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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def load(cls, path, name):
 """Imports the specified ``proteindb`` file from the hard disk. :param path: filedirectory of the ``proteindb`` file :param name: filename without the file extension ".proteindb" .. note:: this generates rather large files, which actually take longer to import than to newly generate. Maybe saving / loading should be limited to the protein database whitout in silico digestion information. """


        filepath = aux.joinpath(path, name + '.proteindb')
        with zipfile.ZipFile(filepath, 'r', allowZip64=True) as containerZip:
            #Convert the zipfile data into a str object, necessary since
            #containerZip.read() returns a bytes object.
            proteinsString = io.TextIOWrapper(containerZip.open('proteins'),
                                              encoding='utf-8'
                                              ).read()
            peptidesString = io.TextIOWrapper(containerZip.open('peptides'),
                                              encoding='utf-8'
                                              ).read()
            infoString = io.TextIOWrapper(containerZip.open('info'),
                                          encoding='utf-8'
                                          ).read()
        newInstance = cls()
        newInstance.proteins = json.loads(proteinsString,
                                          object_hook=ProteinSequence.jsonHook)
        newInstance.peptides = json.loads(peptidesString,
                                          object_hook=PeptideSequence.jsonHook)
        newInstance.info.update(json.loads(infoString))
        return newInstance