class_docstr: Trainer using distinct optimizers for the encoder, decoder and discriminator.
  
  Args:
      model (BaseAE): The model to train
  
      train_dataset (BaseDataset): The training dataset of type
          :class:`~pythae.data.dataset.BaseDataset`
  
      training_args (CoupledOptimizerAdversarialTrainerConfig): The training arguments summarizing
          the main parameters used for training. If None, a basic training instance of
          :class:`AdversarialTrainerConfig` is used. Default: None.
  
      encoder_optimizer (~torch.optim.Optimizer): An instance of `torch.optim.Optimizer`
          used for training the encoder. If None, a :class:`~torch.optim.Adam` optimizer is
          used. Default: None.
  
      decoder_optimizer (~torch.optim.Optimizer): An instance of `torch.optim.Optimizer`
          used for training the decoder. If None, a :class:`~torch.optim.Adam` optimizer is
          used. Default: None.
  
      discriminator_optimizer (~torch.optim.Optimizer): An instance of `torch.optim.Optimizer`
          used for training the discriminator. If None, a :class:`~torch.optim.Adam` optimizer is
          used. Default: None.
code: def _optimizers_step(self, model_output):
      encoder_loss = model_output.encoder_loss
      decoder_loss = model_output.decoder_loss
      discriminator_loss = model_output.discriminator_loss
  
      # Reset optimizers
      if model_output.update_encoder:
          self.encoder_optimizer.zero_grad()
          encoder_loss.backward(retain_graph=True)
  
      if model_output.update_decoder:
          self.decoder_optimizer.zero_grad()
          decoder_loss.backward(retain_graph=True)
  
      if model_output.update_discriminator:
          self.discriminator_optimizer.zero_grad()
          discriminator_loss.backward()
  
      if model_output.update_encoder:
          self.encoder_optimizer.step()
  
      if model_output.update_decoder:
          self.decoder_optimizer.step()
  
      if model_output.update_discriminator:
          self.discriminator_optimizer.step()
  
imports: import datetime
  import dis
  import logging
  import os
  from copy import deepcopy
  from typing import List, Optional
  import torch
  import torch.distributed
  import torch.optim
  import torch.optim.lr_scheduler
  from ...data.datasets import BaseDataset
  from ...models import BaseAE
  from ..base_trainer import BaseTrainer
  from ..trainer_utils import set_seed
  from ..training_callbacks import TrainingCallback
  from .coupled_optimizer_adversarial_trainer_config import CoupledOptimizerAdversarialTrainerConfig
label_desc: Use image node_id 8 for calling the CoupledOptimizerAdversarialTrainer obj's underlying member method code with example usage: obj._optimizers_step(model_output) without return types