code: def forward(self, x):
      word, lens, entity_pos, attribute_value_pos = (
          x["word"],
          x["lens"],
          x["entity_pos"],
          x["attribute_value_pos"],
      )
      mask = seq_len_to_mask(lens)
  
      inputs = self.embedding(word, entity_pos, attribute_value_pos)
      out, out_pool = self.cnn(inputs, mask=mask)
  
      if self.use_pcnn:
          out = out.unsqueeze(-1)  # [B, L, Hs, 1]
          pcnn_mask = x["pcnn_mask"]
          pcnn_mask = self.pcnn_mask_embedding(pcnn_mask).unsqueeze(
              -2
          )  # [B, L, 1, 3]
          out = out + pcnn_mask  # [B, L, Hs, 3]
          out = out.max(dim=1)[0] - 100  # [B, Hs, 3]
          out_pool = out.view(out.size(0), -1)  # [B, 3 * Hs]
          out_pool = F.leaky_relu(self.fc_pcnn(out_pool))  # [B, Hs]
          out_pool = self.dropout(out_pool)
  
      output = self.fc1(out_pool)
      output = F.leaky_relu(output)
      output = self.dropout(output)
      output = self.fc2(output)
  
      return output
  
file_path: DeepKE/src/deepke/attribution_extraction/standard/models/PCNN.py
imports: import os
  import sys
  import torch
  import torch.nn
  import torch.nn.functional
  from .None import BasicModule
  from module import Embedding, CNN
  from utils import seq_len_to_mask
label_desc: Use image node_id 2 for calling the PCNN obj's underlying member method code with example usage: obj.forward(x) and returns: output