// RoleARN parameter processing: If a role ARN is given in the request, we
	// use that and validate the authentication request. If not, we assume this
	// is an STS request for a claim based IDP (if one is present) and set
	// roleArn = openid.DummyRoleARN.
	//
	// Currently, we do not support multiple claim based IDPs, as there is no

    // objects indefinitely.
    private void drainQueue() {
      Reference<? extends L> ref;
      while ((ref = queue.poll()) != null) {
        // We only ever register ArrayReferences with the queue so this is always safe.
        ArrayReference<? extends L> arrayRef = (ArrayReference<? extends L>) ref;

## Parameterized dependencies

All the dependencies we have seen are a fixed function or class.

But there could be cases where you want to be able to set parameters on the dependency, without having to declare many different functions or classes.

Let's imagine that we want to have a dependency that checks if the query parameter `q` contains some fixed content.

But we want to be able to parameterize that fixed content.

   * @param length the length of the new array
   */
  /*
   * The new array contains nulls, even if the old array did not. If we wanted to be accurate, we
   * would declare a return type of `@Nullable T[]`. However, we've decided not to think too hard
   * about arrays for now, as they're a mess. (We previously discussed this in the review of
   * ObjectArrays, which is the main caller of this method.)
   */

    ```

## Verify the `username` and data shape

We verify that we get a `username`, and extract the scopes.

And then we validate that data with the Pydantic model (catching the `ValidationError` exception), and if we get an error reading the JWT token or validating the data with Pydantic, we raise the `HTTPException` we created before.

For that, we update the Pydantic model `TokenData` with a new property `scopes`.

Complex logic ahead...
We have three sets of values, in order of precedence (last wins):
1. The builtin values.yaml defaults
2. The profile the user selects
3. Users input (-f or --set)

Unfortunately, Helm provides us (1) and (3) together (as .Values), making it hard to insert (2).

However, we can workaround this by placing all of (1) under a specific key (.Values.defaults).

      // If either of these reads return null then we must be after a successful cancel or another
      // call to this method.
      TimeoutFuture<V> timeoutFuture = timeoutFutureRef;
      if (timeoutFuture == null) {
        return;
      }
      ListenableFuture<V> delegate = timeoutFuture.delegateRef;
      if (delegate == null) {
        return;
      }

      /*

//
// Below here we do the gradient algorithm. It works as follows:
//
// First we filter the tape to just the subset of operations we want to
// differentiate. In the process of doing so we count how many times each Tensor
// is used as an input to an op (so we know when we're done computing gradients
// for that Tensor). We also count, for each tape entry, how many of its output

  ---
  Getting to Plexus-configured Mojos
  ---
  John Casey
  ---
  2005-04-29

Abstract

  We're moving toward integrating mojos as first-class plexus components, while
  at the same time avoiding introducing required plexus dependencies into the
  mojo development model.

  In order to really achieve this, we need mojo configurations (which are
  provided both in terms of static expressions that are just looked up, and

We are going to include this `APIRouter` in the main `FastAPI` app, but first, let's check the dependencies and another `APIRouter`.

## Dependencies

We see that we are going to need some dependencies used in several places of the application.

So we put them in their own `dependencies` module (`app/dependencies.py`).