flux | Flux is a lightweight scripting language for querying databases (like InfluxDB) and working with dat | Emulator library

I broke it down to two flows 1st getting all operators and then getting all devices for each operator.

For pagination I'm using Flux.expand to extract all pages.

this looks like a simplified version of what the reactor-pool does, in essence. have you considered using that with eg. a maximum size of 1?

https://github.com/reactor/reactor-pool/

https://projectreactor.io/docs/pool/0.2.7/api/reactor/pool/Pool.html

The pool is probably overkill, because it has the overhead of having to deal with multiple resources on top of multiple competing borrowers like in your case, but maybe it could provide some inspiration for you to go further.

I am afraid Project Reactor doesn't provide any implementation of either rate or time limit.

However, you can find a bunch of 3rd party libraries that provide such functionality and are compatible with Project Reactor. As far as I know, resilience4-reactor supports that and is also compatible with Spring and Spring Boot frameworks.

The RateLimiterOperator checks if a downstream subscriber/observer can acquire a permission to subscribe to an upstream Publisher. If the rate limit would be exceeded, the RateLimiterOperator could either delay requesting data from the upstream or it can emit a RequestNotPermitted error to the downstream subscriber.

Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.

Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:

can you give your

• package.json
• node version

I think that's because of the babel issue / your node version, because it cannot transpile the optional chaining https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Optional_chaining

maybe tried using latest LTS node version? because as far as I know, the latest LTS node version already support optional chaining

To figure out where a specific function comes from, you can use the parentmodule function:

I'm not terribly familiar with your stack, so this is a high-level answer to hit on your "Why". There WILL be a more specific answer for you, somewhere down the pipe (e.g. someone that can confirm whether this thread is relevant).

While I'm no Spring Daisy (or Spring dev), you bind an expression to filmMono that resolves as the query select film.* from film..... You reference that expression four times, and it's resolved four times, in separate contexts. The ordering of the statements is likely a partially-successful attempt by the lib author to lazily evaluate the expression that you bound locally, such that it's able to batch the four accidentally identical queries. You most likely resolved this by collecting into an actual container, and then mapping on that container instead of the expression bound to filmMono.

In general, this situation is because the options available to library authors aren't good when the language doesn't natively support lazy evaluation. Because any operation might alter the dataset, the library author has to choose between:

• A, construct just enough scaffolding to fully record all resources needed, copy the dataset for any operations that need to mutate records in some way, and hope that they can detect any edge-cases that might leak the scaffolding when the resolved dataset was expected (getting this right is...hard).
• B, resolve each level of mapping as a query, for each context it appears in, lest any operations mutate the dataset in ways that might surprise the integrator (e.g. you).
• C, as above, except instead of duplicating the original request, just duplicate the data...at every step. Pass-by-copy gets real painful real fast on the JVM, and languages like Clojure and Scala handle this by just making the dev be very specific about whether they want to mutate in-place, or copy then mutate.

In your case, B made the most sense to the folks that wrote that lib. In fact, they apparently got close enough to A that they were able to batch all the queries that were produced by resolving the expression bound to filmMono (which are only accidentally identical), so color me a bit impressed.

Many access patterns can be rewritten to optimize for the resulting queries instead. Your milage may vary...wildly. Getting familiar with raw SQL, or else a special-purpose language like GraphQL, can give much more consistent results than relational mappers, but I'm ever more appreciative of good IDE support, and mixing domains like that often means giving up auto-complete, context highlighting, lang-server solution-proofs and linting.

Given that the scope of the question was "why did this happen?", even noting my lack of familiarity with your stack, the answer is "lazy evaluation in a language that doesn't natively support it is really hard."

Instead of using ReactiveRedisOperations, MessageListener is the way to go here. You can register a listener once, and use the following as the listener.

Sorry, I am really not an expert, but is not the problem that imgs[:, :, :, 2] crates a 3-dimensional tensor? May-be imgs[:, :, :, 2:2] would work, as it makes a four dimensional tensor with the last dimension equal to one (since you have one image)