code: def main(args):
      if not args.use_cuda:
          paddle.set_device("cpu")
      if paddle.distributed.get_world_size() > 1:
          paddle.distributed.init_parallel_env()
  
      dataset = load(args.dataset)
      graph = dataset.graph
  
      model = Model(
          graph.num_nodes, args.embed_size, dataset.num_groups
      )
      model = paddle.DataParallel(model)
  
      batch_size = len(dataset.train_index)
  
      train_steps = (
          int(len(dataset.train_index) / batch_size) * args.epoch
      )
      scheduler = paddle.optimizer.lr.PolynomialDecay(
          learning_rate=args.multiclass_learning_rate,
          decay_steps=train_steps,
          end_lr=0.0001,
      )
  
      optim = Adam(
          learning_rate=scheduler, parameters=model.parameters()
      )
  
      if args.load_from_static:
          model.set_state_dict(load_from_files("./model"))
      else:
          model.set_state_dict(paddle.load("model.pdparams"))
  
      train_data_loader = node_classify_generator(
          graph, dataset.train_index, batch_size=batch_size, epoch=1
      )
      test_data_loader = node_classify_generator(
          graph, dataset.test_index, batch_size=batch_size, epoch=1
      )
  
      best_test_macro_f1 = -1
      for epoch in tqdm.tqdm(range(args.epoch)):
          train_loss = train(model, train_data_loader(), optim)
          test_loss, test_macro_f1, test_micro_f1 = test(
              model, test_data_loader()
          )
          best_test_macro_f1 = max(best_test_macro_f1, test_macro_f1)
      log.info("Best test macro f1 is %s." % best_test_macro_f1)
  
imports: import argparse
  import time
  import os
  import math
  import glob
  import numpy
  import paddle
  from easydict import EasyDict
  import pgl
  import yaml
  from paddle.optimizer import Adam
  import tqdm
  from pgl.utils.logger import log
  from sklearn.metrics import f1_score
  from dataset import ShardedDataset
label_desc: Use image node_id 7 for calling a global function with example usage: main(args) without return types