You can choose freely between the approaches, but please remember that if you choose to use type annotations, you **must** annotate **all** attributes!
If you define a class with a {func}`attrs.field` that **lacks** a type annotation, *attrs* will **ignore** other fields that have a type annotation, but are not defined using {func}`attrs.field`:
Even when going all-in on type annotations, you will need {func}`attrs.field` for some advanced features though.
One of those features are the decorator-based features like defaults.
It's important to remember that *attrs* doesn't do any magic behind your back.
All the decorators are implemented using an object that is returned by the call to {func}`attrs.field`.
Attributes that only carry a class annotation do not have that object so trying to call a method on it will inevitably fail.
---
Please note that types -- regardless how added -- are *only metadata* that can be queried from the class and they aren't used for anything out of the box!
Because Python does not allow references to a class object before the class is defined,
types may be defined as string literals, so-called *forward references* ({pep}`526`).
You can enable this automatically for a whole module by using `from __future__ import annotations` ({pep}`563`) as of Python 3.7.
In this case *attrs* simply puts these string literals into the `type` attributes.
If you need to resolve these to real types, you can call {func}`attrs.resolve_types` which will update the attribute in place.
In practice though, types show their biggest usefulness in combination with tools like [*Mypy*], [*pytype*], or [*Pyright*] that have dedicated support for *attrs* classes.
The addition of static types is certainly one of the most exciting features in the Python ecosystem and helps you write *correct* and *verified self-documenting* code.
If you don't know where to start, Carl Meyer gave a great talk on [*Type-checked Python in the Real World*](https://www.youtube.com/watch?v=pMgmKJyWKn8) at PyCon US 2018 that will help you to get started in no time.
## Mypy
While having a nice syntax for type metadata is great, it's even greater that [*Mypy*] as of 0.570 ships with a dedicated *attrs* plugin which allows you to statically check your code.
Imagine you add another line that tries to instantiate the defined class using `SomeClass("23")`.
Mypy will catch that error for you:
```console
$ mypy t.py
t.py:12: error: Argument 1 to "SomeClass" has incompatible type "str"; expected "int"
```
This happens *without* running your code!
And it also works with *both* Python 2-style annotation styles.
To *Mypy*, this code is equivalent to the one above:
This provides static type inference for a subset of *attrs* equivalent to standard-library {mod}`dataclasses`,
and requires explicit type annotations using the {func}`attrs.define` or `@attr.s(auto_attribs=True)` API.
Given the following definition, *Pyright* will generate static type signatures for `SomeClass` attribute access, `__init__`, `__eq__`, and comparison methods:
The *Pyright* inferred types are a tiny subset of those supported by *Mypy*, including:
- The `attrs.frozen` decorator is not typed with frozen attributes, which are properly typed via `attrs.define(frozen=True)`.
Your constructive feedback is welcome in both [attrs#795](https://github.com/python-attrs/attrs/issues/795) and [pyright#1782](https://github.com/microsoft/pyright/discussions/1782).
Generally speaking, the decision on improving *attrs* support in *Pyright* is entirely Microsoft's prerogative, though.