code: def test_rnnt_loss(self, data_func):
      def get_data(data_func, device):
          data = data_func()
          if type(data) == tuple:
              data = data[0]
          return data
  
      data = get_data(data_func, self.device)
      inputs = (
          data["logits"].to(torch.float32),  # logits
          data["targets"],  # targets
          data["logit_lengths"],  # logit_lengths
          data["target_lengths"],  # target_lengths
          data["blank"],  # blank
          -1,  # clamp
      )
  
      self.assert_grad(F.rnnt_loss, inputs, enable_all_grad=False)
  
imports: from functools import partial
  from typing import Callable, Tuple
  import torch
  import torchaudio.functional
  from parameterized import parameterized
  from torch import Tensor
  from torch.autograd import gradcheck, gradgradcheck
  from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, rnnt_utils, TestBaseMixin, use_deterministic_algorithms
label_desc: Use image node_id 2 for calling the AutogradFloat32 obj's underlying member method code with example usage: obj.test_rnnt_loss(data_func) and returns: data