from torch import rand, randint
from torchmetrics.classification import MultilabelPrecision
metric = MultilabelPrecision(num_labels=3)
values = [ ]
for _ in range(10):
    values.append(metric(randint(2, (20, 3)), randint(2, (20, 3))))
fig_, ax_ = metric.plot(values)