code: def _test_forward(self, gpu):
      in_size = 2
      out_size = 6
  
      rseq = RecurrentSequential(
          nn.Linear(in_size, 3),
          nn.ELU(),
          nn.LSTM(num_layers=1, input_size=3, hidden_size=4),
          nn.Linear(4, 5),
          nn.RNN(num_layers=1, input_size=5, hidden_size=out_size),
          nn.Tanh(),
      )
  
      if gpu >= 0:
          device = torch.device("cuda:{}".format(gpu))
          rseq.to(device)
      else:
          device = torch.device("cpu")
  
      assert len(rseq.recurrent_children) == 2
      assert rseq.recurrent_children[0] is rseq[2]
      assert rseq.recurrent_children[1] is rseq[4]
  
      linear1 = rseq[0]
      lstm = rseq[2]
      linear2 = rseq[3]
      rnn = rseq[4]
  
      seqs_x = [
          torch.rand(4, in_size, requires_grad=True, device=device),
          torch.rand(1, in_size, requires_grad=True, device=device),
          torch.rand(3, in_size, requires_grad=True, device=device),
      ]
  
      packed_x = nn.utils.rnn.pack_sequence(
          seqs_x, enforce_sorted=False
      )
  
      out, _ = rseq(packed_x, None)
      self.assertEqual(out.data.shape, (8, out_size))
  
      # Check if the output matches that of step-by-step execution
      def manual_forward(seqs_x):
          seqs_y = []
          for seq_x in seqs_x:
              lstm_st = None
              rnn_st = None
              seq_y = []
              for i in range(len(seq_x)):
                  h = seq_x[i : i + 1]
                  h = linear1(h)
                  h = F.elu(h)
                  h, lstm_st = _step_lstm(lstm, h, lstm_st)
                  h = linear2(h)
                  h, rnn_st = _step_rnn_tanh(rnn, h, rnn_st)
                  y = F.tanh(h)
                  seq_y.append(y[0])
              seqs_y.append(torch.stack(seq_y))
          return nn.utils.rnn.pack_sequence(
              seqs_y, enforce_sorted=False
          )
  
      manual_out = manual_forward(seqs_x)
      torch_assert_allclose(out.data, manual_out.data, atol=1e-4)
  
      # Finally, check the gradient (wrt input)
      grads = torch.autograd.grad([torch.sum(out.data)], seqs_x)
      manual_grads = torch.autograd.grad(
          [torch.sum(manual_out.data)], seqs_x
      )
      assert len(grads) == len(manual_grads) == 3
      for grad, manual_grad in zip(grads, manual_grads):
          torch_assert_allclose(grad, manual_grad, atol=1e-4)
  
file_path: pfrl/tests/nn_tests/test_recurrent_sequential.py
imports: import unittest
  import pytest
  import torch
  import torch.nn.functional
  from torch import nn
  from pfrl.nn import Lambda, RecurrentSequential
  from pfrl.testing import torch_assert_allclose
label_desc: Use image node_id 1 for calling the TestRecurrentSequential obj's underlying member method code with example usage: obj._test_forward(gpu) and returns: nn