class_docstr: Class for importing LightGBM models.
class_docstr_tok: ["Class","for","importing","LightGBM","models","."]
docstr: Perform prediction for a batch of inputs.
  
  :param x: Input samples.
  :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
docstr_tok: ["Perform","prediction","for","a","batch","of","inputs",".",":","param","x",":","Input","samples",".",":","return",":","Array","of","predictions","of","shape","`","(","nb_inputs",",","nb_classes",")","`","."]
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 of shape `(nb_inputs, nb_classes)`.
      """
      # Apply preprocessing
      x_preprocessed, _ = self._apply_preprocessing(
          x, y=None, fit=False
      )
  
      # Perform prediction
      predictions = self._model.predict(x_preprocessed)
  
      # Apply postprocessing
      predictions = self._apply_postprocessing(
          preds=predictions, fit=False
      )
  
      return predictions
  
file_path: adversarial-robustness-toolbox/art/estimators/classification/lightgbm.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from copy import deepcopy
  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 LightGBMClassifier obj's underlying member method code with example usage: obj.predict(x) and returns: predictions