code: def skip_connections_distance(list_a, list_b):
      """The distance between the skip-connections of two neural networks."""
      distance_matrix = np.zeros((len(list_a), len(list_b)))
      for i, a in enumerate(list_a):
          for j, b in enumerate(list_b):
              distance_matrix[i][j] = skip_connection_distance(a, b)
      return distance_matrix[
          linear_sum_assignment(distance_matrix)
      ].sum() + abs(len(list_a) - len(list_b))
  
imports: import math
  import random
  from copy import deepcopy
  from functools import total_ordering
  from queue import PriorityQueue
  import numpy
  from scipy.linalg import LinAlgError, cho_solve, cholesky, solve_triangular
  from scipy.optimize import linear_sum_assignment
  from sklearn.metrics.pairwise import rbf_kernel
  from nni.utils import OptimizeMode
  from .graph_transformer import transform
  from .utils import Constant
  from .layers import is_layer
label_desc: Use image node_id 5 for calling a global function with example usage: skip_connections_distance(list_a, list_b) and returns: unknown