class_docstr: A representation of the sigma term in the model.
  Specifically, this is the sigma of y itself, i.e. the sigma in
      y ~ Normal(sum_of_trees, sigma)
  
  The default prior is an inverse gamma distribution on the variance
  The parametrization is slightly different to the numpy gamma version, with the scale parameter inverted
  
  Parameters
  ----------
  alpha - the shape of the prior
  beta - the scale of the prior
  scaling_factor - the range of the original distribution
                   needed to rescale the variance into the original scale rather than on (-0.5, 0.5)
class_docstr_tok: ["A","representation","of","the","sigma","term","in","the","model",".","Specifically",",","this","is","the","sigma","of","y","itself",",","i.e",".","the","sigma","in","y","~","Normal","(","sum_of_trees",",","sigma",")","The","default","prior","is","an","inverse","gamma","distribution","on","the","variance","The","parametrization","is","slightly","different","to","the","numpy","gamma","version",",","with","the","scale","parameter","inverted","Parameters","--","--","--","--","--","alpha","-","the","shape","of","the","prior","beta","-","the","scale","of","the","prior","scaling_factor","-","the","range","of","the","original","distribution","needed","to","rescale","the","variance","into","the","original","scale","rather","than","on","(","-0.5",",","0.5",")"]
code: def current_unnormalized_value(self) -> float:
      return self.current_value() * self.scaling_factor
  
label_desc: Use image node_id 4 for calling the Sigma obj's underlying member method code with example usage: obj.current_unnormalized_value() and returns: unknown