llvm-ar error

Unfortunately this is the error I get when trying to run the same command. How are you able to build it? What version of the llvm project are you building?

((1,"c"), (23, "a"), (32,"b"))

same problem here (other tables) but the "kids-table" aren't filtered as expected.

I have tried several attempts and when I run sudo plank, it works without any issues. However, when I run plank normally (without sudo), the problem occurs. Could anyone suggest what kind of permissions or adjustments are needed to make it work without running as root?

Thanks in advance for your help!

I know that each format has its own compression and I know that decompression is long and complicated.

But I would like to do the same thing using libraries that allow conversion to a same single format that is similar to .ppm.

any suggestions?

PS. trying .ppm, it stores RGB values ​​as unsigned

this info is also available via their api without web scraping

https://github.com/EnderLocke/pyespn

Just published an article few days ago: https://stripearmy.medium.com/i-fixed-a-decade-long-ios-safari-problem-0d85f76caec0
And the npm package: https://www.npmjs.com/package/react-ios-scroll-lock

Hope this fixes your problem.

Someone help me with the script where it will give first level second level and thrid approval details configrued in the access policy

I am experiencing the same problem!

can´t make it work. ive tried other options but they never put qty, just one product. yours just come with error and i cant see de qty field. any sugestions?

It is also not working for me!
Maybe a mistake in the hook?

I faced a similar problem earlier. Try to see the solution in this question: How to stretch the DropdownMenu width to the full width of the screen?

@Raja Talha Do you find the Solution to this

it works just fine and gave me my exact location good job!

Can someone please guide me on how to convert a PyTorch .ckpt model to a Hugging Face-supported format so that I can use it with pre-trained models?

The model I'm trying to convert was trained using PyTorch Lightning, and you can find it here:
🔗 hydroxai/pii_model_longtransfomer_version

I need to use this model with the following GitHub repository for testing:
🔗 HydroXai/pii-masker

I tried using Hugging Face Spaces to convert the model to .safetensors format. However, the resulting model produces poor results and triggers several warnings.

These are the warnings I'm seeing:

