code: def before_loss_init(
      policy: Policy,
      obs_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      config: AlgorithmConfigDict,
  ) -> None:
      ComputeTDErrorMixin.__init__(policy)
      TargetNetworkMixin.__init__(policy)
  
imports: from typing import Dict, List, Tuple
  import gymnasium
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, Q_SCOPE, Q_TARGET_SCOPE, postprocess_nstep_and_prio
  from ray.rllib.algorithms.dqn.dqn_torch_model import DQNTorchModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import get_torch_categorical_class_with_temperature, TorchDistributionWrapper
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.exploration.parameter_noise import ParameterNoise
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, FLOAT_MIN, huber_loss, l2_loss, reduce_mean_ignore_inf, softmax_cross_entropy_with_logits
  from ray.rllib.utils.typing import TensorType, AlgorithmConfigDict
label_desc: Use image node_id 7 for calling a global function with example usage: before_loss_init(policy, obs_space, action_space, config) without return types