sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: ant-design
Issue: 在table中设置了fixed:'right'的列中使用popConfirm，当设置<popConfirm getPopUpContainer={(trigger) => trigger.parentNode}, 气泡框出现在下面时，层级太低，导致不能显示
Labels: Inactive

- [ ] I have searched the [issues](https://github.com/ant-design/ant-design/issues) of this repository and believe that this is not a duplicate.

### Reproduction link
[https://gitee.com/weixiaoyun/umi-app](https://gitee.com/weixiaoyun/umi-app)

### Steps to reproduce
进入用户管理，页面滚动到底部，点击表格可视区域第一个删除

### What is expected?
希望无论气泡框出现在上面还是出现在下面，都能正常显示

### What is actually happening?
气泡框出现的上面，样式正常；气泡框出现在下面，被表格覆盖，设置z-index也不管用

| Environment | Info |
|---|---|
| antd | 4.18.4 |
| React | 17.0.2 |
| System | macOs Catalina 10.15.7 |
| Browser | chrome 94.0.4606.81 |


<!-- generated by ant-design-issue-helper. DO NOT REMOVE -->
Repository: flutter
Issue: [Proposal] allow rootBundle access from spawned isolates
Labels: c: new feature,engine,a: assets,c: proposal,P3,team-engine,triaged-engine

Currently as of flutter 2.8.1, isolates cannot use rootBundle. I faced this issue and had to make a workaround for it but it introduces unnecessary code and looks ugly.

Example:

```
Future<void> function() async{
    String data= await rootBundle.loadString(filepath);
    compute(f,data)
}
void f(String data){
    print(data);
}
```
should be

```
Future<void> function() async{
    String data= await rootBundle.loadString(filepath);
print(data);
}

```
//just call function like
`compute(function,args);`
I hope my example shows why rootBundle should be allowed in isolates.
Repository: pytorch
Issue: `torch.sub` behaves differently with `add`, `mul`, `div`
Labels: module: error checking,triaged

### 🐛 Describe the bug

`torch.sub` behaves differently with `add`, `mul`, `div`. For example,

```python
import torch
a = torch.rand([2, 2])
b = True
torch.mul(a, b)
torch.div(a, b)
torch.add(a, b)
torch.sub(a, b)
# RuntimeError: Subtraction, the `-` operator, with a bool tensor is not supported. If you are trying to invert a mask, use the `~` or `logical_not()` operator instead.
```

I think `div` should be consistent with other 3 operations.

### Versions

pytorch: 1.10.1
Repository: ant-design
Issue: 当message最外层包裹元素div dom被移除，之后message实例便不会再创建
Labels: 🐛 Bug,Inactive

- [ ] I have searched the [issues](https://github.com/ant-design/ant-design/issues) of this repository and believe that this is not a duplicate.

### Reproduction link
[![Edit on CodeSandbox](https://codesandbox.io/static/img/play-codesandbox.svg)](https://codesandbox.io/s/antd-reproduction-template-forked-2q52f?file=/index.js)

### Steps to reproduce
//操作：1 打开弹窗
// 2. 点击show message
// 3. 点击ok 关闭弹窗
// 4. 打开弹窗
// 5. 点击show message 

### What is expected?
message正常显示

### What is actually happening?
message未正常显示

建议： 在rc-notification，对于返回的notificationInstance对象添加判断方法，如
messageWrapperExit() {
        if (getContainer) {
          const root = getContainer();
          return root.contains(div);
        }
        return document.body.contains(div);
      }
并且在上层message组件中添加是否新创建 实例的判断
if (messageInstance && msgInstance.messageWrapperExit()) {
    callback({ prefixCls, rootPrefixCls, iconPrefixCls, instance: messageInstance });
    return;
  }




| Environment | Info |
|---|---|
| antd | 4.18.4 |
| React | react-dom16.X.X |
| System | macos |
| Browser | chrome |


<!-- generated by ant-design-issue-helper. DO NOT REMOVE -->
Repository: pytorch
Issue: Initialize DataLoader workers in parallel
Labels: triaged,module: data

### 🚀 The feature, motivation and pitch

Currently, DataLoader workers seems to get initialized sequentially. I'm looking at `persistent_workers` but I suspect the same applies for the non-persistent kind as well.

Some implementation of `Dataset.__setstate__()` are very expensive (e.g. establish a database connection or fire a slow query) so unpickling the `Dataset` for each worker process it may take a long time.

