!!! 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):

<?xml version="1.0" encoding="UTF-8"?>

<!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements.  See the NOTICE file
distributed with this work for additional information
regarding copyright ownership.  The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License.  You may obtain a copy of the License at

### Build session state

A "build session" represents a single invocation of Gradle, for example when you run `gradlew build`.
A session runs the build one or more times.
For example, when continuous build is enabled, the session may run the build many times, but when it is disabled, the session will run the build once only.