def METHOD_NAME(x, config):
    parameters = {
        "num_filter":  64,
        "filter_size":  3,
        "stride":       1,
    }
    for k in parameters.keys():
        if k in config:
            parameters[k] = int(config[k])
    activated = Activation('relu')(x)
    conved = Conv2D(
        filters=parameters['num_filter'],
        kernel_size=parameters['filter_size'],
        strides=parameters['stride'],
        padding='same')(activated)
    result = BatchNormalization()(conved)
    return result