¡Perfecto! Te paso un mensaje que puedes copiar y pegar para reportarlo a Instagram:

---

Mensaje para reportar:

\> Hola, tengo un problema con mi cuenta. No puedo acceder correctamente desde ningún dispositivo. Ya he probado actualizar, borrar caché, reinstalar la app, cambiar de red, y probar en otros teléfonos. A otras personas sí les funciona Instagram normalmente.

Por favor, revisen mi cuenta, ya que creo que puede estar bloqueada por error.

Gracias.

---

Manually set mem.maxVSize(vsize=MAX_SIZE) where max size can be previous size times N

do it your own beacuese i dont know about this hop you understand

You can customize the permission for ngrok.exe file to run in "protection history -> threat blocked -> Severe -> action -> allow" and the problem will be solved. I think this is a silly trick of windows 11.

In 2025 installing the CLI in WSL side is no longer required.

The WSL option is available for example in Remote Explorer > WSL Targets.

Source: https://github.com/microsoft/vscode-docs/blob/vnext/remote-release-notes/v1_79.md#tunnels (at that time the feature was only supported in vscode.dev).

I got it fixed ,it was incompatibility issue , react native version 0.78 and 0.79 not compatible with latest expo version .

I downgraded react native to 0.77.2 and OfCourse react to 18.3.0 and kept expo in latest version it worked as expected .

but should react native community gives us a development warning about incompatibility with expo , I spent over 3 weeks trying to narrow the issue and I was thinking it was something wrong with my code .

An expanded version of the GetImage function in C#, that allows for bitmaps and icons in the resource file.

public Bitmap GetImage(string imageId)

{
    Bitmap ErrorBitmap = Properties.Resources.Exclamation.ToBitmap();
    //'see https://stackoverflow.com/questions/4656883/how-enumerate-resources-inside-a-resx-file-programmatically
     
    // Convert the ResourceSet to IEnumerable and iterate over its entries
    ResourceSet resourceSet = Properties.Resources.ResourceManager.GetResourceSet(CultureInfo.CurrentUICulture, true, true);
    foreach (DictionaryEntry entry in resourceSet)
    {
        string resName = entry.Key as string;
        if (resName == imageId)
        {
            object temp  = Properties.Resources.ResourceManager.GetObject(resName) as Icon;
            if (temp != null)  
                if (temp is Icon)
                {
                    ErrorBitmap = ((Icon)temp).ToBitmap();
                }
                else if (temp is Bitmap)
                {
                    ErrorBitmap = (Bitmap)temp;
                }
            
            break;
        }
    }

    // return the bitmap
    return ErrorBitmap;
}

orphanRemoval = true

and

FetchType.LAZY

in Hibernate 6.6.X:

Title: Issue with

orphanRemoval = true

and

FetchType.LAZY

in Hibernate 6.6.X

Body:

I'm encountering an issue while using Hibernate 6.6.X with

orphanRemoval = true

in combination with

FetchType.LAZY

.

Context:

I have a parent entity that has a one-to-many relationship with child entities. I've configured the relationship as follows:

@Entity
public class Parent {
    @OneToMany(mappedBy = "parent", cascade = CascadeType.ALL, orphanRemoval = true, fetch = FetchType.LAZY)
    private Set<Child> children = new HashSet<>();
}

Problem:

When I try to remove a child entity from the collection of children in the parent entity, it does not seem to get removed properly if the parent entity is loaded lazily. I suspect that it might be related to the way Hibernate handles the lazy loading of collections in conjunction with orphan removal.

Question:

1. Has anyone experienced this issue with

   orphanRemoval = true
   

   and

   FetchType.LAZY
   

   in Hibernate 6.6.X?

2. What would be the best approach to ensure that orphaned entities are removed correctly when using lazy loading?

3. Is there a known workaround or solution to this problem?

Any insights or suggestions would be greatly appreciated!

Feel free to modify any part of the question to better fit your situation or style!

I'm assuming (I may wrong), if we kept resources under separate module & tried to access from different module, above runtime error we are getting; this is because main module can't actually able to access the resources from other modules.

sgdsfgs vdgdfr sdrgvsergsrdgergdvdg

I tested your exact code, it works as expected, no issues found, the size of list is 2 after updating,

note that I used hibernate 6.6.9.Final as you mentioned.

It seems the problem is something else, please give me your project repository address for better debugging

Welcome to the Waffle House Menu Price USA 2025. Waffle House is a popular restaurant chain in the United States, known for its delicious breakfast, lunch, and dinner options at affordable prices. As we enter 2025, many customers are curious about the latest Waffle House menu prices in the USA. If you are craving their famous waffles, crispy hash browns, or a hearty All-Star Special, knowing the updated prices can help you plan your meal better. This guide will provide a detailed look at Waffle House menu with prices in 2025, helping you enjoy your favourite meals without surprises.

https://waffelhouse.org/category/waffle-house-menu/

With rising food costs and market changes, menu prices can vary slightly by location. However, Waffle House remains committed to offering budget-friendly meals that satisfy taste and value. Whether you are a regular visitor or planning your first visit, understanding the latest prices can help you make the best choices. Keep reading to explore the Waffle House menu prices in 2025 and see what is new this year.

The code doesn't cause an error because:

1. _name is protected in Person.

2. teacher is a derived class of Person, so it can access protected members of Person.

3. This is intentional design in C++ to allow derived classes to interact with base class members while still restricting access from outside the hierarchy.

