Author here. It's a design choice, but there's two reasons I chose to use imports like this:
1) For demonstrative purposes. The title of the post is `A GPT in 60 Lines of NumPy`, I kinda wanted to show "hey it's just numpy, nothing to be scared about!". Also if an import is ONLY used in a single function, I find it visually helps show that "hey, this import is only used in this function" vs when it's at the top of the file you're not really sure when/where and how many times an import is used.
2) Scoping. `load_encoder_hparams_and_params` imports tensorflow, which is really slow to import. When I was testing, I used randomly initialized weights instead of loading the checkpoint which is slower, so I was only making use of the `gpt2` function. If I kept the import at the top level, it would've slowed things down unnecessarily.
1) For demonstrative purposes. The title of the post is `A GPT in 60 Lines of NumPy`, I kinda wanted to show "hey it's just numpy, nothing to be scared about!". Also if an import is ONLY used in a single function, I find it visually helps show that "hey, this import is only used in this function" vs when it's at the top of the file you're not really sure when/where and how many times an import is used.
2) Scoping. `load_encoder_hparams_and_params` imports tensorflow, which is really slow to import. When I was testing, I used randomly initialized weights instead of loading the checkpoint which is slower, so I was only making use of the `gpt2` function. If I kept the import at the top level, it would've slowed things down unnecessarily.