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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def training_loop_hparams_from_scoped_overrides(scoped_overrides, trial_id):
 """Create HParams suitable for training loop from scoped HParams. Args: scoped_overrides: HParams, with keys all scoped by one of HP_SCOPES. These parameters are overrides for the base HParams created by create_loop_hparams. trial_id: str, trial identifier. This is used to register unique HParams names for the underlying model and ppo HParams. Returns: HParams suitable for passing to training_loop. """

  trial_hp_overrides = scoped_overrides.values()

  # Create loop, model, and ppo base HParams
  loop_hp = create_loop_hparams()
  model_hp_name = trial_hp_overrides.get(
      "loop.generative_model_params", loop_hp.generative_model_params)
  model_hp = registry.hparams(model_hp_name).parse(FLAGS.hparams)
  base_algo_params_name = trial_hp_overrides.get(
      "loop.base_algo_params", loop_hp.base_algo_params)
  algo_hp = registry.hparams(base_algo_params_name)

  # Merge them and then override with the scoped overrides
  combined_hp = merge_unscoped_hparams(
      zip(HP_SCOPES, [loop_hp, model_hp, algo_hp]))
  combined_hp.override_from_dict(trial_hp_overrides)

  # Split out the component hparams
  loop_hp, model_hp, algo_hp = (
      split_scoped_hparams(HP_SCOPES, combined_hp))

  # Dynamic register the model hp and set the new name in loop_hp
  model_hp_name = "model_hp_%s" % str(trial_id)
  dynamic_register_hparams(model_hp_name, model_hp)
  loop_hp.generative_model_params = model_hp_name

  # Dynamic register the algo hp and set the new name in loop_hp
  algo_hp_name = "algo_hp_%s" % str(trial_id)
  dynamic_register_hparams(algo_hp_name, algo_hp)
  loop_hp.base_algo_params = algo_hp_name

  return loop_hp