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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def attack(self, imgs, targets):
 """ Perform the EAD attack on the given instance for the given targets. If self.targeted is true, then the targets represents the target labels If self.targeted is false, then targets are the original class labels """


    batch_size = self.batch_size
    r = []
    for i in range(0, len(imgs) // batch_size):
      _logger.debug(
          ("Running EAD attack on instance %s of %s",
           i * batch_size, len(imgs)))
      r.extend(
          self.attack_batch(
              imgs[i * batch_size:(i + 1) * batch_size],
              targets[i * batch_size:(i + 1) * batch_size]))
    if len(imgs) % batch_size != 0:
      last_elements = len(imgs) - (len(imgs) % batch_size)
      _logger.debug(
          ("Running EAD attack on instance %s of %s",
           last_elements, len(imgs)))
      temp_imgs = np.zeros((batch_size, ) + imgs.shape[2:])
      temp_targets = np.zeros((batch_size, ) + targets.shape[2:])
      temp_imgs[:(len(imgs) % batch_size)] = imgs[last_elements:]
      temp_targets[:(len(imgs) % batch_size)] = targets[last_elements:]
      temp_data = self.attack_batch(temp_imgs, temp_targets)
      r.extend(temp_data[:(len(imgs) % batch_size)],
               targets[last_elements:])
    return np.array(r)