[FEEDSTOCK REQUEST] Pola-rs

Describe the package you'd like added
Website: https://www.pola.rs/
Source: https://github.com/pola-rs/polars
Describe how this package fits in with the project
A fast library written in Rust for working with DataFrames in memory, built to the Apache Arrow spec. Speed is the primary goal.
Additional context
* submitted on behalf from Open-CE users at US Bank

Closing this, as it is duplicate of #1029


Change the color of the borders?

I want to have black calendar cells with a white border. Is this possible to do?

found that calendarView.backgroundColor does the trick!


feat: 🎸 add assetId in GET /assets/{asset} response

JIRA Link
N/A
Changelog / Description
add assetId in GET /assets/{asset} response
Checklist -

[ ] New Feature ?
[ ] Updated swagger annotation (if API structure is changed) ?
[ ] Unit Test (if possible) ?
[ ] Updated the Readme.md (if required) ?

/fast-forward


adds fix for navbar dropdown and float-right issue

This is a fix for issue #137  - Dropdown with float-right appears off screen. I have documented the issue and the fix below. Please let me know if there are any changes or if there is anything else I can do.

Hi, I have fixed this bug in https://github.com/picturepan2/spectre/pull/144 . I add a display: inline-block to the dropdowns to prevent another bug. Thank you.


helpIntent & stopIntent

How can this standard Intents be handled? My skill missed certification because this two are missing.

You can implement them in a straightforward way. I have a certified skill in https://github.com/artsy/elderfield that could be a good example with things like:
app.intent('AMAZON.HelpIntent', {
    "slots": {},
    "utterances": [
      "help"
    ]
  },
  function(req, res) {
    console.log('app.AMAZON.HelpIntent');
    res.say("Artsy’s mission is to make all the world’s art accessible to anyone with an Internet connection. You can ask Artsy about an artist. For example say ask Artsy about Norman Rockwell. What artist would you like to hear about?");
    res.shouldEndSession(false);
    res.send();
  }
);

You might also want to read http://artsy.github.io/blog/2016/11/30/bringing-artsy-to-amazon-echo-alexa/


User controller

controller should have the following cruds:
/api/users
get all users
get a single user by _id
create (post) a new user
update (put) a user by _id
remove (delete) user by_id
api/users/:userId/friends/:friendId
add (post) friend by _id
remove (delete) friend by _id

DONE!

actually not super done because i've imported thought into the usercontroller but i haven't done the bonus thing where deleteing the user will also delete all associated thoughts


podspec

Those file will be linked to the react native iOS project when you run:
react-native link instabug-reactnative

react-native link instabug-reactnative will link the module to the ios/android project, not generate the podspec file. For people linking the modules via cocoapods (instead of react-native link) this podspec file is needed. Have a look at other modules and you will see that most of them provide a podspec file, even react native itself provide a podspec (https://github.com/facebook/react-native/blob/master/React.podspec) to link it via cocoapods

@yousefhamza any update on this?

@DevHossamHassan can you have a look at this PR, I think @yousefhamza didn't understand the value/purpose of it so it was closed. Linking modules via cocoapods is very common, and I think would be great this module to support it too.

@djhr I'm afraid it doesn't work for us.
The React Pod you refer to is now deprecated and it's version doesn't satisfy our minimum requirement for React, also adding it in a pod cause name conflicts between React in Pods directory and React in node_modules directory

@yousefhamza sorry for the delay getting back to you.

The React Pod you refer to is now deprecated and it's version doesn't satisfy our minimum requirement for React

On the podspec file you can specify which version of the dependency you want, e.g.
s.dependency 'Instabug', '~> 7.0' or
s.dependency 'Instabug ~> 7.0' not sure which one it is, but one of the two works

adding it in a pod cause name conflicts between React in Pods directory and React in node_modules directory

My guess is you mixed react-native link with podspec. When you opt for podspec as your installation method you need to do it for everything, including react native lib itself, e.g.
platform :ios, '8.0'

target 'Sample' do
  use_frameworks!

  # Pods for Sample
  pod 'React', :path => '../node_modules/react-native', :subspecs => [
    'Core',
    'DevSupport',
    'RCTActionSheet',
    'RCTAnimation',
    'RCTGeolocation',
    'RCTImage',
    'RCTLinkingIOS',
    'RCTPushNotification',
    'RCTNetwork',
    'RCTSettings',
    'RCTText',
    'RCTVibration',
    'RCTWebSocket'
  ]
  pod 'Yoga', :path => '../node_modules/react-native/ReactCommon/yoga'
  pod 'instabug-reactnative', :path => '../node_modules/instabug-reactnative'

  target 'SampleTests' do
    inherit! :search_paths
    pod 'React', :path => '../node_modules/react-native', :subspecs => [
      'Core',
      'DevSupport',
      'RCTActionSheet',
      'RCTAnimation',
      'RCTGeolocation',
      'RCTImage',
      'RCTLinkingIOS',
      'RCTPushNotification',
      'RCTNetwork',
      'RCTSettings',
      'RCTText',
      'RCTVibration',
      'RCTWebSocket'
    ]
  end

end

Also important, after you do pod init you should start using the .xcworkspace file to open the project on xcode instead of .xcodeproj file
Some helpful resources:
http://facebook.github.io/react-native/releases/0.47/docs/integration-with-existing-apps.html#configuring-cocoapods-dependencies
As last note, I have it working on a project, so it has to be a matter of configuration/steps

@djhr

On the podspec file you can specify which version of the dependency you want, e.g.
s.dependency 'Instabug', '~> 7.0' or
s.dependency 'Instabug ~> 7.0' not sure which one it is, but one of the two works

The latest React pod version released is below our latest minimum requirement for React as dependency

My guess is you mixed react-native link with podspec. When you opt for podspec as your installation method you need to do it for everything, including react native lib itself, e.g.

In the podspec file, you cannot specify the path as shown in your example, this could be done in the Podfile itself.


rdbloader to support FD. RESP to support delKeyBeforeWrite

Fixing rdb parsing of empty database
To be on the safe side, handling of Incomplete writev()
Extend RespToTcpLoader to support FD
Rebrand RespToTcpLoader to be RespToRedisLoader.
Extend RdbxToResp to support flag delKeyBeforeWrite.
Extend RdbxRespToRedisLoader to know mapping RX RESP error to TX CMD.

This PR was aimed to reaarange commits of another PR.
But eventually force-push was used on original PR.


Update alpaca-lora to use camelcase convention

What does this PR do?
The LLaMA PR was merged with some slight changes to the transformers class names. I wanted to play around with it so I modified alpaca-lora to fit the new convention and use the main branch.
Context:

zphang commented yesterday
I've pushed another breaking change to the code (renaming all LLaMA tokenizer, configuration and model classes to match the camelcase convention in HF Transformers (LLaMA* -> Llama*). This will require updating code, or at minimum, updating the configuration files for the tokenizers and models.

Cool, I'll merge this once I figure out a way to make it less of a breaking change.


Decks should be automatically (or via prompt) assigned to the right archetype

Not just the macro archetype chosen in Gatherling.

Alternatively, if a deck is assigned to one of the macro-archetypes, show a prompt on the deck page, and add it to some kind of moderation queue.  (Yay, more infrastructure)

The real issue here, I guess, is that some decks truly belong in Aggro and should not be prompting us to do anything because they are a one-off and don't have enough siblings to have an archetype added. Whereas some decks are in Aggro just because they were assigned to that on Gatherling and truly belong in Mono W Heroic. I don't like the idea of adding a flag "reviewed" or similar but that is one way to do it. Even if the machine guesses an archetype we probably still want it to be manually checked so maybe we do need a reviewed flag.

Couldn't think of a better way so added reviewed column and use it in 9b7bcb7.


In My DAOs page, fronts size does not unite

Description
In My DAOs page, fronts size does not unite
Steps To Reproduce

Log in...
Enter the My DAOs page

Current Behavior

The fronts size in My own DAOs is bigger than Participated DAOs'

Expected Behavior

Fronts size should unite

Environment

Platform: PC
Browser: Chrome 126.0.6478.114

fixed confirm


[Suggestion] Proper merging strategy for transition hooks

Now when using route transition hooks in mixin they can be redefined by component.
Just look at this demo. Click to "Go to Foo" to see what only component data hook is called. To check that mixin works click to "Go to Bar" you will see that mixin data hook is called.
I think it will be good to achieve Vue hooks merging strategy also for transition hooks.

This has been implemented for data, activate and deactivate in 0.7.3.

@yyx990803 thanks. Works great with v1.0.0-rc.1! http://jsfiddle.net/pnwtq6r0/1/


How to disable animation of children but remain transition of underline

Dear all, I would like to know how to disable the sliding animation of all children components when switching among different tabs, but remaining the animation of underline.
I have read the doc and using the scrollWithoutAnimation  and locked to disable the sliding effect, but it also disables the sliding animation of underline. I want to keep the animation of underline and disable the horizontal dragging, can anyone show me how to make a customize the default ScrollableTabBar?

scrollWithoutAnimation={false}


[BEAM-7386] Introduce EventTimeBoundedEquijoin.

Similar to other inner joins except it includes a temporal predicate,
allowing users to join unbounded PCollections in the GlobalWindow.

Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

[ ] Choose reviewer(s) and mention them in a comment (R: @username).
[ ] Format the pull request title like [BEAM-XXX] Fixes bug in ApproximateQuantiles, where you replace BEAM-XXX with the appropriate JIRA issue, if applicable. This will automatically link the pull request to the issue.
[ ] Update CHANGES.md with noteworthy changes.
[ ] If this contribution is large, please file an Apache Individual Contributor License Agreement.

See the Contributor Guide for more tips on how to make review process smoother.

GitHub Actions Tests Status (on master branch)

See CI.md for more information about GitHub Actions CI.

R: @reuvenlax

/cc @kennknowles

Out of curiosity, why are you adding this here instead of the schema join library (which SQL uses)?

Out of curiosity, why are you adding this here instead of the schema join library (which SQL uses)?

I wasn't aware of the other join library. I saw the join extension library implementations, plus the previously closed PR in BEAM-7386, and thought that this new one should be placed near and made the assumption that SQL would reuse the implementation. Looking at it now though it seems like the SQL joins don't use the join extension library.
Should I keep this one around or refactor into the SQL schema join library?

