docstr: Predict regression value for X.
  
  Parameters
  ----------
  X : array-like or sparse matrix of shape = [n_samples, n_features]
      The input samples. Internally, it will be converted to
      ``dtype=np.float32`` and if a sparse matrix is provided
      to a sparse ``csr_matrix``.
  
  quantile : int, optional
      Value ranging from 0 to 100. By default, the mean is returned.
  
  check_input : boolean, (default=True)
      Allow to bypass several input checking.
      Don't use this parameter unless you know what you do.
  
  Returns
  -------
  y : array of shape = [n_samples]
      If quantile is set to None, then return E(Y | X). Else return
      y such that F(Y=y | x) = quantile.
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code: def predict(self, X, quantile=None, check_input=False):
      """
      Predict regression value for X.
  
      Parameters
      ----------
      X : array-like or sparse matrix of shape = [n_samples, n_features]
          The input samples. Internally, it will be converted to
          ``dtype=np.float32`` and if a sparse matrix is provided
          to a sparse ``csr_matrix``.
  
      quantile : int, optional
          Value ranging from 0 to 100. By default, the mean is returned.
  
      check_input : boolean, (default=True)
          Allow to bypass several input checking.
          Don't use this parameter unless you know what you do.
  
      Returns
      -------
      y : array of shape = [n_samples]
          If quantile is set to None, then return E(Y | X). Else return
          y such that F(Y=y | x) = quantile.
      """
      # apply method requires X to be of dtype np.float32
      X = check_array(X, dtype=np.float32, accept_sparse="csc")
      if quantile is None:
          return super(BaseTreeQuantileRegressor, self).predict(
              X, check_input=check_input
          )
  
      quantiles = np.zeros(X.shape[0])
      X_leaves = self.apply(X)
      unique_leaves = np.unique(X_leaves)
      for leaf in unique_leaves:
          quantiles[X_leaves == leaf] = weighted_percentile(
              self.y_train_[self.y_train_leaves_ == leaf], quantile
          )
      return quantiles
  
imports: import logging
  from functools import partial
  import numpy
  import pandas
  from sklearn.ensemble._forest import ForestRegressor
  from sklearn.tree import BaseDecisionTree, DecisionTreeRegressor, ExtraTreeRegressor
  from sklearn.utils import check_array, check_random_state, check_X_y
label_desc: Use image node_id 1 for calling the BaseTreeQuantileRegressor obj's underlying member method code with example usage: obj.predict(X, quantile, check_input) and returns: quantiles, super