class_docstr: A Causal Tree regressor class.
  The Causal Tree is a decision tree regressor with a split criteria for treatment effects.
  Details are available at Athey and Imbens (2015) (https://arxiv.org/abs/1504.01132)
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docstr: Prepare input data with treatment info for DecisionTreeRegressor
  Args:
      X: : (np.ndarray), feature matrix
      treatment: : (np.ndarray), treatment vector
      y: : (np.ndarray), outcome vector
  Returns: X, y, w
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code: def _prepare_data(
      self, X: np.ndarray, treatment: np.ndarray, y: np.ndarray
  ) -> tuple:
      """
      Prepare input data with treatment info for DecisionTreeRegressor
      Args:
          X: : (np.ndarray), feature matrix
          treatment: : (np.ndarray), treatment vector
          y: : (np.ndarray), outcome vector
      Returns: X, y, w
      """
      if y.shape[0] != treatment.shape[0]:
          raise ValueError(
              f"The number of `treatment` and `y` rows are not equal: {y.shape[0]} {treatment.shape[0]}"
          )
      check_treatment_vector(treatment, self.control_name)
  
      self.is_treatment = treatment != self.control_name
      w = self.is_treatment.astype(int)
  
      X = check_array(X, dtype=DTYPE, accept_sparse="csc")
      y = check_array(y, ensure_2d=False, dtype=None)
  
      self.n_samples, self.n_features = X.shape
  
      return X, y, w
  
imports: import logging
  import sys
  from typing import Union
  import tqdm
  import numpy
  from numpy import float32
  from pathos.pools import ProcessPool
  from scipy.stats import norm
  from sklearn.base import RegressorMixin
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from causalml.inference.meta.utils import check_treatment_vector
  from ._tree import BaseCausalDecisionTree
  from ..utils import get_tree_leaves_mask, timeit
label_desc: Use image node_id 8 for calling the CausalTreeRegressor obj's underlying member method code with example usage: obj._prepare_data(X, treatment, y) and returns: X,  y,  w