code: def alpha_sigma_to_t(alpha, sigma):
      """Returns a timestep, given the scaling factors for the clean image and for
      the noise."""
      return torch.atan2(sigma, alpha) / math.pi * 2
  
imports: import argparse
  import math
  import os
  from copy import deepcopy
  import torch
  from audio_diffusion.models import DiffusionAttnUnet1D
  from diffusion import sampling
  from torch import nn
  from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
label_desc: Use image node_id 1 for calling a global function with example usage: alpha_sigma_to_t(alpha, sigma) and returns: unknown