code: def forward(self, graph_data, correct_candidate_idxs):
      gnn_output: GnnOutput = self._gnn(**graph_data)
  
      # Code assumes that there is one slot per-graph, which is true for the original data
  
      candidate_node_representations = (
          gnn_output.output_node_representations[
              gnn_output.node_idx_references["candidate_nodes"]
          ]
      )  # [num_candidate_nodes, H_out]
      candidate_nodes_slot_idx = gnn_output.node_graph_idx_reference[
          "candidate_nodes"
      ]  # [num_candidate_nodes]
  
      slot_representations = gnn_output.output_node_representations[
          gnn_output.node_idx_references["slot_node_idx"]
      ]  # [num_slot_nodes, H_out]
      slot_representations_per_candidate = slot_representations[
          candidate_nodes_slot_idx
      ]  # [num_candidate_nodes, H]
      candidate_scores = self.__candidate_scores(
          torch.cat(
              (
                  candidate_node_representations,
                  slot_representations_per_candidate,
              ),
              dim=-1,
          )
      ).squeeze(-1)
      candidate_nodes_logprobs = scatter_log_softmax(
          src=candidate_scores, index=candidate_nodes_slot_idx, eps=0
      )
  
      with torch.no_grad():
          self.__sum_acc += int(
              (
                  scatter_max(
                      candidate_scores, index=candidate_nodes_slot_idx
                  )[1]
                  == correct_candidate_idxs
              ).sum()
          )
          self.__num_samples += int(slot_representations.shape[0])
      return -candidate_nodes_logprobs[correct_candidate_idxs].mean()
  
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 4 for calling the VarMisuseGraphModel obj's underlying member method code with example usage: obj.forward(graph_data, correct_candidate_idxs) and returns: unknown