import anyio
from fastapi import FastAPI
from fastapi.responses import StreamingResponse

app = FastAPI()


async def fake_video_streamer():
    for i in range(10):
        yield b"some fake video bytes"
        await anyio.sleep(0)


@app.get("/")
async def main():

from fastapi import FastAPI, File, UploadFile

app = FastAPI()


@app.post("/files/")
async def create_file(file: bytes = File(description="A file read as bytes")):
    return {"file_size": len(file)}


@app.post("/uploadfile/")
async def create_upload_file(
    file: UploadFile = File(description="A file read as UploadFile"),
):

  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  // TODO(gjn): Add support for waiting on async local mirrors
  if (!remote && !async && !remote_func_outputs) {
    auto remote_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h1_task2));
    // The input handles should never change since they have been mirrored.

    name: str
    owner: UserDB


class PetOut(BaseModel):
    name: str
    owner: User


@app.post("/users/", response_model=User)
async def create_user(user: UserCreate):
    return user


@app.get("/pets/{pet_id}", response_model=PetOut)
async def read_pet(pet_id: int):
    user = UserDB(
        email="******@****.***",
        hashed_password="secrethashed",
    )

from fastapi import FastAPI
from fastapi.testclient import TestClient

app = FastAPI()

items = {}


@app.on_event("startup")
async def startup_event():
    items["foo"] = {"name": "Fighters"}
    items["bar"] = {"name": "Tenders"}


@app.get("/items/{item_id}")
async def read_items(item_id: str):
    return items[item_id]


def test_read_items():
    with TestClient(app) as client:

from fastapi import FastAPI, File, UploadFile

app = FastAPI()


@app.post("/files/")
async def create_file(file: Annotated[bytes | None, File()] = None):
    if not file:
        return {"message": "No file sent"}
    else:
        return {"file_size": len(file)}


@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile | None = None):
    if not file:

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


@app.post("/items/")
async def create_item(item: Item) -> Item:
    return item


@app.get("/items/")
async def read_items() -> list[Item]:
    return [
        Item(name="Portal Gun", price=42.0),
        Item(name="Plumbus", price=32.0),

    * This is especially useful if you run `await myfile.read()` once and then need to read the contents again.
* `close()`: Closes the file.

As all these methods are `async` methods, you need to "await" them.

For example, inside of an `async` *path operation function* you can get the contents with:

```Python
contents = await myfile.read()
```

  if (forward_input) {
    TFE_DeleteTensorHandle(n);
  }

  int num_retvals = 1;
  if (async) {
    // Enqueue dummy ops so we backlog async execution & actually test async.
    // This is usually unnecessary, but we've experienced the occasional test
    // failure when testing async mode with no explicit forwarding.
    for (int i = 0; i < 100000; ++i) {
      TFE_Op* add_op_dummy = AddOp(ctx, m, m);

        },
    },
]

app = FastAPI(openapi_tags=tags_metadata)


@app.get("/users/", tags=["users"])
async def get_users():
    return [{"name": "Harry"}, {"name": "Ron"}]


@app.get("/items/", tags=["items"])
async def get_items():