Out of curiosity, why are you adding this here instead of the schema join library (which SQL uses)?

I wasn't aware of the other join library. I saw the join extension library implementations, plus the previously closed PR in BEAM-7386, and thought that this new one should be placed near and made the assumption that SQL would reuse the implementation. Looking at it now though it seems like the SQL joins don't use the join extension library.
Should I keep this one around or refactor into the SQL schema join library?

After looking at the SQL schema join library I think it would be useful to keep this join in join-extension so it can be used with non-schema'd PCollections and support more than equijoins. The schema join should be able to reuse this implementation in the future by refactoring the Join#expand method. Maybe at that point we would discuss inducting the join-extension into core.

Oops - didn't realize that 'Close with Comment' was for the whole PR. I thought it was just for the comment thread.

Sorry for the delay.
AFAIK both this and the schema library are limited today to equijoins. The schema API is designed so that we can extend it later with non equijoins, however doing arbitrary join conditions in a distributed manner can be a hard problem.

Sorry for the delay.
AFAIK both this and the schema library are limited today to equijoins. The schema API is designed so that we can extend it later with non equijoins, however doing arbitrary join conditions in a distributed manner can be a hard problem.

This implementation allows for simple comparisons between records for the join beyond an equijoin by allowing the user to provide a SimpleFunction<KV<V1, V2>, Boolean> compareFn. Do you see an issue with this?

I am a bit confused about the usage of compareFn here. State is per key, so I believe that your DoFn will only join items that have the same key - the compareFn will never even get to compare items with different keys. Is the idea to allow the user to generate a subset of an equijoin?

I am a bit confused about the usage of compareFn here. State is per key, so I believe that your DoFn will only join items that have the same key - the compareFn will never even get to compare items with different keys. Is the idea to allow the user to generate a subset of an equijoin?

Yes, it will be a subset of an equijoin. Sorry for the confusion.

To be clear, this join is a temporal join. You have to have a condition relating the timestamps of the two elements that can translate into garbage collection threshold.

(the garbage collection can be looser than the actual comparison)

IMO it's clearer if we reserve TemporalJoin for cases where the join
condition itself is primarily temporal - e.g. AS OF joins, such as the
quotes/trade examples. Yes this join has a temporal component that bounds
our lookback (and also affects GC), but the primary join condition is not
that.
On Mon, Nov 2, 2020 at 11:15 AM Kenn Knowles<EMAIL_ADDRESS>wrote:

(the garbage collection can be looser than the actual comparison)
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I think there is a hidden innovation here when you refer to AS OF joins. In standard SQL this refers to the past state of a table as it is mutated in processing time. It is primarily useful for inspecting the evolution of a table.
To yield correct results for a quotes/trades pipeline it must refer to event time. If all events from quotes and trades are ordered (and jointly ordered) then AS OF can answer the query. Otherwise it cannot. This is true for standard SQL databases: if a quote is inserted that contradicts a prior result of the quote/trade match, then the ordering used by AS OF cannot be used to determine the price for a trade.
I think the re-interpretation of AS OF to refer to event time is a good change. I know that Flink has done this same thing. I think having a transform that can correctly address the quote/trade problem is also good. In documentation and API just be very careful to make sure it is clear. We already have a lot of users / StackOverflow questions talking about "before" and "after" and "as of" in terms of processing time, mixing it up with event time.

We could call this one "timestamp-bounded equijoin" or some such.

Note that I used as-of joins as.an example reference point, but this code
is not an actual SQL implementation, so I did feel bound to be standards
compliant.
As you mentioned, Flink has reinterpreted AS OF joins to refer to even time
in their SQL dialect. Note though that in SQL you would write this as
SELECT * FROM table INNJER_JOIN temporal_table FOR SYSTEM_TIME AS OF
A simple extension might be to replace SYSTEM_TIME with EVENT_TIME. This
might be a useful extension to our SQL implementation.
On Mon, Nov 2, 2020 at 2:20 PM Kenn Knowles<EMAIL_ADDRESS>wrote:

I think there is a hidden innovation here when you refer to AS OF joins.
In standard SQL this refers to the past state of a table as it is mutated
in processing time. It is primarily useful for inspecting the evolution
of a table.
To yield correct results for a quotes/trades pipeline it must refer to event
time. If all events from quotes and trades are ordered (and jointly
ordered) then AS OF can answer the query. Otherwise it cannot. This is true
for standard SQL databases: if a quote is inserted that contradicts a prior
result of the quote/trade match, then the ordering used by AS OF cannot be
used to determine the price for a trade.
I think the re-interpretation of AS OF to refer to event time is a good
change. I know that Flink has done this same thing. I think having a
transform that can correctly address the quote/trade problem is also good.
In documentation and API just be very careful to make sure it is clear. We
already have a lot of users / StackOverflow questions talking about
"before" and "after" and "as of" in terms of processing time, mixing it up
with event time.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
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.

I am a bit confused about the usage of compareFn here. State is per key, so I believe that your DoFn will only join items that have the same key - the compareFn will never even get to compare items with different keys. Is the idea to allow the user to generate a subset of an equijoin?

Yes, it will be a subset of an equijoin. Sorry for the confusion.

I am a bit confused about the usage of compareFn here. State is per key, so I believe that your DoFn will only join items that have the same key - the compareFn will never even get to compare items with different keys. Is the idea to allow the user to generate a subset of an equijoin?

Yes, it will be a subset of an equijoin. Sorry for the confusion.

Now that i'm thinking about this further, the compareFn may be unnecessarily complicating the API for this join. I imagined it would be helpful for a user who wants to add logic before emitting a matched result, like a filter, but it would be more idiomatic for the user to apply a filter transform to the join result instead.

+1 IMO it would be useful if it somehow happened prior to the CGBK, but
since it's after a subsequent ParDo or Filter will likely be fused anyway.
On Tue, Nov 3, 2020 at 11:23 AM Tyson Hamilton<EMAIL_ADDRESS>wrote:

I am a bit confused about the usage of compareFn here. State is per key,
so I believe that your DoFn will only join items that have the same key -
the compareFn will never even get to compare items with different keys. Is
the idea to allow the user to generate a subset of an equijoin?
Yes, it will be a subset of an equijoin. Sorry for the confusion.
I am a bit confused about the usage of compareFn here. State is per key,
so I believe that your DoFn will only join items that have the same key -
the compareFn will never even get to compare items with different keys. Is
the idea to allow the user to generate a subset of an equijoin?
Yes, it will be a subset of an equijoin. Sorry for the confusion.
Now that i'm thinking about this further, the compareFn may be
unnecessarily complicating the API for this join. I imagined it would be
helpful for a user who wants to add logic before emitting a matched result,
like a filter, but it would be more idiomatic for the user to apply a
filter transform to the join result instead.
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.

We could call this one "timestamp-bounded equijoin" or some such.

Ya this is a tough one to name, more input is welcome. I floated the following: EventTimeLimitedDurationInnerJoin, EventTimeScopedDurationInnerJoin

FYI the actual class name can be a bit longer, as long there is a good builder method. e.g. You could do something like:
Join.boundedInnerJoin(pc1, pc2);
This would be easier to deal with if this contrib Join library used PTransforms instead of functions.

Obsolete.


