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_output_dir(self):
      # Create folder
      if (
          not os.path.exists(self.training_config.output_dir)
          and self.is_main_process
      ):
          os.makedirs(self.training_config.output_dir, exist_ok=True)
          logger.info(
              f"Created {self.training_config.output_dir} folder since did not exist.\n"
          )
  
      self._training_signature = (
          str(datetime.datetime.now())[0:19]
          .replace(" ", "_")
          .replace(":", "-")
      )
  
      training_dir = os.path.join(
          self.training_config.output_dir,
          f"{self.model_name}_training_{self._training_signature}",
      )
  
      self.training_dir = training_dir
  
      if not os.path.exists(training_dir) and self.is_main_process:
          os.makedirs(training_dir, exist_ok=True)
          logger.info(
              f"Created {training_dir}. \n"
              "Training config, checkpoints and final model will be saved here.\n"
          )
  
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 8 for calling the BaseTrainer obj's underlying member method code with example usage: obj._set_output_dir() without return types