class_docstr: Class performing adversarial training following the DP-InstaHide protocol.
  
  Uses data augmentation methods in conjunction with some type of additive noise.
  
  | Paper link: https://arxiv.org/abs/2103.02079
class_docstr_tok: ["Class","performing","adversarial","training","following","the","DP-InstaHide","protocol",".","Uses","data","augmentation","methods","in","conjunction","with","some","type","of","additive","noise",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/2103.02079"]
docstr: Perform prediction using the adversarially trained classifier.
  
  :param x: Input samples.
  :param kwargs: Other parameters to be passed on to the `predict` function of the classifier.
  :return: Predictions for test set.
docstr_tok: ["Perform","prediction","using","the","adversarially","trained","classifier",".",":","param","x",":","Input","samples",".",":","param","kwargs",":","Other","parameters","to","be","passed","on","to","the","`","predict","`","function","of","the","classifier",".",":","return",":","Predictions","for","test","set","."]
code: def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
      """
      Perform prediction using the adversarially trained classifier.
  
      :param x: Input samples.
      :param kwargs: Other parameters to be passed on to the `predict` function of the classifier.
      :return: Predictions for test set.
      """
      return self._classifier.predict(x, **kwargs)
  
file_path: adversarial-robustness-toolbox/art/defences/trainer/dp_instahide_trainer.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import sys
  import time
  from typing import List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from tqdm.auto import trange
  from art.defences.trainer.trainer import Trainer
label_desc: Use image node_id 5 for calling the DPInstaHideTrainer obj's underlying member method code with example usage: obj.predict(x) and returns: self