code: def test_index_select_csc(
      indptr_dtype, indices_dtype, idtype, is_pinned, output_size
  ):
      """Original graph in COO:
      1   0   1   0   1   0
      1   0   0   1   0   1
      0   1   0   1   0   0
      0   1   0   0   1   0
      1   0   0   0   0   1
      0   0   1   0   1   0
      """
      indptr = torch.tensor([0, 3, 5, 7, 9, 12, 14], dtype=indptr_dtype)
      indices = torch.tensor(
          [0, 1, 4, 2, 3, 0, 5, 1, 2, 0, 3, 5, 1, 4],
          dtype=indices_dtype,
      )
      index = torch.tensor([0, 5, 3], dtype=idtype)
  
      cpu_indptr, cpu_indices = torch.ops.graphbolt.index_select_csc(
          indptr, indices, index, None
      )
      if is_pinned:
          indptr = indptr.pin_memory()
          indices = indices.pin_memory()
      else:
          indptr = indptr.cuda()
          indices = indices.cuda()
      index = index.cuda()
  
      if output_size:
          output_size = len(cpu_indices)
  
      gpu_indptr, gpu_indices = torch.ops.graphbolt.index_select_csc(
          indptr, indices, index, output_size
      )
      assert not cpu_indptr.is_cuda
      assert not cpu_indices.is_cuda
  
      assert gpu_indptr.is_cuda
      assert gpu_indices.is_cuda
  
      assert torch.equal(cpu_indptr, gpu_indptr.cpu())
      assert torch.equal(cpu_indices, gpu_indices.cpu())
  
file_path: dgl/tests/python/pytorch/graphbolt/impl/test_in_subgraph_sampler.py
imports: import unittest
  import backend
  import dgl.graphbolt
  import pytest
  import torch
  from ..None import gb_test_utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_index_select_csc(indptr_dtype, indices_dtype, idtype, is_pinned, output_size) without return types