code: def compute_attn_prior(x_len, y_len, scaling_factor=1.0):
      """Compute attention priors for the alignment network."""
      attn_prior = beta_binomial_prior_distribution(
          x_len,
          y_len,
          scaling_factor,
      )
      return attn_prior
  
imports: import numpy
  import torch
  from scipy.stats import betabinom
  from torch.nn import functional
label_desc: Use image node_id 11 for calling a global function with example usage: compute_attn_prior(x_len, y_len, scaling_factor) and returns: attn_prior