class_docstr: Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced
  in Cohen et al. (2019).
  
  | Paper link: https://arxiv.org/abs/1902.02918
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docstr: Uses Clopper-Pearson method to return a (1-alpha) lower confidence bound on bernoulli proportion
  
  :param n_class_samples: Number of samples of a specific class.
  :param n_total_samples: Number of samples for certification.
  :return: Lower bound on the binomial proportion w.p. (1-alpha) over samples.
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code: def _lower_confidence_bound(
      self, n_class_samples: int, n_total_samples: int
  ) -> float:
      """
      Uses Clopper-Pearson method to return a (1-alpha) lower confidence bound on bernoulli proportion
  
      :param n_class_samples: Number of samples of a specific class.
      :param n_total_samples: Number of samples for certification.
      :return: Lower bound on the binomial proportion w.p. (1-alpha) over samples.
      """
      from statsmodels.stats.proportion import proportion_confint
  
      return proportion_confint(
          n_class_samples,
          n_total_samples,
          alpha=2 * self.alpha,
          method="beta",
      )[0]
  
file_path: adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/randomized_smoothing.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from abc import ABC
  import logging
  from typing import Optional, Tuple
  import numpy
  from scipy.stats import norm
  from tqdm.auto import tqdm
  from art.config import ART_NUMPY_DTYPE
label_desc: Use image node_id 9 for calling the RandomizedSmoothingMixin obj's underlying member method code with example usage: obj._lower_confidence_bound(n_class_samples, n_total_samples) and returns: proportion_confint