sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: youtube-dl
Issue: 14tv.com 易趣
Labels: site-support-request

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## Checklist

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- Make sure that all provided video/audio/playlist URLs (if any) are alive and playable in a browser.
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- [x] I'm reporting a new site support request
- [x] I've verified that I'm running youtube-dl version **2020.11.29**
- [x] I've checked that all provided URLs are alive and playable in a browser
- [x] I've checked that none of provided URLs violate any copyrights
- [x] I've searched the bugtracker for similar site support requests including closed ones


## Example URLs

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## Description

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Repository: rust
Issue: Poor error suggestion from failure to `use` trait
Labels: A-diagnostics,A-trait-system,T-compiler,A-suggestion-diagnostics,D-confusing,D-papercut,D-invalid-suggestion,S-needs-repro

Forwarding: https://github.com/rust-num/num-derive/issues/44

As described in the above issue originally filed against `num-derive`, I experienced a very poor suggestion in the error message resulting from forgetting to `use` the trait in question.

I'll duplicate the error here, but please see the linked issue for full details.

```rust
error[E0599]: no variant or associated item named `from_i32` found for enum `capi::pa_port_available_t` in the current scope
   --> pulse-binding/src/context/introspect.rs:351:48
    |
351 |                 available: def::PortAvailable::from_i32(src.available).unwrap(),
    |                                                ^^^^^^^^ variant or associated item not found in `capi::pa_port_available_t`
    |
    = help: items from traits can only be used if the trait is in scope
    = note: the following trait is implemented but not in scope; perhaps add a `use` for it:
            `use crate::channelmap::_IMPL_NUM_ToPrimitive_FOR_Position::_num_traits::FromPrimitive;`
```

Clearly the trait referenced in that suggestion is not one that Rust should be making.

Additionally I pointed out a secondary lesser issue with the type name used in the error being unhelpful as it uses the original name rather than the re-exported name.

I'm currently on Rust 1.47 (Debian Sid).
Repository: rust
Issue: Warn when using `[foo; 0]` where `foo` is a non-const expression that evaluates to a `Drop` type
Labels: A-lints,A-destructors,T-lang,A-const-eval,A-zst

In #74836 it was discovered that `[foo; 0]` will leak `foo`, causing observable effects if `foo` has a destructor. In response #74857 was opened in order to warn users against using `[foo; 0]` to construct a zero-length array in favor of `[]`, but this was deemed to be overkill.

However, since then some new information has emerged that I believe calls for re-evaluating this decision. The problem comes from the use  of array-repeat expressions in constant contexts. Consider the following program, which works on stable Rust today:

```rust
struct Foo;

impl Drop for Foo {
    fn drop(&mut self) {
        println!("dropped!")
    }
}

const FOO: Foo = Foo;

fn main() {
    println!("0 drops follow this");
    [FOO; 0];
    println!("1 drops follow this");
    [FOO; 1];
    println!("2 drops follow this");
    [FOO; 2];
}
```

While the stabilization of array-repeat expressions on constant values was accidental (#79270), people appear to be in agreement that this behavior is correct: https://github.com/rust-lang/rust/pull/79270#issuecomment-735253236 . Specifically, it is considered correct that a constant array repeat expression `[FOO; N]` will run `FOO`'s destructor N times.

Consider how this compares to the use of array-repeat expressions in non-constant contexts. For some non-constant expression `[foo; N]` where foo impls `Drop`:

* When N is greater than 2, this will produce an insurmountable compile-time error (the compiler requires `Copy`, which is incompatible with `Drop`).

* When N is 1, this will have identical results to the const-context array-repeat expression, and is not a problem.

* When N is 0, this will run the destructor exactly once, which differs from the behavior of const-context array-repeat expressions, which will not run any destructor. (Technically this is contingent on #74836 being fixed, but everybody agrees that the current leaking behavior is incorrect).

In light of this divergence of behavior between const and non-const array-repeat expressions, it's worth discussing whether #74857 should be revived, especially since the fix is as simple as *not* using `[foo; 0]` and simply using `[]` instead.
Repository: pytorch
Issue: [FR] loss reduction argument accepts None
Labels: module: loss,triaged

## 🚀 Feature
Currently it accepts string `'none'`.... why not also accept None? :)
Repository: TypeScript
Issue: Consider .node files as module
Labels: Suggestion,Awaiting More Feedback

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## Search Terms
native module, node js native module, .node files
<!-- List of keywords you searched for before creating this issue. Write them down here so that others can find this suggestion more easily -->

## Suggestion
Here is the snippet that load the module:
```ts
import Aboukara from "../../AboukaraLib";
const combo_regex = /\w+@\w+\.[a-zA-Z]+:.+/gm;
```
Currently when I import a .node file the compiler throw me this error:
```
[6:13:25 PM] File change detected. Starting incremental compilation...

src/utils/findCombo.ts:1:22 - error TS2307: Cannot find module '../../AboukaraLib' or its corresponding type declarations.

1 import Aboukara from "../../AboukaraLib";
                       ~~~~~~~~~~~~~~~~~~~

[6:13:25 PM] Found 1 error. Watching for file changes.

