code: def _prepare_representation_module_list(
      max_id: int,
      shapes: Sequence[str],
      label: str,
      representations: OneOrManyHintOrType[Representation] = None,
      representations_kwargs: OneOrManyOptionalKwargs = None,
      skip_checks: bool = False,
  ) -> Sequence[Representation]:
      """
      Normalize list of representations and wrap into nn.ModuleList.
  
      .. note ::
          Important: use ModuleList to ensure that Pytorch correctly handles their devices and parameters
  
      :param representations:
          the representations, or hints for them.
      :param representations_kwargs:
          additional keyword-based parameters for instantiating representations from hints.
      :param max_id:
          the maximum representation ID. Newly instantiated representations will contain that many representations, and
          pre-instantiated ones have to provide at least that many.
      :param shapes:
          the symbolic shapes, which are used for shape verification, if skip_checks is False.
      :param label:
          a label to use for error messages (typically, "entities" or "relations").
      :param skip_checks:
          whether to skip shape verification.
  
      :return:
          a module list of instantiated representation modules.
  
      :raises ValueError:
          if the maximum ID or shapes do not match
      """
      # TODO: allow max_id being present in representation_kwargs; if it matches max_id
      # TODO: we could infer some shapes from the given interaction shape information
      rs = representation_resolver.make_many(
          representations, kwargs=representations_kwargs, max_id=max_id
      )
  
      # check max-id
      for r in rs:
          if r.max_id < max_id:
              raise ValueError(
                  f"{r} only provides {r.max_id} {label} representations, but should provide {max_id}.",
              )
          elif r.max_id > max_id:
              logger.warning(
                  f"{r} provides {r.max_id} {label} representations, although only {max_id} are needed."
                  f"While this is not necessarily wrong, it can indicate an error where the number of {label} "
                  f"representations was chosen wrong.",
              )
  
      rs = cast(Sequence[Representation], nn.ModuleList(rs))
      if skip_checks:
          return rs
  
      # check shapes
      if len(rs) != len(shapes):
          raise ValueError(
              f"Interaction function requires {len(shapes)} {label} representations, but {len(rs)} were given."
          )
      check_shapes(
          *zip(
              (r.shape for r in rs),
              shapes,
          ),
          raise_on_errors=True,
      )
      return rs
  
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 1 for calling a global function with example usage: _prepare_representation_module_list(max_id, shapes, label, representations, representations_kwargs, skip_checks) and returns: rs, rs