How to implement a new search algorithm?

A Searcher interacts with the search space through a simple API. A searcher samples a model from the search space by assigning values to the architecture parameters. The results from sampled architecture are then used to update the architecture parameters of the search space. A searcher also updates the model parameters. A new Searcher should inherit API from nnabla_nas.runner.searcher.search.Searcher. This class has two methods train_on_batch() and valid_on_batch() which should be redefined by users. For further modification, we also provide two methods callback_on_start() and callback_on_finish(), which will be called at the beginning and at the end of the training, respectively.

from nnabla_nas.runner.searcher.search import Searcher

class MyAlgorithm(Searcher):

    def callback_on_start(self):
        # TODO: write your code here

    def train_on_batch(self, key='train'):
        # TODO: write your code here

    def valid_on_batch(self):
        # TODO: write your code here

    def callback_on_finish(self):
        # TODO: write your code here

There are two searcher algorithms implemented in NNablaNAS, including DartsSearcher and ProxylessNasSearcher.