class_docstr: The leaderBoard that can be used to store / sort the model performance automatically.
  
  Parameters
  ----------
  fields: list of `str`
      A list of field name that shows the model performance. The first field is used as
      the major field for sorting the model performances.
  
  is_higher_better: `dict` of *field* -> `bool`
      A mapping of indicator that whether each field is higher better.
class_docstr_tok: ["The","leaderBoard","that","can","be","used","to","store","\/","sort","the","model","performance","automatically",".","Parameters","--","--","--","--","--","fields",":","list","of","`","str","`","A","list","of","field","name","that","shows","the","model","performance",".","The","first","field","is","used","as","the","major","field","for","sorting","the","model","performances",".","is_higher_better",":","`","dict","`","of","*","field","*","-",">","`","bool","`","A","mapping","of","indicator","that","whether","each","field","is","higher","better","."]
docstr: Get the best model according to the performance of the major field.
  
  Parameters
  ----------
  index: `int`
      The index of the model (from good to bad). Default `0`.
  
  Returns
  -------
  name: `str`
      The name/identifier of the required model.
docstr_tok: ["Get","the","best","model","according","to","the","performance","of","the","major","field",".","Parameters","--","--","--","--","--","index",":","`","int","`","The","index","of","the","model","(","from","good","to","bad",")",".","Default","`","0","`",".","Returns","--","--","--","-","name",":","`","str","`","The","name\/identifier","of","the","required","model","."]
code: def get_best_model(self, index=0) -> str:
      """
      Get the best model according to the performance of the major field.
  
      Parameters
      ----------
      index: `int`
          The index of the model (from good to bad). Default `0`.
  
      Returns
      -------
      name: `str`
          The name/identifier of the required model.
      """
      sorted_df = self.perform_dict.sort_values(
          by=self.major_field,
          ascending=not self.is_higher_better[self.major_field],
      )
      name_list = sorted_df["name"].tolist()
      if "ensemble" in name_list:
          name_list.remove("ensemble")
      return name_list[index]
  
imports: import random
  import typing
  import torch
  import torch.backends.cudnn
  import numpy
  import pandas
  from ..backend import DependentBackend
  from ..data import Dataset
  from ..data.graph import GeneralStaticGraph
  from ..utils import get_logger
label_desc: Use image node_id 5 for calling the LeaderBoard obj's underlying member method code with example usage: obj.get_best_model(index) and returns: name_list