code: def test_mvdr_weights_rtf_with_tensor(self):
      batch_size = 2
      channel = 4
      n_fft_bin = 10
      rtf = torch.rand(
          batch_size, n_fft_bin, channel, dtype=self.complex_dtype
      )
      psd_noise = torch.rand(
          batch_size,
          n_fft_bin,
          channel,
          channel,
          dtype=self.complex_dtype,
      )
      reference_channel = torch.zeros(batch_size, channel)
      reference_channel[..., 0].fill_(1)
      self.assert_grad(
          F.mvdr_weights_rtf, (rtf, psd_noise, reference_channel)
      )
  
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 31 for calling the Autograd obj's underlying member method code with example usage: obj.test_mvdr_weights_rtf_with_tensor() without return types