* can't get by with the standard implementations, prefer to derive a new {@code Future} instance
 * with the methods in {@link Futures} or, if necessary, to extend {@link AbstractFuture}.
 *
 * <p>Occasionally, an API will return a plain {@code Future} and it will be impossible to change
 * the return type. For this case, we provide a more expensive workaround in {@code
 * JdkFutureAdapters}. However, when possible, it is more efficient and reliable to create a {@code

 * can't get by with the standard implementations, prefer to derive a new {@code Future} instance
 * with the methods in {@link Futures} or, if necessary, to extend {@link AbstractFuture}.
 *
 * <p>Occasionally, an API will return a plain {@code Future} and it will be impossible to change
 * the return type. For this case, we provide a more expensive workaround in {@code
 * JdkFutureAdapters}. However, when possible, it is more efficient and reliable to create a {@code

                bufferIndex += ((AndXServerMessageBlock) andx).readAndXWireFormat(buffer, bufferIndex);
            } else {

                /*
                 * Just a plain smb. Read it as normal.
                 */

                buffer[bufferIndex++] = (byte) (andx.wordCount & 0xFF);

                if (andx.wordCount != 0 && andx.wordCount > 2) {

{* ../../docs_src/path_operation_advanced_configuration/tutorial006.py hl[19:36, 39:40] *}

In this example, we didn't declare any Pydantic model. In fact, the request body is not even <abbr title="converted from some plain format, like bytes, into Python objects">parsed</abbr> as JSON, it is read directly as `bytes`, and the function `magic_data_reader()` would be in charge of parsing it in some way.

{* ../../docs_src/extra_models/tutorial004_py39.py hl[18] *}


## Response with arbitrary `dict` { #response-with-arbitrary-dict }

You can also declare a response using a plain arbitrary `dict`, declaring just the type of the keys and values, without using a Pydantic model.

This is useful if you don't know the valid field/attribute names (that would be needed for a Pydantic model) beforehand.

# Bucket Replication Design [![slack](https://slack.min.io/slack?type=svg)](https://slack.min.io) [![Docker Pulls](https://img.shields.io/docker/pulls/minio/minio.svg?maxAge=604800)](https://hub.docker.com/r/minio/minio/)

    /**
     * This type is syntactically identical to "multipart/mixed", but the semantics are different.
     * In particular, in a digest, the default `Content-Type` value for a body part is changed from
     * "text/plain" to "message/rfc822".
     */
    @JvmField
    val DIGEST = "multipart/digest".toMediaType()

    /**
     * This type is syntactically identical to "multipart/mixed", but the semantics are different.

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

        return this.username;
    }

    /**
     * Returns the password in plain text or <code>null</code> if the raw password
     * hashes were used to construct this <code>NtlmPasswordAuthentication</code>
     * object which will be the case when NTLM HTTP Authentication is
     * used. There is no way to retrieve a users password in plain text unless
     * it is supplied by the user at runtime.
     *

{* ../../docs_src/path_operation_advanced_configuration/tutorial006.py hl[19:36, 39:40] *}

En este ejemplo, no declaramos ningún modelo Pydantic. De hecho, el cuerpo del request ni siquiera se <abbr title="converted from some plain format, like bytes, into Python objects">parse</abbr> como JSON, se lee directamente como `bytes`, y la función `magic_data_reader()` sería la encargada de parsearlo de alguna manera.