code: def train_pipeline_progressive(
      env,
      player,
      score_threshold,
      batch_size,
      n_episode,
      learn_start=100,
      print_every=10,
  ):
      rewards, losses = [], []
      for i_episode in range(n_episode):
          obs = env.reset()
          reward = 0
          for t in range(env._max_episode_steps):
              action = player.act(obs)
              obs_next, rwd, done, _ = env.step(action)
              player.store(
                  (
                      obs,
                      action,
                      rwd,
                      obs_next,
                      float(
                          False
                          if t == env._max_episode_steps - 1
                          else done
                      ),
                  )
              )
              reward += rwd
              if i_episode > learn_start:
                  loss = player.learn(batch_size)
                  if loss is not None:
                      losses.append(loss)
              if done:
                  break
              obs = obs_next
          rewards.append(reward)
          if i_episode % print_every == 0:
              score = np.mean(rewards[-100:])
              print("================================")
              print("i_episode: {}".format(i_episode))
              print("100 games mean reward: {}".format(score))
              if len(losses) > 0:
                  print(
                      "100 games mean loss: {}".format(
                          np.mean(losses[-100:])
                      )
                  )
              print("================================")
              print()
              if score > score_threshold:
                  break
  
label_desc: Use image node_id 1 for calling a global function with example usage: train_pipeline_progressive(env, player, score_threshold, batch_size, n_episode, learn_start, print_every) without return types