class_docstr: Wrapper class for scikit-learn regression models.
docstr: Perform prediction for a batch of inputs.
  
  :param x: Input samples.
  :return: Array of predictions.
  :raises `ValueError`: If the regressor does not have the method `predict`
docstr_tok: ["Perform","prediction","for","a","batch","of","inputs",".",":","param","x",":","Input","samples",".",":","return",":","Array","of","predictions",".",":","raises","`","ValueError","`",":","If","the","regressor","does","not","have","the","method","`","predict","`"]
code: def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
      """
      Perform prediction for a batch of inputs.
  
      :param x: Input samples.
      :return: Array of predictions.
      :raises `ValueError`: If the regressor does not have the method `predict`
      """
      # Apply defences
      x_preprocessed, _ = self._apply_preprocessing(
          x, y=None, fit=False
      )
  
      if callable(getattr(self.model, "predict", None)):
          y_pred = self.model.predict(x_preprocessed)
      else:
          raise ValueError(
              "The provided model does not have the method `predict`."
          )
  
      # Apply postprocessing
      predictions = self._apply_postprocessing(preds=y_pred, fit=False)
  
      return predictions
  
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 4 for calling the ScikitlearnRegressor obj's underlying member method code with example usage: obj.predict(x) and returns: predictions