class_docstr: A representation with message passing not making use of the relation type.
  
  By only using the connectivity information, but not the relation type information, this module
  can utilize message passing layers defined on uni-relational graphs, which are the majority of
  available layers from the PyTorch Geometric library.
  
  Here, we create a two-layer :class:`torch_geometric.nn.conv.GCNConv` on top of an
  :class:`pykeen.nn.representation.Embedding`:
  
  .. code-block:: python
  
      from pykeen.datasets import get_dataset
  
      embedding_dim = 64
      dataset = get_dataset(dataset="nations")
      r = SimpleMessagePassingRepresentation(
          triples_factory=dataset.training,
          base_kwargs=dict(shape=embedding_dim),
          layers=["gcn"] * 2,
          layers_kwargs=dict(in_channels=embedding_dim, out_channels=embedding_dim),
      )
  
  ---
  name: Simple Message Passing
code: def pass_messages(
      self,
      x: torch.FloatTensor,
      edge_index: torch.LongTensor,
      edge_mask: Optional[torch.BoolTensor] = None,
  ) -> torch.FloatTensor:  # noqa: D102
      for layer, activation in zip(self.layers, self.activations):
          x = activation(layer(x, edge_index=edge_index))
      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 1 for calling the SimpleMessagePassingRepresentation obj's underlying member method code with example usage: obj.pass_messages(x, edge_index, edge_mask) and returns: x