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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 _unfuse(self):
 """Unfuses the fused RNN in to a stack of rnn cells."""

        assert not self._projection_size, "_unfuse does not support projection layer yet!"
        assert not self._lstm_state_clip_min and not self._lstm_state_clip_max, \
                "_unfuse does not support state clipping yet!"
        get_cell = {'rnn_relu': lambda **kwargs: rnn_cell.RNNCell(self._hidden_size,
                                                                  activation='relu',
                                                                  **kwargs),
                    'rnn_tanh': lambda **kwargs: rnn_cell.RNNCell(self._hidden_size,
                                                                  activation='tanh',
                                                                  **kwargs),
                    'lstm': lambda **kwargs: rnn_cell.LSTMCell(self._hidden_size,
                                                               **kwargs),
                    'gru': lambda **kwargs: rnn_cell.GRUCell(self._hidden_size,
                                                             **kwargs)}[self._mode]

        stack = rnn_cell.HybridSequentialRNNCell(prefix=self.prefix, params=self.params)
        with stack.name_scope():
            ni = self._input_size
            for i in range(self._num_layers):
                kwargs = {'input_size': ni,
                          'i2h_weight_initializer': self._i2h_weight_initializer,
                          'h2h_weight_initializer': self._h2h_weight_initializer,
                          'i2h_bias_initializer': self._i2h_bias_initializer,
                          'h2h_bias_initializer': self._h2h_bias_initializer}
                if self._dir == 2:
                    stack.add(rnn_cell.BidirectionalCell(
                        get_cell(prefix='l%d_'%i, **kwargs),
                        get_cell(prefix='r%d_'%i, **kwargs)))
                else:
                    stack.add(get_cell(prefix='l%d_'%i, **kwargs))

                if self._dropout > 0 and i != self._num_layers - 1:
                    stack.add(rnn_cell.DropoutCell(self._dropout))

                ni = self._hidden_size * self._dir

        return stack