code: def forward(
      self, graph: dgl.DGLGraph, *__args, **__kwargs
  ) -> _typing.Sequence[torch.Tensor]:
      if "feat" in graph.ndata:
          x: torch.Tensor = graph.ndata["feat"]
      else:
          x: torch.Tensor = graph.ndata["attr"]
  
      features: _typing.MutableSequence[torch.Tensor] = [x]
      for layer in range(self.__num_layers):
          x: torch.Tensor = self.__gin_layers[layer](graph, x)
          x: torch.Tensor = self.__batch_normalizations[layer](x)
          x: torch.Tensor = _utils.activation.activation_func(
              x, self._act
          )
          features.append(x)
  
      return features
  
imports: import dgl
  import torch.nn.functional
  import typing
  from dgl.nn.pytorch.conv import GINConv
  from ..None import base_encoder, encoder_registry
  from ...None import _utils
label_desc: Use image node_id 2 for calling the _GIN obj's underlying member method code with example usage: obj.forward(graph) and returns: features