code: def test_sparse_tensor(dtype, device):
      kwargs = dict(dtype=dtype, device=device, is_undirected=True)
      adj = EdgeIndex(
          [[0, 1, 1, 2], [1, 0, 2, 1]], sort_order="row", **kwargs
      )
  
      out = EdgeIndex(adj.to_sparse_coo())
      assert out.equal(adj)
      assert out.sort_order == "row"
      assert out.sparse_size() == (3, 3)
      assert out._indptr is None
  
      out = EdgeIndex(adj.to_sparse_csr())
      assert out.equal(adj)
      assert out.sort_order == "row"
      assert out.sparse_size() == (3, 3)
      assert out._indptr.equal(tensor([0, 1, 3, 4], device=device))
  
      out = EdgeIndex(adj.to_sparse_csc())
      assert out.equal(adj.sort_by("col")[0])
      assert out.sort_order == "col"
      assert out.sparse_size() == (3, 3)
      assert out._indptr.equal(tensor([0, 1, 3, 4], device=device))
  
imports: import os.path
  import warnings
  from typing import List, Optional
  import pytest
  import torch
  from torch import Tensor, tensor
  import torch_geometric
  from torch_geometric import EdgeIndex
  from torch_geometric.edge_index import SUPPORTED_DTYPES, ReduceType, SortReturnType, _scatter_spmm, _torch_sparse_spmm, _TorchSPMM, set_tuple_item
  from torch_geometric.profile import benchmark
  from torch_geometric.testing import onlyCUDA, onlyLinux, withCUDA, withoutExtensions, withPackage
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import scatter
label_desc: Use image node_id 3 for calling a global function with example usage: test_sparse_tensor(dtype, device) without return types