from torch import randint
from torchmetrics.classification import MulticlassMatthewsCorrCoef
metric = MulticlassMatthewsCorrCoef(num_classes=3)
values = []
for _ in range(20):
    values.append(metric(randint(3, (20,)), randint(3, (20,))))
fig_, ax_ = metric.plot(values)