To have a Copilot agent in Copilot Studio summarize a URL that isn't a knowledge source, you'll need to configure the agent to access external content by integrating web scraping tools or API services. You can feed the URL to the agent for processing, ensuring it can pull the relevant information and summarize it effectively.

For more on automation tools and digital marketing solutions, visit SMM Trigger.

Another workaround: Start selecting text with left click and (while still holding left button pressed) scroll with your scroll wheel instead of dragging to the edge. Requires some finger acrobatics, but it works faster than shift-select.

it worked for me to disable the config CONFIG_ARM64_PTR_AUTH

# CONFIG_ARM64_PTR_AUTH is not set

**Explanation of this configuration：**https://cateee.net/lkddb/web-lkddb/ARM64_PTR_AUTH.html

If MS Access (.mdb/.accdb) file plays a role in the app, this error might be due to database corruption. Use MS Access Compact and Repair menu option to fix it.

This issue sounds like it might be related to the way the data is being passed or refreshed within the Filament system. I’ve encountered similar display update problems while working with dynamic data. When dealing with such issues, checking how updates are triggered in your app can help. For managing data and updates across platforms, I found services like those offered on terabox mod apk latest version to be quite useful in handling large data efficiently.

In my experience you cant do it, its possible only with JavaScript and HTML. If there is a way to run python + html iwant to know, but i think you cant. Good luck

Downgrading androidx.datastore:datastore-preferences to 1.1.4 helped me.

The cookies() function will work as expected if you modify the Cookie header on the request to match the Set-Cookie header you set on the response.

https://github.com/vercel/next.js/discussions/50374

For debugging purpose only, try to add following piece of code in build.gradle of subproject_2

tasks.named('compileJava'){
  dependsOn(':subproject_1:build')
}

I have some problems on SseEmitter too, So i try to use your code to fix my problems, but the problems exist too. I use curl to request,

 curl -v -H "Accept: text/event-stream" \
http://localhost:8181/sse/events

the response is

Trying 127.0.0.1:8181...
* Connected to localhost (127.0.0.1) port 8181 (#0)
> GET /sse/events HTTP/1.1
> Host: localhost:8181
> User-Agent: curl/8.1.2
> Accept: text/event-stream
>
< HTTP/1.1 200
< Vary: Origin
< Vary: Access-Control-Request-Method
< Vary: Access-Control-Request-Headers
< Content-Type: text/event-stream
< Content-Length: 26
< Date: Sun, 27 Apr 2025 10:56:32 GMT
<
event:myevent
* Connection #0 to host localhost left intact
data:Event 0

it just return first event, but in server log i can see the printing log , i am confused。is there anybody can help，please

Agree with the comment from @Peter regarding the return value checks.

Another potentially useful debug point here would be to convert your poll() example to use the polling itself as a simple delay by changing the final parameter to 1 (meaning a 1 millisecond timeout):

else {
        pollfd pfd{};
        pfd.fd = fd;
        pfd.events = POLLIN;

        int ret = poll(&pfd, 1, 1); // final parameter is 1ms timeout

Then emit explicit debug for the values of ret and pfd.revents every iteration before you run the subsequent error checks on them.

Side note: from personal experience of similar gotchas in the past, try using static_cast<unsigned>(byte) when you log the received bytes, this can sometimes reveal interesting behaviours.

The downside with using the cancelling/resuming instead of pausing is that it can create reporting issues, especially for MRR based companies.

Churnkey has a good guide on how they use pauses: https://docs.churnkey.co/billing-providers/stripe

Churnkey uses Stripe's built-in pause feature which will update the subscription's pause_collection[behavior] to mark_uncollectible. The resumes_at field will automatically be set by Churnkey depending on the length of the pause selected.

You can specify the maximum pause length allowed by customers while configuring your offboarding flow.

First of all, make it clearer by showing the whole code, not separate snippets as you did there ; this will help us understand eventuel placement issues with your variable definitions or errors of such kind. :)

Salam necesen evde deyilem basqa yaerdeyem gelende yigacam Öz'ün kardeşi işten çıkarıldı mı ne verb de bilmirem de bilmirem de bilmirem de bilmirem de bilmirem

What happens in React is that when you execute 'npm run build' command, it replaces %PUBLIC_URL% with ./ or the homepage(if specified) in package.json field. Changing %PUBLIC_URL% to another path made the browser look for images and other files in the wrong location. Changing it back to %PUBLIC_URL% will not solve the issue as browser will be loading files from its cache.

1. You need to first clear browser cache by opening DevTools → Right-click Reload button → Choose Empty Cache and Hard Reload

2. Delete the build/ folder and rebuild -

   npm run build
   

I have the same issue using VS Code for a NestJS + Mongoose server project. The same solution mentioned in several answers above, with a twist: Put it in the eslint.config.mjs` (extension can also be js or whatever you are using for eslint.config.

Put the following in the `rules` object:

rules: {
    ...
    "prettier/prettier": [
        "error",
        {
            "endOfLine": "auto"
        }
    ],
    ...

I have the exact same issue but my greek letters should go into the xlabel.
I've already tried set xlabel "{/Symbol a}/{\U+00B0}" enhanced font ",12" and it didn't work. More precisely the alpha looks weird and just like the latin "a" (and gamma also looks as in the example above).