python-package/lightgbm/sklearn.py:1061: error: Unsupported target for indexed assignment ("Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]")  [index]
python-package/lightgbm/sklearn.py:1063: error: Unsupported target for indexed assignment ("Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]")  [index]
python-package/lightgbm/sklearn.py:1065: error: Argument 1 to "enumerate" has incompatible type "Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]"; expected "Iterable[Union[str, Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]"  [arg-type]
python-package/lightgbm/sklearn.py:1067: error: Unsupported target for indexed assignment ("Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]")  [index]
python-package/lightgbm/sklearn.py:1069: error: Unsupported target for indexed assignment ("Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]")  [index]
python-package/lightgbm/sklearn.py:1079: error: No overload variant of "__setitem__" of "list" matches argument types "int", "Tuple[Any, Any]"  [call-overload]
python-package/lightgbm/sklearn.py:1079: note: Possible overload variants:
python-package/lightgbm/sklearn.py:1079: note:     def __setitem__(self, SupportsIndex, None, /) -> None
python-package/lightgbm/sklearn.py:1079: note:     def __setitem__(self, slice, Iterable[None], /) -> None
python-package/lightgbm/sklearn.py:1081: error: No overload variant of "__setitem__" of "list" matches argument types "int", "Tuple[Any, Any]"  [call-overload]
python-package/lightgbm/sklearn.py:1081: note: Possible overload variants:
python-package/lightgbm/sklearn.py:1081: note:     def __setitem__(self, SupportsIndex, None, /) -> None
python-package/lightgbm/sklearn.py:1081: note:     def __setitem__(self, slice, Iterable[None], /) -> None
python-package/lightgbm/sklearn.py:1148: error: Unsupported operand types for < ("int" and "None")  [operator]
python-package/lightgbm/sklearn.py:1174: error: Incompatible return value type (got "None", expected "int")  [return-value]
python-package/lightgbm/sklearn.py:1187: error: Argument 12 of "fit" is incompatible with supertype "LGBMModel"; supertype defines the argument type as "Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None]"  [override]
python-package/lightgbm/sklearn.py:1187: note: This violates the Liskov substitution principle
python-package/lightgbm/sklearn.py:1187: note: See https://mypy.readthedocs.io/en/stable/common_issues.html#incompatible-overrides
python-package/lightgbm/sklearn.py:1200: error: Incompatible default for argument "eval_at" (default has type "Tuple[int, int, int, int, int]", argument has type "Union[List[int], Tuple[int]]")  [assignment]
python-package/lightgbm/dask.py:593: error: Argument 1 to "append" of "list" has incompatible type "Tuple[List[Optional[Any]], List[Optional[Any]]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:596: error: Value of type "_DatasetNames" is not indexable  [index]
python-package/lightgbm/dask.py:597: error: Value of type "_DatasetNames" is not indexable  [index]
python-package/lightgbm/dask.py:615: error: Argument 1 to "append" of "list" has incompatible type "List[Optional[Any]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:617: error: "_DatasetNames" has no attribute "append"  [attr-defined]
python-package/lightgbm/dask.py:633: error: Argument 1 to "append" of "list" has incompatible type "List[Optional[Any]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:635: error: "_DatasetNames" has no attribute "append"  [attr-defined]
python-package/lightgbm/dask.py:651: error: Argument 1 to "append" of "list" has incompatible type "List[Optional[Any]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:653: error: "_DatasetNames" has no attribute "append"  [attr-defined]
python-package/lightgbm/dask.py:[672](https://github.com/microsoft/LightGBM/actions/runs/3886554691/jobs/6631792813#step:3:673): error: Incompatible return value type (got "Dict[str, Any]", expected "LGBMModel")  [return-value]
python-package/lightgbm/dask.py:[687](https://github.com/microsoft/LightGBM/actions/runs/3886554691/jobs/6631792813#step:3:688): error: "Dict[str, Any]" has no attribute "result"  [attr-defined]
python-package/lightgbm/dask.py:990: error: Incompatible types in assignment (expression has type "Tuple[Any, Any]", variable has type "Tuple[Any]")  [assignment]
python-package/lightgbm/dask.py:1021: error: "_DaskLGBMModel" has no attribute "_other_params"  [attr-defined]
python-package/lightgbm/dask.py:1050: error: "_DaskLGBMModel" has no attribute "get_params"  [attr-defined]
python-package/lightgbm/dask.py:1069: error: Argument "eval_at" to "_train" has incompatible type "Optional[Iterable[int]]"; expected "Union[List[int], Tuple[int], None]"  [arg-type]
python-package/lightgbm/dask.py:1073: error: "_DaskLGBMModel" has no attribute "set_params"  [attr-defined]
python-package/lightgbm/dask.py:1079: error: "_DaskLGBMModel" has no attribute "get_params"  [attr-defined]
python-package/lightgbm/dask.py:1088: error: Item "_DaskLGBMModel" of "Union[_DaskLGBMModel, LGBMModel]" has no attribute "get_params"  [union-attr]
python-package/lightgbm/dask.py:1158: error: Signature of "fit" incompatible with supertype "LGBMClassifier"  [override]
python-package/lightgbm/dask.py:1158: note:      Superclass:
python-package/lightgbm/dask.py:1158: note:          def fit(self, X: Any, y: Any, sample_weight: Any = ..., init_score: Any = ..., eval_set: Any = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Any = ..., eval_class_weight: Any = ..., eval_init_score: Any = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., feature_name: Any = ..., categorical_feature: Any = ..., callbacks: Any = ..., init_model: Union[str, Path, Booster, LGBMModel, None] = ...) -> Any
python-package/lightgbm/dask.py:1158: note:      Subclass:
python-package/lightgbm/dask.py:1158: note:          def fit(self, X: Union[Any, Any], y: Union[Any, Any, Any], sample_weight: Union[Any, Any, None] = ..., init_score: Union[Any, Any, Any, None] = ..., eval_set: Optional[List[Tuple[Union[Any, Any], Union[Any, Any, Any]]]] = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Optional[List[Union[Any, Any]]] = ..., eval_class_weight: Optional[List[Union[Dict[Any, Any], str]]] = ..., eval_init_score: Optional[List[Union[Any, Any, Any]]] = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., **kwargs: Any) -> DaskLGBMClassifier
python-package/lightgbm/dask.py:1158: error: Signature of "fit" incompatible with supertype "LGBMModel"  [override]
python-package/lightgbm/dask.py:1158: note:          def fit(self, X: Any, y: Any, sample_weight: Any = ..., init_score: Any = ..., group: Any = ..., eval_set: Any = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Any = ..., eval_class_weight: Any = ..., eval_init_score: Any = ..., eval_group: Any = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., feature_name: Any = ..., categorical_feature: Any = ..., callbacks: Any = ..., init_model: Union[str, Path, Booster, LGBMModel, None] = ...) -> Any
python-package/lightgbm/dask.py:1158: note:          def fit(self, X: Union[Any, Any], y: Union[Any, Any, Any], sample_weight: Union[Any, Any, None] = ..., init_score: Union[Any, Any, Any, None] = ..., eval_set: Optional[List[Tuple[Union[Any, Any], Union[Any, Any, Any]]]] = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Optional[List[Union[Any, Any]]] = ..., eval_class_weight: Optional[List[Union[Dict[Any, Any], str]]] = ..., eval_init_score: Optional[List[Union[Any, Any, Any]]] = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., **kwargs: Any) -> DaskLGBMClassifier
python-package/lightgbm/dask.py:1173: error: Incompatible return value type (got "_DaskLGBMModel", expected "DaskLGBMClassifier")  [return-value]
python-package/lightgbm/dask.py:1218: error: Signature of "predict" incompatible with supertype "LGBMClassifier"  [override]
python-package/lightgbm/dask.py:1218: note:      Superclass:
python-package/lightgbm/dask.py:1218: note:          def predict(self, X: Any, raw_score: bool = ..., start_iteration: int = ..., num_iteration: Optional[int] = ..., pred_leaf: bool = ..., pred_contrib: bool = ..., validate_features: bool = ..., **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1218: note:      Subclass:
python-package/lightgbm/dask.py:1218: note:          def predict(self, X: Union[Any, Any], **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1218: error: Signature of "predict" incompatible with supertype "LGBMModel"  [override]
python-package/lightgbm/dask.py:1218: note:          def predict(self, X: Any, raw_score: bool = ..., start_iteration: int = ..., num_iteration: Optional[int] = ..., pred_leaf: bool = ..., pred_contrib: bool = ..., validate_features: bool = ..., **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1218: note:          def predict(self, X: Union[Any, Any], **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1237: error: Signature of "predict_proba" incompatible with supertype "LGBMClassifier"  [override]
python-package/lightgbm/dask.py:1237: note:      Superclass:
python-package/lightgbm/dask.py:1237: note:          def predict_proba(self, X: Any, raw_score: bool = ..., start_iteration: int = ..., num_iteration: Optional[int] = ..., pred_leaf: bool = ..., pred_contrib: bool = ..., validate_features: bool = ..., **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1237: note:      Subclass:
python-package/lightgbm/dask.py:1237: note:          def predict_proba(self, X: Union[Any, Any], **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1330: error: Signature of "fit" incompatible with supertype "LGBMRegressor"  [override]
python-package/lightgbm/dask.py:1330: note:      Superclass:
python-package/lightgbm/dask.py:1330: note:          def fit(self, X: Any, y: Any, sample_weight: Any = ..., init_score: Any = ..., eval_set: Any = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Any = ..., eval_init_score: Any = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., feature_name: Any = ..., categorical_feature: Any = ..., callbacks: Any = ..., init_model: Union[str, Path, Booster, LGBMModel, None] = ...) -> Any
python-package/lightgbm/dask.py:1330: note:      Subclass:
python-package/lightgbm/dask.py:1330: note:          def fit(self, X: Union[Any, Any], y: Union[Any, Any, Any], sample_weight: Union[Any, Any, None] = ..., init_score: Union[Any, Any, None] = ..., eval_set: Optional[List[Tuple[Union[Any, Any], Union[Any, Any, Any]]]] = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Optional[List[Union[Any, Any]]] = ..., eval_init_score: Optional[List[Union[Any, Any]]] = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., **kwargs: Any) -> DaskLGBMRegressor
python-package/lightgbm/dask.py:1330: error: Signature of "fit" incompatible with supertype "LGBMModel"  [override]
python-package/lightgbm/dask.py:1330: note:          def fit(self, X: Any, y: Any, sample_weight: Any = ..., init_score: Any = ..., group: Any = ..., eval_set: Any = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Any = ..., eval_class_weight: Any = ..., eval_init_score: Any = ..., eval_group: Any = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., feature_name: Any = ..., categorical_feature: Any = ..., callbacks: Any = ..., init_model: Union[str, Path, Booster, LGBMModel, None] = ...) -> Any
python-package/lightgbm/dask.py:1330: note:          def fit(self, X: Union[Any, Any], y: Union[Any, Any, Any], sample_weight: Union[Any, Any, None] = ..., init_score: Union[Any, Any, None] = ..., eval_set: Optional[List[Tuple[Union[Any, Any], Union[Any, Any, Any]]]] = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Optional[List[Union[Any, Any]]] = ..., eval_init_score: Optional[List[Union[Any, Any]]] = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., **kwargs: Any) -> DaskLGBMRegressor
python-package/lightgbm/dask.py:1344: error: Incompatible return value type (got "_DaskLGBMModel", expected "DaskLGBMRegressor")  [return-value]
python-package/lightgbm/dask.py:1391: error: Signature of "predict" incompatible with supertype "LGBMModel"  [override]
python-package/lightgbm/dask.py:1391: note:      Superclass:
python-package/lightgbm/dask.py:1391: note:          def predict(self, X: Any, raw_score: bool = ..., start_iteration: int = ..., num_iteration: Optional[int] = ..., pred_leaf: bool = ..., pred_contrib: bool = ..., validate_features: bool = ..., **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1391: note:      Subclass:
python-package/lightgbm/dask.py:1391: note:          def predict(self, X: Union[Any, Any], **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1483: error: Signature of "fit" incompatible with supertype "LGBMRanker"  [override]
python-package/lightgbm/dask.py:1483: note:      Superclass:
python-package/lightgbm/dask.py:1483: note:          def fit(self, X: Any, y: Any, sample_weight: Any = ..., init_score: Any = ..., group: Any = ..., eval_set: Any = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Any = ..., eval_init_score: Any = ..., eval_group: Any = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., eval_at: Union[List[int], Tuple[int]] = ..., feature_name: Any = ..., categorical_feature: Any = ..., callbacks: Any = ..., init_model: Union[str, Path, Booster, LGBMModel, None] = ...) -> Any
python-package/lightgbm/dask.py:1483: note:      Subclass:
python-package/lightgbm/dask.py:1483: note:          def fit(self, X: Union[Any, Any], y: Union[Any, Any, Any], sample_weight: Union[Any, Any, None] = ..., init_score: Union[Any, Any, None] = ..., group: Union[Any, Any, None] = ..., eval_set: Optional[List[Tuple[Union[Any, Any], Union[Any, Any, Any]]]] = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Optional[List[Union[Any, Any]]] = ..., eval_init_score: Optional[List[Union[Any, Any]]] = ..., eval_group: Optional[List[Union[Any, Any]]] = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., eval_at: Union[List[int], Tuple[int]] = ..., **kwargs: Any) -> DaskLGBMRanker
python-package/lightgbm/dask.py:1483: error: Signature of "fit" incompatible with supertype "LGBMModel"  [override]
python-package/lightgbm/dask.py:1483: note:          def fit(self, X: Any, y: Any, sample_weight: Any = ..., init_score: Any = ..., group: Any = ..., eval_set: Any = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Any = ..., eval_class_weight: Any = ..., eval_init_score: Any = ..., eval_group: Any = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., feature_name: Any = ..., categorical_feature: Any = ..., callbacks: Any = ..., init_model: Union[str, Path, Booster, LGBMModel, None] = ...) -> Any
python-package/lightgbm/dask.py:1483: note:          def fit(self, X: Union[Any, Any], y: Union[Any, Any, Any], sample_weight: Union[Any, Any, None] = ..., init_score: Union[Any, Any, None] = ..., group: Union[Any, Any, None] = ..., eval_set: Optional[List[Tuple[Union[Any, Any], Union[Any, Any, Any]]]] = ..., eval_names: Optional[List[str]] = ..., eval_sample_weight: Optional[List[Union[Any, Any]]] = ..., eval_init_score: Optional[List[Union[Any, Any]]] = ..., eval_group: Optional[List[Union[Any, Any]]] = ..., eval_metric: Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]], List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]], None] = ..., eval_at: Union[List[int], Tuple[int]] = ..., **kwargs: Any) -> DaskLGBMRanker
python-package/lightgbm/dask.py:1496: error: Incompatible default for argument "eval_at" (default has type "Tuple[int, int, int, int, int]", argument has type "Union[List[int], Tuple[int]]")  [assignment]
python-package/lightgbm/dask.py:1500: error: Incompatible return value type (got "_DaskLGBMModel", expected "DaskLGBMRanker")  [return-value]
python-package/lightgbm/dask.py:1549: error: Signature of "predict" incompatible with supertype "LGBMModel"  [override]
python-package/lightgbm/dask.py:1549: note:      Superclass:
python-package/lightgbm/dask.py:1549: note:          def predict(self, X: Any, raw_score: bool = ..., start_iteration: int = ..., num_iteration: Optional[int] = ..., pred_leaf: bool = ..., pred_contrib: bool = ..., validate_features: bool = ..., **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1549: note:      Subclass:
python-package/lightgbm/dask.py:1549: note:          def predict(self, X: Union[Any, Any], **kwargs: Any) -> Any
Found 123 errors in 5 files (checked 10 source files)

