code: def betas_for_alpha_bar(
      num_diffusion_timesteps, alpha_bar, max_beta=0.999
  ):
      """
      Create a beta schedule based on a discretized alpha_t_bar function.
  
      Parameters:
          num_diffusion_timesteps (int): The number of beta values to produce, corresponding to the number of timesteps in the diffusion process.
          alpha_bar (function): A lambda function that accepts a time value t ranging from 0 to 1 and returns the cumulative product of (1-beta) up to that point in the diffusion process.
          max_beta (float): The maximum allowable value for beta. Setting this to a value lower than 1 helps in preventing singularities in the diffusion process.
  
      Returns:
          list: A list of beta values that correspond to each timestep in the diffusion process.
      """
      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 math
  import numpy
  import torch
  import torch.nn
  from apex.contrib.group_norm import GroupNorm
  from einops import repeat
  from torch._dynamo import disable
  from torch.cuda.amp import custom_bwd, custom_fwd
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