class_docstr: Compute `Fowlkes-Mallows Index`_.
  
  .. math::
      FMI(U,V) = \frac{TP}{\sqrt{(TP + FP) * (TP + FN)}}
  
  Where :math:`TP` is the number of true positives, :math:`FP` is the number of false positives, and :math:`FN` is
  the number of false negatives.
  
  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:
  
  - ``fmi`` (:class:`~torch.Tensor`): A tensor with the Fowlkes-Mallows index.
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example::
      >>> import torch
      >>> from torchmetrics.clustering import FowlkesMallowsIndex
      >>> preds = torch.tensor([2, 2, 0, 1, 0])
      >>> target = torch.tensor([2, 2, 1, 1, 0])
      >>> fmi = FowlkesMallowsIndex()
      >>> fmi(preds, target)
      tensor(0.5000)
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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 FowlkesMallowsIndex
      >>> metric = FowlkesMallowsIndex()
      >>> 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 FowlkesMallowsIndex
      >>> metric = FowlkesMallowsIndex()
      >>> 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 FowlkesMallowsIndex
          >>> metric = FowlkesMallowsIndex()
          >>> 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 FowlkesMallowsIndex
          >>> metric = FowlkesMallowsIndex()
          >>> 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/fowlkes_mallows_index.py
imports: from typing import Any, List, Optional, Sequence, Union
  from torch import Tensor
  from torchmetrics.functional.clustering import fowlkes_mallows_index
  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 FowlkesMallowsIndex obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self