Saravanan666/pypi-org huggingface dataset:
Package: hag
hagA Hotkey AGgregator.hagis a hotkey aggregator, it tries its best to extract the hotkeys of your software and display them.It does so by parsing the config files, man pages, command outputs, ... depending on the software.⌨️ Hotkey parsersBellow is a list of implemented hotkey parsers:AlacrittyBashGimpInkscapeLfMpvNeovimQutebrowserRofiSioyekSxhkdSxivTermiteVimVimivZathuraZsh📦 Installationhagis meant to be minimal, however some parsers require optional dependencies, which can be individually installed:Mpv:python-mpv->pip install 'hag[mpv]'Neovim:pynvim->pip install 'hag[neovim]'Alacritty:pyyaml->pip install 'hag[alacritty]'To installhagwith all the parser dependencies:pip install 'hag[all]'If you just want to use the CLI interface, consider usingpipx.pipx install 'hag[all]'📖 Usage$ hag -h
usage: hag [-h] [-lp | -ld] [-d {json,roff,text}] [-m MODE] [-v]
           [{alacritty,bash,gimp,inkscape,lf,mpv,neovim,qutebrowser,rofi,sioyek,sxhkd,sxiv,termite,vim,vimiv,zathura,zsh}]

Hotkey aggregator. All your hotkeys in one place.

positional arguments:
  {alacritty,bash,gimp,inkscape,lf,mpv,neovim,qutebrowser,rofi,sioyek,sxhkd,sxiv,termite,vim,vimiv,zathura,zsh}
                        Extract hotkeys using parser.

optional arguments:
  -h, --help            show this help message and exit
  -lp, --list-parsers   List available hotkey parsers.
  -ld, --list-displays  List available display methods.
  -d {json,roff,text}, --display {json,roff,text}
                        Display method.
  -m MODE, --modes MODE
                        Filter by mode, if supported by parser.
  -v, --version         Show hag version and exit.🔎 ExamplesA few example uses:Listsxhkdhotkeys:hagsxhkdDisplaysxhkdhotkeys in json format and format withjq:hagsxhkd-djson|jqShowvimNormal and Visual mode hotkeys inrofi:hagvim-mNormal-mVisual|rofi-dmenuUserofito select hotkey parser and show hotkeys:parser="$(hag-le|rofi-dmenu)"&&hag"$parser"|rofi-dmenuList your normal and visual modeneovimhotkeys in a man page:hagneovim-mNormal-mVisual-droff|man-l-Create a pdf of yoursxhkdhotkeys:hagsxhkd-droff|groff-Tps-mmandoc-|ps2pdf-hotkeys.pdf🥳 ContributingIf you want to add support for your favourite software, feel free to open issues/PRs!
Package: hagelslag
HagelslagStorm tracking, machine learning, and probabilistic evaluationHagelslag is an object-based severe storm forecasting system that utilizing image processing and machine learning tools
to derive calibrated probabilities of severe hazards from convection-allowing numerical weather prediction model output.
The package contains modules for storm identification and tracking, spatio-temporal data extraction, and
machine learning model training to predict hazard intensity as well as space and time translations.CitationIf you employ hagelslag in your research, please acknowledge its use with the following citations:Gagne, D. J., A. McGovern, S. E. Haupt, R. A. Sobash, J. K. Williams, M. Xue, 2017: Storm-Based Probabilistic Hail
Forecasting with Machine Learning Applied to Convection-Allowing Ensembles, Wea. Forecasting, 32, 1819-1840. 
https://doi.org/10.1175/WAF-D-17-0010.1. 

