sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: pytorch
Issue: Propose to add constant padding mode to the `torch.nn.functional.grid_sample` function
Labels: module: nn,triaged,has workaround,needs design

### 🚀 The feature, motivation and pitch

I'm working on a registration project. When I am using `torch.nn.functional.grid_sample` to apply the displacement field to the moving image, I find I am unable to use constant values like 255 to pad it (at least it is not that intuitive) just like the way in `np.pad`. So I propose to add one. If possible I would really want to implement it myself, though I am a completely new contributor to the pytorch project.

### Alternatives

As a workaround, I pre-pad the image with 255 pixels on the border and use the `border` mode.

### Additional context

Here are two samples,
![`zeros` mode](https://github.com/pytorch/pytorch/assets/54883050/aa30dc3e-ab8d-4b24-9b11-8b2357e09c8d)
![pre-pad `border` mode](https://github.com/pytorch/pytorch/assets/54883050/9ddca4f5-f7bc-48ca-bb33-5474254b1e23)


cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki
Repository: deno
Issue: npm:got 'agent' and npm:axios 'proxy' configuration not working
Labels: bug,node compat,node API

got & hpagent
```ts
import { HttpProxyAgent, HttpsProxyAgent } from 'npm:hpagent';
import got from 'npm:got';

const ip = await got.get('http://api.ipify.org/', {
    agent: {
        http: new HttpProxyAgent({ proxy: 'http://127.0.0.1:8000' }),
        https: new HttpsProxyAgent({ proxy: 'http://127.0.0.1:8000' }),
    }
}).text();
console.log(ip);
```
axios
```ts
import axios from 'npm:axios';
const ip = await axios.get('http://api.ipify.org/', {
    proxy: {
        protocol: 'http',
        host: '127.0.0.1',
        port: 8000,
    }
});
console.log(ip.data);
```

They don't work at all. But they work well with Node.js.
Repository: vscode
Issue: Add "resourceWebviewId" context key for webviews in `editor/title/context` contribution point
Labels: help wanted,feature-request,api

Currently there isn't a context key that allows extensions to properly scope  `editor/title/context` contributions to their specific webview. Today I am using `activeWebviewPanelId`, but that only works if you right-click on the active tab. If you right-click on an inactive tab, then `activeWebviewPanelId` will still be the "active" tab and show the wrong items.
Repository: transformers
Issue: BarkModel: support multiple history prompt for batch api
Labels: Good Second Issue,Feature request

### Feature request

Add multiple history prompt support for batch api

### Motivation

Batch API speeds up the audio generation process a lot. Unfortunately we can't use it to generate long sequences.

### Your contribution

I may be able to look at it soon
Repository: pytorch
Issue: [dynamo] Implement full `is_` checking
Labels: triaged,oncall: pt2,module: dynamo

### 🚀 The feature, motivation and pitch

This requires checking equality of objects.

For consts, this just requires checking the backing const.

~~For `VariableTracker`s with sources, this requires checking the sources. I believe we do not need to actually reference the original object. Hope this is right? @voznesenskym~~ 

source is not always available. `example_value` `FakeTensor` is better.

### Alternatives

Users cannot do things like deduplicate tensors or other objects in traced code

### Additional context

Required for properly tracing `nn.modules`: https://github.com/pytorch/pytorch/pull/111548
Related: https://github.com/pytorch/pytorch/issues/109504

cc @ezyang @msaroufim @wconstab @bdhirsh @anijain2305 @zou3519 @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @aakhundov @kadeng
Repository: next.js
Issue: Not fetching data always server component
Labels: bug

### Link to the code that reproduces this issue

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

### To Reproduce

1. start dev or production
2. server component fetch with no-store 
3. go to next page
4. make database insert (using tag update)
5. go back first page shows the new fetch
6. if repeat 3-5 again
7. go to first page no fetch shows staled data

