class_docstr: Base model class for hyperbolic embeddings.
class_docstr_tok: ["Base","model","class","for","hyperbolic","embeddings","."]
docstr: Initialize Hyperbolic KG embedding model.
  
  Args:
    sizes: Tuple of size 3 containing (n_entities, n_rels, n_entities).
    args: Namespace with config arguments (see config.py for detailed overview
      of arguments supported).
docstr_tok: ["Initialize","Hyperbolic","KG","embedding","model",".","Args",":","sizes",":","Tuple","of","size","3","containing","(","n_entities",",","n_rels",",","n_entities",")",".","args",":","Namespace","with","config","arguments","(","see","config.py","for","detailed","overview","of","arguments","supported",")","."]
code: def __init__(self, sizes, args):
      """Initialize Hyperbolic KG embedding model.
  
      Args:
        sizes: Tuple of size 3 containing (n_entities, n_rels, n_entities).
        args: Namespace with config arguments (see config.py for detailed overview
          of arguments supported).
      """
      super(BaseH, self).__init__(sizes, args)
      self.c = tf.Variable(
          initial_value=tf.keras.backend.ones(1), trainable=args.train_c
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from kg_hyp_emb.models.base import KGModel
  from kg_hyp_emb.utils import euclidean
  from kg_hyp_emb.utils import hyperbolic
  import numpy
  import tensorflow
label_desc: Use image node_id 1 to create a new BaseH object from inherited base classes: KGModel with example: obj = BaseH(sizes, args)