class_docstr: Recurrent module that bundles parallel branches.
  
  This is a recurrent analog to `pfrl.nn.Branched`. It bundles
  multiple recurrent modules.
  
  Args:
      *modules: Child modules. Each module should be recurrent and callable.
code: def forward(self, sequences, recurrent_state):
      if recurrent_state is None:
          n = len(self)
          recurrent_state = [None] * n
      child_ys, rs = tuple(
          zip(
              *[
                  link(sequences, rs)
                  for link, rs in zip(self, recurrent_state)
              ]
          )
      )
      return child_ys, rs
  
imports: from torch import nn
  from pfrl.nn.recurrent import Recurrent
label_desc: Use image node_id 2 for calling the RecurrentBranched obj's underlying member method code with example usage: obj.forward(sequences, recurrent_state) and returns: child_ys,  rs