code: def test_cat(device):
      row, col = tensor([[0, 0, 1], [0, 1, 2]], torch.long, device)
      mat1 = SparseTensor(row=row, col=col)
      mat1.fill_cache_()
  
      row, col = tensor(
          [[0, 0, 1, 2], [0, 1, 1, 0]], torch.long, device
      )
      mat2 = SparseTensor(row=row, col=col)
      mat2.fill_cache_()
  
      out = cat([mat1, mat2], dim=0)
      assert out.to_dense().tolist() == [
          [1, 1, 0],
          [0, 0, 1],
          [1, 1, 0],
          [0, 1, 0],
          [1, 0, 0],
      ]
      assert out.storage.has_row()
      assert out.storage.has_rowptr()
      assert out.storage.has_rowcount()
      assert out.storage.num_cached_keys() == 1
  
      out = cat([mat1, mat2], dim=1)
      assert out.to_dense().tolist() == [
          [1, 1, 0, 1, 1],
          [0, 0, 1, 0, 1],
          [0, 0, 0, 1, 0],
      ]
      assert out.storage.has_row()
      assert not out.storage.has_rowptr()
      assert out.storage.num_cached_keys() == 2
  
      out = cat([mat1, mat2], dim=(0, 1))
      assert out.to_dense().tolist() == [
          [1, 1, 0, 0, 0],
          [0, 0, 1, 0, 0],
          [0, 0, 0, 1, 1],
          [0, 0, 0, 0, 1],
          [0, 0, 0, 1, 0],
      ]
      assert out.storage.has_row()
      assert out.storage.has_rowptr()
      assert out.storage.num_cached_keys() == 5
  
      value = torch.randn((mat1.nnz(), 4), device=device)
      mat1 = mat1.set_value_(value, layout="coo")
      out = cat([mat1, mat1], dim=-1)
      assert out.storage.value().size() == (mat1.nnz(), 8)
      assert out.storage.has_row()
      assert out.storage.has_rowptr()
      assert out.storage.num_cached_keys() == 5
  
imports: import pytest
  import torch
  from torch_sparse.cat import cat
  from torch_sparse.tensor import SparseTensor
  from torch_sparse.testing import devices, tensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_cat(device) without return types