code: def eval_test(
      parser, program, model, test_exe, graph, y_true, split_idx
  ):
      y_pred = np.zeros_like(y_true)
  
      graph.node_feat["label"] = y_true
      graph.node_feat["nid"] = np.arange(0, graph.num_nodes)
      for subgraph in random_partition(
          num_clusters=5, graph=graph, shuffle=False
      ):
          feed_dict = model.gw.to_feed(subgraph)
          if parser.use_label_e:
              feed_dict["label"] = subgraph.node_feat["label"]
              train_idx_temp = set(split_idx["train"]) & set(
                  subgraph.node_feat["nid"]
              )
              train_idx_temp = subgraph.reindex_from_parrent_nodes(
                  list(train_idx_temp)
              )
              feed_dict["label_idx"] = train_idx_temp
  
          batch_y_pred = test_exe.run(
              program=program,
              feed=feed_dict,
              fetch_list=[model.out_feat],
          )[0]
  
          y_pred[subgraph.node_feat["nid"]] = batch_y_pred
  
      train_acc = evaluator.eval(
          {
              "y_true": y_true[split_idx["train"]],
              "y_pred": y_pred[split_idx["train"]],
          }
      )["rocauc"]
      val_acc = evaluator.eval(
          {
              "y_true": y_true[split_idx["valid"]],
              "y_pred": y_pred[split_idx["valid"]],
          }
      )["rocauc"]
      test_acc = evaluator.eval(
          {
              "y_true": y_true[split_idx["test"]],
              "y_pred": y_pred[split_idx["test"]],
          }
      )["rocauc"]
  
      return train_acc, val_acc, test_acc
  
imports: import math
  import torch
  import paddle
  import pgl
  import numpy
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset
  import time
  import copy
  from ogb.nodeproppred import Evaluator
  from utils import to_undirected, add_self_loop, linear_warmup_decay
  from model import Proteins_baseline_model, Proteins_label_embedding_model
  from partition import random_partition_v2
  import argparse
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: eval_test(parser, program, model, test_exe, graph, y_true, split_idx) and returns: train_acc,  val_acc,  test_acc