class_docstr: Calculate `Cohen's kappa score`_ that measures inter-annotator agreement for multiclass tasks.
  
  .. math::
      \kappa = (p_o - p_e) / (1 - p_e)
  
  where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is
  the expected agreement when both annotators assign labels randomly. Note that
  :math:`p_e` is estimated using a per-annotator empirical prior over the
  class labels.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): Either an int tensor of shape ``(N, ...)` or float tensor of shape
    ``(N, C, ..)``. If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically
    convert probabilities/logits into an int tensor.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
  
  .. note::
     Additional dimension ``...`` will be flattened into the batch dimension.
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``mcck`` (:class:`~torch.Tensor`): A tensor containing cohen kappa score
  
  Args:
      num_classes: Integer specifying the number of classes
      ignore_index:
          Specifies a target value that is ignored and does not contribute to the metric calculation
      weights: Weighting type to calculate the score. Choose from:
  
          - ``None`` or ``'none'``: no weighting
          - ``'linear'``: linear weighting
          - ``'quadratic'``: quadratic weighting
  
      validate_args: bool indicating if input arguments and tensors should be validated for correctness.
          Set to ``False`` for faster computations.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example (pred is integer tensor):
      >>> from torch import tensor
      >>> from torchmetrics.classification import MulticlassCohenKappa
      >>> target = tensor([2, 1, 0, 0])
      >>> preds = tensor([2, 1, 0, 1])
      >>> metric = MulticlassCohenKappa(num_classes=3)
      >>> metric(preds, target)
      tensor(0.6364)
  
  Example (pred is float tensor):
      >>> from torchmetrics.classification import MulticlassCohenKappa
      >>> target = tensor([2, 1, 0, 0])
      >>> preds = tensor([[0.16, 0.26, 0.58],
      ...                 [0.22, 0.61, 0.17],
      ...                 [0.71, 0.09, 0.20],
      ...                 [0.05, 0.82, 0.13]])
      >>> metric = MulticlassCohenKappa(num_classes=3)
      >>> metric(preds, target)
      tensor(0.6364)
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 object and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> from torch import randn, randint
      >>> # Example plotting a single value
      >>> from torchmetrics.classification import MulticlassCohenKappa
      >>> metric = MulticlassCohenKappa(num_classes=3)
      >>> metric.update(randn(20,3).softmax(dim=-1), randint(3, (20,)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> from torch import randn, randint
      >>> # Example plotting a multiple values
      >>> from torchmetrics.classification import MulticlassCohenKappa
      >>> metric = MulticlassCohenKappa(num_classes=3)
      >>> values = []
      >>> for _ in range(20):
      ...     values.append(metric(randn(20,3).softmax(dim=-1), randint(3, (20,))))
      >>> fig_, ax_ = metric.plot(values)
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code: def plot(  # type: ignore[override]
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = 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 object and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> from torch import randn, randint
          >>> # Example plotting a single value
          >>> from torchmetrics.classification import MulticlassCohenKappa
          >>> metric = MulticlassCohenKappa(num_classes=3)
          >>> metric.update(randn(20,3).softmax(dim=-1), randint(3, (20,)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> from torch import randn, randint
          >>> # Example plotting a multiple values
          >>> from torchmetrics.classification import MulticlassCohenKappa
          >>> metric = MulticlassCohenKappa(num_classes=3)
          >>> values = []
          >>> for _ in range(20):
          ...     values.append(metric(randn(20,3).softmax(dim=-1), randint(3, (20,))))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
imports: from typing import Any, Optional, Sequence, Type, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.classification.confusion_matrix import BinaryConfusionMatrix, MulticlassConfusionMatrix
  from torchmetrics.functional.classification.cohen_kappa import _binary_cohen_kappa_arg_validation, _cohen_kappa_reduce, _multiclass_cohen_kappa_arg_validation
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
  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 MulticlassCohenKappa obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self