class_docstr: A smoothed classifier g 
docstr: :param base_classifier: maps from [batch x height x width x channel] to
  [batch x num_classes]
  :param num_classes:
  :param sigma: the noise level hyperparameter
code: def __init__(
      self,
      base_classifier: tf.keras.Model,
      num_classes: int,
      sigma: float,
  ):
      """
      :param base_classifier: maps from [batch x height x width x channel] to
      [batch x num_classes]
      :param num_classes:
      :param sigma: the noise level hyperparameter
      """
      self.base_classifier = base_classifier
      self.num_classes = num_classes
      self.sigma = sigma
  
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 1 to create a new Smooth object  with example: obj = Smooth(base_classifier, num_classes, sigma)