code: def q_posterior(self, x_start, x_t, t):
      posterior_mean = (
          extract(self.posterior_mean_coef1, t, x_t.shape) * x_start
          + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
      )
      posterior_variance = extract(
          self.posterior_variance, t, x_t.shape
      )
      posterior_log_variance_clipped = extract(
          self.posterior_log_variance_clipped, t, x_t.shape
      )
      return (
          posterior_mean,
          posterior_variance,
          posterior_log_variance_clipped,
      )
  
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 6 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj.q_posterior(x_start, x_t, t) and returns: posterior_mean,  posterior_variance,  posterior_log_variance_clipped