code: def forward_pass(log_probs, labels, blank):
      """
      Computes probability of the forward variable alpha.
  
      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 forward variable probabilities - alpha of shape [T, U]
          and the log likelihood of this forward step.
      """
      T, U, _ = log_probs.shape
      alphas = np.zeros((T, U), dtype="f")
  
      for t in range(1, T):
          alphas[t, 0] = alphas[t - 1, 0] + log_probs[t - 1, 0, blank]
  
      for u in range(1, U):
          alphas[0, u] = (
              alphas[0, u - 1] + log_probs[0, u - 1, labels[u - 1]]
          )
      for t in range(1, T):
          for u in range(1, U):
              no_emit = alphas[t - 1, u] + log_probs[t - 1, u, blank]
              emit = (
                  alphas[t, u - 1] + log_probs[t, u - 1, labels[u - 1]]
              )
              alphas[t, u] = np.logaddexp(emit, no_emit)
  
      loglike = alphas[T - 1, U - 1] + log_probs[T - 1, U - 1, blank]
      return alphas, loglike
  
imports: import numpy
  import torch
  from torch.autograd import Function, Variable
  from torch.nn import Module
label_desc: Use image node_id 6 for calling a global function with example usage: forward_pass(log_probs, labels, blank) and returns: alphas,  loglike