train_curve.py [378:401]:
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    trainset, trainloader, testset, testloader = data

    # Unpack optimization parameters
    criterion, optimizer, scheduler = opt_params

    device = "cuda" if torch.cuda.is_available() else "cpu"
    net = net.to(device)

    if device == "cuda":
        cudnn.benchmark = True
    net = torch.nn.DataParallel(net)

    start_epoch = 0

    metric_dict = {"acc": [], "alpha": []}
    if regime_params["regime"] == "quantized":
        metric_dict["num_bits"] = []
    if regime_params["regime"] in (
        "sparse",
        "lec",
        "us",
        "ns",
    ):
        metric_dict["sparsity"] = []
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train_indep.py [403:426]:
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    trainset, trainloader, testset, testloader = data

    # Unpack optimizer parameters
    criterion, optimizer, scheduler = opt_params

    device = "cuda" if torch.cuda.is_available() else "cpu"
    net = net.to(device)

    if device == "cuda":
        cudnn.benchmark = True
    net = torch.nn.DataParallel(net)

    start_epoch = 0

    metric_dict = {"acc": [], "alpha": []}
    if regime_params["regime"] == "quantized":
        metric_dict["num_bits"] = []
    if regime_params["regime"] in (
        "sparse",
        "lec",
        "us",
        "ns",
    ):
        metric_dict["sparsity"] = []
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