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 _train(
      self, train_data: pd.DataFrame, train_config=None
  ) -> pd.DataFrame:
      train_loader = RollingWindowDataset(
          train_data,
          target_seq_index=None,
          shuffle=True,
          flatten=False,
          n_past=self.sequence_length,
          n_future=0,
          batch_size=self.batch_size,
      )
      self.data_dim = train_data.shape[1]
      self.lstmed = self._build_model(train_data.shape[1]).to(
          self.device
      )
      optimizer = torch.optim.Adam(self.lstmed.parameters(), lr=self.lr)
      loss_func = torch.nn.MSELoss(reduction="sum")
      bar = ProgressBar(total=self.num_epochs)
  
      self.lstmed.train()
      for epoch in range(self.num_epochs):
          total_loss = 0
          for batch, _, _, _ in train_loader:
              batch = torch.tensor(
                  batch, dtype=torch.float, device=self.device
              )
              output = self.lstmed(batch)
              loss = loss_func(output, batch)
              self.lstmed.zero_grad()
              loss.backward()
              optimizer.step()
              total_loss += loss
          if bar is not None:
              bar.print(
                  epoch + 1,
                  prefix="",
                  suffix="Complete, Loss {:.4f}".format(
                      total_loss / len(train_loader)
                  ),
              )
      return self._get_anomaly_score(train_data)
  
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 5 for calling the LSTMED obj's underlying member method code with example usage: obj._train(train_data, train_config) and returns: self