code: def test_rnnt_loss(self):
      logits = torch.tensor(
          [
              [
                  [
                      [0.1, 0.6, 0.1, 0.1, 0.1],
                      [0.1, 0.1, 0.6, 0.1, 0.1],
                      [0.1, 0.1, 0.2, 0.8, 0.1],
                  ],
                  [
                      [0.1, 0.6, 0.1, 0.1, 0.1],
                      [0.1, 0.1, 0.2, 0.1, 0.1],
                      [0.7, 0.1, 0.2, 0.1, 0.1],
                  ],
              ]
          ]
      )
      tensor = logits.to(device=self.device, dtype=torch.float32)
      targets = torch.tensor(
          [[1, 2]], device=tensor.device, dtype=torch.int32
      )
      logit_lengths = torch.tensor(
          [2], device=tensor.device, dtype=torch.int32
      )
      target_lengths = torch.tensor(
          [2], device=tensor.device, dtype=torch.int32
      )
  
      self._assert_consistency(
          T.RNNTLoss(), logits, targets, logit_lengths, target_lengths
      )
  
imports: import torch
  import torchaudio.transforms
  from parameterized import parameterized
  from torchaudio_unittest import common_utils
  from torchaudio_unittest.common_utils import skipIfRocm, TestBaseMixin, torch_script
label_desc: Use image node_id 1 for calling the TransformsFloat32Only obj's underlying member method code with example usage: obj.test_rnnt_loss() without return types