code: def test_wrapper_stateful_single_state_gru(self):
      gru = GRU(
          bidirectional=True,
          num_layers=2,
          input_size=3,
          hidden_size=7,
          batch_first=True,
      )
      encoder = PytorchSeq2SeqWrapper(gru, stateful=True)
  
      batch_sizes = [10, 5]
      states = []
      for batch_size in batch_sizes:
          tensor = torch.rand([batch_size, 5, 3])
          mask = torch.ones(batch_size, 5).bool()
          mask.data[0, 3:] = 0
          encoder_output = encoder(tensor, mask)
          states.append(encoder._states)
  
      assert_almost_equal(
          encoder_output[0, 3:, :].data.numpy(), numpy.zeros((2, 14))
      )
      assert_almost_equal(
          states[-1][0][:, -5:, :].data.numpy(),
          states[-2][0][:, -5:, :].data.numpy(),
      )
  
imports: import numpy
  from numpy.testing import assert_almost_equal
  import pytest
  import torch
  from torch.nn import LSTM, GRU
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.modules.seq2seq_encoders import PytorchSeq2SeqWrapper
  from allennlp.nn.util import sort_batch_by_length, get_lengths_from_binary_sequence_mask
label_desc: Use image node_id 11 for calling the TestPytorchSeq2SeqWrapper obj's underlying member method code with example usage: obj.test_wrapper_stateful_single_state_gru() without return types