code: def forward(
      ctx,
      log_probs,
      logit_lengths,
      target_lengths,
      targets,
      blank=-1,
  ):
      device = log_probs.device
      log_probs = log_probs.cpu().data.numpy()
      logit_lengths = logit_lengths.cpu().data.numpy()
      target_lengths = target_lengths.cpu().data.numpy()
      targets = targets.cpu().data.numpy()
  
      gradients, costs, _, _ = __class__.compute(
          log_probs=log_probs,
          logit_lengths=logit_lengths,
          target_lengths=target_lengths,
          targets=targets,
          blank=blank,
      )
  
      costs = torch.FloatTensor(costs).to(device=device)
      gradients = torch.FloatTensor(gradients).to(device=device)
      ctx.grads = torch.autograd.Variable(gradients)
  
      return costs
  
imports: import random
  import unittest
  import numpy
  import torch
  from torchaudio.functional import rnnt_loss
label_desc: Use image node_id 1 for calling the _NumpyTransducer obj's underlying member method code with example usage: obj.forward(ctx, log_probs, logit_lengths, target_lengths, targets, blank) and returns: costs