code: def compute_with_numpy_transducer(data):
      costs = NumpyTransducerLoss(
          blank=data["blank"],
      )(
          logits=data["logits"],
          logit_lengths=data["logit_lengths"],
          target_lengths=data["target_lengths"],
          targets=data["targets"],
      )
  
      loss = torch.sum(costs)
      loss.backward()
      costs = costs.cpu()
      gradients = data["logits"].saved_grad.cpu()
      return costs, gradients
  
imports: import random
  import unittest
  import numpy
  import torch
  from torchaudio.functional import rnnt_loss
label_desc: Use image node_id 1 for calling a global function with example usage: compute_with_numpy_transducer(data) and returns: costs,  gradients