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: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> from torch import randn
      >>> # Example plotting a single value
      >>> from torchmetrics.regression import ConcordanceCorrCoef
      >>> metric = ConcordanceCorrCoef()
      >>> metric.update(randn(10,), randn(10,))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> from torch import randn
      >>> # Example plotting multiple values
      >>> from torchmetrics.regression import ConcordanceCorrCoef
      >>> metric = ConcordanceCorrCoef()
      >>> values = []
      >>> for _ in range(10):
      ...     values.append(metric(randn(10,), randn(10,)))
      >>> fig, ax = metric.plot(values)
code: def plot(
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> from torch import randn
          >>> # Example plotting a single value
          >>> from torchmetrics.regression import ConcordanceCorrCoef
          >>> metric = ConcordanceCorrCoef()
          >>> metric.update(randn(10,), randn(10,))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> from torch import randn
          >>> # Example plotting multiple values
          >>> from torchmetrics.regression import ConcordanceCorrCoef
          >>> metric = ConcordanceCorrCoef()
          >>> values = []
          >>> for _ in range(10):
          ...     values.append(metric(randn(10,), randn(10,)))
          >>> fig, ax = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
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 2 for calling the ConcordanceCorrCoef obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self