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 __init__(
      self,
      model: BaseAE,
      train_dataset: BaseDataset,
      eval_dataset: Optional[BaseDataset] = None,
      training_config: Optional[
          CoupledOptimizerAdversarialTrainerConfig
      ] = None,
      callbacks: List[TrainingCallback] = None,
  ):
      BaseTrainer.__init__(
          self,
          model=model,
          train_dataset=train_dataset,
          eval_dataset=eval_dataset,
          training_config=training_config,
          callbacks=callbacks,
      )
  
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 1 to create a new CoupledOptimizerAdversarialTrainer object from inherited base classes: BaseTrainer with example: obj = CoupledOptimizerAdversarialTrainer(model, train_dataset, eval_dataset, training_config, callbacks)