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.
docstr: Calculate the MSE-LPIPS loss.
  
  Args:
      image: The test image to calculate its distortion.
      reference: The 'ground truth' image.
  
  Returns:
      The MSE-LPIPS loss value.
docstr_tok: ["Calculate","the","MSE-LPIPS","loss",".","Args",":","image",":","The","test","image","to","calculate","its","distortion",".","reference",":","The","'ground","truth","'","image",".","Returns",":","The","MSE-LPIPS","loss","value","."]
code: def __call__(self, image: Tensor, reference: Tensor) -> Tensor:
      """
      Calculate the MSE-LPIPS loss.
  
      Args:
          image: The test image to calculate its distortion.
          reference: The 'ground truth' image.
  
      Returns:
          The MSE-LPIPS loss value.
      """
      return (
          self.mse_param * F.mse_loss(image, reference)
          + self.lpips_param
          * self.lpips_model(
              image, reference, normalize=self.normalize
          ).mean()
      )
  
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 2 for calling the MSELPIPSLoss obj's underlying member method code with example usage: obj.__call__(image, reference) and returns: unknown