docstr: :return: output - Train data loader for the given input
docstr_tok: [":","return",":","output","-","Train","data","loader","for","the","given","input"]
code: def train_dataloader(self):
      """
      :return: output - Train data loader for the given input
      """
      return self.create_data_loader(
          source=self.train_dataset, count=self.train_count
      )
  
imports: import math
  import os
  import lightning
  import pandas
  import torch
  import torch.nn.functional
  import torchtext.datasets
  from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint
  from lightning.pytorch.cli import LightningCLI
  from sklearn.datasets import fetch_20newsgroups
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from torch import nn
  from torch.utils.data import DataLoader
  from torch.utils.data.dataset import random_split
  from torchdata.datapipes.iter import IterDataPipe
  from torchtext.data.functional import to_map_style_dataset
  from torchtext.datasets import AG_NEWS
  from transformers import BertModel, BertTokenizer
  import mlflow.pytorch
label_desc: Use image node_id 4 for calling the BertDataModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: self