code: def __init__(self, args, vocab, pad=False, is_forward_lm=True):
      super().__init__()
      self.args = args
      self.vocab = vocab
      self.is_forward_lm = is_forward_lm
      self.pad = pad
      self.finetune = True  # always finetune unless otherwise specified
  
      # char embeddings
      self.char_emb = nn.Embedding(
          len(self.vocab["char"]),
          self.args["char_emb_dim"],
          padding_idx=None,
      )  # we use space as padding, so padding_idx is not necessary
  
      # 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["char_dropout"],
          rec_dropout=self.args["char_rec_dropout"],
          bidirectional=False,
      )
      self.charlstm_h_init = nn.Parameter(
          torch.zeros(
              self.args["char_num_layers"],
              1,
              self.args["char_hidden_dim"],
          )
      )
      self.charlstm_c_init = nn.Parameter(
          torch.zeros(
              self.args["char_num_layers"],
              1,
              self.args["char_hidden_dim"],
          )
      )
  
      # decoder
      self.decoder = nn.Linear(
          self.args["char_hidden_dim"], len(self.vocab["char"])
      )
      self.dropout = nn.Dropout(args["char_dropout"])
      self.char_dropout = SequenceUnitDropout(
          args.get("char_unit_dropout", 0), UNK_ID
      )
  
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 CharacterLanguageModel object from inherited base classes: nn with example: obj = CharacterLanguageModel(args, vocab, pad, is_forward_lm)