class_docstr: A SFO class, inherited from Optimizer.
  
  This is the designed class to define SFO-related
  variables and methods.
  
  References:
      S. Shadravan, H. Naji and V. Bardsiri.
      The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm
      for solving constrained engineering optimization problems.
      Engineering Applications of Artificial Intelligence (2019).
class_docstr_tok: ["A","SFO","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","SFO-related","variables","and","methods",".","References",":","S.","Shadravan",",","H.","Naji","and","V.","Bardsiri",".","The","Sailfish","Optimizer",":","A","novel","nature-inspired","metaheuristic","algorithm","for","solving","constrained","engineering","optimization","problems",".","Engineering","Applications","of","Artificial","Intelligence","(","2019",")","."]
docstr: Attack power decrease.
docstr_tok: ["Attack","power","decrease","."]
code: def e(self) -> float:
      """Attack power decrease."""
  
      return self._e
  
imports: import copy
  from typing import Any, Dict, List, Optional
  import numpy
  import opytimizer.math.random
  import opytimizer.utils.exception
  from opytimizer.core import Optimizer
  from opytimizer.core.agent import Agent
  from opytimizer.core.function import Function
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 6 for calling the SFO obj's underlying member method code with example usage: obj.e() and returns: self