class_docstr: Utilities for training and sampling diffusion models.
  
  Ported directly from here, and then adapted over time to further experimentation.
  https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
  
  :param betas: a 1-D numpy array of betas for each diffusion timestep,
                starting at T and going to 1.
  :param model_mean_type: a ModelMeanType determining what the model outputs.
  :param model_var_type: a ModelVarType determining how variance is output.
  :param loss_type: a LossType determining the loss function to use.
  :param rescale_timesteps: if True, pass floating point timesteps into the
                            model so that they are always scaled like in the
                            original paper (0 to 1000).
code: def _scale_timesteps(self, t):
      if self.rescale_timesteps:
          return t.float() * (1000.0 / self.num_timesteps)
      return t
  
imports: import enum
  import math
  import numpy
  import torch
  from .nn import mean_flat
  from .losses import normal_kl, discretized_gaussian_log_likelihood
label_desc: Use image node_id 9 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj._scale_timesteps(t) and returns: t, unknown