My minimal example would be:

set encoding utf8
set xlabel "{/Symbol a}"
plot 1

If your data already in DataFrame format ou can used this:

combined_df = pd.concat([df_txt, df_choco], axis=0, ignore_index=True)

df_txt came from df_txt = pd.read_csv('add.txt', delimiter=',')

and df_choco same logic as above.

• Any best practices or recommended libraries to secure structured outputs better (e.g., Guardrails, Outlines, etc.)

This is because your Laravel version is not compatible with the Doctrine DBAL version you are using.

1. Check your version : composer why carbonphp/carbon-doctrine-types

2. Downgrade your version : composer require nesbot/carbon:^2.66(whatever your version is)

3. Remove your dependencies : composer remove carbonphp/carbon-doctrine-types

4. Install the compatible ones : composer require doctrine/dbal:^3.0(whatever your version is)

This is a repo I came across for LLM finetuning

I have a small integration example for VS Code that allows you to deploy the built image directly

to the target device over the network. In my opinion, it’s very convenient.

https://github.com/berserktv/vscode-yocto-helper.git

You don't have to reinvent the wheel, there are great open source solutions that you can try to integrate into your project. Even if you don't want to do that, you can take a look into these solutions to have an understanding of how they implemented it:

• three-mesh-ui has pre-built keyboard layouts, and has an integration example with THREE.Raycaster

• Another alternative would be vr-keyboard which offers a solution for 3D on-screen input in VR.

there is possibilities of later importing sklearn.datasets import load_iris

directly run load_iris block of code .

First run imported libraries code block then run loading dataset block again .

Actually i was getting the same error while using the qwen2.5 model for inference but there was one thing i overlooked by mistake and it was very silly i forgot to edit the pod container size large enough to fit the model weights, after i corrected that it RAN without any error.

I hope this helps .

I'm a bit confused about what you're trying to do. To return to Form1, you just need to set form.Owner = this; when creating Form2, and then use Owner.Show() in Form2 FormClosing event to show Form1 again

You can watch this tutorial,I wish you can find your solution.

https://www.youtube.com/watch?v=4KTSJZcXy6c&t=225s&ab_channel=WinCoderSujon

Hi WhatsApp bro i need answer from this question you can help me?

Update for 2025:

the list of allowed scheduler types from documentation

“linear” = get_linear_schedule_with_warmup
“cosine” = get_cosine_schedule_with_warmup
“cosine_with_restarts” = get_cosine_with_hard_restarts_schedule_with_warmup
“polynomial” = get_polynomial_decay_schedule_with_warmup
“constant” = get_constant_schedule
“constant_with_warmup” = get_constant_schedule_with_warmup
“inverse_sqrt” = get_inverse_sqrt_schedule
“reduce_lr_on_plateau” = get_reduce_on_plateau_schedule
“cosine_with_min_lr” = get_cosine_with_min_lr_schedule_with_warmup
“warmup_stable_decay” = get_wsd_schedule

their parameters can be passed to Trainer using --lr_scheduler_kwargs

hello maybe your looking for the new openturns conditional distribution if you want to condition distribution components by a some scalar values:

https://openturns.github.io/openturns/latest/user_manual/_generated/openturns.experimental.PointConditionalDistribution.html

Maybe we will add vine copulas in the future as I started a module for that.

Try to install it via NPM npm i @symfony/stimulus-bundle and npm i @hotwired/stimulus

This way IDE will check their types in node_modules

This formula it's a bit long, but I think it works: https://sudapollismo.substack.com/p/fifo-capital-gains-excel-formula

screenshoot

In C (and similar languages)

In C#, anonymous types are super handy for temporary structures, but they can't easily be returned from a method because they don't have a named type.

Example of an anonymous type:

csharp

CopyEdit

varresult = new { Name = "Alice", Age = 30 };

Inside a method? Fine.
Returning from a method? Not directly — unless you:

Options to Return Anonymous-Like Data

1. Return as object (not ideal, because you lose type safety)

csharp

CopyEdit

publicobject GetPerson() { return new { Name = "Alice", Age = 30 }; }

Downside: You can't easily use .Name and .Age without casting/reflection.

2. Use dynamic type

csharp

CopyEdit

publicdynamic GetPerson() { return new { Name = "Alice", Age = 30 }; }

Upside: Easier to access properties. Downside: No compile-time checking (errors at runtime if you mistype).

3. Define a real class instead

Best practice for returning structured data:

csharp

CopyEdit

publicclass PersonDto { public string Name { get; set; } public int Age { get; set; } } public PersonDto GetPerson() { return new PersonDto { Name = "Alice", Age = 30 }; }

• Strongly typed

• Safer

• Better for APIs, large projects, etc.

Quick Summary

OptionProsConsobjectQuick hackNo type safetydynamicEasy accessRuntime errorsReal classBest choiceSlightly more code

In Python, JavaScript, etc.

If you meant Python or JavaScript, they naturally return anonymous structures all the time:

• Python:

python

CopyEdit

defget_person(): return {"name": "Alice", "age": 30}

• JavaScript:

javascript

CopyEdit

functiongetPerson() { return { name: "Alice", age: 30 }; }

In dynamic languages, anonymous types are normal and no problem at all.

Refrence site https://stackoverflow.com

It’s unclear which zone you are using for the configuration: "notification.mygatecorp.com" or "mygatecorp.com". If you are configuring it under the "mygatecorp.com" domain, you will need to modify the DKIM name to "selector1-azurecomm-prod-net._domainkey.azurecomm.net.notification". For more information, you can refer to this documentation Verify custom domain

I'm having the same issue, now the version of lxml is 5.4.0 but I still get the error.when I use

from lxml.etree import tostring

I got

ModuleNotFoundError: No module named 'lxml.etree'

Did you solve this problem ?I dont know how to solve it.