96 errors as of https://github.com/microsoft/LightGBM/commits/master.
Found 96 errors in 5 files (checked 10 source files)
python-package/lightgbm/basic.py:149: error: "CDLL" has no attribute "callback"  [attr-defined]
python-package/lightgbm/basic.py:315: error: "int" not callable  [operator]
python-package/lightgbm/basic.py:315: error: Unsupported operand types for * ("type" and "int")  [operator]
python-package/lightgbm/basic.py:1127: error: Incompatible types in assignment (expression has type "_Pointer[c_int64]", variable has type "_Pointer[c_int32]")  [assignment]
python-package/lightgbm/basic.py:1132: error: Incompatible types in assignment (expression has type "_Pointer[c_double]", variable has type "_Pointer[c_float]")  [assignment]
python-package/lightgbm/basic.py:1444: error: Incompatible types in assignment (expression has type "c_void_p", variable has type "None")  [assignment]
python-package/lightgbm/basic.py:1448: error: Argument 1 to "byref" has incompatible type "None"; expected "_CData"  [arg-type]
python-package/lightgbm/basic.py:1499: error: Incompatible types in assignment (expression has type "c_void_p", variable has type "None")  [assignment]
python-package/lightgbm/basic.py:1510: error: Argument 1 to "byref" has incompatible type "None"; expected "_CData"  [arg-type]
python-package/lightgbm/basic.py:1684: error: Incompatible types in assignment (expression has type "c_void_p", variable has type "None")  [assignment]
python-package/lightgbm/basic.py:1689: error: Argument 1 to "byref" has incompatible type "None"; expected "_CData"  [arg-type]
python-package/lightgbm/basic.py:1818: error: Incompatible types in assignment (expression has type "c_void_p", variable has type "None")  [assignment]
python-package/lightgbm/basic.py:1833: error: Argument 1 to "byref" has incompatible type "None"; expected "_CData"  [arg-type]
python-package/lightgbm/basic.py:1848: error: Incompatible types in assignment (expression has type "Array[_Pointer[c_float]]", variable has type "Array[_Pointer[c_double]]")  [assignment]
python-package/lightgbm/basic.py:1874: error: Incompatible types in assignment (expression has type "c_void_p", variable has type "None")  [assignment]
python-package/lightgbm/basic.py:1878: error: Argument 1 to "c_int" has incompatible type "Optional[Any]"; expected "int"  [arg-type]
python-package/lightgbm/basic.py:1884: error: Argument 1 to "byref" has incompatible type "None"; expected "_CData"  [arg-type]
python-package/lightgbm/basic.py:1896: error: Incompatible types in assignment (expression has type "c_void_p", variable has type "None")  [assignment]
python-package/lightgbm/basic.py:1915: error: Argument 1 to "byref" has incompatible type "None"; expected "_CData"  [arg-type]
python-package/lightgbm/basic.py:1927: error: Incompatible types in assignment (expression has type "c_void_p", variable has type "None")  [assignment]
python-package/lightgbm/basic.py:1946: error: Argument 1 to "byref" has incompatible type "None"; expected "_CData"  [arg-type]
python-package/lightgbm/basic.py:2118: error: Incompatible types in assignment (expression has type "List[int]", variable has type "None")  [assignment]
python-package/lightgbm/basic.py:2309: error: Value of type variable "SupportsRichComparisonT" of "sorted" cannot be "object"  [type-var]
python-package/lightgbm/basic.py:2329: error: Incompatible types in assignment (expression has type "_InnerPredictor", variable has type "None")  [assignment]
python-package/lightgbm/basic.py:4200: error: Need type annotation for "values" (hint: "values: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/basic.py:4274: error: "None" has no attribute "ctypes"  [attr-defined]
python-package/lightgbm/basic.py:4280: error: Argument 1 to "len" has incompatible type "None"; expected "Sized"  [arg-type]
python-package/lightgbm/basic.py:4285: error: "None" has no attribute "size"  [attr-defined]
python-package/lightgbm/basic.py:4286: error: "None" has no attribute "reshape"  [attr-defined]
python-package/lightgbm/callback.py:257: error: Need type annotation for "best_score" (hint: "best_score: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/callback.py:258: error: Need type annotation for "best_iter" (hint: "best_iter: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/callback.py:259: error: Need type annotation for "best_score_list" (hint: "best_score_list: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/callback.py:260: error: Need type annotation for "cmp_op" (hint: "cmp_op: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/engine.py:213: error: Argument "stopping_rounds" to "early_stopping" has incompatible type "Optional[Any]"; expected "int"  [arg-type]
python-package/lightgbm/engine.py:257: error: Argument 1 to "eval_train" of "Booster" has incompatible type "Union[Callable[[Any, Dataset], Tuple[str, float, bool]], List[Callable[[Any, Dataset], Tuple[str, float, bool]]], None]"; expected "Union[Union[Callable[[Any, Dataset], Tuple[str, float, bool]], Callable[[Any, Dataset], List[Tuple[str, float, bool]]]], List[Union[Callable[[Any, Dataset], Tuple[str, float, bool]], Callable[[Any, Dataset], List[Tuple[str, float, bool]]]]], None]"  [arg-type]
python-package/lightgbm/engine.py:258: error: Argument 1 to "eval_valid" of "Booster" has incompatible type "Union[Callable[[Any, Dataset], Tuple[str, float, bool]], List[Callable[[Any, Dataset], Tuple[str, float, bool]]], None]"; expected "Union[Union[Callable[[Any, Dataset], Tuple[str, float, bool]], Callable[[Any, Dataset], List[Tuple[str, float, bool]]]], List[Union[Callable[[Any, Dataset], Tuple[str, float, bool]], Callable[[Any, Dataset], List[Tuple[str, float, bool]]]]], None]"  [arg-type]
python-package/lightgbm/engine.py:269: error: Incompatible types in assignment (expression has type "Union[List[Tuple[str, str, float, bool]], List[Tuple[str, str, float, bool, float]]]", variable has type "List[Tuple[str, str, float, bool]]")  [assignment]
python-package/lightgbm/engine.py:504: error: Need type annotation for "cvmap"  [var-annotated]
python-package/lightgbm/engine.py:704: error: Argument "stopping_rounds" to "early_stopping" has incompatible type "Optional[Any]"; expected "int"  [arg-type]
python-package/lightgbm/engine.py:727: error: Unexpected keyword argument "fobj"  [call-arg]
python-package/lightgbm/engine.py:728: error: Too few arguments  [call-arg]
python-package/lightgbm/engine.py:749: error: Incompatible types in assignment (expression has type "CVBooster", target has type "List[float]")  [assignment]
python-package/lightgbm/sklearn.py:129: error: Too few arguments  [call-arg]
python-package/lightgbm/sklearn.py:131: error: Too many arguments  [call-arg]
python-package/lightgbm/sklearn.py:131: error: Too few arguments  [call-arg]
python-package/lightgbm/sklearn.py:133: error: Too many arguments  [call-arg]
python-package/lightgbm/sklearn.py:203: error: Too few arguments  [call-arg]
python-package/lightgbm/sklearn.py:203: error: Incompatible return value type (got "Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]", expected "Tuple[str, float, bool]")  [return-value]
python-package/lightgbm/sklearn.py:205: error: Too many arguments  [call-arg]
python-package/lightgbm/sklearn.py:205: error: Too few arguments  [call-arg]
python-package/lightgbm/sklearn.py:205: error: Incompatible return value type (got "Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]", expected "Tuple[str, float, bool]")  [return-value]
python-package/lightgbm/sklearn.py:207: error: Too many arguments  [call-arg]
python-package/lightgbm/sklearn.py:207: error: Incompatible return value type (got "Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]", expected "Tuple[str, float, bool]")  [return-value]
python-package/lightgbm/sklearn.py:691: error: Incompatible return value type (got "Union[int, Any, None]", expected "int")  [return-value]
python-package/lightgbm/sklearn.py:719: error: List item 0 has incompatible type "Union[str, Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], None]"; expected "Union[str, Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]"  [list-item]
python-package/lightgbm/sklearn.py:809: error: Argument "feval" to "train" has incompatible type "List[_EvalFunctionWrapper]"; expected "Union[Callable[[Any, Dataset], Tuple[str, float, bool]], List[Callable[[Any, Dataset], Tuple[str, float, bool]]], None]"  [arg-type]
python-package/lightgbm/sklearn.py:809: note: "List" is invariant -- see https://mypy.readthedocs.io/en/stable/common_issues.html#variance
python-package/lightgbm/sklearn.py:809: note: Consider using "Sequence" instead, which is covariant
python-package/lightgbm/sklearn.py:879: error: Item "None" of "Optional[Booster]" has no attribute "predict"  [union-attr]
python-package/lightgbm/sklearn.py:897: error: Incompatible return value type (got "None", expected "int")  [return-value]
python-package/lightgbm/sklearn.py:904: error: Incompatible return value type (got "None", expected "int")  [return-value]
python-package/lightgbm/sklearn.py:918: error: Incompatible return value type (got "Optional[int]", expected "int")  [return-value]
python-package/lightgbm/sklearn.py:925: error: Incompatible return value type (got "Union[str, Union[Callable[[Any, Any], Tuple[Any, Any]], Callable[[Any, Any, Any], Tuple[Any, Any]], Callable[[Any, Any, Any, Any], Tuple[Any, Any]]], None]", expected "Union[str, Union[Callable[[Any, Any], Tuple[Any, Any]], Callable[[Any, Any, Any], Tuple[Any, Any]], Callable[[Any, Any, Any, Any], Tuple[Any, Any]]]]")  [return-value]
python-package/lightgbm/sklearn.py:1060: error: Argument 1 to "len" has incompatible type "None"; expected "Sized"  [arg-type]
python-package/lightgbm/sklearn.py:1064: error: List item 0 has incompatible type "Optional[str]"; expected "Union[str, Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]"  [list-item]
python-package/lightgbm/sklearn.py:1066: error: Argument 1 to "enumerate" has incompatible type "Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]"; expected "Iterable[Union[str, Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]"  [arg-type]
python-package/lightgbm/sklearn.py:1068: error: Unsupported target for indexed assignment ("Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]")  [index]
python-package/lightgbm/sklearn.py:1070: error: Unsupported target for indexed assignment ("Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]")  [index]
python-package/lightgbm/sklearn.py:1072: error: Argument 1 to "enumerate" has incompatible type "Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]"; expected "Iterable[Union[str, Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]"  [arg-type]
python-package/lightgbm/sklearn.py:1074: error: Unsupported target for indexed assignment ("Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]")  [index]
python-package/lightgbm/sklearn.py:1076: error: Unsupported target for indexed assignment ("Optional[List[Union[str, Union[Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]]]]")  [index]
python-package/lightgbm/sklearn.py:1086: error: No overload variant of "__setitem__" of "list" matches argument types "int", "Tuple[Any, Any]"  [call-overload]
python-package/lightgbm/sklearn.py:1086: note: Possible overload variants:
python-package/lightgbm/sklearn.py:1086: note:     def __setitem__(self, SupportsIndex, None, /) -> None
python-package/lightgbm/sklearn.py:1086: note:     def __setitem__(self, slice, Iterable[None], /) -> None
python-package/lightgbm/sklearn.py:1088: error: No overload variant of "__setitem__" of "list" matches argument types "int", "Tuple[Any, Any]"  [call-overload]
python-package/lightgbm/sklearn.py:1088: note: Possible overload variants:
python-package/lightgbm/sklearn.py:1088: note:     def __setitem__(self, SupportsIndex, None, /) -> None
python-package/lightgbm/sklearn.py:1088: note:     def __setitem__(self, slice, Iterable[None], /) -> None
python-package/lightgbm/sklearn.py:1155: error: Unsupported operand types for < ("int" and "None")  [operator]
python-package/lightgbm/sklearn.py:1155: note: Left operand is of type "Optional[int]"
python-package/lightgbm/sklearn.py:1181: error: Incompatible return value type (got "Optional[int]", expected "int")  [return-value]
python-package/lightgbm/dask.py:166: error: Incompatible types in assignment (expression has type "str", target has type "Dict[str, List[Any]]")  [assignment]
python-package/lightgbm/dask.py:168: error: Incompatible types in assignment (expression has type "str", target has type "Dict[str, float]")  [assignment]
python-package/lightgbm/dask.py:[599](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:600): error: Argument 1 to "append" of "list" has incompatible type "Tuple[List[Optional[Any]], List[Optional[Any]]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:[602](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:603): error: Value of type "_DatasetNames" is not indexable  [index]
python-package/lightgbm/dask.py:[603](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:604): error: Value of type "_DatasetNames" is not indexable  [index]
python-package/lightgbm/dask.py:[621](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:622): error: Argument 1 to "append" of "list" has incompatible type "List[Optional[Any]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:[623](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:624): error: "_DatasetNames" has no attribute "append"  [attr-defined]
python-package/lightgbm/dask.py:[639](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:640): error: Argument 1 to "append" of "list" has incompatible type "List[Optional[Any]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:[641](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:642): error: "_DatasetNames" has no attribute "append"  [attr-defined]
python-package/lightgbm/dask.py:[657](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:658): error: Argument 1 to "append" of "list" has incompatible type "List[Optional[Any]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:[659](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:660): error: "_DatasetNames" has no attribute "append"  [attr-defined]
python-package/lightgbm/dask.py:[678](https://github.com/microsoft/LightGBM/actions/runs/4177137071/jobs/7234335517#step:3:679): error: Incompatible return value type (got "Dict[str, Any]", expected "LGBMModel")  [return-value]
python-package/lightgbm/dask.py:693: error: "Dict[str, Any]" has no attribute "result"  [attr-defined]
python-package/lightgbm/dask.py:996: error: Incompatible types in assignment (expression has type "Tuple[Any, Any]", variable has type "Tuple[Any]")  [assignment]
python-package/lightgbm/dask.py:1027: error: "_DaskLGBMModel" has no attribute "_other_params"  [attr-defined]
python-package/lightgbm/dask.py:1056: error: "_DaskLGBMModel" has no attribute "get_params"  [attr-defined]
python-package/lightgbm/dask.py:1079: error: "_DaskLGBMModel" has no attribute "set_params"  [attr-defined]
python-package/lightgbm/dask.py:1085: error: "_DaskLGBMModel" has no attribute "get_params"  [attr-defined]
python-package/lightgbm/dask.py:1094: error: Item "_DaskLGBMModel" of "Union[_DaskLGBMModel, LGBMModel]" has no attribute "get_params"  [union-attr]
python-package/lightgbm/dask.py:1179: error: Incompatible return value type (got "_DaskLGBMModel", expected "DaskLGBMClassifier")  [return-value]
python-package/lightgbm/dask.py:1259: error: Signature of "predict_proba" incompatible with supertype "LGBMClassifier"  [override]
python-package/lightgbm/dask.py:1259: note:      Superclass:
python-package/lightgbm/dask.py:1259: note:          def predict_proba(self, X: Any, raw_score: bool = ..., start_iteration: int = ..., num_iteration: Optional[int] = ..., pred_leaf: bool = ..., pred_contrib: bool = ..., validate_features: bool = ..., **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1259: note:      Subclass:
python-package/lightgbm/dask.py:1259: note:          def predict_proba(self, X: Union[Any, Any], **kwargs: Any) -> Any
python-package/lightgbm/dask.py:1366: error: Incompatible return value type (got "_DaskLGBMModel", expected "DaskLGBMRegressor")  [return-value]
python-package/lightgbm/dask.py:1538: error: Incompatible return value type (got "_DaskLGBMModel", expected "DaskLGBMRanker")  [return-value]
Found 96 errors in 5 files (checked 10 source files)

