robot_state_publisher

I have some tf problems (TF_OLD_DATA, etc.) and I think it is related to the change that every component has its own robot state publisher. Why do you think it is better to have so many (in my case 7) robot state publishers that are all publishing the same transforms?
When I run the tf_monitor i get this strange output with unknown publisher:
RESULTS: for all Frames
Frames:
Frame: /base_footprint published by unknown_publisher Average Delay: 0.185526 Max Delay: 0.18663
Frame: arm_0_link published by unknown_publisher Average Delay: -0.312836 Max Delay: 0
Frame: arm_1_link published by unknown_publisher Average Delay: 0.188703 Max Delay: 0.194755
Frame: arm_2_link published by unknown_publisher Average Delay: 0.188705 Max Delay: 0.194758
Frame: arm_3_link published by unknown_publisher Average Delay: 0.188706 Max Delay: 0.194759
Frame: arm_4_link published by unknown_publisher Average Delay: 0.188706 Max Delay: 0.19476
Frame: arm_5_link published by unknown_publisher Average Delay: 0.188707 Max Delay: 0.194761
Frame: arm_6_link published by unknown_publisher Average Delay: 0.188707 Max Delay: 0.194762
Frame: arm_7_link published by unknown_publisher Average Delay: 0.188708 Max Delay: 0.194763
Frame: base_laser_front_link published by unknown_publisher Average Delay: -0.312834 Max Delay: 0
Frame: base_laser_rear_link published by unknown_publisher Average Delay: -0.312833 Max Delay: 0
Frame: base_laser_top_link published by unknown_publisher Average Delay: -0.312832 Max Delay: 0
Frame: base_link published by unknown_publisher Average Delay: -0.312834 Max Delay: 0
Frame: bl_caster_r_wheel_link published by unknown_publisher Average Delay: 0.18574 Max Delay: 0.187387
Frame: bl_caster_rotation_link published by unknown_publisher Average Delay: 0.185742 Max Delay: 0.187388
Frame: br_caster_r_wheel_link published by unknown_publisher Average Delay: 0.185743 Max Delay: 0.187389
Frame: br_caster_rotation_link published by unknown_publisher Average Delay: 0.185743 Max Delay: 0.187389
Frame: cam3d_depth_frame published by unknown_publisher Average Delay: -0.312831 Max Delay: 0
Frame: cam3d_depth_optical_frame published by unknown_publisher Average Delay: -0.31283 Max Delay: 0
Frame: cam3d_link published by unknown_publisher Average Delay: -0.31283 Max Delay: 0
Frame: cam3d_rgb_frame published by unknown_publisher Average Delay: -0.312829 Max Delay: 0
Frame: cam3d_rgb_optical_frame published by unknown_publisher Average Delay: -0.312828 Max Delay: 0
Frame: fl_caster_r_wheel_link published by unknown_publisher Average Delay: 0.185744 Max Delay: 0.18739
Frame: fl_caster_rotation_link published by unknown_publisher Average Delay: 0.185745 Max Delay: 0.187391
Frame: fr_caster_r_wheel_link published by unknown_publisher Average Delay: 0.185746 Max Delay: 0.187391
Frame: fr_caster_rotation_link published by unknown_publisher Average Delay: 0.185746 Max Delay: 0.187392
Frame: head_axis_link published by unknown_publisher Average Delay: 0.185114 Max Delay: 0.185585
Frame: head_base_link published by unknown_publisher Average Delay: -0.312828 Max Delay: 0
Frame: head_cam_reference_link published by unknown_publisher Average Delay: -0.312827 Max Delay: 0
Frame: head_cover_link published by unknown_publisher Average Delay: -0.312826 Max Delay: 0
Frame: lookat_base_link published by unknown_publisher Average Delay: -0.312825 Max Delay: 0
Frame: pg70_finger_left_link published by unknown_publisher Average Delay: 0.18832 Max Delay: 0.188673
Frame: pg70_finger_right_link published by unknown_publisher Average Delay: 0.188322 Max Delay: 0.188675
Frame: pg70_gripper_link published by unknown_publisher Average Delay: -0.312824 Max Delay: 0
Frame: torso_base_link published by unknown_publisher Average Delay: -0.312824 Max Delay: 0
Frame: torso_lower_neck_tilt_link published by unknown_publisher Average Delay: 0.188553 Max Delay: 0.189466
Frame: torso_pan_link published by unknown_publisher Average Delay: 0.188555 Max Delay: 0.189468
Frame: torso_upper_neck_tilt_link published by unknown_publisher Average Delay: 0.188556 Max Delay: 0.18947
Frame: tray_base_link published by unknown_publisher Average Delay: -0.312823 Max Delay: 0
Frame: tray_left_link published by unknown_publisher Average Delay: -0.312822 Max Delay: 0
Frame: tray_link published by unknown_publisher Average Delay: 0.188313 Max Delay: 0.188757
Frame: tray_phidgets_1_link published by unknown_publisher Average Delay: -0.312821 Max Delay: 0
Frame: tray_phidgets_2_link published by unknown_publisher Average Delay: -0.312821 Max Delay: 0
Frame: tray_phidgets_3_link published by unknown_publisher Average Delay: -0.31282 Max Delay: 0
Frame: tray_phidgets_4_link published by unknown_publisher Average Delay: -0.312819 Max Delay: 0
Frame: tray_right_link published by unknown_publisher Average Delay: -0.312819 Max Delay: 0
All Broadcasters:
Node: unknown_publisher 571.44 Hz, Average Delay: -0.0757929 Max Delay: 0.19476
And roswtf:
ERROR TF multiple authority contention:

node [/tray/robot_state_publisher] publishing transform [arm_0_link] with parent [base_link] already published by node [/torso/robot_state_publisher]
node [/tray/robot_state_publisher] publishing transform [base_link] with parent [base_footprint] already published by node [/torso/robot_state_publisher]
node [/tray/robot_state_publisher] publishing transform [base_laser_front_link] with parent [base_link] already published by node [/torso/robot_state_publisher]
node [/tray/robot_state_publisher] publishing transform [base_laser_rear_link] with parent [base_link] already published by node [/torso/robot_state_publisher]
node [/tray/robot_state_publisher] publishing transform [base_laser_top_link] with parent [base_link] already published by node [/torso/robot_state_publisher]
node [/tray/robot_state_publisher] publishing transform [cam3d_depth_frame] with parent [cam3d_link] already published by node [/torso/robot_state_publisher]
node [/tray/robot_state_publisher] publishing transform [cam3d_depth_optical_frame] with parent [cam3d_depth_frame] already published by node [/torso/robot_state_publisher]

and so on...

we identified that too. The reason is that the tf tree for Care-O-bot is quite big and with multiple robot_state_publishers distributed over several pcs this will cause a lot of network traffic.
We've worked out a solution using the joint_state_publisher package and use that to aggregate all joint_states topics into a global /joint_states and then only use one robot_state_publisher again. This will be merged to ipa320/cob_robots soon.

see https://github.com/ipa-fmw/cob_robots/commit/6dfb10885bfffde8f72c487827948a3dc1265a2a
and https://github.com/ipa-fmw/cob_robots/commit/8ccfe5f351756431d28130ef27727e07d5b0ab86

included in https://github.com/ipa320/cob_robots/pull/296


Sonar 100%

Codecov Report

Merging #7 (6cee655) into main (2674579) will not change coverage.
The diff coverage is 100.00%.

