class_docstr: Stateless recurrent link interface.
  
  This class defines the interface of a recurrent link ChainerRL can handle.
  
  In most casese, you can just use ChainerRL's existing containers like
  `chainerrl.links.StatelessRecurrentChainList`,
  `chainerrl.links.StatelessRecurrentSequential`, and
  `chainerrl.links.StatelessRecurrentBranched` to define a recurrent
  link. You can use Chainer's recurrent links such as L.NStepLSTM inside the
  containers.
  
  To write your own recurrent link, you need to implement the interface.
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docstr: Multi-step batch forward computation.
  
  This method sequentially applies layers as chainer.Sequential does.
  
  Args:
      x (list): Input sequences. Each sequence should be a variable whose
          first axis corresponds to time or a tuple of such variables.
      recurrent_state (object): Batched recurrent state. If set to None,
          it is initialized.
      output_mode (str): If set to 'concat', the output value is
          concatenated into a single large batch, which can be suitable
          for loss computation. If set to 'split', the output value is
          a list of output sequences.
  
  Returns:
      object: Output sequences. See the description of the `output_mode`
          argument.
      object: New batched recurrent state.
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code: def n_step_forward(self, x, recurrent_state):
      """Multi-step batch forward computation.
  
      This method sequentially applies layers as chainer.Sequential does.
  
      Args:
          x (list): Input sequences. Each sequence should be a variable whose
              first axis corresponds to time or a tuple of such variables.
          recurrent_state (object): Batched recurrent state. If set to None,
              it is initialized.
          output_mode (str): If set to 'concat', the output value is
              concatenated into a single large batch, which can be suitable
              for loss computation. If set to 'split', the output value is
              a list of output sequences.
  
      Returns:
          object: Output sequences. See the description of the `output_mode`
              argument.
          object: New batched recurrent state.
      """
      raise NotImplementedError
  
imports: from cached_property import cached_property
  import chainer
  import chainer.functions
  import chainer.links
  import numpy
label_desc: Use image node_id 1 for calling the StatelessRecurrent obj's underlying member method code with example usage: obj.n_step_forward(x, recurrent_state) without return types