In write_to_hdf5, the elif isinstance(data, Array): block is skipped if dataset_path in f is True. This means JAX Arrays are not converted to NumPy arrays when overwriting an existing dataset, which can cause h5py to fail or behave unexpectedly. Changing elif to if ensures the conversion always runs.

Using inspect.signature(model_meta_cls.__init__) and then param_names.remove("self") will crash if model_meta_cls does not explicitly define __init__ (like EccentricityMattersCore), because object.__init__ has no self parameter in its signature (or raises ValueError). Using inspect.signature(model_meta_cls) is the standard, safe way to get the constructor signature without self and works correctly for all classes.

Defining a positional argument "options" with nargs=argparse.REMAINDER before "file" causes "options" to consume all remaining arguments (including the file path), leaving "file" unmatched and causing the script to always fail with a required argument error. Removing the "options" argument from the parser allows parse_known_args() to correctly capture all unrecognized options in the options list while successfully parsing "file".

    parser.add_argument("file", help="Path to the HDF5 file to repack")
    args, options = parser.parse_known_args()

The notebook assumes that all training/production datasets (like /acceptances/global/train, loss, /chains/train/...) are always present in the HDF5 file. If any phase was skipped or had no data, these datasets won't exist, causing read_from_hdf5 to raise a ValueError and crash the entire automated report generation. Consider checking if the datasets exist in the file before reading them.