docstr: Build a decision tree classifier from the training set (X, y).
  
  Parameters
  ----------
  X : array-like or sparse matrix, shape = [n_samples, n_features]
      The training input samples. Internally, it will be converted to
      ``dtype=np.float32`` and if a sparse matrix is provided
      to a sparse ``csc_matrix``.
  
  y : array-like, shape = [n_samples] or [n_samples, n_outputs]
      The target values (class labels) as integers or strings.
  
  sample_weight : array-like, shape = [n_samples] or None
      Sample weights. If None, then samples are equally weighted. Splits
      that would create child nodes with net zero or negative weight are
      ignored while searching for a split in each node. Splits are also
      ignored if they would result in any single class carrying a
      negative weight in either child node.
  
  check_input : boolean, (default=True)
      Allow to bypass several input checking.
      Don't use this parameter unless you know what you do.
  
  Returns
  -------
  self : object
      Returns self.
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code: def fit(self, X, y, sample_weight=None, check_input=True):
      """
      Build a decision tree classifier from the training set (X, y).
  
      Parameters
      ----------
      X : array-like or sparse matrix, shape = [n_samples, n_features]
          The training input samples. Internally, it will be converted to
          ``dtype=np.float32`` and if a sparse matrix is provided
          to a sparse ``csc_matrix``.
  
      y : array-like, shape = [n_samples] or [n_samples, n_outputs]
          The target values (class labels) as integers or strings.
  
      sample_weight : array-like, shape = [n_samples] or None
          Sample weights. If None, then samples are equally weighted. Splits
          that would create child nodes with net zero or negative weight are
          ignored while searching for a split in each node. Splits are also
          ignored if they would result in any single class carrying a
          negative weight in either child node.
  
      check_input : boolean, (default=True)
          Allow to bypass several input checking.
          Don't use this parameter unless you know what you do.
  
      Returns
      -------
      self : object
          Returns self.
      """
      # y passed from a forest is 2-D. This is to silence the
      # annoying data-conversion warnings.
      y = np.asarray(y)
      if np.ndim(y) == 2 and y.shape[1] == 1:
          y = np.ravel(y)
  
      # apply method requires X to be of dtype np.float32
      X, y = check_X_y(
          X,
          y,
          accept_sparse="csc",
          dtype=np.float32,
          multi_output=False,
      )
      super(BaseTreeQuantileRegressor, self).fit(
          X, y, sample_weight=sample_weight, check_input=check_input
      )
      self.y_train_ = y
  
      # Stores the leaf nodes that the samples lie in.
      self.y_train_leaves_ = self.tree_.apply(X)
      return self
  
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 2 for calling the BaseTreeQuantileRegressor obj's underlying member method code with example usage: obj.fit(X, y, sample_weight, check_input) and returns: self