code: def q_mean_variance(self, x_start, t):
      mean = (
          extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
      )
      variance = extract(1.0 - self.alphas_cumprod, t, x_start.shape)
      log_variance = extract(
          self.log_one_minus_alphas_cumprod, t, x_start.shape
      )
      return mean, variance, 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 4 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj.q_mean_variance(x_start, t) and returns: mean,  variance,  log_variance