Some weights of the model checkpoint at /content/pii-masker/pii-masker/output_model/deberta3base_1024 were not used when initializing DebertaV2ForTokenClassification: ['deberta.head.lstm.bias_hh_l0', 'deberta.head.lstm.bias_hh_l0_reverse', 'deberta.head.lstm.bias_ih_l0', 'deberta.head.lstm.bias_ih_l0_reverse', 'deberta.head.lstm.weight_hh_l0', 'deberta.head.lstm.weight_hh_l0_reverse', 'deberta.head.lstm.weight_ih_l0', 'deberta.head.lstm.weight_ih_l0_reverse', 'deberta.output.bias', 'deberta.output.weight', 'deberta.transformers_model.embeddings.LayerNorm.bias', 'deberta.transformers_model.embeddings.LayerNorm.weight', 'deberta.transformers_model.embeddings.token_type_embeddings.weight', 'deberta.transformers_model.embeddings.word_embeddings.weight', 'deberta.transformers_model.encoder.layer.0.attention.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.0.attention.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.0.attention.output.dense.bias', 'deberta.transformers_model.encoder.layer.0.attention.output.dense.weight', 'deberta.transformers_model.encoder.layer.0.attention.self.key.bias', 'deberta.transformers_model.encoder.layer.0.attention.self.key.weight', 'deberta.transformers_model.encoder.layer.0.attention.self.key_global.bias', 'deberta.transformers_model.encoder.layer.0.attention.self.key_global.weight', 'deberta.transformers_model.encoder.layer.0.attention.self.query.bias', 'deberta.transformers_model.encoder.layer.0.attention.self.query.weight', 'deberta.transformers_model.encoder.layer.0.attention.self.query_global.bias', 'deberta.transformers_model.encoder.layer.0.attention.self.query_global.weight', 'deberta.transformers_model.encoder.layer.0.attention.self.value.bias', 'deberta.transformers_model.encoder.layer.0.attention.self.value.weight', 'deberta.transformers_model.encoder.layer.0.attention.self.value_global.bias', 'deberta.transformers_model.encoder.layer.0.attention.self.value_global.weight', 'deberta.transformers_model.encoder.layer.0.intermediate.dense.bias', 'deberta.transformers_model.encoder.layer.0.intermediate.dense.weight', 'deberta.transformers_model.encoder.layer.0.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.0.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.0.output.dense.bias', 'deberta.transformers_model.encoder.layer.0.output.dense.weight', 'deberta.transformers_model.encoder.layer.1.attention.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.1.attention.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.1.attention.output.dense.bias', 'deberta.transformers_model.encoder.layer.1.attention.output.dense.weight', 'deberta.transformers_model.encoder.layer.1.attention.self.key.bias', 'deberta.transformers_model.encoder.layer.1.attention.self.key.weight', 'deberta.transformers_model.encoder.layer.1.attention.self.key_global.bias', 'deberta.transformers_model.encoder.layer.1.attention.self.key_global.weight', 'deberta.transformers_model.encoder.layer.1.attention.self.query.bias', 'deberta.transformers_model.encoder.layer.1.attention.self.query.weight', 'deberta.transformers_model.encoder.layer.1.attention.self.query_global.bias', 'deberta.transformers_model.encoder.layer.1.attention.self.query_global.weight', 'deberta.transformers_model.encoder.layer.1.attention.self.value.bias', 'deberta.transformers_model.encoder.layer.1.attention.self.value.weight', 'deberta.transformers_model.encoder.layer.1.attention.self.value_global.bias', 'deberta.transformers_model.encoder.layer.1.attention.self.value_global.weight', 'deberta.transformers_model.encoder.layer.1.intermediate.dense.bias', 'deberta.transformers_model.encoder.layer.1.intermediate.dense.weight', 'deberta.transformers_model.encoder.layer.1.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.1.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.1.output.dense.bias', 'deberta.transformers_model.encoder.layer.1.output.dense.weight', 'deberta.transformers_model.encoder.layer.10.attention.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.10.attention.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.10.attention.output.dense.bias', 'deberta.transformers_model.encoder.layer.10.attention.output.dense.weight', 'deberta.transformers_model.encoder.layer.10.attention.self.key.bias', 'deberta.transformers_model.encoder.layer.10.attention.self.key.weight', 'deberta.transformers_model.encoder.layer.10.attention.self.key_global.bias', 'deberta.transformers_model.encoder.layer.10.attention.self.key_global.weight', 'deberta.transformers_model.encoder.layer.10.attention.self.query.bias', 'deberta.transformers_model.encoder.layer.10.attention.self.query.weight', 'deberta.transformers_model.encoder.layer.10.attention.self.query_global.bias', 'deberta.transformers_model.encoder.layer.10.attention.self.query_global.weight', 'deberta.transformers_model.encoder.layer.10.attention.self.value.bias', 'deberta.transformers_model.encoder.layer.10.attention.self.value.weight', 'deberta.transformers_model.encoder.layer.10.attention.self.value_global.bias', 'deberta.transformers_model.encoder.layer.10.attention.self.value_global.weight', 'deberta.transformers_model.encoder.layer.10.intermediate.dense.bias', 'deberta.transformers_model.encoder.layer.10.intermediate.dense.weight', 'deberta.transformers_model.encoder.layer.10.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.10.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.10.output.dense.bias', 'deberta.transformers_model.encoder.layer.10.output.dense.weight', 'deberta.transformers_model.encoder.layer.11.attention.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.11.attention.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.11.attention.output.dense.bias', 'deberta.transformers_model.encoder.layer.11.attention.output.dense.weight', 'deberta.transformers_model.encoder.layer.11.attention.self.key.bias', 'deberta.transformers_model.encoder.layer.11.attention.self.key.weight', 'deberta.transformers_model.encoder.layer.11.attention.self.key_global.bias', 'deberta.transformers_model.encoder.layer.11.attention.self.key_global.weight', 'deberta.transformers_model.encoder.layer.11.attention.self.query.bias', 'deberta.transformers_model.encoder.layer.11.attention.self.query.weight', 'deberta.transformers_model.encoder.layer.11.attention.self.query_global.bias', 'deberta.transformers_model.encoder.layer.11.attention.self.query_global.weight', 'deberta.transformers_model.encoder.layer.11.attention.self.value.bias', 'deberta.transformers_model.encoder.layer.11.attention.self.value.weight', 'deberta.transformers_model.encoder.layer.11.attention.self.value_global.bias', 'deberta.transformers_model.encoder.layer.11.attention.self.value_global.weight', 'deberta.transformers_model.encoder.layer.11.intermediate.dense.bias', 'deberta.transformers_model.encoder.layer.11.intermediate.dense.weight', 'deberta.transformers_model.encoder.layer.11.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.11.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.11.output.dense.bias', 'deberta.transformers_model.encoder.layer.11.output.dense.weight', 'deberta.transformers_model.encoder.layer.2.attention.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.2.attention.