code: def run(self, results_file):
      runtimes = collections.defaultdict(dict)
  
      sequences, lengths = self.generate_training_sequences()
  
      for implementation in ["scaling", "log"]:
          model = self.new_model(implementation)
          LOG.info(
              f"{model.__class__.__name__}: testing {implementation}"
          )
          key = (
              f"{model.__class__.__name__}|EM|hmmlearn-{implementation}"
          )
          elapsed = self.benchmark(sequences, lengths, model, key)
          runtimes[key]["mean"] = elapsed.mean()
          runtimes[key]["std"] = elapsed.std()
  
      with open(results_file, mode="w") as fd:
          fd.write("configuration,mean,std,n_iterations,repeat\n")
          for key, value in runtimes.items():
              fd.write(
                  f"{key},{value['mean']},{value['std']},"
                  f"{self.n_iter},{self.repeat}\n"
              )
  
imports: import argparse
  import collections
  import logging
  import time
  import hmmlearn.hmm
  import numpy
  import sklearn.base
label_desc: Use image node_id 5 for calling the Benchmark obj's underlying member method code with example usage: obj.run(results_file) without return types