Type hints with user defined classes

The former is correct, if arg accepts an instance of CustomClass:

def FuncA(arg: CustomClass):
    #     ^ instance of CustomClass

In case you want the class CustomClass itself (or a subtype), then you should write:

from typing import Type  # you have to import Type

def FuncA(arg: Type[CustomClass]):
    #     ^ CustomClass (class object) itself

Like it is written in the documentation about Typing:

class typing.Type(Generic[CT_co])

A variable annotated with C may accept a value of type C. In
contrast, a variable annotated with Type[C] may accept values that
are classes themselves - specifically, it will accept the class
object of C.

The documentation includes an example with the int class:

a = 3         # Has type 'int'
b = int       # Has type 'Type[int]'
c = type(a)   # Also has type 'Type[int]'

How do I type hint a method with the type of the enclosing class?

TL;DR: As of today (2019), in Python 3.7+ you can turn this feature on using a "future" statement, from __future__ import annotations.

(The behaviour enabled by from __future__ import annotations might become the default in future versions of Python, and was going to be made the default in Python 3.10. However, the change in 3.10 was reverted at the last minute, and now may not happen at all.)

In Python 3.6 or below, you should use a string.

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I guess you got this exception:

NameError: name 'Position' is not defined

This is because Position must be defined before you can use it in an annotation, unless you are using Python with PEP 563 changes enabled.

Python 3.7+: from __future__ import annotations

Python 3.7 introduces PEP 563: postponed evaluation of annotations. A module that uses the future statement from __future__ import annotations will store annotations as strings automatically:

from __future__ import annotations

class Position:
    def __add__(self, other: Position) -> Position:
        ...

This had been scheduled to become the default in Python 3.10, but this change has now been postponed. Since Python still is a dynamically typed language so no type-checking is done at runtime, typing annotations should have no performance impact, right? Wrong! Before Python 3.7, the typing module used to be one of the slowest python modules in core so for code that involves importing the typing module, you will see an up to 7 times increase in performance when you upgrade to 3.7.

Python <3.7: use a string

According to PEP 484, you should use a string instead of the class itself:

class Position:
    ...
    def __add__(self, other: 'Position') -> 'Position':
       ...

If you use the Django framework, this may be familiar, as Django models also use strings for forward references (foreign key definitions where the foreign model is self or is not declared yet). This should work with Pycharm and other tools.

Sources

The relevant parts of PEP 484 and PEP 563, to spare you the trip:

Forward references

When a type hint contains names that have not been defined yet, that definition may be expressed as a string literal, to be resolved later.

A situation where this occurs commonly is the definition of a container class, where the class being defined occurs in the signature of some of the methods. For example, the following code (the start of a simple binary tree implementation) does not work:

class Tree:
    def __init__(self, left: Tree, right: Tree):
        self.left = left
        self.right = right

To address this, we write:

class Tree:
    def __init__(self, left: 'Tree', right: 'Tree'):
        self.left = left
        self.right = right

The string literal should contain a valid Python expression (i.e., compile(lit, '', 'eval') should be a valid code object) and it should evaluate without errors once the module has been fully loaded. The local and global namespace in which it is evaluated should be the same namespaces in which default arguments to the same function would be evaluated.

and PEP 563:

Implementation

In Python 3.10, function and variable annotations will no longer be evaluated at definition time. Instead, a string form will be preserved in the respective __annotations__ dictionary. Static type checkers will see no difference in behavior, whereas tools using annotations at runtime will have to perform postponed evaluation.

...

Enabling the future behavior in Python 3.7

The functionality described above can be enabled starting from Python 3.7 using the following special import:

from __future__ import annotations

Things that you may be tempted to do instead

A. Define a dummy Position

Before the class definition, place a dummy definition:

class Position(object):
    pass

class Position(object):
    ...

This will get rid of the NameError and may even look OK:

>>> Position.__add__.__annotations__
{'other': __main__.Position, 'return': __main__.Position}

But is it?

>>> for k, v in Position.__add__.__annotations__.items():
...     print(k, 'is Position:', v is Position)                                                                                                                                                                                                                  
return is Position: False
other is Position: False

B. Monkey-patch in order to add the annotations:

You may want to try some Python metaprogramming magic and write a decorator
to monkey-patch the class definition in order to add annotations:

class Position:
    ...
    def __add__(self, other):
        return self.__class__(self.x + other.x, self.y + other.y)

The decorator should be responsible for the equivalent of this:

Position.__add__.__annotations__['return'] = Position
Position.__add__.__annotations__['other'] = Position

At least it seems right:

>>> for k, v in Position.__add__.__annotations__.items():
...     print(k, 'is Position:', v is Position)                                                                                                                                                                                                                  
return is Position: True
other is Position: True

Probably too much trouble.

Python type hinting own class in method

The name Foo doesn't yet exist, so you need to use 'Foo' instead. (mypy and other type checkers should recognize this as a forward reference.)

def __eq__(self, other: 'Foo'):
    return self.id == other.id

Alternately, you can use

from __future__ import annotations

which prevents evaluation of all annotations and simply stores them as strings for later reference. (This will be the default in Python 3.10.)

Finally, as also pointed out in the comments, __eq__ should not be hinted this way in the first place. The second argument should be an arbitrary object; you'll return NotImplemented if you don't know how to compare your instance to it. (Who knows, maybe it knows how to compare itself to your instance. If Foo.__eq__(Foo(), Bar()) returns NotImplemented, then Python will try Bar.__eq__(Bar(), Foo()).)

from typing import Any

def __eq__(self, other: Any) -> bool:
    if isinstance(other, Foo):
        return self.id == other.id
    return NotImplemented

or using duck-typing,

def __eq__(self, other: Any) -> bool:
    # Compare to anything with an `id` attribute
    try:
        return self.id == other.id
    except AttributeError:
        return NotImplemented

In either case, the Any hint is optional.

type hinting within a class

"self" references in type checking are typically done using strings:

class Node:
    def append_child(self, node: 'Node'):
       if node != None:
        self.first_child = node
    self.child_nodes += [node]

This is described in the "Forward references" section of PEP-0484.

Please note that this doesn't do any type-checking or casting. This is a type hint which python (normally) disregards completely¹. However, third party tools (e.g. mypy), use type hints to do static analysis on your code and can generate errors before runtime.

Also, starting with python3.7, you can implicitly convert all of your type-hints to strings within a module by using the from __future__ import annotations (and in python4.0, this will be the default).

^{1The hints are introspectable -- So you could use them to build some kind of runtime checker using decorators or the like if you really wanted to, but python doesn't do this by default.}

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