docstr: Compute feature level outlier scores.
  
  Parameters
  ----------
  X_orig
      Original time series.
  X_recon
      Reconstructed time series.
  threshold_est
      Estimated threshold from the decoder's latent space.
  
  Returns
  -------
  Feature level outlier scores. Scores above 0 are outliers.
docstr_tok: ["Compute","feature","level","outlier","scores",".","Parameters","--","--","--","--","--","X_orig","Original","time","series",".","X_recon","Reconstructed","time","series",".","threshold_est","Estimated","threshold","from","the","decoder","'s","latent","space",".","Returns","--","--","--","-","Feature","level","outlier","scores",".","Scores","above","0","are","outliers","."]
code: def feature_score(
      self,
      X_orig: np.ndarray,
      X_recon: np.ndarray,
      threshold_est: np.ndarray,
  ) -> np.ndarray:
      """
      Compute feature level outlier scores.
  
      Parameters
      ----------
      X_orig
          Original time series.
      X_recon
          Reconstructed time series.
      threshold_est
          Estimated threshold from the decoder's latent space.
  
      Returns
      -------
      Feature level outlier scores. Scores above 0 are outliers.
      """
      fscore = (X_orig - X_recon) ** 2
      # TODO: check casting if nb of features equals time dimension
      fscore_adj = fscore - threshold_est - self.threshold
      return fscore_adj
  
imports: import logging
  import numpy
  import tensorflow
  from tensorflow.keras.layers import Dense, InputLayer
  from typing import Dict, Tuple, Union
  from alibi_detect.models.tensorflow.autoencoder import Seq2Seq, EncoderLSTM, DecoderLSTM
  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 OutlierSeq2Seq obj's underlying member method code with example usage: obj.feature_score(X_orig, X_recon, threshold_est) and returns: fscore_adj