code: def _unpack(self, vec):
      ii = 0
  
      # Fixed effects parameters
      fep = vec[: ii + self.k_fep]
      ii += self.k_fep
  
      # Variance component structure parameters (standard
      # deviations).  These are on the log scale.  The standard
      # deviation for random effect j is exp(vcp[ident[j]]).
      vcp = vec[ii : ii + self.k_vcp]
      ii += self.k_vcp
  
      # Random effect realizations
      vc = vec[ii:]
  
      return fep, vcp, vc
  
imports: import numpy
  from scipy.optimize import minimize
  from scipy import sparse
  import statsmodels.base.model
  from statsmodels.iolib import summary2
  from statsmodels.genmod import families
  import pandas
  import warnings
  import patsy
label_desc: Use image node_id 2 for calling the _BayesMixedGLM obj's underlying member method code with example usage: obj._unpack(vec) and returns: fep,  vcp,  vc