scalars. A lower bound on the learning rate of all param
        groups or each group respectively. Default: 0.
  * **eps** (*float*) -- Minimal decay applied to lr. If the
    difference between new and old lr is smaller than eps, the
    update is ignored. Default: 1e-8.

  * **verbose** (*bool*) -- If "True", prints a message to stdout
    for each update. Default: "False".

-[ Example ]-



optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
scheduler = ReduceLROnPlateau(optimizer, 'min')
for epoch in range(10):
    train(...)
    val_loss = validate(...)
    # Note that step should be called after validate()
    scheduler.step(val_loss)


