docstr: Correct adversarial instances if the adversarial score is above the threshold.
  
  Parameters
  ----------
  X
      Batch of instances.
  batch_size
      Batch size used when computing scores.
  return_instance_score
      Whether to return instance level adversarial scores.
  return_all_predictions
      Whether to return the predictions on the original and the reconstructed data.
  
  Returns
  -------
  Dict with corrected predictions and information whether an instance is adversarial or not.
docstr_tok: ["Correct","adversarial","instances","if","the","adversarial","score","is","above","the","threshold",".","Parameters","--","--","--","--","--","X","Batch","of","instances",".","batch_size","Batch","size","used","when","computing","scores",".","return_instance_score","Whether","to","return","instance","level","adversarial","scores",".","return_all_predictions","Whether","to","return","the","predictions","on","the","original","and","the","reconstructed","data",".","Returns","--","--","--","-","Dict","with","corrected","predictions","and","information","whether","an","instance","is","adversarial","or","not","."]
code: def correct(
      self,
      X: np.ndarray,
      batch_size: int = int(1e10),
      return_instance_score: bool = True,
      return_all_predictions: bool = True,
  ) -> Dict[Dict[str, str], Dict[str, np.ndarray]]:
      """
      Correct adversarial instances if the adversarial score is above the threshold.
  
      Parameters
      ----------
      X
          Batch of instances.
      batch_size
          Batch size used when computing scores.
      return_instance_score
          Whether to return instance level adversarial scores.
      return_all_predictions
          Whether to return the predictions on the original and the reconstructed data.
  
      Returns
      -------
      Dict with corrected predictions and information whether an instance is adversarial or not.
      """
      adv_score, y, y_recon = self.score(
          X, batch_size=batch_size, return_predictions=True
      )
  
      # values above threshold are adversarial
      adv_pred = (adv_score > self.threshold).astype(int)
      idx_adv = np.where(adv_pred == 1)[0]
  
      # correct predictions on adversarial instances
      y = y.argmax(axis=-1)
      y_recon = y_recon.argmax(axis=-1)
      y_correct = y.copy()
      y_correct[idx_adv] = y_recon[idx_adv]
  
      # populate output dict
      ad = adversarial_correction_dict()
      ad["meta"] = self.meta
      ad["data"]["is_adversarial"] = adv_pred
      if return_instance_score:
          ad["data"]["instance_score"] = adv_score
      ad["data"]["corrected"] = y_correct
      if return_all_predictions:
          ad["data"]["no_defense"] = y
          ad["data"]["defense"] = y_recon
      return ad
  
imports: import logging
  from typing import Callable, Dict, List, Tuple, Union, cast
  import numpy
  import tensorflow
  from alibi_detect.base import BaseDetector, FitMixin, ThresholdMixin, adversarial_correction_dict, adversarial_prediction_dict
  from alibi_detect.models.tensorflow.autoencoder import AE
  from alibi_detect.models.tensorflow.losses import loss_adv_ae
  from alibi_detect.models.tensorflow.trainer import trainer
  from alibi_detect.utils.tensorflow.prediction import predict_batch
  from alibi_detect.utils._types import OptimizerTF
  from tensorflow.keras.layers import Dense, Flatten
  from tensorflow.keras.losses import kld
  from tensorflow.keras.models import Model
label_desc: Use image node_id 6 for calling the AdversarialAE obj's underlying member method code with example usage: obj.correct(X, batch_size, return_instance_score, return_all_predictions) and returns: ad