code: def _step_rnn_tanh(rnn, x, state):
      assert isinstance(rnn, nn.RNN)
      rnn_cell = nn.RNNCell(
          input_size=rnn.input_size, hidden_size=rnn.hidden_size
      )
      rnn_cell.weight_ih = rnn.weight_ih_l0
      rnn_cell.weight_hh = rnn.weight_hh_l0
      rnn_cell.bias_ih = rnn.bias_ih_l0
      rnn_cell.bias_hh = rnn.bias_hh_l0
      h = rnn_cell(x, state)
      return h, h
  
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 2 for calling a global function with example usage: _step_rnn_tanh(rnn, x, state) and returns: h,  h