code: def _concordance_corrcoef_compute(
      mean_x: Tensor,
      mean_y: Tensor,
      var_x: Tensor,
      var_y: Tensor,
      corr_xy: Tensor,
      nb: Tensor,
  ) -> Tensor:
      """Compute the final concordance correlation coefficient based on accumulated statistics."""
      pearson = _pearson_corrcoef_compute(var_x, var_y, corr_xy, nb)
      return (
          2.0
          * pearson
          * var_x.sqrt()
          * var_y.sqrt()
          / (var_x + var_y + (mean_x - mean_y) ** 2)
      )
  
imports: import torch
  from torch import Tensor
  from torchmetrics.functional.regression.pearson import _pearson_corrcoef_compute, _pearson_corrcoef_update
label_desc: Use image node_id 1 for calling a global function with example usage: _concordance_corrcoef_compute(mean_x, mean_y, var_x, var_y, corr_xy, nb) and returns: unknown