", dtype=", DataType_Name(DataType()),
                      ", type=", FullType().DebugString(), ")");
}

absl::Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method
  // should always return OK status.
  return absl::OkStatus();
}

//     system.xml. Vendors can work with Veeam Product Management and the Alliances team on getting approval to integrate
//     specific system recommendations based on current support case statistics and storage performance possibilities.
//     Vendors might change the settings based on the configuration and scale out of the solution (more storage nodes =>

from typing import Union

from fastapi import Body, FastAPI, status
from fastapi.responses import JSONResponse

app = FastAPI()

items = {"foo": {"name": "Fighters", "size": 6}, "bar": {"name": "Tenders", "size": 3}}


@app.put("/items/{item_id}")
async def upsert_item(
    item_id: str,
    name: Union[str, None] = Body(default=None),
    size: Union[int, None] = Body(default=None),
):
    if item_id in items:

from typing import Annotated

from fastapi import Body, FastAPI, status
from fastapi.responses import JSONResponse

app = FastAPI()

items = {"foo": {"name": "Fighters", "size": 6}, "bar": {"name": "Tenders", "size": 3}}


@app.put("/items/{item_id}")
async def upsert_item(
    item_id: str,
    name: Annotated[str | None, Body()] = None,
    size: Annotated[int | None, Body()] = None,
):
    if item_id in items:

                                                  TF_Status* s) {
  auto handle = dyn_cast<ImmediateExecutionTensorHandle>(unwrap(at));
  if (!handle) {
    string msg =
        StrCat("Not an eager tensor handle.", reinterpret_cast<uintptr_t>(at));
    TF_SetStatus(s, TF_INVALID_ARGUMENT, msg.c_str());
    return nullptr;
  }
  return wrap(handle);
}

}
TF_AbstractTensor* TF_OutputListGet(TF_OutputList* o, int i) {
  return wrap(unwrap(o)->outputs[i]);
}
void TF_OutputListPushBack(TF_OutputList* o, TF_AbstractTensor* tensor,
                           TF_Status* s) {
  unwrap(o)->outputs.push_back(unwrap(tensor));
}

void TF_AbstractOpSetOpType(TF_AbstractOp* op, const char* const op_type,
                            TF_Status* s) {
  tsl::Set_TF_Status_from_Status(

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

from fastapi import Body, FastAPI, status
from fastapi.responses import JSONResponse

app = FastAPI()

items = {"foo": {"name": "Fighters", "size": 6}, "bar": {"name": "Tenders", "size": 3}}


@app.put("/items/{item_id}")
async def upsert_item(
    item_id: str,
    name: str | None = Body(default=None),
    size: int | None = Body(default=None),
):
    if item_id in items:
        item = items[item_id]
        item["name"] = name

from fastapi import FastAPI

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):

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:
        response = client.get("/items/foo")