From 3256d2cc0f91dad1f2c91ebad2c6efbe6611d688 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Thu, 9 May 2024 00:28:54 +0200
Subject: [PATCH 01/15] feat: added MNIST, Fashion-MNIST and KMNIST datasets

---
 poetry.lock                                  | 423 ++++++++++---------
 pyproject.toml                               |  12 +-
 src/safeds_datasets/image/__init__.py        |   5 +
 src/safeds_datasets/image/_mnist/__init__.py |   5 +
 src/safeds_datasets/image/_mnist/_mnist.py   | 202 +++++++++
 5 files changed, 437 insertions(+), 210 deletions(-)
 create mode 100644 src/safeds_datasets/image/__init__.py
 create mode 100644 src/safeds_datasets/image/_mnist/__init__.py
 create mode 100644 src/safeds_datasets/image/_mnist/_mnist.py

diff --git a/poetry.lock b/poetry.lock
index 10be993..7ad6647 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "anyio"
@@ -671,13 +671,13 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.13.4"
+version = "3.14.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.13.4-py3-none-any.whl", hash = "sha256:404e5e9253aa60ad457cae1be07c0f0ca90a63931200a47d9b6a6af84fd7b45f"},
-    {file = "filelock-3.13.4.tar.gz", hash = "sha256:d13f466618bfde72bd2c18255e269f72542c6e70e7bac83a0232d6b1cc5c8cf4"},
+    {file = "filelock-3.14.0-py3-none-any.whl", hash = "sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f"},
+    {file = "filelock-3.14.0.tar.gz", hash = "sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a"},
 ]
 
 [package.extras]
@@ -894,38 +894,6 @@ files = [
     {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
-[[package]]
-name = "imageio"
-version = "2.34.1"
-description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "imageio-2.34.1-py3-none-any.whl", hash = "sha256:408c1d4d62f72c9e8347e7d1ca9bc11d8673328af3913868db3b828e28b40a4c"},
-    {file = "imageio-2.34.1.tar.gz", hash = "sha256:f13eb76e4922f936ac4a7fec77ce8a783e63b93543d4ea3e40793a6cabd9ac7d"},
-]
-
-[package.dependencies]
-numpy = "*"
-pillow = ">=8.3.2"
-
-[package.extras]
-all-plugins = ["astropy", "av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
-all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
-build = ["wheel"]
-dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
-docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
-ffmpeg = ["imageio-ffmpeg", "psutil"]
-fits = ["astropy"]
-full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
-gdal = ["gdal"]
-itk = ["itk"]
-linting = ["black", "flake8"]
-pillow-heif = ["pillow-heif"]
-pyav = ["av"]
-test = ["fsspec[github]", "pytest", "pytest-cov"]
-tifffile = ["tifffile"]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -937,6 +905,20 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "intel-openmp"
+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = false
+python-versions = "*"
+files = [
+    {file = "intel_openmp-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:41c01e266a7fdb631a7609191709322da2bbf24b252ba763f125dd651bcc7675"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:3b921236a38384e2016f0f3d65af6732cf2c12918087128a9163225451e776f2"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"},
+]
+
 [[package]]
 name = "ipykernel"
 version = "6.29.4"
@@ -1080,13 +1062,13 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.4.0"
+version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "joblib-1.4.0-py3-none-any.whl", hash = "sha256:42942470d4062537be4d54c83511186da1fc14ba354961a2114da91efa9a4ed7"},
-    {file = "joblib-1.4.0.tar.gz", hash = "sha256:1eb0dc091919cd384490de890cb5dfd538410a6d4b3b54eef09fb8c50b409b1c"},
+    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
 ]
 
 [[package]]
@@ -1555,25 +1537,6 @@ files = [
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 ]
 
-[[package]]
-name = "lazy-loader"
-version = "0.4"
-description = "Makes it easy to load subpackages and functions on demand."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc"},
-    {file = "lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1"},
-]
-
-[package.dependencies]
-packaging = "*"
-
-[package.extras]
-dev = ["changelist (==0.5)"]
-lint = ["pre-commit (==3.7.0)"]
-test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
-
 [[package]]
 name = "levenshtein"
 version = "0.25.1"
@@ -2125,6 +2088,24 @@ files = [
 griffe = ">=0.44"
 mkdocstrings = ">=0.24.2"
 
+[[package]]
+name = "mkl"
+version = "2021.4.0"
+description = "Intel® oneAPI Math Kernel Library"
+optional = false
+python-versions = "*"
+files = [
+    {file = "mkl-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:67460f5cd7e30e405b54d70d1ed3ca78118370b65f7327d495e9c8847705e2fb"},
+    {file = "mkl-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:636d07d90e68ccc9630c654d47ce9fdeb036bb46e2b193b3a9ac8cfea683cce5"},
+    {file = "mkl-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:398dbf2b0d12acaf54117a5210e8f191827f373d362d796091d161f610c1ebfb"},
+    {file = "mkl-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:439c640b269a5668134e3dcbcea4350459c4a8bc46469669b2d67e07e3d330e8"},
+    {file = "mkl-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:ceef3cafce4c009dd25f65d7ad0d833a0fbadc3d8903991ec92351fe5de1e718"},
+]
+
+[package.dependencies]
+intel-openmp = "==2021.*"
+tbb = "==2021.*"
+
 [[package]]
 name = "mpmath"
 version = "1.3.0"
@@ -2447,12 +2428,13 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.19.3"
+version = "2.20.5"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl", hash = "sha256:a9734707a2c96443331c1e48c717024aa6678a0e2a4cb66b2c364d18cee6b48d"},
+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
 ]
 
 [[package]]
@@ -2781,6 +2763,50 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
+[[package]]
+name = "polars"
+version = "0.20.25"
+description = "Blazingly fast DataFrame library"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "polars-0.20.25-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:126e3b7d9394e4b23b4cc48919b7188203feeeb35d861ad808f281eaa06d76e2"},
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+    {file = "polars-0.20.25-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:62c8826e81c759f07bf5c0ae00f57a537644ae05fe68737185666b8ad8430664"},
+    {file = "polars-0.20.25-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:0fb5e7a4a9831fba742f1c706e01656607089b6362a5e6f8d579b134a99795ce"},
+    {file = "polars-0.20.25-cp38-abi3-win_amd64.whl", hash = "sha256:9eaeb9080c853e11b207d191025e0ba8fd59ea06a36c22d410a48f2f124e18cd"},
+    {file = "polars-0.20.25.tar.gz", hash = "sha256:4308d63f956874bac9ae040bdd6d62b2992d0b1e1349301bc7a3b59458189108"},
+]
+
+[package.dependencies]
+numpy = {version = ">=1.16.0", optional = true, markers = "extra == \"numpy\""}
+pyarrow = {version = ">=7.0.0", optional = true, markers = "extra == \"pyarrow\""}
+
+[package.extras]
+adbc = ["adbc-driver-manager", "adbc-driver-sqlite"]
+all = ["polars[adbc,async,cloudpickle,connectorx,deltalake,fastexcel,fsspec,gevent,numpy,pandas,plot,pyarrow,pydantic,pyiceberg,sqlalchemy,timezone,torch,xlsx2csv,xlsxwriter]"]
+async = ["nest-asyncio"]
+cloudpickle = ["cloudpickle"]
+connectorx = ["connectorx (>=0.3.2)"]
+deltalake = ["deltalake (>=0.15.0)"]
+fastexcel = ["fastexcel (>=0.9)"]
+fsspec = ["fsspec"]
+gevent = ["gevent"]
+matplotlib = ["matplotlib"]
+numpy = ["numpy (>=1.16.0)"]
+openpyxl = ["openpyxl (>=3.0.0)"]
+pandas = ["pandas", "pyarrow (>=7.0.0)"]
+plot = ["hvplot (>=0.9.1)"]
+pyarrow = ["pyarrow (>=7.0.0)"]
+pydantic = ["pydantic"]
+pyiceberg = ["pyiceberg (>=0.5.0)"]
+pyxlsb = ["pyxlsb (>=1.0)"]
+sqlalchemy = ["pandas", "sqlalchemy"]
+timezone = ["backports-zoneinfo", "tzdata"]
+torch = ["torch"]
+xlsx2csv = ["xlsx2csv (>=0.8.0)"]
+xlsxwriter = ["xlsxwriter"]
+
 [[package]]
 name = "prometheus-client"
 version = "0.20.0"
@@ -2862,6 +2888,54 @@ files = [
 [package.extras]
 tests = ["pytest"]
 
+[[package]]
+name = "pyarrow"
+version = "16.0.0"
+description = "Python library for Apache Arrow"
+optional = false
+python-versions = ">=3.8"
+files = [
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+url = "https://github.com/Safe-DS/Library.git"
+reference = "HEAD"
+resolved_reference = "0564b523c46df74ac235c22783dcbf3d515ed122"
 
 [[package]]
 name = "scikit-learn"
@@ -3928,6 +3956,19 @@ files = [
 [package.dependencies]
 mpmath = ">=0.19"
 
