sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: storybook
Issue: Custom addons not removing their content from `#panel-tab-content` which is causing sizing issues
Labels: bug,needs triage

**Describe the bug**
- We've created 2 (private) addons to cover things we need (will call them `@company/intl` & `@company/config`)
- they just show a big textarea with live-editable JSON which has a `height: 100%;`
- I noticed that the 2nd addon was always shrunk down
- Realised there's a `> *:first-child{ height: 100%; }` rule attached to storybook's `#panel-tab-content`
- But for some reason my addons aren't removing their HTML from the `#panel-tab-content` (other addons like `@storybookjs/a11y` do get removed) so this doesn't seem to be working as intended


**To Reproduce**

Not sure how well I can detail a reproduction here since the addons are private but I followed https://storybook.js.org/docs/react/addons/writing-addons/ and ended up with a block that simplifies to...

```jsx
addons.register("@company/intl", (api) => {
  addons.add("@company/intl/panel", {
    type: types.PANEL,
    title: "@company/intl",
    render: ({ active, key }) => (
      <AddonPanel active={active} key={key}>
            <textarea style={{ width: "100%", height: "100%" }} />
      </AddonPanel>
    ),
  })
})
```

&

```jsx
addons.register("@company/config", (api) => {
  addons.add("@company/config/panel", {
    type: types.PANEL,
    title: "@company/config",
    render: ({ active, key }) => (
      <AddonPanel active={active} key={key}>
            <textarea style={{ width: "100%", height: "100%" }} />
      </AddonPanel>
    ),
  })
})
```

- both addons set global values in their panels & read them in a decorator they add
- both addons come from separate dependencies 
- there's no console errors related to this
- not sure what else would be relevant

**System**
```
Environment Info:

  System:
    OS: Linux 5.10 Debian GNU/Linux 10 (buster) 10 (buster)
    CPU: (4) x64 Intel(R) Core(TM) i7-4870HQ CPU @ 2.50GHz
  Binaries:
    Node: 14.16.1 - /usr/local/bin/node
    Yarn: 1.22.5 - /usr/local/bin/yarn
    npm: 6.14.12 - /usr/local/bin/npm
  Browsers:
    Chrome: 89.0.4389.114
  npmPackages:
    @storybook/addon-a11y: ^6.3.4 => 6.3.4 
    @storybook/addon-actions: ^6.3.4 => 6.3.4 
    @storybook/addon-backgrounds: ^6.3.4 => 6.3.4 
    @storybook/addon-docs: ^6.3.4 => 6.3.4 
    @storybook/addon-essentials: ^6.3.4 => 6.3.4 
    @storybook/addon-knobs: ^6.3.0 => 6.3.0 
    @storybook/addon-links: ^6.3.4 => 6.3.4 
    @storybook/addon-storyshots: ^6.3.4 => 6.3.4 
    @storybook/react: ^6.3.4 => 6.3.4 
    @storybook/react-testing: ^0.0.3 => 0.0.3 
    @storybook/testing-react: ^0.0.18 => 0.0.18 
```

**Additional context**
- Both addons get loaded into `#panel-tab-content` on load, unlike other addons - which suggests maybe there's a bit of config somewhere to _not_ do that that I've just missed?
- if needed I can DM a link to the published storybook on S3
Repository: pytorch
Issue: Allow `ScriptModule`s to be symbolically traced
Labels: triaged,FX-TorchScript Compatibility,module: fx

## Motivation
Currently, we can't trace ScriptModules (the object produced by calling `torch.jit.script` on an nn.Module) because of the way the `forward` function is stored. We should support this to ensure full FX-TorchScript compatibility.

## Example

```python
import torch
from torch.fx import symbolic_trace

class M(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.bn = torch.nn.BatchNorm2d(100)

    def forward(self, x: torch.Tensor):
        return torch.add(x, x)

m = torch.jit.script(M())
traced = symbolic_trace(m)
```

```
Traceback (most recent call last):
  File "test.py", line 21, in <module>
    traced = symbolic_trace(m)
  File "/usr/local/anaconda3/lib/python3.8/site-packages/torch/fx/symbolic_trace.py", line 674, in symbolic_trace
    graph = tracer.trace(root, concrete_args)
  File "/usr/local/anaconda3/lib/python3.8/site-packages/torch/fx/symbolic_trace.py", line 358, in trace
    fn = type(root).forward
  File "/usr/local/anaconda3/lib/python3.8/site-packages/torch/jit/_script.py", line 291, in __get__
    return self.__getattr__("forward")  # type: ignore
AttributeError: '_CachedForward' object has no attribute '__getattr__'
```

## Notes

This is a related issue to #57124. It's not exactly the same, as it fails for a different reason. However, a `ScriptFunction` can be transformed into a `ScriptModule`, so fixing this issue would also fix the original issue.

cc @ezyang @SherlockNoMad @gmagogsfm
Repository: go
Issue: net: LookupSRV doesn't validate record names are consistent
Labels: NeedsDecision

LookupSRV returns the 'canonical name' alongside the slice of SRV records, which is typically used to determine whether the name that was passed to LookupSRV was CNAME'd to another name. The returned canonical name is extracted from the first returned record header NAME, but it is not verified that the rest of the records are consistent.

