code: def get_representation(
      self, chars, charoffsets, charlens, char_orig_idx
  ):
      with torch.no_grad():
          output, _, _ = self.forward(chars, charlens)
          res = [
              output[i, offsets]
              for i, offsets in enumerate(charoffsets)
          ]
          res = unsort(res, char_orig_idx)
          res = pack_sequence(res)
          if self.pad:
              res = pad_packed_sequence(res, batch_first=True)[0]
      return res
  
imports: from collections import Counter
  from operator import itemgetter
  import os
  import torch
  import torch.nn
  from torch.nn.utils.rnn import pack_sequence, pad_packed_sequence, pack_padded_sequence, PackedSequence
  from stanza.models.common.data import get_long_tensor
  from stanza.models.common.packed_lstm import PackedLSTM
  from stanza.models.common.utils import open_read_text, tensor_unsort, unsort
  from stanza.models.common.dropout import SequenceUnitDropout
  from stanza.models.common.vocab import UNK_ID, CharVocab
label_desc: Use image node_id 3 for calling the CharacterLanguageModel obj's underlying member method code with example usage: obj.get_representation(chars, charoffsets, charlens, char_orig_idx) and returns: res