Check models used for common tests are small
# What does this PR do?

This is the first PR in a series that will aim at reducing the time spent on the tests (and the corresponding cost 😅 ). A first analysis of the slowest tests show that the common tests sometimes use real-life models instead of tiny ones. This PR adds a check that the size of the model for common tests is not bigger than 1M parameters (which is a wide bar, BERT has a version with 55k parameters for its common tests for instance).

To avoid making the PR too long, the new test is skipped in most of the failures, only the DETR variants, BridgeTower, Canine and CLAP are treated in this PR. For the DETR variants, a full pretrained ResNet-50 was used as the backbone which is replaced by a tiny random ResNet.

The following models will be treated in follow-up PRs:
* CTRL
* CVT
* DETA
* DPT
* DPT Hybrid
* EfficientNet
* Encodec
* ESM
* Flava
* Git
* GPTSan-Japanese
* Graphormer
* LayoutLM
* LayoutLMv2
* LeViT
* Mask2Former
* Maskformer
* MobileViT
* MobileVit2
* OneFormer
* Perceiver
* SegFormer
* SpeechT5
* SwiftFormer
* TableTransformer
* TimmBackbone
* TVLT
* UperNet
* VideoMAE
* ViT-MAE
* ViViT
_The documentation is not available anymore as the PR was closed or merged._