class_docstr: Compute `Theil's U`_ statistic measuring the association between two categorical (nominal) data series.
  
  .. math::
      U(X|Y) = \frac{H(X) - H(X|Y)}{H(X)}
  
  where :math:`H(X)` is entropy of variable :math:`X` while :math:`H(X|Y)` is the conditional entropy of :math:`X`
  given :math:`Y`. It is also know as the Uncertainty Coefficient. Theils's U is an asymmetric coefficient, i.e.
  :math:`TheilsU(preds, target) \neq TheilsU(target, preds)`, so the order of the inputs matters. The output values
  lies in [0, 1], where a 0 means y has no information about x while value 1 means y has complete information about x.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): Either 1D or 2D tensor of categorical (nominal) data from the first data
    series (called X in the above definition) with shape ``(batch_size,)`` or ``(batch_size, num_classes)``,
    respectively.
  - ``target`` (:class:`~torch.Tensor`): Either 1D or 2D tensor of categorical (nominal) data from the second data
    series (called Y in the above definition) with shape ``(batch_size,)`` or ``(batch_size, num_classes)``,
    respectively.
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``theils_u`` (:class:`~torch.Tensor`): Scalar tensor containing the Theil's U statistic.
  
  Args:
      num_classes: Integer specifying the number of classes
      nan_strategy: Indication of whether to replace or drop ``NaN`` values
      nan_replace_value: Value to replace ``NaN``s when ``nan_strategy = 'replace'``
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example::
  
      >>> from torchmetrics.nominal import TheilsU
      >>> _ = torch.manual_seed(42)
      >>> preds = torch.randint(10, (10,))
      >>> target = torch.randint(10, (10,))
      >>> metric = TheilsU(num_classes=10)
      >>> metric(preds, target)
      tensor(0.8530)
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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
  
      >>> # Example plotting a single value
      >>> import torch
      >>> from torchmetrics.nominal import TheilsU
      >>> metric = TheilsU(num_classes=10)
      >>> metric.update(torch.randint(10, (10,)), torch.randint(10, (10,)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.nominal import TheilsU
      >>> metric = TheilsU(num_classes=10)
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.randint(10, (10,)), torch.randint(10, (10,))))
      >>> fig_, ax_ = metric.plot(values)
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code: def plot(
      self,
      val: Union[Tensor, Sequence[Tensor], None] = 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
  
          >>> # Example plotting a single value
          >>> import torch
          >>> from torchmetrics.nominal import TheilsU
          >>> metric = TheilsU(num_classes=10)
          >>> metric.update(torch.randint(10, (10,)), torch.randint(10, (10,)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.nominal import TheilsU
          >>> metric = TheilsU(num_classes=10)
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.randint(10, (10,)), torch.randint(10, (10,))))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/nominal/theils_u.py
imports: from typing import Any, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.nominal.theils_u import _theils_u_compute, _theils_u_update
  from torchmetrics.functional.nominal.utils import _nominal_input_validation
  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 TheilsU obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self