code: def trimmed_mean_ci(
      data,
      limits=(0.2, 0.2),
      inclusive=(True, True),
      alpha=0.05,
      axis=None,
  ):
      """
      Selected confidence interval of the trimmed mean along the given axis.
  
      Parameters
      ----------
      data : array_like
          Input data.
      limits : {None, tuple}, optional
          None or a two item tuple.
          Tuple of the percentages to cut on each side of the array, with respect
          to the number of unmasked data, as floats between 0. and 1. If ``n``
          is the number of unmasked data before trimming, then
          (``n * limits[0]``)th smallest data and (``n * limits[1]``)th
          largest data are masked.  The total number of unmasked data after
          trimming is ``n * (1. - sum(limits))``.
          The value of one limit can be set to None to indicate an open interval.
  
          Defaults to (0.2, 0.2).
      inclusive : (2,) tuple of boolean, optional
          If relative==False, tuple indicating whether values exactly equal to
          the absolute limits are allowed.
          If relative==True, tuple indicating whether the number of data being
          masked on each side should be rounded (True) or truncated (False).
  
          Defaults to (True, True).
      alpha : float, optional
          Confidence level of the intervals.
  
          Defaults to 0.05.
      axis : int, optional
          Axis along which to cut. If None, uses a flattened version of `data`.
  
          Defaults to None.
  
      Returns
      -------
      trimmed_mean_ci : (2,) ndarray
          The lower and upper confidence intervals of the trimmed data.
  
      """
      data = ma.array(data, copy=False)
      trimmed = mstats.trimr(
          data, limits=limits, inclusive=inclusive, axis=axis
      )
      tmean = trimmed.mean(axis)
      tstde = mstats.trimmed_stde(
          data, limits=limits, inclusive=inclusive, axis=axis
      )
      df = trimmed.count(axis) - 1
      tppf = t.ppf(1 - alpha / 2.0, df)
      return np.array((tmean - tppf * tstde, tmean + tppf * tstde))
  
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 4 for calling a global function with example usage: trimmed_mean_ci(data, limits, inclusive, alpha, axis) and returns: np