code: def __init__(self, cfg):
      super(BiLSTM, self).__init__()
  
      if cfg.dim_strategy == "cat":
          cfg.input_size = cfg.word_dim + 2 * cfg.pos_dim
      else:
          cfg.input_size = cfg.word_dim
  
      self.embedding = Embedding(cfg)
      self.bilstm = RNN(cfg)
      self.fc = nn.Linear(cfg.hidden_size, cfg.num_attributes)
      self.dropout = nn.Dropout(cfg.dropout)
  
file_path: DeepKE/src/deepke/attribution_extraction/standard/models/BiLSTM.py
imports: import os
  import sys
  import torch.nn
  from .None import BasicModule
  from module import Embedding, RNN
label_desc: Use image node_id 1 to create a new BiLSTM object from inherited base classes: BasicModule with example: obj = BiLSTM(cfg)