sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: ant-design
Issue: Tooltips in the inline menu doesn't have a consistent design in collapsible state
Labels: Inactive,unconfirmed

### What problem does this feature solve?

When the inline menu is collapsed, we can see a very different design between tooltip for single title, and tooltips to show children of a menu. Ideally, design should look the same (at the moment we can see a black backgrounded tooltip for single item and white backgrounded modal for multiple children or one child).

We can see a much better behavior on vertical menu, but they are not collapsible and we can't expand children on the list using this type of menu. The tooltips are more consistent.

This will solved misleading consistency for users, where for a same menu you can have 2 totally different tooltips.

### What does the proposed API look like?

I think components are already created and present on the library. It's just a matter of exposing the right components at the right place to ensure consistency on what the end user is going to see on screen.

<!-- generated by ant-design-issue-helper. DO NOT REMOVE -->
Repository: PowerToys
Issue: [Workspaces] Pair window positioning to FancyZones
Labels: Needs-Triage,Product-Workspaces

### Description of the new feature / enhancement

- Add the option to assign a FancyZone layout and zone for each window after capture
- If no zone is assigned, create an internal FancyZone used only by Workspaces to place the windows with no assignment in the same positions and sizes they were at the moment of capture
- The positioning capture should be a toggle, so if one doesn't care about positioning, one doesn't need to bother with it

### Scenario when this would be used?

Reopening all programs already in the positions we are used to work with

### Supporting information

_No response_
Repository: PowerToys
Issue: [Workspaces] MS Teams was captured, but doesn't launch
Labels: Issue-Bug,Needs-Triage,Needs-Team-Response,Product-Workspaces

### Microsoft PowerToys version

0.84.0

### Installation method

PowerToys auto-update

### Running as admin

Yes

### Area(s) with issue?

Workspaces

### Steps to reproduce

- Capture MS Teams
- Launch MS Teams

### ✔️ Expected Behavior

It to launch MS Teams

### ❌ Actual Behavior

It does not launch MS Teams, showing a yellow warning icon
![image](https://github.com/user-attachments/assets/905c427a-c54a-492f-94e2-c75d979271ef)


### Other Software

- Windows 10
Repository: PowerToys
Issue: [Workspaces] Workspaces Editor do not snap to FancyZones
Labels: Issue-Bug,Resolution-Fix Committed,Product-Workspaces

### Microsoft PowerToys version

0.84.0

### Installation method

PowerToys auto-update

### Running as admin

Yes

### Area(s) with issue?

Workspaces

### Steps to reproduce

Try to snap

### ✔️ Expected Behavior

It to snap

### ❌ Actual Behavior

It doesn't snap, even though it's a resizable window

### Other Software

- Windows 10
Repository: PowerToys
Issue: The Workspaces feature captures the opened tabs but the contents like tabs in browsers and path in command prompts are missing when it is launched
Labels: Issue-Bug,Needs-Triage,Product-Workspaces

### Microsoft PowerToys version

0.84.0

### Installation method

PowerToys auto-update

### Running as admin

Yes

### Area(s) with issue?

Workspaces

### Steps to reproduce

Open 5 tabs in Chrome browser and two command prompts from a specific path.
after the capture process, it should open the command prompt and browsers where we captured

### ✔️ Expected Behavior

 it should open the command prompt and browsers where we captured

### ❌ Actual Behavior

it is just opening the tabs but the history is gone

### Other Software

none
Repository: PowerToys
Issue: Gui applications in wsl2 have misaligned context menus with fancyzones enabled
Labels: Issue-Bug,Needs-Triage

### Microsoft PowerToys version

0.84.0

### Installation method

PowerToys auto-update

### Running as admin

Yes

### Area(s) with issue?

FancyZones

### Steps to reproduce

1. Setup fancyzones 
2. Install WSL2 Ubuntu
2. install gitkraken through instructions [here](https://help.gitkraken.com/gitkraken-desktop/windows-subsystem-for-linux/) to ensure all the required dependencies get deployed. Running this in /tmp prevents some apt related issues.
3. Launch gitkraken, observe the window appear
4. Open a repository with it and right click a commit, or open any of the drop-down menus in the application at the top.
5. Observe that the context menus are visually misaligned from the cursor and difficult to use
6. add msrdc.exe to the exclude list for fancyzones (or exit powertoys)
7. Observe the correct behavior for context menus.

