code: def convert_quantum(swarm, rcloud, centre):
      dim = len(swarm[0])
      for part in swarm:
          position = [random.gauss(0, 1) for _ in range(dim)]
          dist = math.sqrt(sum(x**2 for x in position))
  
          # Gaussian distribution
          # u = abs(random.gauss(0, 1.0/3.0))
          # part[:] = [(rcloud * x * u**(1.0/dim) / dist) + c for x, c in zip(position, centre)]
  
          # UVD distribution
          # u = random.random()
          # part[:] = [(rcloud * x * u**(1.0/dim) / dist) + c for x, c in zip(position, centre)]
  
          # NUVD distribution
          u = abs(random.gauss(0, 1.0 / 3.0))
          part[:] = [
              (rcloud * x * u / dist) + c
              for x, c in zip(position, centre)
          ]
  
          del part.fitness.values
          del part.bestfit.values
          part.best = None
  
      return swarm
  
file_path: deap/examples/pso/speciation.py
imports: import itertools
  import math
  import operator
  import random
  import numpy
  from deap import base
  from deap.benchmarks import movingpeaks
  from deap import creator
  from deap import tools
label_desc: Use image node_id 2 for calling a global function with example usage: convert_quantum(swarm, rcloud, centre) and returns: swarm