class_docstr: Implement the acquisition function of Sparsity Exploring Bayesian
  Optimization (SEBO).
  
  The SEBO is a hyperparameter-free method to simultaneously maximize a target
  objective and sparsity. When L0 norm is used, SEBO uses a novel differentiable
  relaxation based on homotopy continuation to efficiently optimize for sparsity.
class_docstr_tok: ["Implement","the","acquisition","function","of","Sparsity","Exploring","Bayesian","Optimization","(","SEBO",")",".","The","SEBO","is","a","hyperparameter-free","method","to","simultaneously","maximize","a","target","objective","and","sparsity",".","When","L0","norm","is","used",",","SEBO","uses","a","novel","differentiable","relaxation","based","on","homotopy","continuation","to","efficiently","optimize","for","sparsity","."]
docstr: Transform torch config to include penalty term (deterministic model) as
  an additional outcomes in BoTorch model.
docstr_tok: ["Transform","torch","config","to","include","penalty","term","(","deterministic","model",")","as","an","additional","outcomes","in","BoTorch","model","."]
code: def _transform_torch_config(
      self,
      torch_opt_config: TorchOptConfig,
      **tkwargs: Any,
  ) -> TorchOptConfig:
      """Transform torch config to include penalty term (deterministic model) as
      an additional outcomes in BoTorch model.
      """
      # update objective weights by appending the weight (-1) of penalty term
      # at the end
      ow_sebo = torch.cat(
          [
              torch_opt_config.objective_weights,
              torch.tensor([-1], **tkwargs),
          ]
      )
      if torch_opt_config.outcome_constraints is not None:
          # update the shape of A matrix in outcome_constraints
          oc_sebo = (
              torch.cat(
                  [
                      torch_opt_config.outcome_constraints[0],
                      torch.zeros(
                          # pyre-ignore
                          torch_opt_config.outcome_constraints[0].shape[
                              0
                          ],
                          1,
                          **tkwargs,
                      ),
                  ],
                  dim=1,
              ),
              torch_opt_config.outcome_constraints[1],
          )
      else:
          oc_sebo = None
      if torch_opt_config.objective_thresholds is not None:
          # append the sparsity threshold at the end if objective_thresholds
          # is not None
          ot_sebo = torch.cat(
              [
                  torch_opt_config.objective_thresholds,
                  torch.tensor([self.sparsity_threshold], **tkwargs),
              ]
          )
      else:
          ot_sebo = None
  
      # update pending observations (if not none) by appending an obs for
      # the new penalty outcome
      pending_observations = torch_opt_config.pending_observations
      if torch_opt_config.pending_observations is not None:
          pending_observations = (
              torch_opt_config.pending_observations
              + [torch_opt_config.pending_observations[0]]
          )
  
      return TorchOptConfig(
          objective_weights=ow_sebo,
          outcome_constraints=oc_sebo,
          objective_thresholds=ot_sebo,
          linear_constraints=torch_opt_config.linear_constraints,
          fixed_features=torch_opt_config.fixed_features,
          pending_observations=pending_observations,
          model_gen_options=torch_opt_config.model_gen_options,
          rounding_func=torch_opt_config.rounding_func,
          opt_config_metrics=torch_opt_config.opt_config_metrics,
          is_moo=torch_opt_config.is_moo,
      )
  
imports: import functools
  from copy import deepcopy
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.models.torch.botorch_modular.acquisition import Acquisition
  from ax.models.torch.botorch_modular.optimizer_argparse import optimizer_argparse
  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.typeutils import not_none
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.multi_objective.monte_carlo import qExpectedHypervolumeImprovement
  from botorch.acquisition.penalized import L0Approximation
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.model import ModelList
  from botorch.optim import Homotopy, HomotopyParameter, LogLinearHomotopySchedule, optimize_acqf_homotopy
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.multi_objective.pareto import is_non_dominated
  from torch import Tensor
  from torch.quasirandom import SobolEngine
label_desc: Use image node_id 3 for calling the SEBOAcquisition obj's underlying member method code with example usage: obj._transform_torch_config(torch_opt_config) and returns: TorchOptConfig