class_docstr: A smoothed classifier g 
docstr: Monte Carlo algorithm for evaluating the prediction of g at x.  With
  probability at least 1 - alpha, the class returned by this method will
  equal g(x).
  This function uses the hypothesis test described in
  https://arxiv.org/abs/1610.03944 for identifying the top category of a
  multinomial distribution.
  
  :param x: the input [height x width x channel]
  :param n: the number of Monte Carlo samples to use
  :param alpha: the failure probability
  :param batch_size: batch size to use when evaluating the base classifier
  :return: the predicted class, or ABSTAIN
code: def predict(
      self, x: np.ndarray, n: int, alpha: float, batch_size: int
  ) -> int:
      """Monte Carlo algorithm for evaluating the prediction of g at x.  With
      probability at least 1 - alpha, the class returned by this method will
      equal g(x).
      This function uses the hypothesis test described in
      https://arxiv.org/abs/1610.03944 for identifying the top category of a
      multinomial distribution.
  
      :param x: the input [height x width x channel]
      :param n: the number of Monte Carlo samples to use
      :param alpha: the failure probability
      :param batch_size: batch size to use when evaluating the base classifier
      :return: the predicted class, or ABSTAIN
      """
      counts = self._sample_noise(x, n, batch_size)
      top2 = counts.argsort()[::-1][:2]
      count1 = counts[top2[0]]
      count2 = counts[top2[1]]
      if binom_test(count1, count1 + count2, p=0.5) > alpha:
          return Smooth.ABSTAIN
      else:
          return top2[0]
  
file_path: neural-structured-learning/research/third_party/denoised_smoothing/scripts/certification_utils.py
imports: from math import ceil
  from statsmodels.stats.proportion import proportion_confint
  from scipy.stats import norm, binom_test
  import tensorflow
  import numpy
label_desc: Use image node_id 3 for calling the Smooth obj's underlying member method code with example usage: obj.predict(x, n, alpha, batch_size) and returns: Smooth, top2