docstr: Compute instance level outlier scores.
  
  Parameters
  ----------
  fscore
      Feature level outlier scores.
  outlier_perc
      Percentage of sorted feature level outlier scores used to predict instance level outlier.
  
  Returns
  -------
  Instance level outlier scores.
docstr_tok: ["Compute","instance","level","outlier","scores",".","Parameters","--","--","--","--","--","fscore","Feature","level","outlier","scores",".","outlier_perc","Percentage","of","sorted","feature","level","outlier","scores","used","to","predict","instance","level","outlier",".","Returns","--","--","--","-","Instance","level","outlier","scores","."]
code: def instance_score(
      self, fscore: np.ndarray, outlier_perc: float = 100.0
  ) -> np.ndarray:
      """
      Compute instance level outlier scores.
  
      Parameters
      ----------
      fscore
          Feature level outlier scores.
      outlier_perc
          Percentage of sorted feature level outlier scores used to predict instance level outlier.
  
      Returns
      -------
      Instance level outlier scores.
      """
      fscore_flat = fscore.reshape(fscore.shape[0], -1).copy()
      n_score_features = int(
          np.ceil(0.01 * outlier_perc * fscore_flat.shape[1])
      )
      sorted_fscore = np.sort(fscore_flat, axis=1)
      sorted_fscore_perc = sorted_fscore[:, -n_score_features:]
      iscore = np.mean(sorted_fscore_perc, axis=1)
      return iscore
  
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 5 for calling the OutlierAE obj's underlying member method code with example usage: obj.instance_score(fscore, outlier_perc) and returns: iscore