class_docstr: Abstract Knowledge Graph embedding model class.
  
  Module to define basic operations in KG embedding models, including embedding
  initialization, computing embeddings and triples' scores.
class_docstr_tok: ["Abstract","Knowledge","Graph","embedding","model","class",".","Module","to","define","basic","operations","in","KG","embedding","models",",","including","embedding","initialization",",","computing","embeddings","and","triples","'","scores","."]
docstr: Compute triple scores using embeddings and biases.
docstr_tok: ["Compute","triple","scores","using","embeddings","and","biases","."]
code: def score(self, lhs, lhs_biases, rhs, rhs_biases, eval_mode):
      """Compute triple scores using embeddings and biases."""
      score = self.similarity_score(lhs, rhs, eval_mode)
      if self.bias == "constant":
          return score + self.gamma
      elif self.bias == "learn":
          if eval_mode:
              return score + lhs_biases + tf.transpose(rhs_biases)
          else:
              return score + lhs_biases + rhs_biases
      else:
          return score
  
file_path: neural-structured-learning/research/kg_hyp_emb/models/base.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  from kg_hyp_emb.learning import regularizers
  import numpy
  import tensorflow
label_desc: Use image node_id 7 for calling the KGModel obj's underlying member method code with example usage: obj.score(lhs, lhs_biases, rhs, rhs_biases, eval_mode) and returns: unknown, score, unknown, unknown