There's a related issue opened for wsgl with video etc. examples, but I opened a bug ticket here because this seems like it may have a fix / workaround in fancyzones itself https://github.com/microsoft/wslg/issues/1116

### ✔️ Expected Behavior

Context menus work as expected

### ❌ Actual Behavior

Context menus are misaligned with the cursor and often un-usable
![Screenshot 2024-09-05 152033](https://github.com/user-attachments/assets/567fce19-9f48-4bc7-9cdf-ba979cd0f4e1)


### Other Software

Windows Subsystem For Linux 2 w. Ubuntu
Remote desktop client
Repository: PowerToys
Issue: "Show this window on all desktops"
Labels: Needs-Triage,Product-Workspaces

### Description of the new feature / enhancement

For Workspaces, it'd be great to have an option to have a particular window show on all desktops.  

### Scenario when this would be used?

This would be used when launching a workspace

### Supporting information

_No response_
Repository: pytorch
Issue: Incorrect calculation of standard deviation when using complex numbers on MPS backend
Labels: triaged,module: complex,module: correctness (silent),module: mps

### 🐛 Describe the bug

When using the MPS backend for generating samples of a complex normal distribution (`torch.randn`), the standard deviation is calculated incorrectly.
This leads to two issues:
1. The generated samples of the normal distribution have twice the standard deviation they should have according to [the documentation of torch.randn](https://pytorch.org/docs/stable/generated/torch.randn.html), and
2. When calculating the standard deviation of a number of samples, the results differ completely depending on whether the calculation is carried out on the CPU or MPS

This example demonstrated the issues:
```
import torch

# Generate samples from a normal distribution
N = 10**5
standard_deviation = 1
samples_cpu = torch.randn(N, dtype=torch.complex64, device='cpu') * standard_deviation
samples_mps = torch.randn(N, dtype=torch.complex64, device='mps') * standard_deviation

# Generate samples seperately using 1. cpu, 2. mps, calculate the standard deviation
print( 'Real part' )
print( 'CPU: std = %.4f' % samples_cpu.real.std() )
print( 'MPS: std = %.4f' % samples_mps.real.std() )

print( '\nImaginary part' )
print( 'CPU: std = %.4f' % samples_cpu.imag.std() )
print( 'MPS: std = %.4f' % samples_mps.imag.std() )

print( '\nComplex standard deviation' )
print( 'CPU: std = %.4f' % samples_cpu.std() )
print( 'MPS: std = %.4f + %.4fi' % (samples_mps.std().real, samples_mps.std().imag) )

# Use the CPU generated samples but calculate std 1. using cpu, 2. using mps
print( '\nUsing only the CPU-generated samples:' )
print( 'CPU: std = %.4f' % samples_cpu.std() )
print( 'MPS: std = %.4f + %.4fi' % (samples_cpu.to('mps').std().real, samples_cpu.to('mps').std().imag) )
```

### Versions

PyTorch version: 2.4.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 14.2.1 (arm64)
GCC version: Could not collect
Clang version: 15.0.0 (clang-1500.1.0.2.5)
CMake version: Could not collect
Libc version: N/A

Python version: 3.9.6 (default, Nov 10 2023, 13:38:27)  [Clang 15.0.0 (clang-1500.1.0.2.5)] (64-bit runtime)
Python platform: macOS-14.2.1-arm64-arm-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Apple M1

Versions of relevant libraries:
[pip3] numpy==2.0.1
[pip3] torch==2.4.0
[pip3] torchaudio==2.4.0
[conda] Could not collect

cc @ezyang @anjali411 @dylanbespalko @mruberry @Lezcano @nikitaved @amjames @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen
Repository: pytorch
Issue: DISABLED test_embedding_bag_dynamic_shapes_cpu (__main__.DynamicShapesCpuTests)
Labels: triaged,module: flaky-tests,skipped,oncall: pt2,oncall: export,module: aotinductor

Platforms: linux

This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_embedding_bag_dynamic_shapes_cpu&suite=DynamicShapesCpuTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/29710808939).

