The build cache works by storing (locally or remotely) build outputs and allowing builds to fetch these outputs from the cache when it is determined that inputs have not changed, avoiding the expensive work of regenerating them.

A first feature using the build cache is _task output caching_.

				t.Fatalf("dial error = %v (%T); want OpError with Err == errCanceled", err, err)
			}
			return // success.
		}
	}
}

func TestCancelAfterDial(t *testing.T) {
	if testing.Short() {
		t.Skip("avoiding time.Sleep")
	}

	ln := newLocalListener(t, "tcp")

	var wg sync.WaitGroup
	wg.Add(1)
	defer func() {
		ln.Close()
		wg.Wait()
	}()

				// space to save the LR.
				autosize += int32(c.ctxt.Arch.FixedFrameSize)
			}

			if p.Mark&LEAF != 0 && autosize < abi.StackSmall {
				// A leaf function with a small stack can be marked
				// NOSPLIT, avoiding a stack check.
				p.From.Sym.Set(obj.AttrNoSplit, true)
			}

			p.To.Offset = int64(autosize)

			q := p

			if !p.From.Sym.NoSplit() {

          absl::StrCat("parameter ", i,
                       ": codegen requires XLA parameters to "
                       "be non-tuples."));
    }
    // Please some compilers (e.g. MSVC) by avoiding the initialization of an
    // array of unknown size an empty initializer. Use "-1" for this; note that
    // this value is never used (the size attribute is set to 0 in ShapeInfo).

    These Pydantic models define more or less a "schema" (a valid data shape).

    So this will help us avoiding confusion while using both.

### Create the Pydantic *models* / schemas

Create all the same Pydantic models as in the SQLAlchemy tutorial:

```Python hl_lines="16-18  21-22  25-30  34-35  38-39  42-48"

      Multiset<E> multisetToModify, Multiset<?> occurrencesToRetain) {
    checkNotNull(multisetToModify);
    checkNotNull(occurrencesToRetain);
    // Avoiding ConcurrentModificationExceptions is tricky.
    Iterator<Entry<E>> entryIterator = multisetToModify.entrySet().iterator();
    boolean changed = false;
    while (entryIterator.hasNext()) {

      Multiset<E> multisetToModify, Multiset<?> occurrencesToRetain) {
    checkNotNull(multisetToModify);
    checkNotNull(occurrencesToRetain);
    // Avoiding ConcurrentModificationExceptions is tricky.
    Iterator<Entry<E>> entryIterator = multisetToModify.entrySet().iterator();
    boolean changed = false;
    while (entryIterator.hasNext()) {

	// Only pass the expanding context to the new instance if their packages
	// match. Since type reference cycles are only possible within a single
	// package, this is sufficient for the purposes of short-circuiting cycles.
	// Avoiding passing the context in other cases prevents unnecessary coupling
	// of types across packages.
	if expanding != nil && expanding.Obj().pkg == obj.pkg {
		inst.ctxt = expanding.inst.ctxt
	}

    These Pydantic models define more or less a "schema" (a valid data shape).

    So this will help us avoiding confusion while using both.

### Create initial Pydantic *models* / schemas

Create an `ItemBase` and `UserBase` Pydantic *models* (or let's say "schemas") to have common attributes while creating or reading data.

____

See the link:https://blog.gradle.org/preview-avoiding-task-configuration-time[_Evolving the Gradle API to reduce configuration time_] blog post and the <<task_configuration_avoidance.adoc#task_configuration_avoidance,Task Configuration Avoidance>> chapter in the user manual for more information.