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"]
code: def reset(
      self,
      *,
      seed: Optional[int] = None,
      options: Optional[dict] = None,
  ) -> Tuple[MultiAgentDict, MultiAgentDict]:
      kaggle_state = self.kaggle_env.reset()
      self.last_cumulative_reward = None
      return {
          f"agent{idx}": self._convert_obs(agent_state["observation"])
          for idx, agent_state in enumerate(kaggle_state)
          if agent_state["status"] == "ACTIVE"
      }, {}
  
file_path: ray/python/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 2 for calling the KaggleFootballMultiAgentEnv obj's underlying member method code with example usage: obj.reset() and returns: unknown,  dict