@@            Coverage Diff            @@
##              main        #7   +/-   ##
=========================================
  Coverage   100.00%   100.00%
=========================================
  Files           24        24
  Lines         1596      1600    +4
=========================================
+ Hits          1596      1600    +4

Impacted Files
Coverage Δ

fastgraphql/application.py
100.00% <100.00%> (ø)

fastgraphql/exceptions.py
100.00% <100.00%> (ø)

fastgraphql/factory.py
100.00% <100.00%> (ø)

fastgraphql/types.py
100.00% <100.00%> (ø)

...ts/fastgraphql/ariadne/test_fastapi_integration.py
100.00% <100.00%> (ø)

tests/fastgraphql/ariadne/test_input_resolver.py
100.00% <100.00%> (ø)

tests/fastgraphql/test_input_rendering.py
100.00% <100.00%> (ø)

tests/fastgraphql/test_type_rendering.py
100.00% <100.00%> (ø)

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


Protocol issue during IPCP

Describe the bug
Hello,
First, a big thanks for this great project and the achievements with this project!
I'm encountering a problem in the IPCP negotiation phase against a LANCOM router with PPPoE server.
After the authentication phase, the LANCOM sends the PPP IPCP Configuration Request message (Wireshark packet 13). This contains the router’s IP address with the explicit value of IP address = '<IP_ADDRESS>' in my test. This is the usual thing. However, the LANCOM also sends in this packet additional options, like DNS Server and NBNS server which are set to <IP_ADDRESS>. I guess the BNG blaster should reject these additional options and accept only the IP address of the BNG.
After this, there seems to be multiple retransmits in negotiating the IP address and DNS servers of BNG blaster, like packet 15 and 16 in the wireshark capture
This is currently only my theory, as these additional options seem to be the only difference against other implementations. This behavior is currently not configurable on the LANCOM.
The history of this LANCOM behavior is that it came from early ISDN days, where two routers could do mutual authentication and exchanging options in both directions in PPP.
Best regards,
John
Edit: Sorry, it seems that I can't attach Wireshark File types... It can be supplied.
To Reproduce
BNG blaster does PPPoE via single tagged VLAN against LANCOM Router. Single PPPoE session.

I‘m not available until end of next week, but your analysis sounds correct
and I will work on a fix as soon as I‘m back to work.
JohnMiller7 @.***> schrieb am Fr. 16. Aug. 2024 um
13:54:

Describe the bug
Hello,
First, a big thanks for this great project and the achievements with this
project!
I'm encountering a problem in the IPCP negotiation phase against a LANCOM
router with PPPoE server.
After the authentication phase, the LANCOM sends the PPP IPCP
Configuration Request message (Wireshark packet 13). This contains the
router’s IP address with the explicit value of IP address = '<IP_ADDRESS>'
in my test. This is the usual thing. However, the LANCOM also sends in this
packet additional options, like DNS Server and NBNS server which are set to
<IP_ADDRESS>. I guess the BNG blaster should reject these additional options and
accept only the IP address of the BNG.
After this, there seems to be multiple retransmits in negotiating the IP
address and DNS servers of BNG blaster, like packet 15 and 16 in the
wireshark capture
This is currently only my theory, as these additional options seem to be
the only difference against other implementations. This behavior is
currently not configurable on the LANCOM.
The history of this LANCOM behavior is that it came from early ISDN days,
where two routers could do mutual authentication and exchanging options in
both directions in PPP.
Best regards,
John
Edit: Sorry, it seems that I can't attach Wireshark File types... It can
be supplied.
To Reproduce
BNG blaster does PPPoE via single tagged VLAN against LANCOM Router.
Single PPPoE session.
Capture.png (view on web)
https://github.com/user-attachments/assets/a14955ae-2438-4d48-b790-18adf1ab6250
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Reply to this email directly, view it on GitHub
https://github.com/rtbrick/bngblaster/issues/273, or unsubscribe
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Sounds good. Thanks for the quick reply!

The issue should be fixed in latest dev branch. Could you please verify!

Thank you. I can confirm the fix is working.


Missing file test.cpp

I want to use Nana as a git submodule for a project.
My project structure is as follows

source/ ->  project source files
external/ -> git submodules, nana is located here
CMakeLists.txt

After attempting an out of source build with cmake, I get the following error

CMake Error at external/nana/CMakeLists.txt:146 (add_executable):
Cannot find source file:
test.cpp

which seems to be caused by the cmake line

add_subdirectory(external/nana)

OS: Ubuntu 14.04 LTS, 64 Bit
CMake: 3.2.2
Nana branch: Master
CMakeLists.txt

Hi, you can remove lines 146-148, thanks for your feedback.

No, they can't. That's the whole point of git submodule. You should remove the reference to the nonexistant file test.cpp from CMakeLists.txt.


[p5.js-es] Translate Events, 3D and Constants references

This PR includes the following translations:

All in Events
Second half of 3D
Constants TWO_PI, DEGREES and RADIANS

Hola @holomorfo , este PR esta listo para ser revisado

Hello @Qianqianye @limzykenneth ! This PR is ready!
Thank you!

Hi @Guirdo @holomorfo, could you fix the PR so it could pass the check? Thanks!

@Qianqianye Done!

Looks great, thanks!


couchbackup not working with shallow mode

Please read these guidelines before opening an issue.

Bug Description
After a recent update to node.js 12.8.2 (from node.js 12.16.1) our scripts that call couchbackup are seeing errors.  I have narrowed down the issue to shallow mode, because running the same command without "--mode shallow" works.
1. Steps to reproduce and the simplest code sample possible to demonstrate the issue
From linux command line, run a shallow couchbackup:
/usr/local/node/bin/couchbackup --url https://user:password@hostname/ --buffer-size 5000 --mode shallow --db ngapi_permissions_pre
2. What you expect to happen
couchbackup completed.  You can see what happened when omitting the "--mode shallow" from the command:
/usr/local/node/bin/couchbackup --url<EMAIL_ADDRESS>--buffer-size 5000 --db ngapi_permissions_pre
================================================================================
Performing backup on<EMAIL_ADDRESS>using configuration:
{
  "bufferSize": 5000,
  "log": "/tmp/tmp-32337S4S3JRNI4vnL",
  "mode": "full",
  "parallelism": 5,
  "requestTimeout": 120000,
  "resume": false
}
================================================================================
  couchbackup:backup Fetching all database changes... +0ms
  couchbackup:backup Total batches received: 1 +223ms

3. What actually happened
With shallow mode option enabled, the following error occurred:
/usr/local/node/bin/couchbackup --url<EMAIL_ADDRESS>--buffer-size 5000 --db ngapi_permissions_pre --mode shallow
================================================================================
Performing backup on<EMAIL_ADDRESS>using configuration:
{
  "bufferSize": 5000,
  "mode": "shallow",
  "requestTimeout": 120000
}
================================================================================
  couchbackup:backup Fetching all database changes... +0ms
events.js:292
      throw er; // Unhandled 'error' event
      ^

Error: Invalid parameters
    at Object.<anonymous> (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/nano/lib/nano.js:20:34)
    at Module._compile (internal/modules/cjs/loader.js:1138:30)
    at Object.Module._extensions..js (internal/modules/cjs/loader.js:1158:10)
    at Module.load (internal/modules/cjs/loader.js:986:32)
    at Function.Module._load (internal/modules/cjs/loader.js:879:14)
    at Module.require (internal/modules/cjs/loader.js:1026:19)
    at require (internal/modules/cjs/helpers.js:72:18)
    at Object.<anonymous> (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/@cloudant/cloudant/cloudant.js:18:12)
    at Module._compile (internal/modules/cjs/loader.js:1138:30)
    at Object.Module._extensions..js (internal/modules/cjs/loader.js:1158:10)
Emitted 'error' event at:
    at /usr/local/node/lib/node_modules/@cloudant/couchbackup/includes/shallowbackup.js:42:14
    at callbackOrRejectError (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/nano/lib/nano.js:47:12)
    at Object.fetchDocs [as fetch] (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/nano/lib/nano.js:649:16)
    at /usr/local/node/lib/node_modules/@cloudant/couchbackup/includes/shallowbackup.js:39:10
    at check (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/async/dist/async.js:2116:13)
    at doWhilst (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/async/dist/async.js:2119:16)
    at awaitable (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/async/dist/async.js:208:32)
    at Object.doUntil (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/async/dist/async.js:2147:16)
    at module.exports (/usr/local/node/lib/node_modules/@cloudant/couchbackup/includes/shallowbackup.js:28:9)
    at /usr/local/node/lib/node_modules/@cloudant/couchbackup/app.js:251:26