63 errors as of https://github.com/microsoft/LightGBM/commit/42df0ff3397aabb06f9f6fda2966f16b9cb3b1f6
Found 63 errors in 5 files (checked 10 source files)
python-package/lightgbm/basic.py:315: error: "int" not callable  [operator]
python-package/lightgbm/basic.py:315: error: Unsupported operand types for * ("type" and "int")  [operator]
python-package/lightgbm/basic.py:1127: error: Incompatible types in assignment (expression has type "_Pointer[c_int64]", variable has type "_Pointer[c_int32]")  [assignment]
python-package/lightgbm/basic.py:1132: error: Incompatible types in assignment (expression has type "_Pointer[c_double]", variable has type "_Pointer[c_float]")  [assignment]
python-package/lightgbm/basic.py:1595: error: Item "None" of "Optional[Dict[str, Any]]" has no attribute "get"  [union-attr]
python-package/lightgbm/basic.py:1857: error: Incompatible types in assignment (expression has type "Array[_Pointer[c_float]]", variable has type "Array[_Pointer[c_double]]")  [assignment]
python-package/lightgbm/basic.py:1887: error: Argument 1 to "c_int" has incompatible type "Optional[Any]"; expected "int"  [arg-type]
python-package/lightgbm/basic.py:2322: error: Value of type variable "SupportsRichComparisonT" of "sorted" cannot be "object"  [type-var]
python-package/lightgbm/basic.py:4256: error: Need type annotation for "values" (hint: "values: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/basic.py:4330: error: "None" has no attribute "ctypes"  [attr-defined]
python-package/lightgbm/basic.py:4336: error: Argument 1 to "len" has incompatible type "None"; expected "Sized"  [arg-type]
python-package/lightgbm/basic.py:4341: error: "None" has no attribute "size"  [attr-defined]
python-package/lightgbm/basic.py:4342: error: "None" has no attribute "reshape"  [attr-defined]
python-package/lightgbm/callback.py:257: error: Need type annotation for "best_score" (hint: "best_score: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/callback.py:258: error: Need type annotation for "best_iter" (hint: "best_iter: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/callback.py:259: error: Need type annotation for "best_score_list" (hint: "best_score_list: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/callback.py:260: error: Need type annotation for "cmp_op" (hint: "cmp_op: List[<type>] = ...")  [var-annotated]
python-package/lightgbm/engine.py:213: error: Argument "stopping_rounds" to "early_stopping" has incompatible type "Optional[Any]"; expected "int"  [arg-type]
python-package/lightgbm/engine.py:257: error: Argument 1 to "eval_train" of "Booster" has incompatible type "Union[Callable[[Any, Dataset], Tuple[str, float, bool]], List[Callable[[Any, Dataset], Tuple[str, float, bool]]], None]"; expected "Union[Union[Callable[[Any, Dataset], Tuple[str, float, bool]], Callable[[Any, Dataset], List[Tuple[str, float, bool]]]], List[Union[Callable[[Any, Dataset], Tuple[str, float, bool]], Callable[[Any, Dataset], List[Tuple[str, float, bool]]]]], None]"  [arg-type]
python-package/lightgbm/engine.py:258: error: Argument 1 to "eval_valid" of "Booster" has incompatible type "Union[Callable[[Any, Dataset], Tuple[str, float, bool]], List[Callable[[Any, Dataset], Tuple[str, float, bool]]], None]"; expected "Union[Union[Callable[[Any, Dataset], Tuple[str, float, bool]], Callable[[Any, Dataset], List[Tuple[str, float, bool]]]], List[Union[Callable[[Any, Dataset], Tuple[str, float, bool]], Callable[[Any, Dataset], List[Tuple[str, float, bool]]]]], None]"  [arg-type]
python-package/lightgbm/engine.py:269: error: Incompatible types in assignment (expression has type "Union[List[Tuple[str, str, float, bool]], List[Tuple[str, str, float, bool, float]]]", variable has type "List[Tuple[str, str, float, bool]]")  [assignment]
python-package/lightgbm/engine.py:704: error: Argument "stopping_rounds" to "early_stopping" has incompatible type "Optional[Any]"; expected "int"  [arg-type]
python-package/lightgbm/engine.py:727: error: Unexpected keyword argument "fobj"  [call-arg]
python-package/lightgbm/engine.py:728: error: Too few arguments  [call-arg]
python-package/lightgbm/engine.py:749: error: Incompatible types in assignment (expression has type "CVBooster", target has type "List[float]")  [assignment]
python-package/lightgbm/sklearn.py:129: error: Too few arguments  [call-arg]
python-package/lightgbm/sklearn.py:131: error: Too many arguments  [call-arg]
python-package/lightgbm/sklearn.py:131: error: Too few arguments  [call-arg]
python-package/lightgbm/sklearn.py:133: error: Too many arguments  [call-arg]
python-package/lightgbm/sklearn.py:207: error: Too few arguments  [call-arg]
python-package/lightgbm/sklearn.py:209: error: Too many arguments  [call-arg]
python-package/lightgbm/sklearn.py:209: error: Too few arguments  [call-arg]
python-package/lightgbm/sklearn.py:211: error: Too many arguments  [call-arg]
python-package/lightgbm/sklearn.py:695: error: Incompatible return value type (got "Union[int, Any, None]", expected "int")  [return-value]
python-package/lightgbm/sklearn.py:723: error: List item 0 has incompatible type "Union[str, Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], None]"; expected "Union[str, Callable[[Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]], Callable[[Any, Any, Any, Any], Union[Tuple[str, float, bool], List[Tuple[str, float, bool]]]]]"  [list-item]
python-package/lightgbm/sklearn.py:813: error: Argument "feval" to "train" has incompatible type "List[_EvalFunctionWrapper]"; expected "Union[Callable[[Any, Dataset], Tuple[str, float, bool]], List[Callable[[Any, Dataset], Tuple[str, float, bool]]], None]"  [arg-type]
python-package/lightgbm/sklearn.py:883: error: Item "None" of "Optional[Booster]" has no attribute "predict"  [union-attr]
python-package/lightgbm/sklearn.py:929: error: Incompatible return value type (got "Union[str, Union[Callable[[Any, Any], Tuple[Any, Any]], Callable[[Any, Any, Any], Tuple[Any, Any]], Callable[[Any, Any, Any, Any], Tuple[Any, Any]]], None]", expected "Union[str, Union[Callable[[Any, Any], Tuple[Any, Any]], Callable[[Any, Any, Any], Tuple[Any, Any]], Callable[[Any, Any, Any, Any], Tuple[Any, Any]]]]")  [return-value]
python-package/lightgbm/sklearn.py:1064: error: Argument 1 to "len" has incompatible type "None"; expected "Sized"  [arg-type]
python-package/lightgbm/sklearn.py:1097: error: No overload variant of "__setitem__" of "list" matches argument types "int", "Tuple[Any, Any]"  [call-overload]
python-package/lightgbm/sklearn.py:1097: note: Possible overload variants:
python-package/lightgbm/sklearn.py:1097: note:     def __setitem__(self, SupportsIndex, None, /) -> None
python-package/lightgbm/sklearn.py:1097: note:     def __setitem__(self, slice, Iterable[None], /) -> None
python-package/lightgbm/sklearn.py:1099: error: No overload variant of "__setitem__" of "list" matches argument types "int", "Tuple[Any, Any]"  [call-overload]
python-package/lightgbm/sklearn.py:1099: note: Possible overload variants:
python-package/lightgbm/sklearn.py:1099: note:     def __setitem__(self, SupportsIndex, None, /) -> None
python-package/lightgbm/sklearn.py:1099: note:     def __setitem__(self, slice, Iterable[None], /) -> None
python-package/lightgbm/sklearn.py:1166: error: Unsupported operand types for < ("int" and "None")  [operator]
python-package/lightgbm/sklearn.py:1166: note: Left operand is of type "Optional[int]"
python-package/lightgbm/sklearn.py:1192: error: Incompatible return value type (got "Optional[int]", expected "int")  [return-value]
python-package/lightgbm/dask.py:[598](https://github.com/microsoft/LightGBM/actions/runs/4265093924/jobs/7424025119#step:3:599): error: Argument 1 to "append" of "list" has incompatible type "Tuple[List[Optional[Any]], List[Optional[Any]]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:[601](https://github.com/microsoft/LightGBM/actions/runs/4265093924/jobs/7424025119#step:3:602): error: Value of type "_DatasetNames" is not indexable  [index]
python-package/lightgbm/dask.py:[602](https://github.com/microsoft/LightGBM/actions/runs/4265093924/jobs/7424025119#step:3:603): error: Value of type "_DatasetNames" is not indexable  [index]
python-package/lightgbm/dask.py:[620](https://github.com/microsoft/LightGBM/actions/runs/4265093924/jobs/7424025119#step:3:621): error: Argument 1 to "append" of "list" has incompatible type "List[Optional[Any]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:[622](https://github.com/microsoft/LightGBM/actions/runs/4265093924/jobs/7424025119#step:3:623): error: "_DatasetNames" has no attribute "append"  [attr-defined]
python-package/lightgbm/dask.py:[638](https://github.com/microsoft/LightGBM/actions/runs/4265093924/jobs/7424025119#step:3:639): error: Argument 1 to "append" of "list" has incompatible type "List[Optional[Any]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:[640](https://github.com/microsoft/LightGBM/actions/runs/4265093924/jobs/7424025119#step:3:641): error: "_DatasetNames" has no attribute "append"  [attr-defined]
python-package/lightgbm/dask.py:656: error: Argument 1 to "append" of "list" has incompatible type "List[Optional[Any]]"; expected "_DatasetNames"  [arg-type]
python-package/lightgbm/dask.py:658: error: "_DatasetNames" has no attribute "append"  [attr-defined]
python-package/lightgbm/dask.py:677: error: Incompatible return value type (got "Dict[str, Any]", expected "LGBMModel")  [return-value]
python-package/lightgbm/dask.py:692: error: "Dict[str, Any]" has no attribute "result"  [attr-defined]
python-package/lightgbm/dask.py:995: error: Incompatible types in assignment (expression has type "Tuple[Any, Any]", variable has type "Tuple[Any]")  [assignment]
python-package/lightgbm/dask.py:1026: error: "_DaskLGBMModel" has no attribute "_other_params"  [attr-defined]
python-package/lightgbm/dask.py:1055: error: "_DaskLGBMModel" has no attribute "get_params"  [attr-defined]
python-package/lightgbm/dask.py:1078: error: "_DaskLGBMModel" has no attribute "set_params"  [attr-defined]
python-package/lightgbm/dask.py:1084: error: "_DaskLGBMModel" has no attribute "get_params"  [attr-defined]
python-package/lightgbm/dask.py:1093: error: Item "_DaskLGBMModel" of "Union[_DaskLGBMModel, LGBMModel]" has no attribute "get_params"  [union-attr]
python-package/lightgbm/dask.py:1178: error: Incompatible return value type (got "_DaskLGBMModel", expected "DaskLGBMClassifier")  [return-value]
python-package/lightgbm/dask.py:1381: error: Incompatible return value type (got "_DaskLGBMModel", expected "DaskLGBMRegressor")  [return-value]
python-package/lightgbm/dask.py:1553: error: Incompatible return value type (got "_DaskLGBMModel", expected "DaskLGBMRanker")  [return-value]
Found 63 errors in 5 files (checked 10 source files)

