code: def rae(self, model_configs, custom_encoder, custom_decoder, request):
      # randomized
  
      alpha = request.param
  
      if alpha < 0.25:
          model = RAE_L2(model_configs)
  
      elif 0.25 <= alpha < 0.5:
          model = RAE_L2(model_configs, encoder=custom_encoder)
  
      elif 0.5 <= alpha < 0.75:
          model = RAE_L2(model_configs, decoder=custom_decoder)
  
      else:
          model = RAE_L2(
              model_configs,
              encoder=custom_encoder,
              decoder=custom_decoder,
          )
  
      return model
  
imports: import os
  from copy import deepcopy
  import pytest
  import torch
  from torch.optim import Adam
  from pythae.customexception import BadInheritanceError
  from pythae.models import RAE_L2, AutoModel, RAE_L2_Config
  from pythae.models.base.base_utils import ModelOutput
  from pythae.pipelines import GenerationPipeline, TrainingPipeline
  from pythae.samplers import GaussianMixtureSamplerConfig, IAFSamplerConfig, MAFSamplerConfig, NormalSamplerConfig
  from pythae.trainers import BaseTrainerConfig, CoupledOptimizerTrainer, CoupledOptimizerTrainerConfig
  from tests.data.custom_architectures import Decoder_AE_Conv, Encoder_AE_Conv, NetBadInheritance
label_desc: Use image node_id 3 for calling the Test_RAE_L2_Training obj's underlying member method code with example usage: obj.rae(model_configs, custom_encoder, custom_decoder, request) and returns: model