class_docstr: Wrapper class for GPy Gaussian Process classification models.
class_docstr_tok: ["Wrapper","class","for","GPy","Gaussian","Process","classification","models","."]
docstr: Perform uncertainty prediction for a batch of inputs.
  
  :param x: Input samples.
  :return: Array of uncertainty predictions of shape `(nb_inputs)`.
docstr_tok: ["Perform","uncertainty","prediction","for","a","batch","of","inputs",".",":","param","x",":","Input","samples",".",":","return",":","Array","of","uncertainty","predictions","of","shape","`","(","nb_inputs",")","`","."]
code: def predict_uncertainty(self, x: np.ndarray) -> np.ndarray:
      """
      Perform uncertainty prediction for a batch of inputs.
  
      :param x: Input samples.
      :return: Array of uncertainty predictions of shape `(nb_inputs)`.
      """
      # Apply preprocessing
      x_preprocessed, _ = self._apply_preprocessing(
          x, y=None, fit=False
      )
  
      # Perform prediction
      out = self.model.predict_noiseless(x_preprocessed)[1]
  
      # Apply postprocessing
      predictions = self._apply_postprocessing(preds=out, fit=False)
  
      return predictions
  
file_path: adversarial-robustness-toolbox/art/estimators/classification/GPy.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  from typing import List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from art.estimators.classification.classifier import ClassifierClassLossGradients
  from art import config
label_desc: Use image node_id 6 for calling the GPyGaussianProcessClassifier obj's underlying member method code with example usage: obj.predict_uncertainty(x) and returns: predictions