from typing import Union

from pydantic.v1 import BaseModel


class Item(BaseModel):
    name: str
    description: Union[str, None] = None

from fastapi import FastAPI

app = FastAPI()


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

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

#
# Environment Variable Prerequisites
#
#   JAVA_HOME           (Optional) Points to a Java installation.
#   MAVEN_OPTS          (Optional) Java runtime options used when Maven is executed.
#   MAVEN_SKIP_RC       (Optional) Flag to disable loading of mavenrc files.
# -----------------------------------------------------------------------------

#
# Environment Variable Prerequisites
#
#   JAVA_HOME           (Optional) Points to a Java installation.
#   MAVEN_OPTS          (Optional) Java runtime options used when Maven is executed.
#   MAVEN_SKIP_RC       (Optional) Flag to disable loading of mavenrc files.
# -----------------------------------------------------------------------------

* 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`.

* Преобразование данных
* Валидацию данных
* Автоматическую документацию

## Особые типы и валидация { #special-types-and-validation }

Помимо обычных простых типов, таких как `str`, `int`, `float` и т.д., вы можете использовать более сложные простые типы, которые наследуются от `str`.

}

unsigned char TF_GetXlaAutoJitEnabled() {
  tensorflow::XlaAutoJitFlag flag =
      tensorflow::GetMarkForCompilationPassFlags()->xla_auto_jit_flag;
  return static_cast<unsigned char>(flag.optimization_level_single_gpu > 0 ||
                                    flag.optimization_level_general > 0);
}

unsigned char TF_GetXlaConstantFoldingDisabled() {
  return static_cast<unsigned char>(

from fastapi import FastAPI
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: Item) -> Item:

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