class_docstr: Compute Spatial Distortion Index (SpatialDistortionIndex_) also now as D_s.
  
  The metric is used to compare the spatial distortion between two images.
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): High resolution multispectral image of shape ``(N,C,H,W)``.
  - ``target`` (:class:`~Dict`): A dictionary containing the following keys:
      - ``ms`` (:class:`~torch.Tensor`): Low resolution multispectral image of shape ``(N,C,H',W')``.
      - ``pan`` (:class:`~torch.Tensor`): High resolution panchromatic image of shape ``(N,C,H,W)``.
      - ``pan_lr`` (:class:`~torch.Tensor`): Low resolution panchromatic image of shape ``(N,C,H',W')``.
  
  where H and W must be multiple of H' and W'.
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``sdi`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average SDI value
    over sample else returns tensor of shape ``(N,)`` with SDI values per sample
  
  Args:
      norm_order: Order of the norm applied on the difference.
      window_size: Window size of the filter applied to degrade the high resolution panchromatic image.
      reduction: a method to reduce metric score over labels.
  
          - ``'elementwise_mean'``: takes the mean (default)
          - ``'sum'``: takes the sum
          - ``'none'``: no reduction will be applied
  
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> import torch
      >>> _ = torch.manual_seed(42)
      >>> from torchmetrics.image import SpatialDistortionIndex
      >>> preds = torch.rand([16, 3, 32, 32])
      >>> target = {
      ...     'ms': torch.rand([16, 3, 16, 16]),
      ...     'pan': torch.rand([16, 3, 32, 32]),
      ... }
      >>> sdi = SpatialDistortionIndex()
      >>> sdi(preds, target)
      tensor(0.0090)
class_docstr_tok: ["Compute","Spatial","Distortion","Index","(","SpatialDistortionIndex_",")","also","now","as","D_s",".","The","metric","is","used","to","compare","the","spatial","distortion","between","two","images",".","As","input","to","``","forward","``","and","``","update","``","the","metric","accepts","the","following","input","-","``","preds","``","(",":","class",":","`","~torch.Tensor","`",")",":","High","resolution","multispectral","image","of","shape","``","(","N",",","C",",","H",",","W",")","``",".","-","``","target","``","(",":","class",":","`","~Dict","`",")",":","A","dictionary","containing","the","following","keys",":","-","``","ms","``","(",":","class",":","`","~torch.Tensor","`",")",":","Low","resolution","multispectral","image","of","shape","``","(","N",",","C",",","H","'",",","W","'",")","``",".","-","``","pan","``","(",":","class",":","`","~torch.Tensor","`",")",":","High","resolution","panchromatic","image","of","shape","``","(","N",",","C",",","H",",","W",")","``",".","-","``","pan_lr","``","(",":","class",":","`","~torch.Tensor","`",")",":","Low","resolution","panchromatic","image","of","shape","``","(","N",",","C",",","H","'",",","W","'",")","``",".","where","H","and","W","must","be","multiple","of","H","'","and","W","'",".","As","output","of","`","forward","`","and","`","compute","`","the","metric","returns","the","following","output","-","``","sdi","``","(",":","class",":","`","~torch.Tensor","`",")",":","if","``","reduction","!","='none","'","``","returns","float","scalar","tensor","with","average","SDI","value","over","sample","else","returns","tensor","of","shape","``","(","N",",",")","``","with","SDI","values","per","sample","Args",":","norm_order",":","Order","of","the","norm","applied","on","the","difference",".","window_size",":","Window","size","of","the","filter","applied","to","degrade","the","high","resolution","panchromatic","image",".","reduction",":","a","method","to","reduce","metric","score","over","labels",".","-","``","'elementwise_mean","'","``",":","takes","the","mean","(","default",")","-","``","'sum","'","``",":","takes","the","sum","-","``","'none","'","``",":","no","reduction","will","be","applied","kwargs",":","Additional","keyword","arguments",",","see",":","ref",":","`","Metric","kwargs","`","for","more","info",".","Example",":",">",">",">","import","torch",">",">",">","_","=","torch.manual_seed","(","42",")",">",">",">","from","torchmetrics.image","import","SpatialDistortionIndex",">",">",">","preds","=","torch.rand","(","[","16",",","3",",","32",",","32","]",")",">",">",">","target","=","{","...","'ms","'",":","torch.rand","(","[","16",",","3",",","16",",","16","]",")",",","...","'pan","'",":","torch.rand","(","[","16",",","3",",","32",",","32","]",")",",","...","}",">",">",">","sdi","=","SpatialDistortionIndex","(",")",">",">",">","sdi","(","preds",",","target",")","tensor","(","0.0090",")"]
