How to use the netron.solvers.Solver function in netron

from netron.solvers import Solver
from netron.grid import NeuralNetGrid
from sklearn.grid_search import ParameterSampler, ParameterGrid
import random
import itertools
import hashlib
import time

class RandomSearch(Solver):
    # If we sample more than this number of already seen networks
    # consecutively, then skip to the next network size
    STRUCT_DUP_THRESHOLD = 100

    def initialize(self, params_sample_size, structure_sample_size):
        self.params_sample_size = params_sample_size
        self.structure_sample_size = structure_sample_size
        self.seen_structures = set()

    def random_product(self, *args, **kwds):
        "Random selection from itertools.product(*args, **kwds)"
        pools = map(tuple, args) * kwds.get('repeat', 1)
        return tuple(random.choice(pool) for pool in pools)

    def create_network_structures(self, layers, layers_num, input_shape):
        """Returns all combinations of given set of layers with given set of sizes"""

from netron.solvers import Solver
from netron.grid import NeuralNetGrid
from sklearn.grid_search import ParameterGrid
import itertools

class GridSearch(Solver):

    def create_network_structures(self, layers, layers_num, input_shape):
        """Returns all combinations of given set of layers with given set of sizes"""
        for i in layers_num:
            for net_struct in itertools.product(layers, repeat=i):
                fixed_net_struct = self.model_factory.fix_or_skip(net_struct, input_shape)
                if fixed_net_struct:
                    yield fixed_net_struct
                else:
                    print "skipping invalid structure: %s" % "->".join(net_struct)
                    continue

    def generate_models(self, input_shape, output_dim):
        loss_type = self.grid.params_grid["loss"][0]
        for layers in self.create_network_structures(self.grid.params_grid["layers"], self.grid.params_grid["layer_nums"], input_shape):
            print "Current network: %s" % "->".join(layers)