class_docstr: Adapts a character model to return embeddings for each character in a word
class_docstr_tok: ["Adapts","a","character","model","to","return","embeddings","for","each","character","in","a","word"]
code: def forward(self, words):
      words = [CHARLM_START + x + CHARLM_END for x in words]
      padded_reps = []
      for charlm in self.charlms:
          rep = charlm.per_char_representation(words)
          padded_rep = torch.zeros(
              len(rep),
              max(x.shape[0] for x in rep),
              rep[0].shape[1],
              dtype=rep[0].dtype,
              device=rep[0].device,
          )
          for idx, row in enumerate(rep):
              padded_rep[idx, : row.shape[0], :] = row
          padded_reps.append(padded_rep)
      padded_rep = torch.cat(padded_reps, dim=2)
      return padded_rep
  
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 2 for calling the CharacterLanguageModelWordAdapter obj's underlying member method code with example usage: obj.forward(words) and returns: padded_rep