code: def _argparse_ehvi(
      acqf: MaybeType[qExpectedHypervolumeImprovement],
      sequential: bool = True,
      init_batch_limit: int = 32,
      batch_limit: int = 5,
      optimizer_options: Optional[Dict[str, Any]] = None,
      **kwargs: Any,
  ) -> Dict[str, Any]:
      return {
          **_argparse_base(
              acqf=acqf,
              init_batch_limit=init_batch_limit,
              batch_limit=batch_limit,
              optimizer_options=optimizer_options,
              **kwargs,
          ),
          "sequential": sequential,
      }
  
file_path: Ax/ax/models/torch/botorch_modular/optimizer_argparse.py
imports: from __future__ import annotations
  from typing import Any, Dict, Optional, Type, TypeVar, Union
  import torch
  from ax.utils.common.constants import Keys
  from ax.utils.common.typeutils import _argparse_type_encoder
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
  from botorch.acquisition.max_value_entropy_search import qMaxValueEntropy
  from botorch.acquisition.multi_objective.monte_carlo import qExpectedHypervolumeImprovement
  from botorch.optim.initializers import gen_one_shot_kg_initial_conditions
  from botorch.utils.dispatcher import Dispatcher
label_desc: Use image node_id 2 for calling a global function with example usage: _argparse_ehvi(acqf, sequential, init_batch_limit, batch_limit, optimizer_options) and returns: dict