```
If the compiler would be able to understand it it would make it useful for people who use native module.
<!-- A summary of what you'd like to see added or changed -->

## Use Cases
Repository: pytorch
Issue: Missing super().__init__() call in nn.Module
Labels: module: nn,triaged

I think I found a quirk with Python that `__init__` of `nn.Module` doesn't account for.
https://github.com/pytorch/pytorch/blob/d386d3323f055397b10cd1c66a8677d9038aa783/torch/nn/modules/module.py#L223

Consider the following:
```
class Module:
    def __init__(self):
        # super().__init__()
        pass

class A:
    def __init__(self):
        super().__init__()
        print("Super class called")

class B(Module, A):
    def __init__(self):
        super().__init__()
        print("All done")

B()
```
The output is:
```
All done
```
However, if we uncomment `super().__init__()` in `Module`, the output is:
```
Super class called
All done
```
See [this stack overflow answer](https://stackoverflow.com/a/30187306) to understand why this happens.

Should this `super().__init__()` call be added or am I misuing `Module`?

cc @albanD @mruberry
Repository: youtube-dl
Issue: Linux_will any package formats allow verifying gpg signatures, AND can be installed w/o compiling, etc?
Labels: question

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## Question

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WRITE QUESTION HERE
Linux: 
 Do any method(s) of updating (upgrading) youtube-dl, actually verify **authenticity** of PGP signed files, at any time after the download or during install of the latest version?  The main question is, are developer signed packages getting verified before they're installed?   Using methods such as:
```
$ sudo youtube-dl -U ; 
or $ sudo -H pip3 install --upgrade youtube-dl
``` 
If those "quick" update methods don't verify signed files, will any of the packages on Github for D/L, install right away (after gpg verifying signed files), and without first compiling them ?  

Or is there any way to verify gpg signatures and install the package w/o having to compile, short of using a PPA to install  latest youtube-dl? ( the PPA packages probably still **won't** be verified w/ gpg signatures).

Thanks.
Repository: pytorch
Issue: Add docs on PyTorch - NumPy interaction
Labels: module: docs,triaged,module: numpy

Mixing PyTorch and NumPy is quite common and there are a lot of subtleties involved (see for example gh-7610), but there is no documentation on this at all right now. I think this belongs as a separate section in the API docs.

_Outline:_

Converting tensors into ndarrays, and vice versa. Should cover:
- Constructor functions (`tensor`, `as_tensor`, `asarray`)
- `Tensor.numpy()`
- The purpose of `__array__`, when it works, how to deal with it when it doesn't (see gh-36560)
- When conversion is or isn't zero-copy, and that if it is memory is shared and mutations affect both tensor and array

Using NumPy functions directly on PyTorch tensors:
- When that works
- When a tensor is returned (this is designed behaviour, see `Tensor.__array_wrap__`) and when an `ndarray` or scalar is produced
- Mention that the reverse, applying PyTorch functions on NumPy arrays, never works (and the rationale for that)
- Reductions have special-case behavior in NumPy (e.g. `np.max[tensor]`) will call `tensor.max()`).

Python operators applied to a tensor and an array:
- `tensor + array` et al., precedence rules and what type gets returned (see gh-46829)

Other places where tensors and arrays can be used interchangeably:
- indexing work both ways, `array[tensor]` and `tensor[array]`, for boolean and fancy indexing

Link to what seems to be [the one relevant tutorials](https://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html), and check that that uses best-practice code.

Also related: `__cuda_array_interface__` is implemented but not documented, mention that it allows zero-copy interop with CuPy, Numba et al. As does DLPack, see `torch.from_dlpack`.

