code: def predict_start_from_noise(self, x_t, t, noise):
      return (
          extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
          - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.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 5 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj.predict_start_from_noise(x_t, t, noise) and returns: unknown