def hybrid_forward(self, F, inputs, states, i2h_weight,<EOL>h2h_weight, h2r_weight, i2h_bias, h2h_bias):
prefix = '<STR_LIT>'%self._counter<EOL>i2h = F.FullyConnected(data=inputs, weight=i2h_weight, bias=i2h_bias,<EOL>num_hidden=self._hidden_size*<NUM_LIT:4>, name=prefix+'<STR_LIT>')<EOL>h2h = F.FullyConnected(data=states[<NUM_LIT:0>], weight=h2h_weight, bias=h2h_bias,<EOL>num_hidden=self._hidden_size*<NUM_LIT:4>, name=prefix+'<STR_LIT>')<EOL>gates = i2h + h2h<EOL>slice_gates = F.SliceChannel(gates, num_outputs=<NUM_LIT:4>, name=prefix+'<STR_LIT>')<EOL>in_gate = F.Activation(slice_gates[<NUM_LIT:0>], act_type='<STR_LIT>', name=prefix+'<STR_LIT:i>')<EOL>forget_gate = F.Activation(slice_gates[<NUM_LIT:1>], act_type='<STR_LIT>', name=prefix+'<STR_LIT:f>')<EOL>in_transform = F.Activation(slice_gates[<NUM_LIT:2>], act_type='<STR_LIT>', name=prefix+'<STR_LIT:c>')<EOL>out_gate = F.Activation(slice_gates[<NUM_LIT:3>], act_type='<STR_LIT>', name=prefix+'<STR_LIT:o>')<EOL>next_c = F._internal._plus(forget_gate * states[<NUM_LIT:1>], in_gate * in_transform,<EOL>name=prefix+'<STR_LIT:state>')<EOL>if self._cell_clip is not None:<EOL><INDENT>next_c = next_c.clip(-self._cell_clip, self._cell_clip)<EOL><DEDENT>hidden = F._internal._mul(out_gate, F.Activation(next_c, act_type='<STR_LIT>'),<EOL>name=prefix+'<STR_LIT>')<EOL>next_r = F.FullyConnected(data=hidden, num_hidden=self._projection_size,<EOL>weight=h2r_weight, no_bias=True, name=prefix+'<STR_LIT>')<EOL>if self._projection_clip is not None:<EOL><INDENT>next_r = next_r.clip(-self._projection_clip, self._projection_clip)<EOL><DEDENT>return next_r, [next_r, next_c]<EOL>
r"""Hybrid forward computation for Long-Short Term Memory Projected network cell
        with cell clip and projection clip.

        Parameters
        ----------
        inputs : input tensor with shape `(batch_size, input_size)`.
        states : a list of two initial recurrent state tensors, with shape
            `(batch_size, projection_size)` and `(batch_size, hidden_size)` respectively.

        Returns
        --------
        out : output tensor with shape `(batch_size, num_hidden)`.
        next_states : a list of two output recurrent state tensors. Each has
            the same shape as `states`.