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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 process_train_set(hdf5_file, train_archive, patch_archive, n_train, wnid_map, shuffle_seed=None):
 """Process the ILSVRC2010 training set. Parameters hdf5_file : :class:`h5py.File` instance HDF5 file handle to which to write. Assumes `features`, `targets` and `filenames` already exist and have first dimension larger than `n_train`. train_archive : str or file-like object Filename or file handle for the TAR archive of training images. patch_archive : str or file-like object Filename or file handle for the TAR archive of patch images. n_train : int The number of items in the training set. wnid_map : dict A dictionary mapping WordNet IDs to class indices. shuffle_seed : int or sequence, optional Seed for a NumPy random number generator that permutes the training set on disk. If `None`, no permutation is performed (this is the default). """

    producer = partial(train_set_producer, train_archive=train_archive,
                       patch_archive=patch_archive, wnid_map=wnid_map)
    consumer = partial(image_consumer, hdf5_file=hdf5_file,
                       num_expected=n_train, shuffle_seed=shuffle_seed)
    producer_consumer(producer, consumer)