from torch import rand, randint
from torchmetrics.classification import BinaryCalibrationError
metric = BinaryCalibrationError(n_bins=2, norm='l1')
values = [ ]
for _ in range(10):
    values.append(metric(rand(10), randint(2,(10,))))
fig_, ax_ = metric.plot(values)