sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: TypeScript
Issue: redux-orm broken by #43624
Labels: Bug,Fix Available,Rescheduled

The nightly dtslint run for 4/28 fails on redux-orm, with new errors on examples like:

```ts
export default class Model<MClass extends typeof AnyModel = typeof AnyModel, Fields extends ModelFieldMap = any> {
    // ...
    readonly ref: Ref<this>;
    // ...
}
export class AnyModel extends Model {}
export type Ref<M extends AnyModel> = {
    [K in keyof RefFields<M>]: ModelFields<M>[K] extends AnyModel ? IdType<ModelFields<M>[K]> : RefFields<M>[K];
};
```

```
ERROR: 118:23  expect  TypeScript@4.3 compile error: 
Type 'this' does not satisfy the constraint 'AnyModel'.
  Type 'Model<MClass, Fields>' is not assignable to type 'Model<typeof AnyModel, any>'.
    Type 'typeof AnyModel' is not assignable to type 'MClass'.
      'typeof AnyModel' is assignable to the constraint of type 'MClass', but 'MClass' could be instantiated with a different subtype of constraint 'typeof AnyModel'.
```

Almost certainly a result of https://github.com/microsoft/TypeScript/pull/43624, but it could be #42449 or #43835, since they went in on the same day.

If this is an intended result of that PR, can you fix up redux-orm? A naive change to `Ref<M extends Model>` doesn't work.
Repository: pytorch
Issue: Using register_full_backward_hook with target module for intermediate activation
Labels: module: docs,module: autograd,module: nn,triaged

## 🐛 Bug

We have a use-case in Captum in which we need to use a forward hook to extract an intermediate layer activation and then use torch.autograd.grad to compute gradients of this activation with respect to inputs. For some methods such as DeepLift and Guided Backpropagation, we use backward hooks in this step to override the gradients for specific modules, sometimes including the target module for which activations were obtained.

This use-case works with register_backward_hook but now leads to a RuntimeError with register_full_backward_hook if a backward hook is registered on the target module.

## To Reproduce

Minimal reproducing example:

```import torch.nn as nn
from torch import Tensor

class ReLULinearModel(nn.Module):

    def __init__(self, inplace: bool = False) -> None:
        super().__init__()
        self.l1 = nn.Linear(3, 1, bias=False)
        self.l2 = nn.Linear(3, 1, bias=False)
        self.l1.weight = nn.Parameter(torch.tensor([[3.0, 1.0, 0.0]]))  # type: ignore
        self.l2.weight = nn.Parameter(torch.tensor([[2.0, 3.0, 0.0]]))  # type: ignore
        self.relu = nn.ReLU(inplace=inplace)
        self.l3 = nn.Linear(2, 1, bias=False)
        self.l3.weight = nn.Parameter(torch.tensor([[1.0, 1.0]]))  # type: ignore

    def forward(self, x1: Tensor, x2: Tensor, x3: int = 1) -> Tensor:
        return self.l3(self.relu(torch.cat([self.l1(x1), x3 * self.l2(x2)], dim=1)))

inp1 = 2*torch.randn(4,3, requires_grad=True)
inp2 = 2*torch.randn(4,3, requires_grad=True)
model = ReLULinearModel()
out_val = None
def forward_hook(mod, inp, out):
  global out_val
  out_val = out

def backward_hook(mod, grad_in, grad_out):
  print("Executed Backward Hook")
forward_hook = model.relu.register_forward_hook(forward_hook)
backward_hook = model.relu.register_backward_hook(backward_hook)
model.forward(inp1, inp2)

torch.autograd.grad(torch.unbind(out_val[:,0]), (inp1, inp2))
```

With register_backward_hook, this works fine and returns:
```
Executed Backward Hook
(tensor([[0., 0., 0.],
         [3., 1., 0.],
         [0., 0., 0.],
         [0., 0., 0.]]), tensor([[0., 0., 0.],
         [0., 0., 0.],
         [0., 0., 0.],
         [0., 0., 0.]]))
```
When switching to register_full_backward_hook, this results in the following RuntimeError:

```
RuntimeError: Module backward hook for grad_input is called before the grad_output one. This happens because the gradient in your nn.Module flows to the Module's input without passing through the Module's output. Make sure that the output depends on the input and that the loss is computed based on the output.
```

cc @ezyang @albanD @zou3519 @gqchen @pearu @nikitaved @soulitzer @Lezcano @Varal7 @brianjo @mruberry @jbschlosser @NarineK
Repository: rust
Issue: Suggest removing `let` when unused variable shadows another variable
Labels: C-enhancement,A-lints,A-diagnostics,A-resolve,T-compiler,A-control-flow

