docstr: Helper function to return a sorted list from a normalization map.
  Also returns the starting index for each feature type
code: def _sort_features_by_normalization(self):
      """
      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
      feature_id_to_index = {}
      sorted_features = []
      feature_starts = []
      assert isinstance(
          list(self.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(self.normalization_parameters.keys()):
              norm = self.normalization_parameters[feature]
              if norm.feature_type == feature_type:
                  feature_id_to_index[feature] = len(sorted_features)
                  sorted_features.append(feature)
      return feature_id_to_index, sorted_features, feature_starts
  
imports: import logging
  from typing import cast, Dict, List, Optional, Tuple
  import torch
  from reagent.core.parameters import NormalizationParameters
  from reagent.preprocessing.identify_types import DO_NOT_PREPROCESS, ENUM, FEATURE_TYPES
  from reagent.preprocessing.normalization import EPS, MAX_FEATURE_VALUE, MIN_FEATURE_VALUE
  from torch.nn import Module, Parameter
label_desc: Use image node_id 26 for calling the Preprocessor obj's underlying member method code with example usage: obj._sort_features_by_normalization() and returns: feature_id_to_index,  sorted_features,  feature_starts