Consider initializing all workers in parallel so that expensive initialization could run concurrently.

### Alternatives

_No response_

### Additional context

_No response_

cc @VitalyFedyunin @ejguan @NivekT
Repository: pytorch
Issue: pip installation SSLError [SSL: CERTIFICATE_VERIFY_FAILED]
Labels: needs reproduction,module: binaries,triaged

### 🐛 Describe the bug

I currently can not install pytorch via pip. I am getting a SSLError.
I found a related closed issue: https://github.com/pytorch/pytorch/issues/33292

Using https://s3.amazonaws.com/pytorch/whl/torch_stable.html instead of https://download.pytorch.org/whl/torch_stable.html works, so I am guessing that the certificate expired again for latter URL?

Looking in links: https://download.pytorch.org/whl/lts/1.8/torch_lts.html
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1131)'))': /whl/lts/1.8/torch_lts.html
WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1131)'))': /whl/lts/1.8/torch_lts.html
WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1131)'))': /whl/lts/1.8/torch_lts.html
WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1131)'))': /whl/lts/1.8/torch_lts.html
WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1131)'))': /whl/lts/1.8/torch_lts.html

### Versions

1.8.1

cc @ezyang @seemethere @malfet
Repository: next.js
Issue: next/image does not work with CDN
Labels: bug,Image (next/image)

### Run `next info` (available from version 12.0.8 and up)

    Operating System:
      Platform: win32
      Arch: x64
      Version: Windows 10 Pro
    Binaries:
      Node: 16.13.0
      npm: 8.1.0
      Yarn: N/A
      pnpm: N/A
    Relevant packages:
      next: 12.0.8
      react: 17.0.2
      react-dom: 17.0.2

### What version of Next.js are you using?

12.0.8

### What version of Node.js are you using?

16.3.0

### What browser are you using?

Chrome

### What operating system are you using?

Windows, Linux

### How are you deploying your application?

Other platform

### Describe the Bug

The issue relates to a [previous bug](https://github.com/vercel/next.js/issues/20968), but that case was closed. However, I believe the issue still persists.

We have the CDN URL specified in the `assetPrefix`:

    // next.config.js
    module.exports = {
        assetPrefix: 'https://cdn.example.com'
    }

Following the guide from the [docs](https://nextjs.org/docs/api-reference/next.config.js/cdn-support-with-asset-prefix):

> Files in the public folder; if you want to serve those assets over a CDN, you'll have to introduce the prefix yourself

In the code we have the following `Image` tag:

    <Image
        src={`${assetPrefix}/images/cat.jpg`}
        alt="Cat"
        width={30}
        height={30}
    />

But the result in the markup is like this:

    <img alt="Cat" src="/_next/image?url=https%3A%2F%2Fcdn.example.com%2Fimages%2Fcat.jpg&amp;w=64&amp;q=75" decoding="async" data-nimg="intrinsic" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%" srcset="/_next/image?url=https%3A%2F%2Fcdn.example.com%2Fimages%2Fcat.jpg&amp;w=32&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.example.com%2Fimages%2Fcat.jpg&amp;w=64&amp;q=75 2x">



### Expected Behavior

The expected behaviour should be that the initiator URL (`src="/_next/image?url...`) should also have the CDN prefix, like `src="https://cdn.example.com/_next/image?url...`.

All compiled assets are loaded fine from the CDN by the way... so the `_next` folder is served from the CDN. That proves the `assetPrefix` setting is working properly, only the `Image` is failing.


### To Reproduce

Use both `<Image>` and `assetPrefix`. Load image from the `public` folder.
Repository: opencv
Issue: Laplacian operator yields different filtered results on PC and Arm machine
Labels: bug,category: imgproc,priority: low

System information (version)

- OpenCV => 4.5.5
- Operating System / Platform => 64-bit windows machine, 64-bit ARM machine
- Compiler => Visual Studio 2017, or GCC. It doesn't matter


Detailed description
We applied cv:Laplacian() for simple filtering on PC and Arm machine. It is found that the filtered results are obviously different between the two kinds of machines when the kernel size is >= 13 pixel. Based on our test, we draw some initial conclusions below
-the problem is not GCC VS Visual Studio.
-the problem is not 32-bit VS 64-bit.
-the problem is not Windows VS Linux
-the problem is not SSE VS non-simd

Below is the filtered image on a 64-bit PC machine (with Ubuntu OS)
![image](https://user-images.githubusercontent.com/15646535/150321282-b095cc43-eff3-4330-ac9f-8e2c8cbe7e2c.png)

