class_docstr: Calculate `Matthews correlation coefficient`_ for multiclass tasks.
  
  This metric measures the general correlation or quality of a classification.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): A 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:
  
  - ``mcmcc`` (:class:`~torch.Tensor`): A tensor containing the Multi-class Matthews Correlation Coefficient.
  
  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
      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 MulticlassMatthewsCorrCoef
      >>> target = tensor([2, 1, 0, 0])
      >>> preds = tensor([2, 1, 0, 1])
      >>> metric = MulticlassMatthewsCorrCoef(num_classes=3)
      >>> metric(preds, target)
      tensor(0.7000)
  
  Example (pred is float tensor):
      >>> from torchmetrics.classification import MulticlassMatthewsCorrCoef
      >>> 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 = MulticlassMatthewsCorrCoef(num_classes=3)
      >>> metric(preds, target)
      tensor(0.7000)
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docstr: Compute metric.
docstr_tok: ["Compute","metric","."]
code: def compute(self) -> Tensor:
      """Compute metric."""
      return _matthews_corrcoef_reduce(self.confmat)
  
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, MultilabelConfusionMatrix
  from torchmetrics.functional.classification.matthews_corrcoef import _matthews_corrcoef_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 MulticlassMatthewsCorrCoef obj's underlying member method code with example usage: obj.compute() and returns: _matthews_corrcoef_reduce