class_docstr: DISTS PyTorch Metric.
  
  This torchmetric class implements the metric described in the following
  paper:
  
  Image Quality Assessment: Unifying Structure and Texture Similarity
  Keyan Ding, Kede Ma, Shiqi Wang, Eero P. Simoncelli
  
  It has similar properties to LPIPS, but with a greater emphasis on texture.
  
  Args:
      reduction: str indicating how to reduce over the batch dimension.
          Choose between `'sum'` or `'mean'`.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more
          info.
class_docstr_tok: ["DISTS","PyTorch","Metric",".","This","torchmetric","class","implements","the","metric","described","in","the","following","paper",":","Image","Quality","Assessment",":","Unifying","Structure","and","Texture","Similarity","Keyan","Ding",",","Kede","Ma",",","Shiqi","Wang",",","Eero","P.","Simoncelli","It","has","similar","properties","to","LPIPS",",","but","with","a","greater","emphasis","on","texture",".","Args",":","reduction",":","str","indicating","how","to","reduce","over","the","batch","dimension",".","Choose","between","`","'sum","'","`","or","`","'mean","'","`",".","kwargs",":","Additional","keyword","arguments",",","see",":","ref",":","`","Metric","kwargs","`","for","more","info","."]
docstr: Compute final perceptual similarity metric.
docstr_tok: ["Compute","final","perceptual","similarity","metric","."]
code: def compute(self) -> Tensor:
      """Compute final perceptual similarity metric."""
      if self.reduction == "mean":
          return self.sum_scores / self.total
      if self.reduction == "sum":
          return self.sum_scores
  
file_path: NeuralCompression/neuralcompression/metrics/_dists.py
imports: from typing import Any, List
  import torch
  from DISTS_pytorch import DISTS
  from torch import Tensor
  from torchmetrics.metric import Metric
  from typing_extensions import Literal
label_desc: Use image node_id 3 for calling the DeepImageStructureTextureSimilarity obj's underlying member method code with example usage: obj.compute() and returns: unknown, self