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 set_decoder_optimizer(self):
      decoder_optimizer_cls = getattr(
          optim, self.training_config.decoder_optimizer_cls
      )
  
      if self.training_config.decoder_optimizer_params is not None:
          if self.distributed:
              decoder_optimizer = decoder_optimizer_cls(
                  self.model.module.decoder.parameters(),
                  lr=self.training_config.decoder_learning_rate,
                  **self.training_config.decoder_optimizer_params,
              )
          else:
              decoder_optimizer = decoder_optimizer_cls(
                  self.model.decoder.parameters(),
                  lr=self.training_config.decoder_learning_rate,
                  **self.training_config.decoder_optimizer_params,
              )
      else:
          if self.distributed:
              decoder_optimizer = decoder_optimizer_cls(
                  self.model.module.decoder.parameters(),
                  lr=self.training_config.decoder_learning_rate,
              )
          else:
              decoder_optimizer = decoder_optimizer_cls(
                  self.model.decoder.parameters(),
                  lr=self.training_config.decoder_learning_rate,
              )
  
      self.decoder_optimizer = decoder_optimizer
  
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 4 for calling the CoupledOptimizerAdversarialTrainer obj's underlying member method code with example usage: obj.set_decoder_optimizer() without return types