class_docstr: Keys we can group by. 
class_docstr_tok: ["Keys","we","can","group","by","."]
docstr: How much we don't want to show this key.
  
  Should be positive.
  
  When trying to find a "minimal" group we will pick the group with smallest sum of costs.
  
  Keys the user is more likely to care about should have a small cost, while keys the user is
  unlikely to care about should have a larger cost.
docstr_tok: ["How","much","we","do","n't","want","to","show","this","key",".","Should","be","positive",".","When","trying","to","find","a","``","minimal","''","group","we","will","pick","the","group","with","smallest","sum","of","costs",".","Keys","the","user","is","more","likely","to","care","about","should","have","a","small","cost",",","while","keys","the","user","is","unlikely","to","care","about","should","have","a","larger","cost","."]
code: def key_cost(self) -> float:
      """
      How much we don't want to show this key.
  
      Should be positive.
  
      When trying to find a "minimal" group we will pick the group with smallest sum of costs.
  
      Keys the user is more likely to care about should have a small cost, while keys the user is
      unlikely to care about should have a larger cost.
      """
      return self.value[3]
  
imports: import heapq
  from dataclasses import dataclass
  from enum import Enum
  from typing import Any, Collection, Iterator, List, Sequence, Tuple, Type
  import pandas
  from benchmark.metadata import BenchmarkMetadata
  from benchmark.plotter_api import Plotter
label_desc: Use image node_id 5 for calling the GroupingKey obj's underlying member method code with example usage: obj.key_cost() and returns: self