code: def test_optimize_l1(self, mock_optimize_acqf: Mock) -> None:
      mock_optimize_acqf.return_value = (
          torch.tensor(
              [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]], dtype=torch.double
          ),
          torch.tensor([1.0, 2.0], dtype=torch.double),
      )
      acquisition = self.get_acquisition_function(
          fixed_features=self.fixed_features,
          options={
              "penalty": "L1_norm",
              "target_point": self.target_point,
          },
      )
      acquisition.optimize(
          n=2,
          search_space_digest=self.search_space_digest,
          inequality_constraints=self.inequality_constraints,
          fixed_features=self.fixed_features,
          rounding_func=self.rounding_func,
          optimizer_options=self.optimizer_options,
      )
  
      args, kwargs = mock_optimize_acqf.call_args
      self.assertEqual(kwargs["acq_function"], acquisition.acqf)
      self.assertEqual(kwargs["q"], 2)
      self.assertEqual(
          kwargs["inequality_constraints"], self.inequality_constraints
      )
      self.assertEqual(
          kwargs["post_processing_func"], self.rounding_func
      )
      self.assertEqual(
          kwargs["num_restarts"], self.optimizer_options["num_restarts"]
      )
      self.assertEqual(
          kwargs["raw_samples"], self.optimizer_options["raw_samples"]
      )
  
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 4 for calling the TestSebo obj's underlying member method code with example usage: obj.test_optimize_l1(mock_optimize_acqf) without return types