code: def __init__(
      self,
      output_dimension: _typing.Optional[int] = ...,
      device: _typing.Union[torch.device, str, int, None] = ...,
      *args,
      **kwargs,
  ):
      super(DiffPoolDecoderMaintainer, self).__init__(
          output_dimension, device, *args, **kwargs
      )
      self.num_graph_features = kwargs.get("num_graph_features", 0)
      self.hyper_parameter_space = [
          {
              "parameterName": "ratio",
              "type": "DOUBLE",
              "maxValue": 0.9,
              "minValue": 0.1,
              "scalingType": "LINEAR",
          },
          {
              "parameterName": "dropout",
              "type": "DOUBLE",
              "maxValue": 0.9,
              "minValue": 0.1,
              "scalingType": "LINEAR",
          },
          {
              "parameterName": "act",
              "type": "CATEGORICAL",
              "feasiblePoints": ["leaky_relu", "relu", "elu", "tanh"],
          },
      ]
      self.hyper_parameters = {
          "ratio": 0.8,
          "dropout": 0.5,
          "act": "relu",
      }
  
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 to create a new DiffPoolDecoderMaintainer object from inherited base classes: base_decoder with example: obj = DiffPoolDecoderMaintainer(output_dimension, device)