class_docstr: Compute `Adjusted Rand Score`_ (also known as Adjusted Rand Index).
  
  .. math::
      ARS(U, V) = (\text{RS} - \text{Expected RS}) / (\text{Max RS} - \text{Expected RS})
  
  The adjusted rand score :math:`\text{ARS}` is in essence the :math:`\text{RS}` (rand score) adjusted for chance.
  The score ensures that completely randomly cluster labels have a score close to zero and only a perfect match will
  have a score of 1 (up to a permutation of the labels). The adjusted rand score is symmetric, therefore swapping
  :math:`U` and :math:`V` yields the same adjusted 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 is generally 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:
  
  - ``adj_rand_score`` (:class:`~torch.Tensor`): Scalar tensor with the adjusted rand score
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example::
      >>> import torch
      >>> from torchmetrics.clustering import AdjustedRandScore
      >>> metric = AdjustedRandScore()
      >>> metric(torch.tensor([0, 0, 1, 1]), torch.tensor([0, 0, 1, 1]))
      tensor(1.)
      >>> metric(torch.tensor([0, 0, 1, 1]), torch.tensor([0, 1, 0, 1]))
      tensor(-0.5000)
class_docstr_tok: ["Compute","`","Adjusted","Rand","Score","`","_","(","also","known","as","Adjusted","Rand","Index",")",".","..","math",":",":","ARS","(","U",",","V",")","=","(","\\text","{","RS","}","-","\\text","{","Expected","RS","}",")","\/","(","\\text","{","Max","RS","}","-","\\text","{","Expected","RS","}",")","The","adjusted","rand","score",":","math",":","`","\\text","{","ARS","}","`","is","in","essence","the",":","math",":","`","\\text","{","RS","}","`","(","rand","score",")","adjusted","for","chance",".","The","score","ensures","that","completely","randomly","cluster","labels","have","a","score","close","to","zero","and","only","a","perfect","match","will","have","a","score","of","1","(","up","to","a","permutation","of","the","labels",")",".","The","adjusted","rand","score","is","symmetric",",","therefore","swapping",":","math",":","`","U","`","and",":","math",":","`","V","`","yields","the","same","adjusted","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","is","generally","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",":","-","``","adj_rand_score","``","(",":","class",":","`","~torch.Tensor","`",")",":","Scalar","tensor","with","the","adjusted","rand","score","Args",":","kwargs",":","Additional","keyword","arguments",",","see",":","ref",":","`","Metric","kwargs","`","for","more","info",".","Example",":",":",">",">",">","import","torch",">",">",">","from","torchmetrics.clustering","import","AdjustedRandScore",">",">",">","metric","=","AdjustedRandScore","(",")",">",">",">","metric","(","torch.tensor","(","[","0",",","0",",","1",",","1","]",")",",","torch.tensor","(","[","0",",","0",",","1",",","1","]",")",")","tensor","(","1",".",")",">",">",">","metric","(","torch.tensor","(","[","0",",","0",",","1",",","1","]",")",",","torch.tensor","(","[","0",",","1",",","0",",","1","]",")",")","tensor","(","-0.5000",")"]
docstr: Update state with predictions and targets.
docstr_tok: ["Update","state","with","predictions","and","targets","."]
code: def update(self, preds: Tensor, target: Tensor) -> None:
      """Update state with predictions and targets."""
      self.preds.append(preds)
      self.target.append(target)
  
file_path: torchmetrics/src/torchmetrics/clustering/adjusted_rand_score.py
imports: from typing import Any, List, Optional, Sequence, Union
  from torch import Tensor
  from torchmetrics.functional.clustering.adjusted_rand_score import adjusted_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 2 for calling the AdjustedRandScore obj's underlying member method code with example usage: obj.update(preds, target) without return types