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()

    filesystem, it will continue to be available.

  The question remaining is: Which level of offline mode will we support? It
  seems reasonable to assume that users will be able to tell when localhost is
  not active (in most cases, localhost should be available, even if the rest of
  the network is not). Therefore, let's concentrate on the state where no
  network <beyond localhost> exists, and leave the more extreme state to users

    # 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
    #

  // Requests with the dryRun attribute will be auto-rejected if they match a webhook with
  // sideEffects == Unknown or Some. Defaults to Unknown.
  // +optional
  optional string sideEffects = 6;

  // TimeoutSeconds specifies the timeout for this webhook. After the timeout passes,
  // the webhook call will be ignored or the API call will fail based on the
  // failure policy.

    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(
            """

    ```Python hl_lines="13-14"
    {!> ../../../docs_src/dependencies/tutorial008c.py!}
    ```

In this case, the client will see an *HTTP 500 Internal Server Error* response as it should, given that we are not raising an `HTTPException` or similar, but the server will **not have any logs** or any other indication of what was the error. 😱

### Always `raise` in Dependencies with `yield` and `except`

    ```

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**.