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])
docstr: Arguments
  x : Tensor
      Tensor shape is [M, Kpad, H].
  
  Returns
  -------
  x : Tensor
      Tensor shape is [M, K, H].
docstr_tok: ["Arguments","x",":","Tensor","Tensor","shape","is","[","M",",","Kpad",",","H","]",".","Returns","--","--","--","-","x",":","Tensor","Tensor","shape","is","[","M",",","K",",","H","]","."]
code: def forward(self, x):
      """
      Arguments
      x : Tensor
          Tensor shape is [M, Kpad, H].
  
      Returns
      -------
      x : Tensor
          Tensor shape is [M, K, H].
      """
      return x[:, : -self.chomp_size, :].contiguous()
  
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 2 for calling the Chomp1d obj's underlying member method code with example usage: obj.forward(x) and returns: x