code: def test_contiguous(dtype, device):
      kwargs = dict(dtype=dtype, device=device)
      data = tensor([[0, 1], [1, 0], [1, 2], [2, 1]], **kwargs).t()
  
      with pytest.raises(ValueError, match="needs to be contiguous"):
          EdgeIndex(data)
  
      adj = EdgeIndex(data.contiguous()).contiguous()
      assert isinstance(adj, EdgeIndex)
      assert adj.is_contiguous()
  
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 12 for calling a global function with example usage: test_contiguous(dtype, device) without return types