singleton | Python singleton - A singleton metaclass for Python | Architecture library

The following only deals with the first of the errors you mention (as this one is pretty clear thanks to @BenBolkers comment), but hopefully it points you to the right tools to use.

I'd use htmltools::tagQuery to make the needed modifications - please check the following:

I suspect the Android emulator might be interfering with you here. I’ve seen issues with it misbehaving, particularly on Windows.

https://issuetracker.google.com/issues/119027639

If you'd like to workaround, consider changing your server to use something other than Connection: close to terminate your response body. Perhaps chunked encoding or a content-length header.

This had very little (read no) connection to fragments, lifecycle, flows and coroutines blocking - which I thought was behind this.

There's a few misunderstandings in this attempt.

1. There is one instance of a meta-class per type. Thus if we want to allow a given type to only be instantiated once, the correct scoping is an attribute in the meta-class, not a my. A my would mean there's one global object no matter which type we create.
2. The compose method, when subclassing ClassHOW, should always call back up to the base compose method (which can be done using callsame). Otherwise, the class will not be composed.
3. The method_table method returns the table of methods for this exact type. However, most classes won't have a new method. Rather, they will inherit the default one. If we wrap that, however, we'd be having a very global effect.

While new is relatively common to override to change the interface to construction, the bless method - which new calls after doing any mapping work - is not something we'd expect language users to be overriding. So one way we could proceed is to just try installing a bless method that does the required logic. (We could also work with new, but really we'd need to check if there was one in this class, wrap it if so, and add a copy of the default one that we then wrap if not, which is a bit more effort.)

Here's a solution that works:

The basic answer is that for an a = Array(T) Julia always allocates sizeof(T)*length(a)+40 bytes. Since sizeof(Missing) == 0, this means it doesn't allocate any bytes related to the length.

Indexing occurs on line 569 of array.c, and the relevant line is

If you could put pattern synonyms in the class instance, then your solution would be even simpler: you wouldn't need empty or singleton in the class at all as they would be definable in terms of PEmpty and PSingleton. Alas, as you know, this is impossible in the current GHC.

As is, you can only define functions and types in your class, and without access to constructors (like [] and : in your list example), you need two functions to define a pattern synonym: one for extracting data from the type and one for embedding into it.

As to your specific points about ugliness, some are unavoidable, but for others, there may be a slightly cleaner approach.

It's annoying that these need to be explicitly bi-directional patterns; especially since the 'under where' line is so directly comparable to the instance overload.

Alas, this one is unavoidable. Without access to constructors in the class, you need to use explicitly bidirectional patterns.

For the empty pattern I have to introduce an explicit (==) test and supporting Eq constraint -- to achieve a simple pattern match. (I suppose I could call an isEmpty method.)

I personally think your code would be cleaner with an isEmpty method. For what it's worth, you can use a default signature if you like, like:

Rotating the PerspectiveCamera does not work with OrbitControls. This is because the OrbitControls overwrites the camera's rotation.

To solve this, I used OrbitControls.target which makes the perspective camera orbit around this target coordinates.

sealed class is a good choice when you have routes with arguments, like shown in Jetcaster Compose sample app:

As already explained in this answer, id implementation is platform specific and is not a good method to guarantee unique identifiers across multiple processes.

In CPython specifically, id returns the pointer of the object within its own process address space. Most of modern OSes abstract the computer memory using a methodology known as Virtual Memory.

What you are observing are actual different objects. Nevertheless, they appear to have the same identifiers as each process allocated that object in the same offset of its own memory address space.

The reason why this does not happen in the pool is most likely due to the fact the Pool is allocating several resources in the worker process (pipes, counters, etc..) before running the task function. Hence, it randomizes the process address space utilization enough such that the object IDs appear different across their sibling processes.