Environment details
couchbackup npm package version:   2.5.2
node.js version:   v12.18.2
OS:  RHEL 7.8

Did some additional testing and went back to node.js 12.16.1 and couchbackup 2.5.2.  Still failing as shown above.
Downgraded the couchbackup to 2.4.2, then 2.4.1 and 2.4.0  all with the following results:
/usr/local/node/bin/couchbackup --url<EMAIL_ADDRESS>--buffer-size 5000 --db ngapi_permissions_pre --mode shallow
================================================================================
Performing backup on<EMAIL_ADDRESS>using configuration:
{
  "bufferSize": 5000,
  "mode": "shallow",
  "requestTimeout": 120000
}
================================================================================
  couchbackup:backup Fetching all database changes... +0ms
ERROR: Invalid parameters

I tried going back further to 2.3.1 and 2.3.0 of couchbackup and both of those worked correctly:
/usr/local/node/bin/couchbackup --url<EMAIL_ADDRESS>--buffer-size 5000 --db ngapi_permissions_pre --mode shallow > test.out
================================================================================
Performing backup on<EMAIL_ADDRESS>using configuration:
{
  "bufferSize": 5000,
  "log": "/tmp/tmp-30602clI505USoUeB.tmp",
  "mode": "shallow",
  "parallelism": 5
}
================================================================================
  couchbackup:backup Fetching all database changes... +0ms
  couchbackup:backup Written batch ID: 0 Total document revisions written: 386 Time: 0.142 +399ms
  couchbackup:backup Finished - Total document revisions written: 386 +1ms

It's possible that during the node.js upgrade our automated processes updated the couchbackup plugin as well.  I can't say 100% sure though what version was installed prior to the node.js upgrade.

One last test, just to cover all the bases.   Using node.js 12.18.2 and couchbackup 2.3.1 also works.  So it was not the node.js version after all, but the upgrade of node.js that also updated the couchbackup version at the same time.
Output from the couchbackup command on node.js 12.18.2 and couckbackup 2.3.1:
 /usr/local/node/bin/couchbackup --url<EMAIL_ADDRESS>--buffer-size 5000 --db ngapi_permissions_pre --mode shallow > test.out
================================================================================
Performing backup on<EMAIL_ADDRESS>using configuration:
{
  "bufferSize": 5000,
  "log": "/tmp/tmp-15845k7OctlX2Z5is.tmp",
  "mode": "shallow",
  "parallelism": 5
}
================================================================================
  couchbackup:backup Fetching all database changes... +0ms
  couchbackup:backup Written batch ID: 0 Total document revisions written: 386 Time: 0.149 +380ms
  couchbackup:backup Finished - Total document revisions written: 386 +1ms

Hi @wyattmw ,
Thank you for opening this ticket!
Looking at your last comment, it's like the issue is resolved now.
Is that correct?

It is resolved if I got back to v2.3.1 of the couchbackup npm.   However, it is still broken on every stable version from 2.4.0 onwards.

@wyattmw please note we revoked the apikey you leaked in this ticket, we have notified the cluster admins as well.

It is resolved if I got back to v2.3.1 of the couchbackup npm. However, it is still broken on every stable version from 2.4.0 onwards.

For any broken environment, can you please include logs with debug (DEBUG=couchbackup environment variable) enabled so that we get more information?

I put the DEBUG=couchbackup environment variable but didn't get any additional information:
[root@s11129 ~]# echo $DEBUG
couchbackup

[root@s11129 ~]# /usr/local/node/bin/couchbackup --url<EMAIL_ADDRESS>--buffer-size 5000 --db ngapi_permissions_pre --mode shallow
================================================================================
Performing backup on<EMAIL_ADDRESS>using configuration:
{
  "bufferSize": 5000,
  "mode": "shallow",
  "requestTimeout": 120000
}
================================================================================
  couchbackup:backup Fetching all database changes... +0ms
events.js:292
      throw er; // Unhandled 'error' event
      ^

Error: Invalid parameters
    at Object.<anonymous> (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/nano/lib/nano.js:20:34)
    at Module._compile (internal/modules/cjs/loader.js:1138:30)
    at Object.Module._extensions..js (internal/modules/cjs/loader.js:1158:10)
    at Module.load (internal/modules/cjs/loader.js:986:32)
    at Function.Module._load (internal/modules/cjs/loader.js:879:14)
    at Module.require (internal/modules/cjs/loader.js:1026:19)
    at require (internal/modules/cjs/helpers.js:72:18)
    at Object.<anonymous> (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/@cloudant/cloudant/cloudant.js:18:12)
    at Module._compile (internal/modules/cjs/loader.js:1138:30)
    at Object.Module._extensions..js (internal/modules/cjs/loader.js:1158:10)
Emitted 'error' event at:
    at /usr/local/node/lib/node_modules/@cloudant/couchbackup/includes/shallowbackup.js:42:14
    at callbackOrRejectError (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/nano/lib/nano.js:47:12)
    at Object.fetchDocs [as fetch] (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/nano/lib/nano.js:649:16)
    at /usr/local/node/lib/node_modules/@cloudant/couchbackup/includes/shallowbackup.js:39:10
    at check (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/async/dist/async.js:2116:13)
    at doWhilst (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/async/dist/async.js:2119:16)
    at awaitable (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/async/dist/async.js:208:32)
    at Object.doUntil (/usr/local/node/lib/node_modules/@cloudant/couchbackup/node_modules/async/dist/async.js:2147:16)
    at module.exports (/usr/local/node/lib/node_modules/@cloudant/couchbackup/includes/shallowbackup.js:28:9)
    at /usr/local/node/lib/node_modules/@cloudant/couchbackup/app.js:251:26

The debug mode really doesn't seem to provide a lot of extra information right now. Just to see if we can do anything, what does the log file look like? Can you please share an anonymized excerpt? Also, what happens if you try a lower (maybe much lower) bufferSize?

I think this is caused by a behaviour change upstream in Nano. It seems that 8.2.2 added this additional (IMO invalid) check that the POST to _all_docs includes a body with a keys element. The server doesn't reject those requests so I don't see why Nano should.

I tried with a smaller buffer count (100), but there are only 386 docs in the database.  Failed the same way.  Buffer size doesn't seem like it would be an issue since the same command works with --mode full and the buffer size set to either 100 or 5000.
Seems to me like an issue checking the parameters (and only --mode shallow?) as it never gets to the point where it looks like it's pulling data from the db.

I tried with a smaller buffer count (100), but there are only 386 docs in the database. Failed the same way. Buffer size doesn't seem like it would be an issue since the same command works with --mode full and the buffer size set to either 100 or 5000.
Seems to me like an issue checking the parameters (and only --mode shallow?) as it never gets to the point where it looks like it's pulling data from the db.

Hi @wyattmw ,
If you are able, can you please try using master (with the recent change #340 of @ricellis )?
That should resolve the issue you're currently facing.

We're doing some long running regression buckets, we'll release the fix ASAP.

