parameters: (points, queries, neighbors)
return_statement: return avg_radii
docstring: Computes the radii based on the number of neighbors
docstring_summary: Computes the radii based on the number of neighbors
docstring_tokens: Computes the radii based on the number of neighbors
function: def compute_avg_radii(points, queries, neighbors):
      """Computes the radii based on the number of neighbors"""
      tree = cKDTree(points.numpy())
      avg_radii = []
      for k in neighbors:
          dist, _ = tree.query(queries.numpy(), k=k + 1)
          avg_radii.append(np.mean(dist.max(axis=-1)))
      return avg_radii
function_tokens: def compute_avg_radii ( points , queries , neighbors ) : tree = cKDTree ( points . numpy ( ) ) avg_radii = [ ] for k in neighbors : dist , _ = tree . query ( queries . numpy ( ) , k = k + 1 ) avg_radii . append ( np . mean ( dist . max ( axis = - 1 ) ) ) return avg_radii