​Manual Base64 Decoding Guide​ (using "TWFu" example):

1. ​Handle URL-Safe Characters​ (if applicable):

   • Replace "-" with "+" and "_" with "/"

   • Example: "TWFu" remains unchanged

2. ​Add Padding:

   • Ensure string length is multiple of 4 by adding "="

   • Original "TWFu" (length 4) needs no padding

3. ​Base64 Character Table:

   base64
   

   复制

   A-Z → 0-25    a-z → 26-51
   0-9 → 52-61   + → 62   / → 63
   

4. ​Convert to 6-bit Binary:

   markdown
   

   复制

   T(19) → 010011
   W(22) → 010110
   F(5)  → 000101
   u(46) → 101110
   

5. ​Combine & Split into 8-bit Bytes:

   binary
   

   复制

   01001101 | 01100001 | 01101110
   

6. ​Convert to ASCII:

   markdown
   

   复制

   0x4D → M
   0x61 → a
   0x6E → n
   

   Final Result: "Man"

​Recommended Automation Tool:
For practical implementation, using the GoTools Base64 Converter which offers:

✓ Bidirectional Conversion (Text ↔ Base64)
✓ URL-Safe Encoding/Decoding
✓ Automatic Padding Handling
✓ Binary/Hex Data Preview
✓ Error Detection for Malformed Input
✓ Multi-Format Export Options

Ideal for developers working with:

• API authentication tokens

• JSON/XML data serialization

• Binary file encoding

• Certificate management

​Pro Tips:

1. For non-ASCII characters (e.g., Chinese), verify UTF-8 encoding layers

2. Validate binary outputs using hex editors like HxD

3. Always sanitize inputs when decoding untrusted data

4. Use chunking (76-char lines) for large datasets

This combination of fundamental understanding and modern tooling provides both educational value and production-ready efficiency. The manual process helps debug encoding issues, while the tool accelerates daily development workflows.

Would you like me to elaborate on any specific aspect of the decoding process or tool features?

This is what worked for me I uninstalled NodeJS Then installed NVM which allows you to install multiple NodeJS So switched to NodeJS version 12.0.0 and error was solved

nvm install 12.0.0 nvm use 12.0.0

You can try using different online base64 decoding tools for processing, such as: https://go-tools.org/tools/base64-converter

If I understood your question correctly, it looks like you simply need $_SERVER['REQUEST_URI']

If that's not what you're trying to do, I suggest you to take a look at PHP's parse_url function, which parses any given URL and returns all its parts in a tidy array. It can also return a single specific part.

This formula it's a bit long, but I think it works: https://sudapollismo.substack.com/p/fifo-capital-gains-excel-formula

In my situation:

Im using import { useSpring, animated } from '@react-spring/web';
instead of import { useSpring, animated } from 'react-spring/web';

• I installed only "@react-spring/web": "^9.7.5",

• I dont need to install the @types, I dont need to install the full react-spring.

• Im using pnpm.

Can you post the steps how to achieve this ?

Installing hrsh7th/vscode-langservers-extracted via NPM and since it's preconfigured in nvim kickstart i can simply add cssls = {} or somesass_ls = {} within local servers = {...} as long as i have the packages installed via NPM globally, which help the LS. Those are documented in the docs for cssls and somesass_ls.

gpu GPU hpu i here for gpu network

It looks like you're facing performance issues with autocomplete in both Android Studio and VSCode

I'm not sure about VSCode but in Android Studio it's a Line endings issue

Check out this guide: https://axat.hashnode.dev/fixing-android-studios-slowness-while-developing-with-flutter

I found this question when trying to work out why users on a second LDAP server could not log in. It was because Gitlab doesn't allow multiple LDAP servers to be configured in the non-EE version. See https://gitlab.com/gitlab-org/gitlab/-/issues/355835 which I found via this discussion of why the second server's login tab did not appear (up to this point, I'd not noticed the login screen had tabs, since there is only ever one tab!) https://forum.gitlab.com/t/only-first-ldap-server-is-shown-as-tab-on-the-login-screen/76599

Thank you, @Tanaike, for your solution! While my original issue was with inserting a QR code image in the body of a Google Document (replacing a placeholder), the approach you suggested of using getParent().asParagraph().appendInlineImage() and then removing the placeholder element (removeChild()) also worked perfectly for my case. This was after trying many other methods that resulted in the "Element does not contain the specified child element" error. Your solution was the one that finally resolved it. Much appreciated!

You can try like his

<button
    onMouseDown={() => setActive(true)}
    onMouseUp={() => setActive(false)}
    onMouseLeave={() => setActive(false)}
    style={{ 
         backgroundColor: active ? '#fff' : '#000', 
         color: active ? '#000' : 'fff'
    }}
>
    Button Label
</button>

The short answer is that you can't—a GF grammar needs a pre-defined lexicon. The words in a GF grammar contain a full inflection table, which for many languages can be quite complex, and pattern matching on a string isn't allowed at runtime. (More on that here.) Furthermore, even if you enter only fully-formed noun phrases and don't need to guess any inflected forms, you would still miss information about agreement: "apple pie is delicious" vs "pretzels are delicious". This is going to be much trickier in languages other than English.