Version 2.6.0 with the fix is now available.

I tested v2.6.0 and it seems to be working.  Will do some additional testing with our full backup process and if I find anything else I'll open another issue. Thank you all.
`]# /usr/local/node/bin/couchbackup --url<EMAIL_ADDRESS>--buffer-size 100 --db ngapi_permissions_pre --mode shallow > test.out
Performing backup on<EMAIL_ADDRESS>using configuration:
{
"bufferSize": 100,
"mode": "shallow",
"requestTimeout": 120000
}
couchbackup:backup Fetching all database changes... +0ms
couchbackup:backup Written batch ID: 0 Total document revisions written: 100 Time: 0.022 +166ms
couchbackup:backup Written batch ID: 1 Total document revisions written: 200 Time: 0.044 +22ms
couchbackup:backup Written batch ID: 2 Total document revisions written: 300 Time: 0.063 +19ms
couchbackup:backup Written batch ID: 3 Total document revisions written: 387 Time: 0.082 +19ms
couchbackup:backup Finished - Total document revisions written: 387 +0ms`


Refactor extraneous registration logic to support plugins that are registered

This PR makes a change to allow extraneous values to be registered in the case where they were depended on at some point during the application setup process. This is common during testing when you might register a mock that doesn't share all the same dependencies as the real implementation. This also could be useful when sharing code.

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.


[WebAPI] TC for DynamicsCompressor.reduction for Chromium 52

Impacted tests(approved): new 1, update 1, delete 0
Unit test platform: Crosswalk for Android 22.52.557.0
Unit test result summary: pass 2, fail 0, block 0
https://crosswalk-project.org/jira/browse/CTS-1851

@Honry PTAK, Thanks a lot!

@Honry I have modified the code, ptal, thanks!

LGTM


Realization of String.prototype.split is inconsistent with ecma-262

Hello, executing following code:
let arg_0 = /(.)\1/i;
let handler_0 = {get:function (oTarget, sKey) {
    print(sKey.toString());
    return Reflect.get(oTarget, sKey);
}};
var p_0 = new Proxy(arg_0, handler_0);
(0 + "\u1234\0").split(p_0);

In ecma-262,  seperator's Symbol.split should be got firstly. However, ch get Symbol.toPrimitive firstly.
Output of ch:
Symbol(Symbol.toPrimitive)
toString
TypeError: RegExp.prototype.toString: 'this' is not a RegExp object

Output of other engines:
Symbol(Symbol.split)
constructor
flags
Symbol(Symbol.match)
source

BT group
2018.09.10

This is because by default we disable support for the ES6 regex symbols. If you enable that support (such as we do for node-chakracore, or via -ES6RegExSymbols in a test or debug build) then this case works as expected.

I feel like we should just take the jump and enable all of the ES6 bits at this point...

This is pretty much why I have JsRuntimeAttributeEnableExperimentalFeatures in my runtime creation for miniSphere (well... that, and also for import()).  I'd be giving up too much spec compliance to leave it off.

Hosts can get more fine-grained control the same way that ch.exe does with the HostConfigFlags/TestHooks infrastructure, I believe. If you wanted to be more precise about what you enabled.

Ah yes, I forgot that they required test builds.

Consolidating as part of #6390


module generator does not create the same variable between EditResponse.php and edit.blade.php

in EditResponse.php module generator always created variable end with "s" or "es" (according to table name created in the module generator).
but in edit.blade.php variable end without "s" or "es".
when i check at the faqs, it seems module generator create wrong variable name in EditResponse.php, It should be without the "s" or "es" suffix.

Thanks, for pointing that out, I will see if that's actually the issue and fix it. Thanks again @hijaumuda

This bug is fixed in develop branch and will be released soon.


Is bincode deterministic?

Will the serialized data for two structs of the same type with the same values always be the same?

Assuming that the Serialize implementation on the structs is also deterministic (if its not I would be very worried) then yes.


Scale effect on github button - dashboard

Great work, please get the task assigned before generate the PR. Thank you.


Test Issue

Issue needs to be addressed asap!

Issue is being addressed!

Issue is being addressed!


Atlantis plan

Ran Plan for dir: . workspace: default
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+ create

Terraform will perform the following actions:

  # google_compute_instance.main will be created
+ resource "google_compute_instance" "main" {
      + allow_stopping_for_update = true
      + can_ip_forward            = false
      + cpu_platform              = (known after apply)
      + current_status            = (known after apply)
      + deletion_protection       = false
      + guest_accelerator         = (known after apply)
      + id                        = (known after apply)
      + instance_id               = (known after apply)
      + label_fingerprint         = (known after apply)
      + machine_type              = "f1-micro"
      + metadata_fingerprint      = (known after apply)
      + min_cpu_platform          = (known after apply)
      + name                      = "test-instance-atlantis"
      + project                   = (known after apply)
      + self_link                 = (known after apply)
      + tags_fingerprint          = (known after apply)
      + zone                      = "asia-southeast2-a"

      + boot_disk {
          + auto_delete                = true
          + device_name                = (known after apply)
          + disk_encryption_key_sha256 = (known after apply)
          + kms_key_self_link          = (known after apply)
          + mode                       = "READ_WRITE"
          + source                     = (known after apply)

          + initialize_params {
              + image  = "centos-cloud/centos-7-v20200309"
              + labels = (known after apply)
              + size   = (known after apply)
              + type   = (known after apply)
            }
        }

      + confidential_instance_config {
          + enable_confidential_compute = (known after apply)
        }

      + network_interface {
          + name               = (known after apply)
          + network            = (known after apply)
          + network_ip         = (known after apply)
          + subnetwork         = "default"
          + subnetwork_project = (known after apply)

          + access_config {
              + nat_ip       = (known after apply)
              + network_tier = (known after apply)
            }
        }

      + scheduling {
          + automatic_restart   = (known after apply)
          + min_node_cpus       = (known after apply)
          + on_host_maintenance = (known after apply)
          + preemptible         = (known after apply)

          + node_affinities {
              + key      = (known after apply)
              + operator = (known after apply)
              + values   = (known after apply)
            }
        }

      + service_account {
          + email  = (known after apply)
          + scopes = [
              + "https://www.googleapis.com/auth/devstorage.read_write",
            ]
        }
    }

Plan: 1 to add, 0 to change, 0 to destroy.

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dir: . workspace: default


List of CSRs

The list of CSRs is currently defined here: https://github.com/CTSRD-CHERI/QuickCheckVEngine/blob/9fc7ec5c2790f8074dab6c77a0b2cf28f2543ced/src/RISCV/RV_CSRs.hs#L130
I found two issues with that list and its usage:

It is out of date and needs to be updated
The CSRs are used independently of what the arch verification string is (https://github.com/CTSRD-CHERI/QuickCheckVEngine/blob/9fc7ec5c2790f8074dab6c77a0b2cf28f2543ced/src/QuickCheckVEngine/Main.hs#L281)

As I understand the code it will return floating point CSRs even if the arch verification string does not specify "F" or "D".

The CSRs are used independently of what the arch verification string is

Ah! I reckon the way to tackle that is probably to turn csrs_map :: [(CSRIdx, CSRName)]   into csrs_map :: [(CSRIdx, CSRName, String)]  where the last String is an archstring subset that needs to be present, and push the test up to, I wanna say, this csr function which would now expect the archstring as an extra argument... ? What do you guys think @francislaus @PeterRugg ?

@gameboo Yes, we need to somehow tag the crsrs_map with the information to which extension they belong. Your way might be the most straightforward one. I briefly thought about a function that would maps some a range of indexes to some string, but that seemingly does not provide any advantage and is far more complicated...
Can we make it part of the filter function? As in it does not only filter the ones the user does not want, but also the ones that are implicitly not allowed by the arch string?

