class_docstr: A base module for KGE models.
  
  Subclasses of :class:`Model` can decide however they want on how to store entities' and
  relations' representations, how they want to be looked up, and how they should
  be scored. The :class:`OModel` provides a commonly used interface for models storing entity
  and relation representations in the form of :class:`pykeen.nn.Embedding`.
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docstr: Calculate the scores for triples.
  
  This method takes head, relation and tail of each triple and calculates the corresponding score.
  
  Additionally, the model is set to evaluation mode.
  
  :param hrt_batch: shape: (number of triples, 3), dtype: long
      The indices of (head, relation, tail) triples.
  :param mode:
      The pass mode. Is None for transductive and "training" / "validation" / "testing" in inductive.
  
  :return: shape: (number of triples, 1), dtype: float
      The score for each triple.
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code: def predict_hrt(
      self,
      hrt_batch: torch.LongTensor,
      *,
      mode: Optional[InductiveMode] = None,
  ) -> torch.FloatTensor:
      """Calculate the scores for triples.
  
      This method takes head, relation and tail of each triple and calculates the corresponding score.
  
      Additionally, the model is set to evaluation mode.
  
      :param hrt_batch: shape: (number of triples, 3), dtype: long
          The indices of (head, relation, tail) triples.
      :param mode:
          The pass mode. Is None for transductive and "training" / "validation" / "testing" in inductive.
  
      :return: shape: (number of triples, 1), dtype: float
          The score for each triple.
      """
      self.eval()  # Enforce evaluation mode
      scores = self.score_hrt(
          self._prepare_batch(batch=hrt_batch, index_relation=1),
          mode=mode,
      )
      if self.predict_with_sigmoid:
          scores = torch.sigmoid(scores)
      return scores
  
imports: from __future__ import annotations
  import inspect
  import logging
  import os
  import pickle
  from abc import ABC, abstractmethod
  from typing import Any, ClassVar, Iterable, Mapping, Optional, Type, Union
  import torch
  from class_resolver import HintOrType
  from docdata import parse_docdata
  from torch import nn
  from ..inverse import RelationInverter, relation_inverter_resolver
  from ..losses import Loss, MarginRankingLoss, loss_resolver
  from ..triples import KGInfo
  from ..typing import LABEL_HEAD, LABEL_RELATION, LABEL_TAIL, InductiveMode, MappedTriples, Target
  from ..utils import NoRandomSeedNecessary, get_preferred_device, set_random_seed
label_desc: Use image node_id 22 for calling the Model obj's underlying member method code with example usage: obj.predict_hrt(hrt_batch) and returns: scores