code: def _input_transform_argparse_warp(
      input_transform_class: Type[Warp],
      dataset: SupervisedDataset,
      search_space_digest: SearchSpaceDigest,
      input_transform_options: Optional[Dict[str, Any]] = None,
  ) -> Dict[str, Any]:
      """Extract the base input transform kwargs form the given arguments.
  
      Args:
          input_transform_class: Input transform class.
          dataset: Dataset containing feature matrix and the response.
          search_space_digest: Search space digest.
          input_transform_options: Input transform kwargs.
  
      Returns:
          A dictionary with input transform kwargs.
      """
      input_transform_options = input_transform_options or {}
      d = len(dataset.feature_names)
      indices = list(range(d))
      task_features = normalize_indices(
          search_space_digest.task_features, d=d
      )
  
      for task_feature in sorted(task_features, reverse=True):
          del indices[task_feature]
  
      input_transform_options.setdefault("indices", indices)
      return input_transform_options
  
file_path: Ax/ax/models/torch/botorch_modular/input_constructors/input_transforms.py
imports: from __future__ import annotations
  from typing import Any, Dict, Optional, Type
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.models.torch.utils import normalize_indices
  from ax.utils.common.typeutils import _argparse_type_encoder
  from botorch.models.transforms.input import InputPerturbation, InputTransform, Normalize, Warp
  from botorch.utils.containers import SliceContainer
  from botorch.utils.datasets import RankingDataset, SupervisedDataset
  from botorch.utils.dispatcher import Dispatcher
label_desc: Use image node_id 2 for calling a global function with example usage: _input_transform_argparse_warp(input_transform_class, dataset, search_space_digest, input_transform_options) and returns: input_transform_options