How to use the dtlpy.executions.get function in dtlpy

def _collect_metrics(self, inputs_dict, trial_id, results_dict):
        thread_name = threading.currentThread().getName()
        logger.info('starting thread: ' + thread_name)
        if self.remote:
            try:
                # checkpoint_path = 'best_' + trial_id + '.pt'
                checkpoint_path = 'checkpoint.pt'
                path_to_tensorboard_dir = 'runs'
                logger.info("trying to get execution objects")
                execution_obj = self._run_trial_remote_execution(inputs_dict)
                logger.info("got execution objects")
                # TODO: Turn execution_obj into metrics
                while execution_obj.latest_status['status'] != 'success':
                    #TODO: make time sleep in env variable
                    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")
                if os.path.exists(checkpoint_path):
                    logger.info('overwriting checkpoint.pt . . .')
                    os.remove(checkpoint_path)
                if os.path.exists(path_to_tensorboard_dir):
                    logger.info('overwriting tenorboards runs . . .')
                    shutil.rmtree(path_to_tensorboard_dir)
                # download artifacts, should contain metrics and tensorboard runs
                # TODO: download many different metrics then should have id hash as well..
                self.project.artifacts.download(package_name=self.package_name,
                                                execution_id=execution_obj.id,
                                                local_path=os.getcwd())
                logger.info('going to load ' + checkpoint_path + ' into checkpoint')
                if torch.cuda.is_available():

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
            # TODO: download many different metrics then should have id hash as well..
            self.project.artifacts.download(package_name=self.package_name,
                                            execution_id=execution_obj.id,
                                            local_path=os.getcwd())

        except Exception as e:
            Exception(' had an exception: \n', repr(e))

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

download_and_organize(path_to_dataset=path_to_dataset, dataset_obj=test_dataset, filters=filters)

        json_file_path = os.path.join(path_to_dataset, 'json')
        self.model_obj = self.project.models.get(model_name='retinanet')
        self.adapter = self.model_obj.build(local_path=os.getcwd())
        logger.info('model built')
        while 1:

            self.compute = precision_recall_compute()
            self.compute.add_dataloop_local_annotations(json_file_path)
            logger.info("running new execution")
            execution_obj = self.service.execute(function_name='search', execution_input=[self.configs_input],
                                                 project_id='72bb623f-517f-472b-ad69-104fed8ee94a')
            while execution_obj.latest_status['status'] != 'success':
                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")
            self.project.artifacts.download(package_name='zazuml',
                                            execution_id=execution_obj.id,
                                            local_path=os.getcwd())
            logs_file_name = 'timer_logs_' + str(execution_obj.id) + '.conf'
            graph_file_name = 'precision_recall_' + str(execution_obj.id) + '.png'
            self.cycle_logger = init_logging(__name__, filename=logs_file_name)
            logger.info('artifact download finished')
            logger.info(str(os.listdir('.')))

            # load new checkpoint and change to unique name
            new_checkpoint_name = 'checkpoint_' + str(execution_obj.id) + '.pt'
            logger.info(str(os.listdir('.')))