class_docstr: An EPO class, inherited from Optimizer.
  
  This is the designed class to define EPO-related
  variables and methods.
  
  References:
      G. Dhiman and V. Kumar. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems.
      Knowledge-Based Systems (2018).
class_docstr_tok: ["An","EPO","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","EPO-related","variables","and","methods",".","References",":","G.","Dhiman","and","V.","Kumar",".","Emperor","penguin","optimizer",":","A","bio-inspired","algorithm","for","engineering","problems",".","Knowledge-Based","Systems","(","2018",")","."]
code: def l(self, l: float) -> None:
      if not isinstance(l, (float, int)):
          raise e.TypeError("`l` should be a float or integer")
  
      self._l = l
  
file_path: opytimizer/opytimizer/optimizers/population/epo.py
imports: from typing import Any, Dict, Optional
  import numpy
  import opytimizer.math.random
  import opytimizer.utils.exception
  from opytimizer.core import Optimizer
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 5 for calling the EPO obj's underlying member method code with example usage: obj.l(l) without return types