code: def test_main(self):
      for wrapper in self.wrappers.values():
          # Test setter/getter
          state = get_tensors_as_ndarray_1d(wrapper.closure.parameters)
          other = np.random.randn(*state.shape).astype(state.dtype)
  
          wrapper.state = other
          self.assertTrue(np.allclose(other, wrapper.state))
  
          index = 0
          for param in wrapper.closure.parameters.values():
              size = param.numel()
              self.assertTrue(
                  np.allclose(
                      other[index : index + size],
                      wrapper.as_array(param.view(-1)),
                  )
              )
              index += size
  
          wrapper.state = state
          self.assertTrue(np.allclose(state, wrapper.state))
  
          # Test __call__
          value, grads = wrapper(other)
          self.assertTrue(np.allclose(other, wrapper.state))
          self.assertIsInstance(value, np.ndarray)
          self.assertIsInstance(grads, np.ndarray)
  
          # Test return values
          (
              value_tensor,
              grad_tensors,
          ) = wrapper.closure()  # get raw Tensor equivalents
          self.assertTrue(
              np.allclose(value, wrapper.as_array(value_tensor))
          )
          index = 0
          for x, dx in zip(wrapper.parameters.values(), grad_tensors):
              size = x.numel()
              grad = grads[index : index + size]
              if dx is None:
                  self.assertTrue((grad == wrapper.fill_value).all())
              else:
                  self.assertTrue(
                      np.allclose(grad, wrapper.as_array(dx))
                  )
              index += size
  
          module = wrapper.closure.forward
          self.assertTrue(np.allclose(grads[0], as_ndarray(module.x)))
          self.assertTrue(np.allclose(grads[1], as_ndarray(module.w)))
          self.assertEqual(grads[2], wrapper.fill_value)
  
          # Test persistent buffers
          for mode in (False, True):
              wrapper.persistent = mode
              self.assertEqual(
                  mode,
                  wrapper._get_gradient_ndarray()
                  is wrapper._get_gradient_ndarray(),
              )
  
imports: from contextlib import nullcontext
  from functools import partial
  from typing import Dict
  from unittest.mock import MagicMock
  import numpy
  import torch
  from botorch.optim.closures.core import ForwardBackwardClosure, get_tensors_as_ndarray_1d, NdarrayOptimizationClosure
  from botorch.optim.utils import as_ndarray
  from botorch.utils.context_managers import zero_grad_ctx
  from botorch.utils.testing import BotorchTestCase
  from linear_operator.utils.errors import NanError, NotPSDError
  from torch.nn import Module, Parameter
label_desc: Use image node_id 2 for calling the TestNdarrayOptimizationClosure obj's underlying member method code with example usage: obj.test_main() without return types