docstr: Updates summary table with values associated with the saved click.
docstr_tok: ["Updates","summary","table","with","values","associated","with","the","saved","click","."]
code: def get_summary_table(
      self, saved_click: pd.DataFrame
  ) -> Dict[str, Union[str, int]]:
      """Updates summary table with values associated with the saved click."""
      if self.mode == "normal":
          cols = [
              "sentence",
              "target",
              f"pred_{self.map_model_id_to_name['model1']}",
          ]
          sentence_df = pd.DataFrame(
              [saved_click[el].split(" ") for el in cols]
          ).T
          sentence_df.columns = [
              "words",
              "target",
              f"pred: {self.map_model_id_to_name['model1']}",
          ]
      elif self.mode == "compare":
          cols = [
              "sentence",
              "target",
              f"pred_{self.map_model_id_to_name['model1']}",
              f"pred_{self.map_model_id_to_name['model2']}",
          ]
          sentence_df = pd.DataFrame(
              [saved_click[el].split(" ") for el in cols]
          ).T
          sentence_df.columns = [
              "words",
              "target",
              f"pred: {self.map_model_id_to_name['model1']}",
              f"pred: {self.map_model_id_to_name['model2']}",
          ]
  
      return sentence_df.to_dict("records")
  
imports: from typing import List, Tuple, Dict, Union
  from abc import ABC, abstractmethod
  import torch
  import pandas
  import numpy
label_desc: Use image node_id 10 for calling the ABSATask obj's underlying member method code with example usage: obj.get_summary_table(saved_click) and returns: sentence_df