code: def check_pickling(self, dtype):
      for proto in range(pickle.HIGHEST_PROTOCOL + 1):
          buf = pickle.dumps(dtype, proto)
          # The dtype pickling itself pickles `np.dtype` if it is pickled
          # as a singleton `dtype` should be stored in the buffer:
          assert b"_DType_reconstruct" not in buf
          assert b"dtype" in buf
          pickled = pickle.loads(buf)
          assert_equal(pickled, dtype)
  
          # XXX: out dtypes do not have .descr
          #         assert_equal(pickled.descr, dtype.descr)
          #         if dtype.metadata is not None:
          #             assert_equal(pickled.metadata, dtype.metadata)
          # Check the reconstructed dtype is functional
  
          x = np.zeros(3, dtype=dtype)
          y = np.zeros(3, dtype=pickled)
          assert_equal(x, y)
          assert_equal(x[0], y[0])
  
imports: import functools
  import operator
  import pickle
  import sys
  import types
  from itertools import permutations
  from typing import Any
  from unittest import skipIf
  import pytest
  from pytest import raises
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests, subtest, TEST_WITH_TORCHDYNAMO, TestCase, xpassIfTorchDynamo
  import numpy
label_desc: Use image node_id 1 for calling the TestPickling obj's underlying member method code with example usage: obj.check_pickling(dtype) without return types