class_docstr: Class implementing a simple linear scheduler to grow the certification radius.
class_docstr_tok: ["Class","implementing","a","simple","linear","scheduler","to","grow","the","certification","radius","."]
docstr: Grow the certification radius by self.step_per_epoch
docstr_tok: ["Grow","the","certification","radius","by","self.step_per_epoch"]
code: def step(self) -> float:
      """
      Grow the certification radius by self.step_per_epoch
      """
      self.bound += self.step_per_epoch
      return self.bound
  
file_path: adversarial-robustness-toolbox/art/defences/trainer/certified_adversarial_trainer_pytorch.py
imports: import logging
  import math
  import random
  import sys
  import numpy
  from sklearn.utils import shuffle
  from tqdm import tqdm
  from art.defences.trainer.trainer import Trainer
  from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 2 for calling the DefaultLinearScheduler obj's underlying member method code with example usage: obj.step() and returns: self