How to use the dtlpy.FunctionIO function in dtlpy

def _launch_predict_remote(self, checkpoint_path):
        self.service = self.global_project.services.get(service_name='predict')
        model_specs = self.optimal_model.unwrap()
        dataset_input = dl.FunctionIO(type='Dataset', name='dataset', value={"dataset_id": self.dataset_id})
        checkpoint_path_input = dl.FunctionIO(type='Json', name='checkpoint_path', value={"checkpoint_path": checkpoint_path})
        val_query_input = dl.FunctionIO(type='Json', name='val_query', value=self.val_query)
        model_specs_input = dl.FunctionIO(type='Json', name='model_specs', value=model_specs)
        inputs = [dataset_input, val_query_input, checkpoint_path_input, model_specs_input]
        logger.info('checkpoint is type: ' + str(type(checkpoint_path)))
        try:
            logger.info("trying to get execution object")
            execution_obj = self._run_pred_remote_execution(inputs)
            logger.info("got execution object")
            # TODO: Turn execution_obj into metrics
            while execution_obj.latest_status['status'] != 'success':
                time.sleep(5)
                execution_obj = dl.executions.get(execution_id=execution_obj.id)
                if execution_obj.latest_status['status'] == 'failed':
                    raise Exception("plugin execution failed")
            logger.info("execution object status is successful")

def push_package(project):
    dataset_input = dl.FunctionIO(type='Dataset', name='dataset')
    train_query_input = dl.FunctionIO(type='Json', name='train_query')
    val_query_input = dl.FunctionIO(type='Json', name='val_query')
    hp_value_input = dl.FunctionIO(type='Json', name='hp_values')
    model_specs_input = dl.FunctionIO(type='Json', name='model_specs')
    checkpoint_path_input = dl.FunctionIO(type='Json', name='checkpoint_path')
    package_name_input = dl.FunctionIO(type='Json', name='package_name')

    configs_input = dl.FunctionIO(type='Json', name='configs')
    time_input = dl.FunctionIO(type='Json', name='time')
    test_dataset_input = dl.FunctionIO(type='Json', name='test_dataset_id')
    query_input = dl.FunctionIO(type='Json', name='query')

    item_input = dl.FunctionIO(type='Item', name='item')
    model_input = dl.FunctionIO(type='Json', name='model_id')
    checkpoint_input = dl.FunctionIO(type='Json', name='checkpoint_id')

    predict_inputs = [dataset_input, val_query_input, checkpoint_path_input, model_specs_input]
    model_inputs = [dataset_input, train_query_input, val_query_input, hp_value_input, model_specs_input]
    zazu_inputs = [configs_input]

    predict_function = dl.PackageFunction(name='run', inputs=predict_inputs, outputs=[], description='')
    model_function = dl.PackageFunction(name='run', inputs=model_inputs, outputs=[], description='')
    zazu_search_function = dl.PackageFunction(name='search', inputs=zazu_inputs, outputs=[], description='')
    zazu_predict_function = dl.PackageFunction(name='predict', inputs=zazu_inputs, outputs=[], description='')
    timer_update_function = dl.PackageFunction(name='update_time', inputs=time_input, outputs=[], description='')
    predict_item_function = dl.PackageFunction(name='predict_single_item', inputs=[item_input], outputs=[],
                                               description='')

def _launch_predict_remote(self, checkpoint_path):
        self.service = self.global_project.services.get(service_name='predict')
        model_specs = self.optimal_model.unwrap()
        dataset_input = dl.FunctionIO(type='Dataset', name='dataset', value={"dataset_id": self.dataset_id})
        checkpoint_path_input = dl.FunctionIO(type='Json', name='checkpoint_path', value={"checkpoint_path": checkpoint_path})
        val_query_input = dl.FunctionIO(type='Json', name='val_query', value=self.val_query)
        model_specs_input = dl.FunctionIO(type='Json', name='model_specs', value=model_specs)
        inputs = [dataset_input, val_query_input, checkpoint_path_input, model_specs_input]
        logger.info('checkpoint is type: ' + str(type(checkpoint_path)))
        try:
            logger.info("trying to get execution object")
            execution_obj = self._run_pred_remote_execution(inputs)
            logger.info("got execution object")
            # TODO: Turn execution_obj into metrics
            while execution_obj.latest_status['status'] != 'success':
                time.sleep(5)
                execution_obj = dl.executions.get(execution_id=execution_obj.id)
                if execution_obj.latest_status['status'] == 'failed':
                    raise Exception("plugin execution failed")
            logger.info("execution object status is successful")
            # download artifacts, should contain dir with txt file annotations

def push_and_deploy_package(self, project, package_name):
    logger.info('dtlpy version:', dl.__version__)
    dataset_input = dl.FunctionIO(type='Dataset', name='dataset')
    hp_value_input = dl.FunctionIO(type='Json', name='hp_values')
    model_specs_input = dl.FunctionIO(type='Json', name='model_specs')
    init_specs_input = dl.FunctionIO(type='Json', name='package_name')
    input_to_init = {
        'package_name': package_name
    }

    inputs = [dataset_input, hp_value_input, model_specs_input]
    function = dl.PackageFunction(name='run', inputs=inputs, outputs=[], description='')
    module = dl.PackageModule(entry_point='dataloop_services/service_executor.py', name='service_executor',
                              functions=[function],
                              init_inputs=init_specs_input)

    package = project.packages.push(
        package_name=package_name,
        src_path=os.getcwd(),
        modules=[module])

    logger.info('deploying package . . .')

