src/pixparse/task/task_cruller_finetune_CORD.py [425:450]:
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        targets = torch.stack([self.text_input_to_target(text) for text in text_inputs])

        transform = self.image_preprocess_train
        images = torch.stack([transform(img) for img in images])
        text_inputs = text_inputs[:, :-1]
        targets = targets[:, 1:]
        return {
            "image": images,
            "label": text_inputs,
            "text_target": targets,
        }  

    def train_step(self, sample: Dict[str, Any]) -> Dict[str, Any]:
        image_input = sample["image"]
        label = sample["label"]
        text_target = sample["text_target"]
        result = {}
        image_input = image_input.to(self.device_env.device, non_blocking=True)
        label = label.to(self.device_env.device, non_blocking=True)
        text_target = text_target.to(self.device_env.device, non_blocking=True)

        accum_steps = self.cfg.opt.grad_accum_steps
        need_update = (self.interval_batch_idx + 1) % accum_steps == 0

        def _forward():
            with self.autocast():
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src/pixparse/task/task_cruller_finetune_docvqa.py [307:335]:
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        targets = torch.stack([self.text_input_to_target(text) for text in text_inputs])

        transform = self.image_preprocess_train
        images = torch.stack([transform(img) for img in images])
        
        text_inputs = text_inputs[:, :-1]
        targets = targets[:, 1:]

        return {
            "image": images,
            "label": text_inputs,
            "text_target": targets,
        }


    def train_step(self, sample: Dict[str, Any]) -> Dict[str, Any]:
        image_input = sample["image"]
        label = sample["label"]
        text_target = sample["text_target"]
        result = {}
        image_input = image_input.to(self.device_env.device, non_blocking=True)
        label = label.to(self.device_env.device, non_blocking=True)
        text_target = text_target.to(self.device_env.device, non_blocking=True)

        accum_steps = self.cfg.opt.grad_accum_steps
        need_update = (self.interval_batch_idx + 1) % accum_steps == 0

        def _forward():
            with self.autocast():
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