code: def from_raw_corpus(
      data_dir,
      text_list,
      tokenizer,
      batch_size_per_gpu=32,
      max_seq_length=512,
      multi_gpu=True,
      test_size=0.1,
      model_type="bert",
      logger=None,
      clear_cache=False,
      no_cache=False,
  ):
      train_file = "lm_train.txt"
      val_file = "lm_val.txt"
  
      train_list, val_list = train_test_split(
          text_list, test_size=test_size, shuffle=True
      )
      # Create train corpus
      create_corpus(
          train_list, str(data_dir / train_file), logger=logger
      )
  
      # Create val corpus
      create_corpus(val_list, str(data_dir / val_file), logger=logger)
  
      return BertLMDataBunch(
          data_dir,
          tokenizer,
          train_file=train_file,
          val_file=val_file,
          batch_size_per_gpu=batch_size_per_gpu,
          max_seq_length=max_seq_length,
          multi_gpu=multi_gpu,
          model_type=model_type,
          logger=logger,
          clear_cache=clear_cache,
          no_cache=no_cache,
      )
  
imports: import re
  import html
  import logging
  import pandas
  import os
  import random
  import torch
  from pathlib import Path
  import pickle
  import shutil
  import itertools
  import more_itertools
  from sklearn.model_selection import train_test_split
  from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler, Dataset
  from torch.utils.data.distributed import DistributedSampler
  import spacy
  from tqdm import tqdm, trange
  from fastprogress.fastprogress import master_bar, progress_bar
  from transformers import WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer, RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer, CamembertConfig, CamembertForSequenceClassification, CamembertTokenizer, ElectraConfig, ElectraForSequenceClassification, ElectraTokenizer
label_desc: Use image node_id 1 for calling the BertLMDataBunch obj's underlying member method code with example usage: obj.from_raw_corpus(data_dir, text_list, tokenizer, batch_size_per_gpu, max_seq_length, multi_gpu, test_size, model_type, logger, clear_cache, no_cache) and returns: BertLMDataBunch