code: def hyperparameters(self):
          
          hyperparameters = super(TensorFlow, self).hyperparameters()
  
          self.checkpoint_path = self.checkpoint_path or self._default_s3_path('checkpoints')
          mpi_enabled = False
  
          if self._script_mode_enabled():
              additional_hyperparameters = {}
  
              if 'parameter_server' in self.distributions:
                  ps_enabled = self.distributions['parameter_server'].get('enabled', False)
                  additional_hyperparameters[self.LAUNCH_PS_ENV_NAME] = ps_enabled
  
              if 'mpi' in self.distributions:
                  mpi_dict = self.distributions['mpi']
                  mpi_enabled = mpi_dict.get('enabled', False)
                  additional_hyperparameters[self.LAUNCH_MPI_ENV_NAME] = mpi_enabled
                  additional_hyperparameters[self.MPI_NUM_PROCESSES_PER_HOST] = mpi_dict.get('processes_per_host', 1)
                  additional_hyperparameters[self.MPI_CUSTOM_MPI_OPTIONS] = mpi_dict.get('custom_mpi_options', '')
  
              self.model_dir = self.model_dir or self._default_s3_path('model', mpi=mpi_enabled)
              additional_hyperparameters['model_dir'] = self.model_dir
          else:
              additional_hyperparameters = {'checkpoint_path': self.checkpoint_path,
                                            'training_steps': self.training_steps,
                                            'evaluation_steps': self.evaluation_steps,
                                            'sagemaker_requirements': self.requirements_file}
  
          hyperparameters.update(Framework._json_encode_hyperparameters(additional_hyperparameters))
          return hyperparameters
docs: Return hyperparameters used by your custom TensorFlow code during model training.