class_docstr: Compute `Homogeneity Score`_.
  
  The homogeneity score is a metric to measure the homogeneity of a clustering. A clustering result satisfies
  homogeneity if all of its clusters contain only data points which are members of a single class. The metric is not
  symmetric, therefore swapping ``preds`` and ``target`` yields a different 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 HomogeneityScore
      >>> preds = torch.tensor([2, 1, 0, 1, 0])
      >>> target = torch.tensor([0, 2, 1, 1, 0])
      >>> metric = HomogeneityScore()
      >>> metric(preds, target)
      tensor(0.4744)
code: def __init__(self, **kwargs: Any) -> None:
      super().__init__(**kwargs)
  
      self.add_state("preds", default=[], dist_reduce_fx="cat")
      self.add_state("target", default=[], dist_reduce_fx="cat")
  
imports: from typing import Any, List, Optional, Sequence, Union
  from torch import Tensor
  from torchmetrics.functional.clustering.homogeneity_completeness_v_measure import completeness_score, homogeneity_score, v_measure_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 1 to create a new HomogeneityScore object from inherited base classes: Metric with example: obj = HomogeneityScore()