code: def train_dataloader(self) -> DataLoader:
      dataloader = self.get_dataloader(
          "train",
          batch_size=self.hparams.train_batch_size,
          shuffle=True,
      )
      return dataloader
  
imports: import argparse
  import glob
  import logging
  import os
  import sys
  import time
  from collections import defaultdict
  from pathlib import Path
  from typing import Dict, List, Tuple
  import numpy
  import pytorch_lightning
  import torch
  from torch import nn
  from torch.utils.data import DataLoader
  from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
  from transformers import MBartTokenizer, T5ForConditionalGeneration
  from transformers.models.bart.modeling_bart import shift_tokens_right
  from utils import ROUGE_KEYS, LegacySeq2SeqDataset, Seq2SeqDataset, assert_all_frozen, calculate_bleu, calculate_rouge, check_output_dir, flatten_list, freeze_embeds, freeze_params, get_git_info, label_smoothed_nll_loss, lmap, pickle_save, save_git_info, save_json, use_task_specific_params
  from lightning_base import BaseTransformer, add_generic_args, generic_train
label_desc: Use image node_id 16 for calling the SummarizationModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: dataloader