code: def test_to_numpy(self, device) -> None:
      def get_castable_tensor(shape, dtype):
          if dtype.is_floating_point:
              dtype_info = torch.finfo(dtype)
              # can't directly use min and max, because for double, max - min
              # is greater than double range and sampling always gives inf.
              low = max(dtype_info.min, -1e10)
              high = min(dtype_info.max, 1e10)
              t = torch.empty(shape, dtype=torch.float64).uniform_(
                  low, high
              )
          else:
              # can't directly use min and max, because for int64_t, max - min
              # is greater than int64_t range and triggers UB.
              low = max(torch.iinfo(dtype).min, int(-1e10))
              high = min(torch.iinfo(dtype).max, int(1e10))
              t = torch.empty(shape, dtype=torch.int64).random_(
                  low, high
              )
          return t.to(dtype)
  
      dtypes = [
          torch.uint8,
          torch.int8,
          torch.short,
          torch.int,
          torch.half,
          torch.float,
          torch.double,
          torch.long,
      ]
  
      for dtp in dtypes:
          # 1D
          sz = 10
          x = get_castable_tensor(sz, dtp)
          y = x.numpy()
          for i in range(sz):
              self.assertEqual(x[i], y[i])
  
          # 1D > 0 storage offset
          xm = get_castable_tensor(sz * 2, dtp)
          x = xm.narrow(0, sz - 1, sz)
          self.assertTrue(x.storage_offset() > 0)
          y = x.numpy()
          for i in range(sz):
              self.assertEqual(x[i], y[i])
  
          def check2d(x, y):
              for i in range(sz1):
                  for j in range(sz2):
                      self.assertEqual(x[i][j], y[i][j])
  
          # empty
          x = torch.tensor([]).to(dtp)
          y = x.numpy()
          self.assertEqual(y.size, 0)
  
          # contiguous 2D
          sz1 = 3
          sz2 = 5
          x = get_castable_tensor((sz1, sz2), dtp)
          y = x.numpy()
          check2d(x, y)
          self.assertTrue(y.flags["C_CONTIGUOUS"])
  
          # with storage offset
          xm = get_castable_tensor((sz1 * 2, sz2), dtp)
          x = xm.narrow(0, sz1 - 1, sz1)
          y = x.numpy()
          self.assertTrue(x.storage_offset() > 0)
          check2d(x, y)
          self.assertTrue(y.flags["C_CONTIGUOUS"])
  
          # non-contiguous 2D
          x = get_castable_tensor((sz2, sz1), dtp).t()
          y = x.numpy()
          check2d(x, y)
          self.assertFalse(y.flags["C_CONTIGUOUS"])
  
          # with storage offset
          xm = get_castable_tensor((sz2 * 2, sz1), dtp)
          x = xm.narrow(0, sz2 - 1, sz2).t()
          y = x.numpy()
          self.assertTrue(x.storage_offset() > 0)
          check2d(x, y)
  
          # non-contiguous 2D with holes
          xm = get_castable_tensor((sz2 * 2, sz1 * 2), dtp)
          x = xm.narrow(0, sz2 - 1, sz2).narrow(1, sz1 - 1, sz1).t()
          y = x.numpy()
          self.assertTrue(x.storage_offset() > 0)
          check2d(x, y)
  
          if dtp != torch.half:
              # check writeable
              x = get_castable_tensor((3, 4), dtp)
              y = x.numpy()
              self.assertTrue(y.flags.writeable)
              y[0][1] = 3
              self.assertTrue(x[0][1] == 3)
              y = x.t().numpy()
              self.assertTrue(y.flags.writeable)
              y[0][1] = 3
              self.assertTrue(x[0][1] == 3)
  
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 3 for calling the TestNumPyInterop obj's underlying member method code with example usage: obj.test_to_numpy(device) and returns: t