const char* name) {
  unique_tensor_ptr tensor(BoolTensor(v), TF_DeleteTensor);
  return Const(tensor.get(), graph, s, name);
}

TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s,
                          const char* name) {
  unique_tensor_ptr tensor(Int32Tensor(v), TF_DeleteTensor);
  return Const(tensor.get(), graph, s, name);
}

    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {
  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

// Runs model as is if output is a scalar,
// else sums the output tensor before returning.
absl::Status RunAndMaybeSum(AbstractContext* ctx, Model forward,

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TF_Tensor* tensor =
      TF_AllocateTensor(TF_FLOAT, dims.data(), dims.size(), sizeof(float));
  float tensor_data[] = {1};
  memcpy(TF_TensorData(tensor), tensor_data, TF_TensorByteSize(tensor));
  TFE_TensorHandle* tensor_handle = TFE_NewTensorHandle(tensor, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  if (reader_ != nullptr) return reader_->DebugString();
  return v2_reader_->DebugString();
}

void CheckpointReader::GetTensor(
    const string& name, std::unique_ptr<tensorflow::Tensor>* out_tensor,
    TF_Status* out_status) const {
  absl::Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;

        assert items == {"foo": {"name": "Fighters"}, "bar": {"name": "Tenders"}}

        response = client.get("/items/foo")
        assert response.status_code == 200
        assert response.json() == {"name": "Fighters"}

        # After the requests is done, the items are still there
        assert items == {"foo": {"name": "Fighters"}, "bar": {"name": "Tenders"}}

                      ", 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();
}

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")

                                                  TF_Status* s) {
  auto handle = dyn_cast<ImmediateExecutionTensorHandle>(unwrap(at));
  if (!handle) {
    string msg = absl::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);
}

//     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 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: