code: def __init__(
      self, args, vocab, pad=False, bidirectional=False, attention=True
  ):
      super().__init__()
      self.args = args
      self.pad = pad
      self.num_dir = 2 if bidirectional else 1
      self.attn = attention
  
      # char embeddings
      self.char_emb = nn.Embedding(
          len(vocab["char"]), self.args["char_emb_dim"], padding_idx=0
      )
      if self.attn:
          self.char_attn = nn.Linear(
              self.num_dir * self.args["char_hidden_dim"], 1, bias=False
          )
          self.char_attn.weight.data.zero_()
  
      # modules
      self.charlstm = PackedLSTM(
          self.args["char_emb_dim"],
          self.args["char_hidden_dim"],
          self.args["char_num_layers"],
          batch_first=True,
          dropout=0
          if self.args["char_num_layers"] == 1
          else args["dropout"],
          rec_dropout=self.args["char_rec_dropout"],
          bidirectional=bidirectional,
      )
      self.charlstm_h_init = nn.Parameter(
          torch.zeros(
              self.num_dir * self.args["char_num_layers"],
              1,
              self.args["char_hidden_dim"],
          )
      )
      self.charlstm_c_init = nn.Parameter(
          torch.zeros(
              self.num_dir * self.args["char_num_layers"],
              1,
              self.args["char_hidden_dim"],
          )
      )
  
      self.dropout = nn.Dropout(args["dropout"])
  
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 1 to create a new CharacterModel object from inherited base classes: nn with example: obj = CharacterModel(args, vocab, pad, bidirectional, attention)