class_docstr: Generate beamformed signal from multi-mic data using DelaySum beamforming.
  
  Arguments
  ---------
  sampling_rate : int (default: 16000)
      Sampling rate of audio signals.
code: def __init__(self, sampling_rate=16000):
      super().__init__()
      self.fs = sampling_rate
      self.stft = STFT(sample_rate=self.fs)
      self.cov = Covariance()
      self.gccphat = GccPhat()
      self.delaysum = DelaySum()
      self.istft = ISTFT(sample_rate=self.fs)
  
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 1 to create a new DelaySum_Beamformer object from inherited base classes: torch.nn with example: obj = DelaySum_Beamformer(sampling_rate)