code: def __init__(
      self,
      data_name,
      X_names,
      model_name,
      model_type,
      latent_variables,
      data,
      index,
      multivariate_model,
      objective,
      method,
      z_hide,
      max_lag,
      ses,
      signal=None,
      scores=None,
      states=None,
      states_var=None,
      elbo_records=None,
  ):
      self.data_name = data_name
      self.X_names = X_names
      self.max_lag = max_lag
      self.model_name = model_name
      self.model_type = model_type
      self.z = latent_variables
      self.method = method
  
      self.ses = ses
      self.scores = scores
      self.states = states
      self.states_var = states_var
      self.data = data
      self.index = index
      self.signal = signal
      self.multivariate_model = multivariate_model
      self.z_hide = z_hide
      self.elbo_records = elbo_records
  
      if self.multivariate_model is True:
          self.data_length = self.data[0].shape[0]
          self.data_name = ",".join(self.data_name)
      else:
          self.data_length = self.data.shape[0]
  
      z_values = self.z.get_z_values(transformed=False)
  
      self.objective = objective
      self.aic = 2 * len(z_values) + 2 * self.objective
      self.bic = 2 * self.objective + len(z_values) * np.log(
          self.data_length
      )
  
      if self.model_type in ["LLT", "LLEV"]:
          self.rounding_points = 10
      else:
          self.rounding_points = 4
  
imports: import numpy
  from .output import TablePrinter
  from .tests import find_p_value
  from .inference import norm_post_sim
label_desc: Use image node_id 1 to create a new BBVISSResults object from inherited base classes: Results with example: obj = BBVISSResults(data_name, X_names, model_name, model_type, latent_variables, data, index, multivariate_model, objective, method, z_hide, max_lag, ses, signal, scores, states, states_var, elbo_records)