There's a few things that make this annoying.
One is that you may want to test the CSR behaviour even if you don't support the extension, i.e. check you get the correct illegal instruction exception: in the past we've tried to split out the set of tested features from the set of supported features, but it's possible this isn't actually very important to anyone and we should just drop it.
Another is that the CSRs are almost all WARL or implementation-defined in some way. Even if you do support the extension, you likely still expect divergence between implementations in all but the simplest cases.
That said, something like what @gameboo said sounds sensible, and also applies to instructions themselves. There are two cases:

A template asked for a specific instruction or CSR, e.g. a template to setup page tables writes to satp: this should cause a runtime error or skipped test if the CSR dependencies aren't met.
A template asked for a random CSR or random instruction: the distribution should never output something where the dependencies aren't met, and should give an error if there are no available options.

Hope that makes sense to everyone?


Remove support for loading state in secondary button style

The usage rules correctly state that it should only be available to primary buttons but our component currently supports the loading indicator in primary and secondary. 😬
https://github.com/thumbtack/thumbprint/blob/171bd7d8ce1f9b86a190d256addeb39f987d4f54/packages/thumbprint-react/components/UIAction/themed.jsx#L12-L15

The Solution Center is currently using the "caution" theme with isLoading:

I assume this would be a breaking change in this case.

Good call. A few days ago we decided against making this change but I had forgotten to close the issue. Will close it now.

I'm not sure I understand where this landed.  Can we use the blue loader dots for a button with a caution theme?

Based on Figma it seems like we should be supporting it:

(But I've confirmed that we don't currently support it in code.)
cc: @bgando


tsx file error: 'interface' declarations can only be used in TypeScript files.

I am trying to use interface inside a tsx file but it produce the following error
'interface' declarations can only be used in TypeScript files.

Ok I found a weird solution. just renaming the file to .ts and then renaming it back to .tsx solve the issue. maybe the problems is with linter or vscode or something else

You will simply need to change your file to ".tsx", and enable JSX. :)
https://www.typescriptlang.org/docs/handbook/jsx.html

TypeScript: restart TS server, in vscode command pallete solves the problem

TypeScript: restart TS server, in vscode command pallete solves the problem

@BulatDashiev ok thanks that solve the issue.
the problem was with TS server it some times produce wired errors like this

@BulatDashiev ok thanks that solve the issue.
the problem was with TS server it some times produce wired errors like this

TypeScript: restart TS server

Obrigado!

You should rename your file name and change it back it again. For example: .vue rename to .ts and renaming it to back .vue from this aproach problem solved for me.

This started again in 2023 July, in Next project as well.

Please file a new bug with repro steps. Thanks!


centerpoint_pillar pretrained model error

Hello.
when i am using the following command:
python demo/pcd_demo.py --pcd demo/data/nuscenes/1.bin --config configs/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus.py --checkpoint checkpoints/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20210816_064624-0f3299c0.pth --show
and the model is downloaded from the model link in configs/centerpoint
Run the above command and it will get the following results：
load checkpoint from local path: checkpoints/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20210816_064624-0f3299c0.pth
The model and loaded state dict do not match exactly
size mismatch for pts_voxel_encoder.pfn_layers.0.linear.weight: copying a param with shape torch.Size([64, 10]) from checkpoint, the shape in current model is torch.Size([64, 11])
and my env list:
mmcls                         0.23.1
mmcv-full                     1.5.3
mmdeploy                      0.5.0
mmdet                         2.25.0
mmdet3d                       1.0.0rc3
mmsegmentation                0.25.0
Is this a bug, or the model of centerpoint pillar is not updated?

@ZCMax Please check this problem. Maybe it is another problem related to the previous KITTI performance upgrading of PointPillars.

also encountered this problem. What caused this problem? Has it been solved?

@ZCMax Hello! When can you solve this problem? I need the pretrained model.

This checkpoint will be provided in one or two days, and other centerpoint checkpoints will be provided in this week.

@ZCMax Hello! When can you solve this problem? I need the pretrained model.

This checkpoint will be provided in one or two days, and other centerpoint checkpoints will be provided in this week.

@ZCMax Hello! When can you solve this problem? I need the pretrained model.

Sorry for the inconvenience caused by the model checkpoint problem

@ZCMax Hello! When can you solve this problem? I need the pretrained model.

Sorry for the inconvenience caused by the model checkpoint problem

Thank you for your reply. I will wait for the new checkpoint.

@ZCMax Hello! When can you solve this problem? I need the pretrained model.

This checkpoint will be provided in one or two days, and other centerpoint checkpoints will be provided in this week.

@ZCMax Hello! I wonder if the new checkpoints are available. It seems the checkpoint links are not updated in this page

@ZCMax I wonder if the new checkpoints are available?

I've prepared a pretrained model of centerpoint_02pillar_second_secfpn_4x8_cyclic_20e_nus for temporal link:
link: https://pan.baidu.com/s/1u1dS6XPbzhvrMNfquuoeuA?pwd=g5qi
password: g5qi

Is there a non baidu link?

I tried to use pretrained model from the documentation for centerpoint_pillar02_second_secfpn_8xb4-cyclic-20e_nus-3d.py to test Centerpoint with the following command.
python tools/test.py --task lidar_det configs/centerpoint/centerpoint_pillar02_second_secfpn_8xb4-cyclic-20e_nus-3d.py checkpoints/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_030004-9061688e.pth --show --show-dir ./data/centerpoint/show_results
I'm facing weights mismatch issue with the following log....
Loads checkpoint by local backend from path: checkpoints/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_030004-9061688e.pth
The model and loaded state dict do not match exactly
size mismatch for pts_backbone.blocks.0.0.weight: copying a param with shape torch.Size([128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for pts_backbone.blocks.0.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.3.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for pts_backbone.blocks.0.4.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.4.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.4.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.4.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.6.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for pts_backbone.blocks.0.7.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.7.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.7.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.7.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.9.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for pts_backbone.blocks.0.10.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.10.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.10.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.0.10.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for pts_backbone.blocks.1.0.weight: copying a param with shape torch.Size([256, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 64, 3, 3]).
size mismatch for pts_backbone.blocks.1.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for pts_backbone.blocks.1.4.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.4.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.4.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.6.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for pts_backbone.blocks.1.7.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.7.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.7.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.7.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.9.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for pts_backbone.blocks.1.10.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.10.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.10.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.10.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.12.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for pts_backbone.blocks.1.13.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.13.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.13.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.13.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.15.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for pts_backbone.blocks.1.16.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.16.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.16.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_backbone.blocks.1.16.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_neck.deblocks.0.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 64, 2, 2]).
size mismatch for pts_neck.deblocks.0.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_neck.deblocks.0.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_neck.deblocks.0.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_neck.deblocks.0.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_neck.deblocks.1.0.weight: copying a param with shape torch.Size([256, 256, 2, 2]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]).
size mismatch for pts_neck.deblocks.1.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_neck.deblocks.1.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_neck.deblocks.1.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_neck.deblocks.1.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for pts_bbox_head.shared_conv.conv.weight: copying a param with shape torch.Size([64, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 384, 3, 3]).
unexpected key in source state_dict: pts_middle_encoder.conv_input.0.weight, pts_middle_encoder.conv_input.1.weight, pts_middle_encoder.conv_input.1.bias, pts_middle_encoder.conv_input.1.running_mean, pts_middle_encoder.conv_input.1.running_var, pts_middle_encoder.conv_input.1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.0.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.0.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.0.bn2.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.1.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.1.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.bias, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.1.bn2.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer1.2.0.weight, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.weight, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.bias, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.running_var, pts_middle_encoder.encoder_layers.encoder_layer1.2.1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer2.0.conv1.weight, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.weight, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.bias, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.running_mean, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.running_var, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn1.num_batches_tracked, pts_middle_encoder.encoder_layers.encoder_layer2.0.conv2.weight, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn2.weight, pts_middle_encoder.encoder_layers.encoder_layer2.0.bn2.bias, 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missing keys in source state_dict: pts_voxel_encoder.pfn_layers.0.norm.weight, pts_voxel_encoder.pfn_layers.0.norm.bias, pts_voxel_encoder.pfn_layers.0.norm.running_mean, pts_voxel_encoder.pfn_layers.0.norm.running_var, pts_voxel_encoder.pfn_layers.0.linear.weight, pts_backbone.blocks.2.0.weight, pts_backbone.blocks.2.1.weight, pts_backbone.blocks.2.1.bias, pts_backbone.blocks.2.1.running_mean, pts_backbone.blocks.2.1.running_var, pts_backbone.blocks.2.3.weight, pts_backbone.blocks.2.4.weight, pts_backbone.blocks.2.4.bias, pts_backbone.blocks.2.4.running_mean, pts_backbone.blocks.2.4.running_var, pts_backbone.blocks.2.6.weight, pts_backbone.blocks.2.7.weight, pts_backbone.blocks.2.7.bias, pts_backbone.blocks.2.7.running_mean, pts_backbone.blocks.2.7.running_var, pts_backbone.blocks.2.9.weight, pts_backbone.blocks.2.10.weight, pts_backbone.blocks.2.10.bias, pts_backbone.blocks.2.10.running_mean, pts_backbone.blocks.2.10.running_var, pts_backbone.blocks.2.12.weight, pts_backbone.blocks.2.13.weight, pts_backbone.blocks.2.13.bias, pts_backbone.blocks.2.13.running_mean, pts_backbone.blocks.2.13.running_var, pts_backbone.blocks.2.15.weight, pts_backbone.blocks.2.16.weight, pts_backbone.blocks.2.16.bias, pts_backbone.blocks.2.16.running_mean, pts_backbone.blocks.2.16.running_var, pts_neck.deblocks.2.0.weight, pts_neck.deblocks.2.1.weight, pts_neck.deblocks.2.1.bias, pts_neck.deblocks.2.1.running_mean, pts_neck.deblocks.2.1.running_var
@ZCMax , is there any reason for not making the model available in Baidu as official?

