model_inversion.py [251:263]:
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        num_classes=2, root=args.data_folder, regression=regression)
    if args.subsample > 0:
        data = dataloading.subsample(data, args.subsample)

    if args.weights_file is not None:
        all_weights = torch.load(args.weights_file)
    else:
        all_weights = [torch.ones(len(data["targets"]))]

    results = []
    for it, weights in enumerate(all_weights):
        if len(all_weights) > 1:
            logging.info(f"Iteration {it} weights for model inversion.")
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private_model_inversion.py [107:120]:
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        num_classes=2, root=args.data_folder, regression=regression)

    if args.subsample > 0:
        data = dataloading.subsample(data, args.subsample)

    if args.weights_file is not None:
        all_weights = torch.load(args.weights_file)
    else:
        all_weights = [torch.ones(len(data["targets"]))]

    results = []
    for it, weights in enumerate(all_weights):
        if len(all_weights) > 1:
            logging.info(f"Iteration {it} weights for model inversion.")
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