code: import numpy as np
  import torch
  
  CONST = 100000.0
  def calc_dist(p, q):
      return np.sqrt(((p[1] - q[1])**2)+((p[0] - q[0]) **2)) * CONST
  
  def get_ref_reward(pointset):
  
      if isinstance(pointset, torch.cuda.FloatTensor) or  isinstance(pointset, torch.FloatTensor):
          pointset = pointset.detach().numpy()
  
      num_points = len(pointset)
      ret_matrix = np.zeros((num_points, num_points))
      for i in range(num_points):
          for j in range(i+1, num_points):
              ret_matrix[i,j] = ret_matrix[j,i] = calc_dist(pointset[i], pointset[j])
      q = elkai.solve_float_matrix(ret_matrix) # Output: [0, 2, 1]
      dist = 0
      for i in range(num_points):
          dist += ret_matrix[q[i], q[(i+1) % num_points]]
      return dist / CONST
  
apis: numpy.sqrt numpy.zeros