code: def __init__(self, gnn: GraphNeuralNetwork):
      super().__init__()
      self._gnn = gnn
      self.__candidate_scores = nn.Linear(
          self._gnn.output_node_state_dim
          + self._gnn.output_node_state_dim,
          1,
          bias=False,
      )
  
imports: from typing_extensions import Final, TypedDict
  import re
  import torch
  from dpu_utils.codeutils import split_identifier_into_parts
  from itertools import chain
  from torch import nn
  from torch_scatter import scatter_log_softmax, scatter_max
  from typing import Any, Dict, Iterator, List, NamedTuple, Optional, Tuple, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.neuralmodels.gnn.graphneuralnetwork import GraphNeuralNetwork, GraphNeuralNetworkModel
  from ptgnn.neuralmodels.gnn.structs import GnnOutput, GraphData, TensorizedGraphData
label_desc: Use image node_id 1 to create a new VarMisuseGraphModel object from inherited base classes: ModuleWithMetrics with example: obj = VarMisuseGraphModel(gnn)