docstr: Compute feature and instance level outlier scores.
  
  Parameters
  ----------
  X
      Batch of instances.
  outlier_perc
      Percentage of sorted feature level outlier scores used to predict instance level outlier.
  batch_size
      Batch size used when making predictions with the VAE.
  
  Returns
  -------
  Feature and instance level outlier scores.
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code: def score(
      self,
      X: np.ndarray,
      outlier_perc: float = 100.0,
      batch_size: int = int(1e10),
  ) -> Tuple[np.ndarray, np.ndarray]:
      """
      Compute feature and instance level outlier scores.
  
      Parameters
      ----------
      X
          Batch of instances.
      outlier_perc
          Percentage of sorted feature level outlier scores used to predict instance level outlier.
      batch_size
          Batch size used when making predictions with the VAE.
  
      Returns
      -------
      Feature and instance level outlier scores.
      """
      # sample reconstructed instances
      X_samples = np.repeat(X, self.samples, axis=0)
      X_recon = predict_batch(
          X_samples, self.vae, batch_size=batch_size
      )
  
      # compute feature and instance level scores
      fscore = self.feature_score(X_samples, X_recon)  # type: ignore[arg-type]
      iscore = self.instance_score(fscore, outlier_perc=outlier_perc)
  
      return fscore, iscore
  
imports: import logging
  import numpy
  import tensorflow
  import tensorflow_probability
  from typing import Dict, Tuple
  from alibi_detect.models.tensorflow.autoencoder import VAE
  from alibi_detect.models.tensorflow.trainer import trainer
  from alibi_detect.models.tensorflow.losses import elbo
  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 6 for calling the OutlierVAE obj's underlying member method code with example usage: obj.score(X, outlier_perc, batch_size) and returns: fscore,  iscore