Below is the filtered image on a 64-bit ARM machine
![image](https://user-images.githubusercontent.com/15646535/150321340-10c5b0f8-ea99-4bd7-ba34-956fbd50b22e.png)


Steps to reproduce

Compile the following sample codes, run it on the two kinds of machines and check the results

 ```
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#if defined(HAVE_OPENCV_HIGHGUI)
#   include <opencv2/highgui/highgui.hpp>
#endif

int main()
{
    std::vector<unsigned short> data;

    for (size_t i = 0; i < 200 * 200; i++)
    {
        data.push_back(i * (32767 / 100) % 32767);
    }
    auto matSrc = cv::Mat(cv::Size(200,200), CV_16S, data.data());

    cv::Laplacian(matSrc, matSrc, matSrc.type(), 13, 1, 0, 0);

#if defined(HAVE_OPENCV_HIGHGUI)
    cv::imshow("Hello", matSrc);
    cv::waitKey();
#endif
}
```

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
 - [x] 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: godot
Issue: 3D Kinematic Body collision/snap error when both CollisionShape and RayShape are enabled (Causing object to bolt/jolt downward instantly)
Labels: bug,topic:physics,topic:3d

### Godot version

v3.4.2.stable.official [45eaa2daf]

### System information

Windows 10, GLES3, Nvidia RTX 2080 SUPER

### Issue description

Not sure if anybody is aware but I discovered a potential collision/slide snap bug:

What happened is that there are two CollisionShapes, one Capsule Shape and the other a RayShape, when both are enabled, it causes the object to bolt/jolt instantly downward. Where in comparision if you have one or the other disabled it causes the object to fall off a steep platform:

https://youtu.be/rWM5ZIxeb3I

### Steps to reproduce

Create a KinematicBody in 3D, then give the KinematicBody two CollisionShape nodes, have the first one be a capsuleshape and the other a rayshape.

try disabling and enabling one another to see the difference:

### Minimal reproduction project

[3point4_Test.zip](https://github.com/godotengine/godot/files/7905047/3point4_Test.zip)

I've included a demo file project so then you can take a look and experiment the collision/snap error.
Repository: PowerToys
Issue: Behaviour like 2 monitors on 1 widescreen monitor, no snapping functions
Labels: Idea-Enhancement,Product-FancyZones,Needs-Triage

### Description of the new feature / enhancement

It would have the same behaviour like 2 monitors (extended displays)
- the maximize button would maximize the window only to the full size of 1 monitor
- all key combinations for moving a window would be the same as before (Windows key + arrow keys or Windows key + shift + right arrow/ left arrow)
- it should be the default that you can move the window to the center of the 2 monitors and when you click the maximize button the window will be maximized to the full size of both monitors (for i.e.: it's not possible with Visual Studio but it's possible with the Citrix Workspace app)


### Scenario when this would be used?

It is more flexible than the snapping functions (grabbing the window with the mouse) and you could still size the window to the half of 1 monitor by the key commands.

### Supporting information

_No response_
Repository: pytorch
Issue: Support freezing of models containing ModuleContainerIndex
Labels: oncall: jit

### 🚀 The feature, motivation and pitch

Architectures such as normalizing flows require reverse iterations over `nn.ModuleList` and non-literal indexing. Torchscript does not support non-literal indexing, but there is a trick - by using  the `torch.jit.interface` decorator for the problematic modules, the network can be torchscripted eg as follows:

```
@torch.jit.interface
class ISubModule(torch.nn.Module):
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        pass


class SubModule(IModule):

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x


class Module(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.submodules = torch.nn.ModuleList([SubModule(), SubModule()])

    def forward(self, x):
        ....

    @torch.jit.export
    def reverse(self, x):

        # iterate over modules in reverse
        for idx in range(len(self.submodules), 0):
            submod: ISubModule = self.submodules[idx]
            x = submod.forward(x)

        return x


m = Module()
m.eval()
m = torch.jit.script(m)  # this works
m = torch.jit.freeze(m)  # this fails
```

But freezing the torchscripted model raises the following error:
```
  File "/fsx/home/name/project/env/lib/python3.8/site-packages/torch/jit/_freeze.py", line 111, in freeze
    out = RecursiveScriptModule(torch._C._freeze_module(mod._c, preserved_attrs))
RuntimeError: Freezing modules containing prim::ModuleContainerIndex is not supported
```
See [this line ](https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/freeze_module.cpp#L300) in torch source code
It makes deployment of models such as Glow, WaveGlow etc. super difficult as long as we want to leverage torchscript. Any chance to implement this feature soon? :)

