class_docstr: Class for importing CatBoost models.
class_docstr_tok: ["Class","for","importing","CatBoost","models","."]
docstr: Fit the classifier on the training set `(x, y)`.
  
  :param x: Training data.
  :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
  :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
         `fit` function in `catboost.core.CatBoostClassifier` and will be passed to this function as such.
docstr_tok: ["Fit","the","classifier","on","the","training","set","`","(","x",",","y",")","`",".",":","param","x",":","Training","data",".",":","param","y",":","Target","values","(","class","labels",")","one-hot-encoded","of","shape","(","nb_samples",",","nb_classes",")",".",":","param","kwargs",":","Dictionary","of","framework-specific","arguments",".","These","should","be","parameters","supported","by","the","`","fit","`","function","in","`","catboost.core.CatBoostClassifier","`","and","will","be","passed","to","this","function","as","such","."]
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.
      :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
      :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
             `fit` function in `catboost.core.CatBoostClassifier` and will be passed to this function as such.
      """
      # Apply preprocessing
      x_preprocessed, y_preprocessed = self._apply_preprocessing(
          x, y, fit=True
      )
  
      self._model.fit(x_preprocessed, y_preprocessed, **kwargs)
      self.nb_classes = self._get_nb_classes()
  
file_path: adversarial-robustness-toolbox/art/estimators/classification/catboost.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import pickle
  from typing import List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from art.estimators.classification.classifier import ClassifierDecisionTree
  from art import config
label_desc: Use image node_id 4 for calling the CatBoostARTClassifier obj's underlying member method code with example usage: obj.fit(x, y) without return types