code: def get_batch(self, i, bptt=None):
      if bptt is None:
          bptt = self.bptt
      seq_len = min(bptt, self.data.size(0) - 1 - i)
  
      end_idx = i + seq_len
      beg_idx = max(0, i - self.ext_len)
  
      data = self.data[beg_idx:end_idx]
      target = self.data[i + 1 : i + 1 + seq_len]
  
      data_out = data.transpose(0, 1).contiguous().to(self.device)
      target_out = target.transpose(0, 1).contiguous().to(self.device)
  
      return data_out, target_out, seq_len
  
imports: import glob
  import os
  import pickle
  import re
  from collections import Counter, OrderedDict
  from typing import List, Optional, Tuple
  import numpy
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import cached_file, is_sacremoses_available, is_torch_available, logging, requires_backends, torch_only_method
label_desc: Use image node_id 2 for calling the LMOrderedIterator obj's underlying member method code with example usage: obj.get_batch(i, bptt) and returns: data_out,  target_out,  seq_len