class_docstr: Sequential model that can contain stateless recurrent modules.
  
  This is a recurrent analog to `torch.nn.Sequential`. It supports
  the recurrent interface by automatically detecting recurrent
  modules and handles recurrent states properly.
  
  For non-recurrent layers, this module automatically concatenates
  the input to the layers for efficient computation.
  
  Args:
      *layers: Callable objects.
docstr: Return recurrent child modules.
  
  Returns:
      tuple: Child modules that are recurrent.
docstr_tok: ["Return","recurrent","child","modules",".","Returns",":","tuple",":","Child","modules","that","are","recurrent","."]
code: def recurrent_children(self):
      """Return recurrent child modules.
  
      Returns:
          tuple: Child modules that are recurrent.
      """
      return tuple(child for child in self if is_recurrent(child))
  
imports: from torch import nn
  from pfrl.nn.recurrent import Recurrent
  from pfrl.utils.recurrent import get_packed_sequence_info, is_recurrent, unwrap_packed_sequences_recursive, wrap_packed_sequences_recursive
label_desc: Use image node_id 2 for calling the RecurrentSequential obj's underlying member method code with example usage: obj.recurrent_children() and returns: tuple