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 _build_model(self, dim):
      return LSTMEDModule(
          dim,
          self.hidden_size,
          self.n_layers,
          self.dropout,
          self.device,
      )
  
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 4 for calling the LSTMED obj's underlying member method code with example usage: obj._build_model(dim) and returns: LSTMEDModule