class_docstr: This tuner is essentially grid search, but it guarantees all the parameters with alpha * beta^2 * gamma^2 is
  approximately `product`.
class_docstr_tok: ["This","tuner","is","essentially","grid","search",",","but","it","guarantees","all","the","parameters","with","alpha","*","beta^2","*","gamma^2","is","approximately","`","product","`","."]
docstr: Filter out all qualified parameters
docstr_tok: ["Filter","out","all","qualified","parameters"]
code: def _expand_parameters(self, para):
      """
      Filter out all qualified parameters
      """
      para = super()._expand_parameters(para)
      if all(
          [key in para[0] for key in ["alpha", "beta", "gamma"]]
      ):  # if this is an interested set
          ret_para = []
          for p in para:
              prod = p["alpha"] * (p["beta"] ** 2) * (p["gamma"] ** 2)
              if abs(prod - self.product) < 0.1:
                  ret_para.append(p)
          return ret_para
      return para
  
imports: from nni.algorithms.hpo.gridsearch_tuner import GridSearchTuner
label_desc: Use image node_id 2 for calling the FixedProductTuner obj's underlying member method code with example usage: obj._expand_parameters(para) and returns: para, ret_para