code: def mean_average_precision() -> tuple:
      """Plot MAP metric."""
      from torchmetrics.detection.mean_ap import MeanAveragePrecision
  
      preds = lambda: [
          {
              "boxes": torch.tensor([[258.0, 41.0, 606.0, 285.0]])
              + torch.randint(10, (1, 4)),
              "scores": torch.tensor([0.536]) + 0.1 * torch.rand(1),
              "labels": torch.tensor([0]),
          }
      ]
      target = [
          {
              "boxes": torch.tensor([[214.0, 41.0, 562.0, 285.0]]),
              "labels": torch.tensor([0]),
          }
      ]
  
      # plot single value
      metric = MeanAveragePrecision()
      metric.update(preds(), target)
      fig, ax = metric.plot()
  
      # plot multiple values
      metric = MeanAveragePrecision()
      vals = [metric(preds(), target) for _ in range(10)]
      fig, ax = metric.plot(vals)
  
      return fig, ax
  
imports: import argparse
  import matplotlib.pyplot
  import torch
label_desc: Use image node_id 18 for calling a global function with example usage: mean_average_precision() and returns: fig,  ax