class_docstr: Wrapper around LC-Net
docstr: predict the loss of an unseen configuration
  
  Parameters:
  -----------
  
  times: numpy array
      times where to predict the loss
  config: numpy array
      the numerical representation of the config
  
  Returns:
  --------
  
  mean and variance prediction at input times for the given config
code: def predict_unseen(self, times, config):
      """
      predict the loss of an unseen configuration
  
      Parameters:
      -----------
  
      times: numpy array
          times where to predict the loss
      config: numpy array
          the numerical representation of the config
  
      Returns:
      --------
  
      mean and variance prediction at input times for the given config
      """
  
      assert np.all(times > 0) and np.all(times <= self.max_num_epochs)
  
      x = np.array(config)[None, :]
  
      idx = times / self.max_num_epochs
      x = np.repeat(x, idx.shape[0], axis=0)
  
      x = np.concatenate((x, idx[:, None]), axis=1)
  
      mean, var = self.model.predict(x)
      return 1 - mean, var
  
file_path: HpBandSter/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 3 for calling the LCNetWrapper obj's underlying member method code with example usage: obj.predict_unseen(times, config) and returns: unknown,  var