class_docstr: A mock model returning fixed scores.
  
  .. math ::
      score(h, r, t) = h \cdot |\mathcal{E}| \cdot |\mathcal{R}| + r \cdot |\mathcal{E}| + t
  
  ---
  name: Fixed Model
  citation:
      author: Berrendorf
      year: 2021
      link: https://github.com/pykeen/pykeen/pull/691
      github: pykeen/pykeen
docstr: Initialize the model.
  
  :param triples_factory:
      the (training) triples factory
  :param _kwargs:
      ignored keyword-based parameters
docstr_tok: ["Initialize","the","model",".",":","param","triples_factory",":","the","(","training",")","triples","factory",":","param","_kwargs",":","ignored","keyword-based","parameters"]
code: def __init__(self, *, triples_factory: KGInfo, **_kwargs):
      """
      Initialize the model.
  
      :param triples_factory:
          the (training) triples factory
      :param _kwargs:
          ignored keyword-based parameters
      """
      super().__init__(
          triples_factory=triples_factory,
      )
      self.num_entities = triples_factory.num_entities
      self.num_relations = triples_factory.num_relations
  
      # This empty 1-element tensor doesn't actually do anything,
      # but is necessary since models with no grad params blow
      # up the optimizer
      self.dummy = torch.nn.Parameter(
          torch.empty(1), requires_grad=True
      )
  
file_path: pykeen/src/pykeen/models/mocks.py
imports: from typing import Any, ClassVar, Mapping, Optional
  import torch
  from .base import Model
  from ..triples.triples_factory import KGInfo
  from ..typing import InductiveMode
label_desc: Use image node_id 1 to create a new FixedModel object from inherited base classes: Model with example: obj = FixedModel()