code: def __init__(
      self,
      num_features: int = ...,
      num_classes: int = ...,
      num_graph_features: int = ...,
      device: _typing.Union[str, torch.device] = ...,
      hyper_parameter_space: _typing.Sequence[_typing.Any] = ...,
      hyper_parameter: _typing.Dict[str, _typing.Any] = ...,
      init: bool = False,
      **kwargs,
  ):
      if "initialize" in kwargs:
          del kwargs["initialize"]
      super(ClassificationModel, self).__init__(
          initialize=init,
          hyper_parameter_space=hyper_parameter_space,
          hyper_parameter=hyper_parameter,
          device=device,
          **kwargs,
      )
      if num_classes != Ellipsis and type(num_classes) == int:
          self.__num_classes: int = (
              num_classes if num_classes > 0 else 0
          )
      else:
          self.__num_classes: int = 0
      if num_features != Ellipsis and type(num_features) == int:
          self.__num_features: int = (
              num_features if num_features > 0 else 0
          )
      else:
          self.__num_features: int = 0
      if (
          num_graph_features != Ellipsis
          and type(num_graph_features) == int
      ):
          if num_graph_features > 0:
              self.__num_graph_features: int = num_graph_features
          else:
              self.__num_graph_features: int = 0
      else:
          self.__num_graph_features: int = 0
  
imports: import copy
  import logging
  import typing
  import torch
  import torch.nn.functional
  from copy import deepcopy
  from autogl.module.hpo.auto_module import AutoModule
label_desc: Use image node_id 3 to create a new ClassificationModel object from inherited base classes: _BaseModel with example: obj = ClassificationModel(num_features, num_classes, num_graph_features, device, hyper_parameter_space, hyper_parameter, init)