pkg unique, func Make[$0 comparable]($0) Handle[$0] #62483
pkg unique, method (Handle[$0]) Value() $0 #62483

        for (projection in typeArguments) {
            // E.g., Test : Comparable<Test>
            val type = (projection as? ConeKotlinTypeProjection)?.type ?: continue
            // E.g., Comparable<Test>
            val newType = substituteOrNull(type) ?: continue
            // Visit new type: e.g., Test, as a type argument, is substituted with Comparable<Test>, again.
            if (newType.hasRecursiveTypeArgument(visited)) return true

### New unique package

The new [unique](/pkg/unique) package provides facilities for
canonicalizing values (like "interning" or "hash-consing").

Any value of comparable type may be canonicalized with the new
`Make[T]` function, which produces a reference to a canonical copy of
the value in the form of a `Handle[T]`.
Two `Handle[T]` are equal if and only if the values used to produce the
handles are equal, allowing programs to deduplicate values and reduce

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

Let's see the same phrase from above:

* 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.

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

Pero toda esta funcionalidad de usar código asincrónico con `async` y `await` se resume muchas veces como usar "coroutines". Es comparable a la característica principal de Go, las "Goroutines".

## Conclusión

Veamos la misma frase de arriba:

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).

### Examples of Replication Tools and Strategies

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"

Pydantic est une bibliothèque permettant de définir la validation, la sérialisation et la documentation des données (à l'aide de JSON Schema) en se basant sur les Python type hints.

Cela le rend extrêmement intuitif.

Il est comparable à Marshmallow. Bien qu'il soit plus rapide que Marshmallow dans les benchmarks. Et comme il est
basé sur les mêmes type hints Python, le support de l'éditeur est grand.

!!! check "**FastAPI** l'utilise pour"