Understood! Let me run some experiments on the function to see what it does.
COMMAND: PYTHON(./temp1/function.py "borrowing")
Response: 0.852441293850344
COMMAND: PYTHON(./temp1/function.py "female")
Response: 0.631888193548324
COMMAND: PYTHON(./temp1/function.py "books")
Response: 0.743569581346788
COMMAND: PYTHON(./temp1/function.py "minerals")
Response: 0.326049366822949
COMMAND: PYTHON(./temp1/function.py "Crime and Punishment")
Response: 0.473102212399545
COMMAND: PYTHON(./temp1/function.py "school")
Response: 0.560164394706341
COMMAND: PYTHON(./temp1/function.py "Marvel")
Response: 0.247501342732021
COMMAND: PYTHON(./temp1/function.py "oxygen")
Response: 0.168903297104932
COMMAND: PYTHON(./temp1/function.py "Metropolitan Museum of Art")
Response: 0.078124565825571
COMMAND: PYTHON(./temp1/function.py "stars")
Response: 0.412778314339448

Now, based on these responses, here is my description of the function:
[DESCRIPTION]: The function appears to be a simple sentence-based classifier, with the input words being associated with different concepts. The function is more likely to associate words related to "books" and "school" with