code: def test_convert_dgl_partition_to_csc_sampling_graph_homo(
      part_method, num_parts
  ):
      with tempfile.TemporaryDirectory() as test_dir:
          g = create_random_graph(1000)
          graph_name = "test"
          partition_graph(
              g,
              graph_name,
              num_parts,
              test_dir,
              part_method=part_method,
          )
          part_config = os.path.join(test_dir, f"{graph_name}.json")
          convert_dgl_partition_to_csc_sampling_graph(part_config)
          for part_id in range(num_parts):
              orig_g = dgl.load_graphs(
                  os.path.join(test_dir, f"part{part_id}/graph.dgl")
              )[0][0]
              new_g = th.load(
                  os.path.join(
                      test_dir,
                      f"part{part_id}/fused_csc_sampling_graph.pt",
                  )
              )
              orig_indptr, orig_indices, _ = orig_g.adj().csc()
              assert th.equal(orig_indptr, new_g.csc_indptr)
              assert th.equal(orig_indices, new_g.indices)
              assert new_g.node_type_offset is None
              assert all(new_g.type_per_edge == 0)
              for node_type, type_id in new_g.node_type_to_id.items():
                  assert g.get_ntype_id(node_type) == type_id
              for edge_type, type_id in new_g.edge_type_to_id.items():
                  assert (
                      g.get_etype_id(_etype_str_to_tuple(edge_type))
                      == type_id
                  )
  
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 12 for calling a global function with example usage: test_convert_dgl_partition_to_csc_sampling_graph_homo(part_method, num_parts) without return types