code: def get_loss(self, model_output, target, w):
      if self.loss_type == "l2":
          return torch.mean(
              (w * (model_output - target) ** 2).reshape(
                  target.shape[0], -1
              ),
              1,
          )
      elif self.loss_type == "l1":
          return torch.mean(
              (w * (model_output - target).abs()).reshape(
                  target.shape[0], -1
              ),
              1,
          )
      elif self.loss_type == "lpips":
          loss = self.lpips(model_output, target).reshape(-1)
          return loss
      else:
          raise NotImplementedError(
              f"Unknown loss type {self.loss_type}"
          )
  
file_path: generative-models/sgm/modules/diffusionmodules/loss.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from ...modules.autoencoding.lpips.loss.lpips import LPIPS
  from ...modules.encoders.modules import GeneralConditioner
  from ...util import append_dims, instantiate_from_config
  from .denoiser import Denoiser
label_desc: Use image node_id 5 for calling the StandardDiffusionLoss obj's underlying member method code with example usage: obj.get_loss(model_output, target, w) and returns: torch, torch, loss