+[[package]]
+name = "tbb"
+version = "2021.12.0"
+description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
+optional = false
+python-versions = "*"
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 [[package]]
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 version = "0.18.1"
@@ -3951,32 +3992,15 @@ typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "threadpoolctl"
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 description = "threadpoolctl"
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 python-versions = ">=3.8"
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-[package.extras]
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 [[package]]
 name = "tinycss2"
 version = "1.3.0"
@@ -4008,42 +4032,28 @@ files = [
 
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 python-versions = ">=3.8.0"
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+    {file = "torch-2.3.0+cu121-cp38-cp38-linux_x86_64.whl", hash = "sha256:9598b959f564ee3ebe3603b0ba01d24174ca8016feca98104f0301f1490617ca"},
+    {file = "torch-2.3.0+cu121-cp38-cp38-win_amd64.whl", hash = "sha256:d7620f3c92e33030274b7b369a93d13ec3b35c965e790d6df27fc6d964a4c829"},
+    {file = "torch-2.3.0+cu121-cp39-cp39-linux_x86_64.whl", hash = "sha256:3cc15e4c2682a85518121a2050d6be7976d98fc4843bbc13b6f5bee275a1b6ee"},
+    {file = "torch-2.3.0+cu121-cp39-cp39-win_amd64.whl", hash = "sha256:77b690e7e0fd472a5d0146394f74fac82ab1e10b822baa9b955dec0667fe83c6"},
 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = "*"
 jinja2 = "*"
+mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
 networkx = "*"
 nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
@@ -4054,58 +4064,53 @@ nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linu
 nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
+triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
 optree = ["optree (>=0.9.1)"]
 
+[package.source]
+type = "legacy"
+url = "https://download.pytorch.org/whl/cu121"
+reference = "torch_cuda121"
+
 [[package]]
 name = "torchvision"
-version = "0.17.2"
+version = "0.18.0+cu121"
 description = "image and video datasets and models for torch deep learning"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "torchvision-0.17.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:1f2910fe3c21ad6875b2720d46fad835b2e4b336e9553d31ca364d24c90b1d4f"},
-    {file = "torchvision-0.17.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ecc1c503fa8a54fbab777e06a7c228032b8ab78efebf35b28bc8f22f544f51f1"},
-    {file = "torchvision-0.17.2-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:f400145fc108833e7c2fc28486a04989ca742146d7a2a2cc48878ebbb40cdbbd"},
-    {file = "torchvision-0.17.2-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:e9e4bed404af33dfc92eecc2b513d21ddc4c242a7fd8708b3b09d3a26aa6f444"},
-    {file = "torchvision-0.17.2-cp310-cp310-win_amd64.whl", hash = "sha256:ba2e62f233eab3d42b648c122a3a29c47cc108ca314dfd5cbb59cd3a143fd623"},
-    {file = "torchvision-0.17.2-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:9b83e55ee7d0a1704f52b9c0ac87388e7a6d1d98a6bde7b0b35f9ab54d7bda54"},
-    {file = "torchvision-0.17.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e031004a1bc432c980a7bd642f6c189a3efc316e423fc30b5569837166a4e28d"},
-    {file = "torchvision-0.17.2-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:3bbc24b7713e8f22766992562547d8b4b10001208d372fe599255af84bfd1a69"},
-    {file = "torchvision-0.17.2-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:833fd2e4216ced924c8aca0525733fe727f9a1af66dfad7c5be7257e97c39678"},
-    {file = "torchvision-0.17.2-cp311-cp311-win_amd64.whl", hash = "sha256:6835897df852fad1015e6a106c167c83848114cbcc7d86112384a973404e4431"},
-    {file = "torchvision-0.17.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:14fd1d4a033c325bdba2d03a69c3450cab6d3a625f85cc375781d9237ca5d04d"},
-    {file = "torchvision-0.17.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9c3acbebbe379af112b62b535820174277b1f3eed30df264a4e458d58ee4e5b2"},
-    {file = "torchvision-0.17.2-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:77d680adf6ce367166a186d2c7fda3a73807ab9a03b2c31a03fa8812c8c5335b"},
-    {file = "torchvision-0.17.2-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:f1c9ab3152cfb27f83aca072cac93a3a4c4e4ab0261cf0f2d516b9868a4e96f3"},
-    {file = "torchvision-0.17.2-cp312-cp312-win_amd64.whl", hash = "sha256:3f784381419f3ed3f2ec2aa42fb4aeec5bf4135e298d1631e41c926e6f1a0dff"},
-    {file = "torchvision-0.17.2-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:b83aac8d78f48981146d582168d75b6c947cfb0a7693f76e219f1926f6e595a3"},
-    {file = "torchvision-0.17.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:1ece40557e122d79975860a005aa7e2a9e2e6c350a03e78a00ec1450083312fd"},
-    {file = "torchvision-0.17.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:32dbeba3987e20f2dc1bce8d1504139fff582898346dfe8ad98d649f97ca78fa"},
-    {file = "torchvision-0.17.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:35ba5c1600c3203549d2316422a659bd20c0cfda1b6085eec94fb9f35f55ca43"},
-    {file = "torchvision-0.17.2-cp38-cp38-win_amd64.whl", hash = "sha256:2f69570f50b1d195e51bc03feffb7b7728207bc36efcfb1f0813712b2379d881"},
-    {file = "torchvision-0.17.2-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:4868bbfa55758c8107e69a0e7dd5e77b89056035cd38b767ad5b98cdb71c0f0d"},
-    {file = "torchvision-0.17.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:efd6d0dd0668e15d01a2cffadc74068433b32cbcf5692e0c4aa15fc5cb250ce7"},
-    {file = "torchvision-0.17.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:7dc85b397f6c6d9ef12716ce0d6e11ac2b803f5cccff6fe3966db248e7774478"},
-    {file = "torchvision-0.17.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:d506854c5acd69b20a8b6641f01fe841685a21c5406b56813184f1c9fc94279e"},
-    {file = "torchvision-0.17.2-cp39-cp39-win_amd64.whl", hash = "sha256:067095e87a020a7a251ac1d38483aa591c5ccb81e815527c54db88a982fc9267"},
+    {file = "torchvision-0.18.0+cu121-cp310-cp310-linux_x86_64.whl", hash = "sha256:13e1b48dc5ce41ccb8100ab3dd26fdf31d8f1e904ecf2865ac524493013d0df5"},
+    {file = "torchvision-0.18.0+cu121-cp310-cp310-win_amd64.whl", hash = "sha256:4ab207a0f35c8c2a43da91f19ee9248520239633dc8e11a9e4a2e77b076bb9db"},
+    {file = "torchvision-0.18.0+cu121-cp311-cp311-linux_x86_64.whl", hash = "sha256:1e516779520fc92157d6cb245ac0fd3f79872fe81c84b8593a8f2e998106f5b1"},
+    {file = "torchvision-0.18.0+cu121-cp311-cp311-win_amd64.whl", hash = "sha256:3e5557512ec9a31a6ce33a1107827427ef50ad65e88cd35012161d7392ef144b"},
+    {file = "torchvision-0.18.0+cu121-cp312-cp312-linux_x86_64.whl", hash = "sha256:700f6019bebee9e0ee8b0bcbdb1588809c94a2eb947a1fde2e06adc34b60da2a"},
+    {file = "torchvision-0.18.0+cu121-cp312-cp312-win_amd64.whl", hash = "sha256:e09966baf7945085d7725ac7c944172c632516643d603bde5855f6b3cf68efcf"},
+    {file = "torchvision-0.18.0+cu121-cp38-cp38-linux_x86_64.whl", hash = "sha256:c2483b2cc62278fbaa6b67d2a9d808d245044f8b64a821596eae68ec6a0d5ed0"},
+    {file = "torchvision-0.18.0+cu121-cp38-cp38-win_amd64.whl", hash = "sha256:1d637b1428a076dfaff71ca0a5b8ef200b5b5ff7dfe2ed9359be0eff43734e5a"},
+    {file = "torchvision-0.18.0+cu121-cp39-cp39-linux_x86_64.whl", hash = "sha256:1bfe0c67fd5461a3a593f8f17e1544f07a6bd6686e155200dd244947074fcd51"},
+    {file = "torchvision-0.18.0+cu121-cp39-cp39-win_amd64.whl", hash = "sha256:611bb0b42c51de0c1bbcc8e90f88b4d48c8b99451bdb2e4761028b93cb086437"},
 ]
 
 [package.dependencies]
 numpy = "*"
 pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
-torch = "2.2.2"
+torch = "2.3.0"
 
 [package.extras]
 scipy = ["scipy"]
 
