code: def main():
      parser = argparse.ArgumentParser(
          description="Benchmark CG version against Cholesky",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      )
  
      parser.add_argument(
          "--input",
          type=str,
          required=True,
          dest="inputfile",
          help="dataset file in matrix market format",
      )
      parser.add_argument(
          "--graph", help="generates graphs", action="store_true"
      )
      parser.add_argument(
          "--loss", help="test training loss", action="store_true"
      )
      parser.add_argument(
          "--speed", help="test training speed", action="store_true"
      )
  
      args = parser.parse_args()
      if not (args.speed or args.loss):
          print("must specify at least one of --speed or --loss")
          parser.print_help()
  
      else:
          plays = bm25_weight(scipy.io.mmread(args.inputfile)).tocsr()
          logging.basicConfig(level=logging.DEBUG)
  
          if args.loss:
              acc = benchmark_accuracy(plays)
              with open("als_accuracy.json", "w", encoding="utf8") as o:
                  json.dump(acc, o)
              if args.graph:
                  generate_loss_graph(acc, "als_accuracy.png")
  
          if args.speed:
              speed = benchmark_times(plays)
              with open("als_speed.json", "w", encoding="utf8") as o:
                  json.dump(speed, o)
              if args.graph:
                  generate_speed_graph(speed, "als_speed.png")
  
file_path: implicit/benchmarks/benchmark_als.py
imports: import argparse
  import json
  import logging
  from collections import defaultdict
  import matplotlib.pyplot
  import scipy.io
  import seaborn
  from implicit.als import AlternatingLeastSquares
  from implicit.gpu import HAS_CUDA
  from implicit.nearest_neighbours import bm25_weight
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types