multiplicative factor reaches to 1. Default: 5.
  * **last_epoch** (*int*) -- The index of the last epoch.
    Default: -1.

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

-[ Example ]-



Assuming optimizer uses lr = 0.05 for all groups
lr = 0.025    if epoch == 0
lr = 0.03125  if epoch == 1
lr = 0.0375   if epoch == 2
lr = 0.04375  if epoch == 3
lr = 0.05    if epoch >= 4
scheduler = LinearLR(self.opt, start_factor=0.5, total_iters=4)
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()".
