code: def test_convolve(self, fn, mode):
      leading_dims = (4, 3, 2)
      L_x, L_y = 23, 40
      x = torch.rand(
          *leading_dims, L_x, dtype=self.dtype, device=self.device
      )
      y = torch.rand(
          *leading_dims, L_y, dtype=self.dtype, device=self.device
      )
      self.assert_grad(getattr(F, fn), (x, y, mode))
  
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 35 for calling the Autograd obj's underlying member method code with example usage: obj.test_convolve(fn, mode) without return types