class_docstr: Compute the highest possible recall value given the minimum precision thresholds provided.
  
  This is done by first calculating the precision-recall curve for different thresholds and the find the recall for
  a given precision level.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(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 sigmoid per element.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing
    ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
    1 always encodes the positive class.
  
  .. note::
     Additional dimension ``...`` will be flattened into the batch dimension.
  
  As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:
  
  - ``recall`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum recall for the
    given precision level per class
  - ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold
    level per class
  
  .. note::
     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_{labels})` (constant memory).
  
  Args:
      num_labels: Integer specifying the number of labels
      min_precision: float value specifying minimum precision threshold.
      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 :class:`~torch.Tensor` of floats, will use the indicated thresholds in the tensor as
            bins for the 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:
      >>> from torch import tensor
      >>> from torchmetrics.classification import MultilabelRecallAtFixedPrecision
      >>> preds = 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 = tensor([[1, 0, 1],
      ...                  [0, 0, 0],
      ...                  [0, 1, 1],
      ...                  [1, 1, 1]])
      >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5, thresholds=None)
      >>> metric(preds, target)
      (tensor([1., 1., 1.]), tensor([0.0500, 0.5500, 0.0500]))
      >>> mlrafp = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5, thresholds=5)
      >>> mlrafp(preds, target)
      (tensor([1., 1., 1.]), tensor([0.0000, 0.5000, 0.0000]))
class_docstr_tok: ["Compute","the","highest","possible","recall","value","given","the","minimum","precision","thresholds","provided",".","This","is","done","by","first","calculating","the","precision-recall","curve","for","different","thresholds","and","the","find","the","recall","for","a","given","precision","level",".","As","input","to","``","forward","``","and","``","update","``","the","metric","accepts","the","following","input",":","-","``","preds","``","(",":","class",":","`","~torch.Tensor","`",")",":","A","float","tensor","of","shape","``","(","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","sigmoid","per","element",".","-","``","target","``","(",":","class",":","`","~torch.Tensor","`",")",":","An","int","tensor","of","shape","``","(","N",",","...",")","``",".","Target","should","be","a","tensor","containing","ground","truth","labels",",","and","therefore","only","contain","{","0,1","}","values","(","except","if","`","ignore_index","`","is","specified",")",".","The","value","1","always","encodes","the","positive","class",".","..","note",":",":","Additional","dimension","``","...","``","will","be","flattened","into","the","batch","dimension",".","As","output","to","``","forward","``","and","``","compute","``","the","metric","returns","a","tuple","of","either","2","tensors","or","2","lists","containing",":","-","``","recall","``","(",":","class",":","`","~torch.Tensor","`",")",":","A","1d","tensor","of","size","``","(","n_classes",",",")","``","with","the","maximum","recall","for","the","given","precision","level","per","class","-","``","threshold","``","(",":","class",":","`","~torch.Tensor","`",")",":","A","1d","tensor","of","size","``","(","n_classes",",",")","``","with","the","corresponding","threshold","level","per","class","..","note",":",":","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_","{","labels","}",")","`","(","constant","memory",")",".","Args",":","num_labels",":","Integer","specifying","the","number","of","labels","min_precision",":","float","value","specifying","minimum","precision","threshold",".","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",":","class",":","`","~torch.Tensor","`","of","floats",",","will","use","the","indicated","thresholds","in","the","tensor","as","bins","for","the","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",":",">",">",">","from","torch","import","tensor",">",">",">","from","torchmetrics.classification","import","MultilabelRecallAtFixedPrecision",">",">",">","preds","=","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","=","tensor","(","[","[","1",",","0",",","1","]",",","...","[","0",",","0",",","0","]",",","...","[","0",",","1",",","1","]",",","...","[","1",",","1",",","1","]","]",")",">",">",">","metric","=","MultilabelRecallAtFixedPrecision","(","num_labels=3",",","min_precision=0.5",",","thresholds=None",")",">",">",">","metric","(","preds",",","target",")","(","tensor","(","[","1.",",","1.",",","1",".","]",")",",","tensor","(","[","0.0500",",","0.5500",",","0.0500","]",")",")",">",">",">","mlrafp","=","MultilabelRecallAtFixedPrecision","(","num_labels=3",",","min_precision=0.5",",","thresholds=5",")",">",">",">","mlrafp","(","preds",",","target",")","(","tensor","(","[","1.",",","1.",",","1",".","]",")",",","tensor","(","[","0.0000",",","0.5000",",","0.0000","]",")",")"]
docstr: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure object and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> from torch import rand, randint
      >>> # Example plotting a single value
      >>> from torchmetrics.classification import MultilabelRecallAtFixedPrecision
      >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5)
      >>> metric.update(rand(20, 3), randint(2, (20, 3)))
      >>> fig_, ax_ = metric.plot()  # the returned plot only shows the maximum recall value by default
  
