code: def test_identity(dtype, device, is_undirected):
      kwargs = dict(
          dtype=dtype, device=device, is_undirected=is_undirected
      )
      adj = EdgeIndex(
          [[0, 1, 1, 2], [1, 0, 2, 1]], sparse_size=(3, 3), **kwargs
      )
  
      out = EdgeIndex(adj)
      assert out.data_ptr() == adj.data_ptr()
      assert out.dtype == adj.dtype
      assert out.device == adj.device
      assert out.sparse_size() == adj.sparse_size()
      assert out.sort_order == adj.sort_order
      assert out.is_undirected == adj.is_undirected
  
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 2 for calling a global function with example usage: test_identity(dtype, device, is_undirected) without return types