code: def q_posterior(self, x_start, x_t, t, *, t_next=None):
      t_next = default(
          t_next, lambda: (t - 1.0 / self.num_timesteps).clamp(min=0.0)
      )
  
      """ https://openreview.net/attachment?id=2LdBqxc1Yv&name=supplementary_material """
      log_snr = self.log_snr(t)
      log_snr_next = self.log_snr(t_next)
      log_snr, log_snr_next = map(
          partial(right_pad_dims_to, x_t), (log_snr, log_snr_next)
      )
  
      alpha, sigma = log_snr_to_alpha_sigma(log_snr)
      alpha_next, sigma_next = log_snr_to_alpha_sigma(log_snr_next)
  
      # c - as defined near eq 33
      c = -expm1(log_snr - log_snr_next)
      posterior_mean = alpha_next * (
          x_t * (1 - c) / alpha + c * x_start
      )
  
      # following (eq. 33)
      posterior_variance = (sigma_next**2) * c
      posterior_log_variance_clipped = log(
          posterior_variance, eps=1e-20
      )
      return (
          posterior_mean,
          posterior_variance,
          posterior_log_variance_clipped,
      )
  
imports: import math
  from functools import partial, wraps
  import torch
  import torch.nn
  from einops import repeat
  from torch.special import expm1
  from nemo.collections.multimodal.parts.utils import randn_like
label_desc: Use image node_id 6 for calling the GaussianDiffusionContinuousTimes 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