8 errors as of https://github.com/microsoft/LightGBM/commit/8f577de01fdad1cc973987971d4e30ceaa8d80ff
Found 8 errors in 2 files (checked 9 source files) (click me)
python-package/lightgbm/basic.py:739: error: Incompatible return value type (got "tuple[Any, list[str], list[str] | list[int], list[list[Any]]]", expected "tuple[Any, list[str], list[str], list[list[Any]]]")  [return-value]
python-package/lightgbm/basic.py:2809: error: Array constructor argument 1 of type "map[int]" is not convertible to the array element type "Iterable[c_char_p]"  [misc]
python-package/lightgbm/basic.py:2823: error: Array constructor argument 1 of type "map[int]" is not convertible to the array element type "Iterable[c_char_p]"  [misc]
python-package/lightgbm/basic.py:4484: error: Array constructor argument 1 of type "map[int]" is not convertible to the array element type "Iterable[c_char_p]"  [misc]
python-package/lightgbm/basic.py:4498: error: Array constructor argument 1 of type "map[int]" is not convertible to the array element type "Iterable[c_char_p]"  [misc]
python-package/lightgbm/basic.py:4708: error: Array constructor argument 1 of type "map[int]" is not convertible to the array element type "Iterable[c_char_p]"  [misc]
python-package/lightgbm/basic.py:4724: error: Array constructor argument 1 of type "map[int]" is not convertible to the array element type "Iterable[c_char_p]"  [misc]
python-package/lightgbm/engine.py:294: error: Incompatible types in assignment (expression has type "list[tuple[str, str, float, bool]] | list[tuple[str, str, float, bool, float]]", variable has type "list[tuple[str, str, float, bool]]")  [assignment]
Found 8 errors in 2 files (checked 9 source files)

That's especially good given that the type hints are so much more complete in the project than they were the last time I posted an update here back in February: https://github.com/microsoft/LightGBM/issues/3867#issuecomment-1444927932
Getting close!

