code: def test_non_contiguous_initial_states_handled_on_gpu(self):
      # Some PyTorch operations which produce contiguous tensors on the CPU produce
      # non-contiguous tensors on the GPU (e.g. forward pass of an RNN when batch_first=True).
      # Accordingly, we perform the same checks from previous test on the GPU to ensure the
      # encoder is not affected by which device it is on.
  
      # Case 1: Encoder is not stateful
  
      # A transposition will make the tensors non-contiguous, start them off at the wrong shape
      # and transpose them into the right shape.
      encoder_base = _EncoderBase(stateful=False).cuda()
      initial_states = (
          torch.randn(5, 6, 7).cuda().permute(1, 0, 2),
          torch.randn(5, 6, 7).cuda().permute(1, 0, 2),
      )
      assert (
          not initial_states[0].is_contiguous()
          and not initial_states[1].is_contiguous()
      )
      assert initial_states[0].size() == torch.Size([6, 5, 7])
      assert initial_states[1].size() == torch.Size([6, 5, 7])
  
      # We'll pass them through an LSTM encoder and a vanilla RNN encoder to make sure it works
      # whether the initial states are a tuple of tensors or just a single tensor.
      encoder_base.sort_and_run_forward(
          self.lstm.cuda(),
          self.tensor.cuda(),
          self.mask.cuda(),
          initial_states,
      )
      encoder_base.sort_and_run_forward(
          self.rnn.cuda(),
          self.tensor.cuda(),
          self.mask.cuda(),
          initial_states[0],
      )
  
      # Case 2: Encoder is stateful
  
      # For stateful encoders, the initial state may be non-contiguous if its state was
      # previously updated with non-contiguous tensors. As in the non-stateful tests, we check
      # that the encoder still works on initial states for RNNs and LSTMs.
      final_states = initial_states
      # Check LSTM
      encoder_base = _EncoderBase(stateful=True).cuda()
      encoder_base._update_states(
          final_states, self.restoration_indices.cuda()
      )
      encoder_base.sort_and_run_forward(
          self.lstm.cuda(), self.tensor.cuda(), self.mask.cuda()
      )
      # Check RNN
      encoder_base.reset_states()
      encoder_base._update_states(
          [final_states[0]], self.restoration_indices.cuda()
      )
      encoder_base.sort_and_run_forward(
          self.rnn.cuda(), self.tensor.cuda(), self.mask.cuda()
      )
  
file_path: allennlp/tests/modules/encoder_base_test.py
imports: import numpy
  import pytest
  import torch
  from torch.nn import LSTM, RNN
  from allennlp.modules.encoder_base import _EncoderBase
  from allennlp.common.testing import AllenNlpTestCase, requires_gpu
  from allennlp.nn.util import sort_batch_by_length, get_lengths_from_binary_sequence_mask
label_desc: Use image node_id 7 for calling the TestEncoderBase obj's underlying member method code with example usage: obj.test_non_contiguous_initial_states_handled_on_gpu() without return types