CYCLICLR Plotting range of Learning Rate for Accuracy

I want to plot a graph for accuracy for base_lr= 0.01 and max_lr=0.1, similar given in attached photo

accuracy = []
for cycle in range(CYCLES):            
            subset = unlabeled_set[:SUBSET]
            # Model - create new instance for every cycle so that it resets
            with torch.cuda.device(CUDA_VISIBLE_DEVICES):            
                resnet18    = resnet.ResNet18(num_classes=NO_CLASSES).cuda()
            models = resnet18
            torch.backends.cudnn.benchmark = True
            models = torch.nn.DataParallel(models, device_ids=[0])
            # Loss, criterion and scheduler (re)initialization
            criterion      = nn.CrossEntropyLoss(reduction='none')
            optim_backbone = optim.SGD(models.parameters(), lr=LR, 
                momentum=MOMENTUM, weight_decay=WDECAY)
            schedulers = torch.optim.lr_scheduler.CyclicLR(optim_backbone, base_lr= 0.01, max_lr=0.1, step_size_up=2000, step_size_down=None, mode='triangular')
optimizers =  optim_backbone
            #schedulers = sched_backbone
            train(models, criterion, optimizers, schedulers, dataloaders, Epochs)
            acc = test(models, dataloaders, mode='test')
            print('Trial {}/{} || Cycle {}/{} || Label set size {}: Test acc {}'.format(trial+1, TRIALS, cycle+1, CYCLES, len(labeled_set), acc))
            #np.array([method, trial+1, TRIALS, cycle+1, CYCLES, len(labeled_set), acc]).tofile(results, sep=" ")
            if cycle == (CYCLES-1):
                # Reached final training cycle
            # Get the indices of the unlabeled samples to train on next cycle
            arg = query_samples(models, data_unlabeled, subset, labeled_set, cycle)

            # Update the labeled dataset and the unlabeled dataset, respectively
            labeled_set += list(torch.tensor(subset)[arg][-ADDENDUM:].numpy())
            listd = list(torch.tensor(subset)[arg][:-ADDENDUM].numpy())
            unlabeled_set = listd + unlabeled_set[SUBSET:]
            # Create a new dataloader for the updated labeled dataset
            dataloaders['train'] = DataLoader(data_train, batch_size=BATCH, 

Screenshot from 2022-09-11 20-30-36