code: def multiclass_precision_recall_curve(
      preds: Tensor,
      target: Tensor,
      num_classes: int,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      average: Optional[Literal["micro", "macro"]] = None,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
  ) -> Union[
      Tuple[Tensor, Tensor, Tensor],
      Tuple[List[Tensor], List[Tensor], List[Tensor]],
  ]:
      r"""Compute the precision-recall curve for multiclass tasks.
  
      The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the
      tradeoff between the two values can been seen.
  
      Accepts the following input tensors:
  
      - ``preds`` (float tensor): ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each
        observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
        softmax per sample.
      - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
        only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified).
  
      Additional dimension ``...`` will be flattened into the batch dimension.
  
      The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
      that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
      non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
      argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
      size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory).
  
      Args:
          preds: Tensor with predictions
          target: Tensor with true labels
          num_classes: Integer specifying the number of classes
          thresholds:
              Can be one of:
  
              - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
                all the data. Most accurate but also most memory consuming approach.
              - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
                0 to 1 as bins for the calculation.
              - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
              - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
                bins for the calculation.
  
          average:
              If aggregation of curves should be applied. By default, the curves are not aggregated and a curve for
              each class is returned. If `average` is set to ``"micro"``, the metric will aggregate the curves by one hot
              encoding the targets and flattening the predictions, considering all classes jointly as a binary problem.
              If `average` is set to ``"macro"``, the metric will aggregate the curves by first interpolating the curves
              from each class at a combined set of thresholds and then average over the classwise interpolated curves.
              See `averaging curve objects`_ for more info on the different averaging methods.
          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 class is returned with an 1d tensor of size (n_thresholds+1, )
            with precision values (length may differ between classes). If `thresholds` is set to something else,
            then a single 2d tensor of size (n_classes, n_thresholds+1) with precision values is returned.
          - recall: if `thresholds=None` a list for each class is returned with an 1d tensor of size (n_thresholds+1, )
            with recall values (length may differ between classes). If `thresholds` is set to something else,
            then a single 2d tensor of size (n_classes, n_thresholds+1) with recall values is returned.
          - thresholds: if `thresholds=None` a list for each class is returned with an 1d tensor of size (n_thresholds, )
            with increasing threshold values (length may differ between classes). If `threshold` is set to something else,
            then a single 1d tensor of size (n_thresholds, ) is returned with shared threshold values for all classes.
  
      Example:
          >>> from torchmetrics.functional.classification import multiclass_precision_recall_curve
          >>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
          ...                       [0.05, 0.75, 0.05, 0.05, 0.05],
          ...                       [0.05, 0.05, 0.75, 0.05, 0.05],
          ...                       [0.05, 0.05, 0.05, 0.75, 0.05]])
          >>> target = torch.tensor([0, 1, 3, 2])
          >>> precision, recall, thresholds = multiclass_precision_recall_curve(
          ...    preds, target, num_classes=5, thresholds=None
          ... )
          >>> precision  # doctest: +NORMALIZE_WHITESPACE
          [tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]),
           tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])]
          >>> recall
          [tensor([1., 1., 0.]), tensor([1., 1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])]
          >>> thresholds
          [tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]),
           tensor([0.0500])]
          >>> multiclass_precision_recall_curve(
          ...     preds, target, num_classes=5, thresholds=5
          ... )  # doctest: +NORMALIZE_WHITESPACE
          (tensor([[0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
                   [0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
                   [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
                   [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
                   [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]),
           tensor([[1., 1., 1., 1., 0., 0.],
                   [1., 1., 1., 1., 0., 0.],
                   [1., 0., 0., 0., 0., 0.],
                   [1., 0., 0., 0., 0., 0.],
                   [0., 0., 0., 0., 0., 0.]]),
           tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))
  
      """
      if validate_args:
          _multiclass_precision_recall_curve_arg_validation(
              num_classes, thresholds, ignore_index, average
          )
          _multiclass_precision_recall_curve_tensor_validation(
              preds, target, num_classes, ignore_index
          )
      (
          preds,
          target,
          thresholds,
      ) = _multiclass_precision_recall_curve_format(
          preds,
          target,
          num_classes,
          thresholds,
          ignore_index,
          average,
      )
      state = _multiclass_precision_recall_curve_update(
          preds, target, num_classes, thresholds, average
      )
      return _multiclass_precision_recall_curve_compute(
          state, num_classes, thresholds, average
      )
  
imports: from typing import List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor, tensor
  from torch.nn import functional
  from typing_extensions import Literal
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.compute import _safe_divide, interp
  from torchmetrics.utilities.data import _bincount, _cumsum
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 18 for calling a global function with example usage: multiclass_precision_recall_curve(preds, target, num_classes, thresholds, average, ignore_index, validate_args) and returns: _multiclass_precision_recall_curve_compute