rate at each step becomes:
  \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} -
  \eta_{min})\left(1 +
  \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right)

It has been proposed in SGDR: Stochastic Gradient Descent with Warm
   Restarts. Note that this only implements the cosine annealing part
   of SGDR, and not the restarts.
Parameters:
      * optimizer (Optimizer) -- Wrapped optimizer.
  * **T_max** (*int*) -- Maximum number of iterations.

  * **eta_min** (*float*) -- Minimum learning rate. Default: 0.

  * **last_epoch** (*int*) -- The index of last epoch. Default:
    -1.

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

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
