code: def scipy_optimizer(
      acq_function: AcquisitionFunction,
      bounds: Tensor,
      n: int,
      inequality_constraints: Optional[
          List[Tuple[Tensor, Tensor, float]]
      ] = None,
      equality_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,
      joint_optimization: bool = False,
      options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
  ) -> Tuple[Tensor, Tensor]:
      r"""Optimizer using scipy's minimize module on a numpy-adpator.
  
      Args:
          acq_function: A botorch AcquisitionFunction.
          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`
          equality constraints: A list of tuples (indices, coefficients, rhs),
              with each tuple encoding an equality 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.
          - In the case of joint optimization, a scalar tensor containing
            the joint acquisition value of the `n` points. In the case of
            sequential optimization, 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`.
      """
  
      sequential = not joint_optimization
      optimize_acqf_options: Dict[str, Union[bool, float, int, str]] = {
          "batch_limit": 5,
          "init_batch_limit": 32,
      }
      if options is not None:
          optimize_acqf_options.update(options)
      X, expected_acquisition_value = optimize_acqf(
          acq_function=acq_function,
          bounds=bounds,
          q=n,
          num_restarts=num_restarts,
          raw_samples=50 * num_restarts
          if raw_samples is None
          else raw_samples,
          options=optimize_acqf_options,
          inequality_constraints=inequality_constraints,
          equality_constraints=equality_constraints,
          fixed_features=fixed_features,
          sequential=sequential,
          post_processing_func=rounding_func,
      )
      return X, expected_acquisition_value
  
imports: import functools
  from copy import deepcopy
  from random import randint
  from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple, Type, Union
  import torch
  from ax.models.model_utils import best_observed_point, get_observed
  from ax.models.torch.utils import _to_inequality_constraints
  from ax.models.torch_base import TorchModel
  from ax.models.types import TConfig
  from botorch.acquisition import get_acquisition_function
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
  from botorch.acquisition.objective import ConstrainedMCObjective, GenericMCObjective
  from botorch.acquisition.utils import get_infeasible_cost
  from botorch.exceptions.errors import UnsupportedError
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP
  from botorch.models.gpytorch import GPyTorchModel
  from botorch.models.model import Model
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms.input import Warp
  from botorch.optim.optimize import optimize_acqf
  from botorch.utils import get_objective_weights_transform, get_outcome_constraint_transforms
  from botorch.utils.multi_objective.scalarization import get_chebyshev_scalarization
  from botorch.utils.transforms import is_ensemble
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.kernels.kernel import Kernel
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.mlls.leave_one_out_pseudo_likelihood import LeaveOneOutPseudoLikelihood
  from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
  from gpytorch.priors import Prior
  from gpytorch.priors.lkj_prior import LKJCovariancePrior
  from gpytorch.priors.torch_priors import GammaPrior, LogNormalPrior
  from torch import Tensor
label_desc: Use image node_id 9 for calling a global function with example usage: scipy_optimizer(acq_function, bounds, n, inequality_constraints, equality_constraints, fixed_features, rounding_func) and returns: X,  expected_acquisition_value