!important;\n}\n\n.overflow-y-visible {\n  overflow-y: visible !important;\n}\n\n.overflow-y-scroll {\n  overflow-y: scroll !important;\n}\n\n.d-inline {\n  display: inline !important;\n}\n\n.d-inline-block {\n  display: inline-block !important;\n}\n\n.d-block {\n  display: block !important;\n}\n\n.d-grid {\n  display: grid !important;\n}\n\n.d-inline-grid {\n  display: inline-grid !important;\n}\n\n.d-table {\n  display: table !important;\n}\n\n.d-table-row {\n  display: table-row !important;\n}\n\n.d-table-cell...

        //
        if (tagNo == 0x1f) {
            int b = s.read();
            if (b < 31) {
                if (b < 0) {
                    throw new EOFException("EOF found inside tag value.");
                }
                throw new IOException("corrupted stream - high tag number < 31 found");
            }

            tagNo = b & 0x7f;

            // X.690-0207 8.1.2.4.2

        super.processWebInfLib();

        final WebResource[] possibleJars = listResources("/WEB-INF/plugin", false);

        for (final WebResource possibleJar : possibleJars) {
            if (possibleJar.isFile() && possibleJar.getName().endsWith(".jar")) {
                try (final JarFile jarFile = new JarFile(possibleJar.getCanonicalPath())) {
                    final Manifest manifest = jarFile.getManifest();

# Body - Fields { #body-fields }

The same way you can declare additional validation and metadata in *path operation function* parameters with `Query`, `Path` and `Body`, you can declare validation and metadata inside of Pydantic models using Pydantic's `Field`.

## Import `Field` { #import-field }

First, you have to import it:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[4] *}


/// warning

```Python
app.dependency_overrides = {}
```

/// tip

If you want to override a dependency only during some tests, you can set the override at the beginning of the test (inside the test function) and reset it at the end (at the end of the test function).

But there are specific cases where it's useful to get the `Request` object.

## Use the `Request` object directly { #use-the-request-object-directly }

Let's imagine you want to get the client's IP address/host inside of your *path operation function*.

For that you need to access the request directly.

{* ../../docs_src/using_request_directly/tutorial001_py39.py hl[1,7:8] *}

# Additional Status Codes { #additional-status-codes }

By default, **FastAPI** will return the responses using a `JSONResponse`, putting the content you return from your *path operation* inside of that `JSONResponse`.

It will use the default status code or the one you set in your *path operation*.

## Additional status codes { #additional-status-codes_1 }

  (
    readByte() and 0xff shl 16
      or (readByte() and 0xff shl 8)
      or (readByte() and 0xff)
  )

/** Run [block] until it either throws an [IOException] or completes. */
internal inline fun ignoreIoExceptions(block: () -> Unit) {
  try {
    block()
  } catch (_: IOException) {
  }
}

internal fun Buffer.skipAll(b: Byte): Int {
  var count = 0
  while (!exhausted() && this[0] == b) {

errors.crud_failed_to_delete_crud_table=Não foi possível excluir os dados. ({0})
errors.crud_could_not_find_crud_table = Dados {0} não encontrados.
errors.could_not_find_backup_index=Não foi possível encontrar o índice de backup.
errors.no_user_for_changing_password=A senha atual está incorreta.
errors.failed_to_change_password=Não foi possível alterar a senha.
errors.unknown_version_for_upgrade=Informações de versão desconhecidas.

Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.