code: def fit(self, times, losses, configs=None):
      """
      function to train the model on the observed data
  
      Parameters:
      -----------
  
      times: list
          list of numpy arrays of the timesteps for each curve
      losses: list
          list of numpy arrays of the loss (the actual learning curve)
      configs: list or None
          list of the configurations for each sample. Each element
          has to be a numpy array. Set to None, if no configuration
          information is available.
      """
      raise NotImplementedError()
  
label_desc: Use image node_id 1 for calling the LCModel obj's underlying member method code with example usage: obj.fit(times, losses, configs) without return types