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 _run_model_sanity_check(self, model, loader):
      try:
          inputs = next(iter(loader))
          train_dataset = self._set_inputs_to_device(inputs)
          model(train_dataset)
  
      except Exception as e:
          raise ModelError(
              "Error when calling forward method from model. Potential issues: \n"
              " - Wrong model architecture -> check encoder, decoder and metric architecture if "
              "you provide yours \n"
              " - The data input dimension provided is wrong -> when no encoder, decoder or metric "
              "provided, a network is built automatically but requires the shape of the flatten "
              "input data.\n"
              f"Exception raised: {type(e)} with message: " + str(e)
          ) from e
  
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 11 for calling the BaseTrainer obj's underlying member method code with example usage: obj._run_model_sanity_check(model, loader) without return types