same problem,
(openmmlab) D:\Datasets\mmdetection3d-main>python demo/multi_modality_demo.py demo/data/kitti/000008.bin demo/data/kitti/000008.png demo/data/kitti/000008.pkl dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class-8963258a.pth --show
12/30 23:45:35 - mmengine - WARNING - Display device not found. --show is forced to False
d:\datasets\3d-alrithem\mmdetection3d-main\mmdet3d\models\dense_heads\anchor3d_head.py:94: UserWarning: dir_offset and dir_limit_offset will be depressed and be incorporated into box coder in the future
warnings.warn(
Loads checkpoint by local backend from path: mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class-8963258a.pth
The model and loaded state dict do not match exactly
size mismatch for pts_middle_encoder.conv_input.0.weight: copying a param with shape torch.Size([16, 3, 3, 3, 128]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 128, 16]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer1.0.0.weight: copying a param with shape torch.Size([16, 3, 3, 3, 16]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 16, 16]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer2.0.0.weight: copying a param with shape torch.Size([32, 3, 3, 3, 16]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 16, 32]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer2.1.0.weight: copying a param with shape torch.Size([32, 3, 3, 3, 32]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 32, 32]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer2.2.0.weight: copying a param with shape torch.Size([32, 3, 3, 3, 32]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 32, 32]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer3.0.0.weight: copying a param with shape torch.Size([64, 3, 3, 3, 32]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 32, 64]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer3.1.0.weight: copying a param with shape torch.Size([64, 3, 3, 3, 64]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 64, 64]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer3.2.0.weight: copying a param with shape torch.Size([64, 3, 3, 3, 64]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 64, 64]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer4.0.0.weight: copying a param with shape torch.Size([64, 3, 3, 3, 64]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 64, 64]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer4.1.0.weight: copying a param with shape torch.Size([64, 3, 3, 3, 64]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 64, 64]).
size mismatch for pts_middle_encoder.encoder_layers.encoder_layer4.2.0.weight: copying a param with shape torch.Size([64, 3, 3, 3, 64]) from checkpoint, the shape in current model is torch.Size([3, 3, 3, 64, 64]).
size mismatch for pts_middle_encoder.conv_out.0.weight: copying a param with shape torch.Size([128, 3, 1, 1, 64]) from checkpoint, the shape in current model is torch.Size([3, 1, 1, 64, 128]).


feature/28: Implemented Browser special keys (Escape, Backspace, Enter and Supr)

PR Details
Added new keys such as Escape, Backspace, Enter and Supr available to call thought Browser class
Related Issue
#28
Types of changes

[ ] Docs change / refactoring / dependency upgrade
[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Checklist

[x] I have read the CONTRIBUTING document
[x] My code follows the code style of this project
[ ] My change requires a change to the documentation
[ ] I have updated the documentation accordingly (README.md for each widget)
[ ] I have added tests to cover my changes (at least 1 spec for each widget with the same coverage as the master branch)
[x] All tests (new and existing) passed
[ ] A new branch needs to be created from master to evolve previous versions
[x] Increase version in package.json following Semantic Versioning
[x] Add the issue into the right project with the proper status (In progress)

A nice one! (I was on holidays when I was asked for review, so I am just taking a "retrospective look" now)


HBASE-22263 Master creates duplicate ServerCrashProcedure on initiali…

…zation, leading to assignment hanging in region-dense clusters

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unit
https://builds.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-2084/2/artifact/out/patch-unit-hbase-server.txt

Test Results
https://builds.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-2084/2/testReport/

Max. process+thread count
4159 (vs. ulimit of 10000)

modules
C: hbase-server U: hbase-server

Console output
https://builds.apache.org/job/HBase-PreCommit-GitHub-PR/job/PR-2084/2/console

versions
git=1.9.1 maven=3.0.5 findbugs=3.0.1

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[FLINK-33024][table-planner][JUnit5 Migration] Module: flink-table-planner (JsonPlanTestBase)

What is the purpose of the change
[FLINK-33024][table-planner][JUnit5 Migration] Module: flink-table-planner (JsonPlanTestBase)
Brief change log
JUnit5 Migration Module: flink-table-planner (JsonPlanTestBase)
Verifying this change
This change is already covered by existing tests
Does this pull request potentially affect one of the following parts:

Dependencies (does it add or upgrade a dependency): no
The public API, i.e., is any changed class annotated with @Public(Evolving): no
The serializers: no
The runtime per-record code paths (performance sensitive): no
Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn, ZooKeeper: no
The S3 file system connector: no

Documentation

Does this pull request introduce a new feature? no
If yes, how is the feature documented? not applicable

Thanks @luoyuxia for the hard review.
Fixed.

Thanks @luoyuxia, the CI passed.


Typo in the doc

Just to let you know a typo, the peopleId param should not be here:
List People Profiles: CrispClient->websitePeople->listPeopleProfiles(websiteId, peopleId, page)
should be
List People Profiles: CrispClient->websitePeople->listPeopleProfiles(websiteId, page)

Closed with https://github.com/crisp-im/php-crisp-api/commit/b18ae793a6d8a2c004e2aa23265aea439b153540, thanks!


UI based phonenumber validation on registration is weak

UI Validation only checks for length of 3.

