// parameters substituted for the formals.
// Invoking a macro does not touch the PC/line history.
func (in *Input) invokeMacro(macro *Macro) {
	// If the macro has no arguments, just substitute the text.
	if macro.args == nil {
		in.Push(NewSlice(in.Base(), in.Line(), macro.tokens))
		return
	}
	tok := in.Stack.Next()
	if tok != '(' {

# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

load("@rules_shell//shell:repositories.bzl", "rules_shell_dependencies", "rules_shell_toolchains")

In the runtime package, assembly code can include <code>go_tls.h</code>, which defines
an OS- and architecture-dependent macro <code>get_tls</code> for accessing this register.
The <code>get_tls</code> macro takes one argument, which is the register to load the
<code>g</code> pointer into.
</p>

<p>
For example, the sequence to load <code>g</code> and <code>m</code>

        Map<String, Object> attributes = new HashMap<>();
        configureCodeHighlightingAttributes(attributes);
        attributes.put("toc", "macro");
        attributes.put("toclevels", 2);

        // TODO: This breaks if version is changed later
        String versionUrl = DOCS_GRADLE_ORG + project.getVersion();

pillow==11.3.0
# For image processing by Material for MkDocs
cairosvg==2.8.2
mkdocstrings[python]==0.30.1
griffe-typingdoc==0.3.0
griffe-warnings-deprecated==1.1.0
# For griffe, it formats with black
black==25.1.0
mkdocs-macros-plugin==1.4.1
markdown-include-variants==0.0.8

For instructions or titles in imperative, keep them in imperative, for example "Edit it" to "Edítalo".

---

For the next terms, use the following translations:

* framework: framework (do not translate to "marco")
* performance: rendimiento
* program (verb): programar
* code (verb): programar
* type hints: anotaciones de tipos
* type annotations: anotaciones de tipos
* autocomplete: autocompletado

Dentro de la función, puedes acceder a todos los atributos del objeto modelo directamente:

{* ../../docs_src/body/tutorial002_py310.py *}

/// info | Información

En Pydantic v1 el método se llamaba `.dict()`, se marcó como obsoleto (pero sigue soportado) en Pydantic v2, y se renombró a `.model_dump()`.

Los ejemplos aquí usan `.dict()` por compatibilidad con Pydantic v1, pero deberías usar `.model_dump()` si puedes usar Pydantic v2.

///

  language: en
repo_name: fastapi/fastapi
repo_url: https://github.com/fastapi/fastapi
plugins:
  social:
    cards_layout_options:
      logo: ../en/docs/img/icon-white.svg
  typeset: null
  search: null
  macros:
    include_yaml:
    - github_sponsors: ../en/data/github_sponsors.yml
    - people: ../en/data/people.yml
    - contributors: ../en/data/contributors.yml
    - translators: ../en/data/translators.yml

Asia/Hovd Asia/Irkutsk Asia/Istanbul Asia/Jakarta Asia/Jayapura Asia/Jerusalem Asia/Kabul Asia/Kamchatka Asia/Karachi Asia/Kashgar Asia/Kathmandu Asia/Katmandu Asia/Khandyga Asia/Kolkata Asia/Krasnoyarsk Asia/Kuala_Lumpur Asia/Kuching Asia/Kuwait Asia/Macao Asia/Macau Asia/Magadan Asia/Makassar Asia/Manila Asia/Muscat Asia/Nicosia Asia/Novokuznetsk Asia/Novosibirsk Asia/Omsk Asia/Oral Asia/Phnom_Penh Asia/Pontianak Asia/Pyongyang Asia/Qatar Asia/Qostanay Asia/Qyzylorda Asia/Rangoon Asia/Riyadh Asia/Saigon...

Com **FastAPI** você terá todos os recursos do **Pydantic** (já que FastAPI utiliza o Pydantic para todo o controle dos dados):

* **Sem pegadinhas**:
    * Sem novas definições de esquema de micro-linguagem para aprender.
    * Se você conhece os tipos do Python, você sabe como usar o Pydantic.