code: def q_sample(self, x_start, t, noise=None):
      noise = default(noise, lambda: torch.randn_like(x_start))
  
      return (
          extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
          + extract(
              self.sqrt_one_minus_alphas_cumprod, t, x_start.shape
          )
          * noise
      )
  
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 12 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj.q_sample(x_start, t, noise) and returns: unknown