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: Compute Theil's U statistic.
docstr_tok: ["Compute","Theil","'s","U","statistic","."]
code: def compute(self) -> Tensor:
      """Compute Theil's U statistic."""
      return _theils_u_compute(self.confmat)
  
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 3 for calling the TheilsU obj's underlying member method code with example usage: obj.compute() and returns: _theils_u_compute