class_docstr: The LSTM-encoder-decoder module. Both the encoder and decoder are LSTMs.
  
  :meta private:
docstr: :param n_features: The input feature dimension
  :param hidden_size: The LSTM hidden size
  :param n_layers: The number of LSTM layers
  :param dropout: The dropout rate
  :param device: CUDA or CPU
docstr_tok: [":","param","n_features",":","The","input","feature","dimension",":","param","hidden_size",":","The","LSTM","hidden","size",":","param","n_layers",":","The","number","of","LSTM","layers",":","param","dropout",":","The","dropout","rate",":","param","device",":","CUDA","or","CPU"]
code: def __init__(
      self, n_features, hidden_size, n_layers, dropout, device
  ):
      """
      :param n_features: The input feature dimension
      :param hidden_size: The LSTM hidden size
      :param n_layers: The number of LSTM layers
      :param dropout: The dropout rate
      :param device: CUDA or CPU
      """
      super().__init__()
      self.n_features = n_features
      self.hidden_size = hidden_size
      self.n_layers = n_layers
      self.dropout = dropout
      self.device = device
  
      self.encoder = nn.LSTM(
          self.n_features,
          self.hidden_size,
          batch_first=True,
          num_layers=self.n_layers[0],
          bias=True,
          dropout=self.dropout[0],
      )
      self.decoder = nn.LSTM(
          self.n_features,
          self.hidden_size,
          batch_first=True,
          num_layers=self.n_layers[1],
          bias=True,
          dropout=self.dropout[1],
      )
      self.output_layer = nn.Linear(self.hidden_size, self.n_features)
  
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 1 to create a new LSTMEDModule object from inherited base classes: nn with example: obj = LSTMEDModule(n_features, hidden_size, n_layers, dropout, device)