WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
Migrated issue, originally created by Anonymous

using 0.6beta1

this used to work in 0.5

from sqlalchemy import create_engine, Table, MetaData, __version__

print __version__

engine = create_engine('oracle://xxx:xxx@XXX', echo=False)

metadata = MetaData(engine)

engine.execute("""create or replace function ADDONE (a number) return number
deterministic
is
begin
return a+1;
end;
""")
try:
engine.execute('drop table BOB')
except:
pass
engine.execute('create table BOB (a number)')
engine.execute('create index fn_idx on BOB (ADDONE(a))')

bob_table = Table(
'bob',
metadata,
autoload=True
)

Gives

Traceback (most recent call last):
File "bob.py", line 28, in <module>
autoload=True
File "/home/geraint/work/sqlalchemy/lib/sqlalchemy/schema.py", line 202, in __new__
table._init(name, metadata, *args, **kw)
File "/home/geraint/work/sqlalchemy/lib/sqlalchemy/schema.py", line 250, in _init
_bind_or_error(metadata).reflecttable(self, include_columns=include_columns)
File "/home/geraint/work/sqlalchemy/lib/sqlalchemy/engine/base.py", line 1450, in reflecttable
self.dialect.reflecttable(conn, table, include_columns)
File "/home/geraint/work/sqlalchemy/lib/sqlalchemy/engine/default.py", line 122, in reflecttable
return insp.reflecttable(table, include_columns)
File "/home/geraint/work/sqlalchemy/lib/sqlalchemy/engine/reflection.py", line 362, in reflecttable
sa_schema.Index(name, *[table.columns[c](table.columns[c) for c in columns],
File "/home/geraint/work/sqlalchemy/lib/sqlalchemy/util.py", line 666, in __getitem__
return self._data[key](key)
KeyError: u'sys_nc00002$'

Issue 2:
Migrated issue, originally created by Anonymous

all is in the title ... :)

http://wiki.postgresql.org/wiki/What%27s_new_in_PostgreSQL_9.3#LATERAL_JOIN
Issue 1:
There are many methods that expect a list or a tensor specifying a shape as argument. It is often the case that I want to pass an argument such as shape=[t1, v2] where t1 is a tensor with shape = TensorShape([]) and v2 is a value. This is currently not possible in many (maybe all) methods.

My current work around is the following.

1- convert v2 to a tensor t2 with tf.convert_to_tensor

2- use t1 = tf.expand_dims(0, t1) and t2 = tf.expand_dims(0, t2) to add a dimension of size 1 to the tensors

3- pass the argument as shape=tf.concat(0, [t1, t2])

Am I missing a simpler way to accomplish that? Is it a good idea to add a feature that allows passing mixture of tensors and values inside a list as argument?

Edit: I just found out that the method tf.pack allows lists with tensors and values as argument. But the method tf.truncated_normal for instance, doesn't allow.

I also realized that I can use tf.pack to replace the steps (1,2,3) above. But it still would be nice if all methods had the same behaviour.

Issue 2:
I'd like to do things such as the following:

>>> tf.convert_to_tensor([tf.constant(1), 2])
>>> tf.slice(..., [0, 1, some_tensor], ...)

Currently both of these raise exceptions. The alternative is to use a combination of tf.expand_dims and tf.concat, which is a bit unwieldy.
Issue 1:
Electron version: 1.21

Operating system: OS X 10.11.2 (15C50)

Electron is called by VMD:

$ vmd --versions
vmd: 1.26.2
electron: 1.2.1
node: 6.1.0
chrome: 51.0.2704.63
v8: 5.1.281.47
openssl: 1.0.2h
zlib: 1.2.8

I'm having trouble reproducing it outside my environment for you. The short version is, Emacs has a VMD preview mode, which ultimately shells out to VMD, which calls Electron like this:

/usr/local/lib/node_modules/vmd/node_modules/electron-prebuilt/dist/Electron.app/Contents/MacOS/Electron /usr/local/lib/node_modules/vmd/main/main.js /tmp/vmd-preview

I'm hoping the stack trace is enough to identify the issue:

crash.txt

Issue 2:
Version: v6.0.0

Platform: OSX: Darwin steph-mbp 15.4.0 Darwin Kernel Version 15.4.0: Fri Feb 26 22:08:05 PST 2016; root:xnu-3248.40.184~3/RELEASE_X86_64 x86_64

Subsystem: GNU Emacs 25.0.93.1 (might affect other clients?)

