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


# Don't do this in production!
@app.post("/user/")
async def create_user(user: UserIn) -> UserIn:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Image(BaseModel):
    url: str
    name: str


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()
    image: Image | None = None


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

		// If we did, remove it from ztunnel
		if util.PodRedirectionActive(pod) {
			log.Debugf("pod is deleted and was captured, removing from ztunnel")
			err := s.dataplane.RemovePodFromMesh(s.ctx, pod, true)
			if err != nil {
				log.Warnf("DelPodFromMesh returned: %v", err)
			}
		} else {
			log.Debugf("skipped deleting from mesh for pod, pod not in mesh")
		}
	}
	return nil

from typing import Annotated, Union

from fastapi import FastAPI, Header

app = FastAPI()


@app.get("/items/")
async def read_items(
    strange_header: Annotated[
        Union[str, None], Header(convert_underscores=False)
    ] = None,
):

from fastapi import FastAPI, Path
from typing_extensions import Annotated

app = FastAPI()


@app.get("/items/{item_id}")
async def read_items(
    q: str, item_id: Annotated[int, Path(title="The ID of the item to get")]
):
    results = {"item_id": item_id}
    if q:
        results.update({"q": q})

## Example using Python API

### 1. Prerequisites

- Install MinIO Server from [here](https://min.io/docs/minio/linux/index.html#procedure).
- Familiarity with AWS S3 API.
- Familiarity with Python and installing dependencies.

### 2. Install boto3

Install `aws-sdk-python` from AWS SDK for Python official docs [here](https://aws.amazon.com/sdk-for-python/)

### 3. Example

// a map (`tensorflow::eager::TensorTape`) from the wrapped tensor to the id of
// the op that produced it (or -1 if this tensor was watched using
// `GradientTape::Watch`.) The op_id is simply a unique index assigned to each
// op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,

from typing import List, Union

from fastapi import FastAPI, Header

app = FastAPI()


@app.get("/items/")
async def read_items(x_token: Union[List[str], None] = Header(default=None)):

from typing import Annotated, List, Union

from fastapi import FastAPI, Header

app = FastAPI()


@app.get("/items/")
async def read_items(x_token: Annotated[Union[List[str], None], Header()] = None):

 */
@Deprecated
class ProjectInheritanceTest extends AbstractProjectInheritanceTestCase {
    // ----------------------------------------------------------------------
    //
    // p1 inherits from p0
    // p0 inherits from super model
    //
    // or we can show it graphically as:
    //
    // p1 ---> p0 --> super model
    //
    // ----------------------------------------------------------------------

    @Test