code: def betas_for_alpha_bar(
      num_diffusion_timesteps: int, max_beta=0.999, dtype=jnp.float32
  ) -> jnp.ndarray:
      """
      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].
  
      Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
      to that part of the diffusion process.
  
  
      Args:
          num_diffusion_timesteps (`int`): the number of betas to produce.
          max_beta (`float`): the maximum beta to use; use values lower than 1 to
                       prevent singularities.
  
      Returns:
          betas (`jnp.ndarray`): the betas used by the scheduler to step the model outputs
      """
  
      def alpha_bar(time_step):
          return (
              math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
          )
  
      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 jnp.array(betas, dtype=dtype)
  
imports: import importlib
  import math
  import os
  from dataclasses import dataclass
  from enum import Enum
  from typing import Optional, Tuple, Union
  import flax
  import jax.numpy
  from huggingface_hub.utils import validate_hf_hub_args
  from ..utils import BaseOutput, PushToHubMixin
label_desc: Use image node_id 2 for calling a global function with example usage: betas_for_alpha_bar(num_diffusion_timesteps, max_beta, dtype) and returns: jnp, unknown