code: def create_random_graph(n):
      arr = (
          spsp.random(
              n, n, density=0.001, format="coo", random_state=100
          )
          != 0
      ).astype(np.int64)
      return dgl.from_scipy(arr)
  
imports: import json
  import os
  import tempfile
  import backend
  import dgl
  import numpy
  import pytest
  import torch
  from dgl import function
  from dgl.distributed import convert_dgl_partition_to_csc_sampling_graph, load_partition, load_partition_book, load_partition_feats, partition_graph
  from dgl.distributed.graph_partition_book import _etype_str_to_tuple, _etype_tuple_to_str, DEFAULT_ETYPE, DEFAULT_NTYPE, EdgePartitionPolicy, HeteroDataName, NodePartitionPolicy, RangePartitionBook
  from dgl.distributed.partition import _get_inner_edge_mask, _get_inner_node_mask, RESERVED_FIELD_DTYPE
  from scipy import sparse
  from utils import reset_envs
label_desc: Use image node_id 3 for calling a global function with example usage: create_random_graph(n) and returns: dgl