@dataclass
class Item:
    name: str
    count: int


class DictablePerson(Person):
    def __iter__(self):
        return ((k, v) for k, v in self.__dict__.items())


class DictablePet(Pet):
    def __iter__(self):
        return ((k, v) for k, v in self.__dict__.items())


class Unserializable:
    def __iter__(self):
        raise NotImplementedError()

    @property
    def __dict__(self):

from fastapi import Depends, FastAPI, WebSocket
from fastapi.testclient import TestClient


class Session:
    def __init__(self) -> None:
        self.data = ["foo", "bar", "baz"]
        self.open = True

    def __iter__(self) -> Generator[str, None, None]:
        for item in self.data:
            if self.open:
                yield item
            else:
                raise ValueError("Session closed")


@contextmanager

from fastapi.responses import StreamingResponse
from fastapi.testclient import TestClient


class Session:
    def __init__(self) -> None:
        self.data = ["foo", "bar", "baz"]
        self.open = True

    def __iter__(self) -> Generator[str, None, None]:
        for item in self.data:
            if self.open:
                yield item
            else:
                raise ValueError("Session closed")


@contextmanager

class MySuperContextManager:
    def __init__(self):
        self.db = DBSession()

    def __enter__(self):
        return self.db

    def __exit__(self, exc_type, exc_value, traceback):
        self.db.close()


async def get_db():
    with MySuperContextManager() as db:

        files.
*   Eager Execution:
    *   With eager execution Datasets can now be used as standard python
        iterators (`for batch in dataset:`). Both `Dataset.__iter__()` and
        `Dataset.make_one_shot_iterator()` can now be used to create iterators
        when eager execution is enabled.
    *   Automatic device placement has been enabled (i.e., use a GPU if

    # since we're creating a new limiter for each call, any non-zero limit
    # works (1 is arbitrary)
    exit_limiter = CapacityLimiter(1)
    try:
        yield await run_in_threadpool(cm.__enter__)
    except Exception as e:
        ok = bool(
            await anyio.to_thread.run_sync(
                cm.__exit__, type(e), e, e.__traceback__, limiter=exit_limiter
            )
        )
        if not ok:

import pytest
from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel


class MyUuid:
    def __init__(self, uuid_string: str):
        self.uuid = uuid_string

    def __str__(self):
        return self.uuid

    @property  # type: ignore
    def __class__(self):
        return uuid.UUID

    @property
    def __dict__(self):

        if self.endpoint_path:
            context += f"\n    {self.endpoint_path}"
        return context

    def __str__(self) -> str:
        message = f"{len(self._errors)} validation error{'s' if len(self._errors) != 1 else ''}:\n"
        for err in self._errors:
            message += f"  {err}\n"

        self.reset_at: datetime = datetime.fromtimestamp(0, timezone.utc)
        self.last_request_start_time: datetime = datetime.fromtimestamp(0, timezone.utc)
        self.speed_multiplier: float = 1.0

    def __enter__(self) -> "RateLimiter":
        now = datetime.now(tz=timezone.utc)

        # Handle primary rate limits
        primary_limit_wait_time = 0.0
        if self.remaining_points <= self.last_query_cost:

Bu, az çok "ileri seviye" bir fikirdir.

**FastAPI**'ye yeni başlıyorsanız şimdilik bunu atlamak isteyebilirsiniz.

///

Python'da Context Manager'ları, [iki method'a sahip bir class oluşturarak: `__enter__()` ve `__exit__()`](https://docs.python.org/3/reference/datamodel.html#context-managers) yaratabilirsiniz.