code: def betas_for_alpha_bar(
      num_diffusion_timesteps, alpha_bar, max_beta=0.999
  ):
      r"""
      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].
  
      Args:
          num_diffusion_timesteps (int): the number of betas to produce.
          alpha_bar (Callable): 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.
          max_beta (int): the maximum beta to use; use values lower than 1 to
                    prevent singularities.
      Returns:
          np.ndarray: a 1-D array of beta values.
      """
      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 enum
  import math
  import copy
  import numpy
  import torch
  import torch.nn.functional
  import torch.nn
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils.enum_type import InputType
  from recbole.model.abstract_recommender import AutoEncoderMixin, GeneralRecommender
  from recbole.model.layers import MLPLayers
  import typing
label_desc: Use image node_id 2 for calling a global function with example usage: betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta) and returns: np