code: def _calc(self, h, t, r, mode):
      if self.norm_flag:
          h = F.normalize(h, 2, -1)
          r = F.normalize(r, 2, -1)
          t = F.normalize(t, 2, -1)
      if mode != "normal":
          h = h.view(-1, r.shape[0], h.shape[-1])
          t = t.view(-1, r.shape[0], t.shape[-1])
          r = r.view(-1, r.shape[0], r.shape[-1])
      if mode == "head_batch":
          score = h + (r - t)
      else:
          score = (h + r) - t
      score = torch.norm(score, self.p_norm, -1).flatten()
      return score
  
file_path: OpenKE/openke/module/model/TransD.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .Model import Model
label_desc: Use image node_id 3 for calling the TransD obj's underlying member method code with example usage: obj._calc(h, t, r, mode) and returns: score