### Current vs. Expected behavior

1. start dev or production
2. server component fetch with no-store 
3. go to next page
4. make database insert (using tag update)
5. go back first page shows the new fetch
6. if repeat 3-5 again
7. go to first page no fetch shows staled data

### Verify canary release

- [X] I verified that the issue exists in the latest Next.js canary release

### Provide environment information

```bash
Operating System:
  Platform: linux
  Arch: x64
  Version: #96~20.04.1-Ubuntu SMP Thu Sep 21 13:23:37 UTC 2023
Binaries:
  Node: 21.0.0
  npm: 10.2.0
  Yarn: 1.22.19
  pnpm: 8.9.0
Relevant Packages:
  next: 13.5.5
  eslint-config-next: 13.5.5
  react: 18.2.0
  react-dom: 18.2.0
  typescript: 5.2.2
Next.js Config:
  output: N/A
```


### Which area(s) are affected? (Select all that apply)

Not sure, App Router

### Additional context

_No response_
Repository: next.js
Issue: NextJS 13.5 + Pages router + MUI: Issue with CssVarsProviders & useColorScheme
Labels: bug

### Link to the code that reproduces this issue

https://github.com/rodrigonzalz/nextjs-mui-issue

### To Reproduce

1. Either run `next dev` or `next build`
2. You get error `MUI: useColorScheme must be called under CssVarsProvider`

### Current vs. Expected behavior

This is NextJS application with Pages router that uses MUI CSSVarsProvider fot theming.

At `_app.tsx` there is a `Wrapper` component that uses the hook `useColorScheme`.

As `Wrapper` is under `CssVarsProvider` it should work without any issue.

When using NextJS `13.5.6` we get error the error`MUI: useColorScheme must be called under CssVarsProvider` with the static generated pages (index, 404 and 500)

This does not happen with NextJS `13.4.19` and earlier versions.

### Verify canary release

- [X] I verified that the issue exists in the latest Next.js canary release

### Provide environment information

```bash
Operating System:
  Platform: darwin
  Arch: arm64
  Version: Darwin Kernel Version 22.5.0: Thu Jun  8 22:22:20 PDT 2023; root:xnu-8796.121.3~7/RELEASE_ARM64_T6000
Binaries:
  Node: 18.3.0
  npm: 8.11.0
  Yarn: 1.22.15
  pnpm: 8.9.0
Relevant Packages:
  next: 13.5.6
  eslint-config-next: 13.5.6
  react: 18.2.0
  react-dom: 18.2.0
  typescript: 5.2.2
Next.js Config:
  output: N/A
```


### Which area(s) are affected? (Select all that apply)

Not sure

### Additional context

_No response_
Repository: PowerToys
Issue: [File Locksmith] ISO support
Labels: Idea-Enhancement,Product-File Locksmith

### Description of the new feature / enhancement

ISO files also become occupied after mounted, and it would be better to include this detection to let users know why.

### Scenario when this would be used?

File Locksmith

### Supporting information

_No response_
Repository: kubernetes
Issue: Check if CRI runtime supports CDI
Labels: kind/cleanup,sig/node,lifecycle/frozen,needs-triage

As [Dynamic Resource Allocation](https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/3063-dynamic-resource-allocation) and [Device Plugins](https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/4009-add-cdi-devices-to-device-plugin-api) depend on [CDI](https://github.com/cncf-tags/container-device-interface), Kubelet should check if CRI runtime supports CDI.

Not all CRI runtimes support CDI. It's is supported and enabled by default in `CRI-O >= 1.23.2` and supported, but not enabled by default in `Containerd >= 1.7.0`

Kubelet should issue a warning if runtime doesn't support CDI.
Repository: material-ui
Issue: [RFC][material-ui] Improve the Tailwind CSS integration
Labels: docs,umbrella,package: material-ui,RFC

### What's the problem? 🤔

At this moment we have only [this information](https://mui.com/material-ui/guides/interoperability/#tailwind-css) in the docs around how people can customize Material UI using Tailwind CSS. We also provide one example related to how to set up a project using these two libraries.

