code: def test_convert_to_hypotheses(self, device: torch.device):
      hyps = BatchedHyps(batch_size=2, init_length=1, device=device)
      hyps.add_results_(
          active_indices=torch.tensor([0], device=device),
          labels=torch.tensor([5], device=device),
          time_indices=torch.tensor([1], device=device),
          scores=torch.tensor([0.5], device=device),
      )
      hyps.add_results_(
          active_indices=torch.tensor([0, 1], device=device),
          labels=torch.tensor([2, 4], device=device),
          time_indices=torch.tensor([1, 2], device=device),
          scores=torch.tensor([1.0, 1.0], device=device),
      )
      hypotheses = batched_hyps_to_hypotheses(hyps)
      assert (
          hypotheses[0].y_sequence
          == torch.tensor([5, 2], device=device)
      ).all()
      assert (
          hypotheses[1].y_sequence == torch.tensor([4], device=device)
      ).all()
      assert hypotheses[0].score == pytest.approx(1.5)
      assert hypotheses[1].score == pytest.approx(1.0)
      assert (
          hypotheses[0].timestep == torch.tensor([1, 1], device=device)
      ).all()
      assert (
          hypotheses[1].timestep == torch.tensor([2], device=device)
      ).all()
  
imports: from typing import List
  import pytest
  import torch
  from nemo.collections.asr.parts.utils.rnnt_utils import BatchedAlignments, BatchedHyps, batched_hyps_to_hypotheses
label_desc: Use image node_id 1 for calling the TestConvertToHypotheses obj's underlying member method code with example usage: obj.test_convert_to_hypotheses(device) without return types