class_docstr: This class implements a Classifier extension that wraps a classifier and a detector.
  More details in https://arxiv.org/abs/1705.07263
class_docstr_tok: ["This","class","implements","a","Classifier","extension","that","wraps","a","classifier","and","a","detector",".","More","details","in","https",":","\/\/arxiv.org\/abs\/1705.07263"]
docstr: Perform prediction for a batch of inputs.
  
  :param x: Input samples.
  :param batch_size: Size of batches.
  :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
docstr_tok: ["Perform","prediction","for","a","batch","of","inputs",".",":","param","x",":","Input","samples",".",":","param","batch_size",":","Size","of","batches",".",":","return",":","Array","of","predictions","of","shape","`","(","nb_inputs",",","nb_classes",")","`","."]
code: def predict(
      self, x: np.ndarray, batch_size: int = 128, **kwargs
  ) -> np.ndarray:
      """
      Perform prediction for a batch of inputs.
  
      :param x: Input samples.
      :param batch_size: Size of batches.
      :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
      """
      # Compute the prediction logits
      classifier_outputs = self.classifier.predict(
          x=x, batch_size=batch_size
      )
      detector_outputs = self.detector.predict(
          x=x, batch_size=batch_size
      )
      detector_outputs = (
          np.reshape(detector_outputs, [-1]) + 1
      ) * np.max(classifier_outputs, axis=1)
      detector_outputs = np.reshape(detector_outputs, [-1, 1])
      combined_outputs = np.concatenate(
          [classifier_outputs, detector_outputs], axis=1
      )
  
      # Apply postprocessing
      predictions = self._apply_postprocessing(
          preds=combined_outputs, fit=False
      )
  
      return predictions
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from art.estimators.classification.classifier import ClassifierNeuralNetwork
label_desc: Use image node_id 3 for calling the DetectorClassifier obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: predictions