class_docstr: Wrapper around LC-Net
docstr: extends a partially observed curve
  
  Parameters:
  -----------
  
  times: numpy array
      times where to predict the loss
  obs_times: numpy array
      times where the curve has already been observed
  obs_losses: numpy array
      corresponding observed losses
  config: numpy array
      numerical reperesentation of the config; None if no config
      information is available
      
  Returns:
  --------
  
  mean and variance prediction at input times
      
      
code: def extend_partial(self, times, obs_times, obs_losses, config=None):
      """
      extends a partially observed curve
  
      Parameters:
      -----------
  
      times: numpy array
          times where to predict the loss
      obs_times: numpy array
          times where the curve has already been observed
      obs_losses: numpy array
          corresponding observed losses
      config: numpy array
          numerical reperesentation of the config; None if no config
          information is available
  
      Returns:
      --------
  
      mean and variance prediction at input times
  
  
      """
      return self.predict_unseen(times, config)
  
file_path: auptimizer/src/aup/Proposer/hpbandster/optimizers/learning_curve_models/lcnet.py
imports: import numpy
  from hpbandster.learning_curve_models.base import LCModel
  from robo.models.lcnet import LCNet, get_lc_net
label_desc: Use image node_id 4 for calling the LCNetWrapper obj's underlying member method code with example usage: obj.extend_partial(times, obs_times, obs_losses, config) and returns: self