class_docstr: This class learns the adaptive frontend for the ConvTasnet model.
  
  Arguments
  ---------
  L : int
      The filter kernel size. Needs to be an odd number.
  N : int
      Number of dimensions at the output of the adaptive front end.
  
  Example
  -------
  >>> inp = torch.rand(10, 100)
  >>> encoder = Encoder(11, 20)
  >>> h = encoder(inp)
  >>> h.shape
  torch.Size([10, 20, 20])
code: def __init__(self, L, N):
      super(Encoder, self).__init__()
  
      # 50% overlap
      self.conv1d_U = sb.nnet.CNN.Conv1d(
          in_channels=1,
          out_channels=N,
          kernel_size=L,
          stride=L // 2,
          bias=False,
      )
  
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 Encoder object from inherited base classes: nn with example: obj = Encoder(L, N)