code: def ae(self, ae_config, custom_encoder, custom_decoder, request):
      # randomized
  
      alpha = request.param
  
      if alpha < 0.25:
          model = RAE_L2(ae_config)
  
      elif 0.25 <= alpha < 0.5:
          model = RAE_L2(ae_config, encoder=custom_encoder)
  
      elif 0.5 <= alpha < 0.75:
          model = RAE_L2(ae_config, decoder=custom_decoder)
  
      else:
          model = RAE_L2(
              ae_config, encoder=custom_encoder, decoder=custom_decoder
          )
  
      return model
  
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 5 for calling the Test_Main_Training obj's underlying member method code with example usage: obj.ae(ae_config, custom_encoder, custom_decoder, request) and returns: model