docstr: Predict whether instances are adversarial instances or not.
  
  Parameters
  ----------
  X
      Batch of instances.
  batch_size
      Batch size used when computing scores.
  return_instance_score
      Whether to return instance level adversarial scores.
  
  Returns
  -------
  Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
      - ``'meta'`` has the model's metadata.
      - ``'data'`` contains the adversarial predictions and instance level adversarial scores.
docstr_tok: ["Predict","whether","instances","are","adversarial","instances","or","not",".","Parameters","--","--","--","--","--","X","Batch","of","instances",".","batch_size","Batch","size","used","when","computing","scores",".","return_instance_score","Whether","to","return","instance","level","adversarial","scores",".","Returns","--","--","--","-","Dictionary","containing","``","'meta","'","``","and","``","'data","'","``","dictionaries",".","-","``","'meta","'","``","has","the","model","'s","metadata",".","-","``","'data","'","``","contains","the","adversarial","predictions","and","instance","level","adversarial","scores","."]
code: def predict(
      self,
      X: np.ndarray,
      batch_size: int = int(1e10),
      return_instance_score: bool = True,
  ) -> Dict[Dict[str, str], Dict[str, np.ndarray]]:
      """
      Predict whether instances are adversarial instances or not.
  
      Parameters
      ----------
      X
          Batch of instances.
      batch_size
          Batch size used when computing scores.
      return_instance_score
          Whether to return instance level adversarial scores.
  
      Returns
      -------
      Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
          - ``'meta'`` has the model's metadata.
          - ``'data'`` contains the adversarial predictions and instance level adversarial scores.
      """
      adv_score = self.score(X, batch_size=batch_size)
  
      # values above threshold are adversarial
      adv_pred = (adv_score > self.threshold).astype(int)  # type: ignore
  
      # populate output dict
      ad = adversarial_prediction_dict()
      ad["meta"] = self.meta
      ad["data"]["is_adversarial"] = adv_pred
      if return_instance_score:
          ad["data"]["instance_score"] = adv_score
      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 5 for calling the AdversarialAE obj's underlying member method code with example usage: obj.predict(X, batch_size, return_instance_score) and returns: ad