Over the past 3 hours, it has been determined flaky in 27 workflow(s) with 27 failures and 27 successes.

**Debugging instructions (after clicking on the recent samples link):**
DO NOT ASSUME THINGS ARE OKAY IF THE CI IS GREEN. We now shield flaky tests from developers so CI will thus be green but it will be harder to parse the logs.
To find relevant log snippets:
1. Click on the workflow logs linked above
2. Click on the Test step of the job so that it is expanded. Otherwise, the grepping will not work.
3. Grep for `test_embedding_bag_dynamic_shapes_cpu`
4. There should be several instances run (as flaky tests are rerun in CI) from which you can study the logs.



<details><summary>Sample error message</summary>

```
Traceback (most recent call last):
  File "/var/lib/jenkins/workspace/test/inductor/test_torchinductor.py", line 4936, in test_embedding_bag
    self.common(
  File "/var/lib/jenkins/workspace/test/inductor/test_torchinductor.py", line 381, in check_model
    eager_result = model(*ref_inputs, **ref_kwargs)
  File "/var/lib/jenkins/workspace/test/inductor/test_torchinductor.py", line 4934, in fn
    return aten._embedding_bag(w, i, o, False, 0, False, None)
  File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/_ops.py", line 1116, in __call__
    return self._op(*args, **(kwargs or {}))
MemoryError: std::bad_alloc

To execute this test, run the following from the base repo dir:
    python test/inductor/test_torchinductor_dynamic_shapes.py DynamicShapesCpuTests.test_embedding_bag_dynamic_shapes_cpu

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
```

</details>


Test file path: `inductor/test_torchinductor_dynamic_shapes.py`

ResponseTimeoutError: Response timeout for 5000ms, GET https://raw.githubusercontent.com/pytorch/pytorch/main/test/inductor/test_torchinductor_dynamic_shapes.py -1 (connected: true, keepalive socket: false, socketHandledRequests: 1, socketHandledResponses: 0)
headers: {}

cc @clee2000 @ezyang @chauhang @penguinwu @avikchaudhuri @gmagogsfm @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4 @desertfire @chenyang78
Repository: PowerToys
Issue: Microsoft PowerToys for Windows Server 2019 or Microsoft PowerToys for RDP
Labels: Needs-Triage

### Description of the new feature / enhancement

Good afternoon, Is it possible to implement Microsoft PowerToys for Windows Server 2019 or Microsoft PowerToys for RDP?

The work is related to remote RDP connection. I connect to Windows Server 2019 remotely. 

### Scenario when this would be used?

I would like to see the possibility of implementing at least partial functionality for RDP:
Always On Top
FancyZones
Image Resizer
PowerRename
Text Extractor

### Supporting information

_No response_
Repository: TypeScript
Issue: Allow --build with Intermediate Errors - shouldn't happen on tsconfig errors
Labels: Suggestion,Awaiting More Feedback,Domain: tsc -b

### 🔎 Search Terms

intermediate error

### 🕗 Version & Regression Information

This is about new feature in 5.6

### ⏯ Playground Link

-

### 💻 Code

tsconfig.json

```
{
    "extends": "../wrong/path/to/some/tsconfig.json",
    "compilerOptions": {
        // nothing, expects to inherit things
    }
}
```

### 🙁 Actual behavior

When compiling such project, `tsc` does complain that it cannot find the base config, however it continues building. This is a big problem when the base config contains important things like `"outDir": "${configDir}/lib"` - TS will happily build using the defaults, writing a ton of .js and .d.ts files next to the source files.

### 🙂 Expected behavior

For tsconfig.json file errors, do not continue building the project.

