code: def __init__(
      self,
      mode,
      num_hidden,
      num_layers,
      num_output,
      bidirectional=False,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.num_output = num_output
  
      with self.name_scope():
          self.rnn = RNN(
              mode=mode,
              num_hidden=num_hidden,
              num_layers=num_layers,
              bidirectional=bidirectional,
          )
  
          self.decoder = nn.Dense(
              num_output, in_units=num_hidden, flatten=False
          )
  
imports: from mxnet.gluon import HybridBlock, nn
  from gluonts.core.component import validated
  from gluonts.mx.block.rnn import RNN
label_desc: Use image node_id 1 to create a new RNNModel object from inherited base classes: HybridBlock with example: obj = RNNModel(mode, num_hidden, num_layers, num_output, bidirectional)