class_docstr: Wraps an instance of an :obj:`art.classifiers.Classifier` to extend
  :obj:`~aif360.algorithms.Transformer`.
docstr: Perform prediction for the input.
  
  Args:
      dataset (Dataset): Test dataset.
      logits (bool, optional): True is prediction should be done at the
          logits layer (passed through to ART).
  
  Returns:
      Dataset: Dataset with predicted labels in the `labels` field.
docstr_tok: ["Perform","prediction","for","the","input",".","Args",":","dataset","(","Dataset",")",":","Test","dataset",".","logits","(","bool",",","optional",")",":","True","is","prediction","should","be","done","at","the","logits","layer","(","passed","through","to","ART",")",".","Returns",":","Dataset",":","Dataset","with","predicted","labels","in","the","`","labels","`","field","."]
code: def predict(self, dataset, logits=False):
      """Perform prediction for the input.
  
      Args:
          dataset (Dataset): Test dataset.
          logits (bool, optional): True is prediction should be done at the
              logits layer (passed through to ART).
  
      Returns:
          Dataset: Dataset with predicted labels in the `labels` field.
      """
      pred_labels = self._art_classifier.predict(
          dataset.features, dataset.labels, logits=logits
      )
  
      if isinstance(dataset, BinaryLabelDataset):
          pred_labels = np.argmax(pred_labels, axis=1).reshape((-1, 1))
  
      pred_dataset = dataset.copy()
      pred_dataset.labels = pred_labels
  
      return pred_dataset
  
imports: import numpy
  from aif360.datasets import BinaryLabelDataset
  from aif360.algorithms import Transformer
label_desc: Use image node_id 3 for calling the ARTClassifier obj's underlying member method code with example usage: obj.predict(dataset, logits) and returns: pred_dataset