code: def report_metrics(
      self,
      dataset: Iterable[PPIGraphSample],
      trained_network: PPIClassification,
      device: Union[str, torch.device],
  ) -> Dict[str, float]:
      trained_network.eval()
      trained_network._reset_module_metrics()
      for mb_data, _ in self.minibatch_iterator(
          self.tensorize_dataset(iter(dataset)),
          device,
          max_minibatch_size=50,
      ):
          with torch.no_grad():
              trained_network(**mb_data)
      return trained_network.report_metrics()
  
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 9 for calling the PPIMulticlassClassification obj's underlying member method code with example usage: obj.report_metrics(dataset, trained_network, device) and returns: trained_network