* Copy wandb param dict before training to avoid overwrites.
Copy the hyperparameter dict retrieved from wandb configuration before passing it to `train()`. Training overwrites parameters in the dictionary (eg scaling obj/box/cls gains), which causes the values reported in wandb to not match the input values. This is confusing as it makes it hard to reproduce a run, and also throws off wandb's Bayesian sweep algorithm.
* Cleanup
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* [pre-commit.ci] pre-commit suggestions
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* precommit: yapf
* align isort
* fix
# Conflicts:
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* PyTorch Hub tuple fix
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* update
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* prune unused imports
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* handle exceptions| attempt CI
* update
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Leave as example for future separate CI
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* precommit: isort
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* add callbacks to train function in wandb sweep
Fix following https://github.com/ultralytics/yolov5/pull/4688 which modified the function signature to `train`
* Cleanup
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