class_docstr: Compute `Accuracy`_ for multiclass tasks.
  
  .. math::
      \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)
  
  Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
  
  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:
  
      - ``mca`` (:class:`~torch.Tensor`): A tensor with the accuracy score whose returned shape depends on the
        ``average`` and ``multidim_average`` arguments:
  
          - If ``multidim_average`` is set to ``global``:
  
            - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
            - If ``average=None/'none'``, the shape will be ``(C,)``
  
          - If ``multidim_average`` is set to ``samplewise``:
  
            - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
            - If ``average=None/'none'``, the shape will be ``(N, C)``
  
  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.
  
  Example (preds is int tensor):
      >>> from torch import tensor
      >>> from torchmetrics.classification import MulticlassAccuracy
      >>> target = tensor([2, 1, 0, 0])
      >>> preds = tensor([2, 1, 0, 1])
      >>> metric = MulticlassAccuracy(num_classes=3)
      >>> metric(preds, target)
      tensor(0.8333)
      >>> mca = MulticlassAccuracy(num_classes=3, average=None)
      >>> mca(preds, target)
      tensor([0.5000, 1.0000, 1.0000])
  
  Example (preds is float tensor):
      >>> from torchmetrics.classification import MulticlassAccuracy
      >>> 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 = MulticlassAccuracy(num_classes=3)
      >>> metric(preds, target)
      tensor(0.8333)
      >>> mca = MulticlassAccuracy(num_classes=3, average=None)
      >>> mca(preds, target)
      tensor([0.5000, 1.0000, 1.0000])
  
  Example (multidim tensors):
      >>> from torchmetrics.classification import MulticlassAccuracy
      >>> 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 = MulticlassAccuracy(num_classes=3, multidim_average='samplewise')
      >>> metric(preds, target)
      tensor([0.5000, 0.2778])
      >>> mca = MulticlassAccuracy(num_classes=3, multidim_average='samplewise', average=None)
      >>> mca(preds, target)
      tensor([[1.0000, 0.0000, 0.5000],
              [0.0000, 0.3333, 0.5000]])
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 randint
      >>> # Example plotting a single value per class
      >>> from torchmetrics.classification import MulticlassAccuracy
      >>> metric = MulticlassAccuracy(num_classes=3, average=None)
      >>> metric.update(randint(3, (20,)), randint(3, (20,)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> from torch import randint
      >>> # Example plotting a multiple values per class
      >>> from torchmetrics.classification import MulticlassAccuracy
      >>> metric = MulticlassAccuracy(num_classes=3, average=None)
      >>> values = []
      >>> for _ in range(20):
      ...     values.append(metric(randint(3, (20,)), randint(3, (20,))))
      >>> fig_, ax_ = metric.plot(values)
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code: def plot(
      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 randint
          >>> # Example plotting a single value per class
          >>> from torchmetrics.classification import MulticlassAccuracy
          >>> metric = MulticlassAccuracy(num_classes=3, average=None)
          >>> metric.update(randint(3, (20,)), randint(3, (20,)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> from torch import randint
          >>> # Example plotting a multiple values per class
          >>> from torchmetrics.classification import MulticlassAccuracy
          >>> metric = MulticlassAccuracy(num_classes=3, average=None)
          >>> values = []
          >>> for _ in range(20):
          ...     values.append(metric(randint(3, (20,)), 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.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
  from torchmetrics.functional.classification.accuracy import _accuracy_reduce
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.enums import ClassificationTask
  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 MulticlassAccuracy obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self