class_docstr: Base class to perform model training.
  
  Args:
      model (BaseAE): A instance of :class:`~pythae.models.BaseAE` to train
  
      train_dataset (BaseDataset): The training dataset of type
          :class:`~pythae.data.dataset.BaseDataset`
  
      eval_dataset (BaseDataset): The evaluation dataset of type
          :class:`~pythae.data.dataset.BaseDataset`
  
      training_config (BaseTrainerConfig): The training arguments summarizing the main
          parameters used for training. If None, a basic training instance of
          :class:`BaseTrainerConfig` is used. Default: None.
  
      callbacks (List[~pythae.trainers.training_callbacks.TrainingCallbacks]):
          A list of callbacks to use during training.
code: def _set_optimizer_on_device(self, optim, device):
      for param in optim.state.values():
          # Not sure there are any global tensors in the state dict
          if isinstance(param, torch.Tensor):
              param.data = param.data.to(device)
              if param._grad is not None:
                  param._grad.data = param._grad.data.to(device)
          elif isinstance(param, dict):
              for subparam in param.values():
                  if isinstance(subparam, torch.Tensor):
                      subparam.data = subparam.data.to(device)
                      if subparam._grad is not None:
                          subparam._grad.data = subparam._grad.data.to(
                              device
                          )
  
      return optim
  
imports: import contextlib
  import datetime
  import logging
  import os
  from copy import deepcopy
  from typing import Any, Dict, List, Optional, Union
  import torch
  import torch.distributed
  import torch.optim
  import torch.optim.lr_scheduler
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.data.distributed import DistributedSampler
  from ...customexception import ModelError
  from ...data.datasets import BaseDataset, collate_dataset_output
  from ...models import BaseAE
  from ..trainer_utils import set_seed
  from ..training_callbacks import CallbackHandler, MetricConsolePrinterCallback, ProgressBarCallback, TrainingCallback
  from .base_training_config import BaseTrainerConfig
label_desc: Use image node_id 12 for calling the BaseTrainer obj's underlying member method code with example usage: obj._set_optimizer_on_device(optim, device) and returns: optim