class_docstr: An abstract representation class utilizing PyTorch Geometric message passing layers.
  
  It comprises:
      * base (entity) representations, which can also be passed as hints
      * a sequence of message passing layers. They are utilized in an abstract
        :meth:`MessagePassingRepresentation._message_passing` to enrich the base representations
        by neighborhood information.
      * a sequence of activation layers in between the message passing layers.
      * an `edge_index` buffer, which stores the edge index and is moved to the device alongside
        the module.
code: def _plain_forward(
      self, indices: Optional[torch.LongTensor] = None
  ) -> torch.FloatTensor:  # noqa: D102
      if self.restrict_k_hop and indices is not None:
          # we can restrict the message passing to the k-hop neighborhood of the desired indices;
          # this does only make sense if we do not request *all* indices
          assert k_hop_subgraph is not None
          # k_hop_subgraph returns:
          # (1) the nodes involved in the subgraph
          # (2) the filtered edge_index connectivity
          # (3) the mapping from node indices in node_idx to their new location, and
          # (4) the edge mask indicating which edges were preserved
          (
              neighbor_indices,
              edge_index,
              indices,
              edge_mask,
          ) = k_hop_subgraph(
              node_idx=indices,
              num_hops=len(self.layers),
              edge_index=self.edge_index,
              relabel_nodes=True,
              flow=self.flow,
          )
          # we only need the base representations for the neighbor indices
          x = self.base(indices=neighbor_indices)
      else:
          # get *all* base representations
          x = self.base(indices=None)
          # use *all* edges
          edge_index = self.edge_index
          edge_mask = None
      # perform message passing
      x = self.pass_messages(
          x=x, edge_index=edge_index, edge_mask=edge_mask
      )
      # select desired indices
      if indices is not None:
          x = x[indices]
      return x
  
imports: from abc import ABC, abstractmethod
  from typing import Collection, Literal, Optional, Sequence
  import torch
  from class_resolver import ClassResolver, HintOrType, OneOrManyHintOrType, OneOrManyOptionalKwargs, OptionalKwargs
  from class_resolver.contrib.torch import activation_resolver
  from docdata import parse_docdata
  from torch import nn
  from .representation import Representation
  from .utils import ShapeError
  from ..triples.triples_factory import CoreTriplesFactory
  from ..typing import OneOrSequence
  from ..utils import get_edge_index, upgrade_to_sequence
label_desc: Use image node_id 2 for calling the MessagePassingRepresentation obj's underlying member method code with example usage: obj._plain_forward(indices) and returns: x