How to use the m3d.hadoop.algorithm.scala_classes.ScalaClasses function in m3d

def run_command_in_cluster_patch(cmd, name):
            # Check command name
            assert "Running Spark Application" in str(name)
            print("Command is: {0}".format(cmd))
            command_components = cmd.split()

            # Check algorithm name from the spark command
            algorithm_class_name = command_components[-3]
            assert algorithm_class_name == ScalaClasses.GZIP_DECOMPRESSOR

            # Check configuration file content
            algorithm_config_file_name = command_components[-2]
            actual_config_file_content = self.get_object_content_from_s3(algorithm_config_file_name)
            print("Actual config content: {0}".format(actual_config_file_content))

            algorithm_config_file_dict = json.loads(actual_config_file_content)

            assert algorithm_config_file_dict == expected_param_dict

def get_scala_class(self):
        return ScalaClasses.DELTA_LOAD

def get_scala_class(self):
            return ScalaClasses.PARTITION_QUERY_MATERIALIZATION

def get_scala_class(self):
        return ScalaClasses.ALGORITHM_TEMPLATE

def get_scala_class(self):
        return ScalaClasses.FULL_LOAD

def get_scala_class(self):
        return ScalaClasses.APPEND_LOAD

def get_scala_class(self):
            return ScalaClasses.PARTITION_FULL_MATERIALIZATION

def get_scala_class(self):
            return ScalaClasses.PARTITION_RANGE_MATERIALIZATION

def get_scala_class(self):
        return ScalaClasses.FIXED_LENGTH_STRING_EXTRACTOR