If a resolver returns a response containing records with various names, LookupSRV obscures this by returning just the first name. Ideally it should verify that all of the names are consistent, returning an error if there were multiple names returned.
Repository: PowerToys
Issue: Cascade, stack, side by side, restore windows to a fancy zone
Labels: Idea-Enhancement,Product-FancyZones

### Description of the new feature / enhancement

Would it be possible to cascade, stack, side by side, or restore all windows of an app group to a particular fancy zone?
When a user picks the particular fancy zone... the windows in that app group are all snapped to the fancy zone with the action selected... cascade, stacked, side by side, or restore.

When a group of windows is shift+right clicked on the task bar, the user is given a context menu that allows them to pick a few actions:
Cascade all windows
Show all windows stacked
Show all windows side by side
Restore all windows
Minimize all windows
Close all windows

would it be possible to create a submenu on each of the cascade, stacked, side by side, restore selection options.  The submenu would list the monitor and its fancy zone.  Or because there might be many fancy zones, perhaps list the fancy zones and put the actions as a separate pop up context.  See attached screen mockups (option2).
![fancy zones picture](https://user-images.githubusercontent.com/87201094/125113948-ecff0780-e0ae-11eb-8038-7bc92f8b95cc.png)

The user can still cascade all windows to a monitor... the windows would behave with the same behavior they have currently, or the user can perform the action to the selected fancy zone.   

so no functionality/behavior is lost, but users now have expanded capability.


### Scenario when this would be used?

working on a laptop between office and home... I have different monitor setups.  Fancy zones recognizes this but all my apps are usually on the wrong monitor.  I have to manually move each window to a fancy zone.  It would make it quicker to move all apps in a group to a designated fancy zone.

### Supporting information

_No response_
Repository: pytorch
Issue: Version is set to 1.8.0 in the torch tag v1.8.1
Labels: module: build,oncall: releng,triaged

## 🐛 Version is set to 1.8.0 in the torch tag v1.8.1

Here https://github.com/pytorch/pytorch/blob/v1.8.1/version.txt, the version is 1.8.0. Shouldn't it be 1.8.1? This caused version check failure when installing the pytorch:v1.8.1 from source.

when I install pytorch:v1.8.1 from source https://github.com/pytorch/pytorch/tree/v1.8.1. I got the following version:
'1.8.0a0+56b43f4'


cc @malfet @seemethere @walterddr
Repository: storybook
Issue: The MDX ArgsTable component doesn't reflect argsTypes overrides
Labels: bug,has workaround,addon: docs,block: argstable,needs triage

**Describe the bug**
The `ArgsTable` component renders without custom overrides.

**To Reproduce**
See my repro repo here: https://github.com/aaronmcadam/storybook-mdx-custom-argstable

**System**
Environment Info:

  System:
    OS: macOS 11.4
    CPU: (12) x64 Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
  Binaries:
    Node: 14.17.1 - ~/.asdf/installs/nodejs/14.17.1/bin/node
    Yarn: 1.22.10 - /usr/local/bin/yarn
    npm: 6.14.13 - ~/.asdf/installs/nodejs/14.17.1/bin/npm
  Browsers:
    Chrome: 91.0.4472.114
    Safari: 14.1.1
  npmPackages:
    @storybook/addon-actions: ^6.3.4 => 6.3.4 
    @storybook/addon-essentials: ^6.3.4 => 6.3.4 
    @storybook/addon-links: ^6.3.4 => 6.3.4 
    @storybook/react: ^6.3.4 => 6.3.4
Repository: flutter
Issue: [platform-view][maps] One of the emulator crashes when launching the same app on both iOS and Android emulators 
Labels: c: crash,platform-android,platform-ios,engine,a: platform-views,has reproducible steps,P2,found in release: 2.2,found in release: 2.4,team-engine,triaged-engine

When trying to debug on concurrent emulators (One Android and one iOS), the second emulator to start triggers a crash on the one that was already running with the google maps plugin. This wasn't happening before the upgrade to the latest flutter version 2.2.3.

