How to avoid per-process memory duplication when using rapidgzip with multiprocessing in Python?

I have a Python file:

from concurrent.futures import ProcessPoolExecutor
import tarfile, rapidgzip

def processNdjson(ndjsonName):
    with rapidgzip.open(inTarDir) as myZip:
        myZip.import_index(rapidgzipDir)
        with tarfile.open(fileobj=myZip, mode="r:*") as f:
            member = f.getmember(ndjsonName)
            dataFile = f.extractfile(member)
            for oneLine in dataFile:
                # process oneLine here

if __name__ == "__main__":
    inTarDir = ...
    rapidgzipDir = ...
    nCore = 5
    ndjsonNames = ["name1.ndjson", "name2.ndjson"]

    with ProcessPoolExecutor(nCore) as pool:
        results = pool.map(worker, ndjsonNames)

Above,

• inTarDir is the directory to a .tar.gz file that contains multiple .ndjson files.
• rapidgzipDir is the pre-index file to be used by rapidgzip. This allows fast random access and is a drop-in replacement for the built-in Python gzip.GzipFile.
• Each process will

with rapidgzip.open(inTarDir) as myZip:
    myZip.import_index(rapidgzipDir)
    with tarfile.open(fileobj=myZip, mode="r:*") as f:

My concern: each command myZip.import_index(rapidgzipDir) will take up a certain amount of RAM (for example, 500MB for a 20GB .tar.gz file). This will grow linearly with nCore.

Is there any way to avoid the multiple import of the same rapidgzipDir?