code: def compute_alpha_one_sequence(log_probs, targets, blank=-1):
      max_T, max_U, D = log_probs.shape
      alpha = np.zeros((max_T, max_U), dtype=np.float32)
      for t in range(1, max_T):
          alpha[t, 0] = alpha[t - 1, 0] + log_probs[t - 1, 0, blank]
  
      for u in range(1, max_U):
          alpha[0, u] = (
              alpha[0, u - 1] + log_probs[0, u - 1, targets[u - 1]]
          )
  
      for t in range(1, max_T):
          for u in range(1, max_U):
              skip = alpha[t - 1, u] + log_probs[t - 1, u, blank]
              emit = (
                  alpha[t, u - 1] + log_probs[t, u - 1, targets[u - 1]]
              )
              alpha[t, u] = np.logaddexp(skip, emit)
  
      cost = -(alpha[-1, -1] + log_probs[-1, -1, blank])
      return alpha, cost
  
imports: import random
  import unittest
  import numpy
  import torch
  from torchaudio.functional import rnnt_loss
label_desc: Use image node_id 3 for calling the _NumpyTransducer obj's underlying member method code with example usage: obj.compute_alpha_one_sequence(log_probs, targets, blank) and returns: alpha,  cost