code: def _mac(model, obs, h):
      """Forward pass of the multi-agent controller.
  
      Args:
          model: TorchModelV2 class
          obs: Tensor of shape [B, n_agents, obs_size]
          h: List of tensors of shape [B, n_agents, h_size]
  
      Returns:
          q_vals: Tensor of shape [B, n_agents, n_actions]
          h: Tensor of shape [B, n_agents, h_size]
      """
      B, n_agents = obs.size(0), obs.size(1)
      if not isinstance(obs, dict):
          obs = {"obs": obs}
      obs_agents_as_batches = {
          k: _drop_agent_dim(v) for k, v in obs.items()
      }
      h_flat = [s.reshape([B * n_agents, -1]) for s in h]
      q_flat, h_flat = model(obs_agents_as_batches, h_flat, None)
      return q_flat.reshape([B, n_agents, -1]), [
          s.reshape([B, n_agents, -1]) for s in h_flat
      ]
  
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 2 for calling a global function with example usage: _mac(model, obs, h) and returns: q_flat,  unknown