code: def __getitem__(self, item):
      if not self.cache_tokenization:
          return self.tokenizer(
              self.sentences[item],
              add_special_tokens=True,
              truncation=True,
              max_length=self.max_length,
              return_special_tokens_mask=True,
          )
  
      if isinstance(self.sentences[item], str):
          self.sentences[item] = self.tokenizer(
              self.sentences[item],
              add_special_tokens=True,
              truncation=True,
              max_length=self.max_length,
              return_special_tokens_mask=True,
          )
      return self.sentences[item]
  
file_path: sentence-transformers/examples/unsupervised_learning/MLM/train_mlm.py
imports: from transformers import AutoModelForMaskedLM, AutoTokenizer
  from transformers import DataCollatorForLanguageModeling, DataCollatorForWholeWordMask
  from transformers import Trainer, TrainingArguments
  import sys
  import gzip
  from datetime import datetime
label_desc: Use image node_id 2 for calling the TokenizedSentencesDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: self, self