def generate(self, multiprocessing_queue):
    'Generate the next neural architecture.\n\n        Args:\n            multiprocessing_queue: the Queue for multiprocessing return value.\n\n        Returns:\n            list of 2-element tuples: generated_graph and other_info,\n            for random searcher the length of list is 1.\n            generated_graph: An instance of Graph.\n            other_info: Anything to be saved in the training queue together with the architecture.\n\n        '
    random_index = randrange(len(self.history))
    model_id = self.history[random_index]['model_id']
    graph = self.load_model_by_id(model_id)
    new_father_id = None
    generated_graph = None
    for temp_graph in transform(graph):
        if (not contain(self.descriptors, temp_graph.extract_descriptor())):
            new_father_id = model_id
            generated_graph = temp_graph
            break
    if (new_father_id is None):
        new_father_id = 0
        generated_graph = self.generators[0](self.n_classes, self.input_shape).generate(self.default_model_len, self.default_model_width)
    return [(generated_graph, new_father_id)]
Generate the next neural architecture.

Args:
    multiprocessing_queue: the Queue for multiprocessing return value.

Returns:
    list of 2-element tuples: generated_graph and other_info,
    for random searcher the length of list is 1.
    generated_graph: An instance of Graph.
    other_info: Anything to be saved in the training queue together with the architecture.
def generate(self, multiprocessing_queue):
    'Generate the next neural architecture.\n\n        Args:\n            multiprocessing_queue: the Queue for multiprocessing return value.\n\n        Returns:\n            list of 2-element tuples: generated_graph and other_info,\n            for random searcher the length of list is 1.\n            generated_graph: An instance of Graph.\n            other_info: Anything to be saved in the training queue together with the architecture.\n\n        '
    random_index = randrange(len(self.history))
    model_id = self.history[random_index]['model_id']
    graph = self.load_model_by_id(model_id)
    new_father_id = None
    generated_graph = None
    for temp_graph in transform(graph):
        if (not contain(self.descriptors, temp_graph.extract_descriptor())):
            new_father_id = model_id
            generated_graph = temp_graph
            break
    if (new_father_id is None):
        new_father_id = 0
        generated_graph = self.generators[0](self.n_classes, self.input_shape).generate(self.default_model_len, self.default_model_width)
    return [(generated_graph, new_father_id)]