code: def forward(
      self,
      features: _typing.Sequence[torch.Tensor],
      graph: dgl.DGLGraph,
      *__args,
      **__kwargs,
  ):
      cumulative_result = 0
      for i, h in enumerate(features):
          cumulative_result += self._dropout(
              self.__linear_predictions[i](self.__pool(graph, h))
          )
      return cumulative_result
  
imports: import torch.nn.functional
  import typing
  import dgl
  from dgl.nn.pytorch.glob import SumPooling, AvgPooling, MaxPooling, SortPooling
  from ..None import base_decoder, decoder_registry
  from ...encoders import base_encoder
  from ...None import _utils
label_desc: Use image node_id 2 for calling the _TopKPoolDecoder obj's underlying member method code with example usage: obj.forward(features, graph) and returns: cumulative_result