code: def create_fused_csc_sampling_graph():
      # Initialize data.
      total_num_nodes = 10
      total_num_edges = 9
      ntypes = {"N0": 0, "N1": 1, "N2": 2, "N3": 3}
      etypes = {
          "N0:R0:N1": 0,
          "N0:R1:N2": 1,
          "N0:R2:N3": 2,
      }
      indptr = torch.LongTensor([0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9])
      indices = torch.LongTensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
      node_type_offset = torch.LongTensor([0, 1, 4, 7, 10])
      type_per_edge = torch.LongTensor([0, 0, 0, 1, 1, 1, 2, 2, 2])
      assert indptr[-1] == total_num_edges
      assert indptr[-1] == len(indices)
      assert node_type_offset[-1] == total_num_nodes
      assert all(type_per_edge < len(etypes))
  
      edge_attributes = {
          "mask": torch.BoolTensor([1, 1, 0, 1, 1, 1, 0, 0, 0]),
          "all": torch.BoolTensor([1, 1, 1, 1, 1, 1, 1, 1, 1]),
          "zero": torch.BoolTensor([0, 0, 0, 0, 0, 0, 0, 0, 0]),
      }
  
      # Construct FusedCSCSamplingGraph.
      return gb.fused_csc_sampling_graph(
          indptr,
          indices,
          edge_attributes=edge_attributes,
          node_type_offset=node_type_offset,
          type_per_edge=type_per_edge,
          node_type_to_id=ntypes,
          edge_type_to_id=etypes,
      )
  
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 28 for calling a global function with example usage: create_fused_csc_sampling_graph() and returns: gb