code: def roc_example() -> tuple:
      """Plot roc metric."""
      from torchmetrics.classification import (
          BinaryROC,
          MulticlassROC,
          MultilabelROC,
      )
  
      p = lambda: torch.rand(20)
      t = lambda: torch.randint(2, (20,))
  
      metric = BinaryROC()
      metric.update(p(), t())
      fig, ax = metric.plot()
  
      p = lambda: torch.randn(200, 5)
      t = lambda: torch.randint(5, (200,))
  
      metric = MulticlassROC(5)
      metric.update(p(), t())
      fig, ax = metric.plot()
  
      p = lambda: torch.rand(20, 2)
      t = lambda: torch.randint(2, (20, 2))
  
      metric = MultilabelROC(2)
      metric.update(p(), t())
  
      return fig, ax
  
imports: import argparse
  import matplotlib.pyplot
  import torch
label_desc: Use image node_id 19 for calling a global function with example usage: roc_example() and returns: fig,  ax