docstr: Return distribution parameters converted from model prediction.
  
  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 mean.
  
  Returns
  -------
  (alpha, beta) : tuple of ndarrays
      Parameters for the scipy distribution to evaluate predictive
      distribution.
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code: def get_distribution_params(
      self, params, exog=None, exog_precision=None
  ):
      """
      Return distribution parameters converted from model prediction.
  
      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 mean.
  
      Returns
      -------
      (alpha, beta) : tuple of ndarrays
          Parameters for the scipy distribution to evaluate predictive
          distribution.
      """
      mean = self.predict(params, exog=exog)
      precision = self.predict(
          params, exog_precision=exog_precision, which="precision"
      )
      return precision * mean, precision * (1 - mean)
  
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 20 for calling the BetaModel obj's underlying member method code with example usage: obj.get_distribution_params(params, exog, exog_precision) and returns: unknown,  unknown