Option to render value directly on Input field

I think we need (if it doesn't yet exists) an option to force use a single Input field instead of rendering an span or other custom content...
I will explain with an example (without code, I'm out home)
I'm trying to render an input with bootstrap3 at NavBar (typical search form) I needed to modify less files to make it working properly as it was absolutely messed up (only using with NavBar) but there's a problem that can't be fixed, or better say, it's a hell... When we select/add an option, selectize renders that element with our custom html code or default html code, span or div or whatever...
That's OK if we are selecting some elements like tags, emails or whatever, but when we just need to perform a search and use an autocomplete with selectizejs (by autocomplete I mean an ajax request) we don't want a rendered block with our value, we just want TEXT, because we can delete a char without deleting the entire tag (even using restore_on_backspace is ugly) because it doesn't delete the first char on first DEL press, it deletes the rendered block and then it deletes the first char on the second DEL press..
Another buggy thing is that input (which is position: absolute;) is rendered again next to our value block, causing an overwidth on our final form...
So basically, what I mean (because I can't explain better without screenshots or code XD) is add an option to prevent block rendering and use only the absolute input field and treat our value as it is, a simple text...
What do you think? I think it's needed in some cases, maybe I am too exquisite.

I'm not following 100%, but it sounds like you're looking for something other than selectize. Maybe implementing something from scratch could work? If it's helpful, there's always sifter.js to take care of the option search aspect of it


Prisma throws error on enabling logging

Bug description
Unable to start the application when logs are enabled. Crashes on start with the following error message:
{"level":30,"time":1663116069562,"pid":2825049,"hostname":"nyx","context":"RouterExplorer","msg":"Mapped {/, GET} route"}
{"level":30,"time":1663116069564,"pid":2825049,"hostname":"nyx","context":"RouterExplorer","msg":"Mapped {/how, GET} route"}
{"level":30,"time":1663116069564,"pid":2825049,"hostname":"nyx","context":"RoutesResolver","msg":"PartnerController {/partner} (version: 1):"}
{"level":30,"time":1663116069565,"pid":2825049,"hostname":"nyx","context":"RoutesResolver","msg":"PropertyController {/property} (version: 1):"}
{"level":30,"time":1663116069565,"pid":2825049,"hostname":"nyx","context":"RouterExplorer","msg":"Mapped {/property, GET} (version: 1) route"}
  prisma:client:libraryEngine:loader  loadEngine using /nix/store/z6b2myc9bhgb94d3140hzww50d5y1csf-prisma-engines-4.2.1/lib/libquery_engine.node +0ms
  prisma:client:libraryEngine  library starting +82ms

/home/phv/code/src/github.com/gamezop/partner-service/prisma/generated/gamezop-read/runtime/index.js:25389
      throw new PrismaClientUnknownRequestError(`Response from the Engine was empty`, this.config.clientVersion);
            ^
Error: Response from the Engine was empty
    at LibraryEngine.parseEngineResponse (/home/phv/code/src/github.com/gamezop/partner-service/prisma/generated/gamezop-read/runtime/index.js:25389:13)
    at LibraryEngine.logger (/home/phv/code/src/github.com/gamezop/partner-service/prisma/generated/gamezop-read/runtime/index.js:25440:24)
    at /home/phv/code/src/github.com/gamezop/partner-service/prisma/generated/gamezop-read/runtime/index.js:25424:59

How to reproduce

Install nestjs, prisma
enable logs
See error

Expected behavior
No response
Prisma information

generator client {
  provider = "prisma-client-js"
  output = "./generated/gamezop-read"
}

datasource gamezopRead {
  provider = "postgresql"
  url      = env("POSTGRESQL_GAMEZOP_READ_URL")
}

....

// not working version
import { Injectable, OnModuleInit, INestApplication } from '@nestjs/common';
import { PrismaClient, Prisma } from '../prisma/generated/gamezop-read';

@Injectable()
export class GzpReadPrismaService
  extends PrismaClient<Prisma.PrismaClientOptions, 'info' | 'query'>
  implements OnModuleInit
{
  constructor() {
    // super();
    super({
      log: ['info', 'query'],
    });
  }

  async onModuleInit() {
    await this.$connect();
  }

  async enableShutdownHooks(app: INestApplication) {
    this.$on('beforeExit', async () => {
      await app.close();
    });
  }
}

// working version
import { Injectable, OnModuleInit, INestApplication } from '@nestjs/common';
import { PrismaClient, Prisma } from '../prisma/generated/gamezop-read';

@Injectable()
export class GzpReadPrismaService
  extends PrismaClient<Prisma.PrismaClientOptions, 'info' | 'query'>
  implements OnModuleInit
{
  constructor() {
    super();
  }

  async onModuleInit() {
    await this.$connect();
  }

  async enableShutdownHooks(app: INestApplication) {
    this.$on('beforeExit', async () => {
      await app.close();
    });
  }
}

Environment & setup

OS: NixOS
Database:PostgreSQL
Node.js version: v16.15.0
prisma: 4.2.1: https://github.com/NixOS/nixpkgs/blob/master/pkgs/development/tools/database/prisma-engines/default.nix

Prisma Version
prisma                  : 4.3.1
@prisma/client          : 4.2.1
Current platform        : linux-nixos
Query Engine (Node-API) : libquery-engine  (at ../../../../../../../nix/store/z6b2myc9bhgb94d3140hzww50d5y1csf-prisma-engines-4.2.1/lib/libquery_engine.node, resolved by PRISMA_QUERY_ENGINE_LIBRARY)
Migration Engine        : migration-engine-cli  (at ../../../../../../../nix/store/z6b2myc9bhgb94d3140hzww50d5y1csf-prisma-engines-4.2.1/bin/migration-engine, resolved by PRISMA_MIGRATION_ENGINE_BINARY)
Introspection Engine    : introspection-core  (at ../../../../../../../nix/store/z6b2myc9bhgb94d3140hzww50d5y1csf-prisma-engines-4.2.1/bin/introspection-engine, resolved by PRISMA_INTROSPECTION_ENGINE_BINARY)
Format Binary           : prisma-fmt  (at ../../../../../../../nix/store/z6b2myc9bhgb94d3140hzww50d5y1csf-prisma-engines-4.2.1/bin/prisma-fmt, resolved by PRISMA_FMT_BINARY)
Format Wasm             : @prisma/prisma-fmt-wasm 4.3.0-32.c875e43600dfe042452e0b868f7a48b817b9640b
Default Engines Hash    : c875e43600dfe042452e0b868f7a48b817b9640b
Studio                  : 0.473.0

@prithvihv Do you maybe have a non Nix-OS machine/VM handy where you can try to run the same project? I would be interested if you get the same problem/error there.

I can confirm that this also happens to me with Prisma v4.2.1 on NixOS.
Relevant code:
export const client = new prisma.PrismaClient({
  // crashes if the following line is uncommented:
  // log: ["query"],
});

Console output with export DEBUG='*':
  prisma:tryLoadEnv  Environment variables not found at null +0ms
  prisma:tryLoadEnv  Environment variables not found at undefined +1ms
  prisma:tryLoadEnv  No Environment variables loaded +0ms
  prisma:tryLoadEnv  Environment variables not found at null +6ms
  prisma:tryLoadEnv  Environment variables not found at undefined +0ms
  prisma:tryLoadEnv  No Environment variables loaded +0ms
  prisma:client  dirname /home/diamond/Scripts/chatter/node_modules/.prisma/client +0ms
  prisma:client  relativePath ../../../src/lib/db +0ms
  prisma:client  cwd /home/diamond/Scripts/chatter/src/lib/db +0ms
  prisma:client  clientVersion 4.2.1 +1ms
  prisma:client  clientEngineType library +0ms
  prisma:client:libraryEngine  internalSetup +0ms
  prisma:client:libraryEngine:loader  loadEngine using /nix/store/918kir4dsxfry3d0975pryna285mkkiy-prisma-engines-4.2.1/lib/libquery_engine.node +0ms
  prisma:client:libraryEngine  library starting +101ms
  prisma:client:libraryEngine  library started +266ms
  prisma:client  Prisma Client call: +418ms
  prisma:client  prisma.asset.findMany(undefined) +1ms
  prisma:client  Generated request: +1ms
  prisma:client  query {
  findManyAsset {
    hash
    name
    data
  }
}
 +0ms
  prisma:client:libraryEngine  sending request, this.libraryStarted: true +53ms
  prisma:client:libraryEngine:exitHooks  exit event received: exit +0ms
/home/diamond/Scripts/chatter/node_modules/@prisma/client/runtime/index.js:25389
      throw new PrismaClientUnknownRequestError(`Response from the Engine was empty`, this.config.clientVersion);
            ^
PrismaClientUnknownRequestError: Response from the Engine was empty
    at LibraryEngine.parseEngineResponse (/home/diamond/Scripts/chatter/node_modules/@prisma/client/runtime/index.js:25389:13)
    at LibraryEngine.logger (/home/diamond/Scripts/chatter/node_modules/@prisma/client/runtime/index.js:25440:24)
    at /home/diamond/Scripts/chatter/node_modules/@prisma/client/runtime/index.js:25424:59 {
  clientVersion: '4.2.1'
}

I unfortunately do not have a non-NixOS machine to reproduce this error on. All of my development is done on this distro.


Python Interface

Added an interface for python using pybind11.
This allows users to control the robot from the python program directly.

Thanks @Kashu7100 for your PR!
I will merge this first, but I have already make a simpler version, so advices are welcomed.

Sounds good.


Missing word

"Data warehouse snapshot is also a powerful mechanism to create copies of your data warehouse for test or development purposes"
Snapshot is missing.

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

ID: e7c1ff9c-547c-e90f-36a7-2178a17d7951
Version Independent ID: 27aba9c4-cf0b-865f-ea24-513b65fc696a
Content: Backup and restore - snapshots, geo-redundant - Azure Synapse Analytics
Content Source: articles/synapse-analytics/sql-data-warehouse/backup-and-restore.md
Service: synapse-analytics
Sub-service: sql-dw
GitHub Login: @joannapea
Microsoft Alias: joanpo

Thank you for your detailed feedback.  We are looking into the issue, and we will respond when we have more information.


