class_docstr: Compute Spatial Correlation Coefficient (SCC_).
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)`` or ``(N,H,W)``.
  - ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)`` or ``(N,H,W)``.
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``scc`` (:class:`~torch.Tensor`): Tensor with scc score
  
  Args:
      hp_filter: High-pass filter tensor. default: tensor([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]).
      window_size: Local window size integer. default: 8.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> import torch
      >>> _ = torch.manual_seed(42)
      >>> from torchmetrics.image import SpatialCorrelationCoefficient as SCC
      >>> preds = torch.randn([32, 3, 64, 64])
      >>> target = torch.randn([32, 3, 64, 64])
      >>> scc = SCC()
      >>> scc(preds, target)
      tensor(0.0023)
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docstr: Compute the VIF score based on inputs passed in to ``update`` previously.
docstr_tok: ["Compute","the","VIF","score","based","on","inputs","passed","in","to","``","update","``","previously","."]
code: def compute(self) -> Tensor:
      """Compute the VIF score based on inputs passed in to ``update`` previously."""
      return self.scc_score / self.total
  
file_path: torchmetrics/src/torchmetrics/image/scc.py
imports: from typing import Any, Optional
  import torch
  from torch import Tensor, tensor
  from torchmetrics.functional.image.scc import _scc_per_channel_compute
  from torchmetrics.functional.image.scc import _scc_update
  from torchmetrics.metric import Metric
label_desc: Use image node_id 3 for calling the SpatialCorrelationCoefficient obj's underlying member method code with example usage: obj.compute() and returns: unknown