class_docstr: Calculatees `Fleiss kappa`_ a statistical measure for inter agreement between raters.
  
  .. math::
      \kappa = \frac{\bar{p} - \bar{p_e}}{1 - \bar{p_e}}
  
  where :math:`\bar{p}` is the mean of the agreement probability over all raters and :math:`\bar{p_e}` is the mean
  agreement probability over all raters if they were randomly assigned. If the raters are in complete agreement then
  the score 1 is returned, if there is no agreement among the raters (other than what would be expected by chance)
  then a score smaller than 0 is returned.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``ratings`` (:class:`~torch.Tensor`): Ratings of shape ``[n_samples, n_categories]`` or
    ``[n_samples, n_categories, n_raters]`` depedenent on ``mode``. If ``mode`` is ``counts``, ``ratings`` must be
    integer and contain the number of raters that chose each category. If ``mode`` is ``probs``, ``ratings`` must be
    floating point and contain the probability/logits that each rater chose each category.
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``fleiss_k`` (:class:`~torch.Tensor`): A float scalar tensor with the calculated Fleiss' kappa score.
  
  Args:
      mode: Whether `ratings` will be provided as counts or probabilities.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> # Ratings are provided as counts
      >>> import torch
      >>> from torchmetrics.nominal import FleissKappa
      >>> _ = torch.manual_seed(42)
      >>> ratings = torch.randint(0, 10, size=(100, 5)).long()  # 100 samples, 5 categories, 10 raters
      >>> metric = FleissKappa(mode='counts')
      >>> metric(ratings)
      tensor(0.0089)
  
  Example:
      >>> # Ratings are provided as probabilities
      >>> import torch
      >>> from torchmetrics.nominal import FleissKappa
      >>> _ = torch.manual_seed(42)
      >>> ratings = torch.randn(100, 5, 10).softmax(dim=1)  # 100 samples, 5 categories, 10 raters
      >>> metric = FleissKappa(mode='probs')
      >>> metric(ratings)
      tensor(-0.0105)
docstr: Updates the counts for fleiss kappa metric.
docstr_tok: ["Updates","the","counts","for","fleiss","kappa","metric","."]
code: def update(self, ratings: Tensor) -> None:
      """Updates the counts for fleiss kappa metric."""
      counts = _fleiss_kappa_update(ratings, self.mode)
      self.counts.append(counts)
  
file_path: torchmetrics/src/torchmetrics/nominal/fleiss_kappa.py
imports: from typing import Any, List, Optional, Sequence, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.nominal.fleiss_kappa import _fleiss_kappa_compute, _fleiss_kappa_update
  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 FleissKappa obj's underlying member method code with example usage: obj.update(ratings) without return types