That said, if your application is simple enough and you are working on a language that doesn't have much morphology, maybe you can get away with string literals. You can read this answer, the relevant part starts from subheading “Arbitrary strings as artists”.

https://www.reddit.com/r/unrealengine/comments/7s47zz/how_to_use_outdated_marketplace_code_plugins_with/?rdt=40644
this may be helpful

you need to copy the plugin from engine to the project，regenerate the VS project files and compile from source

Thought I'd tried out everything before asking this question, but apparently not. Here's the way to do it:

import { render } from "solid-js/web";
import { Router, Route,createAsyncStore } from "@solidjs/router";

let ShowDocs = props => (
  <p>
    doc1: {props.doc1.a} <br/>
    doc2: {props.doc2.a} <br/>
    doc3: {props.doc3.a} <br/>
  </p>
);

let Main= props => {
    const docs = {};
    for (let i=1; i<=3; i++)
        docs['doc'+i] = createAsyncStore(() => ({a:'blah'+i}));

    const docs2 = () => {
        const ds = {};
        for (let i=1; i<=3; i++)
            ds['doc'+i] = docs['doc'+i]();
        return ds;
    }

    return (
        <div>
            WORKS:  <ShowDocs doc1={docs.doc1()} doc2={docs.doc2()} doc3={docs.doc3()} /> 

            BUT IS THERE A WAY TO DO SOMETHING LIKE THIS?
                <ShowDocs {...docs2()} />
        </div>
    );
}

render(() => <Router><Route path="/*" component={Main} /></Router>, document.getElementById("app")!);

I think you have to make the method as private so from outside you will not be able to access the method. Therefore you cannot update.

Adding cancel_action inside your paystack meta data to redirect to a page when the cancel button is clicked. Link to the docs

For this need to use mouse events

<button
    onMouseDown={() => setActive(true)}
    onMouseUp={() => setActive(false)}
    onMouseLeave={() => setActive(false)}
    style={{ backgroundColor: active ? 'white' : 'black', color: active ? 'black' : 'white' }}
>
    The-Button
</button>

This did the trick:

 @Expose()
  @Transform(({ obj }) => obj.user.userName)
  createdBy!: string;

SI funciona con gnone, hay que desactivar el wayland, y activar que fucione con X11 en lugar de wayland

In my case, i have to `Link Binary with the libraries` manually... error resolved

Even if your app only uses HTTPS (this is 99% of all apps), you still need to file a French encryption declaration.

Under French law (Article L2332-1 of the Defense Code) and ANSSI guidance (source), any cryptographic functionality, including HTTPS, requires at least a simple declaration.

Apple also highlights the need to comply with local encryption regulations even for standard libraries (Apple docs).

There are services like NovaStore that can handle the filing if you want to stay compliant.

It is not cached as you assume. The statement you show that uses f() is a top-level statement and therefore it only runs once when the module is first imported. It doesn't matter how many times you expect a to equal 1, you're not re-executing any code whatsoever. The only time that the code in the ./asd module ran was when the module was first imported, when zero test cases had been executed.

The reason for this error is because in that SDK version, the project is configured to use expo-router instead of navigation. Expo-router is built on top of navigation. That's why the NavigationContainer nesting is created.

You are still executing the following instructions after an at end condition occurs therefore the record you are getting is the part of the last record in the file which remains in the files buffer.

better code would be:

read file at end move 'y' to at end-sw. (end-sw was defined in working storage as 'n'.)

if end-sw not equal 'y' perform the processing you want. (in another paragraph)

I'm working with same feature and created a db diagram with minimum columns for understanding how it'll work in real application.

Here is my db diagram, where you may get an idea and implement.

Product Variation Wise Price in Ecommerce

Thank you!

If you'd like to stay connected with your friend

1. Go to you repository

2. Go to Settings -> General

   

3. On the General section, scroll down to "Danger Zone".

   

4. Click on the Change Visibility button.

   

5. Click on the Change to private button.

truct TodoItem {
    let title        : String
    let state        : State
    let dueDate      : DueDate
    let location     : Location
    let collaborators: [Collaborator]
}

I managed to fix this by adding a new method in the entity.

public function getIsBookable(): ?bool
{
    return $this->isBookable();
}

I guess behind the scenes ApiPlatform calls something like `$entity->get{$field}` instead of the classic symfony getter

What is the TextInputLayout border stroke color? For more information, see https://m3.material.io/components/text-fields/specs.

You can use ImageConvertHQ.com — a free online tool that converts images to JPEG with no software needed. It’s fast and supports multiple formats. Plus, no watermarks or login required.

https://imageconverthq.com/image-to-jpeg

Migrating apps can be a real headache, huh? I feel your pain. I once had a similar struggle when trying to switch frameworks. It sounds like you've really thrown the kitchen sink at this problem. Have you double-checked that all the dependencies for Expo SecureStore are properly installed? Maybe it's a version compatibility issue. Also, did you try looking at the Expo logs in more detail? That might give a clue about what's going wrong deeper down. Any luck yet?

