fastmri_examples/unet/run_pretrained_unet_inference.py [128:165]:
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        ),
        type=str,
        help="Model to run",
    )
    parser.add_argument(
        "--device",
        default="cuda",
        type=str,
        help="Model to run",
    )
    parser.add_argument(
        "--state_dict_file",
        default=None,
        type=Path,
        help="Path to saved state_dict (will download if not provided)",
    )
    parser.add_argument(
        "--data_path",
        type=Path,
        required=True,
        help="Path to subsampled data",
    )
    parser.add_argument(
        "--output_path",
        type=Path,
        required=True,
        help="Path for saving reconstructions",
    )

    args = parser.parse_args()

    run_inference(
        args.challenge,
        args.state_dict_file,
        args.data_path,
        args.output_path,
        torch.device(args.device),
    )
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fastmri_examples/varnet/run_pretrained_varnet_inference.py [113:150]:
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        ),
        type=str,
        help="Model to run",
    )
    parser.add_argument(
        "--device",
        default="cuda",
        type=str,
        help="Model to run",
    )
    parser.add_argument(
        "--state_dict_file",
        default=None,
        type=Path,
        help="Path to saved state_dict (will download if not provided)",
    )
    parser.add_argument(
        "--data_path",
        type=Path,
        required=True,
        help="Path to subsampled data",
    )
    parser.add_argument(
        "--output_path",
        type=Path,
        required=True,
        help="Path for saving reconstructions",
    )

    args = parser.parse_args()

    run_inference(
        args.challenge,
        args.state_dict_file,
        args.data_path,
        args.output_path,
        torch.device(args.device),
    )
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