class_docstr: Computes true positives, false positives, true negatives, false negatives and the support for multiclass tasks.
  
  Related to `Type I and Type II errors`_.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): 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, ...)``
  
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``mcss`` (:class:`~torch.Tensor`): A tensor of shape ``(..., 5)``, where the last dimension corresponds
    to ``[tp, fp, tn, fn, sup]`` (``sup`` stands for support and equals ``tp + fn``). The shape
    depends on ``average`` and ``multidim_average`` parameters:
  
    - If ``multidim_average`` is set to ``global``:
  
      - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(5,)``
      - If ``average=None/'none'``, the shape will be ``(C, 5)``
  
    - If ``multidim_average`` is set to ``samplewise``:
  
      - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N, 5)``
      - If ``average=None/'none'``, the shape will be ``(N, C, 5)``
  
  If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
  which the reduction will then be applied over instead of the sample dimension ``N``.
  
  Args:
      num_classes: Integer specifying the number of classes
      average:
          Defines the reduction that is applied over labels. Should be one of the following:
  
          - ``micro``: Sum statistics over all labels
          - ``macro``: Calculate statistics for each label and average them
          - ``weighted``: calculates statistics for each label and computes weighted average using their support
          - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
      top_k:
          Number of highest probability or logit score predictions considered to find the correct label.
          Only works when ``preds`` contain probabilities/logits.
      multidim_average:
          Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
  
          - ``global``: Additional dimensions are flatted along the batch dimension
          - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
            The statistics in this case are calculated over the additional dimensions.
  
      ignore_index:
          Specifies a target value that is ignored and does not contribute to the metric calculation
      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 (preds is int tensor):
      >>> from torch import tensor
      >>> from torchmetrics.classification import MulticlassStatScores
      >>> target = tensor([2, 1, 0, 0])
      >>> preds = tensor([2, 1, 0, 1])
      >>> metric = MulticlassStatScores(num_classes=3, average='micro')
      >>> metric(preds, target)
      tensor([3, 1, 7, 1, 4])
      >>> mcss = MulticlassStatScores(num_classes=3, average=None)
      >>> mcss(preds, target)
      tensor([[1, 0, 2, 1, 2],
              [1, 1, 2, 0, 1],
              [1, 0, 3, 0, 1]])
  
  Example (preds is float tensor):
      >>> from torchmetrics.classification import MulticlassStatScores
      >>> 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 = MulticlassStatScores(num_classes=3, average='micro')
      >>> metric(preds, target)
      tensor([3, 1, 7, 1, 4])
      >>> mcss = MulticlassStatScores(num_classes=3, average=None)
      >>> mcss(preds, target)
      tensor([[1, 0, 2, 1, 2],
              [1, 1, 2, 0, 1],
              [1, 0, 3, 0, 1]])
  
  Example (multidim tensors):
      >>> from torchmetrics.classification import MulticlassStatScores
      >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
      >>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
      >>> metric = MulticlassStatScores(num_classes=3, multidim_average="samplewise", average='micro')
      >>> metric(preds, target)
      tensor([[3, 3, 9, 3, 6],
              [2, 4, 8, 4, 6]])
      >>> mcss = MulticlassStatScores(num_classes=3, multidim_average="samplewise", average=None)
      >>> mcss(preds, target)
      tensor([[[2, 1, 3, 0, 2],
               [0, 1, 3, 2, 2],
               [1, 1, 3, 1, 2]],
              [[0, 1, 4, 1, 1],
               [1, 1, 2, 2, 3],
               [1, 2, 2, 1, 2]]])
docstr: Compute the final statistics.
docstr_tok: ["Compute","the","final","statistics","."]
code: def compute(self) -> Tensor:
      """Compute the final statistics."""
      tp, fp, tn, fn = self._final_state()
      return _multiclass_stat_scores_compute(
          tp, fp, tn, fn, self.average, self.multidim_average
      )
  
imports: from typing import Any, Callable, List, Optional, Tuple, Type, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.functional.classification.stat_scores import _binary_stat_scores_arg_validation, _binary_stat_scores_compute, _binary_stat_scores_format, _binary_stat_scores_tensor_validation, _binary_stat_scores_update, _multiclass_stat_scores_arg_validation, _multiclass_stat_scores_compute, _multiclass_stat_scores_format, _multiclass_stat_scores_tensor_validation, _multiclass_stat_scores_update, _multilabel_stat_scores_arg_validation, _multilabel_stat_scores_compute, _multilabel_stat_scores_format, _multilabel_stat_scores_tensor_validation, _multilabel_stat_scores_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 3 for calling the MulticlassStatScores obj's underlying member method code with example usage: obj.compute() and returns: _multiclass_stat_scores_compute