hresholds in the tensor as
              bins for the calculation.

        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:
        (tuple): a tuple of either 3 tensors or 3 lists containing

        - precision: if `thresholds=None` a list for each label is returned with an 1d tensor of size (n_thresholds+1, )
          with precision values (length may differ between labels). If `thresholds` is set to something else,
          then a single 2d tensor of size (n_labels, n_thresholds+1) with precision values is returned.
        - recall: if `thresholds=None` a list for each label is returned with an 1d tensor of size (n_thresholds+1, )
          with recall values (length may differ between labels). If `thresholds` is set to something else,
          then a single 2d tensor of size (n_labels, n_thresholds+1) with recall values is returned.
        - thresholds: if `thresholds=None` a list for each label is returned with an 1d tensor of size (n_thresholds, )
          with increasing threshold values (length may differ between labels). If `threshold` is set to something else,
          then a single 1d tensor of size (n_thresholds, ) is returned with shared threshold values for all labels.

    Example:
        >>> from torchmetrics.functional.classification import multilabel_precision_recall_curve
        >>> preds = torch.tensor([[0.75, 0.05, 0.35],
        ...                       [0.45, 0.75, 0.05],
        ...                       [0.05, 0.55, 0.75],
        ...                       [0.05, 0.65, 0.05]])
        >>> target = torch.tensor([[1, 0, 1],
        ...                        [0, 0, 0],
        ...                        [0, 1, 1],
        ...                        [1, 1, 1]])
        >>> precision, recall, thresholds = multilabel_precision_recall_curve(
        ...    preds, target, num_labels=3, thresholds=None
        ... )
        >>> precision  # doctest: +NORMALIZE_WHITESPACE
        [tensor([0.5000, 0.5000, 1.0000, 1.0000]), tensor([0.5000, 0.6667, 0.5000, 0.0000, 1.0000]),
         tensor([0.7500, 1.0000, 1.0000, 1.0000])]
        >>> recall  # doctest: +NORMALIZE_WHITESPACE
        [tensor([1.0000, 0.5000, 0.5000, 0.0000]), tensor([1.0000, 1.0000, 0.5000, 0.0000, 0.0000]),
         tensor([1.0000, 0.6667, 0.3333, 0.0000])]
        >>> thresholds  # doctest: +NORMALIZE_WHITESPACE
        [tensor([0.0500, 0.4500, 0.7500]), tensor([0.0500, 0.5500, 0.6500, 0.7500]), tensor([0.0500, 0.3500, 0.7500])]
        >>> multilabel_precision_recall_curve(
        ...     preds, target, num_labels=3, thresholds=5
        ... )  # doctest: +NORMALIZE_WHITESPACE
        (tensor([[0.5000, 0.5000, 1.0000, 1.0000, 0.0000, 1.0000],
                 [0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000],
                 [0.7500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000]]),
         tensor([[1.0000, 0.5000, 0.5000, 0.5000, 0.0000, 0.0000],
                 [1.0000, 1.0000, 1.0000, 0.0000, 0.0000, 0.0000],
                 [1.0000, 0.6667, 0.3333, 0.3333, 0.0000, 0.0000]]),
         tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))

    )