Returns the ops for the trajectory generation with given `horizon`
        and `initial_state`.

        The simulation returns states, actions and interms as a
        sequence of tensors (i.e., all representations are factored).
        The reward is a batch sized tensor.
        The trajectoty output is a tuple: (initial_state, states, actions, interms, rewards).
        If initial state is None, use default compiler's initial state.

        Note:
            All tensors have shape: (batch_size, horizon, fluent_shape).
            Except initial state that has shape: (batch_size, fluent_shape).

        Args:
            horizon (int): The number of simulation timesteps.
            initial_state (Optional[Sequence[tf.Tensor]]): The initial state tensors.

        Returns:
            Tuple[StateTensor, StatesTensor, ActionsTensor, IntermsTensor, tf.Tensor]: Trajectory output tuple.

def trajectory(self,
            horizon: int,
            initial_state: Optional[StateTensor] = None) -> TrajectoryOutput:
        '''Returns the ops for the trajectory generation with given `horizon`
        and `initial_state`.

        The simulation returns states, actions and interms as a
        sequence of tensors (i.e., all representations are factored).
        The reward is a batch sized tensor.
        The trajectoty output is a tuple: (initial_state, states, actions, interms, rewards).
        If initial state is None, use default compiler's initial state.

        Note:
            All tensors have shape: (batch_size, horizon, fluent_shape).
            Except initial state that has shape: (batch_size, fluent_shape).

        Args:
            horizon (int): The number of simulation timesteps.
            initial_state (Optional[Sequence[tf.Tensor]]): The initial state tensors.

        Returns:
            Tuple[StateTensor, StatesTensor, ActionsTensor, IntermsTensor, tf.Tensor]: Trajectory output tuple.
        '''
        if initial_state is None:
            initial_state = self._cell.initial_state()

        with self.graph.as_default():
            self.inputs = self.timesteps(horizon)
            outputs, _ = tf.nn.dynamic_rnn(
                                self._cell,
                                self.inputs,
                                initial_state=initial_state,
                                dtype=tf.float32,
                                scope="trajectory")
            states, actions, interms, rewards = outputs

            # fluent types
            state_dtype = map(rddl2tf.utils.range_type_to_dtype, self._cell._compiler.rddl.state_range_type)
            states = self._output(states, state_dtype)
            interm_dtype = map(rddl2tf.utils.range_type_to_dtype, self._cell._compiler.rddl.interm_range_type)
            interms = self._output(interms, interm_dtype)
            action_dtype = map(rddl2tf.utils.range_type_to_dtype, self._cell._compiler.rddl.action_range_type)
            actions = self._output(actions, action_dtype)

            outputs = (initial_state, states, actions, interms, rewards)

        return outputs