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.2.attention.output.dense.bias', 'deberta.transformers_model.encoder.layer.2.attention.output.dense.weight', 'deberta.transformers_model.encoder.layer.2.attention.self.key.bias', 'deberta.transformers_model.encoder.layer.2.attention.self.key.weight', 'deberta.transformers_model.encoder.layer.2.attention.self.key_global.bias', 'deberta.transformers_model.encoder.layer.2.attention.self.key_global.weight', 'deberta.transformers_model.encoder.layer.2.attention.self.query.bias', 'deberta.transformers_model.encoder.layer.2.attention.self.query.weight', 'deberta.transformers_model.encoder.layer.2.attention.self.query_global.bias', 'deberta.transformers_model.encoder.layer.2.attention.self.query_global.weight', 'deberta.transformers_model.encoder.layer.2.attention.self.value.bias', 'deberta.transformers_model.encoder.layer.2.attention.self.value.weight', 'deberta.transformers_model.encoder.layer.2.attention.self.value_global.bias', 'deberta.transformers_model.encoder.layer.2.attention.self.value_global.weight', 'deberta.transformers_model.encoder.layer.2.intermediate.dense.bias', 'deberta.transformers_model.encoder.layer.2.intermediate.dense.weight', 'deberta.transformers_model.encoder.layer.2.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.2.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.2.output.dense.bias', 'deberta.transformers_model.encoder.layer.2.output.dense.weight', 'deberta.transformers_model.encoder.layer.3.attention.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.3.attention.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.3.attention.output.dense.bias', 'deberta.transformers_model.encoder.layer.3.attention.output.dense.weight', 'deberta.transformers_model.encoder.layer.3.attention.self.key.bias', 'deberta.transformers_model.encoder.layer.3.attention.self.key.weight', 'deberta.transformers_model.encoder.layer.3.attention.self.key_global.bias', 'deberta.transformers_model.encoder.layer.3.attention.self.key_global.weight', 'deberta.transformers_model.encoder.layer.3.attention.self.query.bias', 'deberta.transformers_model.encoder.layer.3.attention.self.query.weight', 'deberta.transformers_model.encoder.layer.3.attention.self.query_global.bias', 'deberta.transformers_model.encoder.layer.3.attention.self.query_global.weight', 'deberta.transformers_model.encoder.layer.3.attention.self.value.bias', 'deberta.transformers_model.encoder.layer.3.attention.self.value.weight', 'deberta.transformers_model.encoder.layer.3.attention.self.value_global.bias', 'deberta.transformers_model.encoder.layer.3.attention.self.value_global.weight', 'deberta.transformers_model.encoder.layer.3.intermediate.dense.bias', 'deberta.transformers_model.encoder.layer.3.intermediate.dense.weight', 'deberta.transformers_model.encoder.layer.3.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.3.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.3.output.dense.bias', 'deberta.transformers_model.encoder.layer.3.output.dense.weight', 'deberta.transformers_model.encoder.layer.4.attention.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.4.attention.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.4.attention.output.dense.bias', 'deberta.transformers_model.encoder.layer.4.attention.output.dense.weight', 'deberta.transformers_model.encoder.layer.4.attention.self.key.bias', 'deberta.transformers_model.encoder.layer.4.attention.self.key.weight', 'deberta.transformers_model.encoder.layer.4.attention.self.key_global.bias', 'deberta.transformers_model.encoder.layer.4.attention.self.key_global.weight', 'deberta.transformers_model.encoder.layer.4.attention.self.query.bias', 'deberta.transformers_model.encoder.layer.4.attention.self.query.weight', 'deberta.transformers_model.encoder.layer.4.attention.self.query_global.bias', 'deberta.transformers_model.encoder.layer.4.attention.self.query_global.weight', 'deberta.transformers_model.encoder.layer.4.attention.self.value.bias', 'deberta.transformers_model.encoder.layer.4.attention.self.value.weight', 'deberta.transformers_model.encoder.layer.4.attention.self.value_global.bias', 'deberta.transformers_model.encoder.layer.4.attention.self.value_global.weight', 'deberta.transformers_model.encoder.layer.4.intermediate.dense.bias', 'deberta.transformers_model.encoder.layer.4.intermediate.dense.weight', 'deberta.transformers_model.encoder.layer.4.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.4.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.4.output.dense.bias', 'deberta.transformers_model.encoder.layer.4.output.dense.weight', 'deberta.transformers_model.encoder.layer.5.attention.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.5.attention.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.5.attention.output.dense.bias', 'deberta.transformers_model.encoder.layer.5.attention.output.dense.weight', 'deberta.transformers_model.encoder.layer.5.attention.self.key.bias', 'deberta.transformers_model.encoder.layer.5.attention.self.key.weight', 'deberta.transformers_model.encoder.layer.5.attention.self.key_global.bias', 'deberta.transformers_model.encoder.layer.5.attention.self.key_global.weight', 'deberta.transformers_model.encoder.layer.5.attention.self.query.bias', 'deberta.transformers_model.encoder.layer.5.attention.self.query.weight', 'deberta.transformers_model.encoder.layer.5.attention.self.query_global.bias', 'deberta.transformers_model.encoder.layer.5.attention.self.query_global.weight', 'deberta.transformers_model.encoder.layer.5.attention.self.value.bias', 'deberta.transformers_model.encoder.layer.5.attention.self.value.weight', 'deberta.transformers_model.encoder.layer.5.attention.self.value_global.bias', 'deberta.transformers_model.encoder.layer.5.attention.self.value_global.weight', 'deberta.transformers_model.encoder.layer.5.intermediate.dense.bias', 'deberta.transformers_model.encoder.layer.5.intermediate.dense.weight', 'deberta.transformers_model.encoder.layer.5.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.5.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.5.output.dense.bias', 'deberta.transformers_model.encoder.layer.5.output.dense.weight', 'deberta.transformers_model.encoder.layer.6.attention.output.LayerNorm.bias', 'deberta.transformers_model.encoder.layer.6.attention.output.LayerNorm.weight', 'deberta.transformers_model.encoder.layer.6.attention.output.dense.bias', 'deberta.transformers_model.encoder.layer.6.attention.output.dense.weight', 'deberta.transformers_model.encoder.layer.6.attention.self.key.bias', 'deberta.transformers_model.encoder.layer.6.attention.self.key.weight', 'deberta.transformers_model.encoder.layer.6.attention.self.key_global.bias', 'deberta.transformers_model.encoder.layer.6.attention.self.key_global.weight', 'deberta.transformers_model.encoder.layer.6.attention.self.query.bias', 'deberta.transformers_model.encoder.layer.6.attention.self.query.weight', 'deberta.transformers_model.encoder.layer.6.attention.self.query_global.bias', 'deberta.transformers_model.encoder.layer.6.attention.self.query_global.weight', 'deberta.transformers_model.encoder.layer.6.attention.self.value.bias', 'deberta.transformers_model.encoder.layer.6.attention.self.value.weight', 'deberta.transformers_model.encoder.layer.6.attention.self.value_global.bias', 'deberta.transformers_model.encoder.layer.6.attention.self.............'deberta.encoder.layer.9.output.dense.bias', 'deberta.encoder.layer.9.output.dense.weight', 'deberta.encoder.rel_embeddings.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

