# 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")

    <_comment>Office Open XML Workbook Add-in (macro-enabled)</_comment>
    <glob pattern="*.xlam"/>
    <sub-class-of type="application/x-tika-ooxml"/>
  </mime-type>

  <mime-type type="application/vnd.ms-excel.sheet.macroenabled.12">
    <_comment>Office Open XML Workbook (macro-enabled)</_comment>
    <glob pattern="*.xlsm"/>
    <sub-class-of type="application/x-tika-ooxml"/>

application/x-troff            t tr roff        # Troff file
application/x-troff-man        man              # Troff with MAN macros
application/x-troff-me         me               # Troff with ME macros
application/x-troff-ms         ms               # Troff with MS macros
application/x-ustar            ustar            # Ustar file
application/vnd.ms-access      mdb mda mde      # MS Access

    /*
     * We use double-checked locking. We could the try racy single-check idiom, but that would
     * depend on Logger to not contain mutable state.
     *
     * We could use Suppliers.memoizingSupplier here, but I micro-optimized to this implementation
     * to avoid the extra class for the lambda (and maybe more for memoizingSupplier itself) and the
     * indirection.
     *

## Why is MinIO cloud-native?

 */

package com.google.common.util.concurrent.internal;


/**
 * A future that, if it fails, may <i>optionally</i> provide access to the cause of the failure.
 *
 * <p>This class is used only for micro-optimization. Standard {@code Future} utilities benefit from
 * this optimization, so there is no need to specialize methods to return or accept this type
 * instead of {@code ListenableFuture}.
 *

	buf, err := bio.Create(*flags.OutputFile)
	if err != nil {
		log.Fatal(err)
	}
	defer buf.Close()

	if !*flags.SymABIs {
		buf.WriteString(objabi.HeaderString())
		fmt.Fprintf(buf, "!\n")
	}

	// Set macros for GOEXPERIMENTs so we can easily switch
	// runtime assembly code based on them.
	if objabi.LookupPkgSpecial(ctxt.Pkgpath).AllowAsmABI {
		for _, exp := range buildcfg.Experiment.Enabled() {

Ce découplage des parties, et le fait d'être un "micro-framework" qui puisse être étendu pour couvrir exactement ce
qui est nécessaire, était une caractéristique clé que je voulais conserver.

Compte tenu de la simplicité de Flask, il semblait bien adapté à la création d'API. La prochaine chose à trouver était un "Django REST Framework" pour Flask.

/// check | A inspiré **FastAPI** à

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

    repo: fontawesome/brands/github-alt
  logo: img/icon-white.svg
  favicon: img/favicon.png
  language: en
repo_name: fastapi/fastapi
repo_url: https://github.com/fastapi/fastapi
plugins:
  search: null
  macros:
    include_yaml:
    - external_links: ../en/data/external_links.yml
    - github_sponsors: ../en/data/github_sponsors.yml
    - people: ../en/data/people.yml
    - contributors: ../en/data/contributors.yml