from torch import randn, randint
from torchmetrics.classification import MulticlassCalibrationError
metric = MulticlassCalibrationError(num_classes=3, n_bins=3, norm='l1')
metric.update(randn(20,3).softmax(dim=-1), randint(3, (20,)))
fig_, ax_ = metric.plot()