Kanhaiya vikram shinde mi as director we will be

MiMri	rijalt
cell 1	cell 2
cell 3	cell 4

"This is half Solved!!!"
 In [ ]:
 # I did my best in the so little time :(((( :(
 Image Coloring Problem
 In this project, you will tackle the challenge of image colorization, a process that
 involves adding color to grayscale images. Image colorization has applications in
 various fields, such as restoring old movies and photographs, enhancing satellite
 imagery, and assisting in medical image analysis.
 The goal is to build a deep learning model that can accurately predict the color
 channels of an image given its grayscale version. You will use PyTorch, a popular
 deep learning library, to construct and train your model. The project will be
 structured around several key tasks, each contributing to the development and
 evaluation of your colorization model.
 U-Net Architecture
 The neural network The neural network needs to take in a noised image at a
 particular time step and return the predicted noise. Note that the predicted noise is
 a tensor that has the same size/resolution as the input image. So technically, the
 network takes in and outputs tensors of the same shape. What type of neural
 network can we use for this?
 What is typically used here is very similar to that of an Autoencoder, which you
 may remember from typical "intro to deep learning" tutorials. Autoencoders have a
 so-called "bottleneck" layer in between the encoder and decoder. The encoder first
 encodes an image into a smaller hidden representation called the "bottleneck", and
 the decoder then decodes that hidden representation back into an actual image.
 This forces the network to only keep the most important information in the
 bottleneck layer.
 In terms of architecture, the DDPM authors went for a U-Net, introduced by
 (Ronneberger et al., 2015) (which, at the time, achieved state-of-the-art results for
 medical image segmentation). This network, like any autoencoder, consists of a
 bottleneck in the middle that makes sure the network learns only the most
 important information. Importantly, it introduced residual connections between the
 encoder and decoder, greatly improving gradient flow (inspired by ResNet in He et
 al., 2015).
Here's a description of the UNet architecture:
 1. Contracting Path (Encoder):
 • The input to the UNet is typically a grayscale or multi-channel image.
 • The contracting path starts with a series of convolutional layers
 followed by max-pooling layers.
 • Each convolutional layer is usually followed by a rectified linear unit
 (ReLU) activation function.
 • The number of filters typically increases with the depth of the network,
 capturing increasingly abstract features.
 • Max-pooling layers progressively downsample the spatial dimensions of
 the feature maps, allowing the network to learn hierarchical
 representations.
 2. Bottleneck:
 • At the bottom of the U-shaped architecture lies the bottleneck or
 central layer.
 • It represents the point where the network switches from the contracting
 path to the expanding path.
 • The bottleneck layer typically consists of convolutional layers without
 max-pooling, allowing the network to capture contextual information.
 3. Expanding Path (Decoder):
 • The expanding path involves upsampling the feature maps and
 concatenating them with feature maps from the contracting path.
 • Each step in the expanding path involves an upsampling operation
 (e.g., transposed convolution or upsampling followed by convolution) to
 increase the spatial resolution.
 • The concatenated feature maps from the corresponding contracting
 path stage serve as skip connections.
 • Skip connections help preserve spatial information and assist in the
 precise localization of segmentation boundaries.
This was adapted from Lukman Aliyu
 Requirements
 • Prepare the data
 • Build a U-net architecture
 • Train the model on the prepared dataset
 • Display 5 images from the training set in 3 formats: original color,
 grayscale, and the colorized
 • Run inference on 10 images in the test set
 • Display the 10 images in 3 formats: original color, grayscale, and the
 colorized
 1. Setup and Imports
 In [ ]:
 import torch
 import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader, Dataset, random_split
 from torchvision import datasets, transforms
 from torchvision.transforms.functional import to_pil_image, resize
 import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
 from tqdm import tqdm
 import os
2. Load the Dataset
 class ColorizationDataset(Dataset):
 def __init__(self, dataset, transform_input=None, transform_target=None):
 self.dataset = dataset
 self.transform_input = transform_input
 self.transform_target = transform_target
 def __len__(self):
 return len(self.dataset)
 def __getitem__(self, idx):
 color_img, _ = self.dataset[idx]
 gray_img = transforms.functional.to_grayscale(color_img, num_output_channels
 if self.transform_input:
 gray_img = self.transform_input(gray_img)
 if self.transform_target:
 color_img = self.transform_target(color_img)
 return gray_img, color_img
 transform_input = transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor
 transform_target = transforms.Compose([transforms.Resize((32, 32)), transforms.
 base_train_dataset = datasets.CIFAR10(root='./data', train=True, download=True)
 base_test_dataset = datasets.CIFAR10(root='./data', train=False, download=True)
 train_full = ColorizationDataset(base_train_dataset, transform_input, transform_target
 test_dataset = ColorizationDataset(base_test_dataset, transform_input, transform_target
