code: def forward(self, x):
      word, lens = x["word"], x["lens"]
      mask = seq_len_to_mask(lens, mask_pos_to_true=False)
      a = self.bert(word, attention_mask=mask)
      last_hidden_state = a[0]
      # pooler_output = a[1]
      _, out_pool = self.bilstm(last_hidden_state, lens)
      out_pool = self.dropout(out_pool)
      output = self.fc(out_pool)
  
      return output
  
file_path: DeepKE/src/deepke/attribution_extraction/standard/models/LM.py
imports: import os
  import sys
  from torch import nn
  from .None import BasicModule
  from module import RNN
  from transformers import BertModel
  from utils import seq_len_to_mask
label_desc: Use image node_id 2 for calling the LM obj's underlying member method code with example usage: obj.forward(x) and returns: output