@SuppressWarnings("ThreadPriorityCheck") // The cow told me to
  @Override
  public final void run() {
    /*
     * Set runner thread before checking isDone(). If we were to check isDone() first, the task
     * might be cancelled before we set the runner thread. That would make it impossible to
     * interrupt, yet it will still run, since interruptTask will leave the runner value null,
     * allowing the CAS below to succeed.

                    } catch (ChecksumFailedException e) {
                        // if we catch a ChecksumFailedException, it means the transfer/read succeeded, but the
                        // checksum doesn't match. This could be a problem with the server (ibiblio HTTP-200 error
                        // page), so we'll try this up to two times. On the second try, we'll handle it as a bona-fide

    ```

But that is still not that useful.

Let's make it give us the current user.

## Create a user model

First, let's create a Pydantic user model.

The same way we use Pydantic to declare bodies, we can use it anywhere else:

=== "Python 3.10+"

    ```Python hl_lines="5  12-16"
    {!> ../../../docs_src/security/tutorial002_an_py310.py!}
    ```

=== "Python 3.9+"

// of the payload and verify that it matches the expected signature value.
//
// The last chunk is *always* 0-sized. So, we must only return io.EOF if we have encountered
// a chunk with a chunk size = 0. However, this chunk still has a signature and we must
// verify it.
const maxChunkSize = 16 << 20 // 16 MiB

// Read - implements `io.Reader`, which transparently decodes

**Experimental!**

We'd like to capture our architectural decisions about the build tool as [Architectural Decision Records (ADRs)](https://adr.github.io/).
For now we just have this global repository of ADRs.
If we see fit, we can break these out to per-platform ones, or keep a hybrid approach to having global and platform-specific ADSs.

Our aim is to keep the process lightweight and approachable.

       */

      //
      seenExceptionsLocal = newConcurrentHashSet();
      /*
       * Other handleException() callers may see this as soon as we publish it. We need to populate
       * it with the initial failure before we do, or else they may think that the initial failure
       * has never been seen before.
       */
      addInitialException(seenExceptionsLocal);

          if (rule.startsWith(EXCEPTION_RULE_MARKER)) {
            rule = rule.substring(1)
            // We use '\n' for end of value.
            totalExceptionRuleBytes += rule.size + 1
            sortedExceptionRules.add(rule)
          } else {
            totalRuleBytes += rule.size + 1 // We use '\n' for end of value.
            sortedRules.add(rule)
          }
        }
      }

					throughputHighestPut = totalPut
					// let the client see lower value as well
					throughputHighestGet = totalGet
				}
				sendResult()
				break
			}

			// We break if we did not see 2.5% growth rate in total GET
			// requests, we have reached our peak at this point.
			doBreak := float64(totalGet-throughputHighestGet)/float64(totalGet) < 0.025

			throughputHighestGet = totalGet

      }
      updateLastKnownIndex();
      /*
       * If the entry has been removed from the map, we return null, even though that might not be a
       * valid value. That's the best we can do, short of holding a reference to the most recently
       * seen value. And while we *could* do that, we aren't required to: Map.Entry explicitly says

Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.

Then we use the request directly, and extract the body as `bytes`. This means that FastAPI won't even try to parse the request payload as JSON.