I keep looking this up every time I run into it, so wanted to put it in writing here.
For almost all cases, the .predict() methods in the Python package return a numpy array or scipy.sparse.spmatrix. That makes it safe to do array-like operations on the result from .predict() unconditionally (e.g., accessing .shape, np.concat()-ing them).
There's one case where that isn't true. For the following specific combination, .predict() returns a Python list of scipy.sparse.spmatrix matrices:

getting SHAP feature contributions (.predict(..., pred_contrib=True))
input is a scipy.sparse.spmatrix (e.g., CSC or CSR matrix)
model is using a multiclass classification objective

That's been true since support for SHAP contributions on sparse matrices was added in #3000.
Here's the code path where that happens:
https://github.com/microsoft/LightGBM/blob/6f19edde528904ee3d61bdfa6fcf1e012a3ae2ed/python-package/lightgbm/basic.py#L1328
https://github.com/microsoft/LightGBM/blob/6f19edde528904ee3d61bdfa6fcf1e012a3ae2ed/python-package/lightgbm/basic.py#L1361-L1375
https://github.com/microsoft/LightGBM/blob/6f19edde528904ee3d61bdfa6fcf1e012a3ae2ed/python-package/lightgbm/basic.py#L1472
That one possibility means that everything handling the result of a .predict() call has to account for the possibility of receiving a list.
For example, this added some complexity to the Dask interface:
https://github.com/microsoft/LightGBM/blob/6f19edde528904ee3d61bdfa6fcf1e012a3ae2ed/python-package/lightgbm/dask.py#L957-L1027
For the purpose of this issue and #3756, this can be handled with some mix of the approaches listed at https://mypy.readthedocs.io/en/stable/type_narrowing.html.
Longer-term, I think it's worth investigating whether it's possible to return an array type for that combination. Although that'd have to be done carefully, since (for example), shap relies on LightGBM's implementation of SHAP values:
https://github.com/shap/shap/blob/f83fda473cf9f96356000101d9549bc5c2497638/shap/explainers/pytree.py#L178-L179
I couldn't quite figure out from the description on #3000 why the decision was made to return a list. @guolinke or @imatiach-msft do you remember?


Recommendation for publish() with minimal copying

The library currently has the following publish() APIs:
void publish(
        packet_id_t packet_id,
        std::string topic_name,
        std::string contents,
        publish_options pubopts = {},
        v5::properties props = {},
        any life_keeper = {}
    )

void publish(
        packet_id_t packet_id,
        as::const_buffer topic_name,
        as::const_buffer contents,
        publish_options pubopts,
        v5::properties props,
        any life_keeper
    )

void publish(
        packet_id_t packet_id,
        as::const_buffer topic_name,
        as::const_buffer contents,
        publish_options pubopts,
        any life_keeper
    )

void publish(
        packet_id_t packet_id,
        buffer topic_name,
        buffer contents,
        publish_options pubopts = {},
        any life_keeper = {}
    )

void publish(
        packet_id_t packet_id,
        buffer topic_name,
        buffer contents,
        publish_options pubopts,
        v5::properties props,
        any life_keeper = {}
    )

I'm trying to understand which API would be ideal for my project's use.
I'm currently using QoS 1 for publishing messages and would like to minimize the amount of copying that's done once these APIs are called by my project code.
Reading through the comments in the code, it seems like the APIs that accept buffer and const_buffer are ideal for that but they require the consumer of the API to manage the lifetime of the buffer(s). Is that correct?
I would like to avoid having to do that. If so, is the API that accepts a std::string the only option I can use?
Appreciate your help!

buffer and as::const_buffer are from two different namespaces.
buffer is the MQTT_NS::buffer, which is essentially a std::shared_ptr<char[]> + std::string_view.
You can use this with MQTT_NS::allocate_buffer(mystringhere)
If you keep your data in this form for most of your application, then you want to use this form of the API.
If you want to manage the lifetime yourself, then you want to use the as::const_buffer form.

I see. Just to confirm - if I were to use buffer, I wouldn't have to manage the lifetime of the buffer in my application code, right?
I might opt for that if so. The way we've set up our application so far is to create a new thin library over the mqtt_cpp client which abstracts away the interface of the mqtt_cpp library. In the sense that it's not aware of the MQTT_NS::* namespace constructs.
I am thinking of accepting an std::string or char array/vector in the Publish APIs exposed by this thin wrapper layer and internally creating an MQTT_NS::buffer that copies the string or char array/vector before calling the APIs on the mqtt_cpp library.

I see. Just to confirm - if I were to use buffer, I wouldn't have to manage the lifetime of the buffer in my application code, right?

It has the same lifetime semantics as std::shared_ptr.

The way we've set up our application so far is to create a new thin library over the mqtt_cpp client which abstracts away the interface of the mqtt_cpp library. In the sense that it's not aware of the MQTT_NS::* namespace constructs.

Why?

I am thinking of accepting an std::string or char array/vector in the Publish APIs exposed by this thin wrapper layer and internally creating an MQTT_NS::buffer that copies the string or char array/vector before calling the APIs on the mqtt_cpp library.

Why?
There's no point in doing that, the MQTT_NS::publish function that accepts std::string is the most efficent way of handing data that is stored in an std::string.
Unless your data never lives in an std::string, and only lives in something that's analogous to std::shared_ptr + std::string_view, there's no point in copying things in the way you described.

If you read the publish() functions, you'll see that all that is happening is that the std::string is being std::moved() into a newly allocated std::shared_ptrstd::string.
The MQTT_NS::buffer functions are the most efficient if you already have your data stored in a std::shared_ptr
But the as::const_buffer functions are the most efficent possible if you have some other lifetime management system, as they are equivalent to std::string_view.

Sounds good, I'll just use std::string for now

Sounds good, I'll just use std::string for now

Sorry to bump this, but how do you pass in a std::string as topic or contents? The function mqtt::buffer::buffer(std::string) is deleted (with the comment "to avoid misuse") so it won't compile.


Modify mosaic supply - fee selector not visible

Version: v0.13.1
Mosaic supply pop-up.
Fee selector is not visible (bad z-index) when changing fee I need to use scroll.

Fixed:


[tool_crash] StateError: Bad state: No element while running flutter debug-adapter

Command
flutter debug-adapter

Steps to Reproduce

...
...
...

Logs
StateError: Bad state: No element
%230      ListMixin.firstWhere (dart:collection/list.dart:167:5)
%231      PacketTransformer._parseHeaders (package:dds/src/dap/protocol_stream_transformers.dart:94:17)
%232      PacketTransformer.bind.<anonymous closure>.<anonymous closure> (package:dds/src/dap/protocol_stream_transformers.dart:40:25)
%233      _rootRunUnary (dart:async/zone.dart:1434:47)
%234      _CustomZone.runUnary (dart:async/zone.dart:1335:19)
%235      _CustomZone.runUnaryGuarded (dart:async/zone.dart:1244:7)
%236      _BufferingStreamSubscription._sendData (dart:async/stream_impl.dart:341:11)
%237      _BufferingStreamSubscription._add (dart:async/stream_impl.dart:271:7)
%238      _ForwardingStreamSubscription._add (dart:async/stream_pipe.dart:123:11)
%239      _ExpandStream._handleData (dart:async/stream_pipe.dart:233:14)
%2310     _ForwardingStreamSubscription._handleData (dart:async/stream_pipe.dart:153:13)
%2311     _rootRunUnary (dart:async/zone.dart:1434:47)
%2312     _CustomZone.runUnary (dart:async/zone.dart:1335:19)
%2313     _CustomZone.runUnaryGuarded (dart:async/zone.dart:1244:7)
%2314     _BufferingStreamSubscription._sendData (dart:async/stream_impl.dart:341:11)
%2315     _BufferingStreamSubscription._add (dart:async/stream_impl.dart:271:7)
%2316     _SyncStreamControllerDispatch._sendData (dart:async/stream_controller.dart:774:19)
%2317     _StreamController._add (dart:async/stream_controller.dart:648:7)
%2318     _StreamController.add (dart:async/stream_controller.dart:596:5)
%2319     _Socket._onData (dart:io-patch/socket_patch.dart:2314:41)
%2320     _rootRunUnary (dart:async/zone.dart:1442:13)
%2321     _CustomZone.runUnary (dart:async/zone.dart:1335:19)
%2322     _CustomZone.runUnaryGuarded (dart:async/zone.dart:1244:7)
%2323     _BufferingStreamSubscription._sendData (dart:async/stream_impl.dart:341:11)
%2324     _BufferingStreamSubscription._add (dart:async/stream_impl.dart:271:7)

[✓] Flutter (Channel stable, 2.10.3, on Microsoft Windows [version 10.0.19044.1526], locale fr-CA)
    • Flutter version 2.10.3 at C:\flutter
    • Upstream repository https://github.com/flutter/flutter.git
    • Framework revision 7e9793dee1 (5 days ago), 2022-03-02 11:23:12 -0600
    • Engine revision bd539267b4
    • Dart version 2.16.1
    • DevTools version 2.9.2

[✗] Android toolchain - develop for Android devices
    • Android SDK at C:\Users\HP Probook\AppData\Local\Android\sdk
    ✗ cmdline-tools component is missing
      Run `path/to/sdkmanager --install "cmdline-tools

Labeling it based on the crash log.
/cc @christopherfujino

cc @DanTup

@Mouftakir99 can you describe how you encountered this? Did you trigger this from an editor, or are you running the command manually? If you're running it manually, can you describe what you were trying to do?
The command is intended to be used by tools that must communicate using the debug adapter protocol. It shouldn't fail in this way if you send it input that doesn't conform to the spec, though the fix for that will likely be to emit a clear error message. I'm not sure if that will solve your issue without understanding how you triggered this.