Inspiration: [this StackOverflow post](https://stackoverflow.com/questions/67296177/rust-book-listing-10-15when-i-use-let-largest-item-the-largest-cant-be)

Given the following code:

```rust
fn largest<T>(list: &[T]) -> &T
    where T: PartialOrd + Copy
{
    let largest = &list[0];

    for item in list.iter() {
        if item > largest {
            let largest = item;
        }
    }

    largest
}
```

The current output is:

```
warning: unused variable: `largest`
 --> src/main.rs:8:17
  |
8 |             let largest = item;
  |                 ^^^^^^^ help: if this is intentional, prefix it with an underscore: `_largest`
  |
  = note: `#[warn(unused_variables)]` on by default
```

<!-- The following is not always necessary. -->
Ideally the output should look like:

```
warning: unused variable: `largest`
 --> src/main.rs:8:17
  |
8 |             let largest = item;
  |                 ^^^^^^^
...
4 |    let largest = &list[0];
  |        ------- note: variable with the same name defined here
...
8 |             let largest = item;
  |             ^^^ help: remove this `let` to assign instead of creating a new variable
  |
  = note: `#[warn(unused_variables)]` on by default
```

Some [discussion on how to implement this](https://rust-lang.zulipchat.com/#narrow/stream/182449-t-compiler.2Fhelp/topic/get.20all.20variables.20in.20scope.20and.20check.20for.20shadowing/near/236494421) on Zulip.
Repository: pytorch
Issue: Segmentation fault when using add_graph from tensorboard in combination with try except in forward pass 
Labels: oncall: visualization

## 🐛 Bug

When using try-except in the forward pass of a network, displaying the computational graph using add_graph from Summary Writer leads to a Segmentation fault. 

## To Reproduce

Steps to reproduce the behavior:

1. Run the provided code yields: 
```python
python test.py
[1]    2536 segmentation fault  python test.py
```


```python 
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter("test")

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.l1 = nn.Linear(1, 1)

    def forward(self, x):
        x = self.l1(x)
        try:
            x.reshape(2, 2, 2)
        except:
            pass

        return x

x = torch.randn(1, 1)
net = Net()
writer.add_graph(net, x)
```

## Expected behavior

I did not expect a Segmentation fault

## Environment

PyTorch version: 1.8.1
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.2 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
Clang version: 10.0.0-4ubuntu1
CMake version: Could not collect

Python version: 3.8 (64-bit runtime)
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==1.19.1
[pip3] torch==1.8.1
[pip3] torchaudio==0.8.0a0+e4e171a
[pip3] torchvision==0.9.1
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               10.2.89              hfd86e86_1
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2020.2                      256
[conda] mkl-service               2.3.0            py38he904b0f_0
[conda] mkl_fft                   1.3.0            py38h54f3939_0
[conda] mkl_random                1.1.1            py38h0573a6f_0
[conda] numpy                     1.19.1                   pypi_0    pypi
[conda] numpy-base                1.19.2           py38hfa32c7d_0
[conda] pytorch                   1.8.1           py3.8_cuda10.2_cudnn7.6.5_0    pytorch
[conda] torchaudio                0.8.1                      py38    pytorch
[conda] torchvision               0.9.1                py38_cu102    pytorch
Repository: PowerToys
Issue: [Run][Plugin manager] Show error state on missing plugin requirements
Labels: Idea-Enhancement,Run-Plugin Manager

# Description

In plugin managers' spec we defined that plugins should show the error state.

I think a required  tool (here everything) which missing is such a plugin error too.

Problem: The case plugin can't work because of missing requirements isn't covered by plugin manager at the moment.

# Possible implementation
### Suggestion 1
Dependency settings file, which is checked by pm and includes all program files/processes which are needed.

```json
{
"requiredRunningProcesses" : ["everything.exe"],
"requiredExistingApps" : ["C:\\program files\\...\\everithing.exe"]
}

```

### Suggestion 2
The plugin itself checks for dependencies and writes an `ErrorState.json` which is interpret by pm.

```json
{
"errorState" : true,
"message" : "Something failed!",
"disablePlugin" : true
}
```


---

cc: @mykhailopylyp , @crutkas , @enricogior 

_Originally posted by @htcfreek in https://github.com/microsoft/PowerToys/pull/10937#issuecomment-827077047_
Repository: react
Issue: Bug: Impossible to use on async hooks
Labels: Type: Discussion,Component: ESLint Rules

The rule that prevents using async functions in the hook makes sense in most cases, but there are some valid hooks that have async functions.  See useAsyncEffect as an example.  (https://www.npmjs.com/package/use-async-effect)

Since the no async rule is bundled with the 'react-hooks/exhaustive-deps' rule, there is no way to use this with an async hook.

