```gradle
dependencies {
  // Pick one:

  // 1. Use Guava in your implementation only:
  implementation("com.google.guava:guava:33.4.8-jre")

  // 2. Use Guava types in your public API:
  api("com.google.guava:guava:33.4.8-jre")

  // 3. Android - Use Guava in your implementation only:
  implementation("com.google.guava:guava:33.4.8-android")

  // 4. Android - Use Guava types in your public API:
  api("com.google.guava:guava:33.4.8-android")

After authenticating in the system, you will see it like:

<img src="/img/tutorial/security/image05.png">

### Get your own user data { #get-your-own-user-data }

Now use the operation `GET` with the path `/users/me`.

You will get your user's data, like:

```JSON
{
  "username": "johndoe",
  "email": "******@****.***",
  "full_name": "John Doe",
  "disabled": false,

## Pin your `fastapi` version { #pin-your-fastapi-version }

The first thing you should do is to "pin" the version of **FastAPI** you are using to the specific latest version that you know works correctly for your application.

For example, let's say you are using version `0.112.0` in your app.

Because we are using a relative URL, if your API was located at `https://example.com/`, then it would refer to `https://example.com/token`. But if your API was located at `https://example.com/api/v1/`, then it would refer to `https://example.com/api/v1/token`.

///

## Run your code with your debugger { #run-your-code-with-your-debugger }

Because you are running the Uvicorn server directly from your code, you can call your Python program (your FastAPI application) directly from the debugger.

---

For example, in Visual Studio Code, you can:

* Go to the "Debug" panel.

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

If you have improvements to TensorFlow, send us your pull requests! For those
just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are

You have already seen how to test your **FastAPI** applications using the provided `TestClient`. Up to now, you have only seen how to write synchronous tests, without using `async` functions.

FastAPI will use this return type to:

* **Validate** the returned data.
    * If the data is invalid (e.g. you are missing a field), it means that *your* app code is broken, not returning what it should, and it will return a server error instead of returning incorrect data. This way you and your clients can be certain that they will receive the data and the data shape expected.
* Add a **JSON Schema** for the response, in the OpenAPI *path operation*.

**"Dependency Injection"** means, in programming, that there is a way for your code (in this case, your *path operation functions*) to declare things that it requires to work and use: "dependencies".

And then, that system (in this case **FastAPI**) will take care of doing whatever is needed to provide your code with those needed dependencies ("inject" the dependencies).

This is very useful when you need to: