code: def check_dist_graph_empty(g, num_clients, num_nodes, num_edges):
      # Test API
      assert g.num_nodes() == num_nodes
      assert g.num_edges() == num_edges
  
      # Test init node data
      new_shape = (g.num_nodes(), 2)
      g.ndata["test1"] = dgl.distributed.DistTensor(new_shape, F.int32)
      nids = F.arange(0, int(g.num_nodes() / 2))
      feats = g.ndata["test1"][nids]
      assert np.all(F.asnumpy(feats) == 0)
  
      # create a tensor and destroy a tensor and create it again.
      test3 = dgl.distributed.DistTensor(
          new_shape, F.float32, "test3", init_func=rand_init
      )
      del test3
      test3 = dgl.distributed.DistTensor(
          (g.num_nodes(), 3), F.float32, "test3"
      )
      del test3
  
      # Test write data
      new_feats = F.ones((len(nids), 2), F.int32, F.cpu())
      g.ndata["test1"][nids] = new_feats
      feats = g.ndata["test1"][nids]
      assert np.all(F.asnumpy(feats) == 1)
  
      # Test metadata operations.
      assert g.node_attr_schemes()["test1"].dtype == F.int32
  
      print("end")
  
imports: import os
  import math
  import multiprocessing
  import pickle
  import socket
  import sys
  import time
  import unittest
  from multiprocessing import Condition, Manager, Process, Value
  import backend
  import dgl
  import numpy
  import pytest
  import torch
  from dgl.data.utils import load_graphs, save_graphs
  from dgl.distributed import DistEmbedding, DistGraph, DistGraphServer, edge_split, load_partition, load_partition_book, node_split, partition_graph
  from dgl.distributed.optim import SparseAdagrad
  from dgl.heterograph_index import create_unitgraph_from_coo
  from numpy.testing import assert_almost_equal, assert_array_equal
  from scipy import sparse
  from utils import create_random_graph, generate_ip_config, reset_envs
label_desc: Use image node_id 4 for calling a global function with example usage: check_dist_graph_empty(g, num_clients, num_nodes, num_edges) without return types