code: def test(graphs, model):
      model.eval()
      accs = []
      for graph in graphs:
          graph = graph.to(args.device)
          pred = model(graph, mode="all")
          label = graph.node_label
          pred = pred[graph.node_label_index].max(1)[1]
          acc = pred.eq(label).sum().item()
          acc /= len(label)
          accs.append(acc)
      return accs
  
file_path: deepsnap/examples/sampling/cluster.py
imports: import copy
  import torch
  import random
  import argparse
  import numpy
  import networkx
  import torch.nn
  import torch.nn.functional
  import community
  from torch_geometric.nn import SAGEConv
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.graph import Graph
  from model import *
label_desc: Use image node_id 4 for calling a global function with example usage: test(graphs, model) and returns: accs