code: def test_frechet_distance(self):
      N = 16
      mu_x = torch.rand((N,))
      sigma_x = torch.rand((N, N))
      mu_y = torch.rand((N,))
      sigma_y = torch.rand((N, N))
      self.assert_grad(
          F.frechet_distance, (mu_x, sigma_x, mu_y, sigma_y)
      )
  
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 40 for calling the Autograd obj's underlying member method code with example usage: obj.test_frechet_distance() without return types