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 hidden_dim(self):
      return sum(charlm.hidden_dim() for charlm in self.charlms)
  
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 CharacterLanguageModelWordAdapter obj's underlying member method code with example usage: obj.hidden_dim() and returns: sum