class_docstr: Implementation of a postprocessor based on selecting high confidence predictions to return as classifier output.
docstr: Perform model postprocessing and return postprocessed output.
  
  :param preds: model output to be postprocessed.
  :return: Postprocessed model output.
docstr_tok: ["Perform","model","postprocessing","and","return","postprocessed","output",".",":","param","preds",":","model","output","to","be","postprocessed",".",":","return",":","Postprocessed","model","output","."]
code: def __call__(self, preds: np.ndarray) -> np.ndarray:
      """
      Perform model postprocessing and return postprocessed output.
  
      :param preds: model output to be postprocessed.
      :return: Postprocessed model output.
      """
      post_preds = preds.copy()
      post_preds[post_preds < self.cutoff] = 0.0
  
      return post_preds
  
file_path: adversarial-robustness-toolbox/art/defences/postprocessor/high_confidence.py
imports: import logging
  import numpy
  from art.defences.postprocessor.postprocessor import Postprocessor
label_desc: Use image node_id 2 for calling the HighConfidence obj's underlying member method code with example usage: obj.__call__(preds) and returns: post_preds