+[package.source]
+type = "legacy"
+url = "https://download.pytorch.org/whl/cu121"
+reference = "torch_cuda121"
+
 [[package]]
 name = "tornado"
 version = "6.4"
@@ -4143,17 +4148,17 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "triton"
-version = "2.2.0"
+version = "2.3.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 files = [
-    {file = "triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5"},
-    {file = "triton-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da58a152bddb62cafa9a857dd2bc1f886dbf9f9c90a2b5da82157cd2b34392b0"},
-    {file = "triton-2.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0af58716e721460a61886668b205963dc4d1e4ac20508cc3f623aef0d70283d5"},
-    {file = "triton-2.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8fe46d3ab94a8103e291bd44c741cc294b91d1d81c1a2888254cbf7ff846dab"},
-    {file = "triton-2.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8ce26093e539d727e7cf6f6f0d932b1ab0574dc02567e684377630d86723ace"},
-    {file = "triton-2.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df"},
+    {file = "triton-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ce4b8ff70c48e47274c66f269cce8861cf1dc347ceeb7a67414ca151b1822d8"},
+    {file = "triton-2.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c3d9607f85103afdb279938fc1dd2a66e4f5999a58eb48a346bd42738f986dd"},
+    {file = "triton-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:218d742e67480d9581bafb73ed598416cc8a56f6316152e5562ee65e33de01c0"},
+    {file = "triton-2.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:381ec6b3dac06922d3e4099cfc943ef032893b25415de295e82b1a82b0359d2c"},
+    {file = "triton-2.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:038e06a09c06a164fef9c48de3af1e13a63dc1ba3c792871e61a8e79720ea440"},
+    {file = "triton-2.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d8f636e0341ac348899a47a057c3daea99ea7db31528a225a3ba4ded28ccc65"},
 ]
 
 [package.dependencies]
@@ -4453,4 +4458,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11,<3.13"
-content-hash = "030a0f2df08d4c30995cfc264f110ea46654fa0f4f5895b741cecce3a24524f3"
+content-hash = "eeda7efdf1d52eb79b00ad5def6a8ac416a6d4d81b33e218e5e31743e77f173e"
diff --git a/pyproject.toml b/pyproject.toml
index 9f4acfd..50f2c2c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,7 @@ packages = [
 
 [tool.poetry.dependencies]
 python = "^3.11,<3.13"
-safe-ds = ">=0.17,<0.22"
+safe-ds = "0.23.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = ">=7.2.1,<9.0.0"
@@ -34,6 +34,10 @@ mkdocs-section-index = "^0.3.9"
 mkdocs-jupyter = ">=0.23,<0.25"
 mkdocs-exclude = "^1.0.2"
 
+
+[tool.poetry.group.image_dataset_dev.dependencies]
+safe-ds = {git = "https://github.com/Safe-DS/Library.git"}
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
@@ -46,3 +50,9 @@ markers = [
 
 [tool.black]
 line-length = 120
+
+
+[[tool.poetry.source]]
+name = "torch_cuda121"
+url = "https://download.pytorch.org/whl/cu121"
+priority = "explicit"
diff --git a/src/safeds_datasets/image/__init__.py b/src/safeds_datasets/image/__init__.py
new file mode 100644
index 0000000..4e9487b
--- /dev/null
+++ b/src/safeds_datasets/image/__init__.py
@@ -0,0 +1,5 @@
+"""Image datasets."""
+
+from ._mnist import load_fashion_mnist, load_kmnist, load_mnist
+
+__all__ = ["load_fashion_mnist", "load_kmnist", "load_mnist"]
diff --git a/src/safeds_datasets/image/_mnist/__init__.py b/src/safeds_datasets/image/_mnist/__init__.py
new file mode 100644
index 0000000..7491a04
--- /dev/null
+++ b/src/safeds_datasets/image/_mnist/__init__.py
@@ -0,0 +1,5 @@
+"""MNIST like Datasets."""
+
+from ._mnist import load_fashion_mnist, load_kmnist, load_mnist
+
+__all__ = ["load_fashion_mnist", "load_kmnist", "load_mnist"]
diff --git a/src/safeds_datasets/image/_mnist/_mnist.py b/src/safeds_datasets/image/_mnist/_mnist.py
new file mode 100644
index 0000000..5e25c46
--- /dev/null
+++ b/src/safeds_datasets/image/_mnist/_mnist.py
@@ -0,0 +1,202 @@
+import gzip
+import os
+import struct
+import sys
+import urllib.request
+from array import array
+from pathlib import Path
+from urllib.error import HTTPError
+
+import torch
+from safeds._config import _init_default_device
+from safeds.data.image.containers import ImageList
+from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
+from safeds.data.labeled.containers import ImageDataset
+from safeds.data.tabular.containers import Column
+
+_mnist_links: list[str] = ["http://yann.lecun.com/exdb/mnist/", "https://ossci-datasets.s3.amazonaws.com/mnist/"]
+_mnist_files: dict[str, str] = {
+    "train-images-idx3": "train-images-idx3-ubyte.gz",
+    "train-labels-idx1": "train-labels-idx1-ubyte.gz",
+    "test-images-idx3": "t10k-images-idx3-ubyte.gz",
+    "test-labels-idx1": "t10k-labels-idx1-ubyte.gz",
+}
+_mnist_labels: dict[int, str] = {0: "0", 1: "1", 2: "2", 3: "3", 4: "4", 5: "5", 6: "6", 7: "7", 8: "8", 9: "9"}
+_mnist_folder: str = "mnist"
+
+_fashion_mnist_links: list[str] = ["http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/"]
+_fashion_mnist_files: dict[str, str] = _mnist_files
+_fashion_mnist_labels: dict[int, str] = {0: "T-shirt/top", 1: "Trouser", 2: "Pullover", 3: "Dress", 4: "Coat", 5: "Sandal", 6: "Shirt", 7: "Sneaker", 8: "Bag", 9: "Ankle boot"}
+_fashion_mnist_folder: str = "fashion-mnist"
+
+_kuzushiji_mnist_links: list[str] = ["http://codh.rois.ac.jp/kmnist/dataset/kmnist/"]
+_kuzushiji_mnist_files: dict[str, str] = _mnist_files
+_kuzushiji_mnist_labels: dict[int, str] = {0: "\u304a", 1: "\u304d", 2: "\u3059", 3: "\u3064", 4: "\u306a", 5: "\u306f", 6: "\u307e", 7: "\u3084", 8: "\u308c", 9: "\u3092"}
+_kuzushiji_mnist_folder: str = "kmnist"
+
+
+def load_mnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[Column], ImageDataset[Column]]:
+    """
+    Load the `MNIST <http://yann.lecun.com/exdb/mnist/>`_ datasets.
+
+    Parameters
+    ----------
+    path:
+        the path were the files are stored or will be downloaded to
+    download:
+        weather the files should be downloaded to the given path
+
+    Returns
+    -------
+    train_dataset, test_dataset:
+        The train and test datasets.
+
+    Raises
+    ------
+    FileNotFoundError
+        if a file of the dataset cannot be found
+    """
+    path = Path(path) / _mnist_folder
+    path.mkdir(parents=True, exist_ok=True)
+    path_files = os.listdir(path)
+    missing_files = []
+    for file_path in _mnist_files.values():
+        if file_path not in path_files:
+            missing_files.append(file_path)
+    if len(missing_files) > 0:
+        if download:
+            _download_mnist_like(path, {name: f_path for name, f_path in _mnist_files.items() if f_path in missing_files}, _mnist_links)
+        else:
+            raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
+    return _load_mnist_like(path, _mnist_files, _mnist_labels)
+
+
+def load_fashion_mnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[Column], ImageDataset[Column]]:
+    """
+    Load the `Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ datasets.
+
+    Parameters
+    ----------
+    path:
+        the path were the files are stored or will be downloaded to
+    download:
+        weather the files should be downloaded to the given path
+
+    Returns
+    -------
+    train_dataset, test_dataset:
+        The train and test datasets.
+
+    Raises
+    ------
+    FileNotFoundError
+        if a file of the dataset cannot be found
+    """
+    path = Path(path) / _fashion_mnist_folder
+    path.mkdir(parents=True, exist_ok=True)
+    path_files = os.listdir(path)
+    missing_files = []
+    for file_path in _fashion_mnist_files.values():
+        if file_path not in path_files:
+            missing_files.append(file_path)
+    if len(missing_files) > 0:
+        if download:
+            _download_mnist_like(path, {name: f_path for name, f_path in _fashion_mnist_files.items() if f_path in missing_files}, _fashion_mnist_links)
+        else:
+            raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
+    return _load_mnist_like(path, _fashion_mnist_files, _fashion_mnist_labels)
+
+
+def load_kmnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[Column], ImageDataset[Column]]:
+    """
+    Load the `Kuzushiji-MNIST <https://github.com/rois-codh/kmnist>`_ datasets.
+
+    Parameters
+    ----------
+    path:
+        the path were the files are stored or will be downloaded to
+    download:
+        weather the files should be downloaded to the given path
+
+    Returns
+    -------
+    train_dataset, test_dataset:
+        The train and test datasets.
+
+    Raises
+    ------
+    FileNotFoundError
+        if a file of the dataset cannot be found
+    """
+    path = Path(path) / _kuzushiji_mnist_folder
+    path.mkdir(parents=True, exist_ok=True)
+    path_files = os.listdir(path)
+    missing_files = []
+    for file_path in _kuzushiji_mnist_files.values():
+        if file_path not in path_files:
+            missing_files.append(file_path)
+    if len(missing_files) > 0:
+        if download:
+            _download_mnist_like(path, {name: f_path for name, f_path in _kuzushiji_mnist_files.items() if f_path in missing_files}, _kuzushiji_mnist_links)
+        else:
+            raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
+    return _load_mnist_like(path, _kuzushiji_mnist_files, _kuzushiji_mnist_labels)
+
+
+def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int, str]) -> tuple[ImageDataset[Column], ImageDataset[Column]]:
+    _init_default_device()
+
+    path = Path(path)
+    test_labels: Column | None = None
+    train_labels: Column | None = None
+    test_image_list: ImageList | None = None
+    train_image_list: ImageList | None = None
+    for file_name, file_path in files.items():
+        if "idx1" in file_name:
+            with gzip.open(path / file_path, mode='rb') as label_file:
+                magic, size = struct.unpack(">II", label_file.read(8))
+                if magic != 2049:
+                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2049.")
+                if "train" in file_name:
+                    train_labels = Column(file_name, [labels[l] for l in array("B", label_file.read())])
+                else:
+                    test_labels = Column(file_name, array("B", label_file.read()))
+        else:
+            with gzip.open(path / file_path, mode='rb') as image_file:
+                magic, size, rows, cols = struct.unpack(">IIII", image_file.read(16))
+                if magic != 2051:
+                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2051.")
+                image_data = array("B", image_file.read())
+                image_tensor = torch.empty(size, 1, rows, cols)
+                for i in range(size):
+                    image_tensor[i, 0] = torch.frombuffer(image_data[i * rows * cols:(i + 1) * rows * cols], dtype=torch.uint8).reshape(rows, cols)
+                image_list = _SingleSizeImageList()
+                image_list._tensor = image_tensor
+                image_list._tensor_positions_to_indices = list(range(size))
+                image_list._indices_to_tensor_positions = image_list._calc_new_indices_to_tensor_positions()
+                if "train" in file_name:
+                    train_image_list = image_list
+                else:
+                    test_image_list = image_list
+    if train_image_list is None or test_image_list is None or train_labels is None or test_labels is None:
+        raise ValueError
+    return ImageDataset[Column](train_image_list, train_labels, 32, shuffle=True), ImageDataset[Column](test_image_list, test_labels, 32)
+
+
+def _download_mnist_like(path: str | Path, files: dict[str, str], links: list[str]) -> None:
+    path = Path(path)
+    for file_name, file_path in files.items():
+        for link in links:
+            try:
+                print(f"Trying to download file {file_name} via {link + file_path}")
+                urllib.request.urlretrieve(link + file_path, path / file_path, reporthook=_report_download_progress)
+                print()
+                break
+            except HTTPError as e:
+                print(e)
+
+
+def _report_download_progress(current_packages: int, package_size: int, file_size: int) -> None:
+    percentage = min(((current_packages * package_size) / file_size) * 100, 100)
+    sys.stdout.write(f"\rDownloading... {percentage:.1f}%")
+    sys.stdout.flush()

