class_docstr: A SMA class, inherited from Optimizer.
  
  This is the designed class to define SMA-related
  variables and methods.
  
  References:
      S. Li, H. Chen, M. Wang, A. A. Heidari, S. Mirjalili
      Slime mould algorithm: A new method for stochastic optimization.
      Future Generation Computer Systems (2020).
class_docstr_tok: ["A","SMA","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","SMA-related","variables","and","methods",".","References",":","S.","Li",",","H.","Chen",",","M.","Wang",",","A",".","A.","Heidari",",","S.","Mirjalili","Slime","mould","algorithm",":","A","new","method","for","stochastic","optimization",".","Future","Generation","Computer","Systems","(","2020",")","."]
docstr: Array of weights.
docstr_tok: ["Array","of","weights","."]
code: def weight(self) -> np.ndarray:
      """Array of weights."""
  
      return self._weight
  
imports: from typing import Any, Dict, List, Optional
  import numpy
  import opytimizer.math.random
  import opytimizer.utils.constant
  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 4 for calling the SMA obj's underlying member method code with example usage: obj.weight() and returns: self