ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  float value = 0.0f;
  TF_Tensor* t = TFE_TensorHandleResolve(value_handle, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(sizeof(float), TF_TensorByteSize(t));
  memcpy(&value, TF_TensorData(t), sizeof(float));
  TF_DeleteTensor(t);
  EXPECT_EQ(12.0, value);

  TFE_DeleteTensorHandle(var_handle);

from fastapi import FastAPI
from fastapi.temp_pydantic_v1_params import Body
from pydantic.v1 import BaseModel


class Item(BaseModel):
    name: str
    description: str | None = None
    size: float


app = FastAPI()


@app.post("/items/")
async def create_item(item: Annotated[Item, Body(embed=True)]) -> Item:

<%@page pageEncoding="UTF-8" contentType="text/html; charset=UTF-8"%>
<footer class="main-footer">
	<div class="float-right d-none d-sm-inline-block"></div>
	<la:message key="labels.footer.copyright" />

                            <la:message key="labels.dict_mapping_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>

  }

  ObjectCountLinkedHashMap(int expectedSize, float loadFactor) {
    super(expectedSize, loadFactor);
  }

  ObjectCountLinkedHashMap(ObjectCountHashMap<K> map) {
    init(map.size(), DEFAULT_LOAD_FACTOR);
    for (int i = map.firstIndex(); i != -1; i = map.nextIndex(i)) {
      put(map.getKey(i), map.getValue(i));
    }
  }

  @Override
  void init(int expectedSize, float loadFactor) {
    super.init(expectedSize, loadFactor);

// 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,

* Data conversion
* Data validation
* Automatic documentation

## Special types and validation { #special-types-and-validation }

Apart from normal singular types like `str`, `int`, `float`, etc. you can use more complex singular types that inherit from `str`.

    "aliases" : { },
    "mappings" : {
      "data_config" : {
        "properties" : {
          "available" : {
            "type" : "boolean"
          },
          "boost" : {
            "type" : "float"
          },
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "description" : {
            "type" : "text"

   */
  ObjectCountHashMap(int capacity) {
    this(capacity, DEFAULT_LOAD_FACTOR);
  }

  ObjectCountHashMap(int expectedSize, float loadFactor) {
    init(expectedSize, loadFactor);
  }

  void init(int expectedSize, float loadFactor) {
    Preconditions.checkArgument(expectedSize >= 0, "Initial capacity must be non-negative");
    Preconditions.checkArgument(loadFactor > 0, "Illegal load factor");

      "suggestWord": {
        "type": "keyword"
      },
      "reading": {
        "type": "keyword"
      },
      "permissions": {
        "type": "keyword"
      },
      "boost": {
        "type": "float"
      },
      "createdBy": {
        "type": "keyword"
      },
      "createdTime": {
        "type": "long"
      },
      "updatedBy": {
        "type": "keyword"
      },
      "updatedTime": {