code: def optimize_acqf_discrete(
      acq_function: AcquisitionFunction,
      q: int,
      choices: Tensor,
      max_batch_size: int = 2048,
      unique: bool = True,
      **kwargs: Any,
  ) -> Tuple[Tensor, Tensor]:
      r"""Optimize over a discrete set of points using batch evaluation.
  
      For `q > 1` this function generates candidates by means of sequential
      conditioning (rather than joint optimization), since for all but the
      smalles number of choices the set `choices^q` of discrete points to
      evaluate quickly explodes.
  
      Args:
          acq_function: An AcquisitionFunction.
          q: The number of candidates.
          choices: A `num_choices x d` tensor of possible choices.
          max_batch_size: The maximum number of choices to evaluate in batch.
              A large limit can cause excessive memory usage if the model has
              a large training set.
          unique: If True return unique choices, o/w choices may be repeated
              (only relevant if `q > 1`).
          kwargs: kwargs do nothing. This is provided so that the same arguments can
              be passed to different acquisition functions without raising an error.
  
      Returns:
          A three-element tuple containing
  
          - a `q x d`-dim tensor of generated candidates.
          - an associated acquisition value.
      """
      if isinstance(acq_function, OneShotAcquisitionFunction):
          raise UnsupportedError(
              "Discrete optimization is not supported for"
              "one-shot acquisition functions."
          )
      if choices.numel() == 0:
          raise InputDataError("`choices` must be non-emtpy.")
      _raise_deprecation_warning_if_kwargs(
          "optimize_acqf_discrete", kwargs
      )
      choices_batched = choices.unsqueeze(-2)
      if q > 1:
          candidate_list, acq_value_list = [], []
          base_X_pending = acq_function.X_pending
          for _ in range(q):
              with torch.no_grad():
                  acq_values = _split_batch_eval_acqf(
                      acq_function=acq_function,
                      X=choices_batched,
                      max_batch_size=max_batch_size,
                  )
              best_idx = torch.argmax(acq_values)
              candidate_list.append(choices_batched[best_idx])
              acq_value_list.append(acq_values[best_idx])
              # set pending points
              candidates = torch.cat(candidate_list, dim=-2)
              acq_function.set_X_pending(
                  torch.cat([base_X_pending, candidates], dim=-2)
                  if base_X_pending is not None
                  else candidates
              )
              # need to remove choice from choice set if enforcing uniqueness
              if unique:
                  choices_batched = torch.cat(
                      [
                          choices_batched[:best_idx],
                          choices_batched[best_idx + 1 :],
                      ]
                  )
  
          # Reset acq_func to previous X_pending state
          acq_function.set_X_pending(base_X_pending)
          return candidates, torch.stack(acq_value_list)
  
      with torch.no_grad():
          acq_values = _split_batch_eval_acqf(
              acq_function=acq_function,
              X=choices_batched,
              max_batch_size=max_batch_size,
          )
      best_idx = torch.argmax(acq_values)
      return choices_batched[best_idx], acq_values[best_idx]
  
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 11 for calling a global function with example usage: optimize_acqf_discrete(acq_function, q, choices, max_batch_size, unique) and returns: choices_batched,  acq_values, candidates,  torch