code: def main():
      template = f"""\
  ## Benchmarking and verification of Julius
  
  In order to verify the correctness and speed of the implementations in Julius,
  we compare ourselves to different reference implementations, comparing speed and
  checking how far we are.
  
  ### ResampleFrac
  
  We compare `julius.resample` to `resampy`, on an input of size (32, 8 * 44100),
  i.e. a batch of size 16 of 8 second of audio at 44.1kHz.
  We use the same number of zero crossing as `resampy` for this benchmark.
  The small delta is probably
  due to the different window function used.
  
  
  On CPU we have:
  
  {run_bench('resample')}
  
  On GPU we have:
  
  {run_bench('resample', device='cuda')}
  
  ### FFTConv1d
  
  We compare to `pytorch.nn.functional.conv1d`, on a input of size [32, 32, 10240],
  for a convolution with 32 input channels, 64 output channels and various kernel sizes.
  
  On CPU we have:
  
  {run_bench('fftconv')}
  
  On GPU we have:
  
  {run_bench('fftconv', device='cuda')}
  
  ### LowPassFilter
  
  We do not compare to anything, but measure the attenuation in dB of a pure tone
  at `0.9 * cutoff`, at the `cutoff`, and at `1.1 * cutoff`.
  Note that our implementation automatically choses to use FFTConv1d or not when appropriate.
  
  On CPU we have:
  
  {run_bench('lowpass')}
  
  On GPU we have:
  
  {run_bench('lowpass', device='cuda')}
  
  """
      print(template)
  
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types