def __call__(self, inputs):
        """
        Compute reward scores for the provided inputs.

        This method processes each input by converting dialogue messages into a query, sending the query to the
        reward model for inference, and extracting the reward scores from the model's responses. The final reward
        for each input is the average of all extracted scores.
        Args:
            inputs (List[Dict]): A list of input requests. Each input request is a dictionary containing:
                - 'messages' (List[Dict]): messages from the training model. Each message dictionary includes:
                    - 'role' (str): The role of the speaker (e.g., 'user', 'assistant').
                    - 'content' (str): The content of the message.
                - Additional dataset columns as key-value pairs (e.g., 'solutions', 'images').
        Returns:
            torch.Tensor: A tensor containing the average reward scores for each input. The tensor has a shape of (N,),
            where N is the number of input requests.
        """

        rm_inputs = self.prepare_rm_inputs(inputs)
        results = self.engine.infer(rm_inputs, self.request_config, use_tqdm=False)
        rewards = self.compute_rewards(results)
        return torch.tensor(rewards, dtype=torch.float32)

        Compute reward scores for the provided inputs.

        This method processes each input by converting dialogue messages into a query, sending the query to the
        reward model for inference, and extracting the reward scores from the model's responses. The final reward
        for each input is the average of all extracted scores.
        Args:
            inputs (List[Dict]): A list of input requests. Each input request is a dictionary containing:
                - 'messages' (List[Dict]): messages from the training model. Each message dictionary includes:
                    - 'role' (str): The role of the speaker (e.g., 'user', 'assistant').
                    - 'content' (str): The content of the message.
                - Additional dataset columns as key-value pairs (e.g., 'solutions', 'images').
        Returns:
            torch.Tensor: A tensor containing the average reward scores for each input. The tensor has a shape of (N,),
            where N is the number of input requests.
        
swift/plugin/rm_plugin.py