code: def process_train_set(hdf5_file, train_archive, patch_archive, n_train,
                        wnid_map, shuffle_seed=None):
      
      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)
docs: 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).