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 require_univariate(self) -> bool:
      return False
  
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 3 for calling the LSTMED obj's underlying member method code with example usage: obj.require_univariate() and returns: False