class_docstr: Utilities for training and sampling diffusion models.
  Original ported from this codebase:
  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.
class_docstr_tok: ["Utilities","for","training","and","sampling","diffusion","models",".","Original","ported","from","this","codebase",":","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","."]
code: def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
      return (
          _extract_into_tensor(
              self.sqrt_recip_alphas_cumprod, t, x_t.shape
          )
          * x_t
          - pred_xstart
      ) / _extract_into_tensor(
          self.sqrt_recipm1_alphas_cumprod, t, x_t.shape
      )
  
imports: import enum
  import math
  import numpy
  import torch
label_desc: Use image node_id 7 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj._predict_eps_from_xstart(x_t, t, pred_xstart) and returns: unknown