Gagne II, D. J., A. McGovern, N. Snook, R. Sobash, J. Labriola, J. K. Williams, S. E. Haupt, and M. Xue, 2016: 
Hagelslag: Scalable object-based severe weather analysis and forecasting. Proceedings of the Sixth Symposium on 
Advances in Modeling and Analysis Using Python, New Orleans, LA, Amer. Meteor. Soc., 447.If you discover any issues, please post them to the Github issue tracker page. Questions and comments should be sent to
djgagne at ou dot edu.RequirementsHagelslag is compatible with Python 3.6 or newer. Hagelslag is easiest to install with the help of theMiniconda
Python Distribution, but it should work with other
Python setups as well. Hagelslag requires the following packages and recommends the following versions:numpy >= 1.10scipy >= 0.15matplotlib >= 1.4scikit-learn >= 0.16pandas >= 0.15arrow >= 0.8.0pyprojnetCDF4-pythonxarrayjupyterncepgrib2pygribcythonpipsphinxmockInstall dependencies with the following commands:git clone https://github.com/djgagne/hagelslag.git
cd ~/hagelslag
conda env create -f environment.yml
conda activate hagelslagInstallationInstall the latest version of hagelslag with the following command from the top-level hagelslag directory (where setup.py
is):pip install .Hagelslag will install the libraries in site-packages and will also install 3 applications into thebindirectory
of your Python installation.UseA Jupyter notebook is located in the demos directory that showcases the functionality of the package. For larger scale
use, 3 scripts are provided in the bin directory.hsdataperforms object tracking and matching as well as data processing.hsforetrains and applies machine learning models.hsevalperforms forecast verification.All scripts take input from a config file. The config file should be valid Python code and contain a dictionary called
config. Custom machine learning models and parameters should be contained within the config files. Examples of them can
be found in the config directory.DocumentationAPI Documentation is availablehere.
Package: hagerstrand
hagerstrandA Python package for an interactive space-time Geographic Information System (GIS)GitHub repo:https://github.com/jimmy-feng/hagerstrandDocumentation:https://jimmy-feng.github.io/hagerstrandPyPI:https://pypi.org/project/hagerstrandConda-forge:https://anaconda.org/conda-forge/hagerstrandFree software: MIT licenseIntroductionHagerstrandis a Python package for a space-time GIS for individual-level human phenomena based onTorsten Hägerstrand'stime geographic framework. Investigation of individual activity patterns in a geographic information system (GIS) has been an interest for many geographers and geographic information scientists since Hägerstrand's seminal article,What about people in regional science?in 1970. However, there lacks a uniform implementation of the concepts in the time geographic framework in a GIS across the academic research community. Esri's ArcGIS platforms have various functions for analysis, visualization, and querying of space-time data but are rather limited and only commercially available. This package is continually developed to enable for more comprehensive space-time GIS processes in an open-source Python environment, and can also be used to explore individual human dynamics (e.g. accessibility to various human needs and services.)InstallationPippip install hagerstrandCondaInstallinghagerstrandfrom theconda-forgechannel can be achieved by addingconda-forgeto your channels with:conda config --add channels conda-forgeOnce theconda-forgechannel has been enabled,hagerstrandcan be installed with:conda install hagerstrandIt is possible to list all of the versions ofhagerstrandavailable on your platform with:conda search hagerstrand --channel conda-forgeFeaturesCreate an interactive mapAdd local datasets (e.g. GeoJSON, JSON, Shapefile) to the map either through code or GUIAdd pandas DataFrames and GeoPandas GeoDataFrames to the mapUse a widget for quick viewing of filtered results of a pd.DataFrameFilter any non-TileLayer in the map by a unique value in a field/columnProcess SafeGraph data and unpack json columns into an existing or a new DataFrameCreditsThis package was created withCookiecutterand thegiswqs/pypackageproject template.
Package: haggregate
haggregateAggregates htimeseries to larger stepsFree software: GNU General Public License v3Documentation:https://haggregate.readthedocs.io.History3.0.2 (2023-12-20)Allow htimeseries 6.3.0.1 (2022-12-03)Allow Click to upgrade to 8.3.0.0 (2022-12-03)Compatible with htimeseries 4.2.0.1 (2022-11-13)Fixed inability to install with pip on Python 3.10 (no difference in
code, it was only rebuilt in Python 3.10).2.0.0 (2020-02-23)Only Python>=3.7 is now supported.1.0.2 (2020-01-22)Fixed crash if result had only NaN values.1.0.1 (2020-01-10)Made installation process more robust. Sometimes it could fail,
depending on the environment.1.0.0 (2020-01-05)Now uses version 5 of hts file (i.e. different time step notation).0.2.0 (2019-12-05)Added option target_timestamp_offset.0.1.4 (2019-07-19)Greatly reduced memory usage.0.1.3 (2019-07-03)Fixed bug where Timezone wasn’t being set in the output file.0.1.2 (2019-06-25)Fixed erroneous setting of time_step, timestamp_rounding and
timestamp_offset in the resulting time series.You can now import regularize from haggregate (before you imported it
from haggregate.regularize).0.1.1 (2019-06-24)Initial release(There is also a 0.1.0 release on PyPI, which was uploaded during
experimentation, but it doesn’t work.)