if you struggle to resolve the problem with python libs. Check this article. it helped me a lot. https://aws.plainenglish.io/easiest-way-to-create-lambda-layers-with-the-required-python-version-d205f59d51f6

how can i make noreply mail.Is it enough for me ?

data['h:Reply-To']="" 

Add the folder in which you stored the "my-project-env" to the VSCode workspace.

I have the same problem. I couldn't install the solution. I think the problem may not be in the code. If you find the solution, I would be very happy if you share it.

I'm not sure what it is but it's not working for me either. The link Eric gave redirects to support. I created all relevent id's ibm cloud, ibm etc.. but nothing is working.

Have you found a solution to this problem?

Use Application Like pdAdmin or DBeaver

My answer is only to refute the answer with the first number of votes, because I can't vote or reply. I followed his instructions and added "source ~/.bash_profile" at the beginning of my ~/.zshrc. Then I executed "source ~/.zshrc" and it gave an error, "-bash: export: `': not a valid identifier". At this time,any "sudo" and "vim" command will not work at all.And my "~/.bashrc" content will be replace by 'eval "$(thefuck --alias)"'.My command config went missing!I could only delete that line "source ~/.bash_profile" and execute "echo $PATH" to check my PATH. I found that "/usr/bin" and "/bin" were missing, which made my basic commands completely invalid. Then I executed "export PATH=$PATH:/usr/bin:/bin" to fix it. Don't try that method lightly!

did you resolve this issue? I have working on this issue for days but have no resolutions yet...

