values[0] = v;
  return TF_NewTensor(TF_DOUBLE, nullptr, 0, values, num_bytes,
                      &DoubleDeallocator, nullptr);
}

TF_Tensor* FloatTensor(float v) {
  const int num_bytes = sizeof(float);
  float* values = new float[1];
  values[0] = v;
  return TF_NewTensor(TF_FLOAT, nullptr, 0, values, num_bytes,
                      &FloatDeallocator, nullptr);
}

from fastapi import FastAPI

app = FastAPI()


@app.post("/index-weights/")
async def create_index_weights(weights: dict[int, float]):

    "aliases" : { },
    "mappings" : {
      "web_config" : {
        "properties" : {
          "available" : {
            "type" : "boolean"
          },
          "boost" : {
            "type" : "float"
          },
          "configParameter" : {
            "type" : "keyword"
          },
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {

from fastapi import FastAPI

app = FastAPI()


@app.get("/keyword-weights/", response_model=dict[str, float])
async def read_keyword_weights():

    }
}
```

🔄, 🤸 👈 📄, ⏮️ **FastAPI** 👆 🤚:

* 👨‍🎨 🐕‍🦺 (🛠️, ♒️), 🐦 🏷
* 💽 🛠️
* 💽 🔬
* 🏧 🧾

## 🎁 🆎 & 🔬

↖️ ⚪️➡️ 😐 ⭐ 🆎 💖 `str`, `int`, `float`, ♒️. 👆 💪 ⚙️ 🌅 🏗 ⭐ 🆎 👈 😖 ⚪️➡️ `str`.

👀 🌐 🎛 👆 ✔️, 🛒 🩺 <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Pydantic 😍 🆎</a>. 👆 🔜 👀 🖼 ⏭ 📃.

    }

    public void setBoost_Equal(Float boost) {
        setBoost_Term(boost, null);
    }

    public void setBoost_Equal(Float boost, ConditionOptionCall<TermQueryBuilder> opLambda) {
        setBoost_Term(boost, opLambda);
    }

    public void setBoost_Term(Float boost) {
        setBoost_Term(boost, null);
    }

    public void setBoost_Term(Float boost, ConditionOptionCall<TermQueryBuilder> opLambda) {

      "depth": {
        "type": "integer"
      },
      "parentUrl": {
        "type": "keyword"
      },
      "method": {
        "type": "keyword"
      },
      "weight": {
        "type": "float"
      },
      "createTime": {
        "type": "long"
      },
      "lastModified": {
        "type": "long"
      },
      "sessionId": {
        "type": "keyword"
      },
      "url": {

{
  "fess_config.elevate_word" : {
    "aliases" : { },
    "mappings" : {
      "elevate_word" : {
        "properties" : {
          "boost" : {
            "type" : "float"
          },
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "permissions" : {
            "type" : "keyword"
          },
          "reading" : {

// 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