Unpack fused weight matrices into separate
        weight matrices.

        For example, say you use a module object `mod` to run a network that has an lstm cell.
        In `mod.get_params()[0]`, the lstm parameters are all represented as a single big vector.
        `cell.unpack_weights(mod.get_params()[0])` will unpack this vector into a dictionary of
        more readable lstm parameters - c, f, i, o gates for i2h (input to hidden) and
        h2h (hidden to hidden) weights.

        Parameters
        ----------
        args : dict of str -> NDArray
            Dictionary containing packed weights.
            usually from `Module.get_params()[0]`.

        Returns
        -------
        args : dict of str -> NDArray
            Dictionary with unpacked weights associated with
            this cell.

        See Also
        --------
        pack_weights: Performs the reverse operation of this function.

def unpack_weights(self, args):
        """Unpack fused weight matrices into separate
        weight matrices.

        For example, say you use a module object `mod` to run a network that has an lstm cell.
        In `mod.get_params()[0]`, the lstm parameters are all represented as a single big vector.
        `cell.unpack_weights(mod.get_params()[0])` will unpack this vector into a dictionary of
        more readable lstm parameters - c, f, i, o gates for i2h (input to hidden) and
        h2h (hidden to hidden) weights.

        Parameters
        ----------
        args : dict of str -> NDArray
            Dictionary containing packed weights.
            usually from `Module.get_params()[0]`.

        Returns
        -------
        args : dict of str -> NDArray
            Dictionary with unpacked weights associated with
            this cell.

        See Also
        --------
        pack_weights: Performs the reverse operation of this function.
        """
        args = args.copy()
        if not self._gate_names:
            return args
        h = self._num_hidden
        for group_name in ['i2h', 'h2h']:
            weight = args.pop('%s%s_weight'%(self._prefix, group_name))
            bias = args.pop('%s%s_bias' % (self._prefix, group_name))
            for j, gate in enumerate(self._gate_names):
                wname = '%s%s%s_weight' % (self._prefix, group_name, gate)
                args[wname] = weight[j*h:(j+1)*h].copy()
                bname = '%s%s%s_bias' % (self._prefix, group_name, gate)
                args[bname] = bias[j*h:(j+1)*h].copy()
        return args