code: def smoothing(lc):
      new_lc = []
      curr_best = np.inf
      for i in range(len(lc)):
          if lc[i] < curr_best:
              curr_best = lc[i]
          new_lc.append(curr_best)
      return new_lc
  
file_path: HpBandSter/hpbandster/optimizers/config_generators/lcnet.py
imports: import ConfigSpace
  import numpy
  import threading
  from robo.models.lcnet import LCNet, get_lc_net
  from hpbandster.core.base_config_generator import base_config_generator
label_desc: Use image node_id 1 for calling a global function with example usage: smoothing(lc) and returns: new_lc