from typing import Union


def process_item(item: Union[int, str]):

from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    app_name: str = "Awesome API"
    admin_email: str
    items_per_user: int = 50

from fastapi import FastAPI

description = """
ChimichangApp API helps you do awesome stuff. 🚀

## Items

You can **read items**.

## Users

You will be able to:

* **Create users** (_not implemented_).
* **Read users** (_not implemented_).
"""

app = FastAPI(
    title="ChimichangApp",
    description=description,
    summary="Deadpool's favorite app. Nuff said.",
    version="0.0.1",

                  region_name='us-east-1')

r = s3.select_object_content(
    Bucket='mycsvbucket',
    Key='sampledata/TotalPopulation.csv.gz',
    ExpressionType='SQL',
    Expression="select * from s3object s where s.Location like '%United States%'",
    InputSerialization={
        'CSV': {
            "FileHeaderInfo": "USE",
        },
        'CompressionType': 'GZIP',
    },

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: str | None = Query(
        default=None,
        alias="item-query",
        title="Query string",
        description="Query string for the items to search in the database that have a good match",
        min_length=3,
        max_length=50,
        pattern="^fixedquery$",
        deprecated=True,
    ),
):

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

from docs_src.app_testing.tutorial002_py39 import test_read_main, test_websocket


def test_main():
    test_read_main()


def test_ws():

		xlMeta.AddTestObjectPart(i+1, 67108864)
	}
	return xlMeta
}

// Compare the unmarshaled XLMetaV1 with the one obtained from jsoniter parsing.
func compareXLMetaV1(t *testing.T, unMarshalXLMeta, jsoniterXLMeta xlMetaV1Object) {
	// Start comparing the fields of xlMetaV1Object obtained from jsoniter parsing with one parsed using json unmarshalling.
	if unMarshalXLMeta.Version != jsoniterXLMeta.Version {

from fastapi import Cookie, FastAPI

app = FastAPI()


@app.get("/items/")
async def read_items(ads_id: str | None = Cookie(default=None)):

from docs_src.python_types.tutorial005_py39 import get_items


def test_get_items():
    res = get_items(
        "item_a",
        "item_b",
        "item_c",
        "item_d",
        "item_e",
    )