code: def __init__(self, config):
      super(Model, self).__init__()
      self.use_bert_last_4_layers = config.use_bert_last_4_layers
  
      self.lstm_hid_size = config.lstm_hid_size
      self.conv_hid_size = config.conv_hid_size
  
      lstm_input_size = 0
  
      self.bert = AutoModel.from_pretrained(
          config.bert_name, output_hidden_states=True
      )
      lstm_input_size += config.bert_hid_size
  
      self.dis_embs = nn.Embedding(20, config.dist_emb_size)
      self.reg_embs = nn.Embedding(3, config.type_emb_size)
  
      self.encoder = nn.LSTM(
          lstm_input_size,
          config.lstm_hid_size // 2,
          num_layers=1,
          batch_first=True,
          bidirectional=True,
      )
  
      conv_input_size = (
          config.lstm_hid_size
          + config.dist_emb_size
          + config.type_emb_size
      )
  
      self.convLayer = ConvolutionLayer(
          conv_input_size,
          config.conv_hid_size,
          config.dilation,
          config.conv_dropout,
      )
      self.dropout = nn.Dropout(config.emb_dropout)
      self.predictor = CoPredictor(
          config.label_num,
          config.lstm_hid_size,
          config.biaffine_size,
          config.conv_hid_size * len(config.dilation),
          config.ffnn_hid_size,
          config.out_dropout,
      )
  
      self.cln = LayerNorm(
          config.lstm_hid_size, config.lstm_hid_size, conditional=True
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from transformers import AutoModel
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model(config)