code: def q_retrace(
      rewards, dones, q_i, values, rho_i, n_envs, n_steps, gamma
  ):
      """
      Calculates the target Q-retrace
  
      :param rewards: ([TensorFlow Tensor]) The rewards
      :param dones: ([TensorFlow Tensor])
      :param q_i: ([TensorFlow Tensor]) The Q values for actions taken
      :param values: ([TensorFlow Tensor]) The output of the value functions
      :param rho_i: ([TensorFlow Tensor]) The importance weight for each action
      :param n_envs: (int) The number of environments
      :param n_steps: (int) The number of steps to run for each environment
      :param gamma: (float) The discount value
      :return: ([TensorFlow Tensor]) the target Q-retrace
      """
      rho_bar = batch_to_seq(
          tf.minimum(1.0, rho_i), n_envs, n_steps, True
      )  # list of len steps, shape [n_envs]
      reward_seq = batch_to_seq(
          rewards, n_envs, n_steps, True
      )  # list of len steps, shape [n_envs]
      done_seq = batch_to_seq(
          dones, n_envs, n_steps, True
      )  # list of len steps, shape [n_envs]
      q_is = batch_to_seq(q_i, n_envs, n_steps, True)
      value_sequence = batch_to_seq(values, n_envs, n_steps + 1, True)
      final_value = value_sequence[-1]
      qret = final_value
      qrets = []
      for i in range(n_steps - 1, -1, -1):
          check_shape(
              [
                  qret,
                  done_seq[i],
                  reward_seq[i],
                  rho_bar[i],
                  q_is[i],
                  value_sequence[i],
              ],
              [[n_envs]] * 6,
          )
          qret = reward_seq[i] + gamma * qret * (1.0 - done_seq[i])
          qrets.append(qret)
          qret = (rho_bar[i] * (qret - q_is[i])) + value_sequence[i]
      qrets = qrets[::-1]
      qret = seq_to_batch(qrets, flat=True)
      return qret
  
imports: import time
  import warnings
  import numpy
  import tensorflow
  from gym.spaces import Discrete, Box
  from collections import deque
  from stable_baselines import logger
  from stable_baselines.common.schedules import Scheduler
  from stable_baselines.common.tf_util import batch_to_seq, seq_to_batch, check_shape, avg_norm, gradient_add, q_explained_variance, total_episode_reward_logger
  from stable_baselines.acer.buffer import Buffer
  from stable_baselines.common import ActorCriticRLModel, tf_util, SetVerbosity, TensorboardWriter
  from stable_baselines.common.runners import AbstractEnvRunner
  from stable_baselines.common.policies import ActorCriticPolicy, RecurrentActorCriticPolicy
label_desc: Use image node_id 3 for calling a global function with example usage: q_retrace(rewards, dones, q_i, values, rho_i, n_envs, n_steps, gamma) and returns: qret