code: def trainer(self, factor_ae, train_dataset, training_configs):
      trainer = AdversarialTrainer(
          model=factor_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.customexception import BadInheritanceError
  from pythae.data.preprocessors import DataProcessor
  from pythae.models import AutoModel, FactorVAE, FactorVAEConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.pipelines import GenerationPipeline, TrainingPipeline
  from pythae.samplers import GaussianMixtureSamplerConfig, IAFSamplerConfig, MAFSamplerConfig, NormalSamplerConfig, TwoStageVAESamplerConfig
  from pythae.trainers import AdversarialTrainer, AdversarialTrainerConfig, BaseTrainerConfig
  from tests.data.custom_architectures import Decoder_AE_Conv, Encoder_VAE_Conv, NetBadInheritance
label_desc: Use image node_id 4 for calling the Test_FactorVAE_Training obj's underlying member method code with example usage: obj.trainer(factor_ae, train_dataset, training_configs) and returns: trainer