code: def test_construct_acquisition_and_optimizer_options(self) -> None:
      # Two dicts for `Acquisition` should be concatenated
      acqf_options = {Keys.NUM_FANTASIES: 64}
  
      acquisition_function_kwargs = {
          Keys.CURRENT_VALUE: torch.tensor([1.0])
      }
      optimizer_kwargs = {Keys.NUM_RESTARTS: 40, Keys.RAW_SAMPLES: 1024}
      model_gen_options = {
          Keys.ACQF_KWARGS: acquisition_function_kwargs,
          Keys.OPTIMIZER_KWARGS: optimizer_kwargs,
      }
  
      (
          final_acq_options,
          final_opt_options,
      ) = construct_acquisition_and_optimizer_options(
          # pyre-fixme[6]: For 1st param expected `Dict[str, Union[None, Dict[str,
          #  typing.Any], OptimizationConfig, AcquisitionFunction, float, int, str]]`
          #  but got `Dict[Keys, int]`.
          # pyre-fixme[6]: For 2nd param expected `Optional[Dict[str, Union[None,
          #  Dict[str, typing.Any], OptimizationConfig, AcquisitionFunction, float,
          #  int, str]]]` but got `Dict[Keys, Union[Dict[Keys, int], Dict[Keys,
          #  Tensor]]]`.
          acqf_options=acqf_options,
          # pyre-fixme[6]: For 2nd param expected `Optional[Dict[str, Union[None,
          #  Dict[str, typing.Any], OptimizationConfig, AcquisitionFunction, float,
          #  int, str]]]` but got `Dict[Keys, Union[Dict[Keys, int], Dict[Keys,
          #  Tensor]]]`.
          model_gen_options=model_gen_options,
      )
      self.assertEqual(
          final_acq_options,
          {
              Keys.NUM_FANTASIES: 64,
              Keys.CURRENT_VALUE: torch.tensor([1.0]),
          },
      )
      self.assertEqual(final_opt_options, optimizer_kwargs)
  
imports: import warnings
  from typing import OrderedDict
  import numpy
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.exceptions.core import AxError, AxWarning, UnsupportedError
  from ax.models.torch.botorch_modular.utils import _get_shared_rows, _tensor_difference, check_metric_dataset_match, choose_botorch_acqf_class, choose_model_class, construct_acquisition_and_optimizer_options, convert_to_block_design, get_subset_datasets, subset_state_dict, use_model_list
  from ax.models.torch.utils import _to_inequality_constraints
  from ax.utils.common.constants import Keys
  from ax.utils.common.testutils import TestCase
  from ax.utils.common.typeutils import checked_cast, not_none
  from ax.utils.testing.torch_stubs import get_torch_test_data
  from botorch.acquisition import qLogNoisyExpectedImprovement
  from botorch.acquisition.multi_objective.logei import qLogNoisyExpectedHypervolumeImprovement
  from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP
  from botorch.models.gp_regression_mixed import MixedSingleTaskGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.multitask import MultiTaskGP
  from botorch.utils.datasets import SupervisedDataset
label_desc: Use image node_id 7 for calling the BoTorchModelUtilsTest obj's underlying member method code with example usage: obj.test_construct_acquisition_and_optimizer_options() without return types