code: def predict_start_from_noise(self, x_t, t, noise):
      log_snr = self.log_snr(t)
      log_snr = right_pad_dims_to(x_t, log_snr)
      alpha, sigma = log_snr_to_alpha_sigma(log_snr)
      return (x_t - sigma * noise) / alpha.clamp(min=1e-8)
  
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 10 for calling the GaussianDiffusionContinuousTimes obj's underlying member method code with example usage: obj.predict_start_from_noise(x_t, t, noise) and returns: unknown