 train_size = int(0.8 * len(train_full))
 val_size = len(train_full)-train_size
 train_dataset, val_dataset = random_split(train_full, [train_size, val_size])
 batch_size = 16
 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
 val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
 test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
 # Just Looking at the data and trying too visualize it
 random_img_idx = torch.randint(0, 1000, (1,)).item()
 print(train_dataset[0][0])
 test_image = train_dataset[random_img_idx][0] # 0 for image part in (image, label) tuple.
 test_image = resize(test_image, (250, 250), antialias=None) # better visualization
 #print(test_image.shape)
 #print('Number of channels in test_image: ', test_image.shape[0])
 test_image.show()
 #to_pil_image(test_image)
 In [ ]:
 In [ ]:
3. Define the Model Architecture
 class Autoencoder(nn.Module):
 def __init__(self):
 super(Autoencoder, self).__init__()
 self.encoder = nn.Sequential(
 nn.Conv2d(1, 16, 3, stride=2, padding=1), nn.ReLU(),
 nn.Conv2d(16, 32, 3, stride=2, padding=1), nn.ReLU(),
 nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(),
 nn.Conv2d(64, 128, 3, padding=1), nn.ReLU()
 )
 self.decoder = nn.Sequential(
 nn.ConvTranspose2d(128, 64, 3, padding=1), nn.ReLU(),
 nn.ConvTranspose2d(64, 32, 3, padding=1), nn.ReLU(),
 nn.ConvTranspose2d(32, 16, 4, stride=2, padding=1), nn.ReLU(),
 nn.ConvTranspose2d(16, 3, 4, stride=2, padding=1), nn.Sigmoid()
 )
 def forward(self, x):
 x = self.encoder(x)
 x = self.decoder(x)
 return x
 class ComprehensiveLoss(nn.Module):
 def __init__(self):
 super(ComprehensiveLoss, self).__init__()
 def forward(self, input, target):
 input = torch.clamp(input, 1e-7, 1-1e-7) # Prevent log(0)
 loss =-1 * (target * torch.log(input) + (1-target) * torch.log(1-input
 return loss.mean()
 def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs
 model.to(device)
 for epoch in range(num_epochs):
 model.train()
 running_loss = 0.0
 for gray_imgs, color_imgs in tqdm(train_loader, desc=f"Epoch {epoch+1}/
 gray_imgs, color_imgs = gray_imgs.to(device), color_imgs.to(device)
 outputs = model(gray_imgs)
 loss = criterion(outputs, color_imgs)
 optimizer.zero_grad()
 loss.backward()
 optimizer.step()
 running_loss += loss.item()
 print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss / len(train_loader
 model.eval()
 val_loss = 0.0
 with torch.no_grad():
 for gray_imgs, color_imgs in val_loader:
 In [ ]:
gray_imgs, color_imgs = gray_imgs.to(device), color_imgs.to(device
 outputs = model(gray_imgs)
 val_loss += criterion(outputs, color_imgs).item()
 print(f"Validation Loss: {val_loss / len(val_loader):.4f}")
 4. Training the Model
 def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs
 model.to(device)
 for epoch in range(num_epochs):
 model.train()
 running_loss = 0.0
 for gray_imgs, color_imgs in tqdm(train_loader, desc=f"Epoch {epoch+1}/
 gray_imgs = gray_imgs.to(device)
 color_imgs = color_imgs.to(device)
 outputs = model(gray_imgs)
 loss = criterion(outputs, color_imgs)
 optimizer.zero_grad()
 loss.backward()
 optimizer.step()
 running_loss += loss.item()
 avg_loss = running_loss / len(train_loader)
 print(f"Epoch [{epoch+1}/{num_epochs}], Training Loss: {avg_loss:.4f}")
 # Validation loss
 model.eval()
 val_loss = 0.0
 with torch.no_grad():
 for gray_imgs, color_imgs in val_loader:
 gray_imgs = gray_imgs.to(device)
 color_imgs = color_imgs.to(device)
 outputs = model(gray_imgs)
 loss = criterion(outputs, color_imgs)
 val_loss += loss.item()
 val_loss /= len(val_loader)
 print(f"Validation Loss: {val_loss:.4f}")
 4.1 Loss function
 In [ ]:
 In [ ]:
 In [ ]:
 In [ ]:
# define your training loop with validation
 # ----------------------------
5. Showing Performance on Training
 data
 def visualize_colorization(model, dataset, device='cpu', num_images=5):
 model.eval()
 fig, axs = plt.subplots(num_images, 3, figsize=(10, 4 * num_images))
 with torch.no_grad():
 for i in range(num_images):
 gray, color = dataset[i]
 gray = gray.unsqueeze(0).to(device)
 output = model(gray).squeeze(0).cpu()
 axs[i, 0].imshow(to_pil_image(color))
 axs[i, 0].set_title("Original Color")
 axs[i, 1].imshow(to_pil_image(gray.squeeze(0).cpu()), cmap='gray')
 axs[i, 1].set_title("Grayscale Input")
 axs[i, 2].imshow(to_pil_image(output))
 axs[i, 2].set_title("Colorized Output")
 for j in range(3): axs[i, j].axis("off")
 plt.tight_layout()
 plt.show()
 6. Making Inferences
 def visualize_colorization(model, dataset, device='cpu', num_images=5):
 model.eval()
 fig, axs = plt.subplots(num_images, 3, figsize=(10, 4 * num_images))
 with torch.no_grad():
 for i in range(num_images):
 gray, color = dataset[i]
 gray = gray.unsqueeze(0).to(device)
 output = model(gray).squeeze(0).cpu()
 axs[i, 0].imshow(to_pil_image(color))
 axs[i, 0].set_title("Original Color")
 axs[i, 1].imshow(to_pil_image(gray.squeeze(0).cpu()), cmap='gray')
 axs[i, 1].set_title("Grayscale Input")
 axs[i, 2].imshow(to_pil_image(output))
 axs[i, 2].set_title("Colorized Output")
 for j in range(3): axs[i, j].axis("off")
 plt.tight_layout()
 plt.show()
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 model = Autoencoder()
 In [ ]:
 In [ ]:
 In [ ]:
 In [ ]:
criterion = ComprehensiveLoss()
 optimizer = optim.Adam(model.parameters(), lr=1e-4)
 # Train the model
 train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=10
 # Visualize on training data
 visualize_colorization(model, train_dataset, device=device, num_images=5)
 # Visualize on test data
 visualize_colorization(model, test_dataset, device=device, num_images=10

