code: def _reference_scc(preds, target):
      """Reference implementation of scc from sewar."""
      preds = torch.movedim(preds, 1, -1)
      target = torch.movedim(target, 1, -1)
      preds = preds.cpu().numpy()
      target = target.cpu().numpy()
      hp_filter = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
      window_size = 8
      scc = [
          sewar_scc(
              GT=target[batch],
              P=preds[batch],
              win=hp_filter,
              ws=window_size,
          )
          for batch in range(preds.shape[0])
      ]
      return np.mean(scc)
  
file_path: torchmetrics/tests/unittests/image/test_scc.py
imports: import numpy
  import pytest
  import torch
  from sewar.full_ref import scc
  from torchmetrics.functional.image import spatial_correlation_coefficient
  from torchmetrics.image import SpatialCorrelationCoefficient
  from unittests import BATCH_SIZE, NUM_BATCHES, _Input
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 1 for calling a global function with example usage: _reference_scc(preds, target) and returns: np