code: def compute_loss(
      self,
      x0,
      text_embed,
      text_mask,
      time=None,
      noise=None,
      **model_kwargs,
  ):
      x_noisy, time, noise = self.get_xt(x0=x0, t=time, epsilon=noise)
      pred = self.forward(
          x_noisy, time, text_embed, text_mask, **model_kwargs
      )
      # Determine target
      if self.pred_objective == "noise":
          target = noise
      elif self.pred_objective == "x_start":
          target = x0
      else:
          raise ValueError(f"unknown objective {self.pred_objective}")
      return self.loss_fn(pred, target)
  
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 5 for calling the ContinousDDPMPrecond obj's underlying member method code with example usage: obj.compute_loss(x0, text_embed, text_mask, time, noise) and returns: self