Keyword to permit inferring a union for a type parameter

Suggestion
🔍 Search Terms

Widen
Generics
Open

✅ Viability Checklist
My suggestion meets these guidelines:

[x] This wouldn't be a breaking change in existing TypeScript/JavaScript code
[x] This wouldn't change the runtime behavior of existing JavaScript code
[x] This could be implemented without emitting different JS based on the types of the expressions
[x] This isn't a runtime feature (e.g. library functionality, non-ECMAScript syntax with JavaScript output, new syntax sugar for JS, etc.)
[x] This feature would agree with the rest of TypeScript's Design Goals.

⭐ Suggestion
Right now TypeScript has some rules about when it decides to widen a generic type or not, which can produce confusing results for relatively similar function signatures:
declare function fn1<T>(items: Array<T>): T
declare function fn2<T>(items: Array<() => T>): T
declare function fn3<T>(items: Array<{ prop: T }>): T

let res1: number | string = fn1([42, "hi"])
let res2: number | string = fn2([() => 42, () => "hi"]) // string ("hi") is not a number
let res3: number | string = fn3([{ prop: 42 }, { prop: "hi" }]) // string ("hi") is not a number

Right now its entirely outside of your control what TypeScript will decide to do when a type parameter is not provided and what TypeScript does has changed a few times over the years.
It would help a lot if TypeScript gave you some more control over this behavior, to specify if you want your generics to widen or not.

Please ignore the syntax, just demonstrating where I'd expect it to go

// widen
declare function fn<widen T>(items: Array<() => T>): T;
fn([() => 1, () => 2, () => "three"]) // => number | string

// do not widen
declare function fn<donotwiden T>(items: Array<T>): T;
fn([1, 2, "three"]) // ERR

📃 Motivating Example
An increasingly popular way to use TypeScript is to produce types from runtime values, there are already lots of TypeScript features dedicated to making it easier to infer precise types from values, and there are even libraries like zod and io-ts to do things like:
let type = union(string(), number())

let value: unknown = ...
assert(value, type)
value // >> string | number

Libraries written in TypeScript are increasingly relying on TypeScript's value-to-type inference as part of their public API. So when TypeScript changes its rules about things like when to widen vs not widen, it can be much more dramatic of a breaking change that sometimes requires redesigning these libraries.
type Check<T> = (value: unknown) => value is T

function string(): Check<string> {...}
function number(): Check<number> {...}
function union<T>(...members: Assertion<T>[]): Check<T> {...}

let assert = union(string(), number()) // ERR: number is not a string
let assert = union<string | number>(string(), number())

Right now there are a couple hacks you can do to trick TypeScript into having the desired widening behavior (by lying about the actual types). But there is no guarantee that this behavior will stick around between TypeScript versions.
💻 Use Cases
type Check<T> = (value: unknown) => value is T

function string(): Check<string> {...}
function number(): Check<number> {...}
function union<widen T>(...members: Check<T>[]): Check<T> {...}

let assert1 = union(string(), number())
let assert2 = union<string | number>(string(), number())
type SomeUnionToStayInSyncWith = string | number
let assert3 = union<SomeUnionToStayInSyncWith>(string(), number())

[Playground]

See Distributive Conditional Types
And this will make your example work.
declare function fn1<T extends unknown[]>(items: T): T[number]
declare function fn2<T extends (() => unknown)[]>(items: T): ReturnType<T[number]>
declare function fn3<T extends {prop: unknown}[]>(items: T): T[number]['prop']

let res1: number | string = fn1([42, "hi"])
let res2: number | string = fn2([() => 42, () => "hi"]) // OK
let res3: number | string = fn3([{ prop: 42 }, { prop: "hi" }]) // OK

Sure but you've changed the signature of union()'s type parameters. The point is that the type parameters are part of the public API

Aside: For clarity, this isn't "widening" (that process is only about converting literal types to their corresponding primitives, and some others). I don't think we have a name for the particular process of deciding when it's OK / not OK to infer a union for a type argument in the presence of multiple candidates. But it's clear in context what you mean here.
For nowiden, I'm not sure how to reason about the behavior being described here. Presumably all three of these lines either have an error or don't have an error, but it's IMO very difficult to make an argument that noWiden(arr1) should be an error, and nothing meaningful about the code should change when we inline that expression.
declare function noWiden<nowiden T>(arr: T[]): void;
const arr1 = [Math.random() > 0.5 ? 1 : "two"];
const arr2 = [1, "two"];
noWiden(arr1);
noWiden(arr2);
noWiden([1, "two"]);

There's a ton of subtlety here around the inference candidate collection process - while the examples in the OP look similar, from a type system perspective the inference algorithm sees very different things. I wouldn't defend it as unsurprising but AFAIK we haven't actually broken much about this in the past (counterexamples welcomed for my own learning).
I think the intuition here is something like "don't infer a union unless one of the inference candidates is a union"... but literally one of the inference candidates is a union in these cases. There'd have to be some stronger theoretical principle to rely on to make this work in a way that was at least somewhat consistent in the presence of refactoring an argument to a local.
widen is much clearer IMO. There's a step after candidate collection that roughly says, if we can't produce a unifying type except by making a union (in the absence of a constraint to say otherwise), then issue an error and fall back to the constraint type. That could trivially be made dependent on the type parameter declaration and everything would pretty much work from there.
Some more real-world use cases would be useful to help consider.

I didn't really have a use case for nowiden anyways, I was making assumptions about how this process worked internally, so if that's nonsensical I'm happy to drop it.

There's a step after candidate collection that roughly says, if we can't produce a unifying type except by making a union (in the absence of a constraint to say otherwise), then issue an error and fall back to the constraint type. That could trivially be made dependent on the type parameter declaration and everything would pretty much work from there.

I guess you could rename widen to prefer-union for the sake of discussion:
declare function oneOf<prefer-union T>(a: T, b: T): T

Which operates like this:
oneOf("hello", "world") // string
oneOf("hello", 42) // string | number

There's actually a number of ways TypeScript already has the desired prefer-union behavior:
oneOf(["hello"], [42]) // string[] | number[]
oneOf({ a: "hello" }, [42]) // { a: string } | number[]

Then there are places where TypeScript is producing a union but possibly not ideal for prefer-union:
// what it is today (without prefer-union):
oneOf({ a: "hello" }, { b: 42 }) // { a: string, b?: undefined } | { a?: undefined, b: number }
// should probably be:
oneOf({ a: "hello" }, { b: 42 }) // { a: string } | { b: number }

And finally place where TypeScript bails out instead of producing a union:
// what it is today (without prefer-union):
oneOf(() => "hello", () => 42) // Type 'number' (42) is not assignable to type 'string'
// should probably be:
oneOf(() => "hello", () => 42) // (() => string) | (() => number)

This is our use case for inferring a union type. I didn't manage to work around having to manually declare the union type in the generic when calling our isOneOf type guard. A new keyword seems like an elegant way to fix that, so you have my upvote.
export function isOneOf<T>(
  value: unknown,
  guards: Array<(value: unknown) => value is T>
): value is T {
  return guards.some((guard) => guard(value));
}

let a!: string | number | boolean;
const isString = (v: unknown): v is string => typeof v === "string";
const isNumber = (v: unknown): v is number => typeof v === "number";

if (isOneOf(a, [isString])) {
  a; // string
}
if (isOneOf<string | number>(a, [isString, isNumber])) {
  a; // string | number
}

// Type 'number' is not assignable to type 'string'.ts(2322)
if (isOneOf(a, [isString, isNumber])) {}

Question: Which place makes more sense for TypeScript internally?
function oneOf<widen T>(...guards: Array<(value: unknown) => value is T): value is T
//            ^^^^^ declaration
function oneOf<T>(...guards: Array<(value: unknown) => value is widen T): value is T
//                                                              ^^^^^ reference

If the definition for oneOf is not
declare function oneOf<T,U>(a: T, b: U): T | U

it would not produce an error for
const f = oneOf(() => "hello", () => 42) // (() => string) | (() => number)  NO ERROR

If instead you really wanted to constraint a and b to be the same type (which you don't) then you need something else.
As for this oneOf example, maybe the question should be, are
declare function oneOf<T,U>(a: T, b: T): T

and
declare function oneOf<T,U>(a: T, b: T): T | U

intended to express the same semantics?  (I think so).
Then why don't they behave the same?
The former is 4 character shorter.


stcm-editor has stopped working

Trying to extract GAME00000\event\script\xxxx\main.cl3 (they can be extracted with strtool) in Re;Birth1, It creates a 0kb txt file and crashes.

Thanks, hopefully fixed in v0.3.4.
I should make more unit tests...


