code: def calc_bootstrap_ci_for_mean(samples, level=0.05, tries=999):
      """
      Count confidence intervals for difference each two samples.
  
      Args:
          :param samples: samples
          :param level: (float) Level for the confidence interval.
          :param tries: bootstrap samples to use
          :return: (left, right) border of confidence interval
  
      """
  
      if not (samples == 0).all():
          samples = np.array(samples)
          means = []
          for _ in range(0, tries):
              resample = np.random.choice(samples, len(samples))
              means.append(np.mean(resample))
          means = sorted(means)
          left = means[int(tries * (level / 2))]
          right = means[int(tries * (1.0 - level / 2))]
          return left, right
      else:
          return 0, 0
  
imports: import numpy
  import pandas
  from enum import Enum
  from ..None import CatBoostError
  from ..core import metric_description_or_str_to_str
  from ..utils import compute_wx_test
label_desc: Use image node_id 2 for calling a global function with example usage: calc_bootstrap_ci_for_mean(samples, level, tries) and returns: left,  right, int,  int