code: def _process(exps):
      n = len(exps)
      ret = []
      for i in range(5):
          ret.append([])
          for j in range(n):
              ret[i].append(exps[j][i])
  
      ret = [np.transpose(arr) for arr in ret]
      return ret
  
file_path: dynet/examples/reinforcement-learning/ddpg.py
imports: import dynet
  import numpy
  from memory import Memory
  from network import MLP
label_desc: Use image node_id 5 for calling the DDPG obj's underlying member method code with example usage: obj._process(exps) and returns: ret