Add C implementation for @stdlib/math/base/special/ln

Resolves #639 .
Checklist

[x] update readme.md
[x] include.gypi
[x] binding.gyp
[x] include/stdlib/math/base/special/
[x] src
[x] manifest.json
[x] lib
[x] examples
[x] benchmark
[x] test

@kgryte

All tests and benchmarks passed. This PR is ready to be reviewed :)


import/path: issue title

Go version
go version go1.21.4 darwin/amd64
Output of go env in your module/workspace:
GO111MODULE='on'
GOARCH='amd64'
GOBIN=''
GOCACHE='/Users/51pwn/Library/Caches/go-build'
GOENV='/Users/51pwn/Library/Application Support/go/env'
GOEXE=''
GOEXPERIMENT=''
GOFLAGS=''
GOHOSTARCH='amd64'
GOHOSTOS='darwin'
GOINSECURE=''
GOMODCACHE='/Users/51pwn/go/pkg/mod'
GONOPROXY='github.com/hktalent/zbServer'
GONOSUMDB='github.com/hktalent/zbServer'
GOOS='darwin'
GOPATH='/Users/51pwn/go'
GOPRIVATE='github.com/hktalent/zbServer'
GOPROXY='https://goproxy.cn'
GOROOT='/usr/local/go/'
GOSUMDB='sum.golang.org'
GOTMPDIR=''
GOTOOLCHAIN='auto'
GOTOOLDIR='/usr/local/go/pkg/tool/darwin_amd64'
GOVCS=''
GOVERSION='go1.21.4'
GCCGO='gccgo'
GOAMD64='v1'
AR='ar'
CC='clang'
CXX='clang++'
CGO_ENABLED='1'
GOMOD='/Users/51pwn/MyWork/goSqlite_gorm/go.mod'
GOWORK=''
CGO_CFLAGS='-O2 -g'
CGO_CPPFLAGS=''
CGO_CXXFLAGS='-O2 -g'
CGO_FFLAGS='-O2 -g'
CGO_LDFLAGS='-O2 -g'
PKG_CONFIG='pkg-config'
GOGCCFLAGS='-fPIC -arch x86_64 -m64 -pthread -fno-caret-diagnostics -Qunused-arguments -fmessage-length=0 -ffile-prefix-map=/var/folders/_l/pnb2t_9s0f192bqlz1348vpr0000gn/T/go-build1603043107=/tmp/go-build -gno-record-gcc-switches -fno-common'

What did you do?
go tool pprof -seconds=600 -http=:9999 https://<IP_ADDRESS>:8081/debug/pprof/heap
What did you see happen?
Fetching profile over HTTP from https://<IP_ADDRESS>:8081/debug/pprof/heap?seconds=600
Please wait... (10m0s)
https://<IP_ADDRESS>:8081/debug/pprof/heap: Get "https://<IP_ADDRESS>:8081/debug/pprof/heap?seconds=600": tls: failed to verify certificate: x509: “Cloudflare” certificate is not standards compliant
failed to fetch any source profiles
What did you expect to see?
I expect to ignore the tls error and work properly. Moreover, this certificate is issued by Cloudflare and can work in a formal environment.

Related Issues

tls: failed to verify certificate: x509: “CloudFlare Origin Certificate” certificate is not standards compliant #62361 (closed)
net/http/pprof: pprof over HTTPS #16073 (closed)
cmd/pprof: add HTTPS support with client certificates #20939 (closed)
pprof: go tool pprof cannot read the pprof file normally #66227 (closed)
cmd/go: TestScript/gotoolchain_issue66175 fails on tip locally #67277 (closed)
crypto/x509: trust setting not inherited on Darwin #32891 (closed)
cmd/compile: `PGO error: error opening profile: EOF` in `cmd/go.TestScript/build_pgo` as of CL 529738 #65220 (closed)
crypto/x509: cannot parse certificate IP & net/http cannot ignore this certificate error #65829 (closed)
crypto/tls: panic: runtime error: slice bounds out of range [33554436:274] #64692 (closed)
cmd/pprof: panics if profile is not found #67626 (closed)

(Emoji vote if this was helpful or unhelpful; more detailed feedback welcome in this discussion.)

Duplicate of #51991


Error in latest .ttl file

When I try to open it in Protege, it says: org.xml.sax.SAXParseException; systemId: file:/Users/maryannmartone/Downloads/curation-export-2.ttl; lineNumber: 1; columnNumber: 1; Content is not allowed in prolog.
When I try to load it in GraphDB, it says:  curation-export-2.ttl
RDF Parse Error: Illegal subject value: "n/a" [line 36]

I was getting the same error with the GraphDB load.  If I just removed that line the load produced no errors.

This has been fixed. There was a case where someone included "n/a" for an orcid and it was being serialized as a subject in the graph. Now, when a value that would be used as an identifier in the graph fails to validate in the json schema that part of the record is excluded.


The background of content panel changes to black when click or move the blocks on the content panel

Repro steps:
• Navigate to https://pxt.microbit.org/beta on Edge (only repro on Edge)
• Move or click the blocks on the content panel
Expect result:
The background of content panel doesn't change
Actual result:
The background of content panel changes to black

@yanlxu What build of edge is this? I can't repro on my edge

@abchatra, we use the latest build: RS_ONECORE_WEBPLAT_STAGE_DEV3\16248.1003.170723-1730

close this issue as not repro on the latest build


Feature Request:  Existing Blog with Dynamic Content Creation for Scholarships and Jobs

Is your feature request related to a problem? Please describe.
Description:
Convert the current static blog page into a dynamic platform that allows for creating and publishing content related to scholarships and job opportunities. This will enable fresh, relevant content and improve user engagement.
Key objectives:

[ ] Transform static blog into a dynamic content management system.
[ ] Enable creation of posts specific to scholarships and job opportunities.
[ ] Integrate with existing scholarship and job databases for real-time updates.
[ ] Improve user engagement through commenting and sharing features.

Describe the solution you'd like.
No response
Describe alternatives you've considered.
No response
Additional context.
@aditya-bhaumik I  would  love to work on it, could you please assign this task to me?
Show us the magic with screenshots
Screenshots:

Checklist

[X] I have checked the Existing Issues
[X] I have read the Contributing Guidelines
[X] I want to work on this issue. (optional)

@aditya-bhaumik kindly have a look at this issue.

sure go ahead
Make sure to join the discord server for further communication
Don't forget to star the repository
Complete the task in next 2 days and kindly mention your issue # in your pr like this:
Closes #IssueNumber
Note: the level of the issue may change based on the quality of the PR code
Also please follow the contributing.md steps properly

@aditya-bhaumik ok!


DM-17130: First version

I guess this report is not intended to discuss what was new in the pipelines themselves, so there wasn't really a natural place to mention the HiPS production.  I did add a little more of a shout-out to it in the RSP section.

thanks gregory


support blackbox images

Description

Related Issue

Motivation and Context

How Has This Been Tested?

Screenshots (if appropriate):
Types of changes

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

Checklist:

[ ] I have signed the Adobe Open Source CLA.
[ ] My code follows the code style of this project.
[ ] My change requires a change to the documentation.
[ ] I have updated the documentation accordingly.
[ ] I have read the CONTRIBUTING document.
[ ] I have added tests to cover my changes.
[ ] All new and existing tests passed.

Done in https://github.com/adobe/aio-cli-plugin-runtime/pull/129


Nav does not seem to support a style prop

I want to set a border and an "active" border to the Nav component. Is that possible at the moment?

@deladela I have created a pull request for adding custom classNames.

this is now on latest release. Apologies -- this has been long time coming


Cannot read properties of undefined ...?

Hi, you populate "static/videos" folder with background videos? (minimum 1 is required)

Hi, you populate "static/videos" folder with background videos? (minimum 1 is required)

Where did I go wrong??

It seems right, I'll check what it could be, I haven't tested it on Windows yet, I'll test it soon and fix it.

Hi @alexdo83, you can try now? I think #2 fixes it :)

Fixed #2


Command output with ninja build system broken

Bug description
When generating a ninja based build system instead of a Makefile based one the display of ninjas output during build doesn't work as it should. There are two components to this issue:

Ninjas output only shows up when the command exits
All output is displayed next to each other in a single line

To Reproduce
Steps to reproduce the behavior:

Create a CMake project or use an existing one
Make sure you have ninja installed
open in vim/neovim and run :CMakeGenerate Debug -GNinja
run :CMakeBuild
wait for command to finish and see output

Expected behavior
I would expect ninjas output to be either shown as in a normal terminal or as when piped through a command like cat.
For reference run the following commands in the build folder:
ninja

ninja | cat

Behavior with minimal .vimrc
Same as above with empty vimrc/init.vim
Screenshots
Actual:

Expected normal:

Expected cat:

Other info

OS: ArchLinux 5.18.14
Vim/Neovim version: NVIM v0.8.0-dev-726-gb971547c54
Vim-CMake version (5th line in :help cmake): 0.9.0 / d62f24d4a08c4332fc672a92a4b8bd89e0ee9061
CMake version (cmake --version): 3.23.2

Additional context
Ninja outputs carriage returns instead of  newlines to only have one line of output. Vim-CMake removes carriage returns and waits for the entire line before sending it to the console window. I could experimentally patch the first part by just disabling the relevant bit of code but don't know what that entails other than the intended (code is probably there for a reason), the second part takes some more knowledge of the code than I have to do right probably.

I went a little bit into too much detail perhaps, as I'm curious to hear your thoughts on these issues, if you have any. I'll try to find time soon to split this issue into multiple tickets that can be addressed independently and in parallel. I'll also then prioritize this issue over other feature requests. And as always contributions are welcome!

Thanks for the extensive response. That is indeed more feedback than I expected but I do have some thoughts on these issues. I do have to prefix them with a disclaimer though: I know next to nothing about the APIs and details surrounding embedded terminals in vim and neovim, especially considering the differences between vim's and neovim's versions.
Line endings
Both neovim and vim do the right thing when running :terminal ninja -C <path to build dir> in a ninja based buildtree. Based on this observation I would assume the problem is in how Vim-CMake  buffers/passes lines to the terminal buffer. Ideally there was a way to just open a terminal in something like a managed mode (for it to not close automatically/display a command exited message) and then just run a command in said terminal buffer. Alternatively I would just pass all data as soon as is available as I already know the terminal knows how to handle it. I don't know how this interacts with channels however so this might not work as I expect.
For stripping CRs the only thing I can think of that might remotely have something to do with compatibility would be Windows using CRLF as the platform line endings but I would expect the respective terminal implementations to handle this just fine.
Last I checked ninja didn't have an option to force multi-line output but you can force it rather easily by not allocating a pty to stdout (e.g. piping to a command). Ninja then outputs multiple lines instead (and also doesn't truncate lines) but you also loose colored output for compiler warnings (and probably a few other things).
Stdout line width
Both neovim and vim can automatically set the size for their terminal from the width of the window. Example neovim (:help nvim_open_term()):
nvim_open_term({buffer}, {opts})                            *nvim_open_term()*
                Open a terminal instance in a buffer
[...]
                Note: to directly initiate the terminal using the right size,
                display the buffer in a configured window before calling this.
                For instance, for a floating display, first create an empty
                buffer using |nvim_create_buf()|, then display it using
                |nvim_open_win()|, and then call this function. Then
                |nvim_chan_send()| can be called immediately to process
                sequences in a virtual terminal having the intended size.

