Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

That’s perfectly fine, and it works quite well. But it also requires that you
specify the exponent every time you call the function. There must be another
way! Of course, there is; that’s the purpose of this section!
We need to build another (higher-order) function to build those functions
(square, cube, etc.) for us. The (higher-order) function is just a function
builder. But how do we do that?
First, let’s build the "skeleton" of the functions we are trying to generate; they
all take a single argument x, and they all perform an exponentiation, each
using a different exponent.
Fine. It should look like this:
def skeleton_exponentiation(x):
return x ** exponent
If you try calling this function with any x, say, skeleton_exponentiation(2),
you’ll get the following error:
skeleton_exponentiation(2)
Output
NameError: name 'exponent' is not defined
This is expected: Your "skeleton" function has no idea what the variable
exponent is! And that’s what the higher-order function is going to
accomplish.
We "wrap" our skeleton function with a higher-order function (which will
build the desired functions). Let’s call it, rather unimaginatively,
exponentiation_builder(). What are its arguments, if any? Well, we’re
trying to tell our skeleton function what its exponent should be, so let’s
start with that!
128 | Chapter 2: Rethinking the Training Loop