class_docstr: This class implements a classifier with the Jax framework.
class_docstr_tok: ["This","class","implements","a","classifier","with","the","Jax","framework","."]
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)`.
      """
      # Apply preprocessing
      x_preprocessed, _ = self._apply_preprocessing(
          x, y=None, fit=False
      )
  
      results_list = []
  
      # Run prediction with batch processing
      num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
      for m in range(num_batch):
          # Batch indexes
          begin, end = (
              m * batch_size,
              min((m + 1) * batch_size, x_preprocessed.shape[0]),
          )
  
          output = self.predict_func(
              self.model, x_preprocessed[begin:end]
          )
          results_list.append(output)
  
      results = np.vstack(results_list)
  
      # Apply postprocessing
      predictions = self._apply_postprocessing(preds=results, fit=False)
  
      return predictions
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import random
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
  from art.experimental.estimators.jax import JaxEstimator
label_desc: Use image node_id 7 for calling the JaxClassifier obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: predictions