


for epoch in range(100):
    train(...)
    validate(...)
    scheduler.step()



get_last_lr()
  Return last computed learning rate by current scheduler.

load_state_dict(state_dict)
  Loads the schedulers state.

  Parameters:
     **state_dict** (*dict*) -- scheduler state. Should be an
     object returned from a call to "state_dict()".

print_lr(is_verbose, group, lr, epoch=None)
  Display the current learning rate.

state_dict()
  Returns the state of the scheduler as a "dict".

  It contains an entry for every variable in self.__dict__ which
  is not the optimizer.
