code: def report_accuracy(
      self,
      dataset: Iterator[VarMisuseSample],
      trained_network: VarMisuseGraphModel,
      device: Union[str, torch.device],
  ) -> float:
      trained_network.eval()
      trained_network._reset_module_metrics()
      for mb_data, _ in self.minibatch_iterator(
          self.tensorize_dataset(dataset), device, max_minibatch_size=50
      ):
          with torch.no_grad():
              trained_network(**mb_data)
      return trained_network.report_metrics()["Accuracy"]
  
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 9 for calling the VarMisuseModel obj's underlying member method code with example usage: obj.report_accuracy(dataset, trained_network, device) and returns: trained_network