docstr: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting a single value
      >>> import torch
      >>> _ = torch.manual_seed(42)
      >>> from torchmetrics.image import SpatialDistortionIndex
      >>> preds = torch.rand([16, 3, 32, 32])
      >>> target = {
      ...     'ms': torch.rand([16, 3, 16, 16]),
      ...     'pan': torch.rand([16, 3, 32, 32]),
      ... }
      >>> metric = SpatialDistortionIndex()
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> _ = torch.manual_seed(42)
      >>> from torchmetrics.image import SpatialDistortionIndex
      >>> preds = torch.rand([16, 3, 32, 32])
      >>> target = {
      ...     'ms': torch.rand([16, 3, 16, 16]),
      ...     'pan': torch.rand([16, 3, 32, 32]),
      ... }
      >>> metric = SpatialDistortionIndex()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(preds, target))
      >>> fig_, ax_ = metric.plot(values)
docstr_tok: ["Plot","a","single","or","multiple","values","from","the","metric",".","Args",":","val",":","Either","a","single","result","from","calling","`","metric.forward","`","or","`","metric.compute","`","or","a","list","of","these","results",".","If","no","value","is","provided",",","will","automatically","call","`","metric.compute","`","and","plot","that","result",".","ax",":","An","matplotlib","axis","object",".","If","provided","will","add","plot","to","that","axis","Returns",":","Figure","and","Axes","object","Raises",":","ModuleNotFoundError",":","If","`","matplotlib","`","is","not","installed","..","plot",":",":",":","scale",":","75",">",">",">","#","Example","plotting","a","single","value",">",">",">","import","torch",">",">",">","_","=","torch.manual_seed","(","42",")",">",">",">","from","torchmetrics.image","import","SpatialDistortionIndex",">",">",">","preds","=","torch.rand","(","[","16",",","3",",","32",",","32","]",")",">",">",">","target","=","{","...","'ms","'",":","torch.rand","(","[","16",",","3",",","16",",","16","]",")",",","...","'pan","'",":","torch.rand","(","[","16",",","3",",","32",",","32","]",")",",","...","}",">",">",">","metric","=","SpatialDistortionIndex","(",")",">",">",">","metric.update","(","preds",",","target",")",">",">",">","fig_",",","ax_","=","metric.plot","(",")","..","plot",":",":",":","scale",":","75",">",">",">","#","Example","plotting","multiple","values",">",">",">","import","torch",">",">",">","_","=","torch.manual_seed","(","42",")",">",">",">","from","torchmetrics.image","import","SpatialDistortionIndex",">",">",">","preds","=","torch.rand","(","[","16",",","3",",","32",",","32","]",")",">",">",">","target","=","{","...","'ms","'",":","torch.rand","(","[","16",",","3",",","16",",","16","]",")",",","...","'pan","'",":","torch.rand","(","[","16",",","3",",","32",",","32","]",")",",","...","}",">",">",">","metric","=","SpatialDistortionIndex","(",")",">",">",">","values","=","[","]",">",">",">","for","_","in","range","(","10",")",":","...","values.append","(","metric","(","preds",",","target",")",")",">",">",">","fig_",",","ax_","=","metric.plot","(","values",")"]
code: def plot(
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting a single value
          >>> import torch
          >>> _ = torch.manual_seed(42)
          >>> from torchmetrics.image import SpatialDistortionIndex
          >>> preds = torch.rand([16, 3, 32, 32])
          >>> target = {
          ...     'ms': torch.rand([16, 3, 16, 16]),
          ...     'pan': torch.rand([16, 3, 32, 32]),
          ... }
          >>> metric = SpatialDistortionIndex()
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> _ = torch.manual_seed(42)
          >>> from torchmetrics.image import SpatialDistortionIndex
          >>> preds = torch.rand([16, 3, 32, 32])
          >>> target = {
          ...     'ms': torch.rand([16, 3, 16, 16]),
          ...     'pan': torch.rand([16, 3, 32, 32]),
          ... }
          >>> metric = SpatialDistortionIndex()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(preds, target))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/image/d_s.py
imports: from typing import Any, Dict, List, Optional, Sequence, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.image.d_s import _spatial_distortion_index_compute, _spatial_distortion_index_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCHVISION_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 4 for calling the SpatialDistortionIndex obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self