code: def test_to_dtype(dtype):
      row = torch.tensor([1, 1, 2])
      col = torch.tensor([1, 2, 0])
      mat = from_coo(row, col, shape=(3, 4))
  
      target_val = mat.val.to(dtype=dtype)
  
      mat2 = mat.to(dtype=dtype)
      assert mat2.shape == mat.shape
      assert torch.allclose(mat2.val, target_val)
  
      func_name = {
          torch.float: "float",
          torch.double: "double",
          torch.int: "int",
          torch.long: "long",
      }
      mat2 = getattr(mat, func_name[dtype])()
      assert mat2.shape == mat.shape
      assert torch.allclose(mat2.val, target_val)
  
imports: import unittest
  import warnings
  import backend
  import pytest
  import torch
  from dgl.sparse import diag, from_coo, from_csc, from_csr, from_torch_sparse, identity, to_torch_sparse_coo, to_torch_sparse_csc, to_torch_sparse_csr, val_like
  from .utils import rand_coo, rand_csc, rand_csr, rand_diag, sparse_matrix_to_dense
label_desc: Use image node_id 23 for calling a global function with example usage: test_to_dtype(dtype) without return types