[Feature] Align terms for core concept

Kubevela is an implementation of OAM, in OAM, we have AppConfig and Component.
In kubevela ,  we have lots of concepts, this is really confusing, we must align them.

workload Name, component Name, Service Name all represent OAM Component
App, AppGroup all represent to OAM AppConfig.

I prefer we we component in vela for OAM Component and use app for OAM AppConfig.
\cc @hongchaodeng @resouer

After discuss with @hongchaodeng and @resouer  offline， we all agree to align to :
service => 'OAM Component'
app(appfile) => 'OAM AppConfig'

vela comp => vela svc
term "component" => "service"

vela comp -t <workload-type> --app [AppName] => vela svc -t <workload-type> --app [AppName]
vela <trait> [CompName] --app [AppName] => vela <trait> [AppName] --svc [ServiceName]
And these commands showing the term "component" in the outputs:vela init, vela logs, vela exec,vela up
I would like to revise these terms from "component" into "service".
But is it necessary to change the variable or function names in the code,e.g., getComponents() => getServices()?

@captainroy-hy  workload-type should be service type if we need to tell user this concept

But is it necessary to change the variable or function names in the code,e.g., getComponents() => getServices()?

We could make it the second step, this is no high priority, please don't include them in the first step.

@captainroy-hy workload-type should be service type if we need to tell user this concept

We could make it the second step, this is no high priority, please don't include them in the first step.
Got it.


Raise informative exception from ScenarioInfo.io_units()

Currently, if either the commodity or technology lacks units, the exception is internal to pint and could be hard to interpret:
File "/home/khaeru/vc/iiasa/models/message_ix_models/util/scenarioinfo.py", line 265, in io_units
  return self.units_for("commodity", commodity) / self.units_for(
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/khaeru/.venv/3.11/lib/python3.11/site-packages/pint/facets/plain/unit.py", line 159, in __truediv__
  return self._REGISTRY.Quantity(1 / other, self._units)
                                 ~~^~~~~~~
TypeError: unsupported operand type(s) for /: 'int' and 'NoneType'

How to review
Read the diff and note that the CI checks all pass.
PR checklist

[x] Continuous integration checks all ✅
[x] Add or expand tests; coverage checks both ✅
[x] Add, expand, or update documentation.
[x] Update doc/whatsnew.

FYI @glatterf42, I was able to debug this issue with a temporary commit that made a change like this:
@pytest.fixture(scope="function")
def test_context(request, session_context):
    """A copy of :func:`session_context` scoped to one test function."""
    from message_ix_models.model.config import Config

    ctx = deepcopy(session_context)

    # Defaults are preserved on session_context and thus ctx
    assert session_context.model.regions == Config.regions

    # Ensure there is a report key
    ctx.setdefault("report", dict())

    yield ctx

    ctx.delete()

    # Defaults are preserved on session_context and thus ctx
    assert session_context.model.regions == Config.regions

This helped me see that the assertions failed after tests which used the mix_models_cli fixture. This one was using the session_context fixture, and so changes made to that Context by various CLI commands would be copied within test_context and on to tests.
The last commit on this branch has the fix.


kpm restore failed because of nuget.org certificat expired

I just did a kpm restore and it failed because the certificat on nugget.org has expired.
Is there a way to workaround cert. issue ?

it works now !

Yup, the cert was updated on nuget.org.


How to write test

How to setup test?

https://github.com/Jermolene/TiddlyWiki5/issues/3970#issuecomment-2015125087

tiddlywiki-plugin-dev test after v0.1.0


Feature request: ignore certain differences

Certain resources have differences between a field's value in the Terraform code and in Terraform's state. It would be useful to ignore some of these differences.
For example, the azurerm_api_management_api_policy resource has an xml_content field containing an XML string. Differences in whitespace between the field's value in the Terraform code and its value in Terraform's state are ignored by the azurerm provider but not by tfautomv.
I think it is best if tfautomv does not implement provider-specific rules for comparing resources. However, such rules can be useful. A rule system could help here. For instance, imagine this rule:
For resources with a type that matches the pattern ^azurerm_, ignore differences in whitespace in fields that match the pattern ^xml_content$.
If this rule could be provided to tfautomv as configuration, then users could choose to relax tfautomv's matching logic in certain cases.

Quick update on this.
I have made progress on the feature itself, it seems to work correctly at this point.
However there is still work to be done for automatic testing of this feature, especially in our end-to-end tests, that actually run Terraform code.
Currently we only use the random provider in our tests. This is convenient because there is no external dependency on a cloud provider and Terraform's state can be discarded after each test; no need for cleanup.
However I have not been able to create a test case with the random provider where ignoring a field is necessary to make the right move and where, once the move is made, Terraform plans no changes.
I see two options here: use a provider that allows for such a use case, or write tests that do not expect an empty plan as a result of running tfautomv.
The first option is closest to the reason behind this new feature: different values can be functionally equivalent for some providers. However depending on an external cloud provider for testing is a major decision.
The second option requires refactoring our end-to-end test suite. I think this would be useful for testing other tfautomv behavior, not just this feature.
I plan on starting with the second option. If we decide to explore the second option, then we can think of how to do it without adding an external dependency to our tests.

Implemented in PR #16. Will close this issue after next release.

Release v0.4 adds this feature. Closing issue.


Improve USD support for check requests

It's a "check" (not "checking", which is an account type).
Make address 2 optional since some only have one line.
Make state optional and add country (optional) since we can
send USD checks to other countries, e.g. Canada.

Untested, might have syntax errors.


Policy store is designed to be associated with allowedBpn

As ... ,
I want ... ,
so that ... .
Hints / Details

...

Outcome / Acceptance Criteria

Outcome

...

Acceptance Criteria

...

Out of Scope

...

_Currently, allowed api bpn is used as a 'primaryKey' in policy store. We moved authentication to use API Keys, and API bpn is not supported anymore. This brings requirement to reimplement Policy Store to not use allowed bpn anymore.
Side note: Not known why it was designed to be associated with allowedBpn. Looking at the code it doesn't make much sense, only one BPN was possible to configure anyway._
Please refer to : https://github.com/eclipse-tractusx/item-relationship-service/issues/199
Business requirement is to define policy for one or multiple BPNLs.
API request has to be extended in #199 to accept a list of BPNLS. In case BPN is used as primary key for policies this has to be refactored as well using a ID which is unique for a certain policy.
@jzbmw  Recommendation is to close this issue.

@ds-ext-kmassalski
Please adjust this ticket to the defined story template.

duplicate


Run from file

The REPL works but is limited. It would be better if a method could be implemented to open a file and execute it's contents

If the file is added then more tests could probably be done easily #18


Revert "[CELEBORN-255] Add counter of outstandingFetches, outstanding…

…Rpcs and outstandingPushes to metrics"
This reverts commit bfa341c32f362b64dd69b84fc54934cf8224200c.

What changes were proposed in this pull request?
Why are the changes needed?
https://github.com/apache/incubator-celeborn/pull/1992#issuecomment-1776760369
Does this PR introduce any user-facing change?
How was this patch tested?

thanks, merging to main(v0.4.0).

@cfmcgrady, I will fix the bug of adding counter of outstandingFetches, outstandingRpcs and outstandingPushes to metrics .

thanks, merging to main(v0.4.0).

Hi @cfmcgrady , seems branch-0.3 also needs to revert

thanks, merging to main(v0.4.0).