React version: 16, 17

## Steps To Reproduce

1. Create a file that uses useAsyncHook

2. Configure plugin -> 

    "react-hooks/exhaustive-deps": [
      "warn",
      {
        "additionalHooks": "(useAsyncEffect)"
      }
    ]

3. lint - see error

## The current behavior

linter displays:

 warning  Effect callbacks are synchronous to prevent race conditions. Put the async function inside:

useEffect(() => {
  async function fetchData() {
    // You can await here
    const response = await MyAPI.getData(someId);
    // ...
  }
  fetchData();
}, [someId]); // Or [] if effect doesn't need props or state

Learn more about data fetching with Hooks: https://reactjs.org/link/hooks-data-fetching  react-hooks/exhaustive-deps

## The expected behavior

Several options:

1. Detect the proper use of async in the hook (may be tough)
2. Make a "react-hooks/no-async" rule to control this behavior (allows exhaustive-dpes check w/o this warning)
3. Add settings for the "additionalHooks" property to specify hook by hook functionality

I would say that all 3 of this would be great, but #2 to me seems to be a very simple fix.
Repository: pytorch
Issue: MaxPool2D Returns Wrong Shape With Ceil_Mode
Labels: high priority,module: docs,triaged,module: pooling

## 🐛 Bug

The documentation is clear on the output shape given parameters to the function:

https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html

However the behavior does not match the documentation.

## To Reproduce

Run
```
import torch

out = torch.nn.MaxPool2d(kernel_size=[1, 1], dilation=[1, 1], stride=[2, 2], ceil_mode=True)(
            torch.rand([1, 3, 32, 32]).float()
        )

print(out.shape)
```

>>  torch.Size([1, 3, 16, 16])

## Expected behavior

By my calculation the result should be torch.Size([1, 3, 17, 17]) based on the documentation. 

For example for H_out, plug in padding[0] = 0, dilation=1, kernel_size[0] = 1, stride[0] = 2.

## Environment

Collecting environment information...
PyTorch version: 1.8.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 11.2.3 (arm64)
GCC version: Could not collect
Clang version: 11.1.0
CMake version: version 3.19.7

Python version: 3.8 (64-bit runtime)
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==1.20.1
[pip3] torch==1.8.0
[pip3] torchvision==0.2.2
[conda] numpy                     1.20.1           py38h9e6c65a_0    conda-forge
[conda] pytorch                   1.8.0           cpu_py38hc43b888_0    conda-forge
[conda] torchvision               0.2.2                      py_3    pytorch


cc @ezyang @gchanan @zou3519 @bdhirsh @jbschlosser @anjali411 @brianjo @mruberry @heitorschueroff
Repository: flutter
Issue: Investigate missing stdout/stderr on failed Engine unit tests on Windows
Labels: team,engine,P2,team-engine,triaged-engine

For example in the build here https://ci.chromium.org/ui/p/flutter/builders/prod/Windows%20Host%20Engine/9731/overview, the logs end with:
```
[ RUN      ] ImageDecoderFixtureTest.CanResizeWithoutDecode

Traceback (most recent call last):

  File "C:\b\s\w\ir\cache\builder\src\flutter\testing\run_tests.py", line 552, in <module>
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    sys.exit(main())
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
  File "C:\b\s\w\ir\cache\builder\src\flutter\testing\run_tests.py", line 522, in main
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    RunCCTests(build_dir, engine_filter)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
  File "C:\b\s\w\ir\cache\builder\src\flutter\testing\run_tests.py", line 170, in RunCCTests

    RunEngineExecutable(build_dir, 'ui_unittests', filter, shuffle_flags + ['--timeout=90'])

  File "C:\b\s\w\ir\cache\builder\src\flutter\testing\run_tests.py", line 115, in RunEngineExecutable
Failed Command:
    RunCmd(test_command, cwd=cwd, forbidden_output=forbidden_output)

  File "C:\b\s\w\ir\cache\builder\src\flutter\testing\run_tests.py", line 60, in RunCmd
C:\b\s\w\ir\cache\builder\src\out\host_debug\ui_unittests.exe --gtest_shuffle --gtest_repeat=2 --timeout=90
    raise Exception('Command "%s" exited with code %d.' % (command_string, process.returncode))

Exception: Command "C:\b\s\w\ir\cache\builder\src\out\host_debug\ui_unittests.exe --gtest_shuffle --gtest_repeat=2 --timeout=90" exited with code -1073741819.
Exit Code: -1073741819
```

/cc @chinmaygarde @jason-simmons
Repository: flutter
Issue: iOS embedder corrupts the scene if its Paint() method is not called
Labels: platform-ios,engine,a: platform-views,P2,a: plugins,team-ios,triaged-ios

To fix https://github.com/flutter/flutter/issues/69552 we changed the engine to always Preroll all layers even if they may be culled. (See https://github.com/flutter/engine/pull/22336)

As a result, layers may now be Prerolled even if they won't eventually be painted on a given scene.

