docstr: Compute feature level outlier scores.
  
  Parameters
  ----------
  X_orig
      Batch of original instances.
  X_recon
      Batch of reconstructed instances.
  
  Returns
  -------
  Feature level outlier scores.
docstr_tok: ["Compute","feature","level","outlier","scores",".","Parameters","--","--","--","--","--","X_orig","Batch","of","original","instances",".","X_recon","Batch","of","reconstructed","instances",".","Returns","--","--","--","-","Feature","level","outlier","scores","."]
code: def feature_score(
      self, X_orig: np.ndarray, X_recon: np.ndarray
  ) -> np.ndarray:
      """
      Compute feature level outlier scores.
  
      Parameters
      ----------
      X_orig
          Batch of original instances.
      X_recon
          Batch of reconstructed instances.
  
      Returns
      -------
      Feature level outlier scores.
      """
      fscore = np.power(X_orig - X_recon, 2)
      return fscore
  
file_path: alibi-detect/alibi_detect/od/ae.py
imports: import logging
  import numpy
  import tensorflow
  from typing import Dict, Tuple
  from alibi_detect.models.tensorflow.autoencoder import AE
  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 OutlierAE obj's underlying member method code with example usage: obj.feature_score(X_orig, X_recon) and returns: fscore