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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 optimize_hyperparameters(self, problems, parameter_locks=None, smoothing=20, max_iterations=100, _meta_optimizer=None, _low_memory=True):
 """Optimize hyperparameters for a given problem. Args: parameter_locks: a list of strings, each corresponding to a hyperparamter that should not be optimized. problems: Either a single problem, or a list of problem instances, allowing optimization based on multiple similar problems. smoothing: int; number of runs to average over for each set of hyperparameters. max_iterations: The number of iterations to optimize before stopping. _low_memory: disable performance enhancements to save memory (they use a lot of memory otherwise). """

        if smoothing <= 0:
            raise ValueError('smoothing must be > 0')

        # problems supports either one or many problem instances
        if isinstance(problems, collections.Iterable):
            for problem in problems:
                if not isinstance(problem, Problem):
                    raise TypeError(
                        'problem must be Problem instance or list of Problem instances'
                    )
        elif isinstance(problems, Problem):
            problems = [problems]
        else:
            raise TypeError(
                'problem must be Problem instance or list of Problem instances'
            )

        # Copy to avoid permanent modification
        meta_parameters = copy.deepcopy(self._hyperparameters)

        # First, handle parameter locks, since it will modify our
        # meta_parameters dict
        locked_values = _parse_parameter_locks(self, meta_parameters,
                                               parameter_locks)

        # We need to know the size of our chromosome,
        # based on the hyperparameters to optimize
        solution_size = _get_hyperparameter_solution_size(meta_parameters)

        # We also need to create a decode function to transform the binary solution
        # into parameters for the metaheuristic
        decode = _make_hyperparameter_decode_func(locked_values,
                                                  meta_parameters)

        # A master fitness dictionary can be stored for use between calls
        # to meta_fitness
        if _low_memory:
            master_fitness_dict = None
        else:
            master_fitness_dict = {}

        additional_parameters = {
            '_optimizer': self,
            '_problems': problems,
            '_runs': smoothing,
            '_master_fitness_dict': master_fitness_dict,
        }
        META_FITNESS = Problem(
            _meta_fitness_func,
            decode_function=decode,
            fitness_kwargs=additional_parameters)
        if _meta_optimizer is None:
            # Initialize default meta optimizer
            # GenAlg is used because it supports both discrete and continous
            # attributes
            from optimal import GenAlg

            # Create metaheuristic with computed decode function and soltuion
            # size
            _meta_optimizer = GenAlg(solution_size)
        else:
            # Adjust supplied metaheuristic for this problem
            _meta_optimizer._solution_size = solution_size

        # Determine the best hyperparameters with a metaheuristic
        best_parameters = _meta_optimizer.optimize(
            META_FITNESS, max_iterations=max_iterations)

        # Set the hyperparameters inline
        self._set_hyperparameters(best_parameters)

        # And return
        return best_parameters