code: def _optimize_acqf_batch(
      opt_inputs: OptimizeAcqfInputs,
  ) -> Tuple[Tensor, Tensor]:
      options = opt_inputs.options or {}
  
      initial_conditions_provided = (
          opt_inputs.batch_initial_conditions is not None
      )
  
      if initial_conditions_provided:
          batch_initial_conditions = opt_inputs.batch_initial_conditions
      else:
          # pyre-ignore[28]: Unexpected keyword argument `acq_function` to anonymous call.
          batch_initial_conditions = opt_inputs.get_ic_generator()(
              acq_function=opt_inputs.acq_function,
              bounds=opt_inputs.bounds,
              q=opt_inputs.q,
              num_restarts=opt_inputs.num_restarts,
              raw_samples=opt_inputs.raw_samples,
              fixed_features=opt_inputs.fixed_features,
              options=options,
              inequality_constraints=opt_inputs.inequality_constraints,
              equality_constraints=opt_inputs.equality_constraints,
              **opt_inputs.ic_gen_kwargs,
          )
  
      batch_limit: int = options.get(
          "batch_limit",
          opt_inputs.num_restarts
          if not opt_inputs.nonlinear_inequality_constraints
          else 1,
      )
  
      def _optimize_batch_candidates() -> (
          Tuple[Tensor, Tensor, List[Warning]]
      ):
          batch_candidates_list: List[Tensor] = []
          batch_acq_values_list: List[Tensor] = []
          batched_ics = batch_initial_conditions.split(batch_limit)
          opt_warnings = []
          timeout_sec = (
              opt_inputs.timeout_sec / len(batched_ics)
              if opt_inputs.timeout_sec is not None
              else None
          )
  
          bounds = opt_inputs.bounds
          gen_kwargs: Dict[str, Any] = {
              "lower_bounds": None
              if bounds[0].isinf().all()
              else bounds[0],
              "upper_bounds": None
              if bounds[1].isinf().all()
              else bounds[1],
              "options": {
                  k: v
                  for k, v in options.items()
                  if k not in INIT_OPTION_KEYS
              },
              "fixed_features": opt_inputs.fixed_features,
              "timeout_sec": timeout_sec,
          }
  
          # only add parameter constraints to gen_kwargs if they are specified
          # to avoid unnecessary warnings in _filter_kwargs
          for constraint_name in [
              "inequality_constraints",
              "equality_constraints",
              "nonlinear_inequality_constraints",
          ]:
              if (
                  constraint := getattr(opt_inputs, constraint_name)
              ) is not None:
                  gen_kwargs[constraint_name] = constraint
  
          filtered_gen_kwargs = _filter_kwargs(
              opt_inputs.gen_candidates, **gen_kwargs
          )
  
          for i, batched_ics_ in enumerate(batched_ics):
              # optimize using random restart optimization
              with warnings.catch_warnings(record=True) as ws:
                  warnings.simplefilter(
                      "always", category=OptimizationWarning
                  )
                  (
                      batch_candidates_curr,
                      batch_acq_values_curr,
                  ) = opt_inputs.gen_candidates(
                      batched_ics_,
                      opt_inputs.acq_function,
                      **filtered_gen_kwargs,
                  )
              opt_warnings += ws
              batch_candidates_list.append(batch_candidates_curr)
              batch_acq_values_list.append(batch_acq_values_curr)
              logger.info(
                  f"Generated candidate batch {i+1} of {len(batched_ics)}."
              )
  
          batch_candidates = torch.cat(batch_candidates_list)
          has_scalars = batch_acq_values_list[0].ndim == 0
          if has_scalars:
              batch_acq_values = torch.stack(batch_acq_values_list)
          else:
              batch_acq_values = torch.cat(
                  batch_acq_values_list
              ).flatten()
          return batch_candidates, batch_acq_values, opt_warnings
  
      (
          batch_candidates,
          batch_acq_values,
          ws,
      ) = _optimize_batch_candidates()
  
      optimization_warning_raised = any(
          (issubclass(w.category, OptimizationWarning) for w in ws)
      )
      if (
          optimization_warning_raised
          and opt_inputs.retry_on_optimization_warning
      ):
          first_warn_msg = (
              "Optimization failed in `gen_candidates_scipy` with the following "
              f"warning(s):\n{[w.message for w in ws]}\nBecause you specified "
              "`batch_initial_conditions`, optimization will not be retried with "
              "new initial conditions and will proceed with the current solution."
              " Suggested remediation: Try again with different "
              "`batch_initial_conditions`, or don't provide `batch_initial_conditions.`"
              if initial_conditions_provided
              else "Optimization failed in `gen_candidates_scipy` with the following "
              f"warning(s):\n{[w.message for w in ws]}\nTrying again with a new "
              "set of initial conditions."
          )
          warnings.warn(first_warn_msg, RuntimeWarning)
  
          if not initial_conditions_provided:
              batch_initial_conditions = opt_inputs.get_ic_generator()(
                  acq_function=opt_inputs.acq_function,
                  bounds=opt_inputs.bounds,
                  q=opt_inputs.q,
                  num_restarts=opt_inputs.num_restarts,
                  raw_samples=opt_inputs.raw_samples,
                  fixed_features=opt_inputs.fixed_features,
                  options=options,
                  inequality_constraints=opt_inputs.inequality_constraints,
                  equality_constraints=opt_inputs.equality_constraints,
                  **opt_inputs.ic_gen_kwargs,
              )
  
              (
                  batch_candidates,
                  batch_acq_values,
                  ws,
              ) = _optimize_batch_candidates()
  
              optimization_warning_raised = any(
                  (
                      issubclass(w.category, OptimizationWarning)
                      for w in ws
                  )
              )
              if optimization_warning_raised:
                  warnings.warn(
                      "Optimization failed on the second try, after generating a "
                      "new set of initial conditions.",
                      RuntimeWarning,
                  )
  
      if opt_inputs.post_processing_func is not None:
          batch_candidates = opt_inputs.post_processing_func(
              batch_candidates
          )
          with torch.no_grad():
              acq_values_list = [
                  opt_inputs.acq_function(cand)
                  for cand in batch_candidates.split(batch_limit, dim=0)
              ]
              batch_acq_values = torch.cat(acq_values_list, dim=0)
  
      if opt_inputs.return_best_only:
          best = torch.argmax(batch_acq_values.view(-1), dim=0)
          batch_candidates = batch_candidates[best]
          batch_acq_values = batch_acq_values[best]
  
      if not opt_inputs.full_tree:
          batch_candidates = opt_inputs.acq_function.extract_candidates(
              X_full=batch_candidates
          )
  
      return batch_candidates, batch_acq_values
  
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 5 for calling a global function with example usage: _optimize_acqf_batch(opt_inputs) and returns: batch_candidates,  batch_acq_values, batch_candidates,  batch_acq_values,  opt_warnings