code: def betas_for_alpha_bar(
      num_diffusion_timesteps, alpha_bar, max_beta=0.999
  ):
      """
      Create a beta schedule that discretizes the given alpha_t_bar function,
      which defines the cumulative product of (1-beta) over time from t = [0,1].
      :param num_diffusion_timesteps: the number of betas to produce.
      :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
                        produces the cumulative product of (1-beta) up to that
                        part of the diffusion process.
      :param max_beta: the maximum beta to use; use values lower than 1 to
                       prevent singularities.
      """
      betas = []
      for i in range(num_diffusion_timesteps):
          t1 = i / num_diffusion_timesteps
          t2 = (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
      return np.array(betas)
  
imports: import os
  import math
  import torch
  import torch.nn
  import numpy
  from einops import repeat
  from ldm.util import instantiate_from_config
label_desc: Use image node_id 4 for calling a global function with example usage: betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta) and returns: np