code: def beta_binomial_prior_distribution(
      phoneme_count, mel_count, scaling_factor=1.0
  ):
      P, M = phoneme_count, mel_count
      x = np.arange(0, P)
      mel_text_probs = []
      for i in range(1, M + 1):
          a, b = scaling_factor * i, scaling_factor * (M + 1 - i)
          rv = betabinom(P, a, b)
          mel_i_prob = rv.pmf(x)
          mel_text_probs.append(mel_i_prob)
      return np.array(mel_text_probs)
  
imports: import numpy
  import torch
  from scipy.stats import betabinom
  from torch.nn import functional
label_desc: Use image node_id 10 for calling a global function with example usage: beta_binomial_prior_distribution(phoneme_count, mel_count, scaling_factor) and returns: np