Here is my result

04-08 16:44:30 I/TestInvocation: Starting invocation for 'cts' with '[ DeviceBuildInfo{bid=eng.anqizh, serial=a0f32ff5} on device 'a0f32ff5'] 
04-08 16:44:31 E/TestInvocation: Caught exception while running invocation
04-08 16:44:31 E/TestInvocation: Trying to access android partner remote server over internet but failed: Unsupported or unrecognized SSL message
com.android.tradefed.targetprep.TargetSetupError[ANDROID_PARTNER_SERVER_ERROR|500505|DEPENDENCY_ISSUE]: Trying to access android partner remote server over internet but failed: Unsupported or unrecognized SSL message
        at com.android.compatibility.common.tradefed.targetprep.DynamicConfigPusher.resolveUrl(DynamicConfigPusher.java:318)
        at com.android.compatibility.common.tradefed.targetprep.DynamicConfigPusher.setUp(DynamicConfigPusher.java:172)
        at com.android.tradefed.invoker.InvocationExecution.runPreparationOnDevice(InvocationExecution.java:621)
        at com.android.tradefed.invoker.InvocationExecution.runPreparersSetup(InvocationExecution.java:522)
        at com.android.tradefed.invoker.InvocationExecution.doSetup(InvocationExecution.java:375)
        at com.android.tradefed.invoker.TestInvocation.prepareAndRun(TestInvocation.java:624)
        at com.android.tradefed.invoker.TestInvocation.performInvocation(TestInvocation.java:291)
        at com.android.tradefed.invoker.TestInvocation.invoke(TestInvocation.java:1431)
        at com.android.tradefed.command.CommandScheduler$InvocationThread.run(CommandScheduler.java:692)
Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message
        at java.base/sun.security.ssl.SSLSocketInputRecord.handleUnknownRecord(SSLSocketInputRecord.java:462)
        at java.base/sun.security.ssl.SSLSocketInputRecord.decode(SSLSocketInputRecord.java:175)
        at java.base/sun.security.ssl.SSLTransport.decode(SSLTransport.java:111)
        at java.base/sun.security.ssl.SSLSocketImpl.decode(SSLSocketImpl.java:1506)
        at java.base/sun.security.ssl.SSLSocketImpl.readHandshakeRecord(SSLSocketImpl.java:1421)
        at java.base/sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:455)
        at java.base/sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:426)
        at java.base/sun.net.www.protocol.https.HttpsClient.afterConnect(HttpsClient.java:586)
        at java.base/sun.net.www.protocol.https.AbstractDelegateHttpsURLConnection.connect(AbstractDelegateHttpsURLConnection.java:187)
        at java.base/sun.net.www.protocol.http.HttpURLConnection.getInputStream0(HttpURLConnection.java:1675)
        at java.base/sun.net.www.protocol.http.HttpURLConnection.getInputStream(HttpURLConnection.java:1599)
        at java.base/sun.net.www.protocol.https.HttpsURLConnectionImpl.getInputStream(HttpsURLConnectionImpl.java:223)
        at java.base/java.net.URL.openStream(URL.java:1325)
        at com.android.compatibility.common.tradefed.targetprep.DynamicConfigPusher.resolveUrl(DynamicConfigPusher.java:315)
        ... 8 more
04-08 16:44:31 E/ClearcutClient: Unsupported or unrecognized SSL message
javax.net.ssl.SSLException: Unsupported or unrecognized SSL message
        at java.base/sun.security.ssl.SSLSocketInputRecord.handleUnknownRecord(SSLSocketInputRecord.java:462)
        at java.base/sun.security.ssl.SSLSocketInputRecord.decode(SSLSocketInputRecord.java:175)
        at java.base/sun.security.ssl.SSLTransport.decode(SSLTransport.java:111)
        at java.base/sun.security.ssl.SSLSocketImpl.decode(SSLSocketImpl.java:1506)
        at java.base/sun.security.ssl.SSLSocketImpl.readHandshakeRecord(SSLSocketImpl.java:1421)
        at java.base/sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:455)
        at java.base/sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:426)
        at java.base/sun.net.www.protocol.https.HttpsClient.afterConnect(HttpsClient.java:586)
        at java.base/sun.net.www.protocol.https.AbstractDelegateHttpsURLConnection.connect(AbstractDelegateHttpsURLConnection.java:187)
        at java.base/sun.net.www.protocol.http.HttpURLConnection.getOutputStream0(HttpURLConnection.java:1446)
        at java.base/sun.net.www.protocol.http.HttpURLConnection.getOutputStream(HttpURLConnection.java:1417)
        at java.base/sun.net.www.protocol.https.HttpsURLConnectionImpl.getOutputStream(HttpsURLConnectionImpl.java:219)
        at com.android.tradefed.clearcut.ClearcutClient.sendToClearcut(ClearcutClient.java:344)
        at com.android.tradefed.clearcut.ClearcutClient.lambda$flushEvents$1(ClearcutClient.java:322)
        at java.base/java.util.concurrent.CompletableFuture$AsyncSupply.run(CompletableFuture.java:1768)
        at java.base/java.util.concurrent.CompletableFuture$AsyncSupply.exec(CompletableFuture.java:1760)
        at java.base/java.util.concurrent.ForkJoinTask.doExec(ForkJoinTask.java:387)
        at java.base/java.util.concurrent.ForkJoinPool$WorkQueue.topLevelExec(ForkJoinPool.java:1312)
        at java.base/java.util.concurrent.ForkJoinPool.scan(ForkJoinPool.java:1843)
        at java.base/java.util.concurrent.ForkJoinPool.runWorker(ForkJoinPool.java:1808)
        at java.base/java.util.concurrent.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:188)
04-08 16:44:31 W/NativeDevice: Attempting to stop logcat when not capturing for a0f32ff5