We touched the validation already a bit in this issue: https://github.com/NexdApp/nexd-ios/issues/42
The backend uses the libphonenumber, which is also available for swift.

I looked into libphonenumber for iOS. Can you provide some additional information about the implementation in the backend? I guess were using "DE" as defaultRegion?

I am not passing in a region. It is used for validating without a region, therefore expecting the country code. I think this gives most flexibility. @andre-stefanov had some solution for Android in mind (maybe a country picker?)

yes, we were thinking about having a "drop down" to chose the country code to have a consistent way of phone number formats (i.e. +49 vs. 0049)

related to issue #42 (this also has some comments regarding phone number validation)


[US-14-9] Add an ability to clear the page object list

As user
I want to be able to clear the page object list
In order not to download page objects created by mistake
Add a button Clear all to the page object list

На доработку Маше

Link to Figma - https://www.figma.com/file/BVIiVfeuQfQuD1fi7HP99K/JDN-(v.2)?node-id=4268%3A50987

в случаи удаления всех ПО показывать поп-ап с предупреждением:

Link to Figma - https://www.figma.com/file/BVIiVfeuQfQuD1fi7HP99K/JDN-(v.2)?node-id=4477%3A51873

US was created: https://jdi-family.atlassian.net/wiki/spaces/JDN/pages/30113824/US-14-9+Clear+all+page+objects+in+the+list

3.1.128

JDN v 3.1.130
Back-end v 2022-02-12-11.36.19
Chrome
done


ovn/utilities: add info level log function

There is no INFO level log function. It is either ERROR or
FATAL. This PR adds it.
Signed-off-by: Paul Greenberg<EMAIL_ADDRESS>

@blp , I don’t think that the test failed due to an issue in this PR. I think the failure is unrelated. Could you please take a look.

I posted this to the ovs-dev mailing list to ensure that it gets noticed by the right people to review it (probably Guru; not me): https://patchwork.ozlabs.org/patch/906419/

I posted this to the ovs-dev mailing list to ensure that it gets noticed by the right people to review it (probably Guru; not me): https://patchwork.ozlabs.org/patch/906419/

@blp, thank you! Btw, I am working on modifying the https://github.com/openvswitch/ovs/blob/master/ovn/utilities/ovn-docker-overlay-driver.in. Currently, the code does not have any debugging or logging functionality, e.g. logging Docker requests to the plugin, etc. That's why I added the two functions. I will have a separate PR for that.

@blp , closing this one in favor of https://github.com/openvswitch/ovs/pull/230


Mono OpenType Operator, download

where can I download the Mono OpenType Operator?

It's not a free font. You can purchase at their website. I will warn you that it is not cheap.
https://www.typography.com/fonts/operator/styles/


Add initial support for Nordic nRF5340

This adds initial support for Nordic nRF5340 chip. This is dual core
chip with two Cortex M33. Only application core is supported for now.
Supports timers, GPIO, UART and PWM. Tested with blinky and pwm_test.

remaining style issues are on purpose (this is imported code so this will make future code sync easier)

SPI was tested with BME280
I2C was tested with BMP388

radio driver https://github.com/apache/mynewt-nimble/pull/841

main missing points:

IPC support between cores
option to set app core into insecure mode
support for passing GPIO control to network core

https://github.com/apache/mynewt-nimble/pull/841 was updated with HCI transport utilizing IPC allowing to run controller on network core and host on application core.


Fix support for subscript expressions in ON CONFLICT

Summary of the changes / Why this improves CrateDB
Closes https://github.com/crate/crate/issues/10196
Checklist

[x] Added an entry in CHANGES.txt for user facing changes
[x] Updated documentation & sql_features table for user facing changes
[x] Touched code is covered by tests
[x] CLA is signed
[x] This does not contain breaking changes, or if it does:

It is released within a major release
It is recorded in CHANGES.txt
It was marked as deprecated in an earlier release if possible
You've thought about the consequences and other components are adapted
(E.g. AdminUI)

@mkleen Pushed a fixup to not break compatibility with the dotted format - looks like we at some point deleted the tests
for that (see https://github.com/crate/crate/commit/ed5cd253de1ff2cc7a1eeeb09fe087f46de85cc2#diff-916c68b743911987bc402a5c2ca01ab8R931 )


proposal: rename package for netty 5

given that code written for netty 4 will not run with netty 5 i recommend to rename the package to io.netty5. Similarly to apache commons lang.

It will certainly be useful when you look at examples in the future to know which version they were written for.

maybe ,the comments will be more useful than that way

And what happens with Netty 6? That guarantees breaking changes every major release. Examples should be well documented / mention what version it was written for.

Thats definitely something we want to do.. Anyway let me close this as there is no netty 5 for now.


subword # should be an option.

For bert, there are many models use # for subword symbol, but not all.
Some popular bert-based models defined their own subword symbol.
For example, in e5 the symbol is ▁.
>>> a = '▁'
>>> a.encode('utf-8')
b'\xe2\x96\x81'

Furthermore, there is no such rule to force use #.

In model, the substr symbol always be called as replacement or continuing_subword_prefix.
Actually, it will show in tokenizer.json.

@skeskinen  no, #31  only make vocab not necessary (because it maybe missing).
This issue is another problem for subwords ( I found this since I meet too many unknown token when using e5).
bellow is some token samples in bert-based model.
in m3e, subword is ## like many bert model.
"##a": 8139,
"03": 8140,
"09": 8141,
"08": 8142,
"28": 8143,
"##2": 8144,

in e5, subword is ▁ since they trained a new tokenizer (bellow is part copy from tokenizer.json)
      [
        "▁si",
        -7.355116367340088
      ],
      [
        "▁ja",
        -7.370460510253906
      ],
      [
        "▁za",
        -7.37307596206665
      ],
      [
        "▁v",
        -7.385393142700195
      ],

For now, I do not have a good idea for this issue, so I do not implement a PR for it.
Maybe we need to more research and discuss.

For now, I do not have a good idea for this issue, so I do not implement a PR for it. Maybe we need to more research and discuss.

加油，需要跨平台的中英文向量化~


feat: add favicons

Related Issue

resolves #77
Summary of Changes

[x] Install eleventy-plugin-gen-favicons to generate favicon images and tags.
[x] Add squared logo SVG image (1:1 ratio image is required by the plugin)

favicon

Desktop icon

(probably from the generated manifest.webmanifest)

It seems that this has been implemented now.


Additional endpoints support

Feature Request
Is your feature request related to a problem? Please describe.

You can not fetch your withdrawals or account details using this SDK even API supports that
Describe the solution you'd like

additional endpoints support should solve the issue.

https://developer.coingate.com/reference/get-account
https://developer.coingate.com/reference/get-withdrawals
https://developer.coingate.com/reference/accounts
https://developer.coingate.com/reference/get-withdrawal

Are you willing to resolve this issue by submitting a Pull Request?

[x] Yes, I have the time, and I know how to start.
[ ] Yes, I have the time, but I don't know how to start. I would need guidance.
[ ] No, I don't have the time, although I believe I could do it if I had the time...
[ ] No, I don't have the time and I wouldn't even know how to start.

@arnasledev, please also update the README on how to import the module, some users are getting confused


Trigger recompile on js project change

Hi,
How to trigger a recompile and copy of resources when the jsclient project is updated?
The follow use case:

Change code in scala js
Reload the play page and incrementally recompile, see the code changed.

Now I do the follow

Change scalala js code
Make a empty change in server template (like a empty space)
Play trigger the recompile on server and this cascade to jsclient
Change is recompiled and appear on the page

Today changes in scalajs do not appear in the result of server automatically.
The server recompile is not needed, as it is a resource.
Using version 0.2.4 and play 2.4.0-RC1

Hi,
I confirm that Scala.js compilation is not triggered after upgrading to play 2.4.0-RC1.
Please let me know if you've got a fix. I'll probably wait until this weekend to look at it.
Thanks for reporting the issue.

