code: def forward(self, x):
      word, lens, head_pos, tail_pos = (
          x["word"],
          x["lens"],
          x["head_pos"],
          x["tail_pos"],
      )
      mask = seq_len_to_mask(lens)
      inputs = self.embedding(word, head_pos, tail_pos)
  
      primary, _ = self.cnn(
          inputs
      )  # 由于长度改变，无法定向mask，不mask可可以，毕竟primary capsule 就是粗粒度的信息
      output = self.capsule(primary)
      output = output.norm(p=2, dim=-1)  # 求得模长再返回值
  
      return output
  
file_path: DeepKE/src/deepke/relation_extraction/standard/models/Capsule.py
imports: import os
  import sys
  import torch
  from .None import BasicModule
  from module import Embedding, CNN
  from module import Capsule
  from utils import seq_len_to_mask, to_one_hot
label_desc: Use image node_id 2 for calling the Capsule obj's underlying member method code with example usage: obj.forward(x) and returns: output