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: Runs a single bootstrap.
  
  Fits on bootstrapped sample, then predicts on whole population.
  
  Args:
      X (np.ndarray): a feature matrix
      treatment (np.ndarray): a treatment vector
      y (np.ndarray): an outcome vector
      sample_size (int): bootstrap sample size
      seed: (int): bootstrap seed
  
  Returns:
      (np.ndarray): bootstrap predictions
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code: def bootstrap(
      self,
      X: np.ndarray,
      treatment: np.ndarray,
      y: np.ndarray,
      sample_size: int,
      seed: int,
  ) -> np.ndarray:
      """Runs a single bootstrap.
  
      Fits on bootstrapped sample, then predicts on whole population.
  
      Args:
          X (np.ndarray): a feature matrix
          treatment (np.ndarray): a treatment vector
          y (np.ndarray): an outcome vector
          sample_size (int): bootstrap sample size
          seed: (int): bootstrap seed
  
      Returns:
          (np.ndarray): bootstrap predictions
      """
      _rnd = np.random.RandomState(seed=seed)
      idxs = _rnd.choice(np.arange(0, X.shape[0]), size=sample_size)
      X_b, y_b, treatment_b = X[idxs], y[idxs], treatment[idxs]
      self.fit(X=X_b, treatment=treatment_b, y=y_b)
      te_b = self.predict(X=X)
      return te_b
  
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 7 for calling the CausalTreeRegressor obj's underlying member method code with example usage: obj.bootstrap(X, treatment, y, sample_size, seed) and returns: te_b