I'm trying to downgrade to 2.2.2 and check if this still happens so that I can isolate the issue.

You can see the stack error thrown below.
I haven't tried on a clean project but I assume this should be easy to reproduce with a clean project and the google maps plugin (I will try it too and post the results here)

Thank you!

```console
PlatformException(error, Attempt to invoke virtual method 'double java.lang.Double.doubleValue()' on a null object reference, null, java.lang.NullPointerException: Attempt to invoke virtual method 'double java.lang.Double.doubleValue()' on a null object reference at io.flutter.embedding.engine.systemchannels.PlatformViewsChannel$1.create(PlatformViewsChannel.java:86) at io.flutter.embedding.engine.systemchannels.PlatformViewsChannel$1.onMethodCall(PlatformViewsChannel.java:59) at io.flutter.plugin.common.MethodChannel$IncomingMethodCallHandler.onMessage(MethodChannel.java:233) at io.flutter.embedding.engine.dart.DartMessenger.handleMessageFromDart(DartMessenger.java:85) at io.flutter.embedding.engine.FlutterJNI.handlePlatformMessage(FlutterJNI.java:818) at android.os.MessageQueue.nativePollOnce(Native Method) at android.os.MessageQueue.next(MessageQueue.java:335) at android.os.Looper.loop(Looper.java:183) at android.app.ActivityThread.main(ActivityThread.java:7656) at java.lang.reflect.Method.invoke(Native Method) at com.android.internal.os.RuntimeInit$MethodAndArgsCaller.run(RuntimeInit.java:592) at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:947) ). Error thrown null.
StandardMethodCodec.decodeEnvelope (message_codecs.dart:597)
MethodChannel._invokeMethod (platform_channel.dart:158)
PlatformViewsService.initUiKitView (platform_views.dart:217)
_UiKitViewState._createNewUiKitView (platform_view.dart:630)
```
Repository: pytorch
Issue: torch.nn.utils.weight_norm fails with DDP
Labels: high priority,oncall: distributed,triaged,module: ddp

## torch.nn.utils.weight_norm fails with DDP

Model with `nn.utils.weight_norm` will fail to start DDP training and report the following errors:

```
RuntimeError: Cowardly refusing to serialize non-leaf tensor which requires_grad, since autograd does not support crossing process boundaries.  If you just want to transfer the data, call detach() on the tensor before serializing (e.g., putting it on the queue).
```

This works for single GPU and DataParallel.

cc @ezyang @gchanan @zou3519 @bdhirsh @jbschlosser @anjali411 @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @agolynski @SciPioneer @H-Huang @mrzzd @cbalioglu @gcramer23
Repository: godot
Issue: Editing a duplicated tilemap's tileset does not persist changes upon reload
Labels: bug,topic:core,topic:editor,topic:2d

### Godot version

3.3.2-stable

### System information

Windows 10 64 bit

### Issue description

I've been messing around with tilemaps, and layers within Godot.

I created a Base tilemap, which all of my layers would be created from. This is because I want all of my tilesets to be the same across layers.

I modified a layer's collision by clicking on the second layer's tileset and adding a collision using the TileSet editor.
After doing so I noticed that my player wouldn't collide with the newly added collisions. I reloaded the project, and then my collisions were just gone.

So I did some debugging and found that if I edited the base tilemap's tileset's collision (the one that was not duplicated, but the one that had been duplicated _from_). And of course, the collisions persisted.

I think potentially we should disable editing a duplicated tilemap's tileset. It's pretty misleading that you are still able to edit the tileset especially since the changes won't persist.

In my example, I wanted to have differing collision maps across the same tileset. This is not possible.

I have an example repo here: https://github.com/mikearche/godot-project-1

### Steps to reproduce

- Create a tilemap
- Create a tileset
- Save tilemap as a scene
- Duplicate the scene
- Modify the duplicated scene's tilemap's tileset's collisions
- Note that the changes to collisions do not persist
- Reopen Godot
- Note that your changes to the duplicated scene's tilemap's tileset's collisions are now gone 

### Minimal reproduction project

https://github.com/mikearche/godot-project-1
Repository: go
Issue: crypto/ed25519: add Secure Elliptic Curve Cryptography
Labels: NeedsInvestigation,FeatureRequest

Go implements elliptic curve cryptography in the "crypto/elliptic" package. However, this package only implements the NIST curves P-224, P-256, P-384 and P-521. All of these curves are [have weaknesses](https://safecurves.cr.yp.to/).

There is an implementation of the secure elliptic curve "25519" in the package "crypto/ed25519". However, this implementation only supports signing and not encryption and decryption.

