code: def collect_trajectories(env, agent, scaler, episodes):
      trajectories, all_unscaled_obs = [], []
      for e in range(episodes):
          obs, actions, rewards, unscaled_obs = run_train_episode(
              env, agent, scaler
          )
          trajectories.append(
              {
                  "obs": obs,
                  "actions": actions,
                  "rewards": rewards,
              }
          )
          all_unscaled_obs.append(unscaled_obs)
      # update running statistics for scaling observations
      scaler.update(np.concatenate(all_unscaled_obs))
      return trajectories
  
file_path: PARL/benchmark/fluid/PPO/train.py
imports: from parl.utils import check_version_for_fluid
  import argparse
  import gym
  import numpy
  import parl
  from mujoco_agent import MujocoAgent
  from mujoco_model import MujocoModel
  from parl.utils import logger
  from parl.utils.rl_utils import calc_gae, calc_discount_sum_rewards
  from parl.env.continuous_wrappers import ActionMappingWrapper
  from scaler import Scaler
label_desc: Use image node_id 3 for calling a global function with example usage: collect_trajectories(env, agent, scaler, episodes) and returns: trajectories