However, this is not enough, mainly because we don't have any information about how the Tailwind CSS theme should be structured, nor do we have examples per component.

### What are the requirements? ❓

In order to make sure we have a great integration with Tailwind CSS, we should offer at least the following things:
- tailwind config with a custom theme that will reflect the default Material UI's theme
- tailwind plugin for mapping the states of the component via tailwind selectors, for e.g. ui-disabled, see for more info about this in https://github.com/mui/base-ui/issues/22 - this is around Base UI, but the same ideas can apply for Material UI too
- provide a fully feature example containing the two above-mentioned features
- provide a demo on each component's page about how the component can be customized using Tailwind CSS
- provide `slotProps` that can target every DOM nodes of Material UI components.
- Tailwind v4 requires custom `@layer`.

### What are our options? 💡

The other option is what we have currently, which based on the feedback is not scaling.

### Proposed solution 🟢

The solution is providing the options listed in the requirements section.

### Resources and benchmarks 🔗

_No response_
Repository: tensorflow
Issue: TFLite CNN model quantization error
Labels: stat:awaiting tensorflower,type:bug,TFLiteConverter,TF2.14

### 1. System information

- OS Platform and Distribution : Ubuntu 22.04.3 LTS
- TensorFlow installation: pip install tensorflow (virtual env: venv)
- TensorFlow library: pip package -> tensorflow 2.14.0

### 2. Code

Colab to build the models and reproduce the issue:

[Reproduce the issue](https://colab.research.google.com/drive/1sx7qmXfP5RA1ituF5Fmh8X-cy0-M7Xol?usp=sharing)


### 3. Failure after conversion
Hi, I'm having an issue when trying to use signatures of quantized tflite CNN model.

The conversion and quantization go well, but when I try to use infer or fine_tune signatures, I get the following error which seems to be related to the quantization process:

RuntimeError: tensorflow/lite/kernels/conv.cc:374 affine_quantization->zero_point->data[i] != 0 (-24 != 0)Node number 21 (CONV_2D) failed to prepare.tensorflow/lite/kernels/conv.cc:374 affine_quantization->zero_point->data[i] != 0 (-24 != 0)Node number 41 (CONV_2D) failed to prepare.


**Note**: I don't get this error with the exact same code using linear model instead of CNN.

Thanks for your help
Repository: next.js
Issue: Updating CSS Modules always triggers full reload
Labels: create-next-app,bug,linear: next

### Link to the code that reproduces this issue

https://github.com/vercel/next.js/tree/canary/packages/create-next-app

### To Reproduce

1. Install Next.js app by `npx create-next-app@latest`
2. Select each options for installation like this.
<img width="538" alt="スクリーンショット 2023-10-19 19 39 27" src="https://github.com/vercel/next.js/assets/5787175/bd24b1a3-7770-4778-9342-23d12bb606ee">

3. Update `app/page.module.css` and it triggers full reload instead of fast refresh like this
![ezgif-2-fd370fc814](https://github.com/vercel/next.js/assets/5787175/37cc2087-ef30-4da2-9c39-6505ba97377a)


### Current vs. Expected behavior

It should trigger fast refresh when I edit css modules.
Actually, the following cases execute fast refresh correctly.

1. When I edit a native css file (like global.css)
2. When I didn't choose `App Router` at the install.

### Verify canary release

- [X] I verified that the issue exists in the latest Next.js canary release

### Provide environment information

```bash
Operating System:
  Platform: darwin
  Arch: x64
  Version: Darwin Kernel Version 23.0.0
Binaries:
  Node: 18.11.0
  npm: 10.2.1
  Yarn: 1.22.19
  pnpm: N/A
Relevant Packages:
  next: 13.5.6
  eslint-config-next: 13.5.6
  react: 18.2.0
  react-dom: 18.2.0
  typescript: 5.2.2
Next.js Config:
  output: N/A
```


### Which area(s) are affected? (Select all that apply)

App Router, CLI (create-next-app)

### Additional context

_No response_
Repository: stable-diffusion-webui
Issue: [Bug]:  No module named 'models.blip'; 'models' is not a package
Labels: bug-report

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What happened?

interrogate clip in img2img tab today stopped working.