The interface trick and the NormalizingFlow usecase are already mentioned in
https://github.com/pytorch/pytorch/issues/47496
https://github.com/pytorch/pytorch/pull/53410
https://github.com/pytorch/pytorch/issues/43737
https://github.com/pytorch/pytorch/issues/31772#issuecomment-895449779

### Alternatives

AFAIK there is no way how to iterate over ModuleList in reverse without the interface trick, but the freezing of the intercafe is not implemented so the only option how to torchscript and freeze a normalizing flow is to

1. swap the reverse and forward passes and swap the order in which the modules are stored in the network
    So we would use .reverse() during training in python runtime where we can easily reverse lists.
    And we would use .forward() and the fact that we would store modules in reversed order in torchscript inference.
   So one would avoid using reverse during inference altogether, but its super ugly.
2. Keep 2 implementations of the same module, where one of them would be only used for torchscripting

### Additional context

_No response_
Repository: pytorch
Issue: ONNX: Wrong output shape for ceil_mode Pooling
Labels: module: onnx,triaged,onnx-triaged,bug

### 🐛 Describe the bug

Output shape in the ONNX file exported for the following dummy model using with ceil_mode AvgPooling2d is wrong size.

```python
import torch
import torch.nn as nn

input_tensor = torch.rand(1, 3, 2, 2)
opset_version = 10

class DummyModel(nn.Module):
    def __init__(self):
        super(DummyModel, self).__init__()
        self.layer = nn.AvgPool2d(
            kernel_size=(3, 3),
            stride=(3, 3),
            padding=(1,1),
            ceil_mode=True)
    def forward(self, x):
        x = self.layer(x)
        return x

dummy_model = DummyModel()
output_torch = dummy_model(input_tensor)
print('PyTorch input', input_tensor.shape)
print('PyTorch output', output_torch.shape)

onnx_file_path = "dummy_model.onnx"
torch.onnx.export(model=dummy_model,
                  args=input_tensor,
                  f=onnx_file_path,
                  do_constant_folding=True,
                  opset_version=opset_version,
                  input_names = ['input'],
                  output_names = ['output'],
                  export_params=True
                  )
import onnxruntime
onnxruntime_net = onnxruntime.InferenceSession(onnx_file_path)
print('onnxruntime input', onnxruntime_net.get_inputs()[0].shape)
print('onnxruntime output', onnxruntime_net.get_outputs()[0].shape)
```

### Output

```
PyTorch input torch.Size([1, 3, 2, 2])
PyTorch output torch.Size([1, 3, 1, 1])
onnxruntime input [1, 3, 2, 2]
onnxruntime output [1, 3, 2, 2]
```

Output shape of ONNX file is bigger than PyTorch model.

### Remarks

It happens only ONNX opset version larger equal than 10.

### Versions

Collecting environment information...
PyTorch version: 1.10.1+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.3 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.8.10 (default, Nov 26 2021, 20:14:08)  [GCC 9.3.0] (64-bit runtime)
Python platform: Linux-5.10.74.3-microsoft-standard-WSL2-x86_64-with-glibc2.29
Is CUDA available: False
CUDA runtime version: No CUDA
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

Versions of relevant libraries:
[pip3] numpy==1.22.1
[pip3] torch==1.10.1
[conda] Could not collect
Repository: flutter
Issue: Change RawAutocomplete and Autocomplete initialValue attribute type to T?
Labels: a: text input,framework,P2,team-framework,triaged-framework

## Use case

RawAutocomplete and Autocomplete will show options list on focus even if initialValue was provided.

## Proposal

Issue is that RawAutocomplete's _shouldShowOptions is dependent on _selection value:

    bool get _shouldShowOptions {
      return _focusNode.hasFocus && _selection == null && _options.isNotEmpty;
    }

but _selection is not initialized in initState. Attribute _selection is set only when value is actually seleted from options list:

    void _select(T nextSelection) {
      ...
      _selection = nextSelection;
      ...
    }

My proposal is to change type of _initialValue in RawAutocomplete and Autocomplete to T? instead of TextEditingValue? and initialize both _textEditingController and _selection in initState:

flutter\lib\src\widgets\autocomplete.dart:

    final T? initialValue;

    void initState() {
      ...
      _selection = widget.initialValue;
      _textEditingController = widget.textEditingController 
      ?? (widget.initialValue != null 
        ? TextEditingController(text: widget.displayStringForOption(widget.initialValue!)) 
        : TextEditingController()
      );
      ...
    }