### Additional information about the issue

_No response_
Repository: opencv
Issue: Wrong pixel value after resize with Linear interpolation
Labels: question (invalid tracker)

### System Information

OpenCV version: 4.7.0
Operating System / Platform: Debian 12
Compiler & compiler version: GCC 12.2.0

### Detailed description

Pixel values are sometimes wrong (off by one) when using `cv2.resize` with `cv2.INTER_LINEAR_EXACT`.

### Steps to reproduce

To reproduce run the following script. It resizes 7x7 image to 3x3 image. The bottom right pixel value in the resized image is wrong, it should be 129, but is 128. It should be 129, because the bilinear interpolation for the pixel value is 128.555555555555, which should be rounded to 129. We can get the 128.555555555 by numeric derivation or by the `exact_computation` function in the following script.


```python
import cv2
import numpy as np
import math


def exact_computation(q11, q12, q21, q22, pix_x, pix_y, orig_x, orig_y, scale_x, scale_y):
    
    # 0.5 must be added, because pixel values are in the middle of a pixel
    x1 = math.floor((pix_x + 0.5) * scale_x) + 0.5
    y1 = math.floor((pix_y + 0.5) * scale_y) + 0.5

    x2 = math.ceil((pix_x + 0.5) * scale_x) + 0.5
    y2 = math.ceil((pix_y + 0.5) * scale_y) + 0.5

    x = (pix_x + 0.5) * scale_x
    y = (pix_y + 0.5) * scale_y

    # equations from https://en.wikipedia.org/wiki/Bilinear_interpolation section Repeated linear interpolation

    f_x_y1 = ((x2 - x) / (x2 - x1)) * q11 + ((x - x1) / (x2 - x1)) * q21
    f_x_y2 = ((x2 - x) / (x2 - x1)) * q12 + ((x - x1) / (x2 - x1)) * q22

    f_x_y = ((y2 - y) / (y2 - y1)) * f_x_y1 + ((y - y1) / (y2 - y1)) * f_x_y2

    return f_x_y


image = np.zeros((7, 7), dtype='uint8')

image[5, 5] = 78
image[5, 6] = 151
image[6, 5] = 245
image[6, 6] = 53

new_height = 3
new_width = 3

result = cv2.resize(image, (3, 3), cv2.INTER_LINEAR_EXACT)
print(result[2, 2])

correct_result = exact_computation(image[5, 5], image[6, 5], image[5, 6], image[6, 6], 2, 2, 7, 7, 7 / 3, 7 / 3)
print(correct_result)
assert result[2, 2] == 129
```

### Issue submission checklist

- [X] I report the issue, it's not a question
- [X] I checked the problem with documentation, FAQ, open issues, forum.opencv.org, Stack Overflow, etc and have not found any solution
- [ ] I updated to the latest OpenCV version and the issue is still there
- [X] There is reproducer code and related data files (videos, images, onnx, etc)
Repository: TypeScript
Issue: Constraints of nested conditional types applied to constrained type variables are incorrect
Labels: Bug,Help Wanted

### 🔎 Search Terms

constraints conditional types type variables nested

### 🕗 Version & Regression Information

- This is the behavior in every version I tried

### ⏯ Playground Link

https://www.typescriptlang.org/play/?ts=5.7.0-dev.20240904#code/C4TwDgpgBAkgzgWQIbAMYAsCWA7A5gZWACcdcB5bCAGQgDcIAbAHgBUoIAPYCbAEzihxipAHxQAvFDadufAQAMkAEgDeQkngC+8gFBQoAfijEArhD1QAXFABmSBnAgBuHTpsnsqYJgD22WwBMTPjsXDz8gsJ4IgAUHNbwyGhYeIQa5JQ09Mz4IgCUUCoWDBDAxtam0JIcTlAA9HVQAKJERD5ExaW21nYOVVA19Y0tbR2arqCQsIgoGKRppCwA7j5ZjHCsobIR6qISUlvhCspqUbjaFkbSYXJQ8qq7WgBeuvr6RpUW+j32jhY-fRcbg8Xl8-hsAGZgodbo9cLF4tMknNUmdlqs6OtgvlCp0ysAKkQzPtBg1mq12njurZfv1SY0yABrKAAIxMZTg6B8JgYvFZ0CQ-ggFLGOiAA