According to this it seems you just have to open the window before creating the terminal in the buffer.
In vim I would expect this to work similar based on :help 'termwinsize'.
In addition vim (and probably also neovim based on the wording of the note above) has the option to set/overwrite the size so you could query the necessary information beforehand and set it manually.
Build targets
A separate parser for every build tool does in fact seem unmaintainable and using a CMake feature would be way nicer. Fortunately CMake has a feature for this. It is called cmake-file-api(7) and is meant to be used by 'clients' to retrieve semantic information about the buildsystems CMake generates. So exactly what we want, especially with the Object Kind "codemodel". Unfortunately usage requires parsing of JSON data which is easy enough in neovim but I don't know about vim (maybe fall back to jq for filtering with something like this: jq '.configurations[].targets[] | select(.projectIndex | contains(0)) .name' codemodel-v2-xxxxxxxxxxxxxxxx.json.

Ping?

Pong! :) Apologies for the long radio silence, it's a busy period. And thanks a lot for the extensive comment.

Ideally there was a way to just open a terminal in something like a managed mode (for it to not close automatically/display a command exited message) and then just run a command in said terminal buffer.

Vim-CMake used to work in a similar fashion (up to v0.6.2). It used to run a terminal with a simple bash script running inside of it, where the script would wait for some command string on the stdin and would send some "end of command" text on the stdout. Very hacky and rather not portable.
Now, Vim-CMake runs commands as Vim/Neovim jobs, and sends the commands' output to an "echo" terminal (see :h nvim_open_term() for Neovim for instance). This method gives us more control over how to run jobs, how to parse their output, and what to display in the terminal. And it decouples the execution of commands from the visualization of text in the terminal, which I think makes the architecture of Vim-CMake more modular and would make unit testing easier (if we're ever to add unit tests).

Alternatively I would just pass all data as soon as is available as I already know the terminal knows how to handle it.

This would be the preferred way, unless there's a third options which we don't know about. We should just fix our filtering rules for data to send to the terminal, and make sure that this yields a consistent result on Unix and Windows and in Neovim and Vim (for now actually Vim on Windows does not seem to produce any good results at all, but that's another problem).
I'd suggest we just fine tune our text filtering routine, and also configure the job's PTY correctly so that lines are not truncated when not necessary. Feel free to have a go at this, otherwise I'll do it, but I won't have time these next few weeks. In any case, I'll make sure to test the changes thoroughly on all platforms/editors.

As for the problem with retrieving the build targets consistently, CMake's file API sounds like a promising option, thank you for pointing that out. I opened #62 for that.

Then let's agree that I work on this one and you take up #62, would that be okay?

Works for me


Prototype GBA cart boots on cart insertion, won't dump

So, I have a prototype GBA cart that I recently purchased for a significant amount of money. It seems to work fine on my GBA SP (although there are a few signs that it it isn't a finished build, notably in the form of some bugs).
I want to dump it and preserve it. But there's just one problem:
Whenever I stick the cart in, the game itself starts (showing the nintendo logo).
So I get to the point where the homebrew is loaded onto the GBA, and I insert the cart, only for the homebrew to exit and the game to start, as if I was turning the system on with the cart in the system.
This has never happened for any of the retail carts I've owned, and I've dumped several with this software without issue.
Strangely, if I turn the system on without a cart in it, the normal GBA boot screen shows up, but when I insert that cart at this point, it starts as normal. I don't think this happens for normal GBA carts.
A friend of mine suggested that this may be due to the ROM header having a debug flag set.
Anyway, the end result is that I can't dump the game with my Wii + GBA SP setup.
I'm begging you, PLEASE help me solve this. I want to preserve the contents of this cart.

hey there, have you tried starting your GBA with the cart inserted while holding down start+select? I think that should skip booting the game and may allow you to start the dumper from there.

IT WORKS! THANK YOU SO MUCH!
Or at least, the dumping process has started. I'll close this issue once I'm sure that this program doesn't have any difficulties getting matching dumps from this cart.
My big concern is that the cart has a sticker on it that says "64M", while this program says that the ROM size is 16.00MB. The original game is 8.00MB, which is equal to 64 MegaBITS, which is what I thought the 64M stood for.
Note to myself for future refernce:
Power on the GBA with the cart inserted, while holding start and select
Then start the homebrew on the Wii and plug in the GBA.

Who knows, perhaps it did not detect the size properly and its making an overdump now which would not be the end of the world to cut off.

So, it dumped sucessfully, and I think it is an overdump.
I used
dd if=orig.gba of=firsthalf.gba count=8192 bs=1024
and
dd if=orig.gba of=secondhalf.gba skip=8192 count=8192 bs=1024
to split the output, and both halves were identical.
I dumped it a second time and got the same checksum.

awesome! I suppose I can close this issue now and perhaps try and reword the readme a bit to be a bit more clear about the start+select thing so people understand it better.


Unable to start graph node locally (yarn run-graph-node)

In the docker-compose up console, I see the following error:
graph-node_1  | thread 'tokio-runtime-worker' panicked at 'Database does not use C locale. Please check the graph-node documentation for how to set up the database locale: database collation is `en_US.utf8` but must be `C`', store/postgres/src/connection_pool.rs:976:13

Perhaps it is simple error and happened to me only, anyone here have any clue on how to fix it?
Thanks in advanced.

I fixed the issue using older version of the image graphprotocol/graph-node:v0.29.0.
You can see my changes in docker-compose.yml file at the following commit:
https://github.com/limcheekin/scaffold-eth/commit/ab031722145b7a75e648b5aa306815569e0da487
Kindly let me know if you have better solution.
Thanks


Propose attributes properties on features

Those can be used for flipping strategies or as configurations

Available in 1.3.1


add delete button to modal

I added a delete button to category group and categories modal in admin page.

Works great!


Don't show the heightgraph on small screens

This PR hides the heightgraph on small screens.
In general there are two options for hiding:

css
js

The benefit of the css solution would be that on some devices the heightgraph would show up, when you turn the screen. Right now you would have to recalculate the route.
The benefit of the js solution is that we can avoid unnecessary calculations, which might speed up the overall performance, especially on potentially less powerful devices and long routes.

Is now on GH Maps

Just a minor issue, but if I close the height graph (say because I want to see more of the map) and then drag one of the route markers it pops up again.

:+1: https://github.com/graphhopper/graphhopper/issues/1658


Frontend Test: Add tests for adding forest client number

Describe the task
Add integration tests for the adding forest client number functionality in grant access page.
Acceptance Criteria

[x] test the invalid user input, forest client number is invalid (length<8), will not do this one in this ticket, validation will be tested in ticket #717
[x] test the not found user input, forest client number is not found
[x] test the inactive user input,  forest client number is inactive
[x] test the duplicate user input, forest client number is already added
[x] test the successful case for single forest client number
[x] test the successful case for multiple forest client numbers
[x] test adding multiple forest client number with mixed of successful and error case
[ ] test the adding button in on loading while we call the api
[x] test the delete forest client number case
[x] test the forest client card in error case
[x] test the forest client card in successful case
[x] test when property change, will cleanup the errors
[x] test information card cleanup when property change

Additional context

might need to think about how to mock the forest client api

I could not find a way to have the test 'adding button in on loading while we call the api' to work. It needs a change in the mockImplementation for the API call to have a non-blocking delay while handling the LoadingState. I have tried some approaches:

setTimeout function doesn't work for testing, the delay cannot be seem;
Using Vitest fake timers do apply the delay, but I could not verify the correct value of the LoadingState after the delay, so the test always fails (is fake timer running in a blocking way?).

@MCatherine1994


"tensorflow.python.ops.op_selector.UnliftableError: Unable to lift tensor" Error

Issue type
Support
Have you reproduced the bug with TensorFlow Nightly?
No
Source
source
TensorFlow version
v1.15.0-rc3-22-g590d6ee
Custom code
Yes
OS platform and distribution
Red Hat Enterprise Linux 8.4 (Ootpa)
Mobile device
No response
Python version
Python 3.6.8 :: Anaconda, Inc.
Bazel version
No response
GCC/compiler version
No response
CUDA/cuDNN version
12.3
GPU model and memory
No response
Current behavior?
tensorflow.python.ops.op_selector.UnliftableError: Unable to lift tensor <tf.Tensor 'model_1/lambda_1/Variable/replica_1/Initializer/Identity:0' shape=(64, 196) dtype=float32> because it depends transitively on placeholder <tf.Operation 'model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp/model_1/lambda_1/Variable' type=Placeholder> via at least one path, e.g.: model_1/lambda_1/Variable/replica_1/Initializer/Identity (Identity) <- model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp (ReadVariableOp) <- model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp/model_1/lambda_1/Variable (Placeholder)
Standalone code to reproduce the issue
I am implementing a code from https://github.com/aspuru-guzik-group/chemical_vae GitHub. I am successfully able to run the same. However, I am using multiple GPUs and to distribute the work among devices, I am using custom training for tf.distribute.MirroredStrategy(). I am pasting the code that I have converted for the same for no_main_prop method in chemvae/train_vae.py file in this GitHub repository.