From d87f65a06c6538b6b6bc4e7e984f7f611cd7caa8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Thu, 9 May 2024 00:48:32 +0200
Subject: [PATCH 02/15] refactor: linter

---
 src/safeds_datasets/image/_mnist/_mnist.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/src/safeds_datasets/image/_mnist/_mnist.py b/src/safeds_datasets/image/_mnist/_mnist.py
index 5e25c46..6ce594e 100644
--- a/src/safeds_datasets/image/_mnist/_mnist.py
+++ b/src/safeds_datasets/image/_mnist/_mnist.py
@@ -2,18 +2,22 @@
 import os
 import struct
 import sys
+import tempfile
 import urllib.request
 from array import array
 from pathlib import Path
+from typing import TYPE_CHECKING
 from urllib.error import HTTPError
 
 import torch
 from safeds._config import _init_default_device
-from safeds.data.image.containers import ImageList
 from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
 from safeds.data.labeled.containers import ImageDataset
 from safeds.data.tabular.containers import Column
 
+if TYPE_CHECKING:
+    from safeds.data.image.containers import ImageList
+
 _mnist_links: list[str] = ["http://yann.lecun.com/exdb/mnist/", "https://ossci-datasets.s3.amazonaws.com/mnist/"]
 _mnist_files: dict[str, str] = {
     "train-images-idx3": "train-images-idx3-ubyte.gz",
@@ -158,7 +162,7 @@ def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int,
                 if magic != 2049:
                     raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2049.")
                 if "train" in file_name:
-                    train_labels = Column(file_name, [labels[l] for l in array("B", label_file.read())])
+                    train_labels = Column(file_name, [labels[label_index] for label_index in array("B", label_file.read())])
                 else:
                     test_labels = Column(file_name, array("B", label_file.read()))
         else:
@@ -188,12 +192,12 @@ def _download_mnist_like(path: str | Path, files: dict[str, str], links: list[st
     for file_name, file_path in files.items():
         for link in links:
             try:
-                print(f"Trying to download file {file_name} via {link + file_path}")
+                print(f"Trying to download file {file_name} via {link + file_path}")  # noqa: T201
                 urllib.request.urlretrieve(link + file_path, path / file_path, reporthook=_report_download_progress)
-                print()
+                print()  # noqa: T201
                 break
             except HTTPError as e:
-                print(e)
+                print(f"An error occurred while downloading: {e}")  # noqa: T201
 
 
 def _report_download_progress(current_packages: int, package_size: int, file_size: int) -> None:

From 869011117327f724b8ac29f22876d4a17f7df1a3 Mon Sep 17 00:00:00 2001
From: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>
Date: Wed, 8 May 2024 22:50:03 +0000
Subject: [PATCH 03/15] style: apply automated linter fixes

---
 src/safeds_datasets/image/_mnist/_mnist.py | 63 +++++++++++++++++-----
 1 file changed, 51 insertions(+), 12 deletions(-)

diff --git a/src/safeds_datasets/image/_mnist/_mnist.py b/src/safeds_datasets/image/_mnist/_mnist.py
index 6ce594e..463e41a 100644
--- a/src/safeds_datasets/image/_mnist/_mnist.py
+++ b/src/safeds_datasets/image/_mnist/_mnist.py
@@ -2,7 +2,6 @@
 import os
 import struct
 import sys
-import tempfile
 import urllib.request
 from array import array
 from pathlib import Path
@@ -30,12 +29,34 @@
 
 _fashion_mnist_links: list[str] = ["http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/"]
 _fashion_mnist_files: dict[str, str] = _mnist_files
-_fashion_mnist_labels: dict[int, str] = {0: "T-shirt/top", 1: "Trouser", 2: "Pullover", 3: "Dress", 4: "Coat", 5: "Sandal", 6: "Shirt", 7: "Sneaker", 8: "Bag", 9: "Ankle boot"}
+_fashion_mnist_labels: dict[int, str] = {
+    0: "T-shirt/top",
+    1: "Trouser",
+    2: "Pullover",
+    3: "Dress",
+    4: "Coat",
+    5: "Sandal",
+    6: "Shirt",
+    7: "Sneaker",
+    8: "Bag",
+    9: "Ankle boot",
+}
 _fashion_mnist_folder: str = "fashion-mnist"
 
 _kuzushiji_mnist_links: list[str] = ["http://codh.rois.ac.jp/kmnist/dataset/kmnist/"]
 _kuzushiji_mnist_files: dict[str, str] = _mnist_files
-_kuzushiji_mnist_labels: dict[int, str] = {0: "\u304a", 1: "\u304d", 2: "\u3059", 3: "\u3064", 4: "\u306a", 5: "\u306f", 6: "\u307e", 7: "\u3084", 8: "\u308c", 9: "\u3092"}
+_kuzushiji_mnist_labels: dict[int, str] = {
+    0: "\u304a",
+    1: "\u304d",
+    2: "\u3059",
+    3: "\u3064",
+    4: "\u306a",
+    5: "\u306f",
+    6: "\u307e",
+    7: "\u3084",
+    8: "\u308c",
+    9: "\u3092",
+}
 _kuzushiji_mnist_folder: str = "kmnist"
 
