: 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.

    Returns:
        The metric returns 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)``

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.functional.classification import multiclass_stat_scores
        >>> target = tensor([2, 1, 0, 0])
        >>> preds = tensor([2, 1, 0, 1])
        >>> multiclass_stat_scores(preds, target, num_classes=3, average='micro')
        tensor([3, 1, 7, 1, 4])
        >>> multiclass_stat_scores(preds, target, num_classes=3, average=None)
        tensor([[1, 0, 2, 1, 2],
                [1, 1, 2, 0, 1],
                [1, 0, 3, 0, 1]])

    Example (preds is float tensor):
        >>> from torchmetrics.functional.classification import multiclass_stat_scores
        >>> 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]])
        >>> multiclass_stat_scores(preds, target, num_classes=3, average='micro')
        tensor([3, 1, 7, 1, 4])
        >>> multiclass_stat_scores(preds, target, num_classes=3, average=None)
        tensor([[1, 0, 2, 1, 2],
                [1, 1, 2, 0, 1],
                [1, 0, 3, 0, 1]])

    Example (multidim tensors):
        >>> from torchmetrics.functional.classification import multiclass_stat_scores
        >>> 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]]])
        >>> multiclass_stat_scores(preds, target, num_classes=3, multidim_average='samplewise', average='micro')
        tensor([[3, 3, 9, 3, 6],
                [2, 4, 8, 4, 6]])
        >>> multiclass_stat_scores(preds, target, num_classes=3, multidim_average='samplewise', average=None)
        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]]])

    )