Hi @cfmcgrady , seems branch-0.3 also needs to revert

raised a new PR https://github.com/apache/incubator-celeborn/pull/2039 for branch-0.3


Configure redstone-smartweave package to support webpack v5

Current version of the redstone-smartweave package does not play well with webpack v5+.
Using the latest create-react-app, generate a new sample project.
npm i arweave
npm i redstone-smartweave

add the following code...
const redstone = SmartWeaveWebFactory.memCached(arweave);
const ctr = redstone.contract("t9T7DIOGxx4VWXoCEeYYarFYeERTpWIC1V3y-BPZgKE");
LoggerFactory.INST.logLevel("error");
ctr.readState().then(r => console.log(r));

Observe as node dependency errors ensue...

Apparently there is a way to structure the package.json in the package to better support targeting the browser platform by default.
https://github.com/webpack/webpack/issues/4674
^ provided here for context.
Expected Result:
Including redstone-smartweave should not introduce compile errors when included in a browser/web project.

@DanMacDonald , thanks for the detailed description, we will try to fix this asap.
@asiaziola , as we've discussed - let's try with redstone-isomorphic

@DanMacDonald v0.4.40 with the fix for the webpack 5 support has been released. Feel free to test it out. I prepared a sample app with the new version plugged in https://github.com/redstone-finance/redstone-smartcontracts-webpack5-app. There's been one problem which affects not only our sdk but also e.g. arweave-js sdk. You will see some warnings about webpack not being able to parse sdks' source maps. Again, seems to be a problem only for v5. These are just warnings and and not errors but I still consider them as an issue. You should also see them when not using our sdk but only arweave-js? Anyway, you can read some more about it here: https://github.com/facebook/create-react-app/pull/8227 (basically it's needed for a better debugging in case of ts libraries). As a small workaround I added in my app following flag: "start": "GENERATE_SOURCEMAP=false react-scripts start" which omits generating source maps. However, I would love to hear your opinion if you will have some time to test it out.

Yes, I've seen this source maps issues with many packages on V5. It seems to be a harmless warning however. Will check it out soon!


Occultism adding back forge:storage_blocks/copper to Cut Copper

Describe the bug
the storage block tag was mistakenly added to cut copper sometime back in forge and was removed later
https://github.com/MinecraftForge/MinecraftForge/commit/8f0d3da1e4228c326e9eaa676165bb150a348870
src/main/resources/data/forge/tags/blocks/storage_blocks/copper.json
src/main/resources/data/forge/tags/items/storage_blocks/copper.json
could/should be removed from both 1.18 and 1.19

Closes in 9b468b5d361ab69fdc8e37867464f38104a5a380


add Teritori token on Osmosis frontier

Signed-off-by: dpierret<EMAIL_ADDRESS>

Has conflicts. Replaced with #259


Request price graph data from new endpoint.

https://pro.coinmarketcap.com/
Note: It contains an API secret key, it no suitable implement in Explorer directly. We should implement it to the statistics-service server, and fetch data from there.

Following up about the cost of implementing

The https://min-api.cryptocompare.com/ had already listed XYM in the API endpoint.
we can re-active back the price chart compoment.


Provide Bidi structured text support to MessageFormat (part 2)

This is a continuation of PR #599.
Fixes #539
Ref #599
Breaking change.

cc @ashensis @tomerm

The BiDi support itself is entirely implemented in https://github.com/SlexAxton/messageformat.js/pull/128 and https://github.com/SlexAxton/messageformat.js/pull/129. Therefore, this PR is overall quite simple: it's about updating messageformat.js to the corresponding updated version, adding one argument (options) to the message formatter methods, and updating the documentation and tests accordingly.
The API changes are shown below:
The easy API change:
- .messageFormatter( path ) ➡ function( [variables] )
+ .messageFormatter( path [, options] ) ➡ function( [variables] )

The harder API change:
- .formatMessage( path [, variables ] )
+ .formatMessage( path [, variables, options ] )

Although the inclusion of the referred options argument is somewhat simple, it could represent a challenge for the formatMessage API without the following breaking change:
formatter = Globalize( "en" ).messageFormatter( "hello" );

// Numbered variables using Array.
formatter([ "Wolfgang", "Amadeus", "Mozart" ]);
// > "Hello, Wolfgang Amadeus Mozart"

// Numbered variables using function arguments. (NO LONGER SUPPORTED AFTER THIS CHANGE)
formatter( "Wolfgang", "Amadeus", "Mozart" );
// > "Hello, Wolfgang Amadeus Mozart"

Please, does anyone (cc @jquery/globalize) have any objection or different suggestion?

Breaking compat on ...arguments seems fine, but should combine that with a major version bump.
As for the implementation, the diff here looks good to me. I'm only missing a test that covers the example from the documentation, something with RTL.

@ashensis is it something we can provide ? I refer to tests. Many thanks in advance.

If I recall correctly, @ashensis has some issues with his text editor to include that RTL text in the test file, therefore I've included it via 7f42082 fixup.
I need @tomerm or @ashensis to review my update and confirm if the test expected value is correct.

I see, though that's not quite what I was looking for. You've got this in the new docs: https://github.com/jquery/globalize/pull/623/files#diff-7735b03e77c86b04c1be49471c334107R231
I'd like to see that in a unit or functional test as well.

RTL text in 7f42082 fixup confirmed to be OK.
Regarding @jzaefferer remark on lacking uni test that covers the example from the documentation, both message-formatter.js and format-message.js contains such tests.

// > "Third << Second << First"

I see. I believe the engine that interprets the BiDi marks will render the string as that, but the returned String itself isn't reverted. Therefore, the doc needs fix.
@tomerm @ashensis could you please confirm?

Indeed , the string itself isn't reverted, the "Third << Second << First" sequence demonstrates how the text will look on display.

I am closing all old (and stalled) PRs


[267/Github-Docker-Tests]

Description
Clean branch with docker workflow for running tests the same as we do locally.
resolves #267
Risks
None, we can revert to the old style of testing at any point
Validation
Locally via https://github.com/nektos/act
Issue
https://github.com/WonderTix/WonderTix/issues/267
Operating System
macOS Sonoma (14.0), Intel
Ubuntu 22.04

`Run docker exec --env-file .env.dist wondertix-client-1 /bin/sh -c 'CI=true /usr/local/bin/npm run test -- --ci'
docker exec --env-file .env.dist wondertix-client-1 /bin/sh -c 'CI=true /usr/local/bin/npm run test -- --ci'
shell: sh -e {0}
Error: Process completed with exit code 137`
Currently trying to work out this issue. Exit code 137 means that we hit a resource limit (cpus, memory, disk, etc) for the docker containers. This happens as soon as we start to run the tests. Setting up debug logging and seeing if I can see which container is causing the resource limit to be hit and GitHub OOM-killing our stack.

@ChentaoMa This branch has the do-not-merge tag for a reason. Please revert your merging of main (a72cfaa) into this branch and please pay more attention to the tags on these pull requests in the future.

Commented on the wrong PR earlier, trying to understand the motivation for this


Add QoS for transition config endpoint

This follows from a Discord conversation about using the engine_exchangeTransitionConfigurationV1 endpoint as a "ping" to allow an EL to be aware when it's CL is offline. (cc @djrtwo @mkalinin)
I imagine this to be particularly helpful in the space between:

Beacon chain Bellatrix fork
Terminal PoW block

During this period all ELs must be paired with a CL, but there's presently no way for them to know that the CL is connected and live.

looks good to me
I think the plan @terencechain is to remove this functionality in a subsequent upgrade if it is not of clear value anymore.
I'm okay with putting a restriction on it being only until the transition occurs but am okay either way

I'm okay either way. Prysm doesn't have a restriction. We plan to remove the functionality at some release post-transition, so that means we'll have to coordinate on the timing with all the execution layer client teams


3.13.0 Closes playnite unexpected

On Settings\Common, if I try to set Source priority, it closes Playnite when I tick the blue arrow.

