code: def test_kg(self) -> None:
      with patch(
          "botorch.optim.initializers.gen_one_shot_kg_initial_conditions"
      ) as mock_gen_initial_conditions:
          mock_gen_initial_conditions.return_value = "TEST"
          reload(Argparse)
  
          user_options = {"foo": "bar", "num_restarts": 114}
          generic_options = _argparse_base(
              None, optimizer_options=user_options
          )
          for acqf in (
              qKnowledgeGradient,
              qMultiFidelityKnowledgeGradient,
          ):
              with self.subTest(acqf=acqf):
                  options = optimizer_argparse(
                      acqf,
                      q=None,
                      bounds=None,
                      optimizer_options=user_options,
                  )
                  self.assertEqual(
                      options.pop(Keys.BATCH_INIT_CONDITIONS), "TEST"
                  )
                  self.assertEqual(options, generic_options)
  
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 6 for calling the OptimizerArgparseTest obj's underlying member method code with example usage: obj.test_kg() without return types