code: def forward(
      self, graph: dgl.DGLGraph, *__args, **__kwargs
  ) -> _typing.Sequence[torch.Tensor]:
      graph = dgl.remove_self_loop(graph)
      graph = dgl.add_self_loop(graph)
      if "feat" in graph.ndata:
          h: torch.Tensor = graph.ndata["feat"]
      else:
          h: torch.Tensor = graph.ndata["attr"]
      hidden_rep = [h]
      for i in range(self.__num_layers):
          h = self.__gcn_layers[i](graph, h)
          h = self.__batch_normalizations[i](h)
          h = torch.nn.functional.relu(h)
          hidden_rep.append(h)
      return hidden_rep
  
imports: import dgl
  import torch.nn.functional
  import typing
  from dgl.nn.pytorch.conv import GraphConv
  from ..None import base_encoder, encoder_registry
label_desc: Use image node_id 2 for calling the _TopK obj's underlying member method code with example usage: obj.forward(graph) and returns: hidden_rep