code: def basic_range1(ranged_hparams):
    
    rhp = ranged_hparams
    rhp.set_discrete("batch_size", [1024, 2048, 4096])
    rhp.set_discrete("num_hidden_layers", [1, 2, 3, 4, 5, 6])
    rhp.set_discrete("hidden_size", [32, 64, 128, 256, 512], scale=rhp.LOG_SCALE)
    rhp.set_discrete("kernel_height", [1, 3, 5, 7])
    rhp.set_discrete("kernel_width", [1, 3, 5, 7])
    rhp.set_discrete("compress_steps", [0, 1, 2])
    rhp.set_float("dropout", 0.0, 0.5)
    rhp.set_float("weight_decay", 1e-4, 10.0, scale=rhp.LOG_SCALE)
    rhp.set_float("label_smoothing", 0.0, 0.2)
    rhp.set_float("clip_grad_norm", 0.01, 50.0, scale=rhp.LOG_SCALE)
    rhp.set_float("learning_rate", 0.005, 2.0, scale=rhp.LOG_SCALE)
    rhp.set_categorical("initializer",
                        ["uniform", "orthogonal", "uniform_unit_scaling"])
    rhp.set_float("initializer_gain", 0.5, 3.5)
    rhp.set_categorical("learning_rate_decay_scheme",
                        ["none", "sqrt", "noam", "exp"])
    rhp.set_float("optimizer_adam_epsilon", 1e-7, 1e-2, scale=rhp.LOG_SCALE)
    rhp.set_float("optimizer_adam_beta1", 0.8, 0.9)
    rhp.set_float("optimizer_adam_beta2", 0.995, 0.999)
    rhp.set_categorical(
        "optimizer",
        ["adam", "adagrad", "momentum", "rms_prop", "sgd", "yellow_fin"])
docs: A basic range of hyperparameters.