docstr: Predict values for precision function for given exog_precision.
  
  Parameters
  ----------
  params : array_like
      The model parameters.
  exog_precision : array_like
      Array of predictor variables for precision.
  
  Returns
  -------
  Predicted precision.
docstr_tok: ["Predict","values","for","precision","function","for","given","exog_precision",".","Parameters","--","--","--","--","--","params",":","array_like","The","model","parameters",".","exog_precision",":","array_like","Array","of","predictor","variables","for","precision",".","Returns","--","--","--","-","Predicted","precision","."]
code: def _predict_precision(self, params, exog_precision=None):
      """Predict values for precision function for given exog_precision.
  
      Parameters
      ----------
      params : array_like
          The model parameters.
      exog_precision : array_like
          Array of predictor variables for precision.
  
      Returns
      -------
      Predicted precision.
      """
      if exog_precision is None:
          exog_precision = self.exog_precision
  
      k_mean = self.exog.shape[1]
      params_precision = params[k_mean:]
      linpred_prec = np.dot(exog_precision, params_precision)
      phi = self.link_precision.inverse(linpred_prec)
  
      return phi
  
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 5 for calling the BetaModel obj's underlying member method code with example usage: obj._predict_precision(params, exog_precision) and returns: phi