class_docstr: Compute `explained variance`_.
  
  .. math:: \text{ExplainedVariance} = 1 - \frac{\text{Var}(y - \hat{y})}{\text{Var}(y)}
  
  Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): Predictions from model in float tensor
    with shape ``(N,)`` or ``(N, ...)`` (multioutput)
  - ``target`` (:class:`~torch.Tensor`): Ground truth values in long tensor
    with shape ``(N,)`` or ``(N, ...)`` (multioutput)
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``explained_variance`` (:class:`~torch.Tensor`): A tensor with the explained variance(s)
  
  In the case of multioutput, as default the variances will be uniformly averaged over the additional dimensions.
  Please see argument ``multioutput`` for changing this behavior.
  
  Args:
      multioutput:
          Defines aggregation in the case of multiple output scores. Can be one
          of the following strings (default is ``'uniform_average'``.):
  
          * ``'raw_values'`` returns full set of scores
          * ``'uniform_average'`` scores are uniformly averaged
          * ``'variance_weighted'`` scores are weighted by their individual variances
  
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Raises:
      ValueError:
          If ``multioutput`` is not one of ``"raw_values"``, ``"uniform_average"`` or ``"variance_weighted"``.
  
  Example:
      >>> from torch import tensor
      >>> from torchmetrics.regression import ExplainedVariance
      >>> target = tensor([3, -0.5, 2, 7])
      >>> preds = tensor([2.5, 0.0, 2, 8])
      >>> explained_variance = ExplainedVariance()
      >>> explained_variance(preds, target)
      tensor(0.9572)
  
      >>> target = tensor([[0.5, 1], [-1, 1], [7, -6]])
      >>> preds = tensor([[0, 2], [-1, 2], [8, -5]])
      >>> explained_variance = ExplainedVariance(multioutput='raw_values')
      >>> explained_variance(preds, target)
      tensor([0.9677, 1.0000])
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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 ExplainedVariance
      >>> metric = ExplainedVariance()
      >>> 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 ExplainedVariance
      >>> metric = ExplainedVariance()
      >>> values = []
      >>> for _ in range(10):
      ...     values.append(metric(randn(10,), randn(10,)))
      >>> fig, ax = metric.plot(values)
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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 ExplainedVariance
          >>> metric = ExplainedVariance()
          >>> 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 ExplainedVariance
          >>> metric = ExplainedVariance()
          >>> 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/explained_variance.py
imports: from typing import Any, Optional, Sequence, Union
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics.functional.regression.explained_variance import ALLOWED_MULTIOUTPUT, _explained_variance_compute, _explained_variance_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 4 for calling the ExplainedVariance obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self