code: def _get_mvdr_vector(
      psd_s: torch.Tensor,
      psd_n: torch.Tensor,
      reference_vector: torch.Tensor,
      solution: str = "ref_channel",
      diagonal_loading: bool = True,
      diag_eps: float = 1e-7,
      eps: float = 1e-8,
  ) -> torch.Tensor:
      r"""Compute the MVDR beamforming weights with ``solution`` argument.
  
      Args:
          psd_s (torch.Tensor): The complex-valued power spectral density (PSD) matrix of target speech.
              Tensor with dimensions `(..., freq, channel, channel)`.
          psd_n (torch.Tensor): The complex-valued power spectral density (PSD) matrix of noise.
              Tensor with dimensions `(..., freq, channel, channel)`.
          reference_vector (torch.Tensor): one-hot reference channel matrix.
          solution (str, optional): Solution to compute the MVDR beamforming weights.
              Options: [``ref_channel``, ``stv_evd``, ``stv_power``]. (Default: ``ref_channel``)
          diagonal_loading (bool, optional): If ``True``, enables applying diagonal loading to ``psd_n``.
              (Default: ``True``)
          diag_eps (float, optional): The coefficient multiplied to the identity matrix for diagonal loading.
              It is only effective when ``diagonal_loading`` is set to ``True``. (Default: ``1e-7``)
          eps (float, optional): Value to add to the denominator in the beamforming weight formula.
              (Default: ``1e-8``)
  
      Returns:
          torch.Tensor: the mvdr beamforming weight matrix
      """
      if solution == "ref_channel":
          beamform_vector = F.mvdr_weights_souden(
              psd_s,
              psd_n,
              reference_vector,
              diagonal_loading,
              diag_eps,
              eps,
          )
      else:
          if solution == "stv_evd":
              stv = F.rtf_evd(psd_s)
          else:
              stv = F.rtf_power(
                  psd_s,
                  psd_n,
                  reference_vector,
                  diagonal_loading=diagonal_loading,
                  diag_eps=diag_eps,
              )
          beamform_vector = F.mvdr_weights_rtf(
              stv,
              psd_n,
              reference_vector,
              diagonal_loading,
              diag_eps,
              eps,
          )
  
      return beamform_vector
  
imports: import warnings
  from typing import Optional, Union
  import torch
  from torch import Tensor
  from torchaudio import functional
label_desc: Use image node_id 1 for calling a global function with example usage: _get_mvdr_vector(psd_s, psd_n, reference_vector, solution, diagonal_loading, diag_eps, eps) and returns: beamform_vector