class_docstr: Scale prior distributions parameters.
class_docstr_tok: ["Scale","prior","distributions","parameters","."]
code: def scale_group_specific(self, term):
      if term.prior.args["sigma"].name != "HalfNormal":
          return
  
      # Handle intercepts
      if term.kind == "intercept":
          _, sigma = self.get_intercept_stats()
      # Handle slopes
      else:
          # Recreate the corresponding common effect data
          if len(term.predictor.shape) == 2:
              data_as_common = term.predictor
          else:
              data_as_common = term.predictor[:, None]
          sigma = np.zeros(data_as_common.shape[1])
          for i, value in enumerate(data_as_common.T):
              sigma[i] = self.get_slope_sigma(value)
      term.prior.args["sigma"].update(
          sigma=np.squeeze(np.atleast_1d(sigma))
      )
  
file_path: bambi/bambi/priors/scaler.py
imports: import numpy
  import pymc
  from bambi.families.univariate import Cumulative, Gaussian, StoppingRatio, StudentT, VonMises
  from bambi.model_components import ConstantComponent
  from bambi.priors.prior import Prior
label_desc: Use image node_id 7 for calling the PriorScaler obj's underlying member method code with example usage: obj.scale_group_specific(term) without return types