How to use the mlagents.envs.exception.UnityEnvironmentException function in mlagents

def _create_model_path(model_path):
        try:
            if not os.path.exists(model_path):
                os.makedirs(model_path)
        except Exception:
            raise UnityEnvironmentException('The folder {} containing the '
                                            'generated model could not be '
                                            'accessed. Please make sure the '
                                            'permissions are set correctly.'
                                            .format(model_path))

def _create_model_path(model_path):
        try:
            if not os.path.exists(model_path):
                os.makedirs(model_path)
        except Exception:
            raise UnityEnvironmentException('The folder {} containing the '
                                            'generated model could not be '
                                            'accessed. Please make sure the '
                                            'permissions are set correctly.'
                                            .format(model_path))

else str(expected_continuous_size),
                                    self._brains[brain_name].vector_action_space_type,
                                    str(vector_action[brain_name])))

            outputs = self.communicator.exchange(
                self._generate_step_input(vector_action, memory, text_action, value, custom_action))
            if outputs is None:
                raise KeyboardInterrupt
            rl_output = outputs.rl_output
            state = self._get_state(rl_output)
            self._global_done = state[1]
            for _b in self._external_brain_names:
                self._n_agents[_b] = len(state[0][_b].agents)
            return state[0]
        elif not self._loaded:
            raise UnityEnvironmentException("No Unity environment is loaded.")
        elif self._global_done:
            raise UnityActionException(
                "The episode is completed. Reset the environment with 'reset()'")
        elif self.global_done is None:
            raise UnityActionException(
                "You cannot conduct step without first calling reset. "
                "Reset the environment with 'reset()'")

def _create_model_path(model_path):
        try:
            if not os.path.exists(model_path):
                os.makedirs(model_path)
        except Exception:
            raise UnityEnvironmentException('The folder {} containing the '
                                            'generated model could not be '
                                            'accessed. Please make sure the '
                                            'permissions are set correctly.'
                                            .format(model_path))

def reset(self, config=None, train_mode=True, custom_reset_parameters=None) -> AllBrainInfo:
        """
        Sends a signal to reset the unity environment.
        :return: AllBrainInfo  : A data structure corresponding to the initial reset state of the environment.
        """
        if config is None:
            config = self._resetParameters
        elif config:
            logger.info("Academy reset with parameters: {0}"
                        .format(', '.join([str(x) + ' -> ' + str(config[x]) for x in config])))
        for k in config:
            if (k in self._resetParameters) and (isinstance(config[k], (int, float))):
                self._resetParameters[k] = config[k]
            elif not isinstance(config[k], (int, float)):
                raise UnityEnvironmentException(
                    "The value for parameter '{0}'' must be an Integer or a Float.".format(k))
            else:
                raise UnityEnvironmentException(
                    "The parameter '{0}' is not a valid parameter.".format(k))

        if self._loaded:
            outputs = self.communicator.exchange(
                self._generate_reset_input(train_mode, config, custom_reset_parameters)
            )
            if outputs is None:
                raise KeyboardInterrupt
            rl_output = outputs.rl_output
            s = self._get_state(rl_output)
            self._global_done = s[1]
            for _b in self._external_brain_names:
                self._n_agents[_b] = len(s[0][_b].agents)

)
            if len(candidates) == 0:
                candidates = glob.glob(
                    os.path.join(file_name + ".app", "Contents", "MacOS", "*")
                )
            if len(candidates) > 0:
                launch_string = candidates[0]
        elif platform == "win32":
            candidates = glob.glob(os.path.join(cwd, file_name + ".exe"))
            if len(candidates) == 0:
                candidates = glob.glob(file_name + ".exe")
            if len(candidates) > 0:
                launch_string = candidates[0]
        if launch_string is None:
            self._close()
            raise UnityEnvironmentException(
                "Couldn't launch the {0} environment. "
                "Provided filename does not match any environments.".format(
                    true_filename
                )
            )
        else:
            logger.debug("This is the launch string {}".format(launch_string))
            # Launch Unity environment
            if not docker_training:
                if no_graphics:
                    self.proc1 = subprocess.Popen(
                        [
                            launch_string,
                            "-nographics",
                            "-batchmode",
                            "--port",

self.seed, self.run_id)
            elif trainer_parameters_dict[brain_name]['trainer'] == 'ppo':
                
