def METHOD_NAME():
    # design to debug the encoding scheme
    seed = 0
    np.random.seed(seed)
    budget = 2000
    B, n_ops, n_cell = 5, 7, 2
    networks = []
    design_id = 1
    while len(networks) < budget:
        bit_string = []
        for c in range(n_cell):
            for b in range(B):
                bit_string += [np.random.randint(n_ops),
                               np.random.randint(b + 2),
                               np.random.randint(n_ops),
                               np.random.randint(b + 2)
                               ]
        genome = convert(bit_string)
        # check against evaluated networks in case of duplicates
        doTrain = True
        for network in networks:
            if compare(genome, network):
                doTrain = False
                break
        if doTrain:
            genotype = decode(genome)
            model = Network(16, 10, 8, False, genotype)
            model.drop_path_prob = 0.0
            data = torch.randn(1, 3, 32, 32)
            output, output_aux = model(torch.autograd.Variable(data))
            networks.append(genome)
            design_id += 1
            print(design_id)