After upgrading 6.0, I can't run npm in an Emacs subshell because it crashes on any call to fs.access; I'm aware this might be Emacs-specific (works fine in iTerm + ZSH) but in the slim case this is an actual regression I'm attaching the full stacktrace from my system.

node_2016-05-04-104139_Stephanes-MacBook-Pro.crash.txt
Issue 1:
🐛 Bug

When torch.Tensor is divided by zero tensors with data type as int, including int8, int32, int64, the program is terminated forcibly and throw Floating point exception (core dumped) error.

If run the code in jupyter notebook, the kernel would be killed.

Based on my experiments, the bug happens both in torch 1.0 and torch 1.4.

However, the bug would not happen if the dividend is not torch.Tensor.

To Reproduce

torch.ones(3, dtype=torch.int8) / torch.zeros(3, dtype=torch.int8)

Expected behavior

ZeroDivisionError: division by zero, which is the common bahavior in python when the divisor is zero.

or

get tensor(nan). (when the data type is float, such as torch.zeros(3, dtype=torch.float64) / torch.zeros(3, dtype=torch.float64))

or

get a tensor with very large value. (when the dividend is an integer or float, such as 1 / torch.zeros(3, dtype=torch.int8)))

Environment

PyTorch version: 1.0.1.post2

Is debug build: No

CUDA used to build PyTorch: 9.0.176

OS: Ubuntu 16.04.5 LTS

GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609

CMake version: version 3.6.3

Python version: 3.6

Is CUDA available: Yes

CUDA runtime version: Could not collect

GPU models and configuration:

GPU 0: TITAN Xp

GPU 1: TITAN Xp

GPU 2: TITAN Xp

GPU 3: TITAN Xp

Nvidia driver version: 384.145

cuDNN version: Could not collect

Versions of relevant libraries:

[pip] numpy==1.16.0

[pip] numpydoc==0.6.0

[pip] torch==1.0.1.post2

[pip] torchsummary==1.5.1

[pip] torchvision==0.2.1

[conda] blas 1.0 mkl https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free

[conda] cuda90 1.0 h6433d27_0 pytorch

[conda] mkl 2019.1 144 defaults

[conda] mkl-include 2019.1 144 defaults

[conda] mkl-service 1.1.2 py36_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free

[conda] pytorch 0.4.0 py36_cuda9.0.176_cudnn7.1.2_1 [cuda90] https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch

[conda] torch 1.0.1.post2

[conda] torchsummary 1.5.1

[conda] torchvision 0.2.1

[conda] torchvision 0.2.1 py_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch

Additional context

cc @ezyang @gchanan @zou3519

Issue 2:
This seems to be an issue with TH and not pytorch, the same thing happens in lua torch.

import torch
a = torch.IntTensor([0,1])
b = torch.IntTensor([0,1])
print(a.div(b)) # Floating point exception (core dumped)

Another unrelated weirdness happens with float tensors when the answer is inf

import torch
a = torch.FloatTensor([1,1])
b = torch.FloatTensor([0,0])
a.div(b)

which works on lua torch, but gives

<repr(<torch.FloatTensor at 0x7f8a012eee18>) failed: RuntimeError: inconsistent tensor size at /tmp/pip-zh0a53-build/torch/lib/TH/generic/THTensorMath.c:90>

in pytorch.

cc @ezyang @gchanan @zou3519
Issue 1:
💻

Would you like to work on a fix?

How are you using Babel?

