code: def conv_memory_warm_start(self, results):
      if results is None:
          return results
  
      results.reset_index(inplace=True, drop=True)
  
      df_positions_dict = {}
      for dim_key in self.s_space.dim_keys:
          result_dim_values = list(results[dim_key].values)
          search_dim = self.s_space.func2str[dim_key]
  
          if self.s_space.data_types[dim_key] == "object":
              result_dim_values_tmp = []
              for value in result_dim_values:
                  try:
                      value = value.__name__
                  except:
                      pass
  
                  result_dim_values_tmp.append(value)
  
              result_dim_values = result_dim_values_tmp
  
              list1_positions = self.get_list_positions(
                  result_dim_values, search_dim
              )
          else:
              list1_positions = self.values2positions(
                  result_dim_values, search_dim
              )
  
          # remove None
          # list1_positions_ = [x for x in list1_positions if x is not None]
          df_positions_dict[dim_key] = list1_positions
  
      results_new = pd.DataFrame(df_positions_dict)
  
      results_new["score"] = results["score"]
      results_new.dropna(how="any", inplace=True)
  
      return results_new
  
file_path: Hyperactive/hyperactive/optimizers/hyper_gradient_conv.py
imports: import numpy
  import pandas
label_desc: Use image node_id 13 for calling the HyperGradientConv obj's underlying member method code with example usage: obj.conv_memory_warm_start(results) and returns: results_new, results