code: def assert_grad(
      self,
      transform: Callable[..., Tensor],
      inputs: Tuple[torch.Tensor],
      enable_all_grad: bool = True,
  ):
      inputs_ = []
      for i in inputs:
          if torch.is_tensor(i):
              i = i.to(dtype=self.dtype, device=self.device)
              if enable_all_grad:
                  i.requires_grad = True
          inputs_.append(i)
      # gradcheck with float32 requires higher atol and epsilon
      assert gradcheck(
          transform, inputs, eps=1e-3, atol=1e-3, nondet_tol=0.0
      )
  
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 1 for calling the AutogradFloat32 obj's underlying member method code with example usage: obj.assert_grad(transform, inputs, enable_all_grad) without return types