code: def compute_loss(
      self,
      x0,
      text_embed,
      text_mask,
      time=None,
      noise=None,
      **model_kwargs,
  ):
      x_noisy, time = self.get_xt(x0=x0, t=None, epsilon=noise)
      pred = self.forward(
          x_noisy, time, text_embed, text_mask, **model_kwargs
      )
      sigma = time
      weight = (
          (sigma**2 + self.sigma_data**2)
          / (sigma * self.sigma_data) ** 2
      ).sqrt()
      target = x0
      return self.loss_fn(
          batch_mul(weight, target),
          batch_mul(weight, pred),
      )
  
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 EDMPrecond obj's underlying member method code with example usage: obj.compute_loss(x0, text_embed, text_mask, time, noise) and returns: self