code: def optimize_acqf_list(
      acq_function_list: List[AcquisitionFunction],
      bounds: Tensor,
      num_restarts: int,
      raw_samples: Optional[int] = None,
      options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
      inequality_constraints: Optional[
          List[Tuple[Tensor, Tensor, float]]
      ] = None,
      equality_constraints: Optional[
          List[Tuple[Tensor, Tensor, float]]
      ] = None,
      nonlinear_inequality_constraints: Optional[
          List[Tuple[Callable, bool]]
      ] = None,
      fixed_features: Optional[Dict[int, float]] = None,
      fixed_features_list: Optional[List[Dict[int, float]]] = None,
      post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
      ic_generator: Optional[TGenInitialConditions] = None,
      ic_gen_kwargs: Optional[Dict] = None,
  ) -> Tuple[Tensor, Tensor]:
      r"""Generate a list of candidates from a list of acquisition functions.
  
      The acquisition functions are optimized in sequence, with previous candidates
      set as `X_pending`. This is also known as sequential greedy optimization.
  
      Args:
          acq_function_list: A list of acquisition functions.
          bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`
              (if inequality_constraints is provided, these bounds can be -inf and
              +inf, respectively).
          num_restarts:  Number of starting points for multistart acquisition
              function optimization.
          raw_samples: Number of samples for initialization. This is required
              if `batch_initial_conditions` is not specified.
          options: Options for candidate generation.
          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 inequality constraint of the form
              `\sum_i (X[indices[i]] * coefficients[i]) = rhs`
          nonlinear_inequality_constraints: A list of tuples representing the nonlinear
              inequality constraints. The first element in the tuple is a callable
              representing a constraint of the form `callable(x) >= 0`. In case of an
              intra-point constraint, `callable()`takes in an one-dimensional tensor of
              shape `d` and returns a scalar. In case of an inter-point constraint,
              `callable()` takes a two dimensional tensor of shape `q x d` and again
              returns a scalar. The second element is a boolean, indicating if it is an
              intra-point or inter-point constraint (`True` for intra-point. `False` for
              inter-point). For more information on intra-point vs inter-point
              constraints, see the docstring of the `inequality_constraints` argument to
              `optimize_acqf()`. The constraints will later be passed to the scipy
              solver. You need to pass in `batch_initial_conditions` in this case.
              Using non-linear inequality constraints also requires that `batch_limit`
              is set to 1, which will be done automatically if not specified in
              `options`.
          fixed_features: A map `{feature_index: value}` for features that
              should be fixed to a particular value during generation.
          fixed_features_list: A list of maps `{feature_index: value}`. The i-th
              item represents the fixed_feature for the i-th optimization. If
              `fixed_features_list` is provided, `optimize_acqf_mixed` is invoked.
          post_processing_func: A function that post-processes an optimization
              result appropriately (i.e., according to `round-trip`
              transformations).
          ic_generator: Function for generating initial conditions. Not needed when
              `batch_initial_conditions` are provided. Defaults to
              `gen_one_shot_kg_initial_conditions` for `qKnowledgeGradient` acquisition
              functions and `gen_batch_initial_conditions` otherwise. Must be specified
              for nonlinear inequality constraints.
          ic_gen_kwargs: Additional keyword arguments passed to function specified by
              `ic_generator`
  
      Returns:
          A two-element tuple containing
  
          - a `q x d`-dim tensor of generated candidates.
          - a `q`-dim tensor of expected acquisition values, where the value at
              index `i` is the acquisition value conditional on having observed
              all candidates except candidate `i`.
      """
      if fixed_features and fixed_features_list:
          raise ValueError(
              "Èither `fixed_feature` or `fixed_features_list` can be provided, not both."
          )
      if not acq_function_list:
          raise ValueError("acq_function_list must be non-empty.")
      candidate_list, acq_value_list = [], []
      candidates = torch.tensor(
          [], device=bounds.device, dtype=bounds.dtype
      )
      base_X_pending = acq_function_list[0].X_pending
      for acq_function in acq_function_list:
          if candidate_list:
              acq_function.set_X_pending(
                  torch.cat([base_X_pending, candidates], dim=-2)
                  if base_X_pending is not None
                  else candidates
              )
          if fixed_features_list:
              candidate, acq_value = optimize_acqf_mixed(
                  acq_function=acq_function,
                  bounds=bounds,
                  q=1,
                  num_restarts=num_restarts,
                  raw_samples=raw_samples,
                  options=options or {},
                  inequality_constraints=inequality_constraints,
                  equality_constraints=equality_constraints,
                  nonlinear_inequality_constraints=nonlinear_inequality_constraints,
                  fixed_features_list=fixed_features_list,
                  post_processing_func=post_processing_func,
                  ic_generator=ic_generator,
                  ic_gen_kwargs=ic_gen_kwargs,
              )
          else:
              ic_gen_kwargs = ic_gen_kwargs or {}
              candidate, acq_value = optimize_acqf(
                  acq_function=acq_function,
                  bounds=bounds,
                  q=1,
                  num_restarts=num_restarts,
                  raw_samples=raw_samples,
                  options=options or {},
                  inequality_constraints=inequality_constraints,
                  equality_constraints=equality_constraints,
                  nonlinear_inequality_constraints=nonlinear_inequality_constraints,
                  fixed_features=fixed_features,
                  post_processing_func=post_processing_func,
                  return_best_only=True,
                  sequential=False,
                  ic_generator=ic_generator,
                  **ic_gen_kwargs,
              )
          candidate_list.append(candidate)
          acq_value_list.append(acq_value)
          candidates = torch.cat(candidate_list, dim=-2)
      return candidates, torch.stack(acq_value_list)
  
imports: from __future__ import annotations
  import dataclasses
  import warnings
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  from botorch.acquisition.acquisition import AcquisitionFunction, OneShotAcquisitionFunction
  from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
  from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import qHypervolumeKnowledgeGradient
  from botorch.exceptions import InputDataError, UnsupportedError
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.generation.gen import gen_candidates_scipy, TGenCandidates
  from botorch.logging import logger
  from botorch.optim.initializers import gen_batch_initial_conditions, gen_one_shot_hvkg_initial_conditions, gen_one_shot_kg_initial_conditions, TGenInitialConditions
  from botorch.optim.stopping import ExpMAStoppingCriterion
  from botorch.optim.utils import _filter_kwargs
  from torch import Tensor
label_desc: Use image node_id 9 for calling a global function with example usage: optimize_acqf_list(acq_function_list, bounds, num_restarts, raw_samples, options, inequality_constraints, equality_constraints, nonlinear_inequality_constraints, fixed_features, fixed_features_list, post_processing_func, ic_generator, ic_gen_kwargs) and returns: candidates,  torch