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 get_eval_dataloader(
      self, eval_dataset: BaseDataset
  ) -> torch.utils.data.DataLoader:
      if self.distributed:
          eval_sampler = DistributedSampler(
              eval_dataset, num_replicas=self.world_size, rank=self.rank
          )
      else:
          eval_sampler = None
      return DataLoader(
          dataset=eval_dataset,
          batch_size=self.training_config.per_device_eval_batch_size,
          num_workers=self.training_config.eval_dataloader_num_workers,
          shuffle=(eval_sampler is None),
          sampler=eval_sampler,
          collate_fn=collate_dataset_output,
      )
  
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 5 for calling the BaseTrainer obj's underlying member method code with example usage: obj.get_eval_dataloader(eval_dataset) and returns: DataLoader