class_docstr: Wrapper class for scikit-learn regression models.
docstr: Fit the regressor on the training set `(x, y)`.
  
  :param x: Training data.
  :param y: Target values.
  :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
         `fit` function in `sklearn` regressor and will be passed to this function as such.
docstr_tok: ["Fit","the","regressor","on","the","training","set","`","(","x",",","y",")","`",".",":","param","x",":","Training","data",".",":","param","y",":","Target","values",".",":","param","kwargs",":","Dictionary","of","framework-specific","arguments",".","These","should","be","parameters","supported","by","the","`","fit","`","function","in","`","sklearn","`","regressor","and","will","be","passed","to","this","function","as","such","."]
code: def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
      """
      Fit the regressor on the training set `(x, y)`.
  
      :param x: Training data.
      :param y: Target values.
      :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
             `fit` function in `sklearn` regressor 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._input_shape = self._get_input_shape(self.model)
  
imports: import logging
  import os
  import pickle
  from copy import deepcopy
  from typing import List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.estimators.scikitlearn import ScikitlearnEstimator
  from art.estimators.regression.regressor import RegressorMixin
  from art import config
label_desc: Use image node_id 3 for calling the ScikitlearnRegressor obj's underlying member method code with example usage: obj.fit(x, y) without return types