# 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")

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

## Why is MinIO cloud-native?

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

          "interval": "",
          "legendFormat": "[{{drive}}:{{api}}]",
          "refId": "B"
        }
      ],
      "title": "Drive Latency (micro sec)",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "description": "",
      "fieldConfig": {

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...

With **FastAPI** you get all of **Pydantic**'s features (as FastAPI is based on Pydantic for all the data handling):

* **No brainfuck**:
    * No new schema definition micro-language to learn.
    * If you know Python types you know how to use Pydantic.

Given the simplicity of Flask, it seemed like a good match for building APIs. The next thing to find was a "Django REST Framework" for Flask.

/// check | Inspired **FastAPI** to

Be a micro-framework. Making it easy to mix and match the tools and parts needed.

Have a simple and easy to use routing system.

///

# google internal build on configurable attributes.
#
# For non-configurable distinction between OSS and Google builds, see
# `if_oss()` and `if_google()` macros in tensorflow.bzl.
config_setting(
    name = "oss",
    flag_values = {":oss_setting": "True"},
    visibility = ["//visibility:public"],
)

# Non-configurable setting to indicate open source build.