=================================================================
def main_no_prop(params):
    start_time = time.time()

    # Create a MirroredStrategy.
    strategy = tf.distribute.MirroredStrategy()

    X_train, X_test = vectorize_data(params)

    # Define the model within the strategy scope
    with strategy.scope():
        AE_only_model, encoder, decoder, kl_loss_var = load_models(params)

        # compile models
        if params['optim'] == 'adam':
            optim = Adam(lr=params['lr'], beta_1=params['momentum'])
        elif params['optim'] == 'rmsprop':
            optim = RMSprop(learning_rate=params['lr'], rho=params['momentum'])
        elif params['optim'] == 'sgd':
            optim = SGD(learning_rate=params['lr'], momentum=params['momentum'])
        else:
            raise NotImplemented("Please define valid optimizer")

        model_losses = {'x_pred': params['loss'],
                        'z_mean_log_var': kl_loss}

        # vae metrics, callbacks
        vae_sig_schedule = partial(mol_cb.sigmoid_schedule, slope=params['anneal_sigmod_slope'],
                                   start=params['vae_annealer_start'])
        vae_anneal_callback = mol_cb.WeightAnnealer_epoch(
            vae_sig_schedule, kl_loss_var, params['kl_loss_weight'], 'vae')

        csv_clb = CSVLogger(params["history_file"], append=False)
        callbacks = [vae_anneal_callback, csv_clb]

        def vae_anneal_metric(y_true, y_pred):
            return kl_loss_var

        xent_loss_weight = tf.keras.backend.variable(params['xent_loss_weight'])

        # Distribute the dataset
        batch_size = params['batch_size']*4
        train_dataset = tf.data.Dataset.from_tensor_slices((X_train, {
            'x_pred': X_train,
            'z_mean_log_var': np.ones((np.shape(X_train)[0], params['hidden_dim'] * 2))
        }))
        train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)
        distributed_train_dataset = strategy.experimental_distribute_dataset(train_dataset)

        test_dataset = tf.data.Dataset.from_tensor_slices((X_test, {
            'x_pred': X_test,
            'z_mean_log_var': np.ones((np.shape(X_test)[0], params['hidden_dim'] * 2))
        }))
        test_dataset = test_dataset.batch(batch_size)
        distributed_test_dataset = strategy.experimental_distribute_dataset(test_dataset)

        AE_only_model.compile(
            loss=model_losses,
            loss_weights=[xent_loss_weight, kl_loss_var],
            optimizer=optim,
            metrics={'x_pred': ['categorical_accuracy', vae_anneal_metric]}
        )

        # Custom training loop
        epochs = params['epochs']
        initial_epoch = params['prev_epochs']

        @tf.function
        def training_steps():
            for epoch in range(initial_epoch, epochs):
                total_loss = 0.0
                num_batches = 0

                for inputs in distributed_train_dataset:
                    print("="*100)
                    print("Input (Train): ", inputs)
                    print("="*100)

                    def train_step(inputs):
                        with tf.GradientTape() as tape:
                            y_pred = AE_only_model(inputs[0])
                            loss = AE_only_model.compute_loss(inputs[1], y_pred)

                        gradients = tape.gradient(loss, AE_only_model.trainable_variables)
                        optim.apply_gradients(zip(gradients, AE_only_model.trainable_variables))
                        return loss

                    @tf.function
                    def distributed_train_step(inputs):
                        per_replica_losses = strategy.experimental_run_v2(train_step,  args=(inputs,))
                        return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)

                    total_loss += distributed_train_step(inputs)
                    num_batches += 1

                train_loss = total_loss / num_batches
                print(f'Epoch {epoch + 1}, Loss: {train_loss:.4f}')

                for inputs in distributed_test_dataset:
                    print("="*100)
                    print("Input (Test): ", inputs)
                    print("="*100)
                    def test_step(inputs):
                        y_pred = AE_only_model(inputs[0])
                        return AE_only_model.compute_loss(inputs[1], y_pred)

                    @tf.function
                    def distributed_test_step(inputs):
                        per_replica_losses = strategy.experimental_run(test_step,  args=(inputs,))
                        return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)

                    test_loss = distributed_test_step(inputs)

                print(f'Epoch {epoch + 1}, Validation Loss: {test_loss:.4f}')

        training_steps()

    encoder.save(params['encoder_weights_file'])
    decoder.save(params['decoder_weights_file'])
    print('Time of run: ', time.time() - start_time)
    print('**FINISHED**')
    return

