code: def mquantiles_cimj(
      data, prob=[0.25, 0.50, 0.75], alpha=0.05, axis=None
  ):
      """
      Computes the alpha confidence interval for the selected quantiles of the
      data, with Maritz-Jarrett estimators.
  
      Parameters
      ----------
      data : ndarray
          Data array.
      prob : sequence, optional
          Sequence of quantiles to compute.
      alpha : float, optional
          Confidence level of the intervals.
      axis : int or None, optional
          Axis along which to compute the quantiles.
          If None, use a flattened array.
  
      Returns
      -------
      ci_lower : ndarray
          The lower boundaries of the confidence interval.  Of the same length as
          `prob`.
      ci_upper : ndarray
          The upper boundaries of the confidence interval.  Of the same length as
          `prob`.
  
      """
      alpha = min(alpha, 1 - alpha)
      z = norm.ppf(1 - alpha / 2.0)
      xq = mstats.mquantiles(data, prob, alphap=0, betap=0, axis=axis)
      smj = mjci(data, prob, axis=axis)
      return (xq - z * smj, xq + z * smj)
  
file_path: catboost/contrib/python/scipy/py2/scipy/stats/mstats_extras.py
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy import float_, int_, ndarray
  import numpy.ma
  from numpy.ma import MaskedArray
  from .None import mstats_basic
  from scipy.stats.distributions import norm, beta, t, binom
label_desc: Use image node_id 6 for calling a global function with example usage: mquantiles_cimj(data, prob, alpha, axis) and returns: unknown,  unknown