  .. plot::
      :scale: 75
  
      >>> from torch import rand, randint
      >>> # Example plotting multiple values
      >>> from torchmetrics.classification import MultilabelRecallAtFixedPrecision
      >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5)
      >>> values = [ ]
      >>> for _ in range(10):
      ...     # we index by 0 such that only the maximum recall value is plotted
      ...     values.append(metric(rand(20, 3), randint(2, (20, 3)))[0])
      >>> fig_, ax_ = metric.plot(values)
docstr_tok: ["Plot","a","single","or","multiple","values","from","the","metric",".","Args",":","val",":","Either","a","single","result","from","calling","`","metric.forward","`","or","`","metric.compute","`","or","a","list","of","these","results",".","If","no","value","is","provided",",","will","automatically","call","`","metric.compute","`","and","plot","that","result",".","ax",":","An","matplotlib","axis","object",".","If","provided","will","add","plot","to","that","axis","Returns",":","Figure","object","and","Axes","object","Raises",":","ModuleNotFoundError",":","If","`","matplotlib","`","is","not","installed","..","plot",":",":",":","scale",":","75",">",">",">","from","torch","import","rand",",","randint",">",">",">","#","Example","plotting","a","single","value",">",">",">","from","torchmetrics.classification","import","MultilabelRecallAtFixedPrecision",">",">",">","metric","=","MultilabelRecallAtFixedPrecision","(","num_labels=3",",","min_precision=0.5",")",">",">",">","metric.update","(","rand","(","20",",","3",")",",","randint","(","2",",","(","20",",","3",")",")",")",">",">",">","fig_",",","ax_","=","metric.plot","(",")","#","the","returned","plot","only","shows","the","maximum","recall","value","by","default","..","plot",":",":",":","scale",":","75",">",">",">","from","torch","import","rand",",","randint",">",">",">","#","Example","plotting","multiple","values",">",">",">","from","torchmetrics.classification","import","MultilabelRecallAtFixedPrecision",">",">",">","metric","=","MultilabelRecallAtFixedPrecision","(","num_labels=3",",","min_precision=0.5",")",">",">",">","values","=","[","]",">",">",">","for","_","in","range","(","10",")",":","...","#","we","index","by","0","such","that","only","the","maximum","recall","value","is","plotted","...","values.append","(","metric","(","rand","(","20",",","3",")",",","randint","(","2",",","(","20",",","3",")",")",")","[","0","]",")",">",">",">","fig_",",","ax_","=","metric.plot","(","values",")"]
code: def plot(  # type: ignore[override]
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure object and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> from torch import rand, randint
          >>> # Example plotting a single value
          >>> from torchmetrics.classification import MultilabelRecallAtFixedPrecision
          >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5)
          >>> metric.update(rand(20, 3), randint(2, (20, 3)))
          >>> fig_, ax_ = metric.plot()  # the returned plot only shows the maximum recall value by default
  
      .. plot::
          :scale: 75
  
          >>> from torch import rand, randint
          >>> # Example plotting multiple values
          >>> from torchmetrics.classification import MultilabelRecallAtFixedPrecision
          >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5)
          >>> values = [ ]
          >>> for _ in range(10):
          ...     # we index by 0 such that only the maximum recall value is plotted
          ...     values.append(metric(rand(20, 3), randint(2, (20, 3)))[0])
          >>> fig_, ax_ = metric.plot(values)
  
      """
      val = (
          val or self.compute()[0]
      )  # by default we select the maximum recall value to plot
      return self._plot(val, ax)
  
imports: from typing import Any, List, Optional, Sequence, Tuple, Type, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.classification.precision_recall_curve import BinaryPrecisionRecallCurve, MulticlassPrecisionRecallCurve, MultilabelPrecisionRecallCurve
  from torchmetrics.functional.classification.recall_fixed_precision import _binary_recall_at_fixed_precision_arg_validation, _binary_recall_at_fixed_precision_compute, _multiclass_recall_at_fixed_precision_arg_compute, _multiclass_recall_at_fixed_precision_arg_validation, _multilabel_recall_at_fixed_precision_arg_compute, _multilabel_recall_at_fixed_precision_arg_validation
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.enums import ClassificationTask
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 3 for calling the MultilabelRecallAtFixedPrecision obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self