code: def get_mvdr_vector(
      psd_s,
      psd_n,
      reference_vector: Union[torch.Tensor, int],
      diagonal_loading: bool = True,
      diag_eps: float = 1e-7,
      eps: float = 1e-8,
  ):
      """Return the MVDR (Minimum Variance Distortionless Response) vector:
  
          h = (Npsd^-1 @ Spsd) / (Tr(Npsd^-1 @ Spsd)) @ u
  
      Reference:
          On optimal frequency-domain multichannel linear filtering
          for noise reduction; M. Souden et al., 2010;
          https://ieeexplore.ieee.org/document/5089420
  
      Args:
          psd_s (torch.complex64):
              speech covariance matrix (..., F, C, C)
          psd_n (torch.complex64):
              observation/noise covariance matrix (..., F, C, C)
          reference_vector (torch.Tensor): (..., C) or an integer
          diagonal_loading (bool): Whether to add a tiny term to the diagonal of psd_n
          diag_eps (float):
          eps (float):
      Returns:
          beamform_vector (torch.complex64): (..., F, C)
      """  # noqa: D400
      return torchaudio.functional.mvdr_weights_souden(
          psd_s,
          psd_n,
          reference_vector,
          diagonal_loading=diagonal_loading,
          diag_eps=diag_eps,
          eps=eps,
      )
  
imports: from typing import List, Union
  import torch
  import torch_complex.functional
  import torchaudio
label_desc: Use image node_id 3 for calling a global function with example usage: get_mvdr_vector(psd_s, psd_n, reference_vector, diagonal_loading, diag_eps, eps) and returns: torchaudio