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),
          data["targets"],
          data["logit_lengths"],
          data["target_lengths"],
      )
      loss = T.RNNTLoss(blank=data["blank"])
  
      self.assert_grad(loss, inputs)
  
imports: import unittest
  from typing import List
  import torch
  import torchaudio.transforms
  from parameterized import parameterized
  from torch.autograd import gradcheck, gradgradcheck
  from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, rnnt_utils, TestBaseMixin
label_desc: Use image node_id 2 for calling the AutogradTestFloat32 obj's underlying member method code with example usage: obj.test_rnnt_loss(data_func) and returns: data