 
@@ -69,7 +90,9 @@ def load_mnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[Co
             missing_files.append(file_path)
     if len(missing_files) > 0:
         if download:
-            _download_mnist_like(path, {name: f_path for name, f_path in _mnist_files.items() if f_path in missing_files}, _mnist_links)
+            _download_mnist_like(
+                path, {name: f_path for name, f_path in _mnist_files.items() if f_path in missing_files}, _mnist_links,
+            )
         else:
             raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
     return _load_mnist_like(path, _mnist_files, _mnist_labels)
@@ -105,7 +128,11 @@ def load_fashion_mnist(path: str | Path, download: bool = True) -> tuple[ImageDa
             missing_files.append(file_path)
     if len(missing_files) > 0:
         if download:
-            _download_mnist_like(path, {name: f_path for name, f_path in _fashion_mnist_files.items() if f_path in missing_files}, _fashion_mnist_links)
+            _download_mnist_like(
+                path,
+                {name: f_path for name, f_path in _fashion_mnist_files.items() if f_path in missing_files},
+                _fashion_mnist_links,
+            )
         else:
             raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
     return _load_mnist_like(path, _fashion_mnist_files, _fashion_mnist_labels)
@@ -141,13 +168,19 @@ def load_kmnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[C
             missing_files.append(file_path)
     if len(missing_files) > 0:
         if download:
-            _download_mnist_like(path, {name: f_path for name, f_path in _kuzushiji_mnist_files.items() if f_path in missing_files}, _kuzushiji_mnist_links)
+            _download_mnist_like(
+                path,
+                {name: f_path for name, f_path in _kuzushiji_mnist_files.items() if f_path in missing_files},
+                _kuzushiji_mnist_links,
+            )
         else:
             raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
     return _load_mnist_like(path, _kuzushiji_mnist_files, _kuzushiji_mnist_labels)
 
 
-def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int, str]) -> tuple[ImageDataset[Column], ImageDataset[Column]]:
+def _load_mnist_like(
+    path: str | Path, files: dict[str, str], labels: dict[int, str],
+) -> tuple[ImageDataset[Column], ImageDataset[Column]]:
     _init_default_device()
 
     path = Path(path)
@@ -157,23 +190,27 @@ def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int,
     train_image_list: ImageList | None = None
     for file_name, file_path in files.items():
         if "idx1" in file_name:
-            with gzip.open(path / file_path, mode='rb') as label_file:
+            with gzip.open(path / file_path, mode="rb") as label_file:
                 magic, size = struct.unpack(">II", label_file.read(8))
                 if magic != 2049:
                     raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2049.")
                 if "train" in file_name:
-                    train_labels = Column(file_name, [labels[label_index] for label_index in array("B", label_file.read())])
+                    train_labels = Column(
+                        file_name, [labels[label_index] for label_index in array("B", label_file.read())],
+                    )
                 else:
                     test_labels = Column(file_name, array("B", label_file.read()))
         else:
-            with gzip.open(path / file_path, mode='rb') as image_file:
+            with gzip.open(path / file_path, mode="rb") as image_file:
                 magic, size, rows, cols = struct.unpack(">IIII", image_file.read(16))
                 if magic != 2051:
                     raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2051.")
                 image_data = array("B", image_file.read())
                 image_tensor = torch.empty(size, 1, rows, cols)
                 for i in range(size):
-                    image_tensor[i, 0] = torch.frombuffer(image_data[i * rows * cols:(i + 1) * rows * cols], dtype=torch.uint8).reshape(rows, cols)
+                    image_tensor[i, 0] = torch.frombuffer(
+                        image_data[i * rows * cols : (i + 1) * rows * cols], dtype=torch.uint8,
+                    ).reshape(rows, cols)
                 image_list = _SingleSizeImageList()
                 image_list._tensor = image_tensor
                 image_list._tensor_positions_to_indices = list(range(size))
@@ -184,7 +221,9 @@ def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int,
                     test_image_list = image_list
     if train_image_list is None or test_image_list is None or train_labels is None or test_labels is None:
         raise ValueError
-    return ImageDataset[Column](train_image_list, train_labels, 32, shuffle=True), ImageDataset[Column](test_image_list, test_labels, 32)
+    return ImageDataset[Column](train_image_list, train_labels, 32, shuffle=True), ImageDataset[Column](
+        test_image_list, test_labels, 32,
+    )
 
 
 def _download_mnist_like(path: str | Path, files: dict[str, str], links: list[str]) -> None:

From fda72fcdbc970d89dd81ea165a404ab87331724b Mon Sep 17 00:00:00 2001
From: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>
Date: Wed, 8 May 2024 22:51:37 +0000
Subject: [PATCH 04/15] style: apply automated linter fixes

---
 src/safeds_datasets/image/_mnist/_mnist.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/src/safeds_datasets/image/_mnist/_mnist.py b/src/safeds_datasets/image/_mnist/_mnist.py
index 463e41a..c8ebd91 100644
--- a/src/safeds_datasets/image/_mnist/_mnist.py
+++ b/src/safeds_datasets/image/_mnist/_mnist.py
@@ -91,7 +91,9 @@ def load_mnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[Co
     if len(missing_files) > 0:
         if download:
             _download_mnist_like(
-                path, {name: f_path for name, f_path in _mnist_files.items() if f_path in missing_files}, _mnist_links,
+                path,
+                {name: f_path for name, f_path in _mnist_files.items() if f_path in missing_files},
+                _mnist_links,
             )
         else:
             raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
@@ -179,7 +181,9 @@ def load_kmnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[C
 
 
 def _load_mnist_like(
-    path: str | Path, files: dict[str, str], labels: dict[int, str],
+    path: str | Path,
+    files: dict[str, str],
+    labels: dict[int, str],
 ) -> tuple[ImageDataset[Column], ImageDataset[Column]]:
     _init_default_device()
 
@@ -196,7 +200,8 @@ def _load_mnist_like(
                     raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2049.")
                 if "train" in file_name:
                     train_labels = Column(
-                        file_name, [labels[label_index] for label_index in array("B", label_file.read())],
+                        file_name,
+                        [labels[label_index] for label_index in array("B", label_file.read())],
                     )
                 else:
                     test_labels = Column(file_name, array("B", label_file.read()))
@@ -209,7 +214,8 @@ def _load_mnist_like(
                 image_tensor = torch.empty(size, 1, rows, cols)
                 for i in range(size):
                     image_tensor[i, 0] = torch.frombuffer(
-                        image_data[i * rows * cols : (i + 1) * rows * cols], dtype=torch.uint8,
+                        image_data[i * rows * cols : (i + 1) * rows * cols],
+                        dtype=torch.uint8,
                     ).reshape(rows, cols)
                 image_list = _SingleSizeImageList()
                 image_list._tensor = image_tensor
@@ -222,7 +228,9 @@ def _load_mnist_like(
     if train_image_list is None or test_image_list is None or train_labels is None or test_labels is None:
         raise ValueError
     return ImageDataset[Column](train_image_list, train_labels, 32, shuffle=True), ImageDataset[Column](
-        test_image_list, test_labels, 32,
+        test_image_list,
+        test_labels,
+        32,
     )
 
 

From c674e1496cbbf26e16b29f9b5447c41f966ebf3b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Thu, 9 May 2024 01:12:14 +0200
Subject: [PATCH 05/15] test: added tests

---
 src/safeds_datasets/image/_mnist/_mnist.py    | 11 ++-
 tests/safeds_datasets/image/__init__.py       |  0
 .../safeds_datasets/image/_mnist/__init__.py  |  0
 .../image/_mnist/test_mnist.py                | 74 +++++++++++++++++++
 4 files changed, 79 insertions(+), 6 deletions(-)
 create mode 100644 tests/safeds_datasets/image/__init__.py
 create mode 100644 tests/safeds_datasets/image/_mnist/__init__.py
 create mode 100644 tests/safeds_datasets/image/_mnist/test_mnist.py

diff --git a/src/safeds_datasets/image/_mnist/_mnist.py b/src/safeds_datasets/image/_mnist/_mnist.py
index 6ce594e..0ab6f5a 100644
--- a/src/safeds_datasets/image/_mnist/_mnist.py
+++ b/src/safeds_datasets/image/_mnist/_mnist.py
@@ -2,7 +2,6 @@
 import os
 import struct
 import sys
-import tempfile
 import urllib.request
 from array import array
 from pathlib import Path
@@ -160,16 +159,16 @@ def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int,
             with gzip.open(path / file_path, mode='rb') as label_file:
                 magic, size = struct.unpack(">II", label_file.read(8))
                 if magic != 2049:
-                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2049.")
+                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2049.")  # pragma: no cover
                 if "train" in file_name:
                     train_labels = Column(file_name, [labels[label_index] for label_index in array("B", label_file.read())])
                 else:
-                    test_labels = Column(file_name, array("B", label_file.read()))
+                    test_labels = Column(file_name, [labels[label_index] for label_index in array("B", label_file.read())])
         else:
             with gzip.open(path / file_path, mode='rb') as image_file:
                 magic, size, rows, cols = struct.unpack(">IIII", image_file.read(16))
                 if magic != 2051:
-                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2051.")
+                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2051.")  # pragma: no cover
                 image_data = array("B", image_file.read())
                 image_tensor = torch.empty(size, 1, rows, cols)
                 for i in range(size):
@@ -183,7 +182,7 @@ def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int,
                 else:
                     test_image_list = image_list
     if train_image_list is None or test_image_list is None or train_labels is None or test_labels is None:
-        raise ValueError
+        raise ValueError  # pragma: no cover
     return ImageDataset[Column](train_image_list, train_labels, 32, shuffle=True), ImageDataset[Column](test_image_list, test_labels, 32)
 
