code: def get_hetero_graph():
      # COO graph:
      # [0, 0, 1, 1, 2, 2, 3, 3, 4, 4]
      # [2, 4, 2, 3, 0, 1, 1, 0, 0, 1]
      # [1, 1, 1, 1, 0, 0, 0, 0, 0] - > edge type.
      # num_nodes = 5, num_n1 = 2, num_n2 = 3
      ntypes = {"n1": 0, "n2": 1}
      etypes = {"n1:e1:n2": 0, "n2:e2:n1": 1}
      indptr = torch.LongTensor([0, 2, 4, 6, 8, 10])
      indices = torch.LongTensor([2, 4, 2, 3, 0, 1, 1, 0, 0, 1])
      type_per_edge = torch.LongTensor([1, 1, 1, 1, 0, 0, 0, 0, 0, 0])
      node_type_offset = torch.LongTensor([0, 2, 5])
      return gb.fused_csc_sampling_graph(
          indptr,
          indices,
          node_type_offset=node_type_offset,
          type_per_edge=type_per_edge,
          node_type_to_id=ntypes,
          edge_type_to_id=etypes,
      )
  
file_path: dgl/tests/python/pytorch/graphbolt/test_feature_fetcher.py
imports: import random
  from enum import Enum
  import dgl.graphbolt
  import pytest
  import torch
  from torchdata.datapipes.iter import Mapper
  from .None import gb_test_utils
label_desc: Use image node_id 4 for calling a global function with example usage: get_hetero_graph() and returns: gb