def METHOD_NAME(self, requests):
    """ Preprocessing, based on processor defined for MMF model.
        """
    def create_sample(video_transfomred,audio_transfomred,text_tensor, video_label):
        label = [self.class_to_idx[l] for l in video_label]
        one_hot_label = torch.zeros(len(self.class_to_idx))
        one_hot_label[label] = 1
        current_sample= Sample()
        current_sample.video = video_transfomred
        current_sample.audio = audio_transfomred
        current_sample.update(text_tensor)
        current_sample.targets = one_hot_label
        current_sample.dataset_type = 'test'
        current_sample.dataset_name = 'charades'
        return SampleList([current_sample]).to(self.device)
    for idx, data in enumerate(requests):
        raw_script = data.get('script')
        script = raw_script.decode('utf-8')
        raw_label = data.get('labels')
        video_label = raw_label.decode('utf-8')
        video_label = [video_label]
        video = io.BytesIO(data['data'])
        video_tensor, audio_tensor,info = torchvision.io.read_video(video)
        text_tensor = self.processor["text_processor"]({"text": script})
        video_transformed = self.processor["video_test_processor"](video_tensor)
        audio_transformed = self.processor["audio_processor"](audio_tensor)
        samples = create_sample(video_transformed,audio_transformed,text_tensor,video_label)
    return samples