class_docstr: MSE-LPIPS distortion loss.
  
  This class combines both mean-squared error and LPIPS into a single loss
  function. The final loss is calculated as
  
  ``mse_param * mse + lpips_param * lpips``
  
  Args:
      mse_param: A parameter for scaling the MSE term.
      lpips_param: A parameter for scaling the LPIPS term.
      normalize: Whether to normalize inputs to the [-1.0, 1.0] range prior
          to LPIPS calculation.
      backbone: String specifying which classifier model to use as the
          backbone for LPIPS calculation.
code: def __init__(
      self,
      mse_param: float = 255.0**2,
      lpips_param: float = 1.0,
      normalize: bool = True,
      backbone: str = "vgg",
  ):
      super().__init__()
      self.mse_param = mse_param
      self.lpips_param = lpips_param
      self.normalize = normalize
      self.lpips_model = NormFixLPIPS(net=backbone).eval()
      for param in self.lpips_model.parameters():
          param.requires_grad_(False)
  
imports: import torch.nn.functional
  from torch import Tensor
  from ._distortion_loss import DistortionLoss
  from ._normfix_lpips import NormFixLPIPS
label_desc: Use image node_id 1 to create a new MSELPIPSLoss object from inherited base classes: DistortionLoss with example: obj = MSELPIPSLoss(mse_param, lpips_param, normalize, backbone)