class_docstr: Generate beamformed signal from multi-mic data using DelaySum beamforming.
  
  Arguments
  ---------
  sampling_rate : int (default: 16000)
      Sampling rate of audio signals.
docstr: Returns beamformed signal using multi-mic data.
  
  Arguments
  ---------
  mics_sginal : tensor
      Set of audio signals to be transformed.
docstr_tok: ["Returns","beamformed","signal","using","multi-mic","data",".","Arguments","--","--","--","--","-","mics_sginal",":","tensor","Set","of","audio","signals","to","be","transformed","."]
code: def forward(self, mics_signals):
      """Returns beamformed signal using multi-mic data.
  
      Arguments
      ---------
      mics_sginal : tensor
          Set of audio signals to be transformed.
      """
      with torch.no_grad():
          Xs = self.stft(mics_signals)
          XXs = self.cov(Xs)
          tdoas = self.gccphat(XXs)
          Ys_ds = self.delaysum(Xs, tdoas)
          sig = self.istft(Ys_ds)
  
      return sig
  
imports: import torch
  from speechbrain.processing.features import STFT, ISTFT
  from speechbrain.processing.multi_mic import Covariance, GccPhat, DelaySum
label_desc: Use image node_id 2 for calling the DelaySum_Beamformer obj's underlying member method code with example usage: obj.forward(mics_signals) and returns: sig