The iOS embedder was not expecting this behavior and so it can leave visible pieces of its embedded view on the screen after it is scrolled away. (See https://github.com/flutter/flutter/issues/76097) Note that the Android embedder does not have this same issue, so this is a design issue with the way we manage the iOS embedder.

A work-around was added to always Paint all layers in a sub-tree that contains a PlatformViewLayer. (See https://github.com/flutter/engine/pull/25824)

If the iOS embedder is taught to not make assumptions about Paint() being called, then the work-around mentioned above can be deleted.
Repository: flutter
Issue: CupertinoTextThemeData does not keep defaults for text styles
Labels: framework,f: cupertino,has reproducible steps,P2,found in release: 2.3,team-design,triaged-design

## Steps to Reproduce

1.  Set the CupertinoTexThemeData with a primary color and a navActionTextStyle
2. Open up a Cupertino picker and it seems the text style uses the color from the navActionTextStyle

**Expected results:** 
https://api.flutter.dev/flutter/cupertino/CupertinoTextThemeData/CupertinoTextThemeData.html states that any text theme that is not defined will use its defaults

**Actual results:** 
With my primary color set to a blue and my navactiontextstyle set to a white, the picker text seems to end up being white

<details>
  <summary>Logs</summary>

```
[✓] Flutter (Channel stable, 2.0.5, on Mac OS X 10.15.7 19H1030 darwin-x64, locale en-US)
    • Flutter version 2.0.5 at /Users/rmargolinross/dev/flutter
    • Framework revision adc687823a (12 days ago), 2021-04-16 09:40:20 -0700
    • Engine revision b09f014e96
    • Dart version 2.12.3

[✓] Android toolchain - develop for Android devices (Android SDK version 30.0.2)
    • Android SDK at /Users/rmargolinross/Library/Android/sdk
    • Platform android-30, build-tools 30.0.2
    • Java binary at: /Applications/Android Studio.app/Contents/jre/jdk/Contents/Home/bin/java
    • Java version OpenJDK Runtime Environment (build 1.8.0_242-release-1644-b3-6915495)
    • All Android licenses accepted.

[✓] Xcode - develop for iOS and macOS
    • Xcode at /Applications/Xcode.app/Contents/Developer
    • Xcode 12.4, Build version 12D4e
    • CocoaPods version 1.10.0

[✓] Chrome - develop for the web
    • Chrome at /Applications/Google Chrome.app/Contents/MacOS/Google Chrome

[✓] Android Studio (version 4.1)
    • 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 1.8.0_242-release-1644-b3-6915495)

[✓] VS Code (version 1.55.2)
    • VS Code at /Applications/Visual Studio Code.app/Contents
    • Flutter extension version 3.10.2

[✓] Connected device (3 available)
    • iPhone (mobile)         • 07da1517197bd6155d47de15a47759f492e42c8d • ios            • iOS 14.4.2
    • iPhone 12 mini (mobile) • AD9976D1-BE17-47E6-A37B-CB3E3A9F3FBD     • ios            • com.apple.CoreSimulator.SimRuntime.iOS-14-4 (simulator)
    • Chrome (web)            • chrome                                   • web-javascript • Google Chrome 90.0.4430.93

• No issues found!
```

</details>
Repository: pytorch
Issue: Segmentation fault with ITIMER_REAL
Labels: triaged,module: macos

## 🐛 Bug

PyTorch throws SIGSEGV when running alongside timer on MacOS x86

## To Reproduce

Steps to reproduce the behavior:
1. Run code located [here](https://gist.github.com/sternj/a78fbb41819c64906630a863d3be9035) on Mac x86 

