def METHOD_NAME(model_name, batch_size=1):
    if model_name == "resnet18_v1":
        import mxnet as mx
        from mxnet import gluon
        from mxnet.gluon.model_zoo import vision
        gluon_model = vision.METHOD_NAME(model_name, pretrained=True)
        img_size = 224
        data_shape = (batch_size, 3, img_size, img_size)
        net, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape})
        return (net, params)
    elif model_name == "mobilenet_v2":
        import keras
        from keras.applications.mobilenet_v2 import MobileNetV2
        keras.backend.clear_session()  # Destroys the current TF graph and creates a new one.
        weights_url = "".join(
            [
                "https://github.com/JonathanCMitchell/",
                "mobilenet_v2_keras/releases/download/v1.1/",
                "mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.5_224.h5",
            ]
        )
        weights_file = "mobilenet_v2_weights.h5"
        weights_path = download_testdata(weights_url, weights_file, module="keras")
        keras_mobilenet_v2 = MobileNetV2(
            alpha=0.5, include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000
        )
        keras_mobilenet_v2.load_weights(weights_path)
        img_size = 224
        data_shape = (batch_size, 3, img_size, img_size)
        mod, params = relay.frontend.from_keras(keras_mobilenet_v2, {"input_1": data_shape})
        return (mod, params)