code: def per_char_representation(self, words):
      device = next(self.parameters()).device
      vocab = self.char_vocab()
  
      all_data = [
          (vocab.map(word), len(word), idx)
          for idx, word in enumerate(words)
      ]
      all_data.sort(key=itemgetter(1), reverse=True)
      chars = [x[0] for x in all_data]
      char_lens = [x[1] for x in all_data]
      char_tensor = get_long_tensor(
          chars, len(chars), pad_id=vocab.unit2id(CHARLM_END)
      ).to(device=device)
      with torch.no_grad():
          output, _, _ = self.forward(char_tensor, char_lens)
          output = [x[:y, :] for x, y in zip(output, char_lens)]
          output = unsort(output, [x[2] for x in all_data])
      return output
  
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 4 for calling the CharacterLanguageModel obj's underlying member method code with example usage: obj.per_char_representation(words) and returns: output