class_docstr: A model which only implements the methods used for evaluation.
docstr: Non-parametric models take a minimal set of arguments.
  
  :param triples_factory: The training triples factory is used to assign the number of entities, relations,
      and inverse condition in the non-parametric model.
docstr_tok: ["Non-parametric","models","take","a","minimal","set","of","arguments",".",":","param","triples_factory",":","The","training","triples","factory","is","used","to","assign","the","number","of","entities",",","relations",",","and","inverse","condition","in","the","non-parametric","model","."]
code: def __init__(self, triples_factory: CoreTriplesFactory):
      """Non-parametric models take a minimal set of arguments.
  
      :param triples_factory: The training triples factory is used to assign the number of entities, relations,
          and inverse condition in the non-parametric model.
      """
      super().__init__(
          triples_factory=triples_factory,
          # These operations are deterministic and a random seed can be fixed
          # just to avoid warnings
          random_seed=0,
      )
  
imports: from typing import Optional
  import numpy
  import torch
  from .utils import get_csr_matrix, get_relation_similarity, marginal_score
  from ..base import Model
  from ...triples import CoreTriplesFactory
  from ...typing import InductiveMode
label_desc: Use image node_id 1 to create a new EvaluationOnlyModel object from inherited base classes: Model with example: obj = EvaluationOnlyModel(triples_factory)