code: def test():
      import argparse
  
      parser = argparse.ArgumentParser(
          "denoiser.demucs",
          description="Benchmark the streaming Demucs implementation, as well as "
          "checking the delta with the offline implementation.",
      )
      parser.add_argument("--depth", default=5, type=int)
      parser.add_argument("--resample", default=4, type=int)
      parser.add_argument("--hidden", default=48, type=int)
      parser.add_argument("--sample_rate", default=16000, type=float)
      parser.add_argument("--device", default="cpu")
      parser.add_argument("-t", "--num_threads", type=int)
      parser.add_argument("-f", "--num_frames", type=int, default=1)
      args = parser.parse_args()
      if args.num_threads:
          th.set_num_threads(args.num_threads)
      sr = args.sample_rate
      sr_ms = sr / 1000
      demucs = Demucs(
          depth=args.depth, hidden=args.hidden, resample=args.resample
      ).to(args.device)
      x = th.randn(1, int(sr * 4)).to(args.device)
      out = demucs(x[None])[0]
      streamer = DemucsStreamer(demucs, num_frames=args.num_frames)
      out_rt = []
      frame_size = streamer.total_length
      with th.no_grad():
          while x.shape[1] > 0:
              out_rt.append(streamer.feed(x[:, :frame_size]))
              x = x[:, frame_size:]
              frame_size = streamer.demucs.total_stride
      out_rt.append(streamer.flush())
      out_rt = th.cat(out_rt, 1)
      model_size = (
          sum(p.numel() for p in demucs.parameters()) * 4 / 2**20
      )
      initial_lag = streamer.total_length / sr_ms
      tpf = 1000 * streamer.time_per_frame
      print(f"model size: {model_size:.1f}MB, ", end="")
      print(
          f"delta batch/streaming: {th.norm(out - out_rt) / th.norm(out):.2%}"
      )
      print(f"initial lag: {initial_lag:.1f}ms, ", end="")
      print(
          f"stride: {streamer.stride * args.num_frames / sr_ms:.1f}ms"
      )
      print(f"time per frame: {tpf:.1f}ms, ", end="")
      rtf = (1000 * streamer.time_per_frame) / (streamer.stride / sr_ms)
      print(f"RTF: {rtf:.2f}")
      print(f"Total lag with computation: {initial_lag + tpf:.1f}ms")
  
imports: import math
  import time
  import torch
  from torch import nn
  from torch.nn import functional
  from .resample import downsample2, upsample2
  from .utils import capture_init
label_desc: Use image node_id 4 for calling a global function with example usage: test() without return types