Here is what I found on Playnite.log
PlayniteLog.txt


VS Code extension fails to start server

I'm running Arch.
Installed via
git clone https://github.com/rust-analyzer/rust-analyzer && cd rust-analyzer
cargo install-code

Opened Rust VS Code workspace, Output panel, Rust Analyzer Language Server, shows
[Error - ##:##:## PM] Starting client failed
Launching server using command ra_lsp_server failed.

Log (Window) shows
[renderer1] [error] spawn ra_lsp_server ENOENT: Error: spawn ra_lsp_server ENOENT
	at Process.ChildProcess._handle.onexit (internal/child_process.js:232:19)
	at onErrorNT (internal/child_process.js:407:16)
	at process._tickCallback (internal/process/next_tick.js:63:19)
[renderer1] [error] Launching server using command ra_lsp_server failed.

I tried setting "rust-analyzer.raLspServerPath": "~/.cargo/bin/ra_lsp_server" in settings.json, but that didn't have any apparent effect.
NPM: 6.9.0
Node: v11.14.0
VS Code: 1.33.1

cargo install-code
What is the output of this command? Is ra_lsp_server built successfully?

I also have this problem, and it compiled successfully.

@jens1o Does it also not work in rust-analyzer workspace itself?

Can't reproduce locally, here's a check list of things:

can I run ra_lsp_sever from command line?
is ra_lsp_server in PATH?
is ra_lsp_sever in PATH for VS Code (desktop envirnoment might end up with a path slightly different from shell)
does setting the complete path (without ~) in settings help?

If all of the above fails, then I don't have any hypothesis as to what is going on. Some further debugging steps:

set RA_LOG_DIR=/tmp/rust-analyzer-log and RUST_LOG=trace env vars to see if we manage to acutually launch the process
launch VS Code extension under debugger to check if problem is in TypeScript. F5 in this repository should launch the debugger. THe entry point of the extensions is here: https://github.com/rust-analyzer/rust-analyzer/blob/e2835b46f6928eda21b7edb44f305f20473a3a98/editors/code/src/extension.ts#L123

Does it also not work in rust-analyzer workspace itself?

Yeah.

If it still doesn't works, would you try to change rust-analyzer.raLspServerPath to point to a full-path, something like:

That works! Thank you!

Glad to hear that @jens1o ! Opened https://github.com/rust-analyzer/rust-analyzer/issues/1199 for better diagnostics.
Optimistically closing this issue under the assumption that @APlagman is seeing the same issue. If it is not the case, please reopen!


fix(dsi): Use x-forward-host header if available

Change

Use the x-forward-host header (if available) for redirect URI to DSI

If I sign in via localhost all is ok, but if I go to www.localhost and log in, I land on an error page, "Microsoft.AspNetCore.Authentication.AuthenticationFailureException: Correlation failed."
that said this happens on dev too...

That's expected on the current (i.e. development branch) code; we set the redirect URI via configuration variable and it won't be that www.localhost so, once the auth callback is received, it'll error.
On this current branch; are you passing in the x-forwarded-host header with a value of http://www.localhost....?
On a side note: we should handle this error and log more informative information around it. Not for this ticket though.


The Room → black bar at the bottom

with d9vk

without d9vk

System information

GPU: Nvidia GT 730M
Driver: 435-17 (beta)
Wine version: Proton 4.11
DXVK version: #807

Apitrace file(s)
https://mega.nz/#!3mIRiA5Q!HlNck4vxHVYHtX2fEowW4W3XSkNcWtsc7oX11TFz_Yw
Log files
steam-288160-d3d9.log
steam-288160-d9vk.log

Looks correct on latest master,
can you verify on your end?

After testing several versions of d9vk, I found the glitch was introduced with #797 and it was fine before.

That one? Because that doesn't change anything for games that aren't Dead Space.

Yes, that one:
#796:

#797:

Could be any one of these commits inbetween too :b

Ah, there are multiple commits between those two builds. a83a45fa15a267d0a3c2cd7dfd46aa33c6d0a1da most likely caused the issue but that fixed other games.

Could you try this build with at least DXVK_LOG_LEVEL=warn and post the logs?
d3d9.zip

Actually, scratch that, can you make an apitrace with D9VK?

@K0bin https://mega.nz/#!SqY3wYiA!MHfhib_aMvtxyHVv1Y8cs5eupqbOq6QoTITDpknHpH0

I've found an issue but the game also uses a different code path with D9VK compared to what it does with the Nvidia D3D9 driver.
Can you try running the game with d3d9.supportDFFormats = False in your DXVK config file?

Can you try running the game with d3d9.supportDFFormats = False in your DXVK config file?

How? My dxvk.conf is inside the game's folder but there is no effect, so I'm not sure if I'm doing it right.

Sounds about right.
If you change your log level to info, the log should tell you if it found the file and applied the option.

info:  Found config file: dxvk.conf
info:  Effective configuration:
info:    d3d9.supportDFFormats = False

Thanks. But yes, d3d9.supportDFFormats = False has no effect.

Thanks. I wonder why the Windows apitrace doesn't show the issue. Something makes the game do things differently with D9VK. Just to be sure, you did use the same settings for both traces, right?

did use the same settings for both traces, right?

Yes, I think. I can make a new apitrace without d9vk if it helps.

Should be fixed with that.

The glitch is gone with #838 (with #839, I just have a black screen).
BUT, there is an other issue:

You can see a black bar at the bottom but it shouldn't be here. Because of that, the menus are shifted up (so the game is unplayable). A workaround is to alt+enter 2 times (disable fullscreen then enable fullscreen).
This is what we should have:

There is a black bar with Oddworld New 'n' Tasty too:

Could be a bug with Unity 4 games.

Tested with #997 and now the black bar is wider:

With 0.30 for reference:

Can this be re-tested?

Still here =/

The issue is gone! 🥳
Tested with DXVK 1.6.

Really? No idea how that got fixed. Cool!

Yes, really, tested 5 times to be sure.


experiment results repeat problem

hi, I run the command "python train.py --cfg_file xxx" in self-training process in st3d++ branch , but the results can not be repeated. Though the random seed seems to  be set in "common_utils.py"，I still get the differernt results every time in the same parameters setting.

https://github.com/open-mmlab/OpenPCDet/issues/1105

ok, thanks!


Add Magical Gadget category page

I followed the layout of the weapons category.
I also asked a question regarding groups of similar items, if that needs to change feel free to point that out,
Ill try and add some of the magical gadgets pages over the next few pages.
Preferably in multiple PRs (looking at you hacktoberfest), but you can leave this one open and for me to add more.

You can accept and merge so I can get a new PR in for hacktober :)

You should be working on seperate branches anyway, which you're actually doing.
So you can do multiple PRs from independent branches.

Well yes I know but I mentioned it specifically because I first said to leave this open since I was going to add more


Add a reference to the Jupyter paper

I believe the jupyter notebook paper is nowhere to be found in the jupyterverse. Perhaps add it to the website?

+1 I agree - there should be a location where all of the jupyter-based publications are listed and which pieces of tech they refer to.

I will add it. I will need some guidelines though. Can I get this ticket ?


chore: enable coverage action

:tada: This PR is included in version 1.17.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Update domains_txt.md

Actually it can by anywhere you want it to be:
# File containing the list of domains to request certificates for (default: $BASEDIR/domains.txt)
#DOMAINS_TXT="${BASEDIR}/domains.txt"

(see docs/examples/config)


Refactor cherry-pick option in CONTRIBUTING.md

Short description:
More details:
What this PR does / why we need it:
Which issue(s) this PR fixes:
Special notes for reviewer:
Bug:

/verified


Add Claude 3 into Instructor

Is your feature request related to a problem? Please describe.
I'd love to use Instructor for a Claude as well so my users are able to have options between OpenAI and Anthropic.
Describe the solution you'd like
Claude 3 API now has the capabilities to produce structured JSON. It'd be great to include that into Instructor.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context or screenshots about the feature request here.

