code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument("--all", action="store_true")
      parser.add_argument("--categorical", action="store_true")
      parser.add_argument("--gaussian", action="store_true")
      parser.add_argument(
          "--multivariate-gaussian", action="store_true"
      )
      parser.add_argument("--gaussian-mixture", action="store_true")
      parser.add_argument("--repeat", type=int, default=10)
      parser.add_argument("--verbose", action="store_true")
      parser.add_argument("--n-iter", type=int, default=100)
  
      args = parser.parse_args()
      if args.all:
          args.categorical = True
          args.gaussian = True
          args.multivariate_gaussian = True
          args.gaussian_mixture = True
  
      if args.categorical:
          bench = MultinomialBenchmark(
              repeat=args.repeat,
              n_iter=args.n_iter,
              verbose=args.verbose,
          )
          bench.run("categorical.benchmark.csv")
      if args.gaussian:
          bench = GaussianBenchmark(
              repeat=args.repeat,
              n_iter=args.n_iter,
              verbose=args.verbose,
          )
          bench.run("gaussian.benchmark.csv")
      if args.multivariate_gaussian:
          bench = MultivariateGaussianBenchmark(
              repeat=args.repeat,
              n_iter=args.n_iter,
              verbose=args.verbose,
          )
          bench.run("multivariate_gaussian.benchmark.csv")
      if args.gaussian_mixture:
          bench = GMMBenchmark(
              repeat=args.repeat,
              n_iter=args.n_iter,
              verbose=args.verbose,
          )
          bench.run("gmm.benchmark.csv")
  
imports: import argparse
  import collections
  import logging
  import time
  import hmmlearn.hmm
  import numpy
  import sklearn.base
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types