class_docstr: Base model class for complex embeddings.
class_docstr_tok: ["Base","model","class","for","complex","embeddings","."]
docstr: Initialize complex 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","complex","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 complex 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).
      """
      assert args.rank % 2 == 0, (
          "Complex models must have an even embedding " "dimension."
      )
      super(BaseC, self).__init__(sizes, args)
      self.half_rank = self.rank // 2
  
file_path: neural-structured-learning/research/kg_hyp_emb/models/complex.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from kg_hyp_emb.models.base import KGModel
  import tensorflow
label_desc: Use image node_id 1 to create a new BaseC object from inherited base classes: KGModel with example: obj = BaseC(sizes, args)