code: def compute_gradient(
      log_probs, alphas, betas, labels, blank, fastemit_lambda
  ):
      """
      Computes the gradients of the log_probs with respect to the log probability of this step occuring.
  
      Args:
      Args:
          log_probs: Tensor of shape [T, U, V+1]
          alphas: Tensor of shape [T, U] which represents the forward variable.
          betas: Tensor of shape [T, U] which represents the backward variable.
          labels: Labels of shape [B, U]
          blank: Index of the blank token.
  
      Returns:
          Gradients of shape [T, U, V+1] with respect to the forward log probability
      """
      T, U, _ = log_probs.shape
      grads = np.full(log_probs.shape, -float("inf"))
      log_like = betas[
          0, 0
      ]  # == alphas[T - 1, U - 1] + betas[T - 1, U - 1]
  
      # // grad to last blank transition
      grads[T - 1, U - 1, blank] = alphas[T - 1, U - 1]
      grads[: T - 1, :, blank] = alphas[: T - 1, :] + betas[1:, :]
  
      # // grad to label transition
      for u, l in enumerate(labels):
          grads[:, u, l] = alphas[:, u] + betas[:, u + 1]
  
      grads = -np.exp(grads + log_probs - log_like)
  
      if fastemit_lambda > 0.0:
          for u, l in enumerate(labels):
              grads[:, u, l] = (1.0 + fastemit_lambda) * grads[:, u, l]
  
      return grads
  
imports: import numpy
  import torch
  from torch.autograd import Function, Variable
  from torch.nn import Module
label_desc: Use image node_id 8 for calling a global function with example usage: compute_gradient(log_probs, alphas, betas, labels, blank, fastemit_lambda) and returns: grads