code: def psnr_loss(
      image: torch.Tensor, target: torch.Tensor, max_val: float
  ) -> torch.Tensor:
      r"""Function that computes the PSNR loss.
  
      The loss is computed as follows:
  
       .. math::
  
          \text{loss} = -\text{psnr(x, y)}
  
      See :meth:`~kornia.losses.psnr` for details abut PSNR.
  
      Args:
          image: the input image with shape :math:`(*)`.
          labels : the labels image with shape :math:`(*)`.
          max_val: The maximum value in the image tensor.
  
      Return:
          the computed loss as a scalar.
  
      Examples:
          >>> ones = torch.ones(1)
          >>> psnr_loss(ones, 1.2 * ones, 2.) # 10 * log(4/((1.2-1)**2)) / log(10)
          tensor(-20.0000)
      """
      return -1.0 * metrics.psnr(image, target, max_val)
  
file_path: kornia/kornia/losses/psnr.py
imports: from __future__ import annotations
  import torch
  from torch import nn
  from kornia import metrics
label_desc: Use image node_id 1 for calling a global function with example usage: psnr_loss(image, target, max_val) and returns: unknown