code: def _get_transition_probs(self, layers_adj, layers_distances):
      layers_alias = {}
      layers_accept = {}
  
      for layer in layers_adj:
          neighbors = layers_adj[layer]
          layer_distances = layers_distances[layer]
          node_alias_dict = {}
          node_accept_dict = {}
          norm_weights = {}
  
          for v, neighbors in neighbors.items():
              e_list = []
              sum_w = 0.0
  
              for n in neighbors:
                  if (v, n) in layer_distances:
                      wd = layer_distances[v, n]
                  else:
                      wd = layer_distances[n, v]
                  w = np.exp(-float(wd))
                  e_list.append(w)
                  sum_w += w
  
              e_list = [x / sum_w for x in e_list]
              norm_weights[v] = e_list
              accept, alias = create_alias_table(e_list)
              node_alias_dict[v] = alias
              node_accept_dict[v] = accept
  
          pd.to_pickle(
              norm_weights,
              self.temp_path
              + "norm_weights_distance-layer-"
              + str(layer)
              + ".pkl",
          )
  
          layers_alias[layer] = node_alias_dict
          layers_accept[layer] = node_accept_dict
  
      return layers_accept, layers_alias
  
imports: import math
  import os
  import shutil
  from collections import ChainMap, deque
  import numpy
  import pandas
  from fastdtw import fastdtw
  from gensim.models import Word2Vec
  from joblib import Parallel, delayed
  from ..alias import create_alias_table
  from ..utils import partition_dict, preprocess_nxgraph
  from ..walker import BiasedWalker
label_desc: Use image node_id 11 for calling the Struc2Vec obj's underlying member method code with example usage: obj._get_transition_probs(layers_adj, layers_distances) and returns: layers_accept,  layers_alias