code: def id_to_features(id, intervals):
      """Given an index in a feature vector, return the original set of features.
  
      Parameters
      ----------
      id: int
          The index in a feature vector given by the given set of features.
      intervals: list
          List of intervals as returned by `get_intervals()`
  
      Returns
      -------
      features: list
          List of features as returned by `get_feature_list()`
      """
      features = 6 * [0]
  
      # Correct for null
      id -= 1
  
      for k in range(0, 6 - 1):
          # print(6-k-1, id)
          features[6 - k - 1] = id // intervals[6 - k - 1]
          id -= features[6 - k - 1] * intervals[6 - k - 1]
      # Correct for last one
      features[0] = id
      return features
  
imports: import numpy
  import deepchem
  from deepchem.feat.base_classes import MolecularFeaturizer
  from deepchem.feat.mol_graphs import ConvMol, WeaveMol
  from deepchem.data import DiskDataset
  import logging
  from typing import Optional, List, Tuple, Union, Iterable
  from deepchem.utils.typing import RDKitMol, RDKitAtom
  from deepchem.utils.molecule_feature_utils import one_hot_encode
label_desc: Use image node_id 7 for calling a global function with example usage: id_to_features(id, intervals) and returns: features