code: def sort_features_by_normalization(
      normalization_parameters: Dict[int, NormalizationParameters]
  ) -> Tuple[List[int], List[int]]:
      """
      Helper function to return a sorted list from a normalization map.
      Also returns the starting index for each feature type"""
      # Sort features by feature type
      sorted_features: List[int] = []
      feature_starts: List[int] = []
      assert isinstance(
          list(normalization_parameters.keys())[0], int
      ), "Normalization Parameters need to be int"
      for feature_type in FEATURE_TYPES:
          feature_starts.append(len(sorted_features))
          for feature in sorted(normalization_parameters.keys()):
              norm = normalization_parameters[feature]
              if norm.feature_type == feature_type:
                  sorted_features.append(feature)
      return sorted_features, feature_starts
  
imports: import json
  import logging
  from dataclasses import asdict
  from typing import Dict, List, Optional, Tuple
  import numpy
  import reagent.core.types
  import six
  import torch
  from reagent.core.parameters import NormalizationParameters
  from reagent.preprocessing import identify_types
  from reagent.preprocessing.identify_types import DEFAULT_MAX_UNIQUE_ENUM, FEATURE_TYPES
  from scipy import stats
  from scipy.stats.mstats import mquantiles
label_desc: Use image node_id 6 for calling a global function with example usage: sort_features_by_normalization(normalization_parameters) and returns: sorted_features,  feature_starts