code: def _validate(obs_space, action_space):
      if not hasattr(obs_space, "original_space") or not isinstance(
          obs_space.original_space, gym.spaces.Tuple
      ):
          raise ValueError(
              "Obs space must be a Tuple, got {}. Use ".format(
                  obs_space
              )
              + "MultiAgentEnv.with_agent_groups() to group related "
              "agents for QMix."
          )
      if not isinstance(action_space, gym.spaces.Tuple):
          raise ValueError(
              "Action space must be a Tuple, got {}. ".format(
                  action_space
              )
              + "Use MultiAgentEnv.with_agent_groups() to group related "
              "agents for QMix."
          )
      if not isinstance(action_space.spaces[0], gym.spaces.Discrete):
          raise ValueError(
              "QMix requires a discrete action space, got {}".format(
                  action_space.spaces[0]
              )
          )
      if len({str(x) for x in obs_space.original_space.spaces}) > 1:
          raise ValueError(
              "Implementation limitation: observations of grouped agents "
              "must be homogeneous, got {}".format(
                  obs_space.original_space.spaces
              )
          )
      if len({str(x) for x in action_space.spaces}) > 1:
          raise ValueError(
              "Implementation limitation: action space of grouped agents "
              "must be homogeneous, got {}".format(action_space.spaces)
          )
  
imports: import logging
  from typing import Dict, List, Optional, Tuple
  import gymnasium
  import numpy
  import tree
  from rllib_qmix.qmix.mixers import QMixer, VDNMixer
  from rllib_qmix.qmix.model import RNNModel, _get_size
  from ray.rllib.env.multi_agent_env import ENV_STATE
  from ray.rllib.env.wrappers.group_agents_wrapper import GROUP_REWARDS
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import _unpack_obs
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical
  from ray.rllib.policy.rnn_sequencing import chop_into_sequences
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_policy import TorchPolicy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.torch_utils import apply_grad_clipping
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 1 for calling a global function with example usage: _validate(obs_space, action_space) without return types