optimizer = optim.Adam(model.parameters(), lr=1.)
# initial lr should be 1
clr = cyclical_lr(step_size, min_lr=0.001, max_lr=1, scale_func=clr_func, scale_md='iterations')
scheduler = lr_scheduler.LambdaLR(optimizer, [clr])

torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=1e-8, T_mult=2)
# T_max < training epochs if you want to use restart policy

from models.BaseNet_class import BaseNet
# some configs setting
configs = {
    'model': net,
    'opt': opt,
    'criterion': nn.CrossEntropyLoss(),
    'dataloaders': ...,
    'data_sz': ...,
    'lrs_decay': lr_scheduler.StepLR(opt, step_size=50),
    'prt_freq': 5,
    'epochs': 500,
}
sub_model = BaseNet(configs)
# train and test
sub_model.train_m()
sub_model.test_m()