=================================================================

Relevant log output
Error reported to Coordinator: Unable to lift tensor <tf.Tensor 'model_1/lambda_1/Variable/replica_1/Initializer/Identity:0' shape=(64, 196) dtype=float32> because it depends transitively on placeholder <tf.Operation 'model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp/model_1/lambda_1/Variable' type=Placeholder> via at least one path, e.g.: model_1/lambda_1/Variable/replica_1/Initializer/Identity (Identity) <- model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp (ReadVariableOp) <- model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp/model_1/lambda_1/Variable (Placeholder)
Traceback (most recent call last):
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/training/coordinator.py", line 297, in stop_on_exception
    yield
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 880, in run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
  File "/tmp/tmpd4qk3dv8.py", line 46, in train_step
    y_pred = ag__.converted_call(AE_only_model, train_step_scope.callopts, (inputs[0],), None, train_step_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 541, in converted_call
    result = converted_f(*effective_args)
  File "/tmp/tmpg_1oddri.py", line 192, in tf____call__
    output = ag__.converted_call(self.call, __call___scope.callopts, (inputs,), dict(kwargs, **{}), __call___scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmppws4cnkd.py", line 68, in tf__call
    do_return, retval_ = ag__.if_stmt(cond_1, if_true_1, if_false_1, get_state_1, set_state_1, ('do_return', 'retval_'), ())
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 895, in if_stmt
    return _py_if_stmt(cond, body, orelse)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 1004, in _py_if_stmt
    return body() if cond else orelse()
  File "/tmp/tmppws4cnkd.py", line 63, in if_false_1
    output_tensors, _, _ = ag__.converted_call(self.run_internal_graph, call_scope.callopts, (inputs, masks), None, call_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 541, in converted_call
    result = converted_f(*effective_args)
  File "/tmp/tmpupurh3pr.py", line 298, in tf__run_internal_graph
    ag__.for_stmt(depth_keys, None, loop_body_5, get_state_17, set_state_17, (), (), ())
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 339, in for_stmt
    return _py_for_stmt(iter_, extra_test, body, get_state, set_state, init_vars)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 350, in _py_for_stmt
    state = body(target, *state)
  File "/tmp/tmpupurh3pr.py", line 296, in loop_body_5
    ag__.for_stmt(nodes, None, loop_body_4, get_state_16, set_state_16, (), (), ())
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 339, in for_stmt
    return _py_for_stmt(iter_, extra_test, body, get_state, set_state, init_vars)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 350, in _py_for_stmt
    state = body(target, *state)
  File "/tmp/tmpupurh3pr.py", line 294, in loop_body_4
    ag__.if_stmt(cond_11, if_true_11, if_false_11, get_state_15, set_state_15, (), ('x._keras_shape', 'x._uses_learning_phase', "kwargs['mask']"))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 895, in if_stmt
    return _py_if_stmt(cond, body, orelse)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 1004, in _py_if_stmt
    return body() if cond else orelse()
  File "/tmp/tmpupurh3pr.py", line 211, in if_true_11
    computed_tensors, output_tensors, output_masks = ag__.if_stmt(cond_7, if_true_7, if_false_7, get_state_9, set_state_9, ('computed_tensors', 'output_tensors', 'output_masks'), ("kwargs['mask']",))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 895, in if_stmt
    return _py_if_stmt(cond, body, orelse)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 1004, in _py_if_stmt
    return body() if cond else orelse()
  File "/tmp/tmpupurh3pr.py", line 207, in if_false_7
    output_tensors = ag__.converted_call(_to_list, run_internal_graph_scope.callopts, (ag__.converted_call(layer.call, run_internal_graph_scope.callopts, (computed_tensors,), dict(kwargs, **{}), run_internal_graph_scope),), None, run_internal_graph_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmp4nuqr_3n.py", line 31, in tf__call
    retval_ = call_scope.mark_return_value(ag__.converted_call(self.function, call_scope.callopts, (inputs,), dict(arguments, **{}), call_scope))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmphkvehh5i.py", line 13, in tf__sampling
    epsilon = ag__.converted_call(K.random_normal_variable, sampling_scope.callopts, (), {'shape': (params['batch_size'], params['hidden_dim']), 'mean': 0.0, 'scale': 1.0}, sampling_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmp2dyogoo7.py", line 68, in tf__random_normal_variable
    retval_ = random_normal_variable_scope.mark_return_value(ag__.converted_call(variable, random_normal_variable_scope.callopts, (value,), {'dtype': dtype, 'name': name}, random_normal_variable_scope))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmpqaev2zhu.py", line 103, in tf__variable
    do_return, retval_ = ag__.if_stmt(cond_3, if_true_3, if_false_3, get_state_3, set_state_3, ('do_return', 'retval_'), ('v._keras_shape', 'v._uses_learning_phase'))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 895, in if_stmt
    return _py_if_stmt(cond, body, orelse)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 1004, in _py_if_stmt
    return body() if cond else orelse()
  File "/tmp/tmpqaev2zhu.py", line 67, in if_false_3
    v = ag__.converted_call(tf.Variable, variable_scope.callopts, (value,), {'dtype': ag__.converted_call(_convert_string_dtype, variable_scope.callopts, (dtype,), None, variable_scope), 'name': name}, variable_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 422, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 330, in _call_unconverted
    return f(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 258, in __call__
    return cls._variable_v1_call(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 219, in _variable_v1_call
    shape=shape)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 65, in getter
    return captured_getter(captured_previous, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/shared_variable_creator.py", line 69, in create_new_variable
    v = next_creator(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 65, in getter
    return captured_getter(captured_previous, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py", line 1345, in creator_with_resource_vars
    return self._create_variable(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 527, in _create_variable
    values.SyncOnReadVariable, *args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py", line 2311, in create_mirrored_variable
    value_list = real_mirrored_creator(devices, *args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 519, in _real_mirrored_creator
    v = next_creator(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 65, in getter
    return captured_getter(captured_previous, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 380, in variable_capturing_scope
    lifted_initializer_graph=lifted_initializer_graph, **kwds)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 262, in __call__
    return super(VariableMetaclass, cls).__call__(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 175, in __init__
    disallowed_placeholders=placeholder_ops)[initial_value]
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/lift_to_graph.py", line 260, in lift_to_graph
    add_sources=add_sources))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/op_selector.py", line 413, in map_subgraph
    % (repr(init_tensor), repr(op), _path_from(op, init_tensor, sources)))
tensorflow.python.ops.op_selector.UnliftableError: Unable to lift tensor <tf.Tensor 'model_1/lambda_1/Variable/replica_1/Initializer/Identity:0' shape=(64, 196) dtype=float32> because it depends transitively on placeholder <tf.Operation 'model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp/model_1/lambda_1/Variable' type=Placeholder> via at least one path, e.g.: model_1/lambda_1/Variable/replica_1/Initializer/Identity (Identity) <- model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp (ReadVariableOp) <- model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp/model_1/lambda_1/Variable (Placeholder)
Traceback (most recent call last):
  File "chemvae/train_vae.py", line 462, in <module>
    main_no_prop(params)
  File "chemvae/train_vae.py", line 339, in main_no_prop
    training_steps()
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 449, in __call__
    self._initialize(args, kwds, add_initializers_to=initializer_map)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 392, in _initialize
    *args, **kwds))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 1847, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2147, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2038, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py", line 915, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 335, in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py", line 902, in wrapper
    ), args, kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmpd4qk3dv8.py", line 112, in tf__training_steps
    test_loss, = ag__.for_stmt(ag__.converted_call(range, training_steps_scope.callopts, (initial_epoch, epochs), None, training_steps_scope), None, loop_body_2, get_state_2, set_state_2, (test_loss,), ('test_loss',), ())
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 339, in for_stmt
    return _py_for_stmt(iter_, extra_test, body, get_state, set_state, init_vars)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 350, in _py_for_stmt
    state = body(target, *state)
  File "/tmp/tmpd4qk3dv8.py", line 69, in loop_body_2
    total_loss, num_batches = ag__.for_stmt(distributed_train_dataset, None, loop_body, get_state, set_state, (total_loss, num_batches), ('total_loss', 'num_batches'), ())
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 337, in for_stmt
    return custom_handler(extra_test, body, init_vars)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/input_lib.py", line 408, in _autograph_for_loop
    return self.reduce(init_state, reduce_body)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/input_lib.py", line 445, in reduce
    cond, loop_body, [has_data, data, initial_state], parallel_iterations=1)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py", line 2675, in while_loop
    back_prop=back_prop)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/while_v2.py", line 198, in while_loop
    add_control_dependencies=add_control_dependencies)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py", line 915, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/while_v2.py", line 176, in wrapped_body
    outputs = body(*_pack_sequence_as(orig_loop_vars, args))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/input_lib.py", line 440, in loop_body
    state = reduce_fn(state, per_replica_data)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/input_lib.py", line 404, in reduce_body
    new_state = body(iterate, *state)
  File "/tmp/tmpd4qk3dv8.py", line 66, in loop_body
    total_loss += ag__.converted_call(distributed_train_step, training_steps_scope.callopts, (inputs,), None, training_steps_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 401, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 332, in _call_unconverted
    return f(*args)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 449, in __call__
    self._initialize(args, kwds, add_initializers_to=initializer_map)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 392, in _initialize
    *args, **kwds))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 1847, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2147, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2038, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py", line 915, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 335, in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py", line 902, in wrapper
    ), args, kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 401, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 330, in _call_unconverted
    return f(*args, **kwargs)
  File "/tmp/tmpd4qk3dv8.py", line 61, in distributed_train_step
    per_replica_losses = ag__.converted_call(strategy.experimental_run_v2, distributed_train_step_scope.callopts, (train_step,), {'args': (inputs,)}, distributed_train_step_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 439, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 330, in _call_unconverted
    return f(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py", line 764, in experimental_run_v2
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py", line 1810, in call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 662, in _call_for_each_replica
    fn, args, kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 196, in _call_for_each_replica
    coord.join(threads)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/training/coordinator.py", line 389, in join
    six.reraise(*self._exc_info_to_raise)
  File "/home/svc-ds/anaconda3/envs/chemvae/lib/python3.6/site-packages/six.py", line 719, in reraise
    raise value
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/training/coordinator.py", line 297, in stop_on_exception
    yield
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 880, in run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
  File "/tmp/tmpd4qk3dv8.py", line 46, in train_step
    y_pred = ag__.converted_call(AE_only_model, train_step_scope.callopts, (inputs[0],), None, train_step_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 541, in converted_call
    result = converted_f(*effective_args)
  File "/tmp/tmpg_1oddri.py", line 192, in tf____call__
    output = ag__.converted_call(self.call, __call___scope.callopts, (inputs,), dict(kwargs, **{}), __call___scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmppws4cnkd.py", line 68, in tf__call
    do_return, retval_ = ag__.if_stmt(cond_1, if_true_1, if_false_1, get_state_1, set_state_1, ('do_return', 'retval_'), ())
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 895, in if_stmt
    return _py_if_stmt(cond, body, orelse)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 1004, in _py_if_stmt
    return body() if cond else orelse()
  File "/tmp/tmppws4cnkd.py", line 63, in if_false_1
    output_tensors, _, _ = ag__.converted_call(self.run_internal_graph, call_scope.callopts, (inputs, masks), None, call_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 541, in converted_call
    result = converted_f(*effective_args)
  File "/tmp/tmpupurh3pr.py", line 298, in tf__run_internal_graph
    ag__.for_stmt(depth_keys, None, loop_body_5, get_state_17, set_state_17, (), (), ())
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 339, in for_stmt
    return _py_for_stmt(iter_, extra_test, body, get_state, set_state, init_vars)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 350, in _py_for_stmt
    state = body(target, *state)
  File "/tmp/tmpupurh3pr.py", line 296, in loop_body_5
    ag__.for_stmt(nodes, None, loop_body_4, get_state_16, set_state_16, (), (), ())
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 339, in for_stmt
    return _py_for_stmt(iter_, extra_test, body, get_state, set_state, init_vars)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 350, in _py_for_stmt
    state = body(target, *state)
  File "/tmp/tmpupurh3pr.py", line 294, in loop_body_4
    ag__.if_stmt(cond_11, if_true_11, if_false_11, get_state_15, set_state_15, (), ('x._keras_shape', 'x._uses_learning_phase', "kwargs['mask']"))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 895, in if_stmt
    return _py_if_stmt(cond, body, orelse)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 1004, in _py_if_stmt
    return body() if cond else orelse()
  File "/tmp/tmpupurh3pr.py", line 211, in if_true_11
    computed_tensors, output_tensors, output_masks = ag__.if_stmt(cond_7, if_true_7, if_false_7, get_state_9, set_state_9, ('computed_tensors', 'output_tensors', 'output_masks'), ("kwargs['mask']",))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 895, in if_stmt
    return _py_if_stmt(cond, body, orelse)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 1004, in _py_if_stmt
    return body() if cond else orelse()
  File "/tmp/tmpupurh3pr.py", line 207, in if_false_7
    output_tensors = ag__.converted_call(_to_list, run_internal_graph_scope.callopts, (ag__.converted_call(layer.call, run_internal_graph_scope.callopts, (computed_tensors,), dict(kwargs, **{}), run_internal_graph_scope),), None, run_internal_graph_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmp4nuqr_3n.py", line 31, in tf__call
    retval_ = call_scope.mark_return_value(ag__.converted_call(self.function, call_scope.callopts, (inputs,), dict(arguments, **{}), call_scope))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmphkvehh5i.py", line 13, in tf__sampling
    epsilon = ag__.converted_call(K.random_normal_variable, sampling_scope.callopts, (), {'shape': (params['batch_size'], params['hidden_dim']), 'mean': 0.0, 'scale': 1.0}, sampling_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmp2dyogoo7.py", line 68, in tf__random_normal_variable
    retval_ = random_normal_variable_scope.mark_return_value(ag__.converted_call(variable, random_normal_variable_scope.callopts, (value,), {'dtype': dtype, 'name': name}, random_normal_variable_scope))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 539, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmpqaev2zhu.py", line 103, in tf__variable
    do_return, retval_ = ag__.if_stmt(cond_3, if_true_3, if_false_3, get_state_3, set_state_3, ('do_return', 'retval_'), ('v._keras_shape', 'v._uses_learning_phase'))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 895, in if_stmt
    return _py_if_stmt(cond, body, orelse)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/operators/control_flow.py", line 1004, in _py_if_stmt
    return body() if cond else orelse()
  File "/tmp/tmpqaev2zhu.py", line 67, in if_false_3
    v = ag__.converted_call(tf.Variable, variable_scope.callopts, (value,), {'dtype': ag__.converted_call(_convert_string_dtype, variable_scope.callopts, (dtype,), None, variable_scope), 'name': name}, variable_scope)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 422, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 330, in _call_unconverted
    return f(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 258, in __call__
    return cls._variable_v1_call(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 219, in _variable_v1_call
    shape=shape)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 65, in getter
    return captured_getter(captured_previous, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/shared_variable_creator.py", line 69, in create_new_variable
    v = next_creator(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 65, in getter
    return captured_getter(captured_previous, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py", line 1345, in creator_with_resource_vars
    return self._create_variable(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 527, in _create_variable
    values.SyncOnReadVariable, *args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py", line 2311, in create_mirrored_variable
    value_list = real_mirrored_creator(devices, *args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 519, in _real_mirrored_creator
    v = next_creator(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 65, in getter
    return captured_getter(captured_previous, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 380, in variable_capturing_scope
    lifted_initializer_graph=lifted_initializer_graph, **kwds)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py", line 262, in __call__
    return super(VariableMetaclass, cls).__call__(*args, **kwargs)
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 175, in __init__
    disallowed_placeholders=placeholder_ops)[initial_value]
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/lift_to_graph.py", line 260, in lift_to_graph
    add_sources=add_sources))
  File "/home/svc-ds/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/op_selector.py", line 413, in map_subgraph
    % (repr(init_tensor), repr(op), _path_from(op, init_tensor, sources)))
tensorflow.python.ops.op_selector.UnliftableError: Unable to lift tensor <tf.Tensor 'model_1/lambda_1/Variable/replica_1/Initializer/Identity:0' shape=(64, 196) dtype=float32> because it depends transitively on placeholder <tf.Operation 'model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp/model_1/lambda_1/Variable' type=Placeholder> via at least one path, e.g.: model_1/lambda_1/Variable/replica_1/Initializer/Identity (Identity) <- model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp (ReadVariableOp) <- model_1/lambda_1/Variable/replica_1/Initializer/ReadVariableOp/model_1/lambda_1/Variable (Placeholder)

I am looking for a solution to this error. I have never seen this error before and could not understand it through online sources. There is very less resources online about this error.

