code: def __init__(
      self,
      *,
      text: str,
      dataset: TextDataset,
      seq_len: int,
      is_random_offset: bool = True,
  ):
      self.is_random_offset = is_random_offset
      self.seq_len = seq_len
      self.data = dataset.text_to_i(text)
  
imports: import random
  from pathlib import PurePath, Path
  from typing import List, Callable, Dict, Optional
  import torch
  from torch.utils.data import IterableDataset, Dataset
  from labml import monit
  from labml.utils.download import download_file
label_desc: Use image node_id 1 to create a new SequentialUnBatchedDataset object from inherited base classes: Dataset with example: obj = SequentialUnBatchedDataset()