docstr: Initialization of inherited lightning data module
docstr_tok: ["Initialization","of","inherited","lightning","data","module"]
code: def __init__(self, dataset, batch_size, num_workers, num_samples):
      """
      Initialization of inherited lightning data module
      """
      super().__init__()
      self.PRE_TRAINED_MODEL_NAME = "bert-base-uncased"
      self.train_dataset = None
      self.val_dataset = None
      self.test_dataset = None
      self.MAX_LEN = 100
      self.encoding = None
      self.tokenizer = None
      self.batch_size = batch_size
      self.num_workers = num_workers
      self.dataset = dataset
      self.num_samples = num_samples
      self.train_count = None
      self.val_count = None
      self.test_count = None
      self.RANDOM_SEED = 42
      self.news_group_df = None
  
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 1 to create a new BertDataModule object from inherited base class: L with example: obj = BertDataModule(dataset, batch_size, num_workers, num_samples)