@dhruv2103,
Could you please try to check in the latest tensorflow version and let us know if you are facing the same issue. Also Some apis are deprecated in TF v2.x. TF v1.x is not actively supported, please upgrade to the latest TF versions and refer to themigration doc to know more on this. Thank you!


formatter.getText() is executed for undefined properties of ProductSwitch

OpenUI5 version: 1.98.0
Given a ProductSwitch:
<f:ProductSwitch
	change = ".onClickProductSwitcherItem"
	items = "{
		path: '/items',
		templateShareable: false
	}">

	<f:items>
		<f:ProductSwitchItem
			src = "{src}"
			subTitle = "{
				path: 'subTitleI18NKey',
				formatter: '.formatter.getText'
			}"
			targetSrc = "{targetSrc}"
			title = "{
				path: 'titleI18NKey',
				formatter: '.formatter.getText'
			}" />
	</f:items>

</f:ProductSwitch>

Given a formatter.js:
getText(i18nKey) {
	const dialogue = this.getView().getModel("i18n").getProperty(i18nKey);
	return dialogue;
}

If both subTitle and title are specified, everything is OK but if one of them is not defined I get an error in console, since formatter.getText() is executed even if subTitle / title is not specified in a model (e.g. subTitleI18NKey is not defined):
{
	"src": "sap-icon://sap-box",
	"targetSrc": "documents",
	"titleI18NKey": "DOCUMENTS"
},

Such behaviour is leading to:

Assertion failed: could not find any translatable text for key 'undefined' in bundle file(s): './i18n/i18n_en.properties'

I don't know, may be such behaviour is by design, but I would not call formatter.getText() if no subTitle / title is provided in ProductSwitchItem XML.
Possible workaround: is to check for an undefined value of i18nKey inside of formatter.getText().

Hello @pubmikeb,
Thanks for sharing your findings!
I do not see any control's issue in the mentioned scenario.
You specified a subtitle to be used and a path to the subtitle value (subTitle = "{
path: 'subTitleI18NKey',
formatter: '.formatter.getText'}") and then you do not add such a value. It is expected to have an error thrown when you are looking for a non-existent value.
Kind Regards,
Niya

I do not see any control's issue in the mentioned scenario.

Since not all ProductSwitchItem might have a subTitle, which is totally OK (it's up to UI/UX team), the false positive warnings in the DevTool console might occur for the ProductSwitchItem without a subTitle.
Therefore, I propose to call getText() for subTitle only if this property has been actually defined.


[Bug]: v0.7.0-beta.1 dynamic import error.

System Info
System:
OS: macOS 14.4
CPU: (32) x64 13th Gen Intel(R) Core(TM) i9-13900KF
Memory: 10.78 GB / 32.00 GB
Shell: 5.9 - /bin/zsh
Binaries:
Node: 21.6.1 - /usr/local/bin/node
npm: 10.2.4 - /usr/local/bin/npm
pnpm: 9.0.4 - /usr/local/bin/pnpm
Watchman: 2<IP_ADDRESS> - /usr/local/bin/watchman
Details

v0.6.5 => v0.7.0-beta.1 ,disable lazyCompilation, dynamic import show above error.
Reproduce link
No response
Reproduce Steps
above.

Could you provide a reproduce? I tried to reproduce with the image you provided but didn't see the error

Could you provide a reproduce? I tried to reproduce with the image you provided but didn't see the error

I guess this bug is cause by path(@/locales/dayjs/${lang} => './en'), I will submit repro later.

Thanks for reporting, the fix will release in v0.7.0


Cldfbench support

@johenglisch merging this would require merging the cldfbench branches of all repos and recreating the prod db from these, right?

No, that is not needed.  The contributions.json file is set up, so that clld initdb checks out the cldfbench branch after cloning the data repos.

So, we can merge later (and/or create a new release on Zenodo and point contribution.json to the DOIs)

ah, ok. But we would want the cldfbench  branch to be the deafult branch in the future, I guess? E.g. to make sure we always make releases from it?

Ah, ok. I think I understand. We'd only switch contributions.json to use the default branch after the merge, right?

Japp


Adds a new hook to kustomize custom resources

This patch adds a new hook that can be used to customize OpenStackControlPlane CR before deploying the control plane. It also adds a template to configure services to use Ceph (toy) and enable its backends (cinder and glance for now)
As a pull request owner and reviewers, I checked that:

[x] Appropriate testing is done and actually running
[x] Appropriate documentation exists and/or is up-to-date:

[x] README in the role
[x] Content of the docs/source is reflecting the changes

/hold

recheck

recheck

/hold cancel

/hold

recheck

recheck

recheck
2023-07-13 13:52:19.570921 | controller | TASK [install_yamls_makes : Run openstack output_dir={{ cifmw_basedir|default(ansible_user_dir ~ '/ci-framework-data') }}/artifacts, chdir=/home/zuul/src/github.com/openstack-k8s-operators/install_yamls, target=openstack, dry_run={{ make_openstack_dryrun|default(false)|bool }}, params={{ make_openstack_params|default(omit) }}] ***
2023-07-13 13:52:19.570930 | controller | Thursday 13 July 2023  13:52:19 -0400 (0:00:00.039)       0:11:54.261 *********
2023-07-13 13:52:26.346056 | controller | [WARNING]: conditional statements should not include jinja2 templating
2023-07-13 13:52:26.346134 | controller | delimiters such as {{ }} or {% %}. Found: {{ make_openstack_until |
2023-07-13 13:52:26.346140 | controller | default(true) }}
2023-07-13 13:52:26.364815 | controller | fatal: [localhost]: FAILED! => {"attempts": 1, "changed": true, "checksum": "d011eb2b8e198168db4577b7951b9119464fe778", "cmd": "/usr/bin/gmake openstack", "dest": "/home/zuul/ci-framework-data/artifacts/ci_make_017_run_openstack.sh", "gid": 1000, "group": "zuul", "md5sum": "08f8f9e717e8391f26120d65add17238", "mode": "0755", "msg": "+ '[' -z /home/zuul/ci-framework-data/artifacts/manifests ']'\n+ '[' -z openstack-operators ']'\n+ OUT_DIR=/home/zuul/ci-framework-data/artifacts/manifests/openstack-operators\n+ '[' '!' -d /home/zuul/ci-framework-data/artifacts/manifests/openstack-operators ']'\n+ mkdir -p /home/zuul/ci-framework-data/artifacts/manifests/openstack-operators\n+ cat\nWarning: resource namespaces/openstack-operators is missing the kubectl.kubernetes.io/last-applied-configuration annotation which is required by oc apply. oc apply should only be used on resources created declaratively by either oc create --save-config or oc apply. The missing annotation will be patched automatically.\nerror: unable to default to a user name: the server is currently unable to handle the request (get users.user.openshift.io ~)\ngmake: *** [Makefile:408: operator_namespace] Error 1", "owner": "zuul", "rc": 2, "secontext": "unconfined_u:object_r:user_home_t:s0", "size": 807, "src": "/home/zuul/.ansible/tmp/ansible-tmp-1689270739.6474783-95970-140277480450836/source", "state": "file", "stderr": "+ '[' -z /home/zuul/ci-framework-data/artifacts/manifests ']'\n+ '[' -z openstack-operators ']'\n+ OUT_DIR=/home/zuul/ci-framework-data/artifacts/manifests/openstack-operators\n+ '[' '!' -d /home/zuul/ci-framework-data/artifacts/manifests/openstack-operators ']'\n+ mkdir -p /home/zuul/ci-framework-data/artifacts/manifests/openstack-operators\n+ cat\nWarning: resource namespaces/openstack-operators is missing the kubectl.kubernetes.io/last-applied-configuration annotation which is required by oc apply. oc apply should only be used on resources created declaratively by either oc create --save-config or oc apply. The missing annotation will be patched automatically.\nerror: unable to default to a user name: the server is currently unable to handle the request (get users.user.openshift.io ~)\ngmake: *** [Makefile:408: operator_namespace] Error 1\n", "stderr_lines": ["+ '[' -z /home/zuul/ci-framework-data/artifacts/manifests ']'", "+ '[' -z openstack-operators ']'", "+ OUT_DIR=/home/zuul/ci-framework-data/artifacts/manifests/openstack-operators", "+ '[' '!' -d /home/zuul/ci-framework-data/artifacts/manifests/openstack-operators ']'", "+ mkdir -p /home/zuul/ci-framework-data/artifacts/manifests/openstack-operators", "+ cat", "Warning: resource namespaces/openstack-operators is missing the kubectl.kubernetes.io/last-applied-configuration annotation which is required by oc apply. oc apply should only be used on resources created declaratively by either oc create --save-config or oc apply. The missing annotation will be patched automatically.", "error: unable to default to a user name: the server is currently unable to handle the request (get users.user.openshift.io ~)", "gmake: *** [Makefile:408: operator_namespace] Error 1"], "stdout": "bash scripts/gen-namespace.sh\noc apply -f /home/zuul/ci-framework-data/artifacts/manifests/openstack-operators/namespace.yaml\nnamespace/openstack-operators configured\nsleep 2\noc project openstack-operators\n", "stdout_lines": ["bash scripts/gen-namespace.sh", "oc apply -f /home/zuul/ci-framework-data/artifacts/manifests/openstack-operators/namespace.yaml", "namespace/openstack-operators configured", "sleep 2", "oc project openstack-operators"], "uid": 1000}
2023-07-13 13:52:26.364895 | controller |

/hold cancel
if passes on check, lets get this in.

recheck

recheck


Add balance-with-min strategy

Changes proposed in this pull request:

If balance is below minimum the voting power is replaces with zero

@bonustrack How long does it usually take until it's live after merge.

@bonustrack How long does it usually take until it's live after merge.

Could the this strategy also notify the voter if the balance is below minimum?

Could the this strategy also notify the voter if the balance is below minimum?

@samuveth When there is a new strategy, the frontend need to be redeployed, i'm doing this now it should be live in 5mn. When user will vote and have less than the minimum he will see 0 as voting power, so somehow he will get notified.

@samuveth When there is a new strategy, the frontend need to be redeployed, i'm doing this now it should be live in 5mn. When user will vote and have less than the minimum he will see 0 as voting power, so somehow he will get notified.

Does this affect both voting and proposing? Would it be possible to support a similar strategy that only blocks proposing for users below the threshold?


Fix off-by-one when initializing Data with discontiguous, underestimated Sequences

What's in this pull request?
Applies apple/swift#23244 to here as well.

