code: def lpips(
      preds: Tensor,
      target: Tensor,
      base_network: str = "alex",
      use_linear_calibration: bool = True,
      linear_weights_version: str = "0.1",
      normalize: bool = False,
      reduction: str = "mean",
  ) -> Tensor:
      """
      Learned perceptual image patch similarity.
  
      Implementation of the LPIPS metric introduced in "The Unreasonable
      Effectiveness of Deep Features as a Perceptual Metric" by Richard Zhang,
      Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang.
  
      Note:
          The input images to this function as assumed to have values in the
          range [-1,1], NOT in the range [0,1]. If image values are in the range
          [0,1], pass ``normalize=True``.
  
      Args:
          preds: Predicted images with shape (batch_size, channel, height, width)
          target: Expected images with shape (batch_size, channel, height, width)
          base_network: the pretrained architecture to extract features from.
              Must be one of ``'alex'``, ``'vgg'``, ``'squeeze'``,
              corresponding to AlexNet, VGG, and SqueezeNet, respectively.
          use_linear_calibration: whether to use pretrained weights to
              compute a weighed average across model layers. If ``False``,
              different layers are averaged.
          linear_weights_version: which pretrained linear weights version
              to use. Must be one of ``'0.0'``, ``'0.1'``. This is ignored if
              ``use_linear_calibration==False``.
          normalize: whether to rescale the input images from the
              range [0,1] to the range [-1,1] before computing LPIPS.
          reduction: specifies the reduction to apply across batches:
              ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction
              will be applied, ``'mean'``: the sum of the output will be
              divided by the number of elements in the output, ``'sum'``: the
              output will be summed.
  
      Returns:
          The LPIPS score between ``preds`` and ``target``, reduced across
          batches as specified by the ``reduction`` argument.
      """
  
      if base_network not in ("alex", "vgg", "squeeze"):
          raise ValueError(
              f"Unknown base network {base_network}"
              " - please pass one of 'alex', 'vgg', or 'squeeze'."
          )
  
      if linear_weights_version not in ("0.0", "0.1"):
          raise ValueError(
              f"Unknown linear weights version {linear_weights_version}"
              " - please pass '0.0' or '0.1'."
          )
  
      model = _load_lpips_model(
          base_network=base_network,
          linear_weights_version=linear_weights_version,
          use_linear_calibration=use_linear_calibration,
      )
      outputs = model(preds, target, normalize=normalize).view(-1)
      reduction_op = _get_reduction_op(reduction)
      return reduction_op(outputs)
  
imports: import lpips
  from torch import Tensor
  from ._multiscale_structural_similarity import _get_reduction_op
label_desc: Use image node_id 2 for calling a global function with example usage: lpips(preds, target, base_network, use_linear_calibration, linear_weights_version, normalize, reduction) and returns: reduction_op