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: Get query embeddings using head and relationship for an index tensor.
  
  Args:
    input_tensor: Tensor of size batch_size x 3 containing triples' indices.
  
  Returns:
    Tensor of size batch_size x embedding_dimension representing queries'
    embeddings.
docstr_tok: ["Get","query","embeddings","using","head","and","relationship","for","an","index","tensor",".","Args",":","input_tensor",":","Tensor","of","size","batch_size","x","3","containing","triples","'","indices",".","Returns",":","Tensor","of","size","batch_size","x","embedding_dimension","representing","queries'","embeddings","."]
code: def get_queries(self, input_tensor):
      """Get query embeddings using head and relationship for an index tensor.
  
      Args:
        input_tensor: Tensor of size batch_size x 3 containing triples' indices.
  
      Returns:
        Tensor of size batch_size x embedding_dimension representing queries'
        embeddings.
      """
      pass
  
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 2 for calling the KGModel obj's underlying member method code with example usage: obj.get_queries(input_tensor) without return types