code: def test_as_ndarray(self):
      base = np.random.randn(3)
      tnsr = torch.from_numpy(base)
  
      # Test inplace conversion
      result = as_ndarray(tnsr)
      self.assertTrue(np.shares_memory(base, result))
  
      # Test conversion with memory allocation
      result = as_ndarray(tnsr, inplace=False)
      self.assertTrue(np.allclose(base, result))
      self.assertFalse(np.shares_memory(base, result))
  
      result = as_ndarray(tnsr, dtype=np.float32)
      self.assertTrue(np.allclose(base, result))
      self.assertFalse(np.shares_memory(base, result))
      self.assertEqual(result.dtype, np.float32)
  
      # Test that `clone` does not get called on non-CPU tensors
      mock_tensor = MagicMock()
      mock_tensor.cpu.return_value = mock_tensor
      mock_tensor.device.return_value = "foo"
      mock_tensor.clone.return_value = mock_tensor
  
      as_ndarray(mock_tensor)
      mock_tensor.cpu.assert_called_once()
      mock_tensor.clone.assert_not_called()
      mock_tensor.numpy.assert_called_once()
  
file_path: botorch/test/optim/utils/test_numpy_utils.py
imports: from __future__ import annotations
  from unittest.mock import MagicMock
  import numpy
  import torch
  from botorch.optim.closures.core import as_ndarray, get_tensors_as_ndarray_1d, set_tensors_from_ndarray_1d
  from botorch.optim.utils import get_bounds_as_ndarray
  from botorch.optim.utils.numpy_utils import torch_to_numpy_dtype_dict
  from botorch.utils.testing import BotorchTestCase
  from torch.nn import Parameter
label_desc: Use image node_id 2 for calling the TestNumpyUtils obj's underlying member method code with example usage: obj.test_as_ndarray() without return types