class_docstr: Compute true positives, false positives, true negatives, false negatives and the support for multilabel 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 or float tensor of shape ``(N, C, ...)``. If preds is a floating
    point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid
    per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``mlss`` (: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_labels: Integer specifying the number of labels
      threshold: Threshold for transforming probability to binary (0,1) predictions
      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
  
      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 MultilabelStatScores
      >>> target = tensor([[0, 1, 0], [1, 0, 1]])
      >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
      >>> metric = MultilabelStatScores(num_labels=3, average='micro')
      >>> metric(preds, target)
      tensor([2, 1, 2, 1, 3])
      >>> mlss = MultilabelStatScores(num_labels=3, average=None)
      >>> mlss(preds, target)
      tensor([[1, 0, 1, 0, 1],
              [0, 0, 1, 1, 1],
              [1, 1, 0, 0, 1]])
  
  Example (preds is float tensor):
      >>> from torchmetrics.classification import MultilabelStatScores
      >>> target = tensor([[0, 1, 0], [1, 0, 1]])
      >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
      >>> metric = MultilabelStatScores(num_labels=3, average='micro')
      >>> metric(preds, target)
      tensor([2, 1, 2, 1, 3])
      >>> mlss = MultilabelStatScores(num_labels=3, average=None)
      >>> mlss(preds, target)
      tensor([[1, 0, 1, 0, 1],
              [0, 0, 1, 1, 1],
              [1, 1, 0, 0, 1]])
  
  Example (multidim tensors):
      >>> from torchmetrics.classification import MultilabelStatScores
      >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
      >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
      ...                 [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
      >>> metric = MultilabelStatScores(num_labels=3, multidim_average='samplewise', average='micro')
      >>> metric(preds, target)
      tensor([[2, 3, 0, 1, 3],
              [0, 2, 1, 3, 3]])
      >>> mlss = MultilabelStatScores(num_labels=3, multidim_average='samplewise', average=None)
      >>> mlss(preds, target)
      tensor([[[1, 1, 0, 0, 1],
               [1, 1, 0, 0, 1],
               [0, 1, 0, 1, 1]],
              [[0, 0, 0, 2, 2],
               [0, 2, 0, 0, 0],
               [0, 0, 1, 1, 1]]])
docstr: Update state with predictions and targets.
docstr_tok: ["Update","state","with","predictions","and","targets","."]
code: def update(self, preds: Tensor, target: Tensor) -> None:
      """Update state with predictions and targets."""
      if self.validate_args:
          _multilabel_stat_scores_tensor_validation(
              preds,
              target,
              self.num_labels,
              self.multidim_average,
              self.ignore_index,
          )
      preds, target = _multilabel_stat_scores_format(
          preds,
          target,
          self.num_labels,
          self.threshold,
          self.ignore_index,
      )
      tp, fp, tn, fn = _multilabel_stat_scores_update(
          preds, target, self.multidim_average
      )
      self._update_state(tp, fp, tn, fn)
  
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 2 for calling the MultilabelStatScores obj's underlying member method code with example usage: obj.update(preds, target) without return types