code: def accuracy_example() -> tuple:
      """Plot Accuracy example."""
      from torchmetrics.classification import MulticlassAccuracy
  
      p = lambda: torch.randn(20, 5)
      t = lambda: torch.randint(5, (20,))
  
      # plot single value
      metric = MulticlassAccuracy(num_classes=5)
      metric.update(p(), t())
      fig, ax = metric.plot()
  
      # plot a value per class
      metric = MulticlassAccuracy(num_classes=5, average=None)
      metric.update(p(), t())
      fig, ax = metric.plot()
  
      # plot two values as a series
      metric = MulticlassAccuracy(num_classes=5)
      val1 = metric(p(), t())
      val2 = metric(p(), t())
      fig, ax = metric.plot([val1, val2])
  
      # plot a series of values per class
      metric = MulticlassAccuracy(num_classes=5, average=None)
      val1 = metric(p(), t())
      val2 = metric(p(), t())
      fig, ax = metric.plot([val1, val2])
      return fig, ax
  
imports: import argparse
  import matplotlib.pyplot
  import torch
label_desc: Use image node_id 8 for calling a global function with example usage: accuracy_example() and returns: fig,  ax