import torch
_ = torch.manual_seed(42)
from torchmetrics.image import RelativeAverageSpectralError
metric = RelativeAverageSpectralError()
values = [ ]
for _ in range(10):
    values.append(metric(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16)))
fig_, ax_ = metric.plot(values)