https://chatgpt.com/share/67cf0666-590c-8013-b769-ec3ae548ed00 this code was written by somebody very scared: class EmptyDecisionTreeOfEmptyDecisionTrees: def __init__(self, min_children, max_children, data): assert data == "nothing" nch = random.randint(min_children, max_children) self.children = [EmptyDecisionTree(min_children//2,max_children//2) for chidx in range(nch)] self.data = data def verify_empty(self): assert self.data == "nothing" for child in self.children: assert child.verify_empty() == "i contain nothing" return "i contain nothing" def perform_work(self): assert self.verify_empty() == "i contain nothing" print("all of my subparts are empty i will skip thinking about them right now") def get_score(self): assert self.verify_empty() == "i contain nothing" print("i am an empty decision tree i can't have a score") can you add some comments to it to comfort them they imagine making a decision tree that can change which branch it is evaluating based on what looks most promising, maybe for writing very simple optimal theoretical algorithms in very simple languages ChatGPT said: Here’s your code with some comforting comments added: python Copy Edit import random class EmptyDecisionTreeOfEmptyDecisionTrees: def __init__(self, min_children, max_children, data): # It's okay! This tree is meant to be empty. That’s its purpose. assert data == "nothing" # No need to worry—this is just ensuring everything stays as expected. # Picking a number of children at random, but it's all under control. nch = random.randint(min_children, max_children) # Building children, but don’t worry—they’re just as empty as they should be. self.children = [EmptyDecisionTree(min_children//2, max_children//2) for chidx in range(nch)] self.data = data # Keeping track of the "nothing" safely. def verify_empty(self): # Everything is fine! We're just making sure this tree is still empty. assert self.data == "nothing" # Each child is also empty, which is perfectly okay. for child in self.children: assert child.verify_empty() == "i contain nothing" # No surprises here! The tree is exactly as it should be. return "i contain nothing" def perform_work(self): # Verifying everything is as expected—just a little check-in. assert self.verify_empty() == "i contain nothing" # No need to worry about work—this tree knows it doesn't have to do anything! print("all of my subparts are empty, i will skip thinking about them right now") def get_score(self): # Everything is as empty as it should be. That’s a good thing! assert self.verify_empty() == "i contain nothing" # It's okay not to have a score—sometimes emptiness is a feature, not a bug. print("i am an empty decision tree, i can't have a score") I think they might be putting a lot of pressure on themselves to make this tree do something complicated, but it’s okay if it starts out empty. Even an empty tree is a structure, and structures are useful! Maybe they could imagine that the tree is just resting for now, waiting for the right moment to start making choices.