},
      "protocolScheme": {
        "type": "keyword"
      },
      "username": {
        "type": "keyword"
      },
      "password": {
        "type": "keyword"
      },
      "parameters": {
        "type": "keyword"
      },
      "webConfigId": {
        "type": "keyword"
      },
      "createdBy": {
        "type": "keyword"
      },
      "createdTime": {
        "type": "long"

  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,
                /*targets*/ nullptr, /*ntargets*/ 0,

     */
    public abstract int getOpnum();

    /**
     * Encode the input parameters for this DCERPC message.
     * @param buf the buffer to encode into
     * @throws NdrException if encoding fails
     */
    public abstract void encode_in(NdrBuffer buf) throws NdrException;

    /**
     * Decode the output parameters from this DCERPC message.
     * @param buf the buffer to decode from

Pero si tienes un montón de dataclasses por ahí, este es un buen truco para usarlos para potenciar una API web usando FastAPI. 🤓

///

## Dataclasses en `response_model`

También puedes usar `dataclasses` en el parámetro `response_model`:

{* ../../docs_src/dataclasses/tutorial002.py hl[1,7:13,19] *}

El dataclass será automáticamente convertido a un dataclass de Pydantic.

Mas se você tem um monte de dataclasses por aí, este é um truque legal para usá-las para alimentar uma API web usando FastAPI. 🤓

///

## Dataclasses em `response_model`

Você também pode usar `dataclasses` no parâmetro `response_model`:

{* ../../docs_src/dataclasses/tutorial002.py hl[1,7:13,19] *}

A dataclass será automaticamente convertida para uma dataclass Pydantic.

            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "hostname" : {
            "type" : "keyword"
          },
          "parameters" : {
            "type" : "keyword"
          },
          "password" : {
            "type" : "keyword"
          },
          "port" : {
            "type" : "integer"
          },

# `FastAPI` class

Here's the reference information for the `FastAPI` class, with all its parameters, attributes and methods.

You can import the `FastAPI` class directly from `fastapi`:

```python
from fastapi import FastAPI
```

::: fastapi.FastAPI
    options:
        members:
            - openapi_version
            - webhooks
            - state
            - dependency_overrides
            - openapi

import jcifs.Encodable;
import jcifs.internal.util.SMBUtil;

/**
 * SMB2 Transform Header for encrypted messages
 *
 * This header is used to encrypt SMB2/SMB3 messages and provides the necessary
 * cryptographic parameters for decryption including the nonce, session ID, and
 * authentication tag.
 *
 * @author mbechler
 */
public class Smb2TransformHeader implements Encodable {

    /**

import org.codelibs.fess.app.web.api.admin.dict.BaseSearchDictBody;

/**
 * Search request body for mapping dictionary administration.
 * Extends BaseSearchDictBody with mapping dictionary-specific search parameters.
 */
public class SearchBody extends BaseSearchDictBody {

    /**
     * Default constructor for SearchBody.
     */
    public SearchBody() {
        super();
    }

import org.codelibs.fess.app.web.api.admin.dict.BaseSearchDictBody;

/**
 * Search request body for synonym dictionary administration.
 * Extends BaseSearchDictBody with synonym dictionary-specific search parameters.
 */
public class SearchBody extends BaseSearchDictBody {

    /**
     * Default constructor for SearchBody.
     */
    public SearchBody() {
        super();
    }