code: def get_rtf_matrix(
      psd_speeches,
      psd_noises,
      diagonal_loading: bool = True,
      ref_channel: int = 0,
      rtf_iterations: int = 3,
      diag_eps: float = 1e-7,
      eps: float = 1e-8,
  ):
      """Calculate the RTF matrix with each column the relative transfer function
      of the corresponding source.
      """  # noqa: H405
      assert isinstance(psd_speeches, list) and isinstance(
          psd_noises, list
      )
      rtf_mat = torch.stack(
          [
              get_rtf(
                  psd_speeches[spk],
                  psd_n,
                  reference_vector=ref_channel,
                  iterations=rtf_iterations,
                  diagonal_loading=diagonal_loading,
                  diag_eps=diag_eps,
              )
              for spk, psd_n in enumerate(psd_noises)
          ],
          dim=-1,
      )
      # normalize at the reference channel
      return rtf_mat / rtf_mat[..., ref_channel, None, :]
  
imports: from typing import List, Union
  import torch
  import torch_complex.functional
  import torchaudio
label_desc: Use image node_id 9 for calling a global function with example usage: get_rtf_matrix(psd_speeches, psd_noises, diagonal_loading, ref_channel, rtf_iterations, diag_eps, eps) and returns: unknown