### 💻 Code

```ts
type IsMatchingStringOneLevel<T extends string> = T extends `a${string}`
  ? true
  : false;

function f2<S extends string>(x: IsMatchingStringOneLevel<S>) {
  let t: true = x; // Error
  let f: false = x; // Error
}

type IsMatchingStringTwoLevels<T extends string> = T extends `a${string}`
  ? T extends `${string}z`
    ? true
    : false
  : false;

function f3<S extends string>(x: IsMatchingStringTwoLevels<S>) {
  let t: true = x; // Error
  let f: false = x; // Ok but should be an error
}
```


### 🙁 Actual behavior

It doesn't error on the fourth assignment

### 🙂 Expected behavior

It should error

### Additional information about the issue

This is just a variant of what was fixed for one-level conditionals in https://github.com/microsoft/TypeScript/pull/56004
Repository: kubernetes
Issue: Unable to set new pre-alpha versioned featuregate to enabled during apiserver initialisation in integration tests
Labels: kind/bug,area/test,sig/api-machinery,sig/architecture,triage/accepted

While developing a new in-tree feature, I need to add integration tests which conditionally enable/disable my feature. I've added the gate to the 'versioned feature gates' file, which seems to work ok. However, my feature gate's configuration is read upon initialisation of the apiserver (it's read by an admission plugin). This means I *must* set this feature gate to be enabled *prior* to calling `StartTestServerOrDie` (or at least, prior to this function actually running/starting the apiserver startup functions).

This feature is considered 'pre-alpha' in 1.31, as it'd be only become alpha in 1.32 (assuming it's merged etc.). This means if I attempt to set this flag to 'true' without also changing the 'emulation version', I receive an error that the feature cannot be enabled at this version (1.31) as it is PreAlpha.

So, I have updated my call to StartTestServer to set the BinaryVersion in the instance options to '1.32':

```
	// Force to run in 1.32 mode as we are testing a feature that only exists (in alpha) from 1.32 onwards.
	opts := kubeapiservertesting.NewDefaultTestServerOptions()
	opts.BinaryVersion = "1.32"
	server := kubeapiservertesting.StartTestServerOrDie(t, opts, framework.DefaultTestServerFlags(), framework.SharedEtcd())
```

However, I still need to set the feature gate itself to enabled, as it's disabled by default (it's an alpha feature). This presents a chicken-egg problem for me:

If I add `featuregatetesting.SetFeatureGateDuringTest(t, utilfeature.DefaultFeatureGate, features.SetPodTopologyLabels, true)` before `StartTestServerOrDie`, the call to set the emulation version to 1.32 has not happened yet (it is within StartTestServerOrDie).

If I set the flag *after*, my feature flag value will have already been read by the admission plugin and I won't get the behaviour I need.

The only alternative I can think of here is to set feature flags using the `--feature-flags` argument passed in InstanceOptions, but this is a departure from how we'd have done this prior to the introduction of versioned feature gates, so I wanted to gather some feedback on how we're _supposed_ to enable pre-alpha feature flags that are required during apiserver initialisation.

/area testing
/sig architecture
Repository: kubernetes
Issue: Unclear with Container garbage collection configuration
Labels: area/kubelet,kind/documentation,sig/node,help wanted,priority/important-longterm,needs-kind,triage/accepted

### What happened?

Iam trying to disable maxPerPodContainer feature in my cluster. To achieve this followed the docs [Container garbage collection](https://kubernetes.io/docs/concepts/architecture/garbage-collection/#container-image-garbage-collection) and gone through code related to garbage collection.

Configured Container garbage collection parameters in the kubelet config file and restarted the kubelet sucessfully.
After that if i check, values in kubelet configuration file are persist but those are actually not reflected in cluster.



