code: def transduce_batch(
      log_probs, labels, flen, glen, blank=0, fastemit_lambda=0.0
  ):
      """
      Compute the transducer loss of the batch.
  
      Args:
          log_probs: [B, T, U, V+1]. Activation matrix normalized with log-softmax.
          labels: [B, U+1] - ground truth labels with <SOS> padded as blank token in the beginning.
          flen: Length vector of the acoustic sequence.
          glen: Length vector of the target sequence.
          blank: Id of the blank token.
          fastemit_lambda: Float scaling factor for FastEmit regularization.
  
      Returns:
          Batch of transducer forward log probabilities (loss) and the gradients of the activation matrix.
      """
      grads = np.zeros_like(log_probs)
      costs = []
      for b in range(log_probs.shape[0]):
          t = int(flen[b])
          u = int(glen[b]) + 1
  
          ll, g, alphas, betas = transduce(
              log_probs[b, :t, :u, :],
              labels[b, : u - 1],
              blank,
              fastemit_lambda,
          )
          grads[b, :t, :u, :] = g
  
          reg = fastemit_regularization(
              log_probs[b, :t, :u, :],
              labels[b, : u - 1],
              alphas,
              betas,
              blank,
              fastemit_lambda,
          )
          ll += reg
          costs.append(ll)
      return costs, grads
  
imports: import numpy
  import torch
  from torch.autograd import Function, Variable
  from torch.nn import Module
label_desc: Use image node_id 11 for calling a global function with example usage: transduce_batch(log_probs, labels, flen, glen, blank, fastemit_lambda) and returns: costs,  grads