code: def psnr(
      image: torch.Tensor, target: torch.Tensor, max_val: float
  ) -> torch.Tensor:
      r"""Create a function that calculates the PSNR between 2 images.
  
      PSNR is Peek Signal to Noise Ratio, which is similar to mean squared error.
      Given an m x n image, the PSNR is:
  
      .. math::
  
          \text{PSNR} = 10 \log_{10} \bigg(\frac{\text{MAX}_I^2}{MSE(I,T)}\bigg)
  
      where
  
      .. math::
  
          \text{MSE}(I,T) = \frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1} [I(i,j) - T(i,j)]^2
  
      and :math:`\text{MAX}_I` is the maximum possible input value
      (e.g for floating point images :math:`\text{MAX}_I=1`).
  
      Args:
          image: the input image with arbitrary shape :math:`(*)`.
          labels: the labels image with arbitrary shape :math:`(*)`.
          max_val: The maximum value in the input tensor.
  
      Return:
          the computed loss as a scalar.
  
      Examples:
          >>> ones = torch.ones(1)
          >>> psnr(ones, 1.2 * ones, 2.) # 10 * log(4/((1.2-1)**2)) / log(10)
          tensor(20.0000)
  
      Reference:
          https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition
      """
      if not isinstance(image, torch.Tensor):
          raise TypeError(
              f"Expected torch.Tensor but got {type(image)}."
          )
  
      if not isinstance(target, torch.Tensor):
          raise TypeError(
              f"Expected torch.Tensor but got {type(target)}."
          )
  
      if image.shape != target.shape:
          raise TypeError(
              f"Expected tensors of equal shapes, but got {image.shape} and {target.shape}"
          )
  
      return 10.0 * torch.log10(
          max_val**2 / mse(image, target, reduction="mean")
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: psnr(image, target, max_val) and returns: unknown