code: def process_csc_sampling_graph_on_shared_memory(
      graph, data_queue, flag_queue
  ):
      # Backup the attributes.
      csc_indptr = graph.csc_indptr.clone()
      indices = graph.indices.clone()
      node_type_offset = graph.node_type_offset.clone()
      type_per_edge = graph.type_per_edge.clone()
  
      # Change the value to random integers. Send the new value to the main
      # process.
      v = torch.randint_like(graph.csc_indptr, 100)
      graph.csc_indptr[:] = v
      data_queue.put(v.clone())
  
      v = torch.randint_like(graph.indices, 100)
      graph.indices[:] = v
      data_queue.put(v.clone())
  
      v = torch.randint_like(graph.node_type_offset, 100)
      graph.node_type_offset[:] = v
      data_queue.put(v.clone())
  
      v = torch.randint_like(graph.type_per_edge, 100)
      graph.type_per_edge[:] = v
      data_queue.put(v.clone())
  
      # Wait for the main process to finish.
      flag_queue.get()
  
      graph.csc_indptr[:] = csc_indptr
      graph.indices[:] = indices
      graph.node_type_offset[:] = node_type_offset
      graph.type_per_edge[:] = type_per_edge
  
imports: import os
  import pickle
  import re
  import tempfile
  import unittest
  import backend
  import dgl
  import dgl.graphbolt
  import pytest
  import torch
  import torch.multiprocessing
  from dgl.graphbolt.base import etype_str_to_tuple
  from scipy import sparse
  from ..None import gb_test_utils
label_desc: Use image node_id 24 for calling a global function with example usage: process_csc_sampling_graph_on_shared_memory(graph, data_queue, flag_queue) without return types