 
@@ -197,7 +196,7 @@ def _download_mnist_like(path: str | Path, files: dict[str, str], links: list[st
                 print()  # noqa: T201
                 break
             except HTTPError as e:
-                print(f"An error occurred while downloading: {e}")  # noqa: T201
+                print(f"An error occurred while downloading: {e}")  # noqa: T201  # pragma: no cover
 
 
 def _report_download_progress(current_packages: int, package_size: int, file_size: int) -> None:
diff --git a/tests/safeds_datasets/image/__init__.py b/tests/safeds_datasets/image/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/safeds_datasets/image/_mnist/__init__.py b/tests/safeds_datasets/image/_mnist/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/safeds_datasets/image/_mnist/test_mnist.py b/tests/safeds_datasets/image/_mnist/test_mnist.py
new file mode 100644
index 0000000..a436aa8
--- /dev/null
+++ b/tests/safeds_datasets/image/_mnist/test_mnist.py
@@ -0,0 +1,74 @@
+import os
+import tempfile
+from pathlib import Path
+
+import pytest
+from safeds.data.labeled.containers import ImageDataset
+
+from safeds_datasets.image import load_mnist, _mnist, load_fashion_mnist, load_kmnist
+
+
+class TestMNIST:
+
+    def test_should_download_and_return_mnist(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            train, test = load_mnist(tmpdirname, True)
+            files = os.listdir(Path(tmpdirname) / _mnist._mnist._mnist_folder)
+            for mnist_file in _mnist._mnist._mnist_files.values():
+                assert mnist_file in files
+            assert isinstance(train, ImageDataset)
+            assert isinstance(test, ImageDataset)
+            assert len(train) == 60_000
+            assert len(test) == 10_000
+            train_output = train.get_output()
+            test_output = test.get_output()
+            assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._mnist_labels.values())
+
+    def test_should_raise_if_file_not_found(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with pytest.raises(FileNotFoundError):
+                load_mnist(tmpdirname, False)
+
+
+class TestFashionMNIST:
+
+    def test_should_download_and_return_mnist(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            train, test = load_fashion_mnist(tmpdirname, True)
+            files = os.listdir(Path(tmpdirname) / _mnist._mnist._fashion_mnist_folder)
+            for mnist_file in _mnist._mnist._fashion_mnist_files.values():
+                assert mnist_file in files
+            assert isinstance(train, ImageDataset)
+            assert isinstance(test, ImageDataset)
+            assert len(train) == 60_000
+            assert len(test) == 10_000
+            train_output = train.get_output()
+            test_output = test.get_output()
+            assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._fashion_mnist_labels.values())
+
+    def test_should_raise_if_file_not_found(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with pytest.raises(FileNotFoundError):
+                load_fashion_mnist(tmpdirname, False)
+
+
+class TestKMNIST:
+
+    def test_should_download_and_return_mnist(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            train, test = load_kmnist(tmpdirname, True)
+            files = os.listdir(Path(tmpdirname) / _mnist._mnist._kuzushiji_mnist_folder)
+            for mnist_file in _mnist._mnist._kuzushiji_mnist_files.values():
+                assert mnist_file in files
+            assert isinstance(train, ImageDataset)
+            assert isinstance(test, ImageDataset)
+            assert len(train) == 60_000
+            assert len(test) == 10_000
+            train_output = train.get_output()
+            test_output = test.get_output()
+            assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._kuzushiji_mnist_labels.values())
+
+    def test_should_raise_if_file_not_found(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with pytest.raises(FileNotFoundError):
+                load_kmnist(tmpdirname, False)

From 6d897d0e5421b184fbeec5fb34572f4b80fd6437 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Thu, 9 May 2024 01:17:43 +0200
Subject: [PATCH 06/15] refactor: linter

---
 .../image/_mnist/test_mnist.py                | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/tests/safeds_datasets/image/_mnist/test_mnist.py b/tests/safeds_datasets/image/_mnist/test_mnist.py
index a436aa8..98d2751 100644
--- a/tests/safeds_datasets/image/_mnist/test_mnist.py
+++ b/tests/safeds_datasets/image/_mnist/test_mnist.py
@@ -10,9 +10,9 @@
 
 class TestMNIST:
 
-    def test_should_download_and_return_mnist(self):
+    def test_should_download_and_return_mnist(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdirname:
-            train, test = load_mnist(tmpdirname, True)
+            train, test = load_mnist(tmpdirname, download=True)
             files = os.listdir(Path(tmpdirname) / _mnist._mnist._mnist_folder)
             for mnist_file in _mnist._mnist._mnist_files.values():
                 assert mnist_file in files
@@ -24,17 +24,17 @@ def test_should_download_and_return_mnist(self):
             test_output = test.get_output()
             assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._mnist_labels.values())
 
-    def test_should_raise_if_file_not_found(self):
+    def test_should_raise_if_file_not_found(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdirname:
             with pytest.raises(FileNotFoundError):
-                load_mnist(tmpdirname, False)
+                load_mnist(tmpdirname, download=False)
 
 
 class TestFashionMNIST:
 
-    def test_should_download_and_return_mnist(self):
+    def test_should_download_and_return_mnist(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdirname:
-            train, test = load_fashion_mnist(tmpdirname, True)
+            train, test = load_fashion_mnist(tmpdirname, download=True)
             files = os.listdir(Path(tmpdirname) / _mnist._mnist._fashion_mnist_folder)
             for mnist_file in _mnist._mnist._fashion_mnist_files.values():
                 assert mnist_file in files
@@ -46,17 +46,17 @@ def test_should_download_and_return_mnist(self):
             test_output = test.get_output()
             assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._fashion_mnist_labels.values())
 
-    def test_should_raise_if_file_not_found(self):
+    def test_should_raise_if_file_not_found(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdirname:
             with pytest.raises(FileNotFoundError):
-                load_fashion_mnist(tmpdirname, False)
+                load_fashion_mnist(tmpdirname, download=False)
 
 
 class TestKMNIST:
 
-    def test_should_download_and_return_mnist(self):
+    def test_should_download_and_return_mnist(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdirname:
-            train, test = load_kmnist(tmpdirname, True)
+            train, test = load_kmnist(tmpdirname, download=True)
             files = os.listdir(Path(tmpdirname) / _mnist._mnist._kuzushiji_mnist_folder)
             for mnist_file in _mnist._mnist._kuzushiji_mnist_files.values():
                 assert mnist_file in files
@@ -68,7 +68,7 @@ def test_should_download_and_return_mnist(self):
             test_output = test.get_output()
             assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._kuzushiji_mnist_labels.values())
 
-    def test_should_raise_if_file_not_found(self):
+    def test_should_raise_if_file_not_found(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdirname:
             with pytest.raises(FileNotFoundError):
-                load_kmnist(tmpdirname, False)
+                load_kmnist(tmpdirname, download=False)

From 578272528f49dbac36997751ee5ec4b2f27cf877 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Thu, 9 May 2024 01:21:24 +0200
Subject: [PATCH 07/15] refactor: linter

---
 tests/safeds_datasets/image/_mnist/test_mnist.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/tests/safeds_datasets/image/_mnist/test_mnist.py b/tests/safeds_datasets/image/_mnist/test_mnist.py
index 98d2751..a692053 100644
--- a/tests/safeds_datasets/image/_mnist/test_mnist.py
+++ b/tests/safeds_datasets/image/_mnist/test_mnist.py
@@ -25,8 +25,7 @@ def test_should_download_and_return_mnist(self) -> None:
             assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._mnist_labels.values())
 
     def test_should_raise_if_file_not_found(self) -> None:
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            with pytest.raises(FileNotFoundError):
+        with tempfile.TemporaryDirectory() as tmpdirname, pytest.raises(FileNotFoundError):
                 load_mnist(tmpdirname, download=False)
 
 
@@ -47,8 +46,7 @@ def test_should_download_and_return_mnist(self) -> None:
             assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._fashion_mnist_labels.values())
 
     def test_should_raise_if_file_not_found(self) -> None:
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            with pytest.raises(FileNotFoundError):
+        with tempfile.TemporaryDirectory() as tmpdirname, pytest.raises(FileNotFoundError):
                 load_fashion_mnist(tmpdirname, download=False)
 
 
@@ -69,6 +67,5 @@ def test_should_download_and_return_mnist(self) -> None:
             assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._kuzushiji_mnist_labels.values())
 
     def test_should_raise_if_file_not_found(self) -> None:
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            with pytest.raises(FileNotFoundError):
+        with tempfile.TemporaryDirectory() as tmpdirname, pytest.raises(FileNotFoundError):
                 load_kmnist(tmpdirname, download=False)

From 51902423a8465be0bdec314a92ce0031a3a6dd27 Mon Sep 17 00:00:00 2001
From: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>
Date: Wed, 8 May 2024 23:22:57 +0000
Subject: [PATCH 08/15] style: apply automated linter fixes

