code: def predict_silo(self, X):
      n = X.shape[0]
      pred = np.zeros(n)
      for i in range(
          n
      ):  # The contents of this inner loop are similar to explain(): doesn't use the features selected by MAPLE or return as much information
          x = X[i, :].reshape(1, -1)
  
          curr_leaf_ids = self.fe.apply(x)[0]
          weights = self.training_point_weights(curr_leaf_ids)
  
          # Local linear model
          lr_model = Ridge(alpha=self.regularization)
          lr_model.fit(self.X_train, self.MR_train, weights)
  
          pred[i] = lr_model.predict(x)[0]
  
      return pred
  
imports: import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  from .._explainer import Explainer
  import numpy
  from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
  from sklearn.linear_model import Ridge
  from sklearn.metrics import mean_squared_error
label_desc: Use image node_id 6 for calling the MAPLE obj's underlying member method code with example usage: obj.predict_silo(X) and returns: pred