class_docstr: Compute `Adjusted Mutual Information Score`_.
  
  .. math::
      AMI(U,V) = \frac{MI(U,V) - E(MI(U,V))}{avg(H(U), H(V)) - E(MI(U,V))}
  
  Where :math:`U` is a tensor of target values, :math:`V` is a tensor of predictions, :math:`M_p(U,V)` is the
  generalized mean of order :math:`p` of :math:`U` and :math:`V`, and :math:`MI(U,V)` is the mutual information score
  between clusters :math:`U` and :math:`V`. The metric is symmetric, therefore swapping :math:`U` and :math:`V` yields
  the same mutual information 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:
  
  - ``ami_score`` (:class:`~torch.Tensor`): A tensor with the Adjusted Mutual Information Score
  
  Args:
      average_method: Method used to calculate generalized mean for normalization. Choose between
          ``'min'``, ``'geometric'``, ``'arithmetic'``, ``'max'``.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example::
      >>> import torch
      >>> from torchmetrics.clustering import AdjustedMutualInfoScore
      >>> preds = torch.tensor([2, 1, 0, 1, 0])
      >>> target = torch.tensor([0, 2, 1, 1, 0])
      >>> ami_score = AdjustedMutualInfoScore(average_method="arithmetic")
      >>> ami_score(preds, target)
      tensor(-0.2500)
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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 AdjustedMutualInfoScore
      >>> metric = AdjustedMutualInfoScore()
      >>> 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 AdjustedMutualInfoScore
      >>> metric = AdjustedMutualInfoScore()
      >>> 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 AdjustedMutualInfoScore
          >>> metric = AdjustedMutualInfoScore()
          >>> 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 AdjustedMutualInfoScore
          >>> metric = AdjustedMutualInfoScore()
          >>> 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/adjusted_mutual_info_score.py
imports: from typing import Any, List, Literal, Optional, Sequence, Union
  from torch import Tensor
  from torchmetrics.clustering.mutual_info_score import MutualInfoScore
  from torchmetrics.functional.clustering.adjusted_mutual_info_score import _validate_average_method_arg, adjusted_mutual_info_score
  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 3 for calling the AdjustedMutualInfoScore obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self