code: def __init__(
      self,
      endog,
      exog,
      exog_vc,
      ident,
      vcp_p=1,
      fe_p=2,
      fep_names=None,
      vcp_names=None,
      vc_names=None,
  ):
      super().__init__(
          endog,
          exog,
          exog_vc=exog_vc,
          ident=ident,
          vcp_p=vcp_p,
          fe_p=fe_p,
          family=families.Binomial(),
          fep_names=fep_names,
          vcp_names=vcp_names,
          vc_names=vc_names,
      )
  
      if not np.all(np.unique(endog) == np.r_[0, 1]):
          msg = "endog values must be 0 and 1, and not all identical"
          raise ValueError(msg)
  
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 BinomialBayesMixedGLM object from inherited base classes: _VariationalBayesMixedGLM, _BayesMixedGLM with example: obj = BinomialBayesMixedGLM(endog, exog, exog_vc, ident, vcp_p, fe_p, fep_names, vcp_names, vc_names)