*   [TensorFlow Code Search](https://cs.opensource.google/tensorflow/tensorflow)

Learn more about the
[TensorFlow community](https://www.tensorflow.org/community) and how to
[contribute](https://www.tensorflow.org/community/contribute).

## Courses

* [Coursera](https://www.coursera.org/search?query=TensorFlow)
* [Udacity](https://www.udacity.com/courses/all?search=TensorFlow)

{"attributes":{"description":"","kibanaSavedObjectMeta":{"searchSourceJSON":"{\"query\":{\"query\":\"\",\"language\":\"kuery\"},\"filter\":[],\"indexRefName\":\"kibanaSavedObjectMeta.searchSourceJSON.index\"}"},"title":"search-query-counts-per-sec","uiStateJSON":"{}","version":1,"visState":"{\"title\":\"search-query-counts-per-sec\",\"type\":\"histogram\",\"params\":{\"type\":\"histogram\",\"grid\":{\"categoryLines\":false},\"categoryAxes\":[{\"id\":\"CategoryAxis-1\",\"type\":\"category\",\"position\":\"bottom...

* However, when a Markdown document is translated, the `#hash-parts` in links to its headings may change. Update these links if possible.
    * Search for such links in the translated document using the regex `#[^# ]`.
    * Search in all documents already translated into your language for `your-translated-document.md`. For example VS Code has an option "Edit" -> "Find in Files".

It is used by many companies including Mozilla, Red Hat and Eventbrite.

It was one of the first examples of **automatic API documentation**, and this was specifically one of the first ideas that inspired "the search for" **FastAPI**.

!!! note
    Django REST Framework was created by Tom Christie. The same creator of Starlette and Uvicorn, on which **FastAPI** is based.


!!! check "Inspired **FastAPI** to"

There are also several other companies offering similar services that you can search and find online. 🤓

## Generate a TypeScript Frontend Client

Let's start with a simple FastAPI application:

=== "Python 3.9+"

    ```Python hl_lines="7-9  12-13  16-17  21"

      labelIndex: Int,
    ): String? {
      var low = 0
      var high = size
      var match: String? = null
      while (low < high) {
        var mid = (low + high) / 2
        // Search for a '\n' that marks the start of a value. Don't go back past the start of the
        // array.
        while (mid > -1 && this[mid] != '\n'.code.toByte()) {
          mid--
        }
        mid++

   * given to [connectionUser] who may (for example) assign it to a [RealCall.connection].
   *
   * This confirms the returned connection is healthy before returning it. If this encounters any
   * unhealthy connections in its search, this will clean them up.
   *
   * If [routes] is non-null these are the resolved routes (ie. IP addresses) for the connection.
   * This is used to coalesce related domains to the same HTTP/2 connection, such as `square.com`

    {!> ../../../docs_src/query_params_str_validations/tutorial003_py310.py!}
    ```

## 🚮 🥔 🧬

👆 💪 🔬 <abbr title="A regular expression, regex or regexp is a sequence of characters that define a search pattern for strings.">🥔 🧬</abbr> 👈 🔢 🔜 🏏:

=== "🐍 3️⃣.6️⃣ &amp; 🔛"

    ```Python hl_lines="11"
    {!> ../../../docs_src/query_params_str_validations/tutorial004.py!}
    ```

Donc, pendant ce temps, l'ordinateur pourra effectuer d'autres tâches, pendant que "fichier-lent" 📝 se termine.

Ensuite l'ordinateur / le programme 🤖 reviendra à chaque fois qu'il en a la chance que ce soit parce qu'il attend à nouveau, ou car il 🤖 a fini tout le travail qu'il avait à faire. Il 🤖 regardera donc si les tâches qu'il attend ont terminé d'être effectuées.

With respect to development inside an IDE the workspace from which we would like to resolve and retrieve dependencies is a read-only local repository to Mercury which is put at the front-end of the search order of any repository that Mercury will consult for artifacts.

h4. Generation of plugin metadata and packaging