In one-node multi-GPU setups, the ImageProcessor module in Detr and other object detection models may cause an error in the Trainer.
### System Info

When training object detection models using multiple GPUs in a single node, I encountered an issue with nn.DataParallel. The nn.DataParallel module splits Tensor data across each GPU before model inference, but it does not split List data. Since all object detection ImageProcessor modules return List objects, the targets will not be split, but the inputs will have already been split. Additionally, inputs are of type Tensor, while targets are of type List.

During loss computation, the shapes of inputs and targets may not match along the 0th dimension.

Is this the best practice for single-node multi-GPU setup? Should I avoid using nn.DataParallel?

### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Due to confidentiality reasons, I cannot submit the complete code. I will provide a example later.

### Expected behavior

Training object detection model with multiple GPUs on a single machine as usual.
You should use DistributedDataParallel for multi-GPU setup, which is better supported and the recommended way from the PyTorch team. It is supported by the Trainer, but just launch your training with `torchrun` instead of `python`.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.