code: def from_formula(string: str) -> Union[Column, "Feature"]:
      if string[-1] != ")":
          return Column(string)
  
      def is_trivial_char(c: str) -> bool:
          return not (c in "()+-*/,")
  
      def find_prev(string: str) -> int:
          if string[-1] != ")":
              return max(
                  [
                      (0 if is_trivial_char(c) else i + 1)
                      for i, c in enumerate(string)
                  ]
              )
          level, pos = 0, -1
          for i in range(len(string) - 1, -1, -1):
              if string[i] == ")":
                  level += 1
              if string[i] == "(":
                  level -= 1
              if level == 0:
                  pos = i
                  break
          while (pos > 0) and is_trivial_char(string[pos - 1]):
              pos -= 1
          return pos
  
      p2 = find_prev(string[:-1])
      if string[p2 - 1] == "(":
          return Feature(
              find_op(string[: p2 - 1]),
              [Feature.from_formula(string[p2:-1])],
          )
      p1 = find_prev(string[: p2 - 1])
      if string[0] == "(":
          return Feature(
              find_op(string[p2 - 1]),
              [
                  Feature.from_formula(string[p1 : p2 - 1]),
                  Feature.from_formula(string[p2:-1]),
              ],
          )
      else:
          op = find_op(string[: p1 - 1])
          if op is not None:
              return Feature(
                  op,
                  [
                      Feature.from_formula(string[p1 : p2 - 1]),
                      Feature.from_formula(string[p2:-1]),
                  ],
              )
          else:
              base_features = [
                  Feature.from_formula(string[p2:-1]),
                  Feature.from_formula(string[p1 : p2 - 1]),
              ]
              while op is None:
                  p2 = p1
                  p1 = find_prev(string[: p1 - 1])
                  base_features.append(
                      Feature.from_formula(string[p1 : p2 - 1])
                  )
                  op = find_op(string[: p1 - 1])
              base_features.reverse()
              return Feature(op, base_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 16 for calling the Feature obj's underlying member method code with example usage: obj.from_formula(string) and returns: Column, unknown, pos, Feature, Feature, max, Feature, Feature