It should be possible to add at least Curve25519 for ECC encryption and decryption and I suggest to implement it.
Repository: godot
Issue: MeshLib objects ghosted when attempting to paste between GridMaps
Labels: bug,topic:core,topic:editor,topic:3d

### Godot version

3.3.2.stable.official

### System information

Linux Mint 20 Cinnamon, NVIDIA Corporation GP106 [GeForce GTX 1060 6GB] (460.84), GLES3

### Issue description

**What Doesn't Work**

Selecting Gridmap content, then cut/pasting to another GridMap.

**What I Expected To Work**

To be able to take content from one GridMap, and move it to another GridMap (i.e. in the event of reorganization) via a cut/paste functionality.

### Steps to reproduce

1. Create a new Project.
2. Set the default scene root node to Spatial
3. Create a MeshInstance, Cube, default parameters
4. Select Scene > Convert To...  > MeshLibrary...
5. Save as Cube.meshlib
6. Create a new scene, Spatial root node
7. Add a GridMap
8. Specify Cube.meshlib for the Mesh Library
9. Click the MeshInstance Cube, and place it anywhere on the GridMap.
10. Shift+Select around the Cube, then Ctrl+X to "cut", then click the root Spatial node (Note: when I was doing this I had a second GridMap which I clicked on to cause this bug; using the root node exposes this bug, too).

**What I Expected to Happen**

The "Selection Ghost" would be paste-able on the selected Scene-tree node, if it was of type GridMap and had the same Mesh Library specified.

**What Actually Happened**

When I clicked on the root node, the previously selected Cube appeared "ghosted" in the scene, and not a child of any particular node. I could:
- toggle node visibility
- delete the GridMaps
- create a new Spatial then set that as the root and delete the original root
- create an entirely new Scene

When I did this, the previously bugged Scene with the ghost Cube remained in-place, undeletable, unmovable, and apparently unassociated with any node.

**Workaround**

I am able to close and reopen the Godot project, and the "ghost" meshes are gone. I assume this is just a visual abberation and not a data anomaly.

### Minimal reproduction project

[bug_gridmap.zip](https://github.com/godotengine/godot/files/6793566/bug_gridmap.zip)
Repository: pytorch
Issue: Implement missing torch.nan* operators
Labels: triaged,module: numpy,module: NaNs and Infs,module: reductions,tracker

As part of NumPy compatibility, we want to implement all remaining nan* operators such as torch.nanmean requested here https://github.com/pytorch/pytorch/issues/21987. 

It's been suggested [here](https://github.com/pytorch/pytorch/issues/46544#issuecomment-757556866) and [here](https://github.com/pytorch/pytorch/issues/21987#issuecomment-503756201) that we add a keyword argument for controlling the nan behavior, in which case the nan* operators would call the corresponding operator with the correct nan flag. See SciPy's design for [nan_policy](https://docs.scipy.org/doc/scipy/reference/dev/api-dev/nan_policy.html)

see also: https://github.com/pytorch/pytorch/issues/21987 https://github.com/pytorch/pytorch/issues/46544

## nan* operators to implement

- [x] https://github.com/pytorch/pytorch/issues/21987
- [ ] torch.nanmax
- [ ] torch.nanmin
- [ ] torch.nanstd
- [ ] torch.nanvar
- [ ] torch.nanargmin
- [ ] torch.nanargmax
- [ ] torch.nanprod
- [ ] torch.nancumsum
- [ ] torch.nancumprod

cc @mruberry @rgommers @heitorschueroff
Repository: flutter
Issue: [assets-for-api-docs] Replace import organizer with Dart SDK version when available publicly.
Labels: team,framework,d: api docs,P3,team-framework,triaged-framework

Currently, the file [import_sorter.dart](https://github.com/flutter/assets-for-api-docs/blob/master/packages/snippets/lib/import_sorter.dart) contains a largely copied implementation of [ImportOrganizer](https://github.com/dart-lang/sdk/blob/c7405b9d86b4b47cf7610667491f1db72723b0dd/pkg/analysis_server/lib/src/services/correction/organize_imports.dart#L15), from the dart SDK's internal implementation files.

If the Dart team makes that utility public, we should replace the copied implementation with the public implementation.
Repository: pytorch
Issue: CompositeImplicitAutograd ops should not call out= variants of operators
Labels: module: autograd,triaged

## Motivation

`functorch`’s transforms fail (both loudly and silently) on CompositeImplicitAutograd operations that call out= variants that call `native::resize_output` to resize empty tensors. (If you work at fb, [more details are available here](https://fb.quip.com/FmaJA34Luxfb)).

