code: def _initialize(
      self,
      encoder: base_encoder.AutoHomogeneousEncoderMaintainer,
      *args,
      **kwargs,
  ) -> _typing.Optional[bool]:
      if (
          isinstance(getattr(self, "num_graph_features"), int)
          and getattr(self, "num_graph_features") > 0
      ):
          num_graph_features: _typing.Optional[int] = getattr(
              self, "num_graph_features"
          )
      else:
          num_graph_features: _typing.Optional[int] = None
      self._decoder = _SumPoolMLPDecoder(
          tuple(encoder.get_output_dimensions())[-1],
          self.hyper_parameters["hidden"],
          self.output_dimension,
          self.hyper_parameters["act"],
          self.hyper_parameters["dropout"],
          num_graph_features,
      ).to(self.device)
      return True
  
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 1 for calling the SumPoolMLPDecoderMaintainer obj's underlying member method code with example usage: obj._initialize(encoder) and returns: True