class_docstr: Mean-squared error loss function.
  
  This function assumes the input images are in the [0.0, 1.0] range. To
  match existing neural compression implementations, the MSE result is
  multiplied by ``scale_param``, which is 255.0**2 by default.
  
  Args:
      scale_param: The scaling parameter for the loss term.
docstr: Apply the MSE loss.
  
  Args:
      image: The image to measure MSE loss from.
      reference: The reference or 'ground truth' image.
  
  Returns:
      The MSE loss value.
docstr_tok: ["Apply","the","MSE","loss",".","Args",":","image",":","The","image","to","measure","MSE","loss","from",".","reference",":","The","reference","or","'ground","truth","'","image",".","Returns",":","The","MSE","loss","value","."]
code: def __call__(self, image: Tensor, reference: Tensor) -> Tensor:
      """
      Apply the MSE loss.
  
      Args:
          image: The image to measure MSE loss from.
          reference: The reference or 'ground truth' image.
  
      Returns:
          The MSE loss value.
      """
      return self.scale_param * F.mse_loss(image, reference)
  
imports: import torch.nn.functional
  from torch import Tensor
  from ._distortion_loss import DistortionLoss
label_desc: Use image node_id 2 for calling the MSELoss obj's underlying member method code with example usage: obj.__call__(image, reference) and returns: unknown