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)
      (
          last_layer_hidden_state,
          all_hidden_states,
          all_attentions,
      ) = self.transformer(inputs, key_padding_mask=mask)
      out_pool = last_layer_hidden_state.max(dim=1)[0]
      output = self.fc(out_pool)
  
      return output
  
file_path: DeepKE/src/deepke/attribution_extraction/standard/models/Transformer.py
imports: import os
  import sys
  import torch.nn
  from .None import BasicModule
  from module import Embedding
  from module import Transformer
  from utils import seq_len_to_mask
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(x) and returns: output