code: def reset(self):
          
  
          # live points
          self.live_u = self.rstate.rand(self.nlive, self.npdim)
          if self.use_pool_ptform:
              # Use the pool to compute the prior transform.
              self.live_v = np.array(list(self.M(self.prior_transform,
                                          np.array(self.live_u))))
          else:
              # Compute the prior transform using the default `map` function.
              self.live_v = np.array(list(map(self.prior_transform,
                                              np.array(self.live_u))))
          if self.use_pool_logl:
              # Use the pool to compute the log-likelihoods.
              self.live_logl = np.array(list(self.M(self.loglikelihood,
                                                    np.array(self.live_v))))
          else:
              # Compute the log-likelihoods using the default `map` function.
              self.live_logl = np.array(list(map(self.loglikelihood,
                                                 np.array(self.live_v))))
          self.live_bound = np.zeros(self.nlive, dtype='int')
          self.live_it = np.zeros(self.nlive, dtype='int')
  
          # parallelism
          self.queue = []
          self.nqueue = 0
          self.unused = 0
          self.used = 0
  
          # sampling
          self.it = 1
          self.since_update = 0
          self.ncall = self.nlive
          self.bound = [UnitCube(self.npdim)]
          self.nbound = 1
          self.added_live = False
  
          # results
          self.saved_id = []
          self.saved_u = []
          self.saved_v = []
          self.saved_logl = []
          self.saved_logvol = []
          self.saved_logwt = []
          self.saved_logz = []
          self.saved_logzvar = []
          self.saved_h = []
          self.saved_nc = []
          self.saved_boundidx = []
          self.saved_it = []
          self.saved_bounditer = []
          self.saved_scale = []
docs: Re-initialize the sampler.