code: def partition_into_passed_and_failed_features(
      feature_proportions, thresholds
  ):
      kept = kept_features(feature_proportions, thresholds)
      passed_features = {
          x[0]: x[1]
          for x in feature_proportions.items()
          if x[0] in kept
      }
      failed_features = {
          x[0]: x[1]
          for x in feature_proportions.items()
          if x[0] not in kept
      }
      return passed_features, failed_features
  
file_path: imodels/imodels/experimental/bartpy/diagnostics/features.py
imports: from collections import Counter
  from typing import List, Mapping, Union, Optional
  import numpy
  import pandas
  import seaborn
  from matplotlib import pyplot
  from ..runner import run_models
  from ..sklearnmodel import SklearnModel
label_desc: Use image node_id 9 for calling a global function with example usage: partition_into_passed_and_failed_features(feature_proportions, thresholds) and returns: passed_features,  failed_features