class_docstr: Wrapper class for GPy Gaussian Process classification models.
class_docstr_tok: ["Wrapper","class","for","GPy","Gaussian","Process","classification","models","."]
docstr: Fit the classifier on the training set `(x, y)`.
  
  :param x: Training data. Not used, as given to model in initialized earlier.
  :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
docstr_tok: ["Fit","the","classifier","on","the","training","set","`","(","x",",","y",")","`",".",":","param","x",":","Training","data",".","Not","used",",","as","given","to","model","in","initialized","earlier",".",":","param","y",":","Target","values","(","class","labels",")","one-hot-encoded","of","shape","(","nb_samples",",","nb_classes",")","."]
code: def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
      """
      Fit the classifier on the training set `(x, y)`.
  
      :param x: Training data. Not used, as given to model in initialized earlier.
      :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
      """
      raise NotImplementedError
  
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 7 for calling the GPyGaussianProcessClassifier obj's underlying member method code with example usage: obj.fit(x, y) without return types