EDIT: last topic to add is the status of `__array_ufunc__`, `__array_function__` (won't be implemented probably) and `__array_namespace__` (should be coming soon). Also  relevant: https://github.com/numpy/numpy/pull/20185

cc @jlin27 @mruberry @rgommers @heitorschueroff
Repository: go
Issue: cmd/go: with -compiler=gccgo, only call runtime.setmodinfo if using a version of gccgo that supports it
Labels: help wanted,NeedsFix

### What version of Go are you using (`go version`)?

<pre>
$ go version
go version devel +7f688d18c0 Mon Nov 30 22:14:36 2020 +0000 linux/amd64
</pre>

### Does this issue reproduce with the latest release?

No. It is on tip only.

```
$ gccgo --version
gccgo (Debian 8.3.0-6) 8.3.0
Copyright (C) 2018 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE
```

### 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/dotaheor/.cache/go-build"
GOENV="/home/dotaheor/.config/go/env"
GOEXE=""
GOFLAGS=""
GOHOSTARCH="amd64"
GOHOSTOS="linux"
GOINSECURE=""
GOMODCACHE="/home/dotaheor/go/pkg/mod"
GONOPROXY=""
GONOSUMDB=""
GOOS="linux"
GOPATH="/home/dotaheor/go"
GOPRIVATE=""
GOROOT="/usr/bin/go"
GOSUMDB="sum.golang.org"
GOTMPDIR=""
GOTOOLDIR="/usr/bin/go/pkg/tool/linux_amd64"
GCCGO="/usr/bin/gccgo"
AR="ar"
CC="gcc"
CXX="g++"
CGO_ENABLED="1"
GOMOD=""
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-build239828229=/tmp/go-build -gno-record-gcc-switches"
</pre></details>

### What did you do?

```
go run -compiler=gccgo example.go
```

### What did you expect to see?

Normal run.

### What did you see instead?

```
# command-line-arguments
/usr/bin/ld: $WORK/b001/_pkg_.a(_go_.o): in function `main.main..init0':
/tmp/go-build/b001/_gomod_.go:5: undefined reference to `runtime.setmodinfo
```

It is on Debian 10 stable.
Repository: rust
Issue: Help for missing lifetimes in inherent impl block header could be better
Labels: C-enhancement,A-lifetimes,T-compiler,A-suggestion-diagnostics,D-papercut

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I tried this code:

```rust
struct Repository;
struct EntityPool;
pub struct EntityImporter<'a, 'b> {
    repositories: &'a Vec<Repository>,
    entity_pool: &'b EntityPool,
}
impl EntityImporter {
    pub fn new(repos: &Vec<Repository>, pool: &EntityPool) -> EntityImporter {
        EntityImporter {
            repositories: repos,
            entity_pool: pool,
        }
    }
}
```
https://play.rust-lang.org/?version=nightly&mode=debug&edition=2018&gist=bfd658bcb864cea21a5fa342a464b4b1

That errors (correctly), but suggests putting anonymous lifetimes in the inherent impl block:
```
error[E0726]: implicit elided lifetime not allowed here
 --> src/lib.rs:7:6
  |
7 | impl EntityImporter {
  |      ^^^^^^^^^^^^^^- help: indicate the anonymous lifetimes: `<'_, '_>`
```

That's a great suggestion for a trait impl block (like if I was doing `impl Debug for EntityImporter`), but for an _inherent_ impl block I'm not sure it's the best we can do.

It might be better for it to suggest using named lifetimes here, since it's likely that at least one method in the block will need them.

See https://users.rust-lang.org/t/understanding-static-lifetimes/52139?u=scottmcm for an example where someone followed the `<'_, '_>` suggestion and ended up in the generally-unwanted situation of
```rust
impl<'x, 'y> EntityImporter<'_, '_> {
```
Repository: pytorch
Issue: [bug] `torch.{sinh, cosh}`: Incorrect values for vectorized path
Labels: module: cpu,triaged,module: sleef,module: NaNs and Infs,module: vectorization,module: trigonometric functions

```python
>>> import torch
>>> a = torch.tensor([89.] * 15)
>>> a.sinh()
tensor([2.2448e+38, 2.2448e+38, 2.2448e+38, 2.2448e+38, 2.2448e+38, 2.2448e+38,
        2.2448e+38, 2.2448e+38, 2.2448e+38, 2.2448e+38, 2.2448e+38, 2.2448e+38,
        2.2448e+38, 2.2448e+38, 2.2448e+38])
>>> a = torch.tensor([89.] * 16)
>>> a.sinh()
tensor([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf])
```

Relevant Line:
https://github.com/pytorch/pytorch/blob/55fc0e9e534d9141fcac08e9ab42df39e24a5274/aten/src/ATen/cpu/vec256/vec256_float.h#L175-L177

cc @VitalyFedyunin @ezyang @bhosmer @smessmer @ljk53 @bdhirsh @ailzhang
Repository: flutter
Issue: Webview on Android should support Long press save image
Labels: c: new feature,platform-android,framework,f: gestures,p: webview,package,c: proposal,P3,team-android,triaged-android

