code: def backward_pass(log_probs, labels, blank):
      """
      Computes probability of the backward variable beta.
  
      Args:
          log_probs: Tensor of shape [T, U, V+1]
          labels: Labels of shape [B, U]
          blank: Index of the blank token.
  
      Returns:
          A tuple of the backward variable probabilities - beta of shape [T, U]
          and the log likelihood of this backward step.
      """
      T, U, _ = log_probs.shape
      betas = np.zeros((T, U), dtype="f")
      betas[T - 1, U - 1] = log_probs[T - 1, U - 1, blank]
  
      for t in reversed(range(T - 1)):
          betas[t, U - 1] = (
              betas[t + 1, U - 1] + log_probs[t, U - 1, blank]
          )
  
      for u in reversed(range(U - 1)):
          betas[T - 1, u] = (
              betas[T - 1, u + 1] + log_probs[T - 1, u, labels[u]]
          )
  
      for t in reversed(range(T - 1)):
          for u in reversed(range(U - 1)):
              no_emit = betas[t + 1, u] + log_probs[t, u, blank]
              emit = betas[t, u + 1] + log_probs[t, u, labels[u]]
              betas[t, u] = np.logaddexp(emit, no_emit)
  
      return betas, betas[0, 0]
  
imports: import numpy
  import torch
  from torch.autograd import Function, Variable
  from torch.nn import Module
label_desc: Use image node_id 7 for calling a global function with example usage: backward_pass(log_probs, labels, blank) and returns: betas,  betas