code: def test_optimizer_options(self) -> None:
      skipped_funcs = {  # These should all have bespoke tests
          optimizer_argparse[acqf_class]
          for acqf_class in (
              qExpectedHypervolumeImprovement,
              qKnowledgeGradient,
              qMaxValueEntropy,
          )
      }
      user_options = {"foo": "bar", "num_restarts": 13}
      for func in optimizer_argparse.funcs.values():
          if func in skipped_funcs:
              continue
  
          parsed_options = func(None, optimizer_options=user_options)
          for key, val in user_options.items():
              self.assertEqual(val, parsed_options.get(key))
  
      # Also test sub-options.
      func = _argparse_base
      parsed_options = func(
          None,
          optimizer_options={
              "options": {"batch_limit": 10, "maxiter": 20}
          },
      )
      self.assertEqual(
          parsed_options["options"],
          {"batch_limit": 10, "init_batch_limit": 32, "maxiter": 20},
      )
  
imports: from __future__ import annotations
  from importlib import reload
  from unittest.mock import patch
  from ax.models.torch.botorch_modular import optimizer_argparse
  from ax.models.torch.botorch_modular.optimizer_argparse import _argparse_base, MaybeType, optimizer_argparse
  from ax.utils.common.constants import Keys
  from ax.utils.common.testutils import TestCase
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.knowledge_gradient import qKnowledgeGradient, qMultiFidelityKnowledgeGradient
  from botorch.acquisition.max_value_entropy_search import qMaxValueEntropy, qMultiFidelityMaxValueEntropy
  from botorch.acquisition.multi_objective.monte_carlo import qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
label_desc: Use image node_id 4 for calling the OptimizerArgparseTest obj's underlying member method code with example usage: obj.test_optimizer_options() without return types