code: def test_preemphasis(self):
      waveform = torch.rand(
          3,
          2,
          100,
          device=self.device,
          dtype=self.dtype,
          requires_grad=True,
      )
      coeff = 0.9
      self.assert_grad(F.preemphasis, (waveform, coeff))
  
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 38 for calling the Autograd obj's underlying member method code with example usage: obj.test_preemphasis() without return types