Package: hagi
HagiCLI manager inpired by invoke, with type hints and asyncio.Basic Example:Create a =tasks.py= file with content:importsysfromhagiimporttask,Context@taskasyncdefhello(c:Context):"hello to you"print('hello')awaitc.exec('whoami',hide=False)Then run in shell in the same directory:lindsay@Reisen> hagi
Usage:
 hagi [Command]

Available Commands:
 hello               hello to you

Flags:
 -h  --help          	
lindsay@Reisen> hagi hello
hello
lindsayMore Detailshttps://io.lin.moe/n/prjs/hagi.html
Package: hagis
HagisA GIS clientpip install hagisfromhagisimportLayer,PointclassCity:objectid:intareaname:strpop2000:intgeometry:Pointlayer=Layer("https://sampleserver6.arcgisonline.com/arcgis/rest/services/USA/MapServer/0",City)forcityinlayer.query():print(city.areaname,city.pop2000,city.geometry.x,city.geometry.y)More examples
Package: hagstofan
HagstofanDescriptionAPI wrapper fyrir hagstofu vefþjónustunaInstallationPrerequisitesPython 3.pipInstalling the packageInstalla frá github repository:pipinstallhagstofanpipinstallgit+https://github.com/datador/hagstofan.gitUsageImporting the packagefromhagstofanimportHagstofanExamples# Initializa classinnhagstofan=Hagstofan()# Leitum og finnum möguleg gagnasetthagstofan.search_datasets('visitala neysluverds').head(1)dbididtexturlEfnahagurVIS01000.pxVísitala neysluverðs og breytingar, grunnur 19https://px.hagstofa.is/pxis/api/v1/is/Efnahagu...# Hvenær var taflan síðast uppfærð?hagstofan.get_update('VIS01000.px')'2023-08-30T08:58:02'# Náum í gögnindf=hagstofan.get_data(table='VIS01000.px')dfMánuður_indexMánuður_labelVísitala_indexVísitala_labelLiður_indexLiður_labelValue001988M05CPIVísitala neysluverðsindexVísitala100111988M05CPIVísitala neysluverðschange_MMánaðarbreyting, %nan221988M05CPIVísitala neysluverðschange_AÁrsbreyting, %nan331988M05CPIVísitala neysluverðsA_rate_MÁrsbreyting síðasta mánuð, %nan441988M05CPIVísitala neysluverðsA_rate_3MÁrsbreyting síðustu 3 mánuði, %nanAsync MethodsPakkinn notar asyncio fyrir asynchronous operations. Allt sem keyrir með run_async_method_sync decorateornum keyrir async synchronously til að geta keyrt í notebooks, það heldur samt asynchronous http requestunum og tapar ekki virkni.ConfigurationPakkinn notar JSON config skrá undirconfigs/table_data.json. Hún heldur utan um öll API köll möguleg (gagnaskrá).
Package: hahomematic
hahomematichahomematicis a Python 3 (>= 3.11) module forHome Assistantto interact withHomeMaticandHomematicIPdevices. Some other devices (f.ex. Bosch, Intertechno) might be supported as well.This is intended to become the successor ofpyhomematic.It can be installed by using thecustom_component.
Necessary installation instructions can be foundhere.Project goal and featurespyhomematichas the requirement to manually add support for devices to make them usable inHome Assistant.hahomematicautomatically create entities for each parameter on each channel on every device (if it not black listed). To achieve this, all paramsets (VALUES) are fetched (and cached for quick successive startups).On top of that it is possible to add custom entity-classes to implement more complex entities, if it makes sense for a device, much like thedevicetypesofpyhomematic. This will be needed for thermostats, lights, covers, climate, lock, siren etc..Helpers for automatic re-connecting after a restart of the CCU are provided as well.RequirementsDue to a bug in previous version of the CCU2 / CCU3,hahomematicrequires at least the following version for usage with HomematicIP devices:CCU2: 2.53.27CCU3: 3.53.26More information about this bug can be found here:https://github.com/jens-maus/RaspberryMatic/issues/843. Other CCU-like platforms that leverage the buggy version of theHmIPServeraren't supported as well.
