class_docstr: Signal downsampling (Decimation)
  
  Arguments
  ---------
  downsampling_factor : int
      Factor of downsampling (i.e. ratio (length before ds / length after ds))
  initial_sampling_rate : int
      Sampling_rate of the input audios
  
  Example
  -------
  >>> sd = SignalDownsampler(2,16000)
  >>> a = torch.rand([8,28000])
  >>> a = sd(a)
  >>> print(a.shape)
  torch.Size([8, 14000])
code: def __init__(self, downsampling_factor, initial_sampling_rate):
      super().__init__()
      self.downsampling_factor = downsampling_factor
      self.target_ds_rate = int(
          initial_sampling_rate / downsampling_factor
      )
      self.downsampler = T.Resample(
          initial_sampling_rate,
          self.target_ds_rate,
          dtype=torch.float32,
      )
  
file_path: speechbrain/speechbrain/lobes/downsampling.py
imports: import torch
  import torchaudio.transforms
  from speechbrain.nnet.CNN import Conv1d
  from speechbrain.nnet.pooling import Pooling1d
label_desc: Use image node_id 1 to create a new SignalDownsampler object from inherited base classes: Downsampler with example: obj = SignalDownsampler(downsampling_factor, initial_sampling_rate)