@swift-ci Please test


Can't use env variables defined in Cloud build

Trying to run a Cloud Build file like the next:
steps:
  - name: 'gcr.io/google-appengine/exec-wrapper'
    id: TEST
    args:
      [
        '-i',
        'gcr.io/$PROJECT_ID/${_BUILD_TARGET}',
        '-s',
        '${_CLOUD_SQL_DEV}',
        '-e',
        'DJANGO_SETTINGS_MODULE=$$DJANGO_SETTINGS_MODULE',
        '-e',
        'CLOUD_SQL=/cloudsql/${_CLOUD_SQL_DEV}',
        '--',
       'python',
        'manage.py',
        'migrate',
        '--no-input',
      ]
    env:
      - 'DJANGO_SETTINGS_MODULE=test.production'

Is throwing an error, as it is assigning the env variable in app engine as the string "$DJANGO_SETTINGS_MODULE" instead of the real value that was coming from the env variable.
Step #0 - "apply migrations": ModuleNotFoundError: No module named '$DJANGO_SETTINGS_MODULE' Finished Step #0 - "apply migrations"
Not sure if the problem is in https://github.com/GoogleCloudPlatform/ruby-docker/blob/05dc67368ed07340f648e19a5b1f195df2a397f6/app-engine-exec-wrapper/execute.sh#L32 as it is adding the environment variables in quotes ENV_PARAMS+=(-e "$OPTARG"). Maybe removing the quotes would fix the issue
ENV_PARAMS+=(-e $OPTARG)
or  the problem can also be in https://github.com/GoogleCloudPlatform/ruby-docker/blob/05dc67368ed07340f648e19a5b1f195df2a397f6/app-engine-exec-wrapper/execute.sh#L32 as it is adding the ENV_PARAMS using quotes. Maybe removing the quotes would fix the issue
docker run --rm ${ENTRYPOINT} --volumes-from=${CONTAINER} --network=${CONTAINER_NETWORK} ${ENV_PARAMS[@]} ${IMAGE} "$@"
It is a silly example, as I can use a substituion instead of an env variable there, but what I'm really trying to do is something like https://cloud.google.com/build/docs/securing-builds/use-secrets

If you're using basic plain-text environment variables (no secrets), you don't need to use $$.
DJANGO_SETTINGS_MODULE=$DJANGO_SETTINGS_MODULE should work just fine.

If you're looking to use secrets with this cloud builder, you'll need to use the entrypoint parameter.  This is required to refer to the environment variable for the secret.
You'll need to call the /buildstep/execute.sh script to in the args parameter.
You'll end up with something like this:
steps:
- name: gcr.io/google-appengine/exec-wrapper
  entrypoint: bash
  args:
  - -c
  - |-
    /buildstep/execute.sh -i gcr.io/my-project/appengine/some-long-name \
        -e ENV_VARIABLE_1=value1 -e ENV_2=value2 \
        -s my-project:us-central1:my_cloudsql_instance \
         -- bundle exec rake db:migrate
  secretEnv:
  - ENV_VARIABLE_1
  - ENV_2
availableSecrets:
  secretManager:
  - versionName: projects/my-project/secrets/secret-1/versions/1
    env: ENV_VARIABLE_1
  - versionName: projects/my-project/secrets/secret-2/versions/1
    env: ENV_2

Great! Thanks for the answer. It would be great to have it documented, maybe in the README.md?

@rgalite I'm not sure I understand where value1 and value2 are coming from in your example.  Are those values stored in Secret Manager and populated with the $$ notation?


properly redirect for the tier-1-orgs page

Great question! I think we have that taken care of with the nginx config here: https://github.com/stellar/stellar-docs/blob/3dc16ba636d6e912db7cf8e81d06ac8fc2bb148b/nginx/nginx.conf#L20-L22
i'm certainly not an nginx expert, but i think we've managed to get the redirects/configs right (finally lol) to take care of trailing slashes

Preview is available here:http://developers-pr401.previews.kube001.services.stellar-ops.com

@ElliotFriend sounds good! I was reviewing based on the nearby lines which had that included but it sounds like those are redundant


Build scripts

This PR adds build script for Linux, and improves the build script for windows.

@yatli,
Thanks for your contribution as a Microsoft full-time employee or intern. You do not need to sign a CLA.
Thanks,
Microsoft Pull Request Bot


Client Allocation Request for: Sanjung

Client Allocation Questions
Core Information

Name: Sanjung
Website / Social Media: http://e-samjung.co.kr/gnuboard4/
Region: Asia excl. Greater China
DataCap Requested: 5TiB
Addresses to be Notarized: f3vnnnt6zokj2bfplbzrqiwukdi6y7xprt36vdrnljw752hturceqql5z4grkdwhqws4zxrly5ymyjptdv5oma
Notary Requested: swatchliu

@Samjungee Please subscribe to notifications for this Issue to be aware of updates. Notaries may request additional information on the Issue.

https://github.com/filecoin-project/filecoin-plus-client-onboarding/issues/1059

I will answer if I need any information.

Please choose your local notary, thanks!


[FEATURE REQ] Support WithClientCapabilities() by MsalConfidentialClient

Library name
Azure.Identity
Please describe the feature.
MsalPublicClient already supports WithClientCapabilities() with CP1 during client building. We hope the parity feature on MsalConfidentialClient

//fyi:  @christothes, @schaabs

Hi @dingmeng-xue  I could be wrong, but I believe CP1 is only supported by PublicClientApplicationBuilder.

HI, @christothes , I added it just for tracking.

@christothes - does Azure SDK not support AcquireTokenOnBehalfOf ? I think CAE makes sense in web api scenario (OBO).


Etherpad names

Is it possible to take a user's logged in name and transfer it to be their etherpad name?

It's actually super easy! We just need to append ?userName=#{format_user_name(current_user)} to the etherpad href.
http://etherpad.org/doc/v1.5.1/#index_username


ClientCertificateCredential doesn't support more than one cert in a file

There is a couple of assumptions within the ClientCertificateCredential implementation being made:

Assuming there is only one cert in the file (and that is the signing cert).
Assuming the private key in the file is for the first cert.

In practice, these assumptions won't always hold. The parsing logic should be resilient to these, and these assumptions should be documented otherwise.
https://github.com/Azure/azure-sdk-for-cpp/blob/df6f5c910d3b2b9d913711e0e4d8c0313fb2c27a/sdk/identity/azure-identity/src/client_certificate_credential.cpp#L232-L240

The service only needs a leaf node cert to be passed-in, even when it is part of the chain. Therefore, we don't need to preemptively support multiple certs in a file. We can re-open if a scenario comes up that requires multiple certs to be supported.

Assuming the private key in the file is for the first cert.

We could consider making the cert file reading/parsing to be resilient to order changes as part of MQ, as general goodness, though it doesn't seem to be necessary atm.


ci: rename permaplant service

Basics

[ ] I added a line to /doc/CHANGELOG.md
[x] The PR is rebased with current master.

[ ] References to issues, e.g. close #X, are in the commit messages.
[x] The buildserver is happy.

Checklist

[ ] I have installed and I am using pre-commit hooks
[ ] I fully described what my PR does in the documentation
(not in the PR description)
[ ] I fixed all affected documentation
[ ] I fixed the introduction tour
[ ] I wrote migrations in a way that they are compatible with already present data
[ ] I fixed all affected decisions
[ ] I added unit tests for my code
[ ] I added code comments, logging, and assertions as appropriate
[ ] I translated all strings visible to the user
[ ] I mentioned every code or binary not directly written or done by me in reuse syntax
[ ] I created left-over issues for things that are still to be done
[ ] Code is conforming to our Architecture
[ ] Code is conforming to our Guidelines
(exceptions are documented)
[ ] Code is consistent to our Design Decisions

Review

[ ] I've tested the code
[ ] I've read through the whole code
[ ] Examples are well chosen and understandable

npm run insert fails because of a password error ... I'll investigate right away

Thx!

Pipeline restarted :) Let's see 😄

All E2E tests failed,

successful except of E2E tests, could be a spurious error

jenkins build please

I already found the problem - I accidentally executed the apache playbook with --check (basically no-op). Should be fixed and pipeline is running 😄

@4ydan do we merge this or do you want to pick these changes and put them into #648?

Just ignore https://github.com/ElektraInitiative/PermaplanT/pull/648 for now.

Pipeline is 💚

@lukashartl Great job! Good to have a simpler setup! Migration worked flawlessly! :green_heart:
But probably #780 was introduced?


.Net: GetPromptTemplate option does not work for HandlebarsPlanner

Describe the bug
The GetPromptTemplate planner option is ignored in the plan prompt creation for HandlebarsPlanner.
To Reproduce
Steps to reproduce the behavior:

Create a HandlebarsPlannerOptions, assign to GetPromptTemplate a string func delegate which returns any text
Create HandlebarsPlanner with those options
Create plan
View plan prompt (plan.Prompt)
See that GetPromptTemplate delegate is ignored and plan prompt contains only the text from the CreatePlanPrompt.handlebars embedded resource

Expected behavior
The plan prompt should contain the string returned by the string delegate assigned to the GetPromptTemplate option.
Platform

OS: Windows 10
IDE: Visual Studio 2022
Language: C#
Source: SemanticKernel NuGets 1.1.0, latest code in SK repo main branch

Additional context
Fix should change first line in HandlebarsPlanner.GetHandlebarsTemplateAsync() to:
var createPlanPrompt = this._options.GetPromptTemplate?.Invoke() ?? this.ReadPrompt("CreatePlanPrompt.handlebars");

@matthewbolanos This is my first time participating in an open source repository. Can I code the fix and create a PR?

@matthewbolanos This is my first time participating in an open source repository. Can I code the fix and create a PR?

@banduki That would be great! Please do and I'll review :smile:

Hi @banduki , apologies, we had a couple of stale, unimplemented options in the HandlebarsPlanner. These have since been removed, and new options have been added for prompt configuration, namely two called GetAdditionalPromptContext and CreatePlanPromptHandler. The latter can be used to override the prompt completely, whereas the former allows you to inject domain context into our engineered prompt.
See the two latest samples in Example65_HandlebarsPlanner starting on line 361
Closing this issue as resolved