class_docstr: A term that is compiled to a HSGP term in PyMC
  
  This instance contains information of a bambi.HSGPTerm and knows how to build the distributions
  in PyMC that represent the HSGP latent approximation.
  
  Parameters
  ----------
  term : bambi.terms.HSGPTerm
      An object representing a Bambi hsgp term.
class_docstr_tok: ["A","term","that","is","compiled","to","a","HSGP","term","in","PyMC","This","instance","contains","information","of","a","bambi.HSGPTerm","and","knows","how","to","build","the","distributions","in","PyMC","that","represent","the","HSGP","latent","approximation",".","Parameters","--","--","--","--","--","term",":","bambi.terms.HSGPTerm","An","object","representing","a","Bambi","hsgp","term","."]
code: def name(self):
      if self.term.alias:
          return self.term.alias
      return self.term.name
  
imports: import inspect
  import numpy
  import pymc
  import pytensor.tensor
  from bambi.backend.utils import get_distribution_from_likelihood, get_distribution_from_prior, get_linkinv, has_hyperprior, make_weighted_distribution, GP_KERNELS
  from bambi.families.multivariate import MultivariateFamily, Multinomial, DirichletMultinomial
  from bambi.families.univariate import Categorical
  from bambi.priors import Prior
  from bambi.utils import get_aliased_name
label_desc: Use image node_id 4 for calling the HSGPTerm obj's underlying member method code with example usage: obj.name() and returns: self, self