class_docstr: Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding.
  https://aclanthology.org/2020.acl-main.241/
class_docstr_tok: ["Orthogonal","Relation","Transforms","with","Graph","Context","Modeling","for","Knowledge","Graph","Embedding",".","https",":","\/\/aclanthology.org\/2020.acl-main.241\/"]
code: def _get_score(self, combined_embeds, ent_embeds):
      output = combined_embeds - ent_embeds
      output_shape = output.shape
      embed_dim = output_shape[-1]
      output = output.reshape([-1, self.num_elem]).norm(p=2, axis=-1)
      output = output.reshape([-1, embed_dim // self.num_elem]).sum(
          axis=-1
      )
      score = output.reshape(output_shape[:-1])
      return score
  
imports: import math
  import numpy
  from numpy.random import RandomState
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.functional import log_sigmoid
label_desc: Use image node_id 9 for calling the OTEScore obj's underlying member method code with example usage: obj._get_score(combined_embeds, ent_embeds) and returns: score