code: def __getitem__(self, idx):
      start = idx * self.seq_len
      assert start + self.seq_len + 1 <= self.data.shape[0]
      if self.is_random_offset:
          start += random.randint(
              0,
              min(
                  self.seq_len - 1,
                  self.data.shape[0] - (start + self.seq_len + 1),
              ),
          )
  
      end = start + self.seq_len
      data = self.data[start:end]
      target = self.data[start + 1 : end + 1]
      return data, target
  
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 3 for calling the SequentialUnBatchedDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: data,  target