I had a private registry container that was causing this issue. my fix for this was to re-push the affected images. the manifests from a recent change were affected. the only change that I had done was to move the underlying registry folder on the local docker instance node from /docker-registry to /mnt/nfs/docker-registry. When I rsync'd the files, something must have affected the manifest sha sum. Not sure if that is the "exact" root cause, but that's the only thing I can think happened, and the fix was to repush the images, which was super fast since all the layers were already present, it created a new manifest sha sum instantly and I was able to pull again.

It may be caused by this problem
@TestPropertySource(locations = { "classpath:application.yml", "classpath:contract-test.properties" })@ActiveProfiles({ "local" })

Check application.yml and contract-test.properties

Ensure that the endpoint IDs for all Actuator-related configurations are legal (lowercase letters, numbers, and hyphens are allowed only -)

Are you trying to randomly change the color of a specific layer inside a LayerDrawable? If so, you can find the target drawable within the LayerDrawable either by index (getDrawable(int index)) or by ID (findDrawableByLayerId(int id)), and then change its color using setTint(int tintColor). If your LayerDrawable is created in XML, you can directly assign an ID to each layer like this:

<item
    android:id="@+id/background_layer"
    android:drawable="@drawable/bottom_background_drawable" />

Got the same issue Here!
Date 2025-04-26
Workbench 8.0.40
MySQL 8.4.5
It looks like during the process of wizard importing CSV data or even on single row entering data, it will put a bXXXXX (XXXXX as being your data) in front of the wanted BINARY(1) data.

It will also put a b in front of all next data, even if data type was defined in the table editor carefully.

Moving BINARY(1) data in middle of attributes, note all next attributes:
INSERT INTO `noteluq`.`Cours` (`no_cours`, `cours_nom`, `credit`, `cycle`, `actif`, `departement`, `temps_actif`) VALUES ('ADM 1010', 'ADMIN DE GRANDS', '3', '1', b'1', b'ADMIN', b'0001');

The work around I found is moving your (hopefully only) binary attribute to the end of all attributes.

after correction:
INSERT INTO `noteluq`.`Cours` (`no_cours`, `cours_nom`, `credit`, `cycle`, `departement`, `temps_actif`, `actif`) VALUES ('ADM 1010', 'ADMIN DE TI CULS', '3', '1', 'ADMIN', '0001', b'1');

INSERT INTO `noteluq`.`Cours` (`no_cours`, `cours_nom`, `credit`, `cycle`, `departement`, `temps_actif`, `temps_retrait`, `actif`) VALUES ('ADM 1020', 'ADMIN DE GRANDS', '3', '1', 'ADMIN', '0001', '0004', b'0');

The import data table wizard seemed affected by the order data in table has been set. When I try to import data, even I put "actif" at the very end of attributes and save, it appears elsewhere. and creates an error While loading data.

Hope these informations helps

If you've tried everything that's been written above and you have a WordPress website. It might just be that you're missing an index.php file

Thank you for using DolphinDB.

To resolve this issue, you can forcibly remove the recovering partitions of the table using dropRecoveringPartitions from the ops module.

use ops
dropRecoveringPartitions(dbPath="dfs://stock", tableName="stock")

Once that's done, you can safely delete the table using dropTable.

If you run into any other issues, don't hesitate to ask - we're happy to help!

Solve the problem by upgrading the Windows version，docker windows clients need at least 22H2 version Supports

The workaround is to downgrade the Xdebug version (of course by choosing a version that matches project PHP version).

Others used the same workaround as stated here https://www.reddit.com/r/PHPhelp/comments/q4cd85/xdebug_causing_err_connection_reset/

I don't know where to submit this issue to get it fixed (or at least investigated). Please share if you know where to do it.

Also, please share if you have a better solution.

Hope this helps!

postMessage is the standard approach for sending data from React Native to WebView especially for passing user data. injectJavaScript directly executes code in the WebView context, which is useful for DOM manipulation or calling functions but less useful for just passing data. Your current setup with postMessage and event listeners is the right pattern for this use case

I had this problem with third party sample code, it was linking with the option "-mwindows" in CMakeLists.txt. that's why there was no console output.

make sure not to link with the option "-mwindows"

Multi Architecture

enter image description here

text = input()
exit_text = ['Done', 'done', 'd']
while True:
    for letter in reversed(text):
        print(letter, end='')
    print()
    text = input()
    if text in exit_text:
        break

I’m the maintainer of Plotlars.

Good news—starting with Plotlars 0.9.0 (just released!) you can add a secondary y-axis to any cartesian plot.

The problem was solved by wrapping the prompt with the chat template that Llama models use during instruction tuning. Adding the special tokens to the prompt better steered the model in the right direction.

Here is a code block that demonstrates what worked:

# Prepare a prompt for email re-write task
original_text = "Hi guys, just checking in to see if you finally finished the slides for next week when we meet with Jack. Let me know asap. Cheers, John"

messages = [
    {"role": "system", "content": "You are an AI assistant that revises emails in a professional writing style."},
    {"role": "user", "content": f"Revise the following draft email in a professional voice, preserving meaning. Only provide the revised email.\n\n### Draft:\n{original_text}"}
]


# Apply the chat template (adds special tokens like <|start_header_id|>, etc.)
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False, # We want the string, not tokens yet
    add_generation_prompt=True # Ensures the prompt ends expecting the assistant's turn
)


print("--- Formatted Prompt ---")
print(prompt)
print("------------------------")

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