code: def get_xt(self, x0, t=None, epsilon=None):
      if epsilon is None:
          epsilon = randn_like(x0, generator=self.rng)
      if t is None:
          t = self.sample_time(batch_size=x0.shape[0], device=x0.device)
      x_noisy, log_snr, alpha, sigma = self.scheduler.q_sample(
          x_start=x0,
          t=t,
          noise=epsilon,
      )
      return x_noisy, t, epsilon
  
imports: import torch
  import torch.nn.functional
  from nemo.collections.multimodal.modules.imagen.sampler.batch_ops import batch_mul
  from nemo.collections.multimodal.modules.imagen.sampler.continuous_ddpm import GaussianDiffusionContinuousTimes
  from nemo.collections.multimodal.parts.utils import randn_like
label_desc: Use image node_id 3 for calling the ContinousDDPMPrecond obj's underlying member method code with example usage: obj.get_xt(x0, t, epsilon) and returns: x_noisy,  t,  epsilon