code: def _test_forward_with_modified_recurrent_state(self, gpu):
      in_size = 2
      out0_size = 2
      out1_size = 3
      par = RecurrentBranched(
          nn.GRU(
              num_layers=1, input_size=in_size, hidden_size=out0_size
          ),
          RecurrentSequential(
              nn.LSTM(
                  num_layers=1,
                  input_size=in_size,
                  hidden_size=out1_size,
              ),
          ),
      )
      if gpu >= 0:
          device = torch.device("cuda:{}".format(gpu))
          par.to(device)
      else:
          device = torch.device("cpu")
      seqs_x = [
          torch.rand(2, in_size, device=device),
          torch.rand(2, in_size, device=device),
      ]
      packed_x = nn.utils.rnn.pack_sequence(
          seqs_x, enforce_sorted=False
      )
      x_t0 = torch.stack((seqs_x[0][0], seqs_x[1][0]))
      x_t1 = torch.stack((seqs_x[0][1], seqs_x[1][1]))
  
      (gru_out, lstm_out), (gru_rs, (lstm_rs,)) = par(packed_x, None)
  
      # Check if n_step_forward and forward twice results are same
      def no_mask_forward_twice():
          _, rs = one_step_forward(par, x_t0, None)
          return one_step_forward(par, x_t1, rs)
  
      (
          (nomask_gru_out, nomask_lstm_out),
          (nomask_gru_rs, (nomask_lstm_rs,)),
      ) = no_mask_forward_twice()
  
      # GRU
      torch_assert_allclose(gru_out.data[2:], nomask_gru_out, atol=1e-5)
      torch_assert_allclose(gru_rs, nomask_gru_rs)
  
      # LSTM
      torch_assert_allclose(
          lstm_out.data[2:], nomask_lstm_out, atol=1e-5
      )
      torch_assert_allclose(lstm_rs[0], nomask_lstm_rs[0], atol=1e-5)
      torch_assert_allclose(lstm_rs[1], nomask_lstm_rs[1], atol=1e-5)
  
      # 1st-only mask forward twice: only 2nd should be the same
      def mask0_forward_twice():
          _, rs = one_step_forward(par, x_t0, None)
          rs = mask_recurrent_state_at(rs, 0)
          return one_step_forward(par, x_t1, rs)
  
      (
          (mask0_gru_out, mask0_lstm_out),
          (mask0_gru_rs, (mask0_lstm_rs,)),
      ) = mask0_forward_twice()
  
      # GRU
      with self.assertRaises(AssertionError):
          torch_assert_allclose(
              gru_out.data[2], mask0_gru_out[0], atol=1e-5
          )
      torch_assert_allclose(
          gru_out.data[3], mask0_gru_out[1], atol=1e-5
      )
  
      # LSTM
      with self.assertRaises(AssertionError):
          torch_assert_allclose(
              lstm_out.data[2], mask0_lstm_out[0], atol=1e-5
          )
      torch_assert_allclose(
          lstm_out.data[3], mask0_lstm_out[1], atol=1e-5
      )
  
      # 2nd-only mask forward twice: only 1st should be the same
      def mask1_forward_twice():
          _, rs = one_step_forward(par, x_t0, None)
          rs = mask_recurrent_state_at(rs, 1)
          return one_step_forward(par, x_t1, rs)
  
      (
          (mask1_gru_out, mask1_lstm_out),
          (mask1_gru_rs, (mask1_lstm_rs,)),
      ) = mask1_forward_twice()
  
      # GRU
      torch_assert_allclose(
          gru_out.data[2], mask1_gru_out[0], atol=1e-5
      )
      with self.assertRaises(AssertionError):
          torch_assert_allclose(
              gru_out.data[3], mask1_gru_out[1], atol=1e-5
          )
  
      # LSTM
      torch_assert_allclose(
          lstm_out.data[2], mask1_lstm_out[0], atol=1e-5
      )
      with self.assertRaises(AssertionError):
          torch_assert_allclose(
              lstm_out.data[3], mask1_lstm_out[1], atol=1e-5
          )
  
      # both 1st and 2nd mask forward twice: both should be different
      def mask01_forward_twice():
          _, rs = one_step_forward(par, x_t0, None)
          rs = mask_recurrent_state_at(rs, [0, 1])
          return one_step_forward(par, x_t1, rs)
  
      (
          (mask01_gru_out, mask01_lstm_out),
          (mask01_gru_rs, (mask01_lstm_rs,)),
      ) = mask01_forward_twice()
  
      # GRU
      with self.assertRaises(AssertionError):
          torch_assert_allclose(
              gru_out.data[2], mask01_gru_out[0], atol=1e-5
          )
      with self.assertRaises(AssertionError):
          torch_assert_allclose(
              gru_out.data[3], mask01_gru_out[1], atol=1e-5
          )
  
      # LSTM
      with self.assertRaises(AssertionError):
          torch_assert_allclose(
              lstm_out.data[2], mask01_lstm_out[0], atol=1e-5
          )
      with self.assertRaises(AssertionError):
          torch_assert_allclose(
              lstm_out.data[3], mask01_lstm_out[1], atol=1e-5
          )
  
      # get and concat recurrent states and resume forward
      def get_and_concat_rs_forward():
          _, rs = one_step_forward(par, x_t0, None)
          rs0 = get_recurrent_state_at(rs, 0, detach=True)
          rs1 = get_recurrent_state_at(rs, 1, detach=True)
          concat_rs = concatenate_recurrent_states([rs0, rs1])
          return one_step_forward(par, x_t1, concat_rs)
  
      (
          (getcon_gru_out, getcon_lstm_out),
          (getcon_gru_rs, (getcon_lstm_rs,)),
      ) = get_and_concat_rs_forward()
  
      # GRU
      torch_assert_allclose(
          gru_out.data[2], getcon_gru_out[0], atol=1e-5
      )
      torch_assert_allclose(
          gru_out.data[3], getcon_gru_out[1], atol=1e-5
      )
  
      # LSTM
      torch_assert_allclose(
          lstm_out.data[2], getcon_lstm_out[0], atol=1e-5
      )
      torch_assert_allclose(
          lstm_out.data[3], getcon_lstm_out[1], atol=1e-5
      )
  
file_path: pfrl/tests/nn_tests/test_recurrent_branched.py
imports: import unittest
  import pytest
  import torch
  from torch import nn
  from pfrl.nn import RecurrentBranched, RecurrentSequential
  from pfrl.testing import torch_assert_allclose
  from pfrl.utils.recurrent import concatenate_recurrent_states, get_recurrent_state_at, mask_recurrent_state_at, one_step_forward
label_desc: Use image node_id 4 for calling the TestRecurrentBranched obj's underlying member method code with example usage: obj._test_forward_with_modified_recurrent_state(gpu) and returns: one_step_forward, one_step_forward, one_step_forward, one_step_forward, one_step_forward