code: def __init__(
      self,
      model,
      target_model,
      mixer,
      target_mixer,
      n_agents,
      n_actions,
      double_q=True,
      gamma=0.99,
  ):
      nn.Module.__init__(self)
      self.model = model
      self.target_model = target_model
      self.mixer = mixer
      self.target_mixer = target_mixer
      self.n_agents = n_agents
      self.n_actions = n_actions
      self.double_q = double_q
      self.gamma = gamma
  
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 to create a new QMixLoss object from inherited base classes: nn with example: obj = QMixLoss(model, target_model, mixer, target_mixer, n_agents, n_actions, double_q, gamma)