<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def _getitem_with_mask(self, key, fill_value=dtypes.NA):
 """Index this Variable with -1 remapped to fill_value."""

        # TODO(shoyer): expose this method in public API somewhere (isel?) and
        # use it for reindex.
        # TODO(shoyer): add a sanity check that all other integers are
        # non-negative
        # TODO(shoyer): add an optimization, remapping -1 to an adjacent value
        # that is actually indexed rather than mapping it to the last value
        # along each axis.

        if fill_value is dtypes.NA:
            fill_value = dtypes.get_fill_value(self.dtype)

        dims, indexer, new_order = self._broadcast_indexes(key)

        if self.size:
            if isinstance(self._data, dask_array_type):
                # dask's indexing is faster this way; also vindex does not
                # support negative indices yet:
                # https://github.com/dask/dask/pull/2967
                actual_indexer = indexing.posify_mask_indexer(indexer)
            else:
                actual_indexer = indexer

            data = as_indexable(self._data)[actual_indexer]
            chunks_hint = getattr(data, 'chunks', None)
            mask = indexing.create_mask(indexer, self.shape, chunks_hint)
            data = duck_array_ops.where(mask, fill_value, data)
        else:
            # array cannot be indexed along dimensions of size 0, so just
            # build the mask directly instead.
            mask = indexing.create_mask(indexer, self.shape)
            data = np.broadcast_to(fill_value, getattr(mask, 'shape', ()))

        if new_order:
            data = np.moveaxis(data, range(len(new_order)), new_order)
        return self._finalize_indexing_result(dims, data)