placeholder: e.g., 3.9 validations: required: false

-   type: input id: Bazel attributes: label: Bazel version description: if
    compiling from source placeholder: validations: required: false

-   type: input id: Compiler attributes: label: GCC/Compiler version
    description: if compiling from source placeholder: validations: required:
    false

-   type: input id: Cuda attributes: label: CUDA/cuDNN version description:

from typing import Any

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr

app = FastAPI()


class UserIn(BaseModel):
    username: str
    password: str
    email: EmailStr
    full_name: str | None = None


class UserOut(BaseModel):
    username: str
    email: EmailStr
    full_name: str | None = None


@app.post("/user/", response_model=UserOut)
async def create_user(user: UserIn) -> Any:

from typing import Union

from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item = Body(embed=True)):
    results = {"item_id": item_id, "item": item}

     */
    String IS_STRICT = "org.apache.maven.artifact.repository.metadata.io.isStrict";

    /**
     * Reads the metadata from the specified file.
     *
     * @param input The file to deserialize the metadata from, must not be {@code null}.
     * @param options The options to use for deserialization, may be {@code null} to use the default values.

     */
    String IS_STRICT = "org.apache.maven.settings.io.isStrict";

    /**
     * Reads the settings from the specified file.
     *
     * @param input The file to deserialize the settings from, must not be {@code null}.
     * @param options The options to use for deserialization, may be {@code null} to use the default values.

     */
    String IS_STRICT = "org.apache.maven.toolchains.io.isStrict";

    /**
     * Reads the toolchains from the specified file.
     *
     * @param input The file to deserialize the toolchains from, must not be {@code null}.
     * @param options The options to use for deserialization, may be {@code null} to use the default values.

		{Name: "subquery_raw_4", Age: 40},
	}
	DB.Create(&users)

	var count int64
	err := DB.Raw("select count(*) from (?) tmp where 1 = ? AND name IN (?)", DB.Raw("select name from users where age >= ? and name in (?)", 10, []string{"subquery_raw_1", "subquery_raw_2", "subquery_raw_3"}), 1, DB.Raw("select name from users where age >= ? and name in (?)", 20, []string{"subquery_raw_1", "subquery_raw_2", "subquery_raw_3"})).Scan(&count).Error

from collections.abc import Generator
from contextlib import contextmanager
from typing import Annotated, Any

import pytest
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:

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

from fastapi import APIRouter, FastAPI, Request
from fastapi.responses import JSONResponse
from fastapi.testclient import TestClient

app = FastAPI()
router = APIRouter()


@router.route("/items/")
def read_items(request: Request):
    return JSONResponse({"hello": "world"})


app.include_router(router)

client = TestClient(app)


def test_sub_router():
    response = client.get("/items/")