@babel/eslint-parser

Input code

// "babel-eslint": "^10.0.3",
const asyncFn = () => {
return new Promise((reslove) => {
setTimeout(() => {
reslove({
code: 200,
// The data may be null
data: {
items: [],
total: '',
}
})
})
})
}

const getAsyncResult = async () => {
const res = await asyncFn();
if (res.code === 200 && res.data) {
// The data may be null
// ？with `${variable}` must error
res?.data?.items?.forEach((item) => {
item.name = `${item.otherName || 'test'}`
})
}
}

Configuration file name

babel.config.js

Configuration

No response

Current and expected behavior

I want to use chain operation to avoid errors

Environment

Babel version(s): [^10.0.3]

Node: [v14.7.0]

yarn version: [1.22.15]

OS: [macOS 11.6.1]

Possible solution

No response

Additional context

No response

Issue 2:
It throws error when dynamic import, template literal inline and rule template-curly-spacing as follow:

Tested on:

babel-eslint@10.0.3 (with @babel/parser@7.7.7)

eslint@6.7.2

Sample code:

import(`${'package.json'}`)

.eslintrc.json

{
"parser": "babel-eslint",
"rules": {
"template-curly-spacing": ["error", "never"]
}
}

The problem is coming from @babel/parser@7.7.7, which added ImportExpression.

Originally posted by @yitomok in babel/babel-eslint#799 (comment)

Edits by @JLHwung

If you come across this issue from search engine, see below for the solution:

uninstall babel-eslint

install @babel/eslint-parser

replace babel-eslint by @babel/eslint-parser in your ESLint config

if you use eslint-plugin-babel, do 4.a - 4.c, otherwise skip it

4.a uninstall eslint-plugin-babel

4.b install @babel/eslint-plugin

4.c replace "babel" or "eslint-plugin-babel" to "@babel"

The working ESLint config should look like #10904 (comment)
Issue 1:
Annoying problem where the Pop overs don't detect when they're going to be slightly hidden by the size of the viewport. In Tooltipsy and stuff, it detects the edges and moves the wrapper accordingly.

Issue 2:
I've just seen that since the version 2.1.X typeahead comes with an AJAX option which it actually comes in handy on many projects since I always had to implement my own autocomplete due to the fact that I would need the data to be fetched as typed.

The only thing that I think it would be nice to have is a debounce method on the keyup event. As with each letter typed there will be a query, imagine how much payload that means on the server ( the one that takes the query ), and if I type really fast about 3 to 6 letters, that means 6 queries.

Sometimes you get the second last query result and sometimes ( if you use an external API ) the server might block your API key because of to many queries / second.

I hope that a debounce method with an option to set the interval would be taken into consideration. I could just due it to my local file, but I think that a lot of devs would appreciate this option :)
Issue 1:
It happened somewhere in v0.137.0...v0.138.0

Issue 2:
The Atom appmenu / global menu no longer works with Atom 0.137.0 in Ubuntu 14.10 (with Unity): http://i.imgur.com/1TSYnD5.png

It used to work up until this version and also, Ubuntu 14.10 seems to be the only one affected as I've tested it in Ubuntu 14.04 and it still works there.

Tested with both the Atom deb downloaded from GitHub and with Atom from the WebUpd8 Atom PPA.
Issue 1:
Cross-build binaries under directory test/images,

https://github.com/kubernetes/kubernetes/tree/master/test/images

TODO LIST:

clusterapi-tester

dnsutils

entrypoint-tester

fakegitserver

goproxy

hostexec

iperf

jessie-dnsutils

mount-tester

mount-tester-user

n-way-http

netexec

network-tester

pets

port-forward-tester

porter

resource-consumer

serve_hostname

volumes-tester

@luxas I agree with you that we should get the Makefiles somehow standardized. I would like to do some help.

@AlexMioMio

Issue 2:
TL;DR - Add more release versions to gcr.io/google_containers/ and tag by version

Currently, the kube-apiserver.yaml linked to in the in the 1.1 version of the docs for High Availability (http://kubernetes.io/v1.1/docs/admin/high-availability.html) refers to:

image: gcr.io/google_containers/kube-apiserver:9680e782e08a1a1c94c656190011bd02

This image tag has kube-apiserver version 0.18.0. When I tried to get version 1.1.3 (the version we are running / looking to replace with a containerized version) I realized this was the only image that exists.

