change anchor_image_size None for compatibility
Author: SangbumChoi
Pull request: yes

# What does this PR do?

@qubvel This is the commit that we discussed in earlier for better compatibility!

Currently, RT-DETR can be trained with arbitrary image sizes. However, evaluation will not work if `anchor_image_size` is not properly set. In other words, models behave differently in training and evaluation modes.

```python
import torch
from transformers import RTDetrConfig, RTDetrForObjectDetection

config = RTDetrConfig()
model = RTDetrForObjectDetection(config)

image_size = 320
sample = torch.rand((2, 3, image_size, image_size))

model.train()
model(sample)
print(f"Successfully run model in train mode with image size {image_size}!")

model.eval()
with torch.no_grad():
    try:
        model(sample)
    except Exception as e:
        print(f"Failed to run model in eval mode with image size {image_size}.")

# Fix eval
config = RTDetrConfig(anchor_image_size=[image_size, image_size])
model = RTDetrForObjectDetection(config)
model.eval()
with torch.no_grad():
    model(sample)
    print(f"Successfully run model in eval mode with image size {image_size}!")
```

This issue occurs because anchors are generated dynamically during training but remain static during evaluation. This PR modifies the default behavior, ensuring that anchors are generated dynamically during evaluation, just as they are during the training stage.

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Comments:
Hi @SangbumChoi, thanks for the PR! 

I updated the description, feel free to add if I missed anything!

@amyeroberts could you have a look? Would be nice to merge it before the release