Just use litellm proxy:
litellm --port 8000 --model claude-3-sonnet-20240229  # or claude-3-opus-20240229

Once the local proxy is running, patch the client in the following way and use it as usual:
import instructor
from openai import OpenAI
from pydantic import BaseModel

# Enables `response_model`
client = instructor.patch(OpenAI(api_key="anything", base_url="http://<IP_ADDRESS>:8000"))

class UserDetail(BaseModel):
    name: str
    age: int

user = client.chat.completions.create(
    model="gpt-3.5-turbo",
    response_model=UserDetail,
    messages=[
        {"role": "user", "content": "Extract Jason is 25 years old"},
    ],
)

assert isinstance(user, UserDetail)
assert user.name == "Jason"
assert user.age == 25

@phatlast96 it seems you got covered starting with release 0.6.5


it's impossible to send a comment

in tje newest version of storeman it's impossible to send a comment. after pushing send button content of comment disapearing.

It's not the first time I've received such a report. It seems that Storeman doesn't handle the authentication status in proper way. As a workaround I can suggest you to re-login to OpenRepos.net.

after re-login it's ok. thanks

I can confirm this to happen still, every now and then, but logging out and back in to OpenRepos always resolves this issue with recent Storeman versions.
@mentaljam, thanks for putting so much effort into this, the stepwise announcements "Authorization is going to expire." and " Authorization expired!" are really nice.
But sometimes Storeman still shows being logged in to OpenRepos, although writing a comment fails (so being logged in did not seem to be true).
As this issue appears to be a hard one, a way to alleviate it would be to preserve the written text in the comment field, when submitting a comment fails.
Currently it is extremely frustrating, that the freshly written comment is gone, when this issue hits.

Yes, I ran into exactly the same problem and I absolutely share Olf0s comment.


Error for Bambi/PYMC Model when using TFP

When I run the following, I get the following error:

TypeError: float() argument must be a string or a real number, not 'ShapedArray'

import bayeux as bx
import bambi as bmb
import pymc as pm
import pandas as pd
import jax
import arviz as az

dist = pm.Normal.dist(mu=100, sigma=30)

draws = pm.draw(dist, draws=1_000, random_seed=1000)

df = pd.DataFrame(data=draws, columns=['heights'])

formula = bmb.Formula('heights ~ 1')

model = bmb.Model(formula=formula, family='gaussian', data=df)

model.build()

bx_model = bx.Model.from_pymc(model.backend.model)

idata = bx_model.mcmc.tfp_nuts(seed=jax.random.key(0))

az.summary(idata)

The traceback is as follows:
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[1], [line 22](vscode-notebook-cell:?execution_count=1&line=22)
     [18](vscode-notebook-cell:?execution_count=1&line=18) model.build()
     [20](vscode-notebook-cell:?execution_count=1&line=20) bx_model = bx.Model.from_pymc(model.backend.model)
---> [22](vscode-notebook-cell:?execution_count=1&line=22) idata = bx_model.mcmc.tfp_nuts(seed=jax.random.key(0))
     [24](vscode-notebook-cell:?execution_count=1&line=24) az.summary(idata)

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:205](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:205), in _TFPBase.__call__(self, seed, **kwargs)
    [194](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:194) initial_running_variance = [
    [195](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:195)     tfp.experimental.stats.sample_stats.RunningVariance.from_stats(
    [196](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:196)         num_samples=jnp.array(1, part.dtype),
    [197](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:197)         mean=jnp.zeros_like(part),
    [198](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:198)         variance=jnp.ones_like(part))
    [199](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:199)     for part in initial_transformed_position]
    [201](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:201) # The public API expects a JointDistribution. Much of the above is adapted
    [202](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:202) # from the source code for
    [203](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:203) # `tfp.experimental.mcmc.windowed_adaptive_{nuts|hmc}`, but handling a raw
    [204](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:204) # log density, and doing the structure flattening with `jax.tree_utils`.
--> [205](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:205) draws, trace = tfp.experimental.mcmc.windowed_sampling._do_sampling(
    [206](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:206)     kind=self.algorithm,
    [207](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:207)     proposal_kernel_kwargs=proposal_kernel_kwargs,
    [208](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:208)     dual_averaging_kwargs=dual_averaging_kwargs,
    [209](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:209)     num_draws=extra_parameters["num_draws"],
    [210](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:210)     num_burnin_steps=extra_parameters["num_adaptation_steps"],
    [211](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:211)     initial_position=initial_transformed_position,
    [212](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:212)     initial_running_variance=initial_running_variance,
    [213](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:213)     bijector=None,
    [214](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:214)     trace_fn=_TRACE_FNS[self.algorithm],
    [215](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:215)     return_final_kernel_results=False,
    [216](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:216)     chain_axis_names=None,
    [217](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:217)     shard_axis_names=None,
    [218](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:218)     seed=sample_key)
    [220](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:220) draws = self.transform_fn(jax.tree_util.tree_unflatten(treedef, draws))
    [221](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/bayeux/_src/mcmc/tfp.py:221) if extra_parameters["return_pytree"]:

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:551](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:551), in _do_sampling(kind, proposal_kernel_kwargs, dual_averaging_kwargs, num_draws, num_burnin_steps, initial_position, initial_running_variance, trace_fn, bijector, return_final_kernel_results, seed, chain_axis_names, shard_axis_names)
    [543](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:543) """Sample from base HMC kernel."""
    [544](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:544) kernel = make_windowed_adapt_kernel(
    [545](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:545)     kind=kind,
    [546](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:546)     proposal_kernel_kwargs=proposal_kernel_kwargs,
   (...)
    [549](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:549)     chain_axis_names=chain_axis_names,
    [550](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:550)     shard_axis_names=shard_axis_names)
--> [551](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:551) return sample.sample_chain(
    [552](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:552)     num_draws,
    [553](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:553)     initial_position,
    [554](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:554)     kernel=kernel,
    [555](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:555)     num_burnin_steps=num_burnin_steps,
    [556](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:556)     # pylint: disable=g-long-lambda
    [557](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:557)     trace_fn=lambda state, pkr: trace_fn(
    [558](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:558)         state, bijector, pkr.step <= dual_averaging_kwargs[
    [559](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:559)             'num_adaptation_steps'], pkr.inner_results.inner_results.
    [560](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:560)         inner_results),
    [561](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:561)     # pylint: enable=g-long-lambda
    [562](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:562)     return_final_kernel_results=return_final_kernel_results,
    [563](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/experimental/mcmc/windowed_sampling.py:563)     seed=seed)

