feeds | Imports data from API sources like YouTube | Addon library

In your code I can't see subFolder definition. Is it folder ID? If so, for moveTo() method to work you need to get that folder first:

The answer is, LSTM knows how to do it on its own. You do not have to manually feed each word one by one. An intuitive way to understand is that the shape of the batch that you send, contains seq_length (batch.shape[1]), using which it decides the number of words in the sentence. The words are passed through LSTM Cell generating the hidden states and C.

-n wraps your code in while (<>) { ... }* (cf perldoc perlrun). Thus, your one-liner is equivalent to:

The function you would want to run is get_round().

get_round() is similar to get_price() but you specify a timestamp and it will return the closest price that occurred just before that timestamp.

You can see this function on GitHub.

Full documentation is still underway for the Chainlink+Solana integration so keep your eyes out on this page in the chainlink docs to find it in the future.

@Lube this is the best way to force a re-render of a component. I've needed to do the same for graph components in the past when my data changes.

There's a great article I've linked to below that explains the various ways of forcing a re-render, but ultimately the best way is adding a :key property to your component and updating that key whenever you need a re-render.

Article can be found here.

Well, one way could be to use factory for the corresponding aggregation. In the factory you can clone some template item and instead of binding text, you could dynamically assign 'hard' one, e.g:

You should create a new array from 2 collection lists, .data is what you need in the below example.

I think you have fundamentally mixed up some things here: the links you have provided and are following provide guidance on setting up logging in Azure Functions. However, you appear to be talking about logging in Azure Pipelines, which is an entirely different thing. So just to be clear:

Azure Pipelines run the build and deployment jobs that deploy the code you might have on your GitHub repository to Azure Functions. Pipelines are executed in Azure Pipelines agents, that can be either Microsoft- or Self-hosted. If we assume that you are executing your pipelines with Microsoft-Hosted agents, you should not assume that these agents have any capabilities that Azure Functions might have (nor that you should execute code aimed for Azure Functions in the first place). If you want do execute python code in your pipeline, you should first start looking at what python-related capabilities the hosted agents have pre-installed and work from there: https://docs.microsoft.com/en-us/azure/devops/pipelines/agents/hosted?view=azure-devops&tabs=yaml

If you want to log something about pipeline run, you should first check the "Enable system diagnostics" option when queuing pipeline manually. For implementing more logging by yourself, do check: https://docs.microsoft.com/en-us/azure/devops/pipelines/scripts/logging-commands?view=azure-devops&tabs=bash

For logging in Azure Functions you might want to start here: https://docs.microsoft.com/en-us/azure/azure-functions/functions-monitoring , but that would be an entirely different topic than logging in Azure Pipelines.

Both of the structures you posted are fine based on your requirements. They produce 2 different types of UIs so what's better entirely depends on what kind of UI you want.

In the first one (stack at root, tabs in the first screen), when you navigate to other screens, the tab bar is not visible on those screens. So if this is the UI you want, go with the first one.

In the second one, (tab at root, stacks nested inside each tab), when you navigate to other screens, the tab bar is still present. So if you want this behavior, go with the second one.