code: def get_acquisition_function(
      self,
      fixed_features: Optional[Dict[int, float]] = None,
      options: Optional[Dict[str, Union[str, float]]] = None,
  ) -> SEBOAcquisition:
      return SEBOAcquisition(
          botorch_acqf_class=qNoisyExpectedHypervolumeImprovement,
          surrogates={Keys.ONLY_SURROGATE: self.surrogates},
          search_space_digest=self.search_space_digest,
          torch_opt_config=dataclasses.replace(
              self.torch_opt_config, fixed_features=fixed_features or {}
          ),
          options=options or self.options,
      )
  
imports: from __future__ import annotations
  import dataclasses
  import functools
  from typing import Any, Dict, Optional, Union
  from unittest import mock
  from unittest.mock import Mock
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.models.torch.botorch_modular.acquisition import Acquisition
  from ax.models.torch.botorch_modular.sebo import L1_norm_func, SEBOAcquisition
  from ax.models.torch.botorch_modular.surrogate import Surrogate
  from ax.models.torch_base import TorchOptConfig
  from ax.utils.common.constants import Keys
  from ax.utils.common.testutils import TestCase
  from ax.utils.common.typeutils import not_none
  from ax.utils.testing.mock import fast_botorch_optimize
  from botorch.acquisition import PosteriorMean
  from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement
  from botorch.acquisition.multi_objective.monte_carlo import qNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.penalized import L0Approximation
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model import ModelList
  from botorch.optim import Homotopy, HomotopyParameter, LinearHomotopySchedule
  from botorch.utils.datasets import SupervisedDataset
  from torch.nn import Parameter
label_desc: Use image node_id 2 for calling the TestSebo obj's underlying member method code with example usage: obj.get_acquisition_function(fixed_features, options) and returns: SEBOAcquisition