code: def _extract_itervar_feature_index(args):
      """extract iteration var feature for an index in extract_space"""
      config = _extract_space.get(args)
      with _extract_target:
          sch, fargs = _extract_task.instantiate(config)
  
      fea = feature.get_itervar_feature_flatten(
          sch, fargs, take_log=True
      )
      fea = np.concatenate(
          (fea, list(config.get_other_option().values()))
      )
      return fea
  
imports: import logging
  import time
  from typing import Dict
  import numpy
  from tvm.contrib.popen_pool import PopenPoolExecutor, StatusKind
  from ..None import feature
  from ..utils import get_rank
  from .metric import cover_curve, max_curve, recall_curve
  from .model_based_tuner import CostModel, FeatureCache
label_desc: Use image node_id 2 for calling a global function with example usage: _extract_itervar_feature_index(args) and returns: fea