![image](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/44883288/26e07694-d1e4-4827-8524-aa6758ed7a22)


### Steps to reproduce the problem

.

### What should have happened?

.

### Sysinfo

.

### What browsers do you use to access the UI ?

_No response_

### Console logs

```Shell
.
```


### Additional information

.
Repository: pytorch
Issue: [dynamo] `not aliased -> aliased` Guard only implemented for Tensors
Labels: triaged,enhancement,oncall: pt2,module: dynamo

### 🐛 Describe the bug

For tensors, both `aliased -> not aliased` and `not aliased -> aliased` can lead to guard failure.
- `aliased -> not aliased`: L['x'] is L['y']"
- `not aliased -> aliased`: Duplicate tensor found where not expected! L['y']should not alias to anything, but is aliased"

For other objects, only `aliased -> not aliased`
- `aliased -> not aliased`: L['x'] is L['y']"

### Diagnosis

This is due to the guards deduping tensors:
https://github.com/pytorch/pytorch/blob/aa3243bceb8c84f56f326b9e8c60ecc9794bbce4/torch/csrc/dynamo/guards.cpp#L404
But not objects

### Question:
1. If there is 1 alias, which then increases to 2, will it still trigger recompile? Yes:
```python
def fn(z, x, y):
    if x is y:
        return z + x * 2
    else:
        return z + x + y

fn_opt = torch.compile(backend='eager', fullgraph=True, dynamic=True)(fn)

x = torch.zeros(2)
y = torch.ones(2)

self.assertEqual(fn(x, x, y), fn_opt(x, x, y))
self.assertEqual(fn(x, x, x), fn_opt(x, x, x))
```

### Versions

main

cc @ezyang @msaroufim @bdhirsh @anijain2305 @zou3519 @chauhang @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @wconstab
Repository: pytorch
Issue: Higher-order derivatives extremely slow, increasing exponentially
Labels: module: autograd,triaged,needs research

### 🐛 Describe the bug

In my application, I need to take the nth order mixed derivative of a function. However, I found that the torch.autograd.grad computation time increases exponentially as n increases. Is this expected, and is there any way around it?

This is my code for differentiating a function self.F (from R^n -> R^1):

```
def differentiate(self, x):
    x.requires_grad_(True)
    xi = [x[...,i] for i in range(x.shape[-1])]
    dyi = self.F(torch.stack(xi, dim=-1))
    for i in range(self.dim):
        start_time = time.time()
        dyi = torch.autograd.grad(dyi.sum(), xi[i], retain_graph=True, create_graph=True)[0]
        grad_time = time.time() - start_time
        print(grad_time)
    return dyi
```

And these are the times printed for each iteration of the above loop:
```
0.0037012100219726562
0.005133152008056641
0.008165121078491211
0.019922733306884766
0.059255123138427734
0.1910409927368164
0.6340939998626709
2.1612229347229004
11.042078971862793
```

I assume this is because the size of the computation graph is increasing? Is there any way around this? I thought I might be able to circumvent this issue by taking a functional approach (presumably obviating the need for a computation graph), using torch.func.grad. However, this actually increased the runtime of the same code! Am I not understanding torch.func.grad properly?

