* ascending order.
   *
   * @throws IllegalArgumentException if {@code k < 0} or {@code k > Integer.MAX_VALUE / 2}
   */
  public static <T extends Comparable<? super T>> TopKSelector<T> least(int k) {
    return least(k, Ordering.natural());
  }

  /**
   * Returns a {@code TopKSelector} that collects the lowest {@code k} elements added to it,

* Convert the copied model to something that can be stored in your DB (for example, using the `jsonable_encoder`).
    * This is comparable to using the model's `.model_dump()` method again, but it makes sure (and converts) the values to data types that can be converted to JSON, for example, `datetime` to `str`.
* Save the data to your DB.
* Return the updated model.

But all this functionality of using asynchronous code with `async` and `await` is many times summarized as using "coroutines". It is comparable to the main key feature of Go, the "Goroutines".

## Conclusion { #conclusion }

Let's see the same phrase from above:

A container image is comparable to the **program** file and contents, e.g. `python` and some file `main.py`.

	"errors"
	"hash/crc32"

	"github.com/minio/pkg/v3/sync/errgroup"
)

// counterMap type adds GetValueWithQuorum method to a map[T]int used to count occurrences of values of type T.
type counterMap[T comparable] map[T]int

// GetValueWithQuorum returns the first key which occurs >= quorum number of times.
func (c counterMap[T]) GetValueWithQuorum(quorum int) (T, bool) {
	var zero T
	for x, count := range c {

Pero toda esta funcionalidad de usar código asíncrono con `async` y `await` a menudo se resume como utilizar "coroutines". Es comparable a la funcionalidad clave principal de Go, las "Goroutines".

## Conclusión

Veamos la misma frase de arriba:

Pydantic is a library to define data validation, serialization and documentation (using JSON Schema) based on Python type hints.

That makes it extremely intuitive.

It is comparable to Marshmallow. Although it's faster than Marshmallow in benchmarks. And as it is based on the same Python type hints, the editor support is great.

/// check | **FastAPI** uses it to

An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

    assertEquals(Integer.class, f.resolveType(Function.class.getTypeParameters()[1]).getType());
    assertEquals(Long.class, f.resolveType(Iterable.class.getTypeParameters()[0]).getType());
  }

  public <E extends Comparable<Iterable<String>> & Iterable<Integer>>
      void testResolveType_fromTypeVariable_onlyDirectBoundsAreUsed() {
    TypeToken<?> e = TypeToken.of(new TypeCapture<E>() {}.capture());

    assertEquals(Integer.class, f.resolveType(Function.class.getTypeParameters()[1]).getType());
    assertEquals(Long.class, f.resolveType(Iterable.class.getTypeParameters()[0]).getType());
  }

  public <E extends Comparable<Iterable<String>> & Iterable<Integer>>
      void testResolveType_fromTypeVariable_onlyDirectBoundsAreUsed() {
    TypeToken<?> e = TypeToken.of(new TypeCapture<E>() {}.capture());