class_docstr: Implementation of (De)Randomized Smoothing applied to classifier predictions as introduced
  in Levine et al. (2020).
  
  | Paper link: https://arxiv.org/abs/2002.10733
class_docstr_tok: ["Implementation","of","(","De",")","Randomized","Smoothing","applied","to","classifier","predictions","as","introduced","in","Levine","et","al",".","(","2020",")",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/2002.10733"]
code: def _predict_classifier(
      self,
      x: np.ndarray,
      batch_size: int,
      training_mode: bool,
      **kwargs,
  ) -> np.ndarray:
      import tensorflow as tf
  
      outputs = TensorFlowV2Classifier.predict(
          self,
          x=x,
          batch_size=batch_size,
          training_mode=training_mode,
          **kwargs,
      )
      if self.logits:
          outputs = tf.nn.softmax(outputs)
      return np.asarray(outputs >= self.threshold).astype(int)
  
file_path: adversarial-robustness-toolbox/art/estimators/certification/derandomized_smoothing/tensorflow.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Callable, List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from tqdm import tqdm
  from art.estimators.certification.derandomized_smoothing.derandomized import DeRandomizedSmoothingMixin
  from art.estimators.classification.tensorflow import TensorFlowV2Classifier
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 2 for calling the TensorFlowV2DeRandomizedSmoothing obj's underlying member method code with example usage: obj._predict_classifier(x, batch_size, training_mode) and returns: np