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.
  
  This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
  ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
  :class:`~torchmetrics.classification.BinaryRecallAtFixedPrecision`,
  :class:`~torchmetrics.classification.MulticlassRecallAtFixedPrecision` and
  :class:`~torchmetrics.classification.MultilabelRecallAtFixedPrecision` for the specific details of each argument
  influence and examples.
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docstr: Initialize task metric.
docstr_tok: ["Initialize","task","metric","."]
code: def __new__(  # type: ignore[misc]
      cls: Type["RecallAtFixedPrecision"],
      task: Literal["binary", "multiclass", "multilabel"],
      min_precision: float,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      num_classes: Optional[int] = None,
      num_labels: Optional[int] = None,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
      **kwargs: Any,
  ) -> Metric:
      """Initialize task metric."""
      task = ClassificationTask.from_str(task)
      if task == ClassificationTask.BINARY:
          return BinaryRecallAtFixedPrecision(
              min_precision,
              thresholds,
              ignore_index,
              validate_args,
              **kwargs,
          )
      if task == ClassificationTask.MULTICLASS:
          if not isinstance(num_classes, int):
              raise ValueError(
                  f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`"
              )
          return MulticlassRecallAtFixedPrecision(
              num_classes,
              min_precision,
              thresholds,
              ignore_index,
              validate_args,
              **kwargs,
          )
      if task == ClassificationTask.MULTILABEL:
          if not isinstance(num_labels, int):
              raise ValueError(
                  f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`"
              )
          return MultilabelRecallAtFixedPrecision(
              num_labels,
              min_precision,
              thresholds,
              ignore_index,
              validate_args,
              **kwargs,
          )
      raise ValueError(f"Task {task} not supported!")
  
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 for calling the RecallAtFixedPrecision obj's underlying member method code with example usage: obj.__new__(cls, task, min_precision, thresholds, num_classes, num_labels, ignore_index, validate_args) and returns: BinaryRecallAtFixedPrecision, MulticlassRecallAtFixedPrecision, MultilabelRecallAtFixedPrecision