Same question, any more findings so far? Thanks in advance.

Sorry i can't post comment cause i don't have 25 reputation so i post an answer.
But your result should be SemOrd=10 for UserId = 1 and SubId = 706 ?

Kindly provide the code as reference for better understanding of the problem.

guys if we import math and take to def functions do we have to import it twice?

Since scripts will be sunset, I recommend starting your developments directly with RedApp.
You can check the link below — feel free to reach out if you need any help.

https://developer.sabre.com/sdk/sabre-red-360/25.1/help-documentation/home.html

To help us diagnose the problem, we need a bit more information:
- More Detailed Scenario - Please describe exactly what type of search or which part of the workbench you're using when the issue occurs? The search function appears in multiple places, so a precise description will help.
- Browser Console - Are there any errors or warnings in the Firefox console when the delay happens?
- Setup Details - You've mentioned that you're using GraphDB version 10.8.4 on a self-hosted server. Can you confirm if you're using the free version or a licensed one?
- Data Characteristics - It would also be useful to know the approximate volume and nature of the data in your database.
With these details, we can better investigate your problem.

Best regards,
Stilyana

14 years later, it seems Outlook still doesn't reconize it. Is it limited to Apple iCalendar ?

I know it's a 11 year old topic, but, I just switched to js and webstorm. I'm wondering if anyone knows if I can set project explorer to automatically expand src directory once I expand a module?

I have a same problem. Did you resolve the problem?

could you please provide more details about the specific modifications you made? I am encountering the same issue and would appreciate your guidance.

I am having the same error: [Error: Failed to collect configuration for /_not-found]

Later, I found out that my .env file was missing a variable. Adding that environment variable solved this build error.

Also, try deleting the ".next" folder if you are self-hosting your project.

How did you find the solution to this error? Like, based on the screenshot provided, how was the error identified and solved by looking at the package natively? Can you guide me through the process? @Arjun Singh

I have on premise, oracle 21c EE on Windows 10, receiving same error " Database Connection Error HTTP Status Code: 571 " I am trying to search for solutions but still nothing worked, please help.

¿Podrías explicar a qué te refieres cuando indicas que className se oculta en todos los componentes?

Tu planteamiento inicial resulta muy vago, por lo que sería conveniente que ampliaras tu pregunta.

Es muy difícil brindar ayuda sin contar con la información mínima necesaria. Agradecería que describieras en detalle tu problema y lo que esperas que suceda para considerar tu código como correcto.

