class_docstr: Compute Exact match (also known as subset accuracy) for multilabel tasks.
  
  Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be
  correctly classified.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): An int tensor 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:
  
  - ``mlem`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument:
  
      - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor
      - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)``
  
  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
      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 MultilabelExactMatch
      >>> target = tensor([[0, 1, 0], [1, 0, 1]])
      >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
      >>> metric = MultilabelExactMatch(num_labels=3)
      >>> metric(preds, target)
      tensor(0.5000)
  
  Example (preds is float tensor):
      >>> from torchmetrics.classification import MultilabelExactMatch
      >>> target = tensor([[0, 1, 0], [1, 0, 1]])
      >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
      >>> metric = MultilabelExactMatch(num_labels=3)
      >>> metric(preds, target)
      tensor(0.5000)
  
  Example (multidim tensors):
      >>> from torchmetrics.classification import MultilabelExactMatch
      >>> 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 = MultilabelExactMatch(num_labels=3, multidim_average='samplewise')
      >>> metric(preds, target)
      tensor([0., 0.])
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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,
      )
      correct, total = _multilabel_exact_match_update(
          preds, target, self.num_labels, self.multidim_average
      )
      if self.multidim_average == "samplewise":
          self.correct.append(correct)
          self.total = total
      else:
          self.correct += correct
          self.total += total
  
imports: from typing import Any, Optional, Sequence, Type, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.functional.classification.exact_match import _exact_match_reduce, _multiclass_exact_match_update, _multilabel_exact_match_update
  from torchmetrics.functional.classification.stat_scores import _multiclass_stat_scores_arg_validation, _multiclass_stat_scores_format, _multiclass_stat_scores_tensor_validation, _multilabel_stat_scores_arg_validation, _multilabel_stat_scores_format, _multilabel_stat_scores_tensor_validation
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.enums import ClassificationTaskNoBinary
  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 MultilabelExactMatch obj's underlying member method code with example usage: obj.update(preds, target) without return types