code: def forward(
      self,
      features: _typing.Sequence[torch.Tensor],
      data: torch_geometric.data.Data,
      *__args,
      **__kwargs,
  ):
      feature = features[-1]
      feature = global_add_pool(feature, data.batch)
      if (
          isinstance(self.__num_graph_features, int)
          and self.__num_graph_features > 0
      ):
          if (
              hasattr(data, "gf")
              and isinstance(data.gf, torch.Tensor)
              and data.gf.dim() == 2
              and data.gf.size()
              == (feature.size(0), self.__num_graph_features)
          ):
              graph_features: torch.Tensor = data.gf
          else:
              raise ValueError(
                  f"The provided data is expected to contain property 'gf' "
                  f"with {self.__num_graph_features} dimensions as graph feature"
              )
          feature: torch.Tensor = torch.cat(
              [feature, graph_features], dim=-1
          )
      feature: torch.Tensor = self._fc1(feature)
      feature: torch.Tensor = _utils.activation.activation_func(
          feature, self._act
      )
      if isinstance(self._dropout, float) and 0 <= self._dropout <= 1:
          feature: torch.Tensor = torch.nn.functional.dropout(
              feature, self._dropout, self.training
          )
      feature: torch.Tensor = self._fc2(feature)
      return torch.nn.functional.log_softmax(feature, dim=-1)
  
imports: import torch.nn.functional
  import typing
  import torch_geometric
  from torch_geometric.nn.conv import GraphConv
  from torch_geometric.nn.pool import TopKPooling
  from torch_geometric.nn.glob import global_add_pool, global_max_pool, global_mean_pool
  from ...encoders import base_encoder
  from ..None import base_decoder, decoder_registry
  from ...None import _utils
label_desc: Use image node_id 2 for calling the _SumPoolMLPDecoder obj's underlying member method code with example usage: obj.forward(features, data) and returns: torch