from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float


class ResponseMessage(BaseModel):
    message: str


@app.post("/items/", response_model=ResponseMessage)
async def create_item(item: Item):
    return {"message": "item received"}


@app.get("/items/", response_model=list[Item])
async def get_items():
    return [

// Create 1 dimensional tensor with values from `values`
TF_Tensor* Int32Tensor(const std::vector<int32_t>& values);

TF_Tensor* Int32Tensor(int32_t v);

TF_Tensor* DoubleTensor(double v);

TF_Tensor* FloatTensor(float v);

TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
                          const char* name = "feed",
                          TF_DataType dtype = TF_INT32,

                           &out_components, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

    ExpectScalarEq<float>(out_components[0].get(), 9.);
    ExpectScalarEq<float>(out_components[1].get(), 4.);
  }

  worker_server1.release();
  worker_server2.release();
}
}  // namespace parallel_device

from typing import List

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float


class ResponseMessage(BaseModel):
    message: str


@app.post("/items/", response_model=ResponseMessage)
async def create_item(item: Item):
    return {"message": "item received"}


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

from fastapi.testclient import TestClient
from pydantic.dataclasses import dataclass

app = FastAPI()


@dataclass
class Item:
    name: str
    price: Optional[float] = None
    owner_ids: Optional[List[int]] = None


@app.get("/items/invalid", response_model=Item)
def get_invalid():
    return {"name": "invalid", "price": "foo"}

app = FastAPI()


@app.get("/items/{item_id}")
async def read_items(
    *,
    item_id: Annotated[int, Path(title="The ID of the item to get", ge=0, le=1000)],
    q: str,
    size: Annotated[float, Query(gt=0, lt=10.5)],
):
    results = {"item_id": item_id}
    if q:
        results.update({"q": q})
    if size:
        results.update({"size": size})

        "type": "keyword"
      },
      "handlerParameter": {
        "type": "keyword"
      },
      "handlerScript": {
        "type": "keyword"
      },
      "boost": {
        "type": "float"
      },
      "available": {
        "type": "boolean"
      },
      "permissions": {
        "type": "keyword"
      },
      "virtualHosts": {
        "type": "keyword"
      },

    out.writeChar('a');
    out.writeShort((short) -30000);
    out.writeShort((short) 50000);
    out.writeInt(0xCAFEBABE);
    out.writeLong(0xDEADBEEFCAFEBABEL);
    out.writeUTF("Herby Derby");
    out.writeFloat(Float.intBitsToFloat(0xCAFEBABE));
    out.writeDouble(Double.longBitsToDouble(0xDEADBEEFCAFEBABEL));

    byte[] data = baos.toByteArray();

    /* Setup input streams */

    out.writeChar('a');
    out.writeShort((short) -30000);
    out.writeShort((short) 50000);
    out.writeInt(0xCAFEBABE);
    out.writeLong(0xDEADBEEFCAFEBABEL);
    out.writeUTF("Herby Derby");
    out.writeFloat(Float.intBitsToFloat(0xCAFEBABE));
    out.writeDouble(Double.longBitsToDouble(0xDEADBEEFCAFEBABEL));

    byte[] data = baos.toByteArray();

    /* Setup input streams */

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float


class ResponseMessage(BaseModel):
    message: str


class User(BaseModel):
    username: str
    email: str


@app.post("/items/", response_model=ResponseMessage, tags=["items"])
async def create_item(item: Item):
    return {"message": "Item received"}