code: def layers_distance(list_a, list_b):
      """The distance between the layers of two neural networks."""
      len_a = len(list_a)
      len_b = len(list_b)
      f = np.zeros((len_a + 1, len_b + 1))
      f[-1][-1] = 0
      for i in range(-1, len_a):
          f[i][-1] = i + 1
      for j in range(-1, len_b):
          f[-1][j] = j + 1
      for i in range(len_a):
          for j in range(len_b):
              f[i][j] = min(
                  f[i][j - 1] + 1,
                  f[i - 1][j] + 1,
                  f[i - 1][j - 1]
                  + layer_distance(list_a[i], list_b[j]),
              )
      return f[len_a - 1][len_b - 1]
  
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 3 for calling a global function with example usage: layers_distance(list_a, list_b) and returns: f