                ###############################################################################
                #######         External brain becomes internal brain in here        ##########
                ###############################################################################
                
                self.trainers[brain_name] = PPOTrainer(
                    sess, self.env.brains[brain_name],
                    self.meta_curriculum
                        .brains_to_curriculums[brain_name]
                        .min_lesson_length if self.meta_curriculum else 0,
                    trainer_parameters_dict[brain_name],
                    self.train_model, self.seed, self.run_id)
            else:
                raise UnityEnvironmentException('The trainer config contains '
                                                'an unknown trainer type for '
                                                'brain {}'
                                                .format(brain_name))

def _load_config(self):
        try:
            with open(self.trainer_config_path) as data_file:
                trainer_config = yaml.load(data_file)
                return trainer_config
        except IOError:
            raise UnityEnvironmentException('Parameter file could not be found '
                                            'at {}.'
                                            .format(self.trainer_config_path))
        except UnicodeDecodeError:
            raise UnityEnvironmentException('There was an error decoding '
                                            'Trainer Config from this path : {}'
                                            .format(self.trainer_config_path))

:param value: Value estimates provided by agents.
        :param vector_action: Agent's vector action. Can be a scalar or vector of int/floats.
        :param memory: Vector corresponding to memory used for recurrent policies.
        :param text_action: Text action to send to environment for.
        :param custom_action: Optional instance of a CustomAction protobuf message.
        :return: AllBrainInfo  : A Data structure corresponding to the new state of the environment.
        """
        vector_action = {} if vector_action is None else vector_action
        memory = {} if memory is None else memory
        text_action = {} if text_action is None else text_action
        value = {} if value is None else value
        custom_action = {} if custom_action is None else custom_action

        # Check that environment is loaded, and episode is currently running.
        if not self._loaded:
            raise UnityEnvironmentException("No Unity environment is loaded.")
        elif self._global_done:
            raise UnityActionException(
                "The episode is completed. Reset the environment with 'reset()'"
            )
        elif self.global_done is None:
            raise UnityActionException(
                "You cannot conduct step without first calling reset. "
                "Reset the environment with 'reset()'"
            )
        else:
            if isinstance(vector_action, self.SINGLE_BRAIN_ACTION_TYPES):
                if self._num_external_brains == 1:
                    vector_action = {self._external_brain_names[0]: vector_action}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names a keys, "

:bool train_mode: Whether to run in training mode, speeding up the simulation, by default.
        """

        atexit.register(self._close)
        self.port = base_port + worker_id
        self._buffer_size = 12000
        self._version_ = "API-8"
        self._loaded = False  # If true, this means the environment was successfully loaded
        self.proc1 = None  # The process that is started. If None, no process was started
        self.communicator = self.get_communicator(worker_id, base_port, timeout_wait)

        # If the environment name is None, a new environment will not be launched
        # and the communicator will directly try to connect to an existing unity environment.
        # If the worker-id is not 0 and the environment name is None, an error is thrown
        if file_name is None and worker_id != 0:
            raise UnityEnvironmentException(
                "If the environment name is None, "
                "the worker-id must be 0 in order to connect with the Editor.")
        if file_name is not None:
            self.executable_launcher(file_name, docker_training, no_graphics)
        else:
            logger.info("Start training by pressing the Play button in the Unity Editor.")
        self._loaded = True

        rl_init_parameters_in = UnityRLInitializationInput(
            seed=seed
        )
        try:
            aca_params = self.send_academy_parameters(rl_init_parameters_in)
        except UnityTimeOutException:
            self._close()
            raise