Running Neptune.ai in a loop

so i created a for loop so I can run various batch sizes, where each loop will open and close a neptune run. The first time runs fine, but the following runs, the accuracy doesn't record into neptune, and python does not throw an error? Can anyone think what the problem may be?

for i in range(len(percentage)):

    run = neptune.init(
        project="xxx",
        api_token="xxx",
    )

    epochs = 600
    batch_perc = percentage[i]
    lr = 0.001
    sb = 64 #round((43249*batch_perc)*0.00185)
    params = {
        'lr': lr,
        'bs': sb,
        'epochs': epochs,
        'batch %': batch_perc
    }
    run['parameters'] = params

    torch.manual_seed(12345)
    td = 43249 * batch_perc
    vd = 0.1*(43249 - td) + td

    train_dataset = dataset[:round(td)]
    val_dataset = dataset[round(td):round(vd)]
    test_dataset = dataset[round(vd):]

    print(f'Number of training graphs: {len(train_dataset)}')
    run['train'] = len(train_dataset)
    print(f'Number of validation graphs: {len(val_dataset)}')
    run['val'] = len(val_dataset)
    print(f'Number of test graphs: {len(test_dataset)}')
    run['test'] = len(test_dataset)

    train_loader = DataLoader(train_dataset, batch_size=sb, shuffle=True)
    val_loader = DataLoader(val_dataset, batch_size=sb, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)

    model = GCN(hidden_channels=64).to(device)

    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
    criterion = torch.nn.CrossEntropyLoss()

    for epoch in range(1, epochs):
        train()
        train_acc = test(train_loader)
        run['training/batch/acc'].log(train_acc)
        val_acc = test(val_loader)
        run['training/batch/val'].log(val_acc)