~$ curl -k -s -X GET https://gcr.io/v2/google_containers/kube-apiserver/tags/list | jq -r '.tags[]'
9680e782e08a1a1c94c656190011bd02

The main issue here is that there is only a single version available for the apiserver. This is also the case for the kube-controller-manager, kube-scheduler, and podmaster.

There are multiple versions of etcd but there isn't a standardized pattern as 2.0.3 is preceded by a 'v' and the rest are not:

~$ curl -k -s -X GET https://gcr.io/v2/google_containers/etcd/tags/list | jq -r '.tags[]'
2.0.12
2.0.13
2.0.8
2.0.9
2.0.9.1
2.1.0-alpha.0
2.2.1
v2.0.3

A further improvement would be to tag each image with just a version number.
Issue 1:
I've seen this a couple times, e.g.

http://kubekins.dls.corp.google.com:8081/job/kubernetes-pull-build-test-e2e-gce/28103/#

cc @caesarxuchao

Issue 2:
• Failure [12.590 seconds]
Generated release_1_2 clientset
/go/src/k8s.io/kubernetes/_output/dockerized/go/src/k8s.io/kubernetes/test/e2e/generated_clientset.go:154
should create pods, delete pods, watch pods [It]
/go/src/k8s.io/kubernetes/_output/dockerized/go/src/k8s.io/kubernetes/test/e2e/generated_clientset.go:153

Expected
<int>: 0
to equal
<int>: 1

/go/src/k8s.io/kubernetes/_output/dockerized/go/src/k8s.io/kubernetes/test/e2e/generated_clientset.go:102
Issue 1:
See; tensorflow.org/versions/r0.7/tutorials/mnist/beginners/index.html --> Training section. "Math Processing Error" appears in multiple places in page. Used OSX / Safari, Firefox, and Chrome. Screen shot attached.

Issue 2:
The website tries to load,

https://www.tensorflow.org/MathJax/jax/output/HTML-CSS/fonts/STIX/fontdata.js?rev=2.6.1

and fails. Someone needs to fix the path to get the right version of mathjax, and have all the LaTeX symbols show up.

Thanks.
Issue 1:
Groupby by level of MultiIndex with rolling duplicate index level.

df = pd.DataFrame(data=[[1, 1, 10, 20], [1, 2, 30, 40], [1, 3, 50, 60],
[2, 1, 11, 21], [2, 2, 31, 41], [2, 3, 51, 61]],
columns=['id', 'date', 'd1', 'd2'])

df.set_index(['id', 'date'], inplace=True)
print (df)
d1 d2
id date
1 1 10 20
2 30 40
3 50 60
2 1 11 21
2 31 41
3 51 61

Double id level.

df1 = df.d1.groupby(level='id').rolling(window=2).sum()
print(df1)
id id date
1 1 1 NaN
2 40.0
3 80.0
2 2 1 NaN
2 42.0
3 82.0
Name: d1, dtype: float64

Solution with apply works nice.

df2 = df.groupby(level='id').d1.apply(lambda x: x.rolling(window=2).sum())
print(df2)
id date
1 1 NaN
2 40.0
3 80.0
2 1 NaN
2 42.0
3 82.0
Name: d1, dtype: float64

SO question.

print (pd.show_versions())

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: sk_SK
LOCALE: None.None

pandas: 0.19.2
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
None

Issue 2:
I found another oddity while digging through #13966.

Begin with the initial DataFrame in that issue:

df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
'B': np.arange(40)})

Save the grouping:

In [215]: g = df.groupby('A')

Compute the rolling sum:

In [216]: r = g.rolling(4)

In [217]: r.sum()
Out[217]:
A B
A
1 0 NaN NaN
1 NaN NaN
2 NaN NaN
3 4.0 6.0
4 4.0 10.0
5 4.0 14.0
6 4.0 18.0
7 4.0 22.0
8 4.0 26.0
9 4.0 30.0
... ... ...
2 30 8.0 114.0
31 8.0 118.0
3 32 NaN NaN
33 NaN NaN
34 NaN NaN
35 12.0 134.0
36 12.0 138.0
37 12.0 142.0
38 12.0 146.0
39 12.0 150.0

[40 rows x 2 columns]

It maintains the by column (A)! That column should not be in the resulting DataFrame.

