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
          ),
          "targets": torch.tensor(
              np.concatenate(
                  accumulated_minibatch_data["labels"], axis=0
              ),
              dtype=torch.bool,
              device=device,
          ),
      }
  
imports: import numpy
  import torch
  from torch import nn
  from typing import Any, Dict, Iterable, NamedTuple, Optional, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.baseneuralmodel.utils.data import enforce_not_None
  from ptgnn.implementations.ppi.dataloader import PPIGraphSample
  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 PPIMulticlassClassification obj's underlying member method code with example usage: obj.finalize_minibatch(accumulated_minibatch_data, device) and returns: dict