Package: hahtorch
Figure 1: HaH block for image classification DNNs.Hebbian/Anti-Hebbian Learning for PytorchOfficial repository for the paper entitled "Towards Robust, Interpretable Neural Networks via Hebbian/anti-Hebbian Learning: A Software Framework for
Training with Feature-Based Costs". If you have questions you can contact metehancekic [at] ucsb [dot] edu.Maintainers:WCSL Lab,Metehan Cekic,Can Bakiskan,Dependenciesnumpy==1.20.2torch==1.10.2How to installThe most recent stable version can be installed via python package installer "pip", or you can clone it from the git page.pipinstallhahtorchorgitclonegit@github.com:metehancekic/HaH.gitExperimentsWe used CIFAR-10 image classification to show the effectiveness of our module. We train a VGG16 in a standard fashion and train another VGG16 that contains HaHblocks with layer-wise HaHCost as a supplement. Details of our experiments can be found in our recentpaperCIFAR10 Image Classification with VGG16 model as BackboneFigure 2: HaH VGG16, our proposed architecture for HaH training, seepaperfor more detail.Table 1: CIFAR10 classification: Performance of the HaH trained network against different input corruptions on the test set. For all of the adversarial attacks, we use AutoAttack which is an ensemble of parameter-free attacks, seepaperfor more detail.Current Version0.0.5SourcesPyPi page for the codeGit repo for the code
Package: hahusms
API Guide for Hahu Cloud SMS GatewayThis is simple python api forhahucloud sms. It is a RESTful API, and you can use it to send SMS messages to your customers. It is a simple and easy to use API.This isunofficial API, and it isnotsupported byHahu Clouds. It is just a guide for you to use Hahu SMS API. If you have any questions, please contact us.Contact Information:- Email: chapimenge3@gmail.com
- Telegram: @chapimenge
- Twitter: @chapimenge
- Linkedin: https://www.linkedin.com/in/chapimenge/Installationhahusms is available on PyPI:python-mpipinstallhahusmsHahusms officially supports Python 3.7 and above.User GuideSimple API Reference:fromhahusmsimportSMSsms=SMS(api_key='your_api_key')response=sms.send_message(to='+86xxxxxxxx',message='Hello world!')print(response['status'])print(response['message'])print(response['data'])# Or you can use the following to make the response from json to objectsms=SMS(api_key='your_api_key',response_format="obj")response=sms.send_message(to='+86xxxxxxxx',message='Hello world!')print(response.status)print(response.message)print(response.data.message)API ReferenceThe SMS class the base class for all API calls. You can set the following parameters when you initialize the SMS class:- api_key: Your API key.
- response_format: The response format. It can be "json" or "obj".
- device (optional): The device id to set your own default device.
- sim (optional): The sim slot to set your own default sim slot.
- priority (optional): The priority of the message. It can be 0 or 1.To use the above parameters we can use as belowfromhahusmsimportSMSsms=SMS(api_key='your_api_key',response_format="json",device='your_device_id(int)',sim='your_sim_slot(int)',priority=1)You can pass the parameters when you call the API.sms=SMS(api_key='your_api_key',response_format="obj")params={"id":1}response=sms.get_device(params=params)print(response.status)print(response.message)print(response.data.name)# The response is object type because we set response_format="obj"you can always override the SMS class and define your own custom methods to call the API.Available MethodsSMSclass have the following methods:- create_contact
- create_group
- delete_contact
- delete_group
- get_contacts
- get_groups
- get_device
- get_devices
- get_pending
- get_received
- get_received
- send
- send_message - required parameters: to, messageYou can find the officialPostman Collectionfor each method in theAPI Reference.
Package: hai-chat-gpt
HaiChatGPTThis project is a ChatGPT GUI based on Hai GUI Framework (HaiGF).Quickstartpipinstallhai-chat-gpt--upgrade# installHaiChatGPT# run# parmeters:--cli# run in command line modeAcknowledgementThe project is based onOpenAIand acheong08'sChatGPTproject.
