code: def scipy_optimizer_list(
      acq_function_list: List[AcquisitionFunction],
      bounds: Tensor,
      inequality_constraints: Optional[
          List[Tuple[Tensor, Tensor, float]]
      ] = None,
      fixed_features: Optional[Dict[int, float]] = None,
      rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
      num_restarts: int = 20,
      raw_samples: Optional[int] = None,
      options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
  ) -> Tuple[Tensor, Tensor]:
      r"""Sequential optimizer using scipy's minimize module on a numpy-adaptor.
  
      The ith acquisition in the sequence uses the ith given acquisition_function.
  
      Args:
          acq_function_list: A list of botorch AcquisitionFunctions,
              optimized sequentially.
          bounds: A `2 x d`-dim tensor, where `bounds[0]` (`bounds[1]`) are the
              lower (upper) bounds of the feasible hyperrectangle.
          n: The number of candidates to generate.
          inequality constraints: A list of tuples (indices, coefficients, rhs),
              with each tuple encoding an inequality constraint of the form
              `\sum_i (X[indices[i]] * coefficients[i]) >= rhs`
          fixed_features: A map {feature_index: value} for features that should
              be fixed to a particular value during generation.
          rounding_func: A function that rounds an optimization result
              appropriately (i.e., according to `round-trip` transformations).
  
      Returns:
          2-element tuple containing
  
          - A `n x d`-dim tensor of generated candidates.
          - A `n`-dim tensor of conditional acquisition
            values, where `i`-th element is the expected acquisition value
            conditional on having observed candidates `0,1,...,i-1`.
      """
      # Use SLSQP by default for small problems since it yields faster wall times.
      optimize_options: Dict[str, Union[bool, float, int, str]] = {
          "batch_limit": 5,
          "init_batch_limit": 32,
          "method": "SLSQP",
      }
      if options is not None:
          optimize_options.update(options)
      X, expected_acquisition_value = optimize_acqf_list(
          acq_function_list=acq_function_list,
          bounds=bounds,
          num_restarts=num_restarts,
          raw_samples=50 * num_restarts
          if raw_samples is None
          else raw_samples,
          options=optimize_options,
          inequality_constraints=inequality_constraints,
          fixed_features=fixed_features,
          post_processing_func=rounding_func,
      )
      return X, expected_acquisition_value
  
imports: from __future__ import annotations
  from typing import Callable, cast, Dict, List, Optional, Tuple, Union
  import torch
  from ax.exceptions.core import AxError
  from ax.models.torch.botorch_defaults import NO_FEASIBLE_POINTS_MESSAGE
  from ax.models.torch.utils import _get_X_pending_and_observed, get_outcome_constraint_transforms, subset_model
  from ax.models.torch_base import TorchModel
  from ax.utils.common.typeutils import checked_cast, not_none
  from botorch.acquisition import get_acquisition_function
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.multi_objective.logei import qLogExpectedHypervolumeImprovement, qLogNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.multi_objective.monte_carlo import qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.multi_objective.objective import WeightedMCMultiOutputObjective
  from botorch.acquisition.multi_objective.utils import get_default_partitioning_alpha
  from botorch.models.model import Model
  from botorch.optim.optimize import optimize_acqf_list
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.posteriors.posterior import Posterior
  from botorch.posteriors.posterior_list import PosteriorList
  from botorch.utils.multi_objective.hypervolume import infer_reference_point
  from botorch.utils.multi_objective.pareto import is_non_dominated
  from torch import Tensor
label_desc: Use image node_id 9 for calling a global function with example usage: scipy_optimizer_list(acq_function_list, bounds, inequality_constraints, fixed_features, rounding_func, num_restarts, raw_samples, options) and returns: X,  expected_acquisition_value