def _launch_remote_best_trial(self, best_trial):
        model_specs = self.optimal_model.unwrap()
        dataset_input = dl.FunctionIO(type='Dataset', name='dataset', value={"dataset_id": self.dataset_id})
        train_query_input = dl.FunctionIO(type='Json', name='train_query', value=self.train_query)
        val_query_input = dl.FunctionIO(type='Json', name='val_query', value=self.val_query)
        hp_value_input = dl.FunctionIO(type='Json', name='hp_values', value=best_trial['hp_values'])
        model_specs_input = dl.FunctionIO(type='Json', name='model_specs', value=model_specs)
        inputs = [dataset_input, train_query_input, val_query_input, hp_value_input, model_specs_input]

        execution_obj = self._run_trial_remote_execution(inputs)
        while execution_obj.latest_status['status'] != 'success':
            time.sleep(5)
            execution_obj = dl.executions.get(execution_id=execution_obj.id)
            if execution_obj.latest_status['status'] == 'failed':
                raise Exception("package execution failed")
        return execution_obj

def _launch_remote_best_trial(self, best_trial):
        model_specs = self.optimal_model.unwrap()
        dataset_input = dl.FunctionIO(type='Dataset', name='dataset', value={"dataset_id": self.dataset_id})
        train_query_input = dl.FunctionIO(type='Json', name='train_query', value=self.train_query)
        val_query_input = dl.FunctionIO(type='Json', name='val_query', value=self.val_query)
        hp_value_input = dl.FunctionIO(type='Json', name='hp_values', value=best_trial['hp_values'])
        model_specs_input = dl.FunctionIO(type='Json', name='model_specs', value=model_specs)
        inputs = [dataset_input, train_query_input, val_query_input, hp_value_input, model_specs_input]

        execution_obj = self._run_trial_remote_execution(inputs)
        while execution_obj.latest_status['status'] != 'success':
            time.sleep(5)
            execution_obj = dl.executions.get(execution_id=execution_obj.id)
            if execution_obj.latest_status['status'] == 'failed':
                raise Exception("package execution failed")
        return execution_obj

trial_service.delete()
            zazu_service.delete()

    if args.zazu_timer:
        logger.info('about to launch timer deployment')
        with open('global_configs.json', 'r') as fp:
            global_project_name = json.load(fp)['project']

        global_project = dl.projects.get(project_name=global_project_name)
        global_package_obj = push_package(global_project)

        with open('configs.json', 'r') as fp:
            configs = json.load(fp)

        configs_input = dl.FunctionIO(type='Json', name='configs', value=json.dumps(configs))
        time_input = dl.FunctionIO(type='Json', name='time', value=3600*0.25)
        test_dataset_input = dl.FunctionIO(type='Json', name='test_dataset_id', value='5eb7e0bdd4eb9434c77d80b5')
        query_input = dl.FunctionIO(type='Json', name='query', value=json.dumps({"resource": "items", "sort": {}, "page": 0, "pageSize": 1000, "filter": {"$and": [{"dir": "/items/val*"}, {"hidden": False}, {"type": "file"}]}}))
        init_inputs = [configs_input, time_input, test_dataset_input, query_input]
        deploy_zazu_timer(package=global_package_obj,
                          init_inputs=init_inputs)
        logger.info('timer deployment launched successfully')

    if args.update:
        with open('global_configs.json', 'r') as fp:
            global_project_name = json.load(fp)
        maybe_login()
        global_project = dl.projects.get(project_name=global_project_name)
        update_service(global_project, 'trial')
        update_service(global_project, 'zazu')

if args.zazu_timer:
        logger.info('about to launch timer deployment')
        with open('global_configs.json', 'r') as fp:
            global_project_name = json.load(fp)['project']

        global_project = dl.projects.get(project_name=global_project_name)
        global_package_obj = push_package(global_project)

        with open('configs.json', 'r') as fp:
            configs = json.load(fp)

        configs_input = dl.FunctionIO(type='Json', name='configs', value=json.dumps(configs))
        time_input = dl.FunctionIO(type='Json', name='time', value=3600*0.25)
        test_dataset_input = dl.FunctionIO(type='Json', name='test_dataset_id', value='5eb7e0bdd4eb9434c77d80b5')
        query_input = dl.FunctionIO(type='Json', name='query', value=json.dumps({"resource": "items", "sort": {}, "page": 0, "pageSize": 1000, "filter": {"$and": [{"dir": "/items/val*"}, {"hidden": False}, {"type": "file"}]}}))
        init_inputs = [configs_input, time_input, test_dataset_input, query_input]
        deploy_zazu_timer(package=global_package_obj,
                          init_inputs=init_inputs)
        logger.info('timer deployment launched successfully')

    if args.update:
        with open('global_configs.json', 'r') as fp:
            global_project_name = json.load(fp)
        maybe_login()
        global_project = dl.projects.get(project_name=global_project_name)
        update_service(global_project, 'trial')
        update_service(global_project, 'zazu')