I was facing the same error , downgrading the version pip install --force-reinstall uvicorn<0.24 helped me. Thank you @QuimPuiggalí

Can I ask you if are you calling downloadEvfData into a loop to have refreshed images or this is your complete solution for real time streaming? Because I need to download a liveviewimage continuously in background but using a while loop into a thread causes EdsDownloadEvfImage to crush without errors; the code just stops exiting from the loop.Thank you in advance

Can someone tell me what is this? I remember downloading it on my phone.

load_files.html

<div class='err box_link'>Авторизуйтесь для доступа</div>

Though I am too late, I hope someone will find this helpful.

You can find the clear steps in this article.

https://medium.com/@sp96.info/deploying-vue-js-app-to-firebase-hosting-0d4351714e4c

do you have the dataset?

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv("you dataset here")
sns.scatterplot(data=df, x="Age", y="Sales")
plt.title("Relationship between Age and Car Toy Sales")
plt.show()

Yes. it's work. Replace With > Local History it'd resolved my problem.
Big thank you for the good advice.

This happens because the QB desktop uses auto ref number which has go be switched off for manually set txnunbers.

Which Soap are you using?

I have the same issue. The issue is related to https://github.com/huggingface/transformers/pull/37337

In my case, installing accelerate fix the issue, as the workaround.

pip install accelerate

Were you able to find a fix? One way could be to save the dates/ time in string format. I am in the middle of a bug fix though, and saving it in string format would mean that all the previous use cases would fail.

you are talking rocket science, steve is not proud

New templates and plugins for Sponzy on https://bbest.live/shop

Since you have mentioned voro++ on top of your current options, it seems logic to think that if you could use voro++ in MATLAB you could readily fox the problem at hand.

Good news ! Some one ahead of you has posted in Github the MEX libraries for voro++ .

https://github.com/smr29git/MATLAB-Voro

Please give a go and let us know.

was there ever a fix found for this? Our test email sent in dev show this behavior but not the tests from prod, ie with the prod tests when 'view entire message' is clicked the css is carried over, but not from dev. The esp we are using is SailThru

I have ran into the same problem and seeing very similar training logs to you when using a multi-discrete action space but the evaluation is not good. Did you ever find a solution?

Go to this link, And download all the models under `ComfyUI/models` into your models.

Issue - You might be using the VM and because of this, internet access is blocked.
Reference - https://github.com/chflame163/ComfyUI_LayerStyle_Advance?tab=readme-ov-file#download-model-files

Have you tried giving up on this assignment? worked for me

Anyone please answer to this question. It is required in my university assignment. Please help. ASAP.

I have the same issue, did you ever resolve this?

I got the solution please change gradel dependency

please replace with

implementation 'com.github.smarteist:Android-Image-Slider:1.4.0'

it's working for my project. I hope it will work for your project.

if have any issue please let me know. Thanks

Did you ever get that figured out?

have you found a solution yet? I've been struggling with this issue myself for the past two days

Counterintuitively, removing "Codeable" conformance from the @Model protocol conformance list eliminates the error.

The Macro expansion is the issue.

See: "Cannot Synthesize" -- Why is this class not ready to be declared "@Model" for use with SwiftData?

I also am looking for an answer to this.

did you get the answer of opening the parent app directly from shield action extension?

DOH! I was checking an empty table by accident. My bad!

I am having the exact same issue where Autodesk.Revit.Exceptions.InvalidOperationException: 'This Element cannot have type assigned.' gets thrown.

How did you manage to solve this ?

i can here to understand sigaction(); function because im working on a project that called minitalk in 42 school so i was wondering how can i send a message from server to client using sigaction and other functions also use SIGUSR1 and SIGUSR2

Did you find any solution? libx264 now have flag AV_CODEC_CAP_ENCODER_FLUSH for x264

https://github.com/FFmpeg/FFmpeg/blob/master/libavcodec/libx264.c

But I can't understand - how make it working. When I use avcodec_flush_buffers then after switching to next stream I just got in log

lookahead thread is already stopped

Error sending video frame for encoding: Generic error in an external library

We discussed this on the GitHub issues site: https://github.com/grpc/grpc-java/issues/11763

There are a few reasons this could happen.

1. can you verify the concurrency isn't being overridden on the factory, such as in code with a factory.setConcurreny("10") or something like that?

2. Do you maybe have multiple @JmsListeners in different classes or configs or even another instance of the app listening to the same queue?

If it's neither of those can you share more info around what your app looks like? Are you using Solace's JMS starter? or something else?

