code: def forward(
      ctx, acts, labels, act_lens, label_lens, blank, fastemit_lambda
  ):
      costs, grads = transduce_batch(
          acts.detach().cpu().numpy(),
          labels.cpu().numpy(),
          act_lens.cpu().numpy(),
          label_lens.cpu().numpy(),
          blank,
          fastemit_lambda,
      )
  
      costs = torch.FloatTensor([sum(costs)])
      grads = torch.Tensor(grads).to(acts)
  
      ctx.grads = grads
      return costs
  
imports: import numpy
  import torch
  from torch.autograd import Function, Variable
  from torch.nn import Module
label_desc: Use image node_id 1 for calling the _RNNT obj's underlying member method code with example usage: obj.forward(ctx, acts, labels, act_lens, label_lens, blank, fastemit_lambda) and returns: costs