code: def make_groups(
      candidates: List[Feature],
  ) -> List[Union[Feature, "FeatureGroup"]]:
      grouped_features = []
  
      def groupby_func(
          f: Feature,
      ) -> Tuple[Operand, Union[Column, Feature]]:
          return (
              f.op,
              f.children[0]
              if f.op.is_unary or f.op.is_vector
              else f.children[1],
          )
  
      for op_child, features in itertools.groupby(
          candidates, groupby_func
      ):
          op, main_child = op_child
          feature_list = list(features)
          if op.is_vectorizable:
              if op.is_unary:
                  group = FeatureGroup(
                      op, main_column=None, children=feature_list
                  )
              else:
                  group = FeatureGroup(
                      op, main_column=main_child, children=feature_list
                  )
              grouped_features.append(group)
          else:
              grouped_features.extend(feature_list)
      return grouped_features
  
imports: import hashlib
  import itertools
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from pandas._typing import DtypeObj
  from upgini.autofe.all_operands import find_op
  from upgini.autofe.operand import Operand, PandasOperand
label_desc: Use image node_id 5 for calling the FeatureGroup obj's underlying member method code with example usage: obj.make_groups(candidates) and returns: grouped_features, f,  unknown