class_docstr: Compute `Rand Score`_ (alternatively known as Rand Index).
  
  .. math::
      RS(U, V) = \text{number of agreeing pairs} / \text{number of pairs}
  
  The number of agreeing pairs is every :math:`(i, j)` pair of samples where :math:`i \in U` and :math:`j \in V`
  (the predicted and true clusterings, respectively) that are in the same cluster for both clusterings. The metric is
  symmetric, therefore swapping :math:`U` and :math:`V` yields the same rand score.
  
  This clustering metric is an extrinsic measure, because it requires ground truth clustering labels, which may not
  be available in practice since clustering in generally is used for unsupervised learning.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with predicted cluster labels
  - ``target`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with ground truth cluster labels
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``rand_score`` (:class:`~torch.Tensor`): A tensor with the Rand Score
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example::
      >>> import torch
      >>> from torchmetrics.clustering import RandScore
      >>> preds = torch.tensor([2, 1, 0, 1, 0])
      >>> target = torch.tensor([0, 2, 1, 1, 0])
      >>> metric = RandScore()
      >>> metric(preds, target)
      tensor(0.6000)
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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.clustering import RandScore
      >>> metric = RandScore()
      >>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,)))
      >>> fig_, ax_ = metric.plot(metric.compute())
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.clustering import RandScore
      >>> metric = RandScore()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.randint(0, 4, (10,)), torch.randint(0, 4, (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.clustering import RandScore
          >>> metric = RandScore()
          >>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,)))
          >>> fig_, ax_ = metric.plot(metric.compute())
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.clustering import RandScore
          >>> metric = RandScore()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,))))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/clustering/rand_score.py
imports: from typing import Any, List, Optional, Sequence, Union
  from torch import Tensor
  from torchmetrics.functional.clustering.rand_score import rand_score
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  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 RandScore obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self