code: def _optimize_acqf_all_features_fixed(
      *,
      bounds: Tensor,
      fixed_features: Dict[int, float],
      q: int,
      acq_function: AcquisitionFunction,
  ) -> Tuple[Tensor, Tensor]:
      """
      Helper function for `optimize_acqf` for the trivial case where
      all features are fixed.
      """
      X = torch.tensor(
          [fixed_features[i] for i in range(bounds.shape[-1])],
          device=bounds.device,
          dtype=bounds.dtype,
      )
      X = X.expand(q, *X.shape)
      with torch.no_grad():
          acq_value = acq_function(X)
      return X, acq_value
  
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 2 for calling a global function with example usage: _optimize_acqf_all_features_fixed() and returns: X,  acq_value