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 all candidate tail embeddings in a knowledge graph dataset.
  
  Returns:
    Tensor of size n_entities x embedding_dimension representing embeddings
    for all enitities in the KG.
docstr_tok: ["Get","all","candidate","tail","embeddings","in","a","knowledge","graph","dataset",".","Returns",":","Tensor","of","size","n_entities","x","embedding_dimension","representing","embeddings","for","all","enitities","in","the","KG","."]
code: def get_candidates(self):
      """Get all candidate tail embeddings in a knowledge graph dataset.
  
      Returns:
        Tensor of size n_entities x embedding_dimension representing embeddings
        for all enitities in the KG.
      """
      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 4 for calling the KGModel obj's underlying member method code with example usage: obj.get_candidates() without return types