code: def repeat_if_necessary(
      scores: torch.FloatTensor,
      representations: Sequence[Representation],
      num: Optional[int],
  ) -> torch.FloatTensor:
      """
      Repeat score tensor if necessary.
  
      If a model does not have entity/relation representations, the scores for
      `score_{h,t}` / `score_r` are always the same. For efficiency, they are thus
      only computed once, but to meet the API, they have to be brought into the correct shape afterwards.
  
      :param scores: shape: (batch_size, ?)
          the score tensor
      :param representations:
          the representations. If empty (i.e. no representations for this 1:n scoring), repetition needs to be applied
      :param num:
          the number of times to repeat, if necessary.
  
      :return:
          the score tensor, which has been repeated, if necessary
      """
      if representations:
          return scores
      return scores.repeat(1, num)
  
imports: from __future__ import annotations
  import logging
  from abc import ABC
  from collections import defaultdict
  from operator import itemgetter
  from typing import Any, ClassVar, Generic, Iterable, List, Literal, Mapping, Optional, Sequence, Tuple, Type, Union, cast
  import torch
  from class_resolver import HintOrType, OptionalKwargs
  from class_resolver.utils import OneOrManyHintOrType, OneOrManyOptionalKwargs, normalize_with_default
  from torch import nn
  from .base import Model
  from ..nn import representation_resolver
  from ..nn.modules import Interaction, interaction_resolver, parallel_unsqueeze
  from ..nn.representation import Representation
  from ..regularizers import Regularizer, regularizer_resolver
  from ..triples import KGInfo
  from ..typing import HeadRepresentation, InductiveMode, RelationRepresentation, TailRepresentation
  from ..utils import check_shapes, get_batchnorm_modules
label_desc: Use image node_id 2 for calling a global function with example usage: repeat_if_necessary(scores, representations, num) and returns: scores, scores