class_docstr: Predict several quantiles with one estimator.
  
  This is a wrapper around `GradientBoostingRegressor`'s quantile
  regression that allows you to predict several `quantiles` in
  one go.
  
  Parameters
  ----------
  quantiles : array-like
      Quantiles to predict. By default the 16, 50 and 84%
      quantiles are predicted.
  
  base_estimator : GradientBoostingRegressor instance or None (default)
      Quantile regressor used to make predictions. Only instances
      of `GradientBoostingRegressor` are supported. Use this to change
      the hyper-parameters of the estimator.
  
  n_jobs : int, default=1
      The number of jobs to run in parallel for `fit`.
      If -1, then the number of jobs is set to the number of cores.
  
  random_state : int, RandomState instance, or None (default)
      Set random state to something other than None for reproducible
      results.
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docstr: Predict.
  
  Predict `X` at every quantile if `return_std` is set to False.
  If `return_std` is set to True, then return the mean
  and the predicted standard deviation, which is approximated as
  the (0.84th quantile - 0.16th quantile) divided by 2.0
  
  Parameters
  ----------
  X : array-like, shape=(n_samples, n_features)
      where `n_samples` is the number of samples
      and `n_features` is the number of features.
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code: def predict(self, X, return_std=False, return_quantiles=False):
      """Predict.
  
      Predict `X` at every quantile if `return_std` is set to False.
      If `return_std` is set to True, then return the mean
      and the predicted standard deviation, which is approximated as
      the (0.84th quantile - 0.16th quantile) divided by 2.0
  
      Parameters
      ----------
      X : array-like, shape=(n_samples, n_features)
          where `n_samples` is the number of samples
          and `n_features` is the number of features.
      """
      predicted_quantiles = np.asarray(
          [rgr.predict(X) for rgr in self.regressors_]
      )
      if return_quantiles:
          return predicted_quantiles.T
  
      elif return_std:
          std_quantiles = [0.16, 0.5, 0.84]
          is_present_mask = np.in1d(std_quantiles, self.quantiles)
          if not np.all(is_present_mask):
              raise ValueError(
                  "return_std works only if the quantiles during "
                  "instantiation include 0.16, 0.5 and 0.84"
              )
          low = self.regressors_[self.quantiles.index(0.16)].predict(X)
          high = self.regressors_[self.quantiles.index(0.84)].predict(X)
          mean = self.regressors_[self.quantiles.index(0.5)].predict(X)
          return mean, ((high - low) / 2.0)
  
      # return the mean
      return self.regressors_[self.quantiles.index(0.5)].predict(X)
  
file_path: scikit-optimize/skopt/learning/gbrt.py
imports: import numpy
  from sklearn.base import clone
  from sklearn.base import BaseEstimator, RegressorMixin
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.utils import check_random_state
  from joblib import Parallel, delayed
label_desc: Use image node_id 3 for calling the GradientBoostingQuantileRegressor obj's underlying member method code with example usage: obj.predict(X, return_std, return_quantiles) and returns: self, predicted_quantiles, mean,  unknown