code: def get_wbic(self):
      # This function calculates the WBIC given that MCMC sampling happened with sampling_temperature = log(n)
      training_metrics = (
          self.get_training_metrics()
      )  # get the training metrics
      training_meta = self.get_training_meta()  # get the meta data
      sampling_temp = training_metrics[
          "sampling_temperature"
      ]  # get the sampling temperature
      nobs = training_meta["num_of_obs"]  # the number of observations
      if sampling_temp != np.log(nobs):
          raise ForecasterException(
              "Sampling temperature is not log(n); WBIC calculation is not valid!"
          )
      return -2 * np.nanmean(training_metrics["loglk"]) * nobs
  
file_path: orbit/orbit/forecaster/full_bayes.py
imports: import numpy
  import pandas
  from functools import partial
  from ..constants.constants import PredictMethod, PredictionKeys
  from ..exceptions import ForecasterException
  from ..utils.predictions import prepend_date_column, compute_percentiles
  from .forecaster import Forecaster
label_desc: Use image node_id 7 for calling the FullBayesianForecaster obj's underlying member method code with example usage: obj.get_wbic() and returns: unknown