code: def __init__(
      self,
      endog,
      exog,
      exog_vc=None,
      ident=None,
      family=None,
      vcp_p=1,
      fe_p=2,
      fep_names=None,
      vcp_names=None,
      vc_names=None,
      **kwargs,
  ):
      if exog.ndim == 1:
          if isinstance(exog, np.ndarray):
              exog = exog[:, None]
          else:
              exog = pd.DataFrame(exog)
  
      if exog.ndim != 2:
          msg = "'exog' must have one or two columns"
          raise ValueError(msg)
  
      if exog_vc.ndim == 1:
          if isinstance(exog_vc, np.ndarray):
              exog_vc = exog_vc[:, None]
          else:
              exog_vc = pd.DataFrame(exog_vc)
  
      if exog_vc.ndim != 2:
          msg = "'exog_vc' must have one or two columns"
          raise ValueError(msg)
  
      ident = np.asarray(ident)
      if ident.ndim != 1:
          msg = "ident must be a one-dimensional array"
          raise ValueError(msg)
  
      if len(ident) != exog_vc.shape[1]:
          msg = (
              "len(ident) should match the number of columns of exog_vc"
          )
          raise ValueError(msg)
  
      if not np.issubdtype(ident.dtype, np.integer):
          msg = "ident must have an integer dtype"
          raise ValueError(msg)
  
      # Get the fixed effects parameter names
      if fep_names is None:
          if hasattr(exog, "columns"):
              fep_names = exog.columns.tolist()
          else:
              fep_names = [
                  "FE_%d" % (k + 1) for k in range(exog.shape[1])
              ]
  
      # Get the variance parameter names
      if vcp_names is None:
          vcp_names = [
              "VC_%d" % (k + 1) for k in range(int(max(ident)) + 1)
          ]
      else:
          if len(vcp_names) != len(set(ident)):
              msg = "The lengths of vcp_names and ident should be the same"
              raise ValueError(msg)
  
      if not sparse.issparse(exog_vc):
          exog_vc = sparse.csr_matrix(exog_vc)
  
      ident = ident.astype(int)
      vcp_p = float(vcp_p)
      fe_p = float(fe_p)
  
      # Number of fixed effects parameters
      if exog is None:
          k_fep = 0
      else:
          k_fep = exog.shape[1]
  
      # Number of variance component structure parameters and
      # variance component realizations.
      if exog_vc is None:
          k_vc = 0
          k_vcp = 0
      else:
          k_vc = exog_vc.shape[1]
          k_vcp = max(ident) + 1
  
      # power might be better but not available in older scipy
      exog_vc2 = exog_vc.multiply(exog_vc)
  
      super().__init__(endog, exog, **kwargs)
  
      self.exog_vc = exog_vc
      self.exog_vc2 = exog_vc2
      self.ident = ident
      self.family = family
      self.k_fep = k_fep
      self.k_vc = k_vc
      self.k_vcp = k_vcp
      self.fep_names = fep_names
      self.vcp_names = vcp_names
      self.vc_names = vc_names
      self.fe_p = fe_p
      self.vcp_p = vcp_p
      self.names = fep_names + vcp_names
      if vc_names is not None:
          self.names += vc_names
  
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 1 to create a new _BayesMixedGLM object from inherited base classes: base with example: obj = _BayesMixedGLM(endog, exog, exog_vc, ident, family, vcp_p, fe_p, fep_names, vcp_names, vc_names)