### Versions

torch 2.1.0

cc @ezyang @albanD @zou3519 @gqchen @pearu @nikitaved @soulitzer @Lezcano @Varal7
Repository: node
Issue: globalPreload docs have non-working code
Labels: doc

### Affected URL(s)

https://nodejs.org/docs/latest-v16.x/api/esm.html#globalpreload
https://nodejs.org/docs/latest-v18.x/api/esm.html#globalpreload

### Description of the problem
The example code from the docs doesn't work.

`port.onmessage` doesn't work. the code needs to be `port.on('message', ...)`. Additionally, there is just `data`, not `evt.data`.

I know that globalPreload is gone in 20, so feel free to close this. I am working with making loaders work across multiple versions of node and this was a problem that I ran into.


```
/**
 * This example has the application context send a message to the loader
 * and sends the message back to the application context
 */
export function globalPreload({ port }) {
  port.onmessage = (evt) => {
    port.postMessage(evt.data);
  };
  return `\
    port.postMessage('console.log("I went to the Loader and back");');
    port.onmessage = (evt) => {
      eval(evt.data);
    };
  `;
}
```
Repository: opencv
Issue: Res10_300x300_ssd_iter_140000_fp16 github
Labels: category: documentation,incomplete

### Describe the doc issue

not work 


### Fix suggestion

_No response_
Repository: tensorflow
Issue: tf.map_fn and TensorArray do not seem to support backpropagation
Labels: stat:awaiting tensorflower,type:bug,type:feature,comp:ops,TF2.14

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

binary

### TensorFlow version

tf2.14

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

****
I provide three different snippet implementation of a function. I am calculating multiple losses with respect to different networks. 
The first and third implementation gives `gradients=None` in both graph and eager mode.

The second implementation works in eager mode but not in graph mode. 

It seems that tf.map_fn does not support backpropagation #19897 and this issue could be related to that.

### Standalone code to reproduce the issue

```shell
First snippet ( Gradients are None in both graph and eager mode )


    def _compute_unadjusted_ce(self, label_batch, logits):
        ce = tf.map_fn(
            lambda x: tf.reduce_mean(
                tf.keras.losses.categorical_crossentropy(
                    from_logits=True,
                    y_pred=logits[x, ...],
                    y_true=label_batch
                )
            ),
            elems=tf.range(self.num_learners),
            fn_output_signature=tf.float32,
            parallel_iterations=1,
            back_prop=True
        )
        return ce



# The following function does the work but fails in graph mode (`tf.function`) ( similar to #37512   


 def _compute_unadjusted_ce(self, label_batch, logits):
       ce = list()
         for i in range(self.num_learners):
            ce.append(
                 tf.reduce_mean(
                     tf.keras.losses.categorical_crossentropy(
                         from_logits=True,
                         y_pred=logits[i, ...],
                         y_true=label_batch
                     )
                 )
             )
    
         return ce


# The following function causes gradients as None ( in both eager and graph mode )


 def _compute_unadjusted_ce(self, label_batch, logits):
         ce = tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True, clear_after_read=True)
         for i in range(self.num_learners):
             ce = ce.write(
                 ce.size(),
                 tf.reduce_mean(
                     tf.keras.losses.categorical_crossentropy(
                         from_logits=True,
                         y_pred=logits[i, ...],
                         y_true=label_batch
                     )
                 )
             )
    
         return ce.stack()
```


### Relevant log output

_No response_
Repository: pytorch
Issue: build: failure when building pytorch with TBB
Labels: module: build,triaged,module: tbb


## Issue description

I want to build pytorch from source using TBB but not OMP, and I try v1.10.2 and v1.13.1, both failed. plz help.


