code: def finalize_minibatch(
      self,
      accumulated_minibatch_data: Dict[str, Any],
      device: Union[str, torch.device],
  ) -> Dict[str, Any]:
      return {
          "graph_mb_data": self.__gnn_model.finalize_minibatch(
              accumulated_minibatch_data["graph_mb_data"], device
          ),
          "target_classes": torch.tensor(
              accumulated_minibatch_data["target_classes"],
              dtype=torch.int64,
              device=device,
          ),
          "original_supernode_idxs": accumulated_minibatch_data[
              "original_supernode_idxs"
          ],
      }
  
imports: from typing_extensions import TypedDict
  import numpy
  import torch
  from dpu_utils.mlutils import Vocabulary
  from torch import nn
  from typing import Any, Counter, Dict, Iterator, List, NamedTuple, Optional, Tuple, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.baseneuralmodel.utils.data import enforce_not_None
  from ptgnn.neuralmodels.gnn import GnnOutput, GraphData, TensorizedGraphData
  from ptgnn.neuralmodels.gnn.graphneuralnetwork import GraphNeuralNetwork, GraphNeuralNetworkModel
label_desc: Use image node_id 10 for calling the Graph2Class obj's underlying member method code with example usage: obj.finalize_minibatch(accumulated_minibatch_data, device) and returns: dict