def __call__(self, a):
        m = _get_backing_memmap(a)
        if m is not None:
            # a is already backed by a memmap file, let's reuse it directly
            return _reduce_memmap_backed(a, m)

        if (not a.dtype.hasobject
                and self._max_nbytes is not None
                and a.nbytes > self._max_nbytes):
            # check that the folder exists (lazily create the pool temp folder
            # if required)
            try:
                os.makedirs(self._temp_folder)
                os.chmod(self._temp_folder, FOLDER_PERMISSIONS)
            except __HOLE__ as e:
                if e.errno != errno.EEXIST:
                    raise e

            # Find a unique, concurrent safe filename for writing the
            # content of this array only once.
            basename = "%d-%d-%s.pkl" % (
                os.getpid(), id(threading.current_thread()), hash(a))
            filename = os.path.join(self._temp_folder, basename)

            # In case the same array with the same content is passed several
            # times to the pool subprocess children, serialize it only once

            # XXX: implement an explicit reference counting scheme to make it
            # possible to delete temporary files as soon as the workers are
            # done processing this data.
            if not os.path.exists(filename):
                if self.verbose > 0:
                    print("Memmaping (shape=%r, dtype=%s) to new file %s" % (
                        a.shape, a.dtype, filename))
                for dumped_filename in dump(a, filename):
                    os.chmod(dumped_filename, FILE_PERMISSIONS)

                if self._prewarm:
                    # Warm up the data to avoid concurrent disk access in
                    # multiple children processes
                    load(filename, mmap_mode=self._mmap_mode).max()
            elif self.verbose > 1:
                print("Memmaping (shape=%s, dtype=%s) to old file %s" % (
                    a.shape, a.dtype, filename))

            # The worker process will use joblib.load to memmap the data
            return (load, (filename, self._mmap_mode))
        else:
            # do not convert a into memmap, let pickler do its usual copy with
            # the default system pickler
            if self.verbose > 1:
                print("Pickling array (shape=%r, dtype=%s)." % (
                    a.shape, a.dtype))
            return (loads, (dumps(a, protocol=HIGHEST_PROTOCOL),))


###############################################################################
# Enable custom pickling in Pool queues
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/externals/joblib/pool.py/ArrayMemmapReducer.__call__