code: def _test_n_step_forward_with_tuple_input(self, gpu):
      in_size = 5
      out_size = 3
  
      def concat_input(*args):
          return F.concat(args, axis=1)
  
      rseq = StatelessRecurrentSequential(
          concat_input,
          L.NStepRNNTanh(1, in_size, out_size, 0),
      )
  
      if gpu >= 0:
          chainer.cuda.get_device_from_id(gpu).use()
          rseq.to_gpu()
      xp = rseq.xp
  
      # Input is list of tuples. Each tuple has two variables.
      seqs_x = [
          (
              xp.random.uniform(-1, 1, size=(3, 2)).astype(np.float32),
              xp.random.uniform(-1, 1, size=(3, 3)).astype(np.float32),
          ),
          (
              xp.random.uniform(-1, 1, size=(1, 2)).astype(np.float32),
              xp.random.uniform(-1, 1, size=(1, 3)).astype(np.float32),
          ),
      ]
  
      # Concatenated output should be a variable.
      concat_out, concat_state = rseq.n_step_forward(
          seqs_x, None, output_mode="concat"
      )
      self.assertEqual(concat_out.shape, (4, out_size))
  
      # Split output should be a list of variables.
      split_out, split_state = rseq.n_step_forward(
          seqs_x, None, output_mode="split"
      )
      self.assertIsInstance(split_out, list)
      self.assertEqual(len(split_out), len(seqs_x))
      for seq_x, seq_out in zip(seqs_x, split_out):
          self.assertEqual(seq_out.shape, (len(seq_x), out_size))
  
      # Check if output_mode='concat' and output_mode='split' are consistent
      xp.testing.assert_allclose(
          F.concat(split_out, axis=0).array,
          concat_out.array,
      )
  
imports: import unittest
  import chainer
  from chainer import functions
  from chainer import links
  from chainer import testing
  import numpy
  from chainerrl.links import StatelessRecurrentSequential
label_desc: Use image node_id 4 for calling the TestStatelessRecurrentSequential obj's underlying member method code with example usage: obj._test_n_step_forward_with_tuple_input(gpu) and returns: F