code: def __init__(
      self,
      unet,
      loss_type="l2",
      pred_objective="noise",
      noise_schedule="cosine",
      timesteps=1000,
      noise_cond_aug=False,
  ):
      super().__init__(unet, loss_type)
      self.scheduler = GaussianDiffusionContinuousTimes(
          noise_schedule=noise_schedule, timesteps=timesteps
      )
      self.pred_objective = pred_objective
      assert (
          noise_cond_aug == False
      ), "noise cond aug currently not supported for DDPM"
  
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 1 to create a new ContinousDDPMPrecond object from inherited base classes: PrecondModel with example: obj = ContinousDDPMPrecond(unet, loss_type, pred_objective, noise_schedule, timesteps, noise_cond_aug)