code: def _get_samples(self, N, trace_sort):
          
          # allocate an empty numpy array to store the run and append accept count
          # (importance sampling accepts all points)
          this_run = self.samples.append(N)
  
          # store the proposed points (weights are still to be calculated)
          if trace_sort:
              this_run[:], origin = self.proposal.propose(N, self.rng, trace=True, shuffle=False)
              return this_run, origin
          else:
              this_run[:] = self.proposal.propose(N, self.rng)
              return this_run
docs: Save N samples from ``self.proposal`` to ``self.samples``
          This function does NOT calculate the weights.
  
          Return a reference to this run's samples in ``self.samples``.
          If ``trace_sort`` is True, additionally return an array
          indicating the responsible component. (MixtureDensity only)