code: def to_parameter_specs(self, name_prefix=""):
      
      specs = []
      for name, categories, _ in self._categorical_params.values():
        spec = {
            "parameterName": name_prefix + name,
            "type": "CATEGORICAL",
            "categoricalValues": categories,
        }
        specs.append(spec)
  
      for name, feasible_points, scale, _ in self._discrete_params.values():
        spec = {
            "parameterName": name_prefix + name,
            "type": "DISCRETE",
            "discreteValues": feasible_points,
        }
        if scale:
          spec["scaleType"] = self.SCALES_STR[scale]
        specs.append(spec)
  
      for name, min_val, max_val, scale, _ in self._float_params.values():
        spec = {
            "parameterName": name_prefix + name,
            "type": "DOUBLE",
            "minValue": min_val,
            "maxValue": max_val,
        }
        if scale:
          spec["scaleType"] = self.SCALES_STR[scale]
        specs.append(spec)
  
      for name, min_val, max_val, scale, _ in self._int_params.values():
        spec = {
            "parameterName": name_prefix + name,
            "type": "INTEGER",
            "minValue": min_val,
            "maxValue": max_val,
        }
        if scale:
          spec["scaleType"] = self.SCALES_STR[scale]
        specs.append(spec)
  
      return specs
docs: To list of dicts suitable for Cloud ML Engine hyperparameter tuning.