improper indendtaton in validate_environment_path

ml-agents-envs/mlagents_envs/environment.py

     DEFAULT_EDITOR_PORT = 5004
 
     def __init__(
-        self,
-        file_name: Optional[str] = None,
-        worker_id: int = 0,
-        base_port: int = 5005,
-        seed: int = 0,
-        docker_training: bool = False,
-        no_graphics: bool = False,
-        timeout_wait: int = 60,
-        args: Optional[List[str]] = None,
-        side_channels: Optional[List[SideChannel]] = None,
+            self,
+            file_name: Optional[str] = None,
+            worker_id: int = 0,
+            base_port: int = 5005,
+            seed: int = 0,
+            docker_training: bool = False,
+            no_graphics: bool = False,
+            timeout_wait: int = 60,
+            args: Optional[List[str]] = None,
+            side_channels: Optional[List[SideChannel]] = None,
     ):
         """
         Starts a new unity environment and establishes a connection with the environment.
         # Strip out executable extensions if passed
         env_path = (
             env_path.strip()
-                .replace(".app", "")
-                .replace(".exe", "")
-                .replace(".x86_64", "")
-                .replace(".x86", "")
+            .replace(".app", "")
+            .replace(".exe", "")
+            .replace(".x86_64", "")
+            .replace(".x86", "")
         )
         true_filename = os.path.basename(os.path.normpath(env_path))
         logger.debug("The true file name is {}".format(true_filename))
             self._close()
             raise UnityEnvironmentException(
                 "Couldn't launch the {0} environment. "
-                "Provided filename does not match any environments.".format(
-                    file_name
-                )
+                "Provided filename does not match any environments.".format(file_name)
             )
         else:
             logger.debug("This is the launch string {}".format(launch_string))
         self._env_actions[agent_group] = action
 
     def set_action_for_agent(
-        self, agent_group: AgentGroup, agent_id: AgentId, action: np.ndarray
+            self, agent_group: AgentGroup, agent_id: AgentId, action: np.ndarray
     ) -> None:
         self._assert_group_exists(agent_group)
         if agent_group not in self._env_state:
 
     @staticmethod
     def _parse_side_channel_message(
-        side_channels: Dict[int, SideChannel], data: bytes
+            side_channels: Dict[int, SideChannel], data: bytes
     ) -> None:
         offset = 0
         while offset < len(data):
-                message_data = data[offset : offset + message_len]
+                message_data = data[offset: offset + message_len]
                 offset = offset + message_len
             except Exception:
                 raise UnityEnvironmentException(
 
     @timed
     def _generate_step_input(
-        self, vector_action: Dict[str, np.ndarray]
+            self, vector_action: Dict[str, np.ndarray]
     ) -> UnityInputProto:
         rl_in = UnityRLInputProto()
         for b in vector_action:
         return self.wrap_unity_input(rl_in)
 
     def send_academy_parameters(
-        self, init_parameters: UnityRLInitializationInputProto
+            self, init_parameters: UnityRLInitializationInputProto
     ) -> UnityOutputProto:
         inputs = UnityInputProto()
         inputs.rl_initialization_input.CopyFrom(init_parameters)