class_docstr: This class cuts out a portion of the signal from the end.
  
  It is written as a class to be able to incorporate it inside a sequential
  wrapper.
  
  Arguments
  ---------
  chomp_size : int
      The size of the portion to discard (in samples).
  
  Example
  -------
  >>> x = torch.randn(10, 110, 5)
  >>> chomp = Chomp1d(10)
  >>> x_chomped = chomp(x)
  >>> x_chomped.shape
  torch.Size([10, 100, 5])
code: def __init__(self, chomp_size):
      super(Chomp1d, self).__init__()
      self.chomp_size = chomp_size
  
imports: import torch
  import torch.nn
  import speechbrain
  import torch.nn.functional
  from speechbrain.processing.signal_processing import overlap_and_add
label_desc: Use image node_id 1 to create a new Chomp1d object from inherited base classes: nn with example: obj = Chomp1d(chomp_size)