class_docstr: Class for importing XGBoost models.
docstr: Fit the classifier on the training set `(x, y)`. Only supported for models of type XGBClassifier.
  
  :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 `xgboost.XGBClassifier` and will be passed to this function as such.
  :raises `NotImplementedException`: This method is not supported for Booster objects.
docstr_tok: ["Fit","the","classifier","on","the","training","set","`","(","x",",","y",")","`",".","Only","supported","for","models","of","type","XGBClassifier",".",":","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","`","xgboost.XGBClassifier","`","and","will","be","passed","to","this","function","as","such",".",":","raises","`","NotImplementedException","`",":","This","method","is","not","supported","for","Booster","objects","."]
code: def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
      """
      Fit the classifier on the training set `(x, y)`. Only supported for models of type XGBClassifier.
  
      :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 `xgboost.XGBClassifier` and will be passed to this function as such.
      :raises `NotImplementedException`: This method is not supported for Booster objects.
      """
      import xgboost
  
      if isinstance(self._model, xgboost.XGBClassifier):
          self._model.fit(x, y, **kwargs)
          _nb_classes = self._get_nb_classes(self._nb_classes)
          if _nb_classes is not None:
              self._nb_classes = _nb_classes
      else:
          raise NotImplementedError
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from copy import deepcopy
  import json
  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.utils import to_categorical
  from art import config
label_desc: Use image node_id 4 for calling the XGBoostClassifier obj's underlying member method code with example usage: obj.fit(x, y) without return types