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, ...)``. 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 the following output:
  
  - ``recall`` (:class:`~torch.Tensor`): A scalar tensor with the maximum recall for the given precision level
  - ``threshold`` (:class:`~torch.Tensor`): A scalar tensor with the corresponding threshold level
  
  .. 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})` (constant memory).
  
  Args:
      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 BinaryRecallAtFixedPrecision
      >>> preds = tensor([0, 0.5, 0.7, 0.8])
      >>> target = tensor([0, 1, 1, 0])
      >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5, thresholds=None)
      >>> metric(preds, target)
      (tensor(1.), tensor(0.5000))
      >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5, thresholds=5)
      >>> metric(preds, target)
      (tensor(1.), tensor(0.5000))
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code: def __init__(
      self,
      min_precision: float,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
      **kwargs: Any,
  ) -> None:
      super().__init__(
          thresholds, ignore_index, validate_args=False, **kwargs
      )
      if validate_args:
          _binary_recall_at_fixed_precision_arg_validation(
              min_precision, thresholds, ignore_index
          )
      self.validate_args = validate_args
      self.min_precision = min_precision
  
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 1 to create a new BinaryRecallAtFixedPrecision object from inherited base classes: BinaryPrecisionRecallCurve with example: obj = BinaryRecallAtFixedPrecision(min_precision, thresholds, ignore_index, validate_args)