## Code example

Error messages:
```
[ 98%] Linking CXX executable ../../../../bin/torch_shm_manager
/data/wangjie/dependtool/pytorch/build/lib/libtorch_cpu.so：对‘std::allocator<std::pair<long, std::tuple<torch::jit::SourceRange, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::intrusive_ptr<torch::jit::InlinedCallStack, c10::detail::intrusive_target_default_null_type<torch::jit::InlinedCallStack> > > > >::allocator()’未定义的引用
collect2: error: ld returned 1 exit status
```

- PyTorch or Caffe2:
- How you installed PyTorch (conda, pip, source): source
- Build command you used (if compiling from source): USE_CUDA=0 BUILD_TEST=0 USE_TBB=1 USE_OPENMP=0 MKLDNN_CPU_RUNTIME=TBB MKL_THREADING=TBB ATEN_THREADING=TBB  python setup.py install
- OS: centos7
- PyTorch version: v1.13.1 & v1.10.2
- Python version: 3.10.13
- CUDA/cuDNN version:
- GPU models and configuration:
- GCC version (if compiling from source):3.7.0
- CMake version:3.21.0
- Libc version: glibc-2.17
- Versions of any other relevant libraries:


cc @malfet @seemethere
Repository: flutter
Issue: Running app on macOS Sonoma shows a warning dialog
Labels: tool,platform-mac,a: desktop,P2,team-macos,triaged-macos

### Steps to reproduce

Run a new or a existing app on macOS Sonoma. (Only shows after upgrading to macOS Sonoma)

This warning should either be suppressed or provide documentation on how to get rid of this dialog.

This happens every time when running the app on Sonoma. 

### Expected results

No warning dialog 

### Actual results

![Flutter macOS Sonoma Dialog](https://github.com/flutter/flutter/assets/48603081/cc9c107f-0e13-4e02-bc15-489d215c9840)

### Flutter Doctor output

<details><summary>Doctor output</summary>

```console
[✓] Flutter (Channel stable, 3.13.8, on macOS 14.0 23A344 darwin-arm64, locale en-US)
    • Flutter version 3.13.8 on channel stable at /Users/tahatesser/Code/flutter
    • Upstream repository git@github.com:TahaTesser/flutter.git
    • FLUTTER_GIT_URL = git@github.com:TahaTesser/flutter.git
    • Framework revision 6c4930c4ac (21 hours ago), 2023-10-18 10:57:55 -0500
    • Engine revision 767d8c75e8
    • Dart version 3.1.4
    • DevTools version 2.25.0

[✓] Android toolchain - develop for Android devices (Android SDK version 34.0.0)
    • Android SDK at /Users/tahatesser/Code/android-sdk
    • Platform android-34, build-tools 34.0.0
    • ANDROID_SDK_ROOT = /Users/tahatesser/Code/android-sdk
    • Java binary at: /Applications/Android Studio.app/Contents/jbr/Contents/Home/bin/java
    • Java version OpenJDK Runtime Environment (build 17.0.6+0-17.0.6b829.9-10027231)
    • All Android licenses accepted.

[✓] Xcode - develop for iOS and macOS (Xcode 15.0)
    • Xcode at /Applications/Xcode.app/Contents/Developer
    • Build 15A240d
    • CocoaPods version 1.13.0

[✓] Chrome - develop for the web
    • Chrome at /Applications/Google Chrome.app/Contents/MacOS/Google Chrome

[✓] Android Studio (version 2022.3)
    • Android Studio at /Applications/Android Studio.app/Contents
    • Flutter plugin can be installed from:
      🔨 https://plugins.jetbrains.com/plugin/9212-flutter
    • Dart plugin can be installed from:
      🔨 https://plugins.jetbrains.com/plugin/6351-dart
    • Java version OpenJDK Runtime Environment (build 17.0.6+0-17.0.6b829.9-10027231)

[✓] VS Code (version 1.83.1)
    • VS Code at /Applications/Visual Studio Code.app/Contents
    • Flutter extension version 3.74.0

[✓] Connected device (2 available)
    • macOS (desktop) • macos  • darwin-arm64   • macOS 14.0 23A344 darwin-arm64
    • Chrome (web)    • chrome • web-javascript • Google Chrome 118.0.5993.88

[✓] Network resources
    • All expected network resources are available.

• No issues found!

```

</details>