The forward reference part is where I stumbled.
Start by clarifying the requirements and constraints, such as whether the decorator should handle inheritance, generics, or only simple annotations. Then outline a step-by-step implementation: iterate over the class's __annotations__, resolve forward references using typing.get_type_hints, and replace each annotated attribute with a Feature instance. Finally, discuss trade-offs like performance, error handling, and compatibility with different Python versions.
Pro tip: Mention that using typing.get_type_hints is the most robust way to resolve forward references because it handles string annotations and nested types, but be aware of its limitations with local scopes and circular imports. Also, consider using a metaclass or __init_subclass__ for a more integrated solution, but a class decorator is simpler and sufficient for this use case.
Ask about edge cases: should it handle inherited annotations, generics, or only direct annotations? What should happen if a type cannot be resolved? Are there performance concerns?
Describe using a class decorator that inspects __annotations__, resolves forward references via typing.get_type_hints, and replaces each attribute with a Feature instance.
Walk through: (a) get annotations from the class, (b) resolve types using get_type_hints, (c) for each annotation, create a Feature instance with the resolved type, (d) set the attribute on the class.
Compare using a class decorator vs. a metaclass or __init_subclass__. Mention performance implications of get_type_hints and how to handle errors gracefully.
Write a concise code snippet demonstrating the decorator, and mention how you would test it, including forward references and edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once step 1 was solid, since the annotations are already `Feature` instances by then.
Start by clarifying the requirements and assumptions, then outline the decorator's implementation using Python's typing and dataclasses modules. Focus on extracting parameter and return annotations, handling edge cases, and ensuring the decorator preserves the original function's metadata.
Pro tip: Demonstrate awareness of Python's typing introspection tools like `typing.get_type_hints` and `inspect.signature`, and mention how to handle forward references and `from __future__ import annotations`.
Ask questions to confirm the expected behavior: What is a `Feature`? Should the decorator support async functions? How to handle missing annotations? This shows thoroughness.
Define a `Resolver` dataclass that holds the wrapped function, input features (list of `Feature` objects), and output feature. Consider adding metadata like name and docstring.
Use `typing.get_type_hints` to resolve annotations, then iterate over parameters to collect those annotated as `Feature`. Extract the return annotation for the output feature.
Check for missing annotations, non-`Feature` types, and multiple output features. Raise clear errors or warnings as appropriate.
Use `functools.wraps` to copy metadata, then return a `Resolver` instance. Optionally, make the `Resolver` callable to maintain original behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the part that actually took thought.
Start by clarifying the problem: resolvers are functions that take known features and return new features, and we need to compute desired outputs by chaining them. Then design a dependency graph from resolver inputs to outputs, perform a topological sort to determine execution order, and execute resolvers when all their inputs are available, handling cycles and missing inputs.
Pro tip: Mention that you would cache computed features to avoid redundant work and support incremental updates, and discuss how to handle resolver failures gracefully without crashing the entire pipeline.
Ask about resolver interface, whether multiple resolvers can produce the same feature, and if there are priorities or costs. Confirm that resolvers are pure and deterministic.
Build a directed graph where nodes are features and edges represent resolver dependencies (from input features to output features). Identify which resolvers are needed to compute the desired outputs.
Check for cycles in the dependency graph; if found, report an error. Also verify that all required inputs are either initially known or computable from known features.
Perform a topological sort on the needed resolvers, then execute them in order, updating the known features map. Use a queue or DFS-based approach to process resolvers as soon as their inputs are ready.
Discuss handling missing inputs, resolver failures, and caching results. Consider incremental computation if the known features change, and avoid recomputing already computed features.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.