code: def p_mean_variance(self, x, cond, t, clip_denoised: bool):
      x_recon = self.predict_start_from_noise(
          x, t=t, noise=self.denoise_fn(x, t, cond=cond)
      )
  
      if clip_denoised:
          x_recon.clamp_(-1.0, 1.0)
  
      (
          model_mean,
          posterior_variance,
          posterior_log_variance,
      ) = self.q_posterior(x_start=x_recon, x_t=x, t=t)
      return model_mean, posterior_variance, posterior_log_variance
  
imports: from functools import partial
  from inspect import isfunction
  import numpy
  import torch
  from torch import nn, einsum
  import torch.nn.functional
label_desc: Use image node_id 7 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj.p_mean_variance(x, cond, t, clip_denoised) and returns: model_mean,  posterior_variance,  posterior_log_variance