docstr: Compute outlier scores.
  
  Parameters
  ----------
  X
      Batch of instances to analyze.
  batch_size
      Batch size used when making predictions with the AEGMM.
  
  Returns
  -------
  Array with outlier scores for each instance in the batch.
docstr_tok: ["Compute","outlier","scores",".","Parameters","--","--","--","--","--","X","Batch","of","instances","to","analyze",".","batch_size","Batch","size","used","when","making","predictions","with","the","AEGMM",".","Returns","--","--","--","-","Array","with","outlier","scores","for","each","instance","in","the","batch","."]
code: def score(
      self, X: np.ndarray, batch_size: int = int(1e10)
  ) -> np.ndarray:
      """
      Compute outlier scores.
  
      Parameters
      ----------
      X
          Batch of instances to analyze.
      batch_size
          Batch size used when making predictions with the AEGMM.
  
      Returns
      -------
      Array with outlier scores for each instance in the batch.
      """
      _, z, _ = predict_batch(X, self.aegmm, batch_size=batch_size)
      energy, _ = gmm_energy(
          z,
          self.phi,
          self.mu,
          self.cov,
          self.L,
          self.log_det_cov,
          return_mean=False,
      )
      return energy.numpy()
  
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 4 for calling the OutlierAEGMM obj's underlying member method code with example usage: obj.score(X, batch_size) and returns: energy