code: def beta_binomial_prior_distribution(
      phoneme_count: int, mel_count: int, scaling_factor: float = 1.0
  ) -> np.array:
      x = rearrange(torch.arange(0, phoneme_count), "b -> 1 b")
      y = rearrange(torch.arange(1, mel_count + 1), "b -> b 1")
      a = scaling_factor * y
      b = scaling_factor * (mel_count + 1 - y)
      n = torch.FloatTensor([phoneme_count - 1])
  
      return logbetabinom(n, a, b, x).exp().numpy()
  
imports: import functools
  import os
  import random
  import traceback
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Tuple
  import librosa
  import numpy
  import torch
  from einops import rearrange
  from scipy import ndimage
  from torch.special import gammaln
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
label_desc: Use image node_id 9 for calling a global function with example usage: beta_binomial_prior_distribution(phoneme_count, mel_count, scaling_factor) and returns: logbetabinom