code: def __init__(
      self,
      unet,  # Underlying model.
      loss_type="l2",
      sigma_data=0.5,  # Expected standard deviation of the training data.
      p_mean=-1.2,
      p_std=1.2,
      noise_cond_aug=False,
  ):
      super().__init__(unet, loss_type)
      self.sigma_data = sigma_data
      self.p_mean = p_mean
      self.p_std = p_std
      self.noise_cond_aug = noise_cond_aug
  
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 EDMPrecond object from inherited base classes: PrecondModel with example: obj = EDMPrecond(unet, loss_type, sigma_data, p_mean, p_std, noise_cond_aug)