def METHOD_NAME(self, **kwargs) -> nn.Module:
    rgb_uuid = next(
        (s.uuid for s in self.sensors if isinstance(s, RGBSensor)), None
    )
    depth_uuid = next(
        (s.uuid for s in self.sensors if isinstance(s, DepthSensor)), None
    )
    goal_sensor_uuid = next(
        (
            s.uuid
            for s in self.sensors
            if isinstance(
                s, (GPSCompassSensorRoboThor, TargetCoordinatesSensorHabitat)
            )
        )
    )
    return PointNavActorCritic(
        # Env and Tak
        action_space=gym.spaces.Discrete(len(PointNavTask.class_action_names())),
        observation_space=kwargs["sensor_preprocessor_graph"].observation_spaces,
        rgb_uuid=rgb_uuid,
        depth_uuid=depth_uuid,
        goal_sensor_uuid=goal_sensor_uuid,
        # RNN
        hidden_size=228
        if self.multiple_beliefs and len(self.auxiliary_uuids) > 1
        else 512,
        num_rnn_layers=1,
        rnn_type="GRU",
        add_prev_actions=self.add_prev_actions,
        action_embed_size=4,
        # CNN
        backbone=self.backbone,
        resnet_baseplanes=32,
        embed_coordinates=False,
        coordinate_dims=2,
        # Aux
        auxiliary_uuids=self.auxiliary_uuids,
        multiple_beliefs=self.multiple_beliefs,
        beliefs_fusion=self.belief_fusion,
    )