code: def explain(self, x):
      x = x.reshape(1, -1)
  
      mostImpFeats = np.argsort(-self.feature_scores)
      x_p = np.delete(x, mostImpFeats[self.retain :], axis=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, self.MR_train, weights)
  
      # Get the model coefficients
      coefs = np.zeros(self.num_features + 1)
      coefs[0] = lr_model.intercept_
      coefs[np.sort(mostImpFeats[0 : self.retain]) + 1] = lr_model.coef_
  
      # Get the prediction at this point
      prediction = lr_model.predict(x_p.reshape(1, -1))
  
      out = {}
      out["weights"] = weights
      out["coefs"] = coefs
      out["pred"] = prediction
  
      return out
  
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 3 for calling the MAPLE obj's underlying member method code with example usage: obj.explain(x) and returns: out