code: def params_transform_univariate(
      params, cov_params, link=None, transform=None, row_labels=None
  ):
      """
      results for univariate, nonlinear, monotonicaly transformed parameters
  
      This provides transformed values, standard errors and confidence interval
      for transformations of parameters, for example in calculating rates with
      `exp(params)` in the case of Poisson or other models with exponential
      mean function.
      """
  
      from statsmodels.genmod.families import links
  
      if link is None and transform is None:
          link = links.Log()
  
      if row_labels is None and hasattr(params, "index"):
          row_labels = params.index
  
      params = np.asarray(params)
  
      predicted_mean = link.inverse(params)
      link_deriv = link.inverse_deriv(params)
      var_pred_mean = link_deriv**2 * np.diag(cov_params)
      # TODO: do we want covariance also, or just var/se
  
      dist = stats.norm
  
      # TODO: need ci for linear prediction, method of `lin_pred
      linpred = PredictionResultsMean(
          params,
          np.diag(cov_params),
          dist=dist,
          row_labels=row_labels,
          link=links.Identity(),
      )
  
      res = PredictionResultsMean(
          predicted_mean,
          var_pred_mean,
          dist=dist,
          row_labels=row_labels,
          linpred=linpred,
          link=link,
      )
  
      return res
  
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 7 for calling a global function with example usage: params_transform_univariate(params, cov_params, link, transform, row_labels) and returns: res