import org.apache.maven.api.Session;
import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.model.Dependency;
import org.apache.maven.api.model.Parent;

/**
 * Resolves a POM from its coordinates.
 */
public interface ModelResolver extends Service {

    /**
     * Tries to resolve the POM for the specified parent coordinates possibly updating {@code parent}.
     *

$ python main.py

// As we didn't set the env var, we get the default value

Hello World from Python

// But if we create an environment variable first
$ export MY_NAME="Wade Wilson"

// And then call the program again
$ python main.py

// Now it can read the environment variable

Hello Wade Wilson from Python
```

</div>

    *   Upgrade Flatbuffers v2.0.5 from v1.12.0

*   `tf.keras`:

    *   `EinsumDense` layer is moved from experimental to core. Its import path
        is moved from `tf.keras.layers.experimental.EinsumDense` to
        `tf.keras.layers.EinsumDense`.
    *   Added `tf.keras.utils.audio_dataset_from_directory` utility to easily
        generate audio classification datasets from directories of `.wav` files.

```txt
Hello, World from Flask!
```

And if you go to <a href="http://localhost:8000/v2" class="external-link" target="_blank">http://localhost:8000/v2</a> you will see the response from FastAPI:

```JSON
{
    "message": "Hello World"
}

```{ .dockerfile .annotate }
# (1)
FROM python:3.9 as requirements-stage

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)

     * Gets the relative path for an artifact cached from a remote repository.
     * Note that the artifact need not actually exist yet at the returned location,
     * the path merely indicates where the artifact would eventually be stored.
     * <p>
     * Shortcut for {@code getService(LocalArtifactManager.class).getPathForRemoteArtifact(...)}.
     *
     * @param remote the repository from where artifacts are downloaded

	for _, i := range r.Perm(len(disks)) {
		if disks[i] != nil && disks[i].IsLocal() {
			newDisks = append(newDisks, disks[i])
		}
	}
	return newDisks
}

// readMultipleFiles Reads raw data from all specified files from all disks.
func readMultipleFiles(ctx context.Context, disks []StorageAPI, req ReadMultipleReq, readQuorum int) ([]ReadMultipleResp, error) {
	resps := make([]chan ReadMultipleResp, len(disks))
	for i := range resps {

```Python hl_lines="1  46-47"
{!../../../docs_src/websockets/tutorial001.py!}
```

!!! note "Technical Details"
    You could also use `from starlette.websockets import WebSocket`.

    **FastAPI** provides the same `WebSocket` directly just as a convenience for you, the developer. But it comes directly from Starlette.

## Await for messages and send messages

In your WebSocket route you can `await` for messages and send messages.

	if err != nil {
		log.Errorf("failed to remove pod from mesh: %v", err)
		return err
	}
	log.Debug("removing annotation from pod")
	err = util.AnnotateUnenrollPod(s.kubeClient, &pod.ObjectMeta)
	if err != nil {
		log.Errorf("failed to annotate pod unenrollment: %v", err)
	}
	return err
}

// Delete pod from mesh: pod is deleted. iptables rules will die with it, we just need to update ztunnel

## Example upgrade

Now modify the file `main.py` to receive a body from a `PUT` request.

Declare the body using standard Python types, thanks to Pydantic.

```Python hl_lines="4  9-12  25-27"
from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float