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)
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docstr: Update state with preds and target.
  
  Args:
      preds: High resolution multispectral image.
      target: A dictionary containing the following keys:
  
          - ``'ms'``: low resolution multispectral image.
          - ``'pan'``: high resolution panchromatic image.
          - ``'pan_lr'``: (optional) low resolution panchromatic image.
  
  Raises:
      ValueError:
          If ``target`` doesn't have ``ms`` and ``pan``.
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code: def update(self, preds: Tensor, target: Dict[str, Tensor]) -> None:
      """Update state with preds and target.
  
      Args:
          preds: High resolution multispectral image.
          target: A dictionary containing the following keys:
  
              - ``'ms'``: low resolution multispectral image.
              - ``'pan'``: high resolution panchromatic image.
              - ``'pan_lr'``: (optional) low resolution panchromatic image.
  
      Raises:
          ValueError:
              If ``target`` doesn't have ``ms`` and ``pan``.
  
      """
      if "ms" not in target:
          raise ValueError(
              f"Expected `target` to have key `ms`. Got target: {target.keys()}."
          )
      if "pan" not in target:
          raise ValueError(
              f"Expected `target` to have key `pan`. Got target: {target.keys()}."
          )
      ms = target["ms"]
      pan = target["pan"]
      pan_lr = target["pan_lr"] if "pan_lr" in target else None
      preds, ms, pan, pan_lr = _spatial_distortion_index_update(
          preds, ms, pan, pan_lr
      )
      self.preds.append(preds)
      self.ms.append(target["ms"])
      self.pan.append(target["pan"])
      if "pan_lr" in target:
          self.pan_lr.append(target["pan_lr"])
  
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 2 for calling the SpatialDistortionIndex obj's underlying member method code with example usage: obj.update(preds, target) without return types