Package: haicu
HAICU Control Script (haicu-ctl)OverviewPackage and command line utility for controlling and sequencing the MLD1200InstallationThe recommended installation method is via pipTo install/upgrade:pip install -U haicuTo run it locally from the source (from thescripts/haicudirectory):python -m haicuUsageCommand:haicu-ctl [-h] [--version] [-v] [-l file] [-s SERIAL] [-j] {list,info,set,upload,program,convert,compare}Purpose:
Connects to an MLD1200 device and reads/writes registers, statuses, and memory. Also contains utility functions to create .RLE files for consumption by device.Options:-hor--helpPrint out help for general usage or subcommand. Ie:haicu-ctl -horhaicu-ctl set -h--versionPrint out current version-vor--verboseIncrease logging level, can be used multiple times-l LOG_FILEor--log LOG_FILESpecify log file name, instead of standard naming scheme-s SERIALor--serial SERIALSpecify serial number of MLD1200 to connect to-jor--jsonFor reads, format output in JSON. Only affectsset(with no value),info,status,control,memory, andbreadThe available subcommands are:list- List of available MLD1200 devicesinfo- Get full report on MLD1200, all registers and status bitsset <regname> [data]- Allows human-readable setting of register values. If data is omitted, returns current value in registerupload <file> <section>- Upload a new sequence into MLD1200 from file. Choose section to load ('left','right','extension'). Does not start sequence.program [config_file] [--auto/-a]- Upload and start sequence. Can be passed a path to an INI file, otherwise tries to use 'haicu.ini' in current directory. The '--auto' option auto-reload the sequencer when it finishes, it will then start again on next trigger.convert <infile> <outfile>- Convert a derived format file 'infile' into RLE formatted file 'outfile'compare <derived_file> <rle_file>- Compare a derived file to an RLE fileDiagnostic subcommands:memtest- Test the selected MLD by writing and reading random values to DDR memorystatus <addr>- Low level readback of status register at given addresscontrol <addr> [data]- Low level read/write of control registers. Omit 'data' to return current value at given addressmemory <addr> [data]- Low level read/write of DDR memory. Omit 'data' to return current value at given addressbread <addr> <count>- Low level block read of DDR memory section. Reads 'count' words out of DDR started at given address
Package: hai-gui-framework
HaiGFHaiGF(HAI GUI Framework)是可扩展、轻量化的用于开发基于机器学习算法的应用程序的框架。采用模块化设计，可扩展性强，可灵活添加新的模块。采用基于HAI算法库的前后端分离设计，实现界面的轻量化。主界面主界面分为5个模块：核心功能栏、主侧栏、中央控件、辅助侧栏和面板栏。1. 核心功能栏Core Func Bar核心功能栏在左侧，由图标工具组成，例如：资源管理器、标注工具、AI工具等，可通过点击图标切换到相应的模块。可通过开发插件添加新的功能。2. 主侧栏主侧栏是核心功能的具体展开，由一个不可移动的左侧Dock坞构成，包含标题、标题右侧工具图标和内容工具。可开发控件添加到主侧栏，并与核心功能绑定。3. 中央控件中央控件用于展示核心功能的具体内容、可视化数据并与人进行交互。
中央控件基于选项卡(Tab)和页面(Page)的设计，可通过选项卡切换不同的页面。可通过开发控件添加到中央控件，并与核心功能或主侧栏的操作绑定。
在中央控件内部，通过QSplitter实现自动分屏器，可通过拖动Tab实现自动分屏。4. 辅助侧栏辅助侧栏位于主界面的右侧，用于展示详细属性、信息，设置一些参数等。5. 面板栏面板栏位于主界面的底部，通过对选项卡的方式实现多个输出。
Package: haiku-errors
To have no errors Would be life without meaning No struggle, no joy~ (Brian M. Porter)haiku-errorsJustimport haiku_errorsto print a haiku whenever exception is not
handled:$python-c"import haiku_errors; raise SystemError"Traceback (most recent call last):
  File "<string>", line 1, in <module>
SystemError

    Something has gone wrong.
    Format your disk, because this
    Error won't help you.
    ~ (Cheryl Walker)One liner example without explicitraise:$python3-c"import haiku_errors; f = lambda: f(); f()"Traceback (most recent call last):
  File "<string>", line 1, in <module>
LookupError

    The ten thousand things,
    How long do any persist?
    Netscape, too, has gone.