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:359](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:359), in sample_chain(num_results, current_state, previous_kernel_results, kernel, num_burnin_steps, num_steps_between_results, trace_fn, return_final_kernel_results, parallel_iterations, seed, name)
    [352](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:352)   seed, next_state, current_kernel_results = loop_util.smart_for_loop(
    [353](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:353)       loop_num_iter=num_steps,
    [354](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:354)       body_fn=_seeded_one_step,
    [355](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:355)       initial_loop_vars=list(seed_state_and_results),
    [356](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:356)       parallel_iterations=parallel_iterations)
    [357](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:357)   return seed, next_state, current_kernel_results
--> [359](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:359) (_, _, final_kernel_results), (all_states, trace) = loop_util.trace_scan(
    [360](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:360)     loop_fn=_trace_scan_fn,
    [361](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:361)     initial_state=(seed, current_state, previous_kernel_results),
    [362](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:362)     elems=tf.one_hot(
    [363](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:363)         indices=0,
    [364](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:364)         depth=num_results,
    [365](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:365)         on_value=1 + num_burnin_steps,
    [366](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:366)         off_value=1 + num_steps_between_results,
    [367](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:367)         dtype=tf.int32),
    [368](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:368)     # pylint: disable=g-long-lambda
    [369](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:369)     trace_fn=lambda seed_state_and_results: (
    [370](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:370)         seed_state_and_results[1], trace_fn(*seed_state_and_results[1:])),
    [371](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:371)     # pylint: enable=g-long-lambda
    [372](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:372)     parallel_iterations=parallel_iterations)
    [374](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:374) if return_final_kernel_results:
    [375](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:375)   return CheckpointableStatesAndTrace(
    [376](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:376)       all_states=all_states,
    [377](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:377)       trace=trace,
    [378](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:378)       final_kernel_results=final_kernel_results)

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:232](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:232), in trace_scan(loop_fn, initial_state, elems, trace_fn, trace_criterion_fn, static_trace_allocation_size, condition_fn, parallel_iterations, name)
    [224](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:224)   trace_arrays, num_steps_traced = ps.cond(
    [225](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:225)       trace_criterion_fn(state) if trace_criterion_fn else True,
    [226](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:226)       lambda: (trace_one_step(num_steps_traced, trace_arrays, state),  # pylint: disable=g-long-lambda
    [227](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:227)                num_steps_traced + 1),
    [228](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:228)       lambda: (trace_arrays, num_steps_traced))
    [230](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:230)   return i + 1, state, num_steps_traced, trace_arrays
--> [232](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:232) _, final_state, _, trace_arrays = tf.while_loop(
    [233](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:233)     cond=condition_fn if condition_fn is not None else lambda *_: True,
    [234](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:234)     body=_body,
    [235](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:235)     loop_vars=(0, initial_state, 0, trace_arrays),
    [236](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:236)     maximum_iterations=length,
    [237](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:237)     parallel_iterations=parallel_iterations)
    [239](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:239) # unflatten
    [240](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:240) stacked_trace = tf.nest.pack_sequence_as(
    [241](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:241)     initial_trace, [ta.stack() for ta in trace_arrays],
    [242](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:242)     expand_composites=True)

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:102](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:102), in _while_loop_jax(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, maximum_iterations, name)
     [99](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:99)       args = pack_body(body(*args))
    [100](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:100)     return args, ()
--> [102](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:102)   loop_vars, _ = lax.scan(
    [103](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:103)       override_body_fn, loop_vars, xs=None, length=maximum_iterations)
    [104](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:104)   return loop_vars
    [105](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:105) else:

    [... skipping hidden 9 frame]

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:99](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:99), in _while_loop_jax.<locals>.override_body_fn(args, _)
     [96](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:96)   args = lax.cond(c, args, lambda args: pack_body(body(*args)), args,
     [97](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:97)                   lambda args: args)
     [98](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:98) elif sc:
---> [99](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:99)   args = pack_body(body(*args))
    [100](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:100) return args, ()

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:222](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:222), in trace_scan.<locals>._body(i, state, num_steps_traced, trace_arrays)
    [220](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:220) def _body(i, state, num_steps_traced, trace_arrays):
    [221](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:221)   elem = elems_array.read(i)
--> [222](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:222)   state = loop_fn(state, elem)
    [224](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:224)   trace_arrays, num_steps_traced = ps.cond(
    [225](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:225)       trace_criterion_fn(state) if trace_criterion_fn else True,
    [226](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:226)       lambda: (trace_one_step(num_steps_traced, trace_arrays, state),  # pylint: disable=g-long-lambda
    [227](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:227)                num_steps_traced + 1),
    [228](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:228)       lambda: (trace_arrays, num_steps_traced))
    [230](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:230)   return i + 1, state, num_steps_traced, trace_arrays

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:352](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:352), in sample_chain.<locals>._trace_scan_fn(seed_state_and_results, num_steps)
    [351](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:351) def _trace_scan_fn(seed_state_and_results, num_steps):
--> [352](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:352)   seed, next_state, current_kernel_results = loop_util.smart_for_loop(
    [353](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:353)       loop_num_iter=num_steps,
    [354](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:354)       body_fn=_seeded_one_step,
    [355](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:355)       initial_loop_vars=list(seed_state_and_results),
    [356](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:356)       parallel_iterations=parallel_iterations)
    [357](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/mcmc/sample.py:357)   return seed, next_state, current_kernel_results

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:111](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:111), in smart_for_loop(loop_num_iter, body_fn, initial_loop_vars, parallel_iterations, unroll_threshold, name)
    [101](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:101) if (loop_num_iter_ is None
    [102](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:102)     or tf.executing_eagerly()
    [103](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:103)     # large values for loop_num_iter_ will cause ridiculously slow
   (...)
    [108](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:108)   # Cast to int32 to run the comparison against i in host memory,
    [109](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:109)   # where while/LoopCond needs it.
    [110](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:110)   loop_num_iter = tf.cast(loop_num_iter, dtype=tf.int32)
--> [111](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:111)   return tf.while_loop(
    [112](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:112)       cond=lambda i, *args: i < loop_num_iter,
    [113](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:113)       body=lambda i, *args: [i + 1] + list(body_fn(*args)),
    [114](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:114)       loop_vars=[np.int32(0)] + initial_loop_vars,
    [115](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:115)       parallel_iterations=parallel_iterations
    [116](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:116)   )[1:]
    [117](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:117) result = initial_loop_vars
    [118](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/substrates/jax/internal/loop_util.py:118) for _ in range(loop_num_iter_):

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:90](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:90), in _while_loop_jax(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, maximum_iterations, name)
     [88](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:88)   def override_cond_fn(args):
     [89](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:89)     return cond(*args)
---> [90](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:90)   return lax.while_loop(override_cond_fn, override_body_fn, loop_vars)
     [91](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:91) elif back_prop:
     [92](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/control_flow.py:92)   def override_body_fn(args, _):

    [... skipping hidden 4 frame]

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/linalg.py:102](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/linalg.py:102), in register_pytrees.<locals>.register.<locals>.unflatten(info, xs)
    [100](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/linalg.py:100) keys, metadata = info
    [101](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/linalg.py:101) parameters = dict(list(zip(keys, xs)), **metadata)
--> [102](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/linalg.py:102) return cls(**parameters)

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:171](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:171), in LinearOperatorDiag.__init__(self, diag, is_non_singular, is_self_adjoint, is_positive_definite, is_square, name)
    [161](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:161) parameters = dict(
    [162](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:162)     diag=diag,
    [163](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:163)     is_non_singular=is_non_singular,
   (...)
    [167](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:167)     name=name
    [168](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:168) )
    [170](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:170) with ops.name_scope(name, values=[diag]):
--> [171](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:171)   self._diag = linear_operator_util.convert_nonref_to_tensor(
    [172](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:172)       diag, name="diag")
    [173](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:173)   self._check_diag(self._diag)
    [175](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_diag.py:175)   # Check and auto-set hints.

