code: def compute_beta_one_sequence(log_probs, targets, blank=-1):
      max_T, max_U, D = log_probs.shape
      beta = np.zeros((max_T, max_U), dtype=np.float32)
      beta[-1, -1] = log_probs[-1, -1, blank]
  
      for t in reversed(range(max_T - 1)):
          beta[t, -1] = beta[t + 1, -1] + log_probs[t, -1, blank]
  
      for u in reversed(range(max_U - 1)):
          beta[-1, u] = beta[-1, u + 1] + log_probs[-1, u, targets[u]]
  
      for t in reversed(range(max_T - 1)):
          for u in reversed(range(max_U - 1)):
              skip = beta[t + 1, u] + log_probs[t, u, blank]
              emit = beta[t, u + 1] + log_probs[t, u, targets[u]]
              beta[t, u] = np.logaddexp(skip, emit)
  
      cost = -beta[0, 0]
      return beta, cost
  
imports: import random
  import unittest
  import numpy
  import torch
  from torchaudio.functional import rnnt_loss
label_desc: Use image node_id 4 for calling the _NumpyTransducer obj's underlying member method code with example usage: obj.compute_beta_one_sequence(log_probs, targets, blank) and returns: beta,  cost