Webview on Android currently does not seem to support long press save image, while Webview on iOS does.
Is there some feature I need to turn on to enable this function?

webview_flutter: ^1.0.5
flutter version: 1.22.3 with Hybrid Composite enabled
Repository: TypeScript
Issue: Parameters required by default when generating modules from JavaScript files
Labels: Suggestion,Awaiting More Feedback

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**TypeScript Version:**  4.1.2x

<!-- Search terms you tried before logging this (so others can find this issue more easily) -->
**Search Terms:**
transpile javascript optional parameters

**Code**

```js
export default function example (hello, optionalParam={}) {
  console.log(hello);
}
```

```ts
export default function example(hello: any, optionalParam?: object): void;
```

The _first_ parameter is now required. While there is syntax to make parameters optional as shown above, shouldn't all javascript parameters be optional unless specified by a JSDoc?

Ideally there would be some configuration for this to allow a "optional by default". 

**Expected behavior:**
https://www.typescriptlang.org/docs/handbook/type-checking-javascript-files.html#function-parameters-are-optional-by-default

**Actual behavior:**
Parameters are not optional by default.

**Playground Link:** <!-- A link to a TypeScript Playground "Share" link which demonstrates this behavior -->

**Related Issues:** <!-- Did you find other bugs that looked similar? -->
Repository: pytorch
Issue: Can sublist a nn.Sequential subclass
Labels: module: nn,triaged

## 🐛 Bug

Subclassing nn.Sequential prevents to access children modules as `seq[1:4]`. I use it to encapsulate several layers in one predefined class and have that new when printing the whole model instead of a Sequential. As a workaround, I could define a function that returns a regular nn.Sequential.


## To Reproduce

```python
from torch import nn
class A(nn.Sequential):
    def __init__(self):
        layers = [nn.Conv2d(3,4,3), nn.Conv2d(4,5,3), nn.Conv2d(5,10,3), nn.Conv2d(10, 1,3)]
        super().__init__(*layers)

a = A()
a[:2] # <- doesn't work
a[1] # <- it works
```

I tried to use OrderedDict and add_module without success. It gives me the same error.

If I use a regular nn.Sequential, it works:
```
n = nn.Sequential(nn.Conv2d(3,4,3), nn.Conv2d(4,5,3), nn.Conv2d(5,10,3), nn.Conv2d(10, 1,3))
n[:2] # <- It's fine
```

## Error stack

```
~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/container.py in __getitem__(self, idx)
     79     def __getitem__(self: T, idx) -> T:
     80         if isinstance(idx, slice):
---> 81             return self.__class__(OrderedDict(list(self._modules.items())[idx]))
     82         else:
     83             return self._get_item_by_idx(self._modules.values(), idx)

TypeError: __init__() takes 1 positional argument but 2 were given
```

## Expected behavior

I expect to be able to use a subclassed nn.Sequential as a nn.Sequential.

## Environment
 - PyTorch Version (e.g., 1.0): 1.7.0 and 1.6.0
 - OS (e.g., Linux): Ubuntu 20.04
 - How you installed PyTorch (`conda`, `pip`, source): conda
 - Python version: 3.7
 - CUDA/cuDNN version: 10.2
 - GPU models and configuration: Nvidia RTX 2080



cc @albanD @mruberry
Repository: react
Issue: TypeError: Cannot read property 'setExtraStackFrame' of undefined
Labels: Type: Discussion

I try Upgrade to the New JSX Transform in my project.(According to the official tutorial.)
I used production react  in development  environment，I see this error.
but, if used development react in  development  environment， every thing is ok.

React version:
17.0.1
## The current behavior
TypeError: Cannot read property 'setExtraStackFrame' of undefined

## Additional information
  react.production.min.js:22 Uncaught TypeError: Cannot read property 'setExtraStackFrame' of undefined
    at setCurrentlyValidatingElement (react-jsx-runtime.development.js:574)
    at checkPropTypes (react-jsx-runtime.development.js:618)
    at validatePropTypes (react-jsx-runtime.development.js:1072)
    at jsxWithValidation (react-jsx-runtime.development.js:1192)
    at jsxWithValidationDynamic (react-jsx-runtime.development.js:1209)
    at PartPage (PartPage.js:161)
    at Le (react-dom.production.min.js:100)
    at Re (react-dom.production.min.js:113)
    at Pj (react-dom.production.min.js:232)
    at di (react-dom.production.min.js:168)
Repository: vscode
Issue: Progress: cannot cancel
Labels: feature-request,file-explorer

Testing #111250

It does not seem possible to cancel the operations from the explorer, e.g. download (tried download from a remote Codespace).