Thank u for responding my message i resolve the problem    Envoyé à partir de Courrier pour Windows De : Danny TuppenyEnvoyé le :8 mars 2022 19:26À : flutter/flutterCc : Mouftakir Aiman; MentionObjet :Re: [flutter/flutter] [tool_crash] StateError: Bad state: No element while running `flutter debug-adapter` (Issue<EMAIL_ADDRESS>can you describe how you encountered this? Did you trigger this from an editor, or are you running the command manually? If you're running it manually, can you describe what you were trying to do?The command is intended to be used by tools that must communicate using the debug adapter protocol. It shouldn't fail in this way if you send it input that doesn't conform to the spec, though the fix for that will likely be to emit a clear error message. I'm not sure if that will solve your issue without understanding how you triggered this.—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you were mentioned.Message ID<EMAIL_ADDRESS>


Create Your First MDK Application

Mission: https://developers.sap.com/mission.mobile-dev-kit-get-started.html
Hello guys,
I have a question about Odata source Mobile services. When I create mobile app in mobile service cocpit (MDK app) and set up as SAP shows in developers tutorial. Then I create an MDK app in business studio, but I can't see any application ID (no data available) in mobile services. I am log in in the same space, where is a instance of mobile service.
If I create an app with Odata service, then is error in Deploy. I choosed mobile services, but there is not an option for choose appID created via mobile app in mobile service cocpit. What can I do, please?
Thanks for the response.

@havir-petr can you please share a screensot of the Mobile Services instance?
Are you trying it in Trial or Productive account?
Is it Mobile Services preview?

@jitendrakansal  Hello, thank you for the quick response. We have a Trial account. We are logged in to Cloud foundry space in SAP Business Studio and instance is created in the same space.

if we create an mobile service instance and then go to the SAP Business Studio, create an dev space SAP Mobile Application and then create an app via Create Template (MDK project) we can't see any appID. For this example Picture 1. and 2.

If we create MDK project with Odata source from URL (Service Url), then we can't deploy it. If I click on Application.app and MDK:Deploy -> then I want deploy it as a Mobile Service it doesn't show any options for appID Picture 3 and Picture 4.

Picture 1.

Picture 2.

Picture 3.

Picture 4.

@havir-petr
as i was guessing, this is currently a known limitation in editor, it connects to Mobile services standard edition only (not the Preview edition).
This is covered in this issue https://github.com/SAPDocuments/Issues/issues/127


store "max sane feerate" in config file instead hardcoding it

following my comment here, i think we should store this value in the config file instead of hardcoding it as we already see many tx w/ a feerate > 1000sats/vb

(I do not check if it has already been tackled in the lasts commits of v4)

I opened this, as it's related:
https://github.com/wizardsardine/liana/issues/910

looking at this, in spend.rs we have several constants defined:
/// We would never create a transaction with an output worth less than this.
/// That's 1$ at 20_000$ per BTC.
pub const DUST_OUTPUT_SATS: u64 = 5_000;

/// Long-term feerate (sats/vb) used for coin selection considerations.
pub const LONG_TERM_FEERATE_VB: f32 = 10.0;

/// Assume that paying more than 1BTC in fee is a bug.
pub const MAX_FEE: bitcoin::Amount = bitcoin::Amount::ONE_BTC;

/// Assume that paying more than 1000sat/vb in feerate is a bug.
pub const MAX_FEERATE: u64 = 1_000;

should we move all in the config file instead?

@darosior

Sure we can make the dust output sat, max fee, max feerate and long term feerate configurable.

I am using this thread to give an opinion about configuration: I think we should not use the configuration file for this kind of values but use the wallet table and change it using a new command. It will help us long term for the multiple wallet support and it enable on the fly change of the dust value by the client without having to restart the daemon.

I am using this thread to give an opinion about configuration: I think we should not use the configuration file for this kind of values but use the wallet table and change them using a new command. It will help us long term for the multiple wallet support and it enable "on the fly" change of the dust value by the client without having to restart the daemon.

you mean have some way from GUI (in settings) to allow user to change?


Conjure fails to evaluate in latest Neovim nightly version

Note: there is no problem with Neovim v0.5, this only happens on nightly.
I ran into this issue after updating Neovim nighly to the latest version yesterday using Homebrew.
Could be related to this breaking change.
Environment Information
OS: Ubuntu 20.04.3 LTS inside WSL2 in Windows 10 Pro 21H1
Neovim version: NVIM v0.6.0-dev+304-g924e8e4f2
How to reproduce the issue
I created this Github repository which uses Docker to reproduce the issue: datwaft/conjure-issue_neovim_nightly.
Evidence

I've added some changes to use the new API instead of the older runtimepath string in Aniseed / Conjure. Let me know if that helps at all! No more time to test tonight, but seems okay on the stable version 🤔

I tried with the develop branch, now it fails with another error:
E5108: Error executing lua ...nvim/site/pack/packer/start/conjure/lua/conjure/eval.lua:
147: ...k/packer/start/conjure/lua/conjure/aniseed/nvim/util.lua:53: ...cker/start/conj
ure/lua/conjure/client/fennel/aniseed.lua:152: attempt to concatenate field 'context' (
a nil value)

Huh, well, that doesn't really make any sense from what I understand right now but thank you for trying it. I'll dig in soon with your container.
Horrible timing on this bug while I'm lacking any free time and was 80% through a huge refactor 😞 hopefully I can sort it soon.

😞

I think I spotted it and fixed it on develop!

Seems to work properly now! 😄

Should be fixed! Released!


Azure Active Directory - SQL Server Management Studio - unable to assign permissions

We are testing an environment where within Azure Active Directory. we are using SQL Server management studio to assign permissions to a stored procedure.
SSMS is accepting this, however those permissions are not being reflected in the software. Execute permission denied error - although I am granted access.
This is a VS2015 desktop application.
We appreciate the age of the software - just wondering if there is a workaround to resolve this (other than re-writing to whole thing).
[Enter feedback here]

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

ID: 135900dc-a56e-5136-5593-1d79b6b2bd19
Version Independent ID: 1a6428ef-831f-a09f-a79e-3a03550453a7
Content: Grant Permissions on a Stored Procedure - SQL Server
Content Source: docs/relational-databases/stored-procedures/grant-permissions-on-a-stored-procedure.md
Product: sql
Technology: stored-procedures
GitHub Login: @WilliamDAssafMSFT
Microsoft Alias: wiassaf

@rguyatt73 -- thank you for your feedback.  This repo is solely for documentation issues, and this seems implementation-related.  Please consider these resources:

SQL Server on Microsoft Q&A
DBA Stack Exchange
Stack Overflow

I am closing this issue now.  You are welcome to @ mention me for any followup.  We hope to hear from you again.
cc @WilliamDAssafMSFT for awareness


emacs: fix build on aarch64-darwin

Motivation for this change
Fix emacs build on aarch64-darwin. Requires "codesign", which will be provided by #124570
Things done

[ ] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[ ] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Added a release notes entry if the change is major or breaking
[x] Fits CONTRIBUTING.md.

Is the build issue which your are fixing presenting itself like this?
configure: error: The include files (AppKit/AppKit.h etc) that
are required for a Nextstep build are missing or cannot be compiled.
Either fix this, or re-configure with the option '--without-ns'.
error: builder for '/nix/store/42nxg2bmhpqir9w2b6lrafy79qp6i5lj-emacs-27.2.drv' failed with exit code 1;
       last 10 log lines:
       > configure: WARNING: AppKit/AppKit.h: see the Autoconf documentation
       > configure: WARNING: AppKit/AppKit.h:     section "Present But Cannot Be Compiled"
       > configure: WARNING: AppKit/AppKit.h: proceeding with the compiler's result
       > configure: WARNING:     ## ------------------------------------ ##
       > configure: WARNING:     ## Report this to<EMAIL_ADDRESS>##
       > configure: WARNING:     ## ------------------------------------ ##
       > checking for AppKit/AppKit.h... no
       > configure: error: The include files (AppKit/AppKit.h etc) that
       > are required for a Nextstep build are missing or cannot be compiled.
       > Either fix this, or re-configure with the option '--without-ns'.
       For full logs, run 'nix log /nix/store/42nxg2bmhpqir9w2b6lrafy79qp6i5lj-emacs-27.2.drv'.
error: 1 dependencies of derivation '/nix/store/mndj1354g2yz8hxkpiv5f60wkn9fvqpq-emacs-with-packages-27.2.drv' failed to build

I tested the preConfigure patch included there (the branch didn't build for me with the other changes), but I have the feeling this isn't related?

@hlolli The error you need change CFLAGS  to CFLAGS = "-DMAC_OS_X_VERSION_MAX_ALLOWED=111000";(111000 is macOS 11.1)

I believe we can close this PR?
https://github.com/NixOS/nixpkgs/pull/124888


Migrate to gofrs/uuid from gobuffalo/uuid

This is a properly maintained community fork of the uuid project. I suggest we migrate users and pop to this new package.
https://github.com/gofrs/uuid/releases/tag/v2.0.0

I think it could be good, just let see how they go with go modules before updating the project.

I just merged the module file so I think we should be good to go here. Do y'all need help with having this done?

Waiting for https://github.com/gofrs/uuid/pull/66.

Merged in development, waiting for the next minor release.