It gets weirder if I compute the sum over the entire grouping and then re-do the rolling calculation. Now by column is gone as expected:

In [218]: g.sum()
Out[218]:
B
A
1 190
2 306
3 284

In [219]: r.sum()
Out[219]:
B
A
1 0 NaN
1 NaN
2 NaN
3 6.0
4 10.0
5 14.0
6 18.0
7 22.0
8 26.0
9 30.0
... ...
2 30 114.0
31 118.0
3 32 NaN
33 NaN
34 NaN
35 134.0
36 138.0
37 142.0
38 146.0
39 150.0

[40 rows x 1 columns]

So the grouping summation has some sort of side effect.
Issue 1:
Importing the numpy c-extensions failed.

Original error was: No module named 'numpy.core._multiarray_umath'

python Version:

3.7.0

numpy version

1.18.2

Operating System :Windows

Error message:

Failed to load entry point 'info':

IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!

Importing the numpy c-extensions failed.

Try uninstalling and reinstalling numpy.

If you have already done that, then:

Check that you expected to use Python3.7 from "c:\python37\python_d.exe",

and that you have no directories in your PATH or PYTHONPATH that can

interfere with the Python and numpy version "1.18.2" you're trying to use.

If (1) looks fine, you can open a new issue at

https://github.com/numpy/numpy/issues. Please include details on:

how you installed Python

how you installed numpy

your operating system

whether or not you have multiple versions of Python installed

if you built from source, your compiler versions and ideally a build log

If you're working with a numpy git repository, try git clean -xdf

(removes all files not under version control) and rebuild numpy.

Note: this error has many possible causes, so please don't comment on

an existing issue about this - open a new one instead.

Original error was: No module named 'numpy.core._multiarray_umath'

tried reinstalling numpy. but the problem persist

Issue 2:
I am unable to import numpy (version 1.14.5) when I am using a debug version of Python (python_d.exe). When I attempt to do so I get the following traceback

Python 3.5.4 (v3.5.4:3f56838, Aug 8 2017, 02:20:40) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
Traceback (most recent call last):
File "C:\Python\Python35\lib\site-packages\numpy\core\__init__.py", line 16, in <module>
from . import multiarray
ImportError: cannot import name 'multiarray'

For debugging I often need to step across from Python code into C++ code, but if I have a numpy dependency I cannot do so due to the above behavior.
Issue 1:
Steps to Reproduce

flutter create newapp

...

...

Logs

Issue 2:
Noticed this while investigating #19571

Run this Flutter app:

class Foo { }
void main() { print(new Foo()); }

Add a second class Foo { } declaration, and do a hot reload.

Do another hot reload. The engine will crash inside Dart's ReloadSources.

@a-siva

Stack trace:

