step_list
      assert "on_train_end" in dummy_callback.step_list
      assert "on_epoch_begin" in dummy_callback.step_list
      assert "on_epoch_end" in dummy_callback.step_list
      assert "on_prediction_step" in dummy_callback.step_list
      assert "on_log" in dummy_callback.step_list
  
      count = Counter(dummy_callback.step_list)
  
      assert count["on_train_begin"] == 1
      assert count["on_train_end"] == 1
      assert (
          count["on_prediction_step"]
          == configs_and_models[1].num_epochs
          // configs_and_models[1].steps_predict
      )
      assert count["on_epoch_begin"] == configs_and_models[1].num_epochs
      assert count["on_epoch_end"] == configs_and_models[1].num_epochs
      assert count["on_log"] == configs_and_models[1].num_epochs
  
      assert (
          count["on_train_step_begin"]
          == configs_and_models[1].num_epochs + 1
      )
      assert (
          count["on_train_step_end"]
          == configs_and_models[1].num_epochs + 1
      )
      assert (
          count["on_eval_step_begin"]
          == configs_and_models[1].num_epochs + 1
      )
      assert (
          count["on_eval_step_end"]
          == configs_and_models[1].num_epochs + 1
      )
  
imports: import os
  from collections import Counter
  import pytest
  import torch
  from pythae.models import AE, RAE_L2, VAEGAN, Adversarial_AE, Adversarial_AE_Config, AEConfig, RAE_L2_Config, VAEGANConfig
  from pythae.trainers import *
  from pythae.trainers.training_callbacks import *
label_desc: Use image node_id 5 for calling the Test_TrainersCalls obj's underlying member method code with example usage: obj.test_trainer_callback_calls(trainer_cls, configs_and_models, train_dataset, dummy_callback) without return types