I created a class initializing the attributes you mentioned, and when adding your docstring to your function signature it gave me exactly the answer you were looking for. Note that it was all in first try, and that I did not think at all about the initialization for components, marginalized or observed—I simply auto-completed.
1. if you want a longer init, write a doctring for it
natural language stochastic compiler
I don’t get what you mean here. I’m also not an expert on the Codex’ “Context windows”.
1) in my experience, even if not specified in your prompt, the model still goes or your depency graph (in different files in your repo, not Github) and picks which functions are relevant for the next line 2) if you know which function to. use, then add these function or “API calls” in the docstring;
Thanks for natural language stochastic compiler explanation, makes a lot of sense. I broadly get a sense of what you mean by “context window” since people have been mentioning that quite a lot when talking about GPT-3. As for whether it makes sense to write docstrings for trivial things, I think this is only pointing at the Codex demo examples where people write docstrings and get results, but for most of my use cases, and when it gets really interesting, is when it auto-completes 1) while I’m writing 2) when I’m done writing and it guesses the next line 3) when I start a line by “return ” or “x = ” and wait for his auto-completion. Here, I would have no idea how to formulate it in the docstring, I just generally trust its ability to follow the logic of the code that precedes it (and I find it useful most of the time).
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I created a class initializing the attributes you mentioned, and when adding your docstring to your function signature it gave me exactly the answer you were looking for. Note that it was all in first try, and that I did not think at all about the initialization for components, marginalized or observed—I simply auto-completed.
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1. if you want a longer init, write a doctring for it
I don’t get what you mean here. I’m also not an expert on the Codex’ “Context windows”.
1) in my experience, even if not specified in your prompt, the model still goes or your depency graph (in different files in your repo, not Github) and picks which functions are relevant for the next line 2) if you know which function to. use, then add these function or “API calls” in the docstring;
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Thanks for natural language stochastic compiler explanation, makes a lot of sense. I broadly get a sense of what you mean by “context window” since people have been mentioning that quite a lot when talking about GPT-3. As for whether it makes sense to write docstrings for trivial things, I think this is only pointing at the Codex demo examples where people write docstrings and get results, but for most of my use cases, and when it gets really interesting, is when it auto-completes 1) while I’m writing 2) when I’m done writing and it guesses the next line 3) when I start a line by “return ” or “x = ” and wait for his auto-completion. Here, I would have no idea how to formulate it in the docstring, I just generally trust its ability to follow the logic of the code that precedes it (and I find it useful most of the time).