code: def test_has_storage_numpy(self, device):
      for dtype in [
          np.float32,
          np.float64,
          np.int64,
          np.int32,
          np.int16,
          np.uint8,
      ]:
          arr = np.array([1], dtype=dtype)
          self.assertIsNotNone(
              torch.tensor(
                  arr, device=device, dtype=torch.float32
              ).storage()
          )
          self.assertIsNotNone(
              torch.tensor(
                  arr, device=device, dtype=torch.double
              ).storage()
          )
          self.assertIsNotNone(
              torch.tensor(
                  arr, device=device, dtype=torch.int
              ).storage()
          )
          self.assertIsNotNone(
              torch.tensor(
                  arr, device=device, dtype=torch.long
              ).storage()
          )
          self.assertIsNotNone(
              torch.tensor(
                  arr, device=device, dtype=torch.uint8
              ).storage()
          )
  
imports: import torch
  import numpy
  from itertools import product
  from torch.testing._internal.common_utils import skipIfTorchDynamo, TestCase, run_tests
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCPU, dtypes, skipMeta
  from torch.testing._internal.common_dtype import all_types_and_complex_and
label_desc: Use image node_id 15 for calling the TestNumPyInterop obj's underlying member method code with example usage: obj.test_has_storage_numpy(device) without return types