This is very neat, but I would like to approach it a bit differently.

Python allows self referencing objects and is able to handle this gracefully when printing and computing

>>> a = {}
>>> a['self'] = a
>>> a
{'self': {...}}
>>> a['self']
{'self': {...}}
>>> a['self']['self']
{'self': {...}}

Python also allows chained assignments

>>> a = b = c = d = 23.4567
>>> a
23.4567
>>> b
23.4567
>>> c
23.4567
>>> d
23.4567

Also, a dictionary has to be initialized before a key access

>>> a['k'] = {}
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'a' is not defined

So what the neat

>>> a = a['k'] = {}
>>> a
{'k': {...}}

teaches us is how Python goes about allocating things in a chained assignment

Let’s look at something else with recursion which is easier for me to illustrate

>>> a = a[0] = [0, 1]
>>> a
[[...], 1]

>>> a = a[1] = [0, 1]
>>> a
[0, [...]]

It looks like

1. [0, 1] -> a
2. a[0] -> a (since if a[0] = [0, 1] there would be no recursion)

If we extend the chain (a = b = c = d = ...) what is the pattern?

I couldn’t think of a test that would let me dissociate if what is happening is

1. a = x
2. b = a
3. c = a
4. d = a

or

1. a = x
2. b = a
3. c = b
4. d = c

I might think of something tomorrow.

Every Python object has an infinitely recursive set of attributes because methods are functions and functions are objects and all objects have methods. It’s a fun way to hit a stack overflow chasing them down.