code: def evaluate(exe, prog, model, gw, loader):
      """evaluate"""
      total_loss = []
      total_acc = []
      for idx, batch_data in enumerate(loader):
          g, labels = batch_data
          feed_dict = gw.to_feed(g)
          feed_dict["labels"] = labels
          ret_loss, ret_acc = exe.run(
              prog, feed=feed_dict, fetch_list=[model.loss, model.acc]
          )
          total_loss.append(ret_loss)
          total_acc.append(ret_acc)
  
      total_loss = np.mean(total_loss)
      total_acc = np.mean(total_acc)
  
      return {"loss": total_loss, "acc": total_acc}
  
file_path: PGL/legacy/examples/gin/main.py
imports: import os
  import sys
  import time
  import argparse
  import numpy
  import paddle.fluid
  import paddle.fluid.layers
  import pgl
  from pgl.utils.logger import log
  from Dataset import GINDataset, fold10_split, random_split
  from dataloader import GraphDataloader
  from model import GINModel
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(exe, prog, model, gw, loader) and returns: dict