class_docstr: Sinc Convolution.
  
  This module performs a convolution using Sinc filters in time domain as kernel.
  Sinc filters function as band passes in spectral domain.
  The filtering is done as a convolution in time domain, and no transformation
  to spectral domain is necessary.
  
  This implementation of the Sinc convolution is heavily inspired
  by Ravanelli et al. https://github.com/mravanelli/SincNet,
  and adapted for the ESpnet toolkit.
  Combine Sinc convolutions with a log compression activation function, as in:
  https://arxiv.org/abs/2010.07597
  
  Notes:
  Currently, the same filters are applied to all input channels.
  The windowing function is applied on the kernel to obtained a smoother filter,
  and not on the input values, which is different to traditional ASR.
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docstr: Obtain the output dimension of the filter.
docstr_tok: ["Obtain","the","output","dimension","of","the","filter","."]
code: def get_odim(self, idim: int) -> int:
      """Obtain the output dimension of the filter."""
      D_out = (
          idim
          + 2 * self.padding
          - self.dilation * (self.kernel_size - 1)
          - 1
      )
      D_out = (D_out // self.stride) + 1
      return D_out
  
imports: import math
  from typing import Union
  import torch
  from typeguard import check_argument_types
label_desc: Use image node_id 8 for calling the SincConv obj's underlying member method code with example usage: obj.get_odim(idim) and returns: D_out