How to use the a2ml.api.utils.fsclient.is_file_exists function in a2ml

self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=True, json_result=True, count_in_result=False, prediction_date=prediction_date, 
            model_path=model_path, model_id=options.get('uid'))
        res = json.loads(res)
        self.assertEqual( res['columns'], ds.columns)
        self.assertEqual( len(res['data']), 6)

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        ds.options['data_path'] = None
        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=False, json_result=False, count_in_result=False, prediction_date=prediction_date, 
            model_path=model_path, model_id=options.get('uid'))
        self.assertEqual( type(res[0]), dict)
        self.assertEqual( res[0][options['targetFeature']], 'setosa')

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        ds.options['data_path'] = None
        ds.loaded_columns = ds.columns

prediction_date + '_' + prediction_id + "_results.feather.zstd")
        predicted_file_path = os.path.join(model_path, "predictions", 
            "iris_test_"+prediction_id+"_"+options.get('uid')+"_predicted.csv")

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=True, json_result=False, count_in_result=False, prediction_date=prediction_date, 
            model_path=model_path, model_id=options.get('uid'))
        self.assertEqual(res, predicted_file_path)
        self.assertTrue(fsclient.is_file_exists(predicted_file_path))
        self.assertTrue(fsclient.is_file_exists(results_file_path))

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=True, json_result=True, count_in_result=False, prediction_date=prediction_date, 
            model_path=model_path, model_id=options.get('uid'))
        res = json.loads(res)
        self.assertEqual( res['columns'], ds.columns)
        self.assertEqual( len(res['data']), 6)

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)

def test_save_prediction(self):
        model_path = 'tests/fixtures/test_predict_by_model/iris'
        options = fsclient.read_json_file(os.path.join(model_path, "options.json"))

        prediction_id = "123"
        prediction_date="today"
        results_file_path = os.path.join(model_path, "predictions", 
            prediction_date + '_' + prediction_id + "_results.feather.zstd")
        predicted_file_path = os.path.join(model_path, "predictions", 
            "iris_test_"+prediction_id+"_"+options.get('uid')+"_predicted.csv")

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=True, json_result=False, count_in_result=False, prediction_date=prediction_date, 
            model_path=model_path, model_id=options.get('uid'))
        self.assertEqual(res, predicted_file_path)
        self.assertTrue(fsclient.is_file_exists(predicted_file_path))
        self.assertTrue(fsclient.is_file_exists(results_file_path))

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=True, json_result=True, count_in_result=False, prediction_date=prediction_date,

ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        ds.options['data_path'] = None
        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=False, json_result=False, count_in_result=False, prediction_date=prediction_date, 
            model_path=model_path, model_id=options.get('uid'))
        self.assertEqual( type(res[0]), dict)
        self.assertEqual( res[0][options['targetFeature']], 'setosa')

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        ds.options['data_path'] = None
        ds.loaded_columns = ds.columns
        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=False, json_result=False, count_in_result=False, prediction_date=prediction_date, 
            model_path=model_path, model_id=options.get('uid'))
        self.assertEqual( res['columns'], ds.columns)
        self.assertEqual( len(res['data']), 6)
        self.assertEqual( type(res['data'][0]), list)

def _path_to_credentials(self):
        if self.ctx.config.get('path_to_credentials'):
            creds_path = os.path.abspath(self.ctx.config.get('path_to_credentials'))
        elif os.environ.get('%s_CREDENTIALS_PATH'%self.provider.upper()):
            creds_path = os.environ.get('%s_CREDENTIALS_PATH'%self.provider.upper())
        else:
            cur_path = os.getcwd()
            if self.ctx.config.path:
                cur_path = self.ctx.config.path

            if fsclient.is_file_exists(os.path.join(cur_path, "%s.json"%self.provider)):
                creds_path = cur_path
            else:
                creds_path = os.path.abspath('%s/.a2ml' % os.environ.get('HOME', os.getcwd()))

        return creds_path

def _load(self, name):
        part = SerializableConfigYaml()
        if fsclient.is_file_exists(name):
            part.load_from_file(name)
        return part

def verify_local_model(self, model_id):
        model_path = os.path.join(self.ctx.config.get_model_path(model_id),
            'model.pkl.gz')
        return fsclient.is_file_exists(model_path), model_path

if not fsclient.is_s3_path(data_source_file):
            if config_path is None:
                config_path = os.getcwd()

            data_source_file = os.path.join(config_path, data_source_file)
            if not fsclient.is_s3_path(data_source_file):
                data_source_file = os.path.abspath(data_source_file)

        filename, file_extension = os.path.splitext(data_source_file)
        if not file_extension in SUPPORTED_FORMATS:
            raise AugerException(
                'Source file has to be one of the supported fomats: %s' %
                ', '.join(SUPPORTED_FORMATS))

        if not fsclient.is_file_exists(data_source_file):
            raise AugerException(
                'Can\'t find file to import: %s' % data_source_file)

        return data_source_file, True