З `Form` Ви можете оголошувати ті ж конфігурації, що і з `Body` (та `Query`, `Path`, `Cookie`), включаючи валідацію, приклади, псевдоніми (наприклад, `user-name` замість `username`) тощо.

/// info | Інформація

`Form` — це клас, який безпосередньо наслідується від `Body`.

///

/// tip | Порада

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import jcifs.util.Hexdump;

/**
 * Privilege Attribute Certificate (PAC) decoder for Kerberos tickets.
 * Parses and validates PAC data structures containing user authorization information
 * from Active Directory Kerberos tickets.
 */
public class Pac {

    private static final Logger log = LoggerFactory.getLogger(Pac.class);

    private PacLogonInfo logonInfo;

# History, Design and Future { #history-design-and-future }

Some time ago, <a href="https://github.com/fastapi/fastapi/issues/3#issuecomment-454956920" class="external-link" target="_blank">a **FastAPI** user asked</a>:

> What’s the history of this project? It seems to have come from nowhere to awesome in a few weeks [...]

Here's a little bit of that history.

## Alternatives { #alternatives }

# /---------------------------------------------------------------------------
# commonColumnMap: (Default map:{})
#
# The definition of common column(contains auto set-up).
# For example, the date you registered the record,
# the user who updated the record and so on...
# The column names are treated as case insensitive.
#
# The variable '$$AccessContext$$' means allcommon.AccessContext.
#
# Example:
# map:{
#     ; commonColumnMap = map:{

    with the Library, with the complete machine-readable "work that
    uses the Library", as object code and/or source code, so that the
    user can modify the Library and then relink to produce a modified
    executable containing the modified Library.  (It is understood
    that the user who changes the contents of definitions files in the
    Library will not necessarily be able to recompile the application

from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can

        uses: actions/checkout@v5

      - name: Check links
        uses: lycheeverse/lychee-action@v2.6.1
        with:
          # excluded:
          # - notes.md and notes-templates.md - because they are a part of user guide
          # - Kotlin's Module.md - because it's a part of the Kotlin DSL docs
          args: >
            --no-progress --offline '**/*.md' --include-fragments

    assertThat(recorded.headers["Accept-Encoding"]).isEqualTo("gzip")
    assertThat(recorded.headers["Connection"]).isEqualTo("Keep-Alive")
    assertThat(recorded.headers["User-Agent"]!!).matches(Regex("okhttp/.*"))
  }

The user setting up replication needs *s3:GetReplicationConfiguration* and *s3:GetBucketVersioning* permission on the source cluster. We do not recommend running root credentials/super admin with replication, instead create a dedicated user. The access credentials used at the destination requires *s3:ReplicateObject* permission.

The following minimal permission policy is needed by admin user setting up replication on the `source`:

						<la:text property="username" styleId="username"
							class="form-control" placeholder="${ph_username}" />
						<div class="input-group-append">
							<span class="input-group-text">
								<em class="fa fa-user fa-fw">
            				</span>
						</div>
					</div>
					<div class="input-group mb-3">
						<c:set var="ph_password">
							<la:message key="labels.login.placeholder_password" />
						</c:set>