### What did you expect to happen?

Container garbage collection parameters which i added in config file should reflect in cluster.

**minAge:
  maxPerPodContainer: 
  maxContainers:** 

### How can we reproduce it (as minimally and precisely as possible)?



Ex: 
1. Add below values in kubelet configuration file: /var/lib/kubelet/config.yaml

  ```
minAge: "5m"
  maxPerPodContainer: 1
  maxContainers: 50
```
2. Restart kubelet service: 
`sudo systemctl restart kubelet`
Make sure kubelet restarted sucessfully: sudo systemctl status kubelet

3. Run: kubectl proxy
4. Open another terminal and check whether added parameters are present in configuration or not:
     curl http://localhost:8001/api/v1/nodes/<hostname/proxy/configz




### Anything else we need to know?

I tried many ways to add these parameters but no use. even these are not listed in api-resources.
https://kubernetes.io/docs/reference/config-api/kubelet-config.v1beta1/
https://kubernetes.io/docs/reference/command-line-tools-reference/kubelet/

Is it possible to define these values in kubelet as mentioned in [docs](https://kubernetes.io/docs/concepts/architecture/garbage-collection/#container-image-garbage-collection)? if possible how can we configure them?

### Kubernetes version

<details>

```console
$ kubectl version
Client Version: v1.30.3
Kustomize Version: v5.0.4-0.20230601165947-6ce0bf390ce3
Server Version: v1.30.3
```

</details>


### Cloud provider

<details>

</details>


### OS version

<details>

```console
# On Linux:
$ cat /etc/os-release
# paste output here
$ uname -a
# paste output here

# On Windows:
C:\> wmic os get Caption, Version, BuildNumber, OSArchitecture
# paste output here
```

</details>


### Install tools

<details>

</details>


### Container runtime (CRI) and version (if applicable)

<details>

</details>


### Related plugins (CNI, CSI, ...) and versions (if applicable)

<details>

</details>
Repository: pytorch
Issue: Disable `retain_grad` on non-leaf tensors?
Labels: module: autograd,triaged,enhancement,actionable

### 🚀 The feature, motivation and pitch

Non-leaf tensors can be set to retain gradients with `tensor.retain_grad()`, after which `tensor.retains_grad is True` (for non-leaf tensors only it seems).

Is it possible to revert this? If not (which I believe so far), that would be the feature request.

For example, I'd likde the API `tensor.retain_grad(False)`. By the way, I don't really understand why there is no trailing underscore `retain_grad_()`, but that's another point.


### Alternatives

_No response_

### Additional context

_No response_

cc @ezyang @albanD @gqchen @pearu @nikitaved @soulitzer @Varal7 @xmfan
Repository: vscode
Issue: LSP DocumentColor Refresh Request
Labels: feature-request,api

<!-- ⚠️⚠️ Do Not Delete This! feature_request_template ⚠️⚠️ -->
<!-- Please read our Rules of Conduct: https://opensource.microsoft.com/codeofconduct/ -->
<!-- Please search existing issues to avoid creating duplicates. -->

<!-- Describe the feature you'd like. -->
LSP Servers need a way to trigger documentColor refresh requests in VS Code, just like other refresh requests, including the newly added [foldingRange refresh request](https://microsoft.github.io/language-server-protocol/specifications/lsp/3.18/specification/#workspace_foldingRange_refresh) proposed in the 3.18 version of the LSP spec.

## Scenario
XAML color swatch support. Colors in XAML files should appear in the following places. For example:
1. `Background="#FFFF0000"`
2. `Background="Red"`
3. `Background="{StaticResource RedBrush}"`

The first two are possible with the current capabilities. 3) on the other hand, requires semantic information, which is usually not available yet by the time the `documentColor` request is made. `RedBrush` may also be defined in an external document, and when the value for `RedBrush` is changed there it won't get reflected in the current document until a text change is made. To solve this challenge, we should introduce and support a documentColor refresh request: `workspace/documentColor/refresh`

cc @dbaeumer