code: def feature_normalize(self, obs, mean, std, duplicate_id):
      scaler_len = mean.shape[0]
      assert obs.shape[0] >= scaler_len
      obs[:scaler_len] = (obs[:scaler_len] - mean) / std
      final_obs = []
      for i in range(obs.shape[0]):
          if i not in duplicate_id:
              final_obs.append(obs[i])
      return np.array(final_obs)
  
imports: import abc
  import gym
  import math
  import numpy
  import random
  from collections import OrderedDict
  from osim.env import ProstheticsEnv
  from parl.utils import logger
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling the ObsTranformerBase obj's underlying member method code with example usage: obj.feature_normalize(obs, mean, std, duplicate_id) and returns: np