    ~ (Jason Willoughby)To install run:$ pip install haiku-errors
Package: haiku-lst
This project had been deprecated. Maybe use deep learning.IntroduceA Modification ofLibShortTextandLIBLINEAR.Uses Wissen Text AnalyzerFeature SelectionAPI Exported by Skitai App EngineWin32 support (need MSVC)Installationgitclonehttps://gitlab.com/hansroh/haikucdhaikupythonsetup.pybuildinstallBasic Usageimporthaikumodel_path="./golforbed"analyzer=haiku.StandardAnalyzer(max_term=200,stem_level=2,make_lower_case=1)trainset=[('Golf',"cloudy cold calm"),('Golf',"sunny warm"),('Bed',"rainy hot"),('Golf',"sunny hot windy"),('Bed',"windy cloudy cold"),('Bed',"rainy cloudy cold"),]# trainingh=haiku.Haiku(model_path,haiku.CL_L2,analyzer)# pruning by document frequency and scoring by meth (FS_CF means category frequency)h.select(data,mindf=0,maxdf=0,top=0,meth=haiku.FS_CF)# set training options: uni/bigram and feature representationh.train(haiku.BIGRAM,haiku.FT_BIN)h.close()# guessingh=haiku.Haiku(model_path,haiku.CL_L2,analyzer)h.load()print(h.guess("sunny cold windy"))h.close()Exporting API through Skitai App EnginePlace model data intoapp_root/resources/haikus/golforbed.importhaikuimportskitaiif__name__=="__main__":pref=skitai.pref()pref.config.resource_dir=skitai.joinpath('resources')skitai.mount("/",haiku,"app",pref)skitai.run(port=5005)Go tohttp://127.0.0.1:5000/haiku/golforbed/guess?q=sunny%20cold%20windy.
Package: haiku-mup
MUP for HaikuThis is a (very preliminary) port of Yang and Hu et al.'sμP repoto Haiku and JAX. It's not feature complete, and I'm very open to suggestions on improving the usability.Installationpip install haiku-mupLearning rate demoThese plots show the evolution of the optimal learning rate for a 3-hidden-layer MLP on MNIST, trained for 10 epochs (5 trials per lr/width combination).With standard parameterization, the learning rate optimum (w.r.t. training loss) continues changing as the width increases, but μP keeps it approximately fixed:Here's the same kind of plot for 3 layer transformers on the Penn Treebank, this time showing Validation loss instead of training loss, scaling both the number of heads and the embedding dimension simultaneously:Note that the optima have the same value forn_embd=80. That's because the other hyperparameters were tuned using an SP model with that width, so this shouldn't be biased in favor of μP.Usagefromfunctoolsimportpartialimportjaximportjax.numpyasjnpimporthaikuashkfromoptaximportadam,chainfromhaiku_mupimportapply_mup,Mup,ReadoutclassMyModel(hk.Module):def__init__(self,width,n_classes=10):super().__init__(name='model')self.width=widthself.n_classes=n_classesdef__call__(self,x):x=hk.Linear(self.width)(x)x=jax.nn.relu(x)returnReadout(2)(x)# 1. Replace output layer with Readout layerdeffn(x,width=100):withapply_mup():# 2. Modify parameter creation with apply_mup()returnMyModel(width)(x)mup=Mup()init_input=jnp.zeros(123)base_model=hk.transform(partial(fn,width=1))withmup.init_base():# 3. Use this context manager when initializing the base modelhk.init(fn,jax.random.PRNGKey(0),init_input)model=hk.transform(fn)withmup.init_target():# 4. Use this context manager when initializng the target modelparams=model.init(jax.random.PRNGKey(0),init_input)model=mup.wrap_model(model)# 5. Modify your model with Mupoptimizer=optax.adam(3e-4)optimizer=mup.wrap_optimizer(optimizer,adam=True)# 6. Use wrap_optimizer to get layer specific learning rates# Now the model can be trained as normalSummaryReplace output layers withReadoutlayersModify parameter creation with theapply_mup()context managerInitialize a base model inside aMup.init_base()contextInitialize the target model inside aMup.init_target()contextWrap the model withMup.wrap_modelWrap optimizer withMup.wrap_optimizerShared Input/Output embeddingsIf you want to use the input embedding matrix as the output layer's weight matrix make the following two replacements:# old: embedding_layer = hk.Embed(*args, **kwargs)# new:embedding_layer=haiku_mup.SharedEmbed(*args,**kwargs)input_embeds=embedding_layer(x)#old: output = hk.Linear(n_classes)(x)# new:output=haiku_mup.SharedReadout()(embedding_layer.get_weights(),x)