class_docstr: Defines an RNN block.
  
  Parameters
  ----------
  mode
      type of the RNN. Can be either: rnn_relu (RNN with relu activation),
      rnn_tanh, (RNN with tanh activation), lstm or gru.
  
  num_hidden
      number of units per hidden layer.
  
  num_layers
      number of hidden layers.
  
  bidirectional
      toggle use of bi-directional RNN as encoder.
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docstr: Parameters
  ----------
  F
      A module that can either refer to the Symbol API or the NDArray
      API in MXNet.
  
  inputs
      input tensor with shape (batch_size, num_timesteps, num_dimensions)
  
  Returns
  -------
  Tensor
      rnn output with shape (batch_size, num_timesteps, num_dimensions)
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code: def hybrid_forward(
      self, F, inputs: Tensor
  ) -> Tensor:  # NTC in, NTC out
      """
  
      Parameters
      ----------
      F
          A module that can either refer to the Symbol API or the NDArray
          API in MXNet.
  
      inputs
          input tensor with shape (batch_size, num_timesteps, num_dimensions)
  
      Returns
      -------
      Tensor
          rnn output with shape (batch_size, num_timesteps, num_dimensions)
      """
      return self.rnn(inputs)
  
imports: from mxnet.gluon import HybridBlock, rnn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the RNN obj's underlying member method code with example usage: obj.hybrid_forward(F, inputs) and returns: self