#0 dart::Object::SetRaw (this=0x8b51c63c, value=0x50f05) at ../../third_party/dart/runtime/vm/object.h:9169
#1 dart::String::initializeHandle (obj=0x8b51c63c, raw_ptr=0x50f05) at ../../third_party/dart/runtime/vm/object.h:7374
#2 dart::String::Handle (zone=<optimized out>, raw_ptr=0x50f05) at ../../third_party/dart/runtime/vm/object.h:7374
#3 dart::String::Handle (raw_ptr=0x50f05) at ../../third_party/dart/runtime/vm/object.h:7374
#4 dart::IsolateReloadContext::Reload (this=0x89bb6940, force_reload=false, root_script_url=<optimized out>,
packages_url_=0x8b51c398 "/data/user/0/com.example.bug19571/cache/bug_19571XQXBUU/bug_19571/.packages")
at ../../third_party/dart/runtime/vm/isolate_reload.cc:550
#5 0x8c0b0ca6 in dart::Isolate::ReloadSources (this=0x89a4ea00, js=<optimized out>, force_reload=33,
root_script_url=0x1 <error: Cannot access memory at address 0x1>,
packages_url=0x8b51c398 "/data/user/0/com.example.bug19571/cache/bug_19571XQXBUU/bug_19571/.packages",
dont_delete_reload_context=<optimized out>) at ../../third_party/dart/runtime/vm/isolate.cc:1230
#6 0x8c1e1bce in dart::ReloadSources (thread=<optimized out>, js=<optimized out>) at ../../third_party/dart/runtime/vm/service.cc:3053
#7 0x8c1da36a in dart::Service::InvokeMethod (I=0x89a4ea00, msg=..., parameters_are_dart_objects=false)
at ../../third_party/dart/runtime/vm/service.cc:869
#8 0x8c1da92c in dart::Service::HandleIsolateMessage (isolate=0x89a4ea00, msg=...) at ../../third_party/dart/runtime/vm/service.cc:917
#9 0x8c0ae008 in dart::IsolateMessageHandler::HandleMessage (this=<optimized out>, message=<optimized out>)
at ../../third_party/dart/runtime/vm/isolate.cc:565
#10 0x8c0ccd44 in dart::MessageHandler::HandleMessages (this=0x98cb1ca0, ml=<optimized out>, allow_normal_messages=true,
allow_multiple_normal_messages=<optimized out>) at ../../third_party/dart/runtime/vm/message_handler.cc:231
#11 0x8c0cce10 in dart::MessageHandler::HandleNextMessage (this=0x98cb1ca0) at ../../third_party/dart/runtime/vm/message_handler.cc:290
#12 0x8c3032a4 in Dart_HandleMessage () at ../../third_party/dart/runtime/vm/dart_api_impl.cc:1664
#13 0x8bf07b10 in tonic::DartMessageHandler::OnHandleMessage (this=0x89a701e0, dart_state=0x9af78890)
at ../../third_party/tonic/dart_message_handler.cc:114
Issue 1:
When exporting interfaces from a .d.ts file using the es6 module format, using a * symbol will generate an equivalent line in the javascript even if no file is generated by tsc.

test.d.ts:

export interface Test {

}

module_no_asterisk.ts:

export {Test} from "test";

generates an empty file. However:

module_asterisk.ts:

export * from "test";

generates the following.

module_asterisk.js:

export * from "test";

even though no test file is generated due to it being a definition file.

Would it be possible to detect when an export * is nothing but interfaces and not generate any output?

Issue 2:
I have a fileinterfaces.d.ts which defines some interfaces, e.g:

export interface IFoo {}
export interface IBar {}

I want to reexport all interfaces in another module:

export * from "interfaces";

When targeting AMD, compiled code looks like this:

define(["require", "exports", "interfaces"], function (require, exports, interfaces_1) {
function __export(m) {
for (var p in m) if (!exports.hasOwnProperty(p)) exports[p] = m[p];
}
__export(interfaces_1);
});

Module "interfaces" should not be imported at all, because it doesn't contain any JS code. To fix this I have to list all exported interfaces manually:

export { IFoo, IBar } from "interfaces";

It's a problem if the number of interfaces is large.

I use version 1.5.3.
Issue 1:
In native applications you can navigate between adjacent menu items using left and right arrow keys. In Electron this is impossible. Only up/down navigation works to select items from current open menu.

Windows 7, 64-bit

Electron 0.36.2

Issue 2:
I created the Quick Start app and ran it in Windows 8.1 Professional (64-bit) with "electron-prebuilt": "^0.30.4".

Here are some ways in which using Electron menus from the keyboard don't work like Windows native menus:

Pressing Alt by itself should select the first menu from the menu bar. (Electron just flashes the accelerator keys at you.)

Similarly, pressing and releasing Alt then pressing a shortcut key should bring that menu up (Electron requires you to hold Alt while pressing the shortcut key, per item # 1 ).

When you use the keyboard to choose a menu, the first item in the menu should be pre-selected. (Electron holds the menu open, but no item is selected.)

When you have a menu selected, pressing ← or → should take you to the first item in the menu to the left/right, respectively. (Electron ignores the left and right arrow keys.)

When you have a menu selected, pressing ↑ should highlight the last item in the menu. (Electron highlights the first.)