docstr: predict values for conditional variance V(endog | exog)
  
  Parameters
  ----------
  params : array_like
      The model parameters.
  exog : array_like
      Array of predictor variables for mean.
  exog_precision : array_like
      Array of predictor variables for precision.
  
  Returns
  -------
  Predicted conditional variance.
docstr_tok: ["predict","values","for","conditional","variance","V","(","endog","|","exog",")","Parameters","--","--","--","--","--","params",":","array_like","The","model","parameters",".","exog",":","array_like","Array","of","predictor","variables","for","mean",".","exog_precision",":","array_like","Array","of","predictor","variables","for","precision",".","Returns","--","--","--","-","Predicted","conditional","variance","."]
code: def _predict_var(self, params, exog=None, exog_precision=None):
      """predict values for conditional variance V(endog | exog)
  
      Parameters
      ----------
      params : array_like
          The model parameters.
      exog : array_like
          Array of predictor variables for mean.
      exog_precision : array_like
          Array of predictor variables for precision.
  
      Returns
      -------
      Predicted conditional variance.
      """
      mean = self.predict(params, exog=exog)
      precision = self._predict_precision(
          params, exog_precision=exog_precision
      )
  
      var_endog = mean * (1 - mean) / (1 + precision)
      return var_endog
  
imports: import numpy
  from scipy.special import gammaln
  import patsy
  import statsmodels.base.wrapper
  import statsmodels.regression.linear_model
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.base.model import GenericLikelihoodModel, GenericLikelihoodModelResults, _LLRMixin
  from statsmodels.genmod import families
label_desc: Use image node_id 6 for calling the BetaModel obj's underlying member method code with example usage: obj._predict_var(params, exog, exog_precision) and returns: var_endog