docstr: Compute outlier scores and transform into outlier predictions.
  
  Parameters
  ----------
  X
      Batch of instances.
  batch_size
      Batch size used when making predictions with the AEGMM.
  return_instance_score
      Whether to return instance level outlier scores.
  
  Returns
  -------
  Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
      - ``'meta'`` has the model's metadata.
      - ``'data'`` contains the outlier predictions and instance level outlier scores.
docstr_tok: ["Compute","outlier","scores","and","transform","into","outlier","predictions",".","Parameters","--","--","--","--","--","X","Batch","of","instances",".","batch_size","Batch","size","used","when","making","predictions","with","the","AEGMM",".","return_instance_score","Whether","to","return","instance","level","outlier","scores",".","Returns","--","--","--","-","Dictionary","containing","``","'meta","'","``","and","``","'data","'","``","dictionaries",".","-","``","'meta","'","``","has","the","model","'s","metadata",".","-","``","'data","'","``","contains","the","outlier","predictions","and","instance","level","outlier","scores","."]
code: def predict(
      self,
      X: np.ndarray,
      batch_size: int = int(1e10),
      return_instance_score: bool = True,
  ) -> Dict[Dict[str, str], Dict[np.ndarray, np.ndarray]]:
      """
      Compute outlier scores and transform into outlier predictions.
  
      Parameters
      ----------
      X
          Batch of instances.
      batch_size
          Batch size used when making predictions with the AEGMM.
      return_instance_score
          Whether to return instance level outlier scores.
  
      Returns
      -------
      Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
          - ``'meta'`` has the model's metadata.
          - ``'data'`` contains the outlier predictions and instance level outlier scores.
      """
      # compute outlier scores
      iscore = self.score(X, batch_size=batch_size)
  
      # values above threshold are outliers
      outlier_pred = (iscore > self.threshold).astype(int)
  
      # populate output dict
      od = outlier_prediction_dict()
      od["meta"] = self.meta
      od["data"]["is_outlier"] = outlier_pred
      if return_instance_score:
          od["data"]["instance_score"] = iscore
      return od
  
file_path: alibi-detect/alibi_detect/od/aegmm.py
imports: import logging
  import numpy
  import tensorflow
  from typing import Callable, Dict, Tuple
  from alibi_detect.models.tensorflow.autoencoder import AEGMM, eucl_cosim_features
  from alibi_detect.models.tensorflow.gmm import gmm_energy, gmm_params
  from alibi_detect.models.tensorflow.losses import loss_aegmm
  from alibi_detect.models.tensorflow.trainer import trainer
  from alibi_detect.base import BaseDetector, FitMixin, ThresholdMixin, outlier_prediction_dict
  from alibi_detect.utils.tensorflow.prediction import predict_batch
  from alibi_detect.utils._types import OptimizerTF
label_desc: Use image node_id 5 for calling the OutlierAEGMM obj's underlying member method code with example usage: obj.predict(X, batch_size, return_instance_score) and returns: od