code: def __init__(
      self,
      input_dimension: _typing.Optional[int] = ...,
      final_dimension: _typing.Optional[int] = ...,
      device: _typing.Union[torch.device, str, int, None] = ...,
      *args,
      **kwargs,
  ):
      super(AutoTopKMaintainer, self).__init__(
          input_dimension, final_dimension, device, *args, **kwargs
      )
      self.hyper_parameter_space = [
          {
              "parameterName": "num_layers",
              "type": "DISCRETE",
              "feasiblePoints": "4,5,6",
          },
          {
              "parameterName": "hidden",
              "type": "NUMERICAL_LIST",
              "numericalType": "INTEGER",
              "length": 5,
              "minValue": [8, 8, 8, 8, 8],
              "maxValue": [64, 64, 64, 64, 64],
              "scalingType": "LOG",
              "cutPara": ("num_layers",),
              "cutFunc": lambda x: x[0] - 1,
          },
      ]
      self.hyper_parameters = {
          "num_layers": 5,
          "hidden": [64, 64, 64, 64],
      }
  
imports: import dgl
  import torch.nn.functional
  import typing
  from dgl.nn.pytorch.conv import GraphConv
  from ..None import base_encoder, encoder_registry
label_desc: Use image node_id 1 to create a new AutoTopKMaintainer object from inherited base classes: base_encoder with example: obj = AutoTopKMaintainer(input_dimension, final_dimension, device)