tracing torchvision detection model results in an error
Labels: oncall: jit, module: vision

### 🐛 Describe the bug

The detection API isn't compatible with torch.jit.trace because the forward for detection models results in a list of dictionaries.

```python
torch.jit.trace(func=torchvision.models.detection.fasterrcnn_resnet50_fpn_v2().eval(), example_inputs=torch.rand(1, 3, 512, 512))
```

```
/usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:3908: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  (torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
/usr/local/lib/python3.8/dist-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  boxes_x = torch.min(boxes_x, torch.tensor(width, dtype=boxes.dtype, device=boxes.device))
/usr/local/lib/python3.8/dist-packages/torchvision/ops/boxes.py:159: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  boxes_y = torch.min(boxes_y, torch.tensor(height, dtype=boxes.dtype, device=boxes.device))
/usr/local/lib/python3.8/dist-packages/torch/__init__.py:827: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
/usr/local/lib/python3.8/dist-packages/torchvision/models/detection/transform.py:298: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(s, dtype=torch.float32, device=boxes.device)
/usr/local/lib/python3.8/dist-packages/torchvision/models/detection/transform.py:299: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  / torch.tensor(s_orig, dtype=torch.float32, device=boxes.device)
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
[<ipython-input-54-9a5e30e34328>](https://localhost:8080/#) in <module>
----> 1 torch.jit.trace(func=torchvision.models.detection.fasterrcnn_resnet50_fpn_v2().eval(), example_inputs=torch.rand(1, 3, 512, 512))

1 frames
[/usr/local/lib/python3.8/dist-packages/torch/jit/_trace.py](https://localhost:8080/#) in trace_module(mod, inputs, optimize, check_trace, check_inputs, check_tolerance, strict, _force_outplace, _module_class, _compilation_unit)
    974             example_inputs = make_tuple(example_inputs)
    975 
--> 976             module._c._create_method_from_trace(
    977                 method_name,
    978                 func,

RuntimeError: Only tensors, lists, tuples of tensors, or dictionary of tensors can be output from traced functions
```

### Versions

Collecting environment information...
PyTorch version: 1.13.0+cu116
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: 6.0.0-1ubuntu2 (tags/RELEASE_600/final)
CMake version: version 3.22.6
Libc version: glibc-2.27

Python version: 3.8.16 (default, Dec  7 2022, 01:12:13)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.10.133+-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: 11.2.152
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 460.32.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.1.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.21.6
[pip3] torch==1.13.0+cu116
[pip3] torchaudio==0.13.0+cu116
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.14.0
[pip3] torchvision==0.14.0+cu116
[conda] Could not collect

cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @datumbox @vfdev-5 @pmeier