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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code: def __init__(
      self,
      quantiles=[0.16, 0.5, 0.84],
      base_estimator=None,
      n_jobs=1,
      random_state=None,
  ):
      self.quantiles = quantiles
      self.random_state = random_state
      self.base_estimator = base_estimator
      self.n_jobs = n_jobs
  
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 1 to create a new GradientBoostingQuantileRegressor object from inherited base classes: BaseEstimator, RegressorMixin with example: obj = GradientBoostingQuantileRegressor(quantiles, base_estimator, n_jobs, random_state)