code: def finalize_minibatch(
      self,
      accumulated_minibatch_data: Dict[str, Any],
      device: Union[str, torch.device],
  ) -> Dict[str, Any]:
      return {
          "graph_data": self.__gnn_model.finalize_minibatch(
              accumulated_minibatch_data["graph_data"], device=device
          ),
          "correct_candidate_idxs": torch.tensor(
              accumulated_minibatch_data["correct_candidate_idxs"],
              dtype=torch.int64,
              device=device,
          ),
      }
  
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 8 for calling the VarMisuseModel obj's underlying member method code with example usage: obj.finalize_minibatch(accumulated_minibatch_data, device) and returns: dict