docstr: compute unnormalized transition probability between nodes v and its neighbors give the previous visited node t.
  :param t:
  :param v:
  :return:
docstr_tok: ["compute","unnormalized","transition","probability","between","nodes","v","and","its","neighbors","give","the","previous","visited","node","t.",":","param","t",":",":","param","v",":",":","return",":"]
code: def get_alias_edge(self, t, v):
      """
      compute unnormalized transition probability between nodes v and its neighbors give the previous visited node t.
      :param t:
      :param v:
      :return:
      """
      G = self.G
      p = self.p
      q = self.q
  
      unnormalized_probs = []
      for x in G.neighbors(v):
          weight = G[v][x].get("weight", 1.0)  # w_vx
          if x == t:  # d_tx == 0
              unnormalized_probs.append(weight / p)
          elif G.has_edge(x, t):  # d_tx == 1
              unnormalized_probs.append(weight)
          else:  # d_tx > 1
              unnormalized_probs.append(weight / q)
      norm_const = sum(unnormalized_probs)
      normalized_probs = [
          float(u_prob) / norm_const for u_prob in unnormalized_probs
      ]
  
      return create_alias_table(normalized_probs)
  
imports: import itertools
  import math
  import random
  import pandas
  from joblib import Parallel, delayed
  from .alias import alias_sample, create_alias_table
  from .utils import partition_num
label_desc: Use image node_id 7 for calling the RandomWalker obj's underlying member method code with example usage: obj.get_alias_edge(t, v) and returns: create_alias_table