code: def transduce(log_probs, labels, blank=0, fastemit_lambda=0.0):
      """
      Args:
          log_probs: 3D array with shape
                [input len, output len + 1, vocab size]
          labels: 1D array with shape [output time steps]
          blank: Index of the blank token.
          fastemit_lambda: Float scaling factor for FastEmit regularization.
  
      Returns:
          float: The negative log-likelihood
          3D array: Gradients with respect to the
                      unnormalized input actications
          2d arrays: Alphas matrix (TxU)
          2d array: Betas matrix (TxU)
      """
      alphas, ll_forward = forward_pass(log_probs, labels, blank)
      betas, ll_backward = backward_pass(log_probs, labels, blank)
      grads = compute_gradient(
          log_probs, alphas, betas, labels, blank, fastemit_lambda
      )
      return -ll_forward, grads, alphas, betas
  
imports: import numpy
  import torch
  from torch.autograd import Function, Variable
  from torch.nn import Module
label_desc: Use image node_id 10 for calling a global function with example usage: transduce(log_probs, labels, blank, fastemit_lambda) and returns: unknown,  grads,  alphas,  betas