Caveman | Indie game project driven by a few enthusiasts | Game Engine library

So I decided to take a crack at my first Python/Discord bot to make it easier to play a game me and my friends made up.

I want to know how I can add the local users of my server to a docker container. I don't need to import their files, I just need a username/password/privileges with new home directory in the docker container for every user in my system. For example, suppose my docker container contains the following users:

Host System:
admin: who has root access and rw access to all
bob: a regular non-sudo user
joe: another regular non-sudo user

Then the Docker Container must have users:
admin: who has root access and rw access to all
bob: a regular non-sudo user
joe: another regular non-sudo user

The Docker container and the system are both running linux, though the system is red hat and the container is ubuntu.

EDIT: I don't want to mount /etc/ files if possible, as this can create a two way security vulnerability as pointed out by @caveman

I have a Python iterative quicksort implementation. I'd like to do an argsort instead of a sort though, so that the resulting array has the ranking of the items when sorted instead of the items themselves.

This question is definitely a stupid question. But, coming from C; I'm having trouble adding header files or "an initialization" file to my pari-gp code. This is to mean; I have a 1hr compile of code to make one vector; and I can use that vector once initialized; but I want to make a file of this vector such that I can access it once it's compiled once.

Here's the code without a header file; which takes about an hour to compile (given the series precision/numerical precision which are set to 100).

I am trying to use Torch for Label Propagation. I have a dataframe that looks like

I'd like for a user who starts the application for the first time on a device to be able to access GPS data in that session. Currently, the user must close and then restart the application after providing location permission for the location data to show in the application. I have tried a variety of methods to resolve this. Most recently, I have moved requestPermission into Fragment1 which is also where locationManager is located.

My app has a passcode view, conceptually similar to the iOS unlock screen. It's a UIViewController that I present within a new UIWindow. Works fine. I'm adding the capability to type the passcode using a hardware keyboard. The keyCommands method is not called, thus key presses not recognized, until the user taps anywhere on the screen at least once. It's a full-screen UIWindow/UIViewController so presumably it's a tap within the UIWindow/UIViewController. Once that tap occurs, keyCommands will be called as expected, and all works perfectly. I don't want to require the user to tap their screen before typing their passcode.

Any idea what's going on here, specifically why the user needs to tap the screen (and how to avoid that requirement)?

I have verified the UIViewController is the firstResponder by including a repeating NSTimer call to verify as such.

See EDIT below, the initial post almost has no meaning now but the question still remains.

I developing a neural network to semantically segment imagery. I have worked through various loss functions (categorical cross entropy (CCE), weight CCE, focal loss, tversky loss, jaccard loss, focal tversky loss, etc) which attempt to handle highly skewed class representation, though none are producing the desired effect. My advisor mentioned attempting to create a custom loss function which ignores false negatives for a specific class (but still penalizes false positives).

I have a 6 class problem and my network is setup to work in/with one-hot encoded truth data. As a result my loss function will accept two tensors, y_true, y_pred, of shape (batch, row, col, class) (which is currently (8, 128, 128, 6)). To be able to utilize the losses I have already explored I would like to alter y_pred to set the predicted value for the specific class (the 0th class) to always be correct. That is where y_true == class 0 set y_pred == class 0, otherwise do nothing.

I have spent way too much time attempting to create this loss function as a result of tensorflow tensors being immutable. My first attempt (which I was led to through my experience with numpy)

I'm trying to run some unit tests using moto and unittest based off of this example from muhannad0's blog.

Below is my code (minified the table creation as it's not so important).