Hitting the same issue, any updates on this?

Changes to the example-shared files are detected because they are part of the server's classpath when dependsOn is used https://github.com/vmunier/play-with-scalajs-example/blob/master/build.sbt#L19.
But the source files from the Scala.js project (that is example-client in https://github.com/vmunier/play-with-scalajs-example) are not watched anymore when using Play 2.4.
I spent a bit of time looking into it but I haven't found a solution yet, it's not a quick fix.

Hi @giovannicandido, @zlangbert,
sbt-play-scalajs v0.2.6 has been published to be used along with Play 2.4.
Note that it remains compatible with Play 2.3.x.
Play 2.4 has stopped monitoring some directories that it used to watch on Play run.
The Scala.js projects are now added to unmanagedSourceDirectories in Assets to be part of the directories monitored by Play.


E_INVALIDENTITYUID : Invalid item id Error While using schedule-api

I am trying to bulk delete project tasks with "CallPssDeleteAction" , Each operation set will contain maximum of 97 tasks . but This error pops up randomly ,
{"code":"-1945829329","message":"{\"errorId\":-1945829329,\"errorKey\":\"E_BATCHFAILED\",\"ErrorMessage\":\"One of the batch requests failed\",\"failedBatchRequestIndex\":0,\"failedBatchRequestError\":{\"errorId\":-1946156705,\"errorKey\":\"E_INVALIDENTITYUID\",\"ErrorMessage\":\"Invalid item id\"}}","innerError":null} ,
if retried after some time it works and then again throws an error. Is there any other way to bulk delete all project Tasks

Thanks for contacting us. Unfortunately, we're unable to handle Support requests for the product here. To contact Support, consider one of the options described here: Support contact options. In particular, you might want to go to the Dynamics 365 community page to get help with your questions from the Dynamics community.


fix(jaeger-exporter): set Span Kind as Tags

Which problem is this PR solving?

Fixes #594

Short description of the changes

Jaeger exporter doesn't have direct support for kind attribute, this PR will set SpanKind as Tags similar to Status field.

/cc @OlivierAlbertini

Codecov Report

Merging #596 into master will decrease coverage by 0.22%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #596      +/-   ##
==========================================
- Coverage   89.93%   89.71%   -0.23%
==========================================
  Files         168      160       -8
  Lines        8230     7903     -327
  Branches      739      698      -41
==========================================
- Hits         7402     7090     -312
+ Misses        828      813      -15

Impacted Files
Coverage Δ

...telemetry-node/src/instrumentation/PluginLoader.ts
92.72% <0%> (ø)
:arrow_up:

...ages/opentelemetry-plugin-http/test/utils/utils.ts
33.33% <0%> (-26.67%)
:arrow_down:

packages/opentelemetry-node/src/NodeTracer.ts
100% <0%> (ø)
:arrow_up:

...es/opentelemetry-node/src/instrumentation/utils.ts
100% <0%> (ø)
:arrow_up:

.../opentelemetry-exporter-collector/src/transform.ts
88.88% <0%> (-8.09%)
:arrow_down:

...ackages/opentelemetry-exporter-zipkin/src/utils.ts
100% <0%> (ø)
:arrow_up:

.../opentelemetry-plugin-redis/test/assertionUtils.ts
95.83% <0%> (-4.17%)
:arrow_down:

...ackages/opentelemetry-exporter-zipkin/src/types.ts
100% <0%> (ø)
:arrow_up:

...ges/opentelemetry-exporter-zipkin/test/e2e.test.ts
100% <0%> (ø)
:arrow_up:

...pentelemetry-exporter-prometheus/src/prometheus.ts
92% <0%> (ø)
:arrow_up:

... and 31 more

Both Http & gRPC servers now take new optional parameter spanKind in addition to service. When spanKind is absent or empty, operations from all kinds of spans will be returned.

https://github.com/jaegertracing/jaeger/blob/master/CHANGELOG.md#1160-unreleased
next jaeger version

Both Http & gRPC servers now take new optional parameter spanKind in addition to service. When spanKind is absent or empty, operations from all kinds of spans will be returned.

https://github.com/jaegertracing/jaeger/blob/master/CHANGELOG.md#1160-unreleased
next jaeger version

That's great. I will create a separate issue to track and implement this in the future.


host script in Chrome-Appstore

Normally Greasemonkey Scripts are working also in Google Chrome. But you have to install them from the Chrome App Store. Could you host the Script there?

Hello,
The GM script is now linked here : https://greasyfork.org/scripts/9713-geocaching-com-project-gc
Surfoo


vueify@next needs global envify

With browserify & Vue 2.0 you actually need to install envify yourself and apply it as global transform like described here.
And there's also extract-css plugin included now so browserify users can extract styles into file as well.

@zigomir Thanks for this!


Cannot add count insight

SQL Operations Studio Version: sqlops 0.23.6 (8a3011756318a2ded1e6e84332230a2b2bf9c544, 2017-11-06T21:51:43.232Z)
OS Version: Windows_NT x64 10.0.15063

Steps to Reproduce:

Follow steps at https://docs.microsoft.com/en-gb/sql/sql-operations-studio/tutorial-build-custom-insight-sql-server
When adding widget insight data to user-settings throws error Incorrect type : expected "null"

Closing as duplicate of #80.  Please reactive if #80 doesn't capture this issue.  Thanks!


⚠ Bofrost.lu has degraded performance

In 2aec9ef, Bofrost.lu (https://www.bofrost.lu) experienced degraded performance:

HTTP code: 200
Response time: 1789 ms

Resolved: Bofrost.lu performance has improved in b64546f after 13 minutes.


Adjusting login modal width to fit smartphones

Codecov Report

Merging #32 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master      #32   +/-   ##
=======================================
  Coverage   99.42%   99.42%
=======================================
  Files          13       13
  Lines         175      175
  Branches       27       27
=======================================
  Hits          174      174
  Partials        1        1

Impacted Files
Coverage Δ

src/components/navbar.tsx
100% <ø> (ø)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 5dc0f67...a580bce. Read the comment docs.


'You’re browsing the documentation for v2.x and earlier. For v3.x, click here.' points to https://v3.vuejs.org not https://v3.vuejs.org/guide

Hi,
I thought the prompt below could be improved by pointing to the guide (https://v3.vuejs.org/guide) instead of just the main page (https://v3.vuejs.org)
Bonus points if we can match the exact section from v2 guide to the corresponding one in v3 e.g. from the v2 guide section of 'List rendering' (https://vuejs.org/v2/guide/list.html) to v3 of the same section (https://v3.vuejs.org/guide/list.html), which in this case would be a trivial change, but I bet in some cases there might be sections present in v2 that might not be present in v3
Thanks!

I second this. It's very annoying as a newcomer to experience the following scenario:

Start learning Vue version 3.
Google for something.
Click on the first result, get to version 2.x docs.
Press on that "click here" link, expecting to get to 3.x version of the same page, but getting to the front page instead.
Having to re-Google everything from scratch.


update htlc_config for demo

Codecov Report

Merging #303 into release-rc4 will not change coverage.
The diff coverage is n/a.

@@              Coverage Diff               @@
##             release-rc4     #303   +/-   ##
==============================================
  Coverage          21.75%   21.75%
  Complexity           375      375
==============================================
  Files                129      129
  Lines               5089     5089
  Branches             379      379
==============================================
  Hits                1107     1107
  Misses              3916     3916
  Partials              66       66