code: def trainer(
      self,
      ae,
      train_dataset,
      optimizer_config,
      scheduler_config,
      training_configs,
  ):
      training_configs.encoder_optimizer_cls = optimizer_config[
          "encoder_optimizer_cls"
      ]
      training_configs.encoder_optimizer_params = optimizer_config[
          "encoder_optimizer_params"
      ]
      training_configs.decoder_optimizer_cls = optimizer_config[
          "decoder_optimizer_cls"
      ]
      training_configs.decoder_optimizer_params = optimizer_config[
          "decoder_optimizer_params"
      ]
      training_configs.encoder_scheduler_cls = scheduler_config[
          "encoder_scheduler_cls"
      ]
      training_configs.encoder_scheduler_params = scheduler_config[
          "encoder_scheduler_params"
      ]
      training_configs.decoder_scheduler_cls = scheduler_config[
          "decoder_scheduler_cls"
      ]
      training_configs.decoder_scheduler_params = scheduler_config[
          "decoder_scheduler_params"
      ]
  
      trainer = CoupledOptimizerTrainer(
          model=ae,
          train_dataset=train_dataset,
          eval_dataset=train_dataset,
          training_config=training_configs,
      )
  
      trainer.prepare_training()
  
      return trainer
  
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 8 for calling the Test_Main_Training obj's underlying member method code with example usage: obj.trainer(ae, train_dataset, optimizer_config, scheduler_config, training_configs) and returns: trainer