This will prevent options list to be displayed, if initial value is set and no selection were performed yet.
Repository: go
Issue: cmd/go: list module@version should suggest -m flag
Labels: NeedsFix,GoCommand,BadErrorMessage

<!--
Please answer these questions before submitting your issue. Thanks!
-->

### What version of Go are you using (`go version`)?

<pre>
$ go version
go version devel go1.18-bb7fb8a5fa Wed Jan 19 21:46:55 2022 +0000 linux/amd64
</pre>

### Does this issue reproduce with the latest release?

yes

### What operating system and processor architecture are you using (`go env`)?

<details><summary><code>go env</code> Output</summary><br><pre>
$ go env
GO111MODULE=""
GOARCH="amd64"
GOBIN=""
GOCACHE="/home/arccy/.cache/go-build"
GOENV="/home/arccy/.config/go/env"
GOEXE=""
GOEXPERIMENT=""
GOFLAGS=""
GOHOSTARCH="amd64"
GOHOSTOS="linux"
GOINSECURE=""
GOMODCACHE="/home/arccy/.data/go/pkg/mod"
GONOPROXY=""
GONOSUMDB=""
GOOS="linux"
GOPATH="/home/arccy/.data/go"
GOPRIVATE=""
GOPROXY="https://proxy.golang.org,direct"
GOROOT="/home/arccy/sdk/gotip"
GOSUMDB="sum.golang.org"
GOTMPDIR=""
GOTOOLDIR="/home/arccy/sdk/gotip/pkg/tool/linux_amd64"
GOVCS=""
GOVERSION="devel go1.18-bb7fb8a5fa Wed Jan 19 21:46:55 2022 +0000"
GCCGO="gccgo"
GOAMD64="v1"
AR="ar"
CC="gcc"
CXX="g++"
CGO_ENABLED="1"
GOMOD="/dev/null"
GOWORK=""
CGO_CFLAGS="-g -O2"
CGO_CPPFLAGS=""
CGO_CXXFLAGS="-g -O2"
CGO_FFLAGS="-g -O2"
CGO_LDFLAGS="-g -O2"
PKG_CONFIG="pkg-config"
GOGCCFLAGS="-fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=/tmp/go-build4196401972=/tmp/go-build -gno-record-gcc-switches"
</pre></details>

### What did you do?

```sh
$ go list -f '{{ .Version }}' miniflux.app@686dd92cb9e486c41f407e93d2e10f6b4609f778
package miniflux.app@686dd92cb9e486c41f407e93d2e10f6b4609f778: can only use path@version syntax with 'go get' and 'go install' in module-aware mode
```

### What did you expect to see?

An error message telling me I may have wanted `go list -m ...`

### What did you see instead?

Error message only mentions `go get` and `go install`

cc @bcmills @matloob
Repository: pytorch
Issue: [docs] Tensor.uniform_ docs are not clear about whether from/to boundary values are included in sampling or not
Labels: module: docs,triaged,module: random

### 🐛 Describe the bug

https://pytorch.org/docs/stable/generated/torch.Tensor.uniform_.html?highlight=uniform_#torch.Tensor.uniform_

It may also be nice if torch.rand was extended to allow for from-to arguments (i.e. to have a more convenient uniform random tensor factory function)

I've searched issues for this, but couldn't found an existing issue quickly. Please feel free to close if duplicate

### Versions

1.9.1

cc @brianjo @mruberry @pbelevich
Repository: PowerToys
Issue: [Keyboard Manager] Distinguish Alt Gr and Ctrl+Alt for keyboard shortcuts
Labels: Idea-Enhancement,Product-Keyboard Shortcut Manager,Needs-Triage

### Description of the new feature / enhancement

I propose to add a feature to keyboard manager to be able to distinguish Alt Gr and Ctrl+Alt for keyboard shortcuts.

### Scenario when this would be used?

The international english keyboard layout and many others place characters behind Alt Gr key conbinations.
Since Windows currently treats Alt Gr and Ctrl+Alt to be the same, some characters cannot be typed if a global shortcut is registered to a Ctrl+Alt combination.
This feature would greatly increase the usefullness of keyboard layouts that make use of Alt Gr.

### Supporting information

_No response_