code: def _retrieval_recall_at_fixed_precision(
      precision: Tensor,
      recall: Tensor,
      top_k: Tensor,
      min_precision: float,
  ) -> Tuple[Tensor, Tensor]:
      """Compute maximum recall with condition that corresponding precision >= `min_precision`.
  
      Args:
          top_k: tensor with all possible k
          precision: tensor with all values precisions@k for k from top_k tensor
          recall: tensor with all values recall@k for k from top_k tensor
          min_precision: float value specifying minimum precision threshold.
  
      Returns:
          Maximum recall value, corresponding it best k
  
      """
      try:
          max_recall, best_k = max(
              (r, k)
              for p, r, k in zip(precision, recall, top_k)
              if p >= min_precision
          )
  
      except ValueError:
          max_recall = torch.tensor(
              0.0, device=recall.device, dtype=recall.dtype
          )
          best_k = torch.tensor(len(top_k))
  
      if max_recall == 0.0:
          best_k = torch.tensor(
              len(top_k), device=top_k.device, dtype=top_k.dtype
          )
  
      return max_recall, best_k
  
imports: from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics import Metric
  from torchmetrics.functional.retrieval.precision_recall_curve import retrieval_precision_recall_curve
  from torchmetrics.retrieval.base import _retrieval_aggregate
  from torchmetrics.utilities.checks import _check_retrieval_inputs
  from torchmetrics.utilities.data import _flexible_bincount, dim_zero_cat
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_curve
label_desc: Use image node_id 1 for calling a global function with example usage: _retrieval_recall_at_fixed_precision(precision, recall, top_k, min_precision) and returns: max_recall,  best_k