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_util.py:134](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_util.py:134), in convert_nonref_to_tensor(value, dtype, dtype_hint, name)
    [130](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_util.py:130)     raise TypeError(
    [131](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_util.py:131)         f"Argument `value` must be of dtype `{dtype_name(dtype_base)}` "
    [132](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_util.py:132)         f"Received: `{dtype_name(value_dtype_base)}`.")
    [133](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_util.py:133)   return value
--> [134](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_util.py:134) return ops.convert_to_tensor(
    [135](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_util.py:135)     value, dtype=dtype, dtype_hint=dtype_hint, name=name
    [136](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/gen/linear_operator_util.py:136) )

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:167](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:167), in _convert_to_tensor(value, dtype, dtype_hint, name)
    [164](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:164)     pass
    [166](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:166) if ret is None:
--> [167](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:167)   ret = conversion_func(value, dtype=dtype)
    [168](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:168) return ret

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:243](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:243), in _default_convert_to_tensor(value, dtype)
    [240](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:240) # If no dtype is provided, we try the inferred dtype and fallback to int64 or
    [241](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:241) # float32 depending on the type of conversion error we see.
    [242](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:242) try:
--> [243](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:243)   return _default_convert_to_tensor_with_dtype(value, inferred_dtype)
    [244](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:244) except _Int64ToInt32Error as e:
    [245](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:245)   return np.array(value, dtype=np.int64)

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:286](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:286), in _default_convert_to_tensor_with_dtype(value, dtype, error_if_mismatch)
    [283](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:283) is_arraylike = hasattr(value, 'dtype')
    [284](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:284) if is_arraylike:
    [285](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:285)   # Duck-typed for `onp.array`/`oonp.generic`
--> [286](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:286)   arr = np.array(value)
    [287](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:287)   if dtype is not None:
    [288](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:288)     # arr.astype(None) forces conversion to float64
    [289](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/tensorflow_probability/python/internal/backend/jax/ops.py:289)     return arr.astype(dtype)

File [~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2158](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2158), in array(object, dtype, copy, order, ndmin)
   [2151](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2151) out: ArrayLike
   [2153](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2153) if all(not isinstance(leaf, Array) for leaf in leaves):
   [2154](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2154)   # TODO(jakevdp): falling back to numpy here fails to overflow for lists
   [2155](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2155)   # containing large integers; see discussion in
   [2156](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2156)   # https://github.com/google/jax/pull/6047. More correct would be to call
   [2157](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2157)   # coerce_to_array on each leaf, but this may have performance implications.
-> [2158](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2158)   out = np.array(object, dtype=dtype, ndmin=ndmin, copy=False)  # type: ignore[arg-type]
   [2159](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2159) elif isinstance(object, Array):
   [2160](https://file+.vscode-resource.vscode-cdn.net/Users/zweli/Desktop/~/mambaforge/envs/bambi_tf/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:2160)   assert object.aval is not None

TypeError: float() argument must be a string or a real number, not 'ShapedArray'

Gosh, you're not going to believe this, but the problem is jax.random.key(0) instead of jax.random.PRNGKey(0).  The easiest thing for you is to change the type of jax key you use for now, but it is fixed on tfp-nightly, and will be in the next stable release (here is the tricky fix from @SiegeLordEx)

Yes, changing it to "jax.random.PRNGKey(0)" works! Thank you!

Another question. I am on a M1 Ultra with the silicon GPU. Given that Tensorflow has silicon GPU support, can I leverage that via Bayeux. I installed Tensorflow + the TensorFlow Metal (which is recognized) but I cannot figure out how to tell Tensorflow-Probability to use the Tensorflow and thus the GPU.


extract_tree_with_taxa + str() produces malformed tree

I am using the following Newick-format tree: merged_tree_0.tre.gz
This is my script:
from dendropy import Tree
def label(u):
    return str(u.taxon).replace("'",'')
tree = Tree.get(data=gzip.open('merged_tree_0.tre.gz').read().decode(),schema='newick')
taxa = [leaf.taxon for leaf in tree.leaf_node_iter() if -1 < float(label(leaf).split('|')[-1]) < 9]
print((str(tree.extract_tree_with_taxa(taxa))))

However, the resulting tree is malformed (it's missing a semicolon). I tried with suppress_unifurcations off and it had the same result.
I also tried implementing my own version in which, for each taxon t, I mark t and all ancestors of t as "keep", and I perform a BFS starting at the seed node: if the current node is marked "keep", add all of its children to the BFS queue; if not, delete it from its parent's children via the remove_child function. This code also had the same outcome (malformed output tree missing a semicolon). I'm guessing perhaps there's a more fundamental issue with deleting nodes, and it's happening to trigger this issue in extract_tree_with_taxa due to internal node deletion

Also, I wrote my own version of the extract_tree_with_taxa function that seems to be much faster than the original, in case anyone is interested in using it
def extract_tree_with_taxa(tree, taxa, suppress_unifurcations=True):
    taxon_to_leaf = {}
    for n in tree.preorder_node_iter():
        n.keep = False
        if n.is_leaf():
            taxon_to_leaf[n.taxon] = n
    for t in taxa:
        for n in taxon_to_leaf[t].ancestor_iter(inclusive=True):
            n.keep = True
    out = Tree()
    q_old = Queue(); q_old.put(tree.seed_node)
    q_new = Queue(); q_new.put(out.seed_node)
    while not q_old.empty():
        n_old = q_old.get(); n_new = q_new.get()
        for c_old in n_old.child_node_iter():
            if c_old.keep:
                c_new = Node(taxon=c_old.taxon, label=c_old.label, edge_length=c_old.edge_length); n_new.add_child(c_new)
                q_old.put(c_old); q_new.put(c_new)
    if suppress_unifurcations:
        out.suppress_unifurcations()
    return out

__str__
https://github.com/jeetsukumaran/DendroPy/blob/c31c1d986b0e6d72aa90e4fef61707e055ce3779/src/dendropy/datamodel/treemodel/_tree.py#L3947
appears to delegate to a quick and dirty internal debugging print
https://github.com/jeetsukumaran/DendroPy/blob/c31c1d986b0e6d72aa90e4fef61707e055ce3779/src/dendropy/datamodel/treemodel/_tree.py#L4274
As a potential public-facing endpoint, __str__ method should probably be updated to delegate to the proper as_string("Newick") instead.

I've since written my own tree Python package (TreeSwift) with this functionality implemented if it's of interest 😄
https://niema.net/TreeSwift/#treeswift.Tree.extract_tree_with

This has been fixed for DendroPy 5.


Feature atualizar evento

finalizando #7

Coverage decreased (-2.9%) to 83.851% when pulling ce94faceef89436e24809153996a060a4ad7e4f9 on feature-atualizar-evento into d75e9a72f707b5e052ef431d2c392f36a7bcb40d on master.


Document querybench workload

Title
Add section for workload querybench
Description
querybench is very handy for customer
Outline
follow similar structures and examples.
Expected Audience
Developers and architects

@lin-crl, I don't see any such workload or command in the cockroach cli. Can you clarify what you mean here?
We recently removed cockroach systembench (see https://github.com/cockroachdb/cockroach/pull/62305 for details there). Perhaps you're referring to that?

Based on conversations in a different channel, @lin-crl is referring to querybench built into the stand-alone workload binary at wget https://edge-binaries.cockroachdb.com/cockroach/workload.LATEST, which is used in this benchmarking workshop: https://github.com/cockroachlabs/workshop_benchmark/blob/master/instructor_benchmark_config.md
In general, we have stopped leveraging the stand-alone binary in favor of the cockroach workload commands. For example, we recently changed our TPC-C benchmarking docs to use cockroach workload tpcc instead of workload tpcc. I believe this guidance came from Engineering. @nvanbenschoten, @petermattis, do you have guidance here? Do we want to stop leveraging stand-alone workload just in docs or also in field materials? cc @awoods187 as well.

