class_docstr: This will be superseded by a framework-agnostic approach soon.
code: def __init__(
      self,
      tokenizer: PreTrainedTokenizer,
      file_path: str,
      block_size: int,
  ):
      warnings.warn(
          DEPRECATION_WARNING.format(
              "https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py"
          ),
          FutureWarning,
      )
      if os.path.isfile(file_path) is False:
          raise ValueError(f"Input file path {file_path} not found")
      # Here, we do not cache the features, operating under the assumption
      # that we will soon use fast multithreaded tokenizers from the
      # `tokenizers` repo everywhere =)
      logger.info(f"Creating features from dataset file at {file_path}")
  
      with open(file_path, encoding="utf-8") as f:
          lines = [
              line
              for line in f.read().splitlines()
              if (len(line) > 0 and not line.isspace())
          ]
  
      batch_encoding = tokenizer(
          lines,
          add_special_tokens=True,
          truncation=True,
          max_length=block_size,
      )
      self.examples = batch_encoding["input_ids"]
      self.examples = [
          {"input_ids": torch.tensor(e, dtype=torch.long)}
          for e in self.examples
      ]
  
imports: import json
  import os
  import pickle
  import random
  import time
  import warnings
  from typing import Dict, List, Optional
  import torch
  from torch.utils.data import Dataset
  from filelock import FileLock
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 1 to create a new LineByLineTextDataset object from inherited base classes: Dataset with example: obj = LineByLineTextDataset(tokenizer, file_path, block_size)