## Pitch

### Short term solution: add branch into native::resize_output

Rewrite [resize.cpp](https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Resize.cpp) into the following

```cpp
bool resize_output(const Tensor& output, IntArrayRef shape) {
  if (resize_output_check(output, shape)) {
    if (output.is_cpu() && !is_functorch_wrapper(output)) {
      at::native::resize_(output, shape);
    } else {
      output.resize_(shape);
    }
    return true;
  } else {
    return false;
  }
}
```

### (Short) Long-term solution: rewrite linalg_norm to not call the out variant

Instead of calling the _out variant, we rewrite `linalg_norm` and co. [(`frobenius_norm`](https://github.com/pytorch/pytorch/blob/80797d03e072d32c36fd26589f03368b1536001d/aten/src/ATen/native/LinearAlgebra.cpp#L2071) and other CompositeImplictAutograd ops) to not call the `_out` variants.

For example:

```cpp
Tensor linalg_norm(const Tensor& self, ...) {
  if (cond1)
    return frobenius_norm(self, ...); 
  if (cond2)
    return nuclear_norm(self, ...); 
  if (cond3)
    return vector_norm(self, ...); 
  if (cond1)
    return matrix_norm(self, ...); 
}

Tensor& linalg_norm_out(const Tensor& self, ..., Tensor& result) {
  return result.copy_(linalg_norm(self, ...));
}
```

`linalg_norm_out` calls `linalg_norm` and then performs a `copy_`. This is [no worse than what is being done today](https://github.com/pytorch/pytorch/blob/80797d03e072d32c36fd26589f03368b1536001d/aten/src/ATen/native/LinearAlgebra.cpp#L2104-L2105) in terms of performance; `linalg_norm_out` does not save memory because it is written using out-of-place operations to support autograd.

If we further care about memory savings, then we can have separate `linalg_norm` and `linalg_norm_out` implementations.


### (Distant) Long-term solution: leverage writing ops in Python

We write `linalg_norm` and co. in Python. We can generate an efficient `linalg_norm_out` (and co.) by transforming the `linalg_norm` code into code that uses the out variant.

cc @ezyang @albanD @zou3519 @gqchen @pearu @nikitaved @soulitzer @Lezcano @Varal7
Repository: pytorch
Issue: Some reduction operators have double signatures
Labels: triaged,module: deprecation,module: reductions,module: python array api

Some reduction operators have two signatures, one that takes a `dim` and `keepdim` parameter to reduce along one or more dimensions and another that does not which reduces the flattened tensor. We should update these operators to only have one signature with an optional `dim=None` parameter which is more compatible with the Python Array API Standard.

In particular, the Python Array API Standard specifies the following types for axis (dim) parameter:
`axis : Optional[ Union[ int, Tuple[ int, … ] ] ]`.

## Operators with double signature to update

- [ ] [argmax](https://pytorch.org/docs/master/generated/torch.argmax.html#torch.argmax)
- [ ] [all](https://pytorch.org/docs/master/generated/torch.all.html#torch.all)
- [ ] [any](https://pytorch.org/docs/master/generated/torch.any.html#torch.any)
- [ ] [max](https://pytorch.org/docs/master/generated/torch.max.html#torch.max)
- [ ] [min](https://pytorch.org/docs/master/generated/torch.min.html#torch.min)
- [ ] [mean](https://pytorch.org/docs/master/generated/torch.mean.html#torch.mean)
- [ ] [median](https://pytorch.org/docs/master/generated/torch.median.html#torch.median)
- [ ] [nanmedian](https://pytorch.org/docs/master/generated/torch.nanmedian.html#torch.nanmedian)
- [ ] [nansum](https://pytorch.org/docs/master/generated/torch.nansum.html#torch.nansum)
- [ ] [prod](https://pytorch.org/docs/master/generated/torch.prod.html#torch.prod)
- [ ] [std](https://pytorch.org/docs/master/generated/torch.std.html#torch.std)
- [ ] [std_mean](https://pytorch.org/docs/master/generated/torch.std_mean.html#torch.std_mean)
- [ ] [sum](https://pytorch.org/docs/master/generated/torch.sum.html#torch.sum)
- [ ] [var](https://pytorch.org/docs/master/generated/torch.var.html#torch.var)
- [ ] [var_mean](https://pytorch.org/docs/master/generated/torch.var_mean.html#torch.var_mean)
- [ ] [count_nonzero](https://pytorch.org/docs/master/generated/torch.count_nonzero.html#torch.count_nonzero) [missing keepdim]

cc @mruberry @rgommers @pmeier @asmeurer @leofang @AnirudhDagar @asi1024 @emcastillo @kmaehashi @heitorschueroff