I didn't know querybench wasn't part of cockroach binary. It's a very handy tool to simulate a custom workload using an arbitral sql file for workshop or prototyping. But if it's engineering decision that it will not be, then we in field will have to find a workaround. Currently we install the binary manually on vms. It gets a bit tricky in docker/k8.


replace IMPORT TABLE on Migrate from Serverless doc

Resolves https://cockroachlabs.atlassian.net/browse/DOC-5805.

This change is


[improve][admin] Add option function-type to admin CLI for Functions

Motivation
Add option function-type to admin CLI for Functions.
This option can be used as an alternative to the --jar option to create a built-in Function.
This is the equivalent of the --sink-type/--source-type options of connectors for Functions
Verifying this change

[x] Make sure that the change passes the CI checks.

This change is already covered by existing tests, such as testCreateFunctionWithBuiltinNar
Does this pull request potentially affect one of the following parts:
yes
If yes was chosen, please highlight the changes

Dependencies (does it add or upgrade a dependency): (yes / no)
The public API: (yes / no)
yes. Adds a --function-type option to admin create function command
The schema: (yes / no / don't know)
The default values of configurations: (yes / no)
The wire protocol: (yes / no)
The rest endpoints: (yes / no)
The admin cli options: (yes / no)
Anything that affects deployment: (yes / no / don't know)

Documentation
Check the box below or label this PR directly.
Need to update docs?

[x] doc-required
(Your PR needs to update docs and you will update later)

[ ] doc-not-needed
(Please explain why)

[ ] doc
(Your PR contains doc changes)

[ ] doc-complete
(Docs have been already added)

we will also need something to list available builtin functions (as a follow up work)


fix: min / max validators failed if value was zero

Please ensure your pull request:

~Includes additions to the docs, found in the /docs, as required~
✓The new and changed code you have written is unit tested and passes the eslint
✓The body of the text for this PR includes enough text for a reviewer to understand the context of the change

validators were not being added when value was zero
if (Number(param.minLength)) { // fails the check here, if explicit zero passed

changed to check if is numeric - ie. if number cast does not return NaN

bit of formatting noise - the only changes were the number lines in utils, and the last 2 tests in the unit suite


chore: add debug panel to lu/lg/qna/publish page

Description

Add debug panel to lu/lg/qna/publish page
Delete diagnostic page

Question
What dose shouldShowEditorError mean? I see it just calculate the height of content component.
Task Item
#minor
Screenshots

Coverage decreased (-0.5%) to 53.687% when pulling f22f21447017f17fee4f292e86c68b9a290e83bd on alanlong9278:julong/diagnostic-panel into 93f12c963c5e61df3062dbc58f556ae7eb1d17a6 on microsoft:main.

@alanlong9278 If we are removing the diagnostics panel we still need to add a filter to filter diagnostics by bots.

@yeze322 @alanlong9278 There seems to be a bug when i try this branch. Essentially, the debug panel wont show the errors of the currently seletected project inside the LG/LU/Qna page. It shows the errors and warnings for the current project only inside design page

Fix the bug about show selected project errors/warninigs in the LG/LU/Qna page, but can not do that in publish page because we can select multible project. And revert diagnostics page to the left nav. @srinaath  @yeze322


AttributeError at /login/facebook/ 'tuple' object has no attribute 'rsplit'

Hello,
When I click on Facebook link in the home page to authenticate I get this error:
AttributeError at /login/facebook/
'tuple' object has no attribute 'rsplit'
I couldn't find any answer pleaser HELP !

which version of django you're using?

The problem is that the function module_member over social/utils.py is getting the entire authentication backends tuple defined over the settings.py
def module_member(name):

    print('social > module_member > ', name) # you will se the tuple
    mod, member = name.rsplit('.', 1)
    module = import_module(mod)
    return getattr(module, member)

afaik this should receive the correct backend module path, but i don't know how this magic should be done.

@adilberrada, you need to post your settings.py file. I had a similar error when my EMAIL_* vars inside settings.py had unnecessary trailing commas making them tuples instead of plain string vars.


userspace: remove APP_SHARED_MEM Kconfig

This is an integral part of userspace and cannot be used
on its own. Fold into the main userspace configuration.
Signed-off-by: Andrew Boie<EMAIL_ADDRESS>

Codecov Report

Merging #13694 into master will increase coverage by 0.02%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #13694      +/-   ##
==========================================
+ Coverage    52.3%   52.33%   +0.02%
==========================================
  Files         307      307
  Lines       45350    45355       +5
  Branches    10464    10469       +5
==========================================
+ Hits        23721    23737      +16
+ Misses      16877    16866      -11
  Partials     4752     4752

Impacted Files
Coverage Δ

kernel/init.c
73.43% <ø> (ø)
:arrow_up:

subsys/testsuite/ztest/src/ztest.c
65.28% <ø> (ø)
:arrow_up:

subsys/testsuite/ztest/include/ztest_test.h
100% <ø> (ø)
:arrow_up:

drivers/clock_control/nrf_power_clock.c
50% <0%> (-2.11%)
:arrow_down:

boards/posix/nrf52_bsim/time_machine.c
52.45% <0%> (+4.91%)
:arrow_up:

boards/posix/nrf52_bsim/argparse.c
46.98% <0%> (+15.66%)
:arrow_up:

Continue to review full report at Codecov.

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Package request: tabnine-nvim

Project description
Neovim plugin for code completion with Tabnine.
Metadata

homepage URL: https://github.com/codota/tabnine-nvim
source URL:
license: mit, bsd, gpl2+ , ...
platforms: unix, linux, darwin, ...

I don't know if it is okay to bump requests like this, but I would love it if Tabnine (and the chat module) be added to nixpkgs.

Until a package is available, you can add it to your configuration yourself, but beware, my solution is somewhat ugly.
programs.neovim.plugins =
  with pkgs.vimPlugins;
  let
    tabnine-nvim = pkgs.vimUtils.buildVimPlugin {
      name = "tabnine-nvim";
      src = pkgs.fetchFromGitHub {
        owner = "codota";
        repo = "tabnine-nvim";
        rev = "a9baab62d29f1696f475ef3597e061f0920c9077";
        sha256 = "sha256-D0fKeppxM59NMNeXpGa5AfPcpbGMwGCZkM9IQ2NgcwY=";
      };
      buildInputs = [
        pkgs.tabnine
        pkgs.semver-lua
      ];
      buildPhase = ''
        patchShebangs .

        # Determine the Tabnine version and target platform
        version=${pkgs.tabnine.version}
        platform=${pkgs.tabnine.platform}

        # Create the expected directory structure
        mkdir -p binaries/$version/$platform
        cp ${pkgs.tabnine}/bin/* binaries/$version/$platform

        # Make the binaries executable
        chmod +x binaries/$version/$platform/*

        # Add semver.lua to the correct location
        cp ${pkgs.semver-lua}/semver.lua ./lua/tabnine/third_party/semver/
      '';
    };
  in
  [
    tabnine-nvim
  ];

_Note: I use Home Manager and you should likely use the latest version of the plugin. Also, the semver-lua package is an overlay, because I couldn't find it:
self: super:
let
  version = "1.2.1";
in
{
  semver-lua = super.pkgs.stdenv.mkDerivation {
    pname = "semver-lua";
    version = "v${version}";

    src = super.fetchFromGitHub {
      owner = "kikito";
      repo = "semver.lua";
      rev = "v${version}";
      hash = "sha256-S0Rn6/FR/Dt+GtHZtEv1Ad1/6B4Abep6IBot887BRuc=";
    };
    installPhase = ''
      runHook preInstall
      install -Dm755 semver.lua $out/semver.lua
      runHook postInstall
    '';
  };
}


Get Attached Devices - Missing Logitech MeetUp

The get attached devices script seem to miss the Logitech MeetUp Camera. As in device manager this is under the PNPClass of "Camera" rather than "Image"
Suggest updating to;
gwmi -Class Win32_PnPEntity | where {$.PNPClass -eq "Image" -or $.PNPClass -eq "Camera"}

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: f68cba21-b43e-ed44-449c-da752f5a0f53
Version Independent ID: 5a2a2c5e-ad0e-833d-6ab7-74ed26f63d4a
Content: Skype Room Systems v2 maintenance and operations
Content Source: Skype/SfbServer/manage/skype-room-systems-v2/room-systems-v2-operations.md
Product: skype-for-business-itpro
GitHub Login: @jambirk
Microsoft Alias: jambirk

@officedocsbot unassign @yogkumgit

@rogersau Thank you for pointing this out and helping improve MS Docs. I have updated the wording as per your suggestion.

