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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 slice(self, rng, num_of_slices=None, slice_pos=None,
              slice_start=None, slice_end=None,
              cache_dir=None):
        '''
        Slices the data iterator so that newly generated data iterator has access to limited portion of the original data.

        Args:
            rng (numpy.random.RandomState): Random generator for Initializer.
            num_of_slices(int): Total number of slices to be made. Muts be used together with `slice_pos`. 
            slice_pos(int): Position of the slice to be assigned to the new data iterator. Must be used together with `num_of_slices`.
            slice_start(int): Starting position of the range to be sliced into new data iterator. Must be used together with `slice_end`.
            slice_end(int) : End position of the range to be sliced into new data iterator. Must be used together with `slice_start`.
            cache_dir(str) : Directory to save cache files

        Example:

        .. code-block:: python

            from nnabla.utils.data_iterator import data_iterator_simple
            import numpy as np

            def load_func1(index):
                d = np.ones((2, 2)) * index
                return d

            di = data_iterator_simple(load_func1, 1000, batch_size=3)

            di_s1 = di.slice(None, num_of_slices=10, slice_pos=0)
            di_s2 = di.slice(None, num_of_slices=10, slice_pos=1)

            di_s3 = di.slice(None, slice_start=100, slice_end=200)
            di_s4 = di.slice(None, slice_start=300, slice_end=400) 

        '''

        if num_of_slices is not None and slice_pos is not None and slice_start is None and slice_end is None:
            size = self._size // num_of_slices
            amount = self._size % num_of_slices
            slice_start = slice_pos * size
            if slice_pos < amount:
                slice_start += slice_pos
            else:
                slice_start += amount
            slice_end = slice_start + size
            if slice_end > self._size:
                slice_start -= (slice_end - self._size)
                slice_end = self._size

        elif num_of_slices is None and slice_pos is None and slice_start is not None and slice_end is not None:
            pass
        else:
            logger.critical(
                'You must specify position(num_of_slice and slice_pos) or range(slice_start and slice_end).')
            return None

        if cache_dir is None:
            ds = self._data_source
            while '_data_source' in dir(ds):
                if '_cache_dir' in dir(ds):
                    cache_dir = ds._cache_dir
                ds = ds._data_source

        if cache_dir is None:
            return DataIterator(
                DataSourceWithMemoryCache(
                    SlicedDataSource(
                        self._data_source,
                        self._data_source.shuffle,
                        slice_start=slice_start,
                        slice_end=slice_end),
                    shuffle=self._shuffle,
                    rng=rng),
                self._batch_size)
        else:
            return DataIterator(
                DataSourceWithMemoryCache(
                    DataSourceWithFileCache(
                        SlicedDataSource(
                            self._data_source,
                            self._data_source.shuffle,
                            slice_start=slice_start,
                            slice_end=slice_end),
                        cache_dir=cache_dir,
                        cache_file_name_prefix='cache_sliced_{:08d}_{:08d}'.format(
                            slice_start,
                            slice_end),
                        shuffle=self._shuffle,
                        rng=rng),
                    shuffle=self._shuffle,
                    rng=rng),
                self._batch_size)