class_docstr: Computes delta coefficients (time derivatives).
  
  Arguments
  ---------
  win_length : int
      Length of the window used to compute the time derivatives.
  
  Example
  -------
  >>> inputs = torch.randn([10, 101, 20])
  >>> compute_deltas = Deltas(input_size=inputs.size(-1))
  >>> features = compute_deltas(inputs)
  >>> features.shape
  torch.Size([10, 101, 20])
class_docstr_tok: ["Computes","delta","coefficients","(","time","derivatives",")",".","Arguments","--","--","--","--","-","win_length",":","int","Length","of","the","window","used","to","compute","the","time","derivatives",".","Example","--","--","--","-",">",">",">","inputs","=","torch.randn","(","[","10",",","101",",","20","]",")",">",">",">","compute_deltas","=","Deltas","(","input_size=inputs.size","(","-1",")",")",">",">",">","features","=","compute_deltas","(","inputs",")",">",">",">","features.shape","torch.Size","(","[","10",",","101",",","20","]",")"]
code: def __init__(
      self,
      input_size,
      window_length=5,
  ):
      super().__init__()
      self.n = (window_length - 1) // 2
      self.denom = self.n * (self.n + 1) * (2 * self.n + 1) / 3
  
      self.register_buffer(
          "kernel",
          torch.arange(
              -self.n,
              self.n + 1,
              dtype=torch.float32,
          ).repeat(input_size, 1, 1),
      )
  
imports: import math
  import torch
  import logging
  from speechbrain.utils.checkpoints import mark_as_saver, mark_as_loader, mark_as_transfer, register_checkpoint_hooks
  from speechbrain.dataio.dataio import length_to_mask
label_desc: Use image node_id 1 to create a new Deltas object from inherited base classes: torch.nn with example: obj = Deltas(input_size, window_length)