Problem solved. See the specification and don't believe the ai!

do anyone have a solution for this problem ? I have the same and i can't figure it out.

Thanks in advance.

How to export with tag details from the same code, please post that as well.

Can you send the entire query and an example of the expected result?

Facing same issue here, I am using the custom component for the label, and I need to change some styles depending whether it is in the dropdown or in the tabs

I have similar problem, but I used

Page<T> findAll(@Nullable Specification<T> var1, Pageable var2)

And I can't sort in Specification. Somebody have any idea?

Answer: Its not possible with WMI

good morning.

Have you had any success in solving this problem?

I have a similar problem, the difference is that the tab is created via PowerShell..

Any solution for Debian linux?

grant create database does not work on my fb 4.0.5 - isql simply says "Use CONNECT or CREATE DATABASE to specify a database". If I try to create a database with my test user I get the same error message as before.

okay so it seems like the problem was that i was trying to use VS Code and I should be using Visual Studio to run this project (i'm new to coding but to what i could gather VS Code cant properly run and gives this errors)

I have the exact same issue, but in Flutterflow. Can someone support me doing the same thing in flutterflow?

I searching the same solution. After 7 years you found?

This is not possible and as it seems also not planned in the future. See https://github.com/handsontable/handsontable/issues/191

What have you tried so far to achieve this? Stack Overflow is meant for developers who are actively working through problems and need help refining or debugging their code. Not just a place to request ready-made solutions. It's important to show some effort and experimentation on your part.

Maybe you can take a look at some related questions:
- Create custom Notice-Type, and use it on specific actions(i.e add to cart button)/pages in woommerce (This is a non AJAX solution that involves only PHP - and the default WooCommerce notice behaviar)
- How to display a message on the checkout page after clicking on the proceed to payment button? (This gives you more information about showing messages on custom places like the checkout page)

Or you can create your own modal: https://www.w3schools.com/howto/howto_css_modals.asp
And place your code inside a WooCommerce theme override or action. You can find more about template overrides and inserting your own code in the WooCommerce documentation:
https://developer.woocommerce.com/docs/template-structure-overriding-templates-via-a-theme/

We're happy to help if you run into any code-related issues after trying some of the above solutions.

There is information in this article about how to use SSE to build MCP, which may be helpful to you.
https://github.com/liaokongVFX/MCP-Chinese-Getting-Started-Guide

I was wondering what is the final purpose to have those pets wandering around ? Is to keep active the session ?

Unrelated (kinda), question. Have you been able to change the textures of gltf (.glb) 3d object with sceneCore. In a similar way of what possible with previous 3d/Ar libraries used in android with kotlin like Sceneform?:
What I want to do (example):
An sphere without a mapped texture, plain color. Add a .PNG image as a texture that will be displayed in the sphere 3d object

An improvement over that is to use https://www.npmjs.com/package/express-http-context

Did you save the file? I also had the issue and i was trying to find what the problem was, just to find out i needed to just save the code before the terminal could read it ahah

i was wondering, did you succeed in that task? I'm currently stuck at the same task and i found recommendation about using "originator node" in thingsboard but still didn't succeed in the mission

update-ios-version:
    steps:
      - set-xcode-build-number@1:
          inputs:
            - plist_path: $BITRISE_SOURCE_DIR/apps/mobile/ios/Myapp/Info.plist
            - build_short_version_string: $VERSION_STRING
      - set-xcode-build-number@1:
          inputs:
            - plist_path: $BITRISE_SOURCE_DIR/apps/mobile/ios/Extension/Info.plist
            - build_short_version_string: $VERSION_STRING
    

what's wrong in my step, if you could help @jbeu425

It work in my case thanks Amin !

FYI: as of version 3.5, vue allows component level app instance: https://vuejs.org/guide/extras/web-components.html#app-level-config

Have you referenced the nuget package : System.ServiceModel.Primitives ?

The error implies either a missing using statement or a missing package reference, but it's difficult to help more without more detail. If adding the above library doesn't resolve it please provide more details about your application including what the project type is and what references you already have - but hopefully the above will sort it....

Simply had to select matching target framework for my device.

what's the required RBAC for ADF on Azure Logic App in this case?

Use Shift+CTRL+D and a windows open that you can search and filter all database objects

https://sourceforge.net/projects/mingw/
download from this resource and then install .

All right,Well, that's the problem with my parameters(to_time). It's set too close

echo file_put_contents("");

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