How to use the h5py._hl.base.with_phil function in h5py

    @with_phil
    def __array__(self, dtype=None):
        """ Create a Numpy array containing the whole dataset.  DON'T THINK
        THIS MEANS DATASETS ARE INTERCHANGEABLE WITH ARRAYS.  For one thing,
        you have to read the whole dataset every time this method is called.
        """
        arr = numpy.empty(self.shape, dtype=self.dtype if dtype is None else dtype)

        # Special case for (0,)*-shape datasets
        if numpy.product(self.shape, dtype=numpy.ulonglong) == 0:
            return arr

        self.read_direct(arr)
        return arr

    @with_phil
    def filename(self):
        """File name on disk"""
        return filename_decode(h5f.get_name(self.id))

    @with_phil
    def __repr__(self):
        if not self._id:
            return ""
        return "" % id(self._id)

    @with_phil
    def shuffle(self):
        """Shuffle filter present (T/F)"""
        return 'shuffle' in self._filters

    @with_phil
    def __eq__(self, other):
        return hash(self) == hash(other)

        @with_phil
        def flush(self):
            """ Flush the dataset data and metadata to the file.
            If the dataset is chunked, raw data chunks are written to the file.

            This is part of the SWMR features and only exist when the HDF5
            library version >=1.9.178
            """
            self._id.flush()

    @with_phil
    def __getitem__(self, name):
        """ Open an object in the file """

        if isinstance(name, h5r.Reference):
            oid = h5r.dereference(name, self.id)
            if oid is None:
                raise ValueError("Invalid HDF5 object reference")
        else:
            oid = h5o.open(self.id, self._e(name), lapl=self._lapl)

        otype = h5i.get_type(oid)
        if otype == h5i.GROUP:
            return Group(oid)
        elif otype == h5i.DATASET:
            return dataset.Dataset(oid, readonly=(self.file.mode == 'r'))
        elif otype == h5i.DATATYPE:

    @with_phil
    def __iter__(self):
        """ Iterate over the dimensions. """
        for i in range(len(self)):
            yield self[i]

    @with_phil
    def libver(self):
        """File format version bounds (2-tuple: low, high)"""
        bounds = self.id.get_access_plist().get_libver_bounds()
        return tuple(libver_dict_r[x] for x in bounds)

    @with_phil
    def __len__(self):
        """ Number of dimensions associated with the dataset. """
        return len(Dataset(self._id).shape)