Now create Pydantic *models* (schemas) that will be used when reading data, when returning it from the API.

For example, before creating an item, we don't know what will be the ID assigned to it, but when reading it (when returning it from the API) we will already know its ID.

The same way, when reading a user, we can now declare that `items` will contain the items that belong to this user.

	m.o = m.o[idx:]
}

// mergeEntryChannels will merge entries from in and return them sorted on out.
// To signify no more results are on an input channel, close it.
// The output channel will be closed when all inputs are emptied.
// If file names are equal, compareMeta is called to select which one to choose.
// The entry not chosen will be discarded.
// If the context is canceled the function will return the error,

	Requests chan<- Req
}

// Results returns the results from the remote server one by one.
// If any error is returned by the callback, the stream will be canceled.
// If the context is canceled, the stream will be canceled.
func (s *TypedStream[Req, Resp]) Results(next func(resp Resp) error) (err error) {
	return s.responses.Results(func(b []byte) error {
		resp := s.newResp()

    # meshes will interact with each other, but it is not required to be
    # globally/universally unique. For example, if any of the following are true,
    # then two meshes must have different Mesh IDs:
    # - Meshes will have their telemetry aggregated in one place
    # - Meshes will be federated together
    # - Policy will be written referencing one mesh from the other
    #

    For example, in an HTTP Bearer token scheme, the client will send a header
    like:

    ```
    Authorization: Bearer deadbeef12346
    ```

    In this case:

    * `scheme` will have the value `"Bearer"`
    * `credentials` will have the value `"deadbeef12346"`
    """

    scheme: Annotated[
        str,
        Doc(
            """

    ```

Those parameters are what **FastAPI** will use to "solve" the dependency.

In both cases, it will have:

* An optional `q` query parameter that is a `str`.
* A `skip` query parameter that is an `int`, with a default of `0`.
* A `limit` query parameter that is an `int`, with a default of `100`.

In both cases the data will be converted, validated, documented on the OpenAPI schema, etc.

## Use it

### 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**.

```Python hl_lines="2  7"
{!../../../docs_src/custom_response/tutorial001b.py!}
```

!!! info
    The parameter `response_class` will also be used to define the "media type" of the response.

    In this case, the HTTP header `Content-Type` will be set to `application/json`.

    And it will be documented as such in OpenAPI.

!!! tip
    The `ORJSONResponse` is currently only available in FastAPI, not in Starlette.

    ```

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.

   * multiset itself).
   *
   * <p>Despite the method name, this method attempts to avoid actually copying the data when it is
   * safe to do so. The exact circumstances under which a copy will or will not be performed are
   * undocumented and subject to change.
   *
   * <p>This method is not type-safe, as it may be called on elements that are not mutually
   * comparable.
   *