class_docstr: The LSTM-encoder-decoder-based multivariate time series anomaly detector.
  The time series representation is modeled by an encoder-decoder network where
  both encoder and decoder are LSTMs. The distribution of the reconstruction error
  is estimated for anomaly detection.
code: def _get_anomaly_score(
      self,
      time_series: pd.DataFrame,
      time_series_prev: pd.DataFrame = None,
  ) -> pd.DataFrame:
      self.lstmed.eval()
      ts = (
          pd.concat((time_series_prev, time_series))
          if time_series_prev is None
          else time_series
      )
      data_loader = RollingWindowDataset(
          ts,
          target_seq_index=None,
          shuffle=False,
          flatten=False,
          n_past=self.sequence_length,
          n_future=0,
          batch_size=self.batch_size,
      )
      scores, outputs = [], []
      for idx, (batch, _, _, _) in enumerate(data_loader):
          batch = torch.tensor(
              batch, dtype=torch.float, device=self.device
          )
          output = self.lstmed(batch)
          error = nn.L1Loss(reduction="none")(output, batch)
          score = np.mean(
              error.view(-1, ts.shape[1]).data.cpu().numpy(), axis=1
          )
          scores.append(
              score.reshape(batch.shape[0], self.sequence_length)
          )
  
      scores = np.concatenate(scores)
      lattice = np.full((self.sequence_length, ts.shape[0]), np.nan)
      for i, score in enumerate(scores):
          lattice[
              i % self.sequence_length, i : i + self.sequence_length
          ] = score
      scores = np.nanmean(lattice, axis=0)
      return pd.DataFrame(
          scores[-len(time_series) :], index=time_series.index
      )
  
imports: from typing import Sequence
  import numpy
  import pandas
  from merlion.models.base import NormalizingConfig
  from merlion.models.anomaly.base import DetectorBase, DetectorConfig
  from merlion.post_process.threshold import AggregateAlarms
  from merlion.utils.misc import ProgressBar, initializer
  from merlion.models.utils.rolling_window_dataset import RollingWindowDataset
label_desc: Use image node_id 6 for calling the LSTMED obj's underlying member method code with example usage: obj._get_anomaly_score(time_series, time_series_prev) and returns: pd