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","."]
docstr: Compute what the p_mean_variance output would have been, should the
  model's score function be conditioned by cond_fn.
  See condition_mean() for details on cond_fn.
  Unlike condition_mean(), this instead uses the conditioning strategy
  from Song et al (2020).
docstr_tok: ["Compute","what","the","p_mean_variance","output","would","have","been",",","should","the","model","'s","score","function","be","conditioned","by","cond_fn",".","See","condition_mean","(",")","for","details","on","cond_fn",".","Unlike","condition_mean","(",")",",","this","instead","uses","the","conditioning","strategy","from","Song","et","al","(","2020",")","."]
code: def condition_score(
      self, cond_fn, p_mean_var, x, t, model_kwargs=None
  ):
      """
      Compute what the p_mean_variance output would have been, should the
      model's score function be conditioned by cond_fn.
      See condition_mean() for details on cond_fn.
      Unlike condition_mean(), this instead uses the conditioning strategy
      from Song et al (2020).
      """
      alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
  
      eps = self._predict_eps_from_xstart(
          x, t, p_mean_var["pred_xstart"]
      )
      eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, t, **model_kwargs)
  
      out = p_mean_var.copy()
      out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
      out["mean"], _, _ = self.q_posterior_mean_variance(
          x_start=out["pred_xstart"], x_t=x, t=t
      )
      return out
  
imports: import enum
  import math
  import numpy
  import torch
label_desc: Use image node_id 9 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj.condition_score(cond_fn, p_mean_var, x, t, model_kwargs) and returns: out