code: def __init__(self, unet, loss_type):
      super().__init__()
      self.unet = unet
      self.rng = None
      self.inference = False
      if loss_type == "l1":
          self.loss_fn = F.l1_loss
      elif loss_type == "l2":
          self.loss_fn = F.mse_loss
      elif loss_type == "huber":
          self.loss_fn = F.smooth_l1_loss
      else:
          raise NotImplementedError(
              f"{loss_type} loss is not supported"
          )
  
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 PrecondModel object from inherited base classes: torch.nn with example: obj = PrecondModel(unet, loss_type)