class_docstr: Wrapper for downsampling techniques
      
docstr: Downsampling function
  Arguments
  ---------
  x : tensor
      Speech samples of shape [B,n_samples] with B the batch size
docstr_tok: ["Downsampling","function","Arguments","--","--","--","--","-","x",":","tensor","Speech","samples","of","shape","[","B",",","n_samples","]","with","B","the","batch","size"]
code: def forward(self, x):
      """Downsampling function
      Arguments
      ---------
      x : tensor
          Speech samples of shape [B,n_samples] with B the batch size
      """
  
      return self.downsampler(x)
  
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 for calling the Downsampler obj's underlying member method code with example usage: obj.forward(x) and returns: self