---
 src/safeds_datasets/image/_mnist/_mnist.py    |  4 ++-
 .../image/_mnist/test_mnist.py                | 27 +++++++++++++------
 2 files changed, 22 insertions(+), 9 deletions(-)

diff --git a/src/safeds_datasets/image/_mnist/_mnist.py b/src/safeds_datasets/image/_mnist/_mnist.py
index 1dc6961..f848453 100644
--- a/src/safeds_datasets/image/_mnist/_mnist.py
+++ b/src/safeds_datasets/image/_mnist/_mnist.py
@@ -204,7 +204,9 @@ def _load_mnist_like(
                         [labels[label_index] for label_index in array("B", label_file.read())],
                     )
                 else:
-                    test_labels = Column(file_name, [labels[label_index] for label_index in array("B", label_file.read())])
+                    test_labels = Column(
+                        file_name, [labels[label_index] for label_index in array("B", label_file.read())],
+                    )
         else:
             with gzip.open(path / file_path, mode="rb") as image_file:
                 magic, size, rows, cols = struct.unpack(">IIII", image_file.read(16))
diff --git a/tests/safeds_datasets/image/_mnist/test_mnist.py b/tests/safeds_datasets/image/_mnist/test_mnist.py
index a692053..b526bd8 100644
--- a/tests/safeds_datasets/image/_mnist/test_mnist.py
+++ b/tests/safeds_datasets/image/_mnist/test_mnist.py
@@ -4,8 +4,7 @@
 
 import pytest
 from safeds.data.labeled.containers import ImageDataset
-
-from safeds_datasets.image import load_mnist, _mnist, load_fashion_mnist, load_kmnist
+from safeds_datasets.image import _mnist, load_fashion_mnist, load_kmnist, load_mnist
 
 
 class TestMNIST:
@@ -22,11 +21,15 @@ def test_should_download_and_return_mnist(self) -> None:
             assert len(test) == 10_000
             train_output = train.get_output()
             test_output = test.get_output()
-            assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._mnist_labels.values())
+            assert (
+                set(train_output.get_unique_values())
+                == set(test_output.get_unique_values())
+                == set(_mnist._mnist._mnist_labels.values())
+            )
 
     def test_should_raise_if_file_not_found(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdirname, pytest.raises(FileNotFoundError):
-                load_mnist(tmpdirname, download=False)
+            load_mnist(tmpdirname, download=False)
 
 
 class TestFashionMNIST:
@@ -43,11 +46,15 @@ def test_should_download_and_return_mnist(self) -> None:
             assert len(test) == 10_000
             train_output = train.get_output()
             test_output = test.get_output()
-            assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._fashion_mnist_labels.values())
+            assert (
+                set(train_output.get_unique_values())
+                == set(test_output.get_unique_values())
+                == set(_mnist._mnist._fashion_mnist_labels.values())
+            )
 
     def test_should_raise_if_file_not_found(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdirname, pytest.raises(FileNotFoundError):
-                load_fashion_mnist(tmpdirname, download=False)
+            load_fashion_mnist(tmpdirname, download=False)
 
 
 class TestKMNIST:
@@ -64,8 +71,12 @@ def test_should_download_and_return_mnist(self) -> None:
             assert len(test) == 10_000
             train_output = train.get_output()
             test_output = test.get_output()
-            assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._kuzushiji_mnist_labels.values())
+            assert (
+                set(train_output.get_unique_values())
+                == set(test_output.get_unique_values())
+                == set(_mnist._mnist._kuzushiji_mnist_labels.values())
+            )
 
     def test_should_raise_if_file_not_found(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdirname, pytest.raises(FileNotFoundError):
-                load_kmnist(tmpdirname, download=False)
+            load_kmnist(tmpdirname, download=False)

From aaaa0562a0325f554591fad0c5d9ae25e784e7f2 Mon Sep 17 00:00:00 2001
From: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>
Date: Wed, 8 May 2024 23:24:23 +0000
Subject: [PATCH 09/15] style: apply automated linter fixes

---
 src/safeds_datasets/image/_mnist/_mnist.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/safeds_datasets/image/_mnist/_mnist.py b/src/safeds_datasets/image/_mnist/_mnist.py
index f848453..a5dc11b 100644
--- a/src/safeds_datasets/image/_mnist/_mnist.py
+++ b/src/safeds_datasets/image/_mnist/_mnist.py
@@ -205,7 +205,8 @@ def _load_mnist_like(
                     )
                 else:
                     test_labels = Column(
-                        file_name, [labels[label_index] for label_index in array("B", label_file.read())],
+                        file_name,
+                        [labels[label_index] for label_index in array("B", label_file.read())],
                     )
         else:
             with gzip.open(path / file_path, mode="rb") as image_file:

From 2cdbc5daf8fce14d1eb2681534db2f8dd3e7ce41 Mon Sep 17 00:00:00 2001
From: Alexander <47296670+Marsmaennchen221@users.noreply.github.com>
Date: Thu, 9 May 2024 17:30:55 +0200
Subject: [PATCH 10/15] Apply suggestions from code review

Co-authored-by: Lars Reimann <mail@larsreimann.com>
---
 src/safeds_datasets/image/_mnist/_mnist.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/safeds_datasets/image/_mnist/_mnist.py b/src/safeds_datasets/image/_mnist/_mnist.py
index a5dc11b..0c4c1d5 100644
--- a/src/safeds_datasets/image/_mnist/_mnist.py
+++ b/src/safeds_datasets/image/_mnist/_mnist.py
@@ -69,7 +69,7 @@ def load_mnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[Co
     path:
         the path were the files are stored or will be downloaded to
     download:
-        weather the files should be downloaded to the given path
+        whether the files should be downloaded to the given path
 
     Returns
     -------
@@ -109,7 +109,7 @@ def load_fashion_mnist(path: str | Path, download: bool = True) -> tuple[ImageDa
     path:
         the path were the files are stored or will be downloaded to
     download:
-        weather the files should be downloaded to the given path
+        whether the files should be downloaded to the given path
 
     Returns
     -------
@@ -149,7 +149,7 @@ def load_kmnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[C
     path:
         the path were the files are stored or will be downloaded to
     download:
-        weather the files should be downloaded to the given path
+        whether the files should be downloaded to the given path
 
     Returns
     -------

From 65d6e132f97069e1c45a0b3ad616a41b00c4a6c8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Thu, 9 May 2024 18:06:04 +0200
Subject: [PATCH 11/15] build: bump safe-ds from 0.23.0 to 0.24.0

---
 poetry.lock    | 41 +++++++++++++++++++----------------------
 pyproject.toml | 12 +-----------
 2 files changed, 20 insertions(+), 33 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 53b33e4..d20f097 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3131,6 +3131,7 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -3696,33 +3697,29 @@ files = [
 
 [[package]]
 name = "safe-ds"
-version = "0.23.0"
+version = "0.24.0"
 description = "A user-friendly library for Data Science in Python."
 optional = false
-python-versions = "^3.11,<3.13"
-files = []
-develop = false
+python-versions = "<3.13,>=3.11"
+files = [
+    {file = "safe_ds-0.24.0-py3-none-any.whl", hash = "sha256:1c350900d1b480a0b2aaacfdd3915059a22bc5a635a14cf92efe56223a7931f6"},
+    {file = "safe_ds-0.24.0.tar.gz", hash = "sha256:e8156293f33b7f8dcc4e494327561c9235a54cd8ad3c90876db204fcc0520a2d"},
+]
 
 [package.dependencies]
-apipkg = "^3.0.2"
+apipkg = ">=3.0.2,<4.0.0"
 levenshtein = ">=0.21.1,<0.26.0"
-matplotlib = "^3.6.3"
-openpyxl = "^3.1.2"
-pandas = "^2.0.0"
+matplotlib = ">=3.6.3,<4.0.0"
+openpyxl = ">=3.1.2,<4.0.0"
+pandas = ">=2.0.0,<3.0.0"
 pillow = ">=9.5,<11.0"
-polars = {version = "^0.20.24", extras = ["numpy", "pyarrow"]}
-scikit-learn = "^1.2.0"
-seaborn = "^0.13.0"
-statsmodels = "^0.14.1"
-torch = "^2.3.0"
-torchvision = "^0.18.0"
-xxhash = "^3.4.1"
-
-[package.source]
-type = "git"
-url = "https://github.com/Safe-DS/Library.git"
-reference = "HEAD"
-resolved_reference = "0564b523c46df74ac235c22783dcbf3d515ed122"
+polars = {version = ">=0.20.25,<0.21.0", extras = ["numpy", "pyarrow"]}
+scikit-learn = ">=1.2.0,<2.0.0"
+seaborn = ">=0.13.0,<0.14.0"
+statsmodels = ">=0.14.1,<0.15.0"
+torch = ">=2.3.0,<3.0.0"
+torchvision = ">=0.18.0,<0.19.0"
+xxhash = ">=3.4.1,<4.0.0"
 
 [[package]]
 name = "scikit-learn"
@@ -4458,4 +4455,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11,<3.13"
-content-hash = "eeda7efdf1d52eb79b00ad5def6a8ac416a6d4d81b33e218e5e31743e77f173e"
+content-hash = "f30dadb994c7fc8f7ce65009427c65c56b6f6483344ec825e3dfec452129bad6"
diff --git a/pyproject.toml b/pyproject.toml
index 50f2c2c..306c19b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,7 @@ packages = [
 
