code: def fit(
      self,
      X: pd.DataFrame,
      y: pd.DataFrame,
      minima: typing.Dict[int, typing.Dict[str, float]],
      maxima: typing.Dict[int, typing.Dict[str, float]],
  ) -> None:
      self.X_ = X
      self.strategies_ = y.columns
      self.return_value_ = np.array(
          [
              (
                  len(self.strategies_)
                  - self.default_strategies.index(strategy)
                  - 1
              )
              / (len(self.strategies_) - 1)
              for strategy in self.strategies_
          ]
      )
      self.return_value_ = self.return_value_ / np.sum(
          self.return_value_
      )
      self.selector.fit(X, y, minima, maxima)
  
imports: import typing
  import copy
  import itertools
  import numpy
  import pandas
  import scipy.stats
  import sklearn.ensemble
label_desc: Use image node_id 2 for calling the FallbackWrapper obj's underlying member method code with example usage: obj.fit(X, y, minima, maxima) without return types