def _batch_inference(self, batched_inputs):
        """
        Execute inference on a list of inputs,
        using batch size = self.batch_size, instead of the length of the list.

        Inputs & outputs have the same format as :meth:`GeneralizedRCNN.inference`
        """
        outputs = []
        inputs = []
        for idx, x in enumerate(batched_inputs):
            inputs.append(x)
            if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1:
                # This runs NMS (box and optionally bev) per each augmented image.
                outputs.extend([res['instances'] for res in self.model(inputs)])
                inputs = []
        return outputs

        Execute inference on a list of inputs,
        using batch size = self.batch_size, instead of the length of the list.

        Inputs & outputs have the same format as :meth:`GeneralizedRCNN.inference`
        
tridet/modeling/dd3d/nuscenes_dd3d_tta.py