class_docstr: Compute concordance correlation coefficient that measures the agreement between two variables.
  
  .. math::
      \rho_c = \frac{2 \rho \sigma_x \sigma_y}{\sigma_x^2 + \sigma_y^2 + (\mu_x - \mu_y)^2}
  
  where :math:`\mu_x, \mu_y` is the means for the two variables, :math:`\sigma_x^2, \sigma_y^2` are the corresponding
  variances and \rho is the pearson correlation coefficient between the two variables.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): either single output float tensor with shape ``(N,)`` or multioutput
    float tensor of shape ``(N,d)``
  - ``target`` (:class:`~torch.Tensor`): either single output float tensor with shape ``(N,)`` or multioutput
    float tensor of shape ``(N,d)``
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``concordance`` (:class:`~torch.Tensor`): A scalar float tensor with the concordance coefficient(s) for
    non-multioutput input or a float tensor with shape ``(d,)`` for multioutput input
  
  Args:
      num_outputs: Number of outputs in multioutput setting
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example (single output regression):
      >>> from torchmetrics.regression import ConcordanceCorrCoef
      >>> from torch import tensor
      >>> target = tensor([3, -0.5, 2, 7])
      >>> preds = tensor([2.5, 0.0, 2, 8])
      >>> concordance = ConcordanceCorrCoef()
      >>> concordance(preds, target)
      tensor(0.9777)
  
  Example (multi output regression):
      >>> from torchmetrics.regression import ConcordanceCorrCoef
      >>> target = tensor([[3, -0.5], [2, 7]])
      >>> preds = tensor([[2.5, 0.0], [2, 8]])
      >>> concordance = ConcordanceCorrCoef(num_outputs=2)
      >>> concordance(preds, target)
      tensor([0.7273, 0.9887])
docstr: Compute final concordance correlation coefficient over metric states.
code: def compute(self) -> Tensor:
      """Compute final concordance correlation coefficient over metric states."""
      if (self.num_outputs == 1 and self.mean_x.numel() > 1) or (
          self.num_outputs > 1 and self.mean_x.ndim > 1
      ):
          (
              mean_x,
              mean_y,
              var_x,
              var_y,
              corr_xy,
              n_total,
          ) = _final_aggregation(
              self.mean_x,
              self.mean_y,
              self.var_x,
              self.var_y,
              self.corr_xy,
              self.n_total,
          )
      else:
          mean_x = self.mean_x
          mean_y = self.mean_y
          var_x = self.var_x
          var_y = self.var_y
          corr_xy = self.corr_xy
          n_total = self.n_total
      return _concordance_corrcoef_compute(
          mean_x, mean_y, var_x, var_y, corr_xy, n_total
      )
  
file_path: torchmetrics/src/torchmetrics/regression/concordance.py
imports: from typing import Optional, Sequence, Union
  from torch import Tensor
  from torchmetrics.functional.regression.concordance import _concordance_corrcoef_compute
  from torchmetrics.regression.pearson import PearsonCorrCoef, _final_aggregation
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 1 for calling the ConcordanceCorrCoef obj's underlying member method code with example usage: obj.compute() and returns: _concordance_corrcoef_compute