!!! info
    In Pydantic version 1 the configuration was done in an internal class `Config`, in Pydantic version 2 it's done in an attribute `model_config`. This attribute takes a `dict`, and to get autocompletion and inline errors you can import and use `SettingsConfigDict` to define that `dict`.

## Documenting the callback

The actual callback code will depend heavily on your own API app.

And it will probably vary a lot from one app to the next.

It could be just one or two lines of code, like:

```Python
callback_url = "https://example.com/api/v1/invoices/events/"
httpx.post(callback_url, json={"description": "Invoice paid", "paid": True})
```

		for profile in manifests/helm-profiles/*.yaml ; do \
			sed "1s|^|$${warning}\n\n|" $$profile > manifests/charts/$$chart/files/profile-$$(basename $$profile) ; \
		done; \
		cp manifests/zzz_profile.yaml manifests/charts/$$chart/templates ; \
	done

#-----------------------------------------------------------------------------
# Target: go build
#-----------------------------------------------------------------------------

For any non-trivial change, we need to be able to answer these questions:

* Why is this change done? What's the use case?
* For user facing features, what will the API look like?
* What test cases should it have? What could go wrong?
* How will it roughly be implemented? We'll happily provide code pointers to save you time.

query API metrics for buckets `test1` and `test2`, make a scrape request to `/minio/metrics/v3/api/bucket?buckets=test1,test2`. 

Instead of a metrics scrape, it is also possible to list the metrics that would be returned by a path. This is done by adding a `?list` query parameter. The MinIO server will then list all possible metrics that could be returned. During an actual metrics scrape, only available metrics are returned - not all of them. With the `list` query parameter, the output format...

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

### Dockerfile

Now in the same project directory create a file `Dockerfile` with:

```{ .dockerfile .annotate }
# (1)
FROM python:3.9

from pydantic import BaseModel

class Item(BaseModel):
    name: str
    price: Optional[float] = None
    owner_ids: Optional[List[int]] = None

app = FastAPI()

@app.get("/items/invalidnone", response_model=Union[Item, None])
def get_invalid_none():
    return None
```

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}


@app.put("/items/{item_id}")
def update_item(item_id: int, item: Item):

        of built-in training loops such as `tf.keras` `compile` and `fit`, we
        expect reduction value to be `SUM` or `NONE`. Using `AUTO` in that case
        will raise an error.
    *   `NONE`: Weighted losses with one dimension reduced (axis=-1, or axis
        specified by loss function). When this reduction type used with built-in

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}


@app.put("/items/{item_id}")
def update_item(item_id: int, item: Item):