code: def positions2results(self, positions):
      results_dict = {}
  
      for para_name in self.s_space.dim_keys:
          values_list = self.s_space[para_name]
          pos_ = positions[para_name].values
          values_ = [values_list[idx] for idx in pos_]
          results_dict[para_name] = values_
  
      results = pd.DataFrame.from_dict(results_dict)
  
      diff_list = np.setdiff1d(positions.columns, results.columns)
      results[diff_list] = positions[diff_list]
  
      return results
  
file_path: Hyperactive/hyperactive/optimizers/hyper_gradient_conv.py
imports: import numpy
  import pandas
label_desc: Use image node_id 12 for calling the HyperGradientConv obj's underlying member method code with example usage: obj.positions2results(positions) and returns: results