### Starting and Stopping Containers

To start a stopped container, you can use the [`docker start`](https://docs.docker.com/engine/reference/commandline/start/) command.

```sh
docker start <container_id>
```

To stop a running container, you can use the [`docker stop`](https://docs.docker.com/engine/reference/commandline/stop/) command.

```sh
docker stop <container_id>

Here's the reference information for the request parameters.

These are the special functions that you can put in *path operation function* parameters or dependency functions with `Annotated` to get data from the request.

It includes:

* `Query()`
* `Path()`
* `Body()`
* `Cookie()`
* `Header()`
* `Form()`
* `File()`

You can import them all directly from `fastapi`:

```python

   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.

   *
   * <p>As long as there are applications that assume that all IP addresses are IPv4 addresses and
   * can therefore be converted safely to integers (for whatever purpose) this function can be used
   * to handle IPv6 addresses as well until the application is suitably fixed.
   *
   * <p>NOTE: an IPv6 address coerced to an IPv4 address can only be used for such purposes as

# Templates { #templates }

You can use any template engine you want with **FastAPI**.

A common choice is Jinja2, the same one used by Flask and other tools.

There are utilities to configure it easily that you can use directly in your **FastAPI** application (provided by Starlette).

## Install dependencies { #install-dependencies }

    String[] WINERR_MESSAGES = { "The operation completed successfully.", "Access is denied.",
            "No more connections can be made to this remote computer at this time because there are already as many connections as the computer can accept.",
            "The pipe state is invalid.", "All pipe instances are busy.", "The pipe is being closed.",

    String[] WINERR_MESSAGES = { "The operation completed successfully.", "Access is denied.",
            "No more connections can be made to this remote computer at this time because there are already as many connections as the computer can accept.",
            "The pipe state is invalid.", "All pipe instances are busy.", "The pipe is being closed.",

///

## 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.
* "Add configuration...".
* Select "Python"

var (
	D        MultiFlag
	I        MultiFlag
	PrintOut int
	DebugV   bool
)

func init() {
	flag.Var(&D, "D", "predefined symbol with optional simple value -D=identifier=value; can be set multiple times")
	flag.Var(&I, "I", "include directory; can be set multiple times")
	flag.BoolVar(&DebugV, "v", false, "print debug output")
	flag.Var(objabi.NewDebugFlag(&DebugFlags, nil), "d", "enable debugging settings; try -d help")

    attributes:
      label: Operating System Details
      description: You can add more details about your operating system here, in particular if you chose "Other".
  - type: input
    id: fastapi-version
    attributes:
      label: FastAPI Version
      description: |
        What FastAPI version are you using?

        You can find the FastAPI version with:

        ```bash