We've checked with the product group, and this change to the commands is inadvisable. We'll leave this as it's currently written.


Dual Shock 3 support

Operating system or device: Gnome Ubuntu Linux 16.04
Issue description (what happened, and what was expected): By trying to configure DS3 in my game I realised that the suggestion given by the editor for the buttons is completely wrong (ex: button 0 --> PS = X, Nintenda = A, etc...). Turns out, for instance, that select button is the godot X
Emoticon. How can I get the button mapping so to not going for a "trial and error" approach?
Steps to reproduce: Project settings --> input map: Map the buttons and test it.

According to @Hondres, it would be related to https://github.com/gabomdq/SDL_GameControllerDB/issues/66

I'll look into it. I remember that my ds3 was working over bluetooth, but there may be multiple revisions or something.
Btw, I should get rid of that @Hondres account... causes confusion or even wrongly attributed commits :/

Btw, I should get rid of that @Hondres account... causes confusion or even wrongly attributed commits :/

Hehe, estimate yourself lucky, some guy grabben the "akien" account 5 years ago and never did anything with it :p You could ask the GitHub guys to merge your two accounts into one.

Alright, turns out my system also needs the new mapping with bus type 5.
However a quick google search for "ps3 bus type 006" revealed that a lot of people are indeed running this thing on bus 6, so I also kept the old one.
I guess it was fixed in a (more or less) recent driver/kernel.


Reading List(...) into arrow not supported yet

Describe the bug
When trying to read parquet files with deeply nested fields we end up with the following error:
Parquet reader thread terminated due to error: ParquetError(ArrowError("reading List(GroupType {
...
}) into arrow not supported yet"))

To Reproduce
This is easily visible from the code found at array_reader.rs#L1516-L1522
Expected behavior
To have support for reading nested parquet files into arrow.
Additional context
This issue, in my particular case, has been hidden under #1383.

This may have been fixed as there has been significant work on reading of nested structures by @helgikrs and others

@alamb https://github.com/apache/arrow-rs/blob/master/parquet/src/arrow/array_reader.rs#L1622

The nested data structure support is been pending for long time. I am also eagerly looking for this support. I have a fairly large JSON data which I used apache spark to write to parquet files. I wanted to read them via rust arrow have no success.

Hi @kesavkolla  -- could you possibly share an example file we could use to test any fix?

I have a parquet file https://github.com/Igosuki/arrow2/blob/main/part-00000-b4749aa1-94e4-4ddb-bab2-954c4d3a290f.c000.snappy.parquet it uses List(List(Float64))

Here is a sample parquet file https://github.com/chauhanVritul/sampleparquet/blob/main/part-00000-8e5acb24-eb4e-491c-8c85-88799f25d1f0-c000.snappy.parquet

Yes, this use case is supported in datafusion. For instance, you can read nested avro and json schemas, so the issue should be raised with arrow-rs, I added a comment here https://github.com/apache/arrow-rs/issues/720 with our files.


Read LaunchedOnReservation and Reserved tasks

Summary:
Previously, all tasks were being read as LaunchedEphemeral task
subtypes, and this was leading to any resident information about the
task to be lost.
This change fixes the issue, and adds round-trip serialization tests.
Fixed #5165
Backport of dad4e1f
Test Plan: unit
Reviewers: aquamatthias, meichstedt
Subscribers: marathon-team
Differential Revision: https://phabricator.mesosphere.com/D546

Retest

retest this please


[Fix #6861] Fix a false positive for Layout/IndentationWidth

Fixes #6861.
This is a regression by https://github.com/rubocop-hq/rubocop/pull/6792.
This PR fixes infinite loop for Layout/IndentationWidth cop and Layout/AccessModifierIndentation cop.
# exmaple.rb
class Foo
private

  def do_something
  end
end

# .rubocop.yml
% cat .rubocop.yml
Layout/AccessModifierIndentation:
  EnforcedStyle: outdent

First, auto-corrected by Layout/IndentationWidth.
% rubocop -v
0.66.0
% rubocop -a --only Layout/IndentationWidth
Inspecting 1 file
C

Offenses:

example.rb:2:1: C: [Corrected] Layout/IndentationWidth: Use 2 (not 0)
spaces for indentation.
private

1 file inspected, 1 offense detected, 1 offense corrected

% git diff example.rb
diff --git a/example.rb b/example.rb
index 063e6a5..081b80a 100644
--- a/example.rb
+++ b/example.rb
@@ -1,5 +1,5 @@
 class Foo
-private
+  private

   def do_something
   end

Next, auto-corrected by Layout/Layout/AccessModifierIndentation (EnforcedStyle: outdent).
% rubocop -a --only Layout/AccessModifierIndentation
Inspecting 1 file
C

Offenses:

example.rb:2:3: C: [Corrected] Layout/AccessModifierIndentation: Outdent
access modifiers like private.
  private
  ^^^^^^^

1 file inspected, 1 offense detected, 1 offense corrected

This will return to the original code.
% git diff
diff --git a/example.rb b/example.rb
index 081b80a..063e6a5 100644
--- a/example.rb
+++ b/example.rb
@@ -1,5 +1,5 @@
 class Foo
-  private
+private

   def do_something
   end

That caused the infinite loop in Layout/IndentationWidth and Layout/AccessModifierIndentation (EnforcedStyle: outdent).
With this PR, Layout/indentationWidth cop makes aware of Layout/AccessModifierIndentation cop.

Before submitting the PR make sure the following are checked:

[x] Wrote good commit messages.
[x] Commit message starts with [Fix #issue-number] (if the related issue exists).
[x] Feature branch is up-to-date with master (if not - rebase it).
[x] Squashed related commits together.
[x] Added tests.
[x] Added an entry to the Changelog if the new code introduces user-observable changes. See changelog entry format.
[x] The PR relates to only one subject with a clear title
and description in grammatically correct, complete sentences.
[x] Run bundle exec rake default. It executes all tests and RuboCop for itself, and generates the documentation.

Hi @koic,
Great description of the problem and solution. I found one remaining problem. If I start with EnforcedStyle: outdent for Layout/AccessModifierIndentation and
# example.rb
class Foo
                 private

  def do_something
    # something
  end
end

Auto-correction changes it to
# example.rb
class Foo
private

                 def do_something
                   # something
                 end
end

@jonas054 Sure! I was not aware of this problem 💦 I'll look it up. Thanks for your checking.

Hello @koic! Thanks for working on this, do you still plan to wrap this up?

Wanted to second merging this PR once the remaining issues are fixed. If it helps, this only seems to be a problem if the access modifier is the first line of code in the class/module. So maybe the issue with autocorrect could be fixed by limiting changes to the first line of code in this case?

@koic What's the state here?

Sorry I'm late.  I updated this PR and added the following mentioned issue as a test.
https://github.com/rubocop-hq/rubocop/pull/6866#issuecomment-478321279

Thanks for fixing this @koic!! :heart: