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","."]
code: def __init__(self, fields, is_higher_better):
      assert isinstance(fields, list)
      self.keys = ["name"] + fields
      self.perform_dict = pd.DataFrame(columns=self.keys)
      self.is_higher_better = is_higher_better
      self.major_field = fields[0]
  
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 1 to create a new LeaderBoard object  with example: obj = LeaderBoard(fields, is_higher_better)