Here is the stack trace from the crashed thread:

```
Thread 6 Crashed:
0   ???                           	0x00007ffeee6d7138 0 + 140732898570552
1   libtorch_cpu.dylib            	0x000000010392478c at::TensorIteratorBase::serial_for_each(c10::function_ref<void (char**, long long const*, long long, long long)>, at::Range) const + 588
2   libtorch_cpu.dylib            	0x000000010390cdf2 std::__1::__function::__func<at::internal::_parallel_run(long long, long long, long long, std::__1::function<void (long long, long long, unsigned long)> const&)::$_1, std::__1::allocator<at::internal::_parallel_run(long long, long long, long long, std::__1::function<void (long long, long long, unsigned long)> const&)::$_1>, void (int, unsigned long)>::operator()(int&&, unsigned long&&) + 114
3   libtorch_cpu.dylib            	0x000000010390b7ca std::__1::__function::__func<at::(anonymous namespace)::_run_with_pool(std::__1::function<void (int, unsigned long)> const&, unsigned long)::$_3, std::__1::allocator<at::(anonymous namespace)::_run_with_pool(std::__1::function<void (int, unsigned long)> const&, unsigned long)::$_3>, void ()>::operator()() + 42
4   libc10.dylib                  	0x00000001020996c9 c10::ThreadPool::main_loop(unsigned long) + 569
5   libc10.dylib                  	0x0000000102099d43 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct> >, c10::ThreadPool::ThreadPool(int, int, std::__1::function<void ()>)::$_0> >(void*) + 67
6   libsystem_pthread.dylib       	0x00007fff5a16a2eb _pthread_body + 126
7   libsystem_pthread.dylib       	0x00007fff5a16d249 _pthread_start + 66
8   libsystem_pthread.dylib       	0x00007fff5a16940d thread_start + 13
```

## Expected behavior
Either the program should run without issue or should pass up the SIGALRM.

## Environment
```
Collecting environment information...
PyTorch version: 1.8.1
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 10.14.6 (x86_64)
GCC version: Could not collect
Clang version: 11.0.0 (clang-1100.0.33.12)
CMake version: Could not collect

Python version: 3.9 (64-bit runtime)
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==1.20.1
[pip3] torch==1.8.1
[conda] Could not collect
```
Repository: create-react-app
Issue: Interpreting exports fields in package.json
Labels: needs triage,issue: bug report

This is related to [this issue](https://github.com/Pomax/bezierjs/issues/149) in the [bezier-js](https://github.com/Pomax/bezierjs) library.

That library has the following targets specified in its `package.json`:
https://github.com/Pomax/bezierjs/blob/cb4fe3e55add7401bdc5ee56ada46abb61b55883/package.json#L6-L11
```js
  "type": "module",
  "exports": {
    "import": "./src/bezier.js",
    "require": "./dist/bezier.cjs"
  },
  "main": "./dist/bezier.cjs",
```

Yet, when an app created with `react-scripts` encounters `import { Bezier } from "bezier-js"`, it will incorrectly link to the commonJs file (`dist/bezier.cjs`), not the ES module`src/bezier.js` as specified there.
This in turn causes an error because the named import `Bezier` does not exist on the cjs bundle. That means every app created with `create-react-app` that has `bezier-js` in its dependency tree will fail the build, as can be seen here: https://github.com/vasturiano/force-graph/issues/182 and here: https://github.com/vasturiano/react-force-graph/issues/282.

Other popular bundlers like `webpack` and `rollup` don't seem to have this issue, and correctly target the ES module file in the import.

I've made a simple sandbox that reproduces the issue with `react-scripts`:
https://codesandbox.io/s/create-react-app-import-bezier-js-5fxc4

I'm not sure if this a known issue or whether it is already resolved in some way. I've searched in the issues and couldn't find anything obvious related.
Repository: TypeScript
Issue: JSDoc @link not resolved across modules
Labels: Bug,Effort: Moderate,Rescheduled

# Bug Report

While verifying https://github.com/microsoft/vscode/issues/119357

### 🔎 Search Terms

- link
- export
- modules

### 🕗 Version & Regression Information

- I was unable to test this on prior versions because it did not previously exist

### ⏯ Playground Link

[Workbench Repro](https://www.typescriptlang.org/dev/bug-workbench/?#code/PTAEAEDMEsBsFMB2BDAtvAXKZA6ALgM4CwAUNKgA4D2ATnqAN6gCyVAJvLKAL6iQ1VUoAOQ5gAI2EBuUqWAAqeaVDzG4WNEQBrFu07dl84LJIgIMBCnRZx+YiXgAPanVABjWMgIFdHLg24gA)

### 💻 Code

```ts repro
// @filename: a.ts
import { Model } from './b';

/**
 * {@link Model}
 */

// @filename: b.ts
export class Model {}
```

### 🙁 Actual behavior

`@link Model` is not clickable

### 🙂 Expected behavior

I expected to be able to click on `@link Model`