class_docstr: An interface to the kaggle's football environment.
  
  See: https://github.com/Kaggle/kaggle-environments
class_docstr_tok: ["An","interface","to","the","kaggle","'s","football","environment",".","See",":","https",":","\/\/github.com\/Kaggle\/kaggle-environments"]
docstr: Convert raw observations
  
  These conversions are necessary to make the observations fall into the
  observation space defined below.
docstr_tok: ["Convert","raw","observations","These","conversions","are","necessary","to","make","the","observations","fall","into","the","observation","space","defined","below","."]
code: def _convert_obs(self, obs: Dict[str, Any]) -> Dict[str, Any]:
      """Convert raw observations
  
      These conversions are necessary to make the observations fall into the
      observation space defined below.
      """
      new_obs = deepcopy(obs)
      if new_obs["players_raw"][0]["ball_owned_team"] == -1:
          new_obs["players_raw"][0]["ball_owned_team"] = 2
      if new_obs["players_raw"][0]["ball_owned_player"] == -1:
          new_obs["players_raw"][0]["ball_owned_player"] = 11
      new_obs["players_raw"][0]["steps_left"] = [
          new_obs["players_raw"][0]["steps_left"]
      ]
      return new_obs
  
file_path: ray/rllib/env/wrappers/kaggle_wrapper.py
imports: from copy import deepcopy
  from gymnasium.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete, Space, Tuple
  import numpy
  from typing import Any, Dict, Optional, Tuple
  from ray.rllib.env import MultiAgentEnv
  from ray.rllib.utils.typing import MultiAgentDict, AgentID
label_desc: Use image node_id 4 for calling the KaggleFootballMultiAgentEnv obj's underlying member method code with example usage: obj._convert_obs(obs) and returns: new_obs