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\/"]
docstr: Orthogonalize relation embeddings.
          
docstr_tok: ["Orthogonalize","relation","embeddings","."]
code: def orth_rel_embedding(self, embeds):
      """Orthogonalize relation embeddings."""
      embed_shape = embeds.shape
      embeds = embeds.reshape(
          [-1, self.num_elem, self.num_elem + int(self.use_scale)]
      )
      embeds = self.gram_schimidt_process(embeds)
      embeds = embeds.reshape(embed_shape)
      return embeds
  
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 11 for calling the OTEScore obj's underlying member method code with example usage: obj.orth_rel_embedding(embeds) and returns: embeds