def METHOD_NAME(test_case):
    dtype = random_dtype(["pod"])
    device = random_device()
    x = random_tensor(ndim=3).to(dtype).to(device)
    of_result = x.oneflow.new()
    th_result = x.pytorch.new()
    test_case.assertTrue(list(of_result.shape) == list(th_result.shape))
    test_case.assertTrue(
        of_result.numpy().dtype == th_result.detach().cpu().numpy().dtype
    )
    test_case.assertTrue(of_result.device.type == th_result.device.type)
    y = random_tensor(ndim=3).to(dtype).to(device)
    of_result = x.oneflow.new(y.oneflow)
    th_result = x.pytorch.new(y.pytorch)
    test_case.assertTrue(list(of_result.shape) == list(th_result.shape))
    test_case.assertTrue(
        of_result.numpy().dtype == th_result.detach().cpu().numpy().dtype
    )
    test_case.assertTrue(of_result.device.type == th_result.device.type)
    np_data = np.random.randn(3, 3)
    of_result = x.oneflow.new(np_data)
    th_result = x.pytorch.new(np_data)
    test_case.assertTrue(list(of_result.shape) == list(th_result.shape))
    test_case.assertTrue(
        of_result.numpy().dtype == th_result.detach().cpu().numpy().dtype
    )
    test_case.assertTrue(of_result.device.type == th_result.device.type)
    of_result = x.oneflow.new([1, 2, 3])
    th_result = x.pytorch.new([1, 2, 3])
    test_case.assertTrue(list(of_result.shape) == list(th_result.shape))
    test_case.assertTrue(
        of_result.numpy().dtype == th_result.detach().cpu().numpy().dtype
    )
    test_case.assertTrue(of_result.device.type == th_result.device.type)