code: def compute_gradients_one_sequence(
      log_probs, alpha, beta, targets, blank=-1
  ):
      max_T, max_U, D = log_probs.shape
      gradients = np.full(log_probs.shape, float("-inf"))
      cost = -beta[0, 0]
  
      gradients[-1, -1, blank] = alpha[-1, -1]
  
      gradients[:-1, :, blank] = alpha[:-1, :] + beta[1:, :]
  
      for u, l in enumerate(targets):
          gradients[:, u, l] = alpha[:, u] + beta[:, u + 1]
  
      gradients = -(np.exp(gradients + log_probs + cost))
      return gradients
  
imports: import random
  import unittest
  import numpy
  import torch
  from torchaudio.functional import rnnt_loss
label_desc: Use image node_id 5 for calling the _NumpyTransducer obj's underlying member method code with example usage: obj.compute_gradients_one_sequence(log_probs, alpha, beta, targets, blank) and returns: gradients