 [tool.poetry.dependencies]
 python = "^3.11,<3.13"
-safe-ds = "0.23.0"
+safe-ds = "^0.24.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = ">=7.2.1,<9.0.0"
@@ -34,10 +34,6 @@ mkdocs-section-index = "^0.3.9"
 mkdocs-jupyter = ">=0.23,<0.25"
 mkdocs-exclude = "^1.0.2"
 
-
-[tool.poetry.group.image_dataset_dev.dependencies]
-safe-ds = {git = "https://github.com/Safe-DS/Library.git"}
-
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
@@ -50,9 +46,3 @@ markers = [
 
 [tool.black]
 line-length = 120
-
-
-[[tool.poetry.source]]
-name = "torch_cuda121"
-url = "https://download.pytorch.org/whl/cu121"
-priority = "explicit"

From af4ba2be6642d30877f0c04df2288007ac0e0956 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Thu, 9 May 2024 18:26:03 +0200
Subject: [PATCH 12/15] build: changed poetry.lock

---
 poetry.lock | 92 +++++++++++++++++++++++++++++------------------------
 1 file changed, 51 insertions(+), 41 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index d20f097..e572e07 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -671,13 +671,13 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.14.0"
+version = "3.13.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.14.0-py3-none-any.whl", hash = "sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f"},
-    {file = "filelock-3.14.0.tar.gz", hash = "sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a"},
+    {file = "filelock-3.13.4-py3-none-any.whl", hash = "sha256:404e5e9253aa60ad457cae1be07c0f0ca90a63931200a47d9b6a6af84fd7b45f"},
+    {file = "filelock-3.13.4.tar.gz", hash = "sha256:d13f466618bfde72bd2c18255e269f72542c6e70e7bac83a0232d6b1cc5c8cf4"},
 ]
 
 [package.extras]
@@ -1062,13 +1062,13 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.4.2"
+version = "1.4.0"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
-    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
+    {file = "joblib-1.4.0-py3-none-any.whl", hash = "sha256:42942470d4062537be4d54c83511186da1fc14ba354961a2114da91efa9a4ed7"},
+    {file = "joblib-1.4.0.tar.gz", hash = "sha256:1eb0dc091919cd384490de890cb5dfd538410a6d4b3b54eef09fb8c50b409b1c"},
 ]
 
 [[package]]
@@ -3989,13 +3989,13 @@ typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "threadpoolctl"
-version = "3.5.0"
+version = "3.4.0"
 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"},
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+    {file = "threadpoolctl-3.4.0.tar.gz", hash = "sha256:f11b491a03661d6dd7ef692dd422ab34185d982466c49c8f98c8f716b5c93196"},
 ]
 
 [[package]]
@@ -4029,21 +4029,31 @@ files = [
 
 [[package]]
 name = "torch"
-version = "2.3.0+cu121"
+version = "2.3.0"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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+    {file = "torch-2.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:1bf023aa20902586f614f7682fedfa463e773e26c58820b74158a72470259459"},
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+    {file = "torch-2.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:729804e97b7cf19ae9ab4181f91f5e612af07956f35c8b2c8e9d9f3596a8e877"},
+    {file = "torch-2.3.0-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:d24e328226d8e2af7cf80fcb1d2f1d108e0de32777fab4aaa2b37b9765d8be73"},
+    {file = "torch-2.3.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:b0de2bdc0486ea7b14fc47ff805172df44e421a7318b7c4d92ef589a75d27410"},
+    {file = "torch-2.3.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:a306c87a3eead1ed47457822c01dfbd459fe2920f2d38cbdf90de18f23f72542"},
+    {file = "torch-2.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:f9b98bf1a3c8af2d4c41f0bf1433920900896c446d1ddc128290ff146d1eb4bd"},
+    {file = "torch-2.3.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:dca986214267b34065a79000cee54232e62b41dff1ec2cab9abc3fc8b3dee0ad"},
+    {file = "torch-2.3.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:20572f426965dd8a04e92a473d7e445fa579e09943cc0354f3e6fef6130ce061"},
+    {file = "torch-2.3.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:e65ba85ae292909cde0dde6369826d51165a3fc8823dc1854cd9432d7f79b932"},
+    {file = "torch-2.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:5515503a193781fd1b3f5c474e89c9dfa2faaa782b2795cc4a7ab7e67de923f6"},
+    {file = "torch-2.3.0-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:6ae9f64b09516baa4ef890af0672dc981c20b1f0d829ce115d4420a247e88fba"},
+    {file = "torch-2.3.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:cd0dc498b961ab19cb3f8dbf0c6c50e244f2f37dbfa05754ab44ea057c944ef9"},
+    {file = "torch-2.3.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:e05f836559251e4096f3786ee99f4a8cbe67bc7fbedba8ad5e799681e47c5e80"},
+    {file = "torch-2.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:4fb27b35dbb32303c2927da86e27b54a92209ddfb7234afb1949ea2b3effffea"},
+    {file = "torch-2.3.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:760f8bedff506ce9e6e103498f9b1e9e15809e008368594c3a66bf74a8a51380"},
 ]
 
 [package.dependencies]
@@ -4071,28 +4081,33 @@ typing-extensions = ">=4.8.0"
 opt-einsum = ["opt-einsum (>=3.3)"]
 optree = ["optree (>=0.9.1)"]
 
-[package.source]
-type = "legacy"
-url = "https://download.pytorch.org/whl/cu121"
-reference = "torch_cuda121"
-
 [[package]]
 name = "torchvision"
-version = "0.18.0+cu121"
+version = "0.18.0"
 description = "image and video datasets and models for torch deep learning"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "torchvision-0.18.0+cu121-cp310-cp310-linux_x86_64.whl", hash = "sha256:13e1b48dc5ce41ccb8100ab3dd26fdf31d8f1e904ecf2865ac524493013d0df5"},
-    {file = "torchvision-0.18.0+cu121-cp310-cp310-win_amd64.whl", hash = "sha256:4ab207a0f35c8c2a43da91f19ee9248520239633dc8e11a9e4a2e77b076bb9db"},
-    {file = "torchvision-0.18.0+cu121-cp311-cp311-linux_x86_64.whl", hash = "sha256:1e516779520fc92157d6cb245ac0fd3f79872fe81c84b8593a8f2e998106f5b1"},
-    {file = "torchvision-0.18.0+cu121-cp311-cp311-win_amd64.whl", hash = "sha256:3e5557512ec9a31a6ce33a1107827427ef50ad65e88cd35012161d7392ef144b"},
-    {file = "torchvision-0.18.0+cu121-cp312-cp312-linux_x86_64.whl", hash = "sha256:700f6019bebee9e0ee8b0bcbdb1588809c94a2eb947a1fde2e06adc34b60da2a"},
-    {file = "torchvision-0.18.0+cu121-cp312-cp312-win_amd64.whl", hash = "sha256:e09966baf7945085d7725ac7c944172c632516643d603bde5855f6b3cf68efcf"},
-    {file = "torchvision-0.18.0+cu121-cp38-cp38-linux_x86_64.whl", hash = "sha256:c2483b2cc62278fbaa6b67d2a9d808d245044f8b64a821596eae68ec6a0d5ed0"},
-    {file = "torchvision-0.18.0+cu121-cp38-cp38-win_amd64.whl", hash = "sha256:1d637b1428a076dfaff71ca0a5b8ef200b5b5ff7dfe2ed9359be0eff43734e5a"},
-    {file = "torchvision-0.18.0+cu121-cp39-cp39-linux_x86_64.whl", hash = "sha256:1bfe0c67fd5461a3a593f8f17e1544f07a6bd6686e155200dd244947074fcd51"},
-    {file = "torchvision-0.18.0+cu121-cp39-cp39-win_amd64.whl", hash = "sha256:611bb0b42c51de0c1bbcc8e90f88b4d48c8b99451bdb2e4761028b93cb086437"},
+    {file = "torchvision-0.18.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dd61628a3d189c6852a12dc5ed4cd2eece66d2d67f35a866cb16f1dcb06c8c62"},
+    {file = "torchvision-0.18.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:493c45f9937dad37aa1b64b14da17c7a589c72b91adc4837d431009cfe29bd53"},
+    {file = "torchvision-0.18.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:5337f6acfa1fe959d5cb340d01a00614d6b31ce7a4824ccb95435a85c5273b95"},
+    {file = "torchvision-0.18.0-cp310-cp310-win_amd64.whl", hash = "sha256:bd8e6f3b5beb49965f15c461302488edfa3d8c2d01d3bb79b150d6fb62711e3a"},
+    {file = "torchvision-0.18.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:6896a52168befe1105fb3c9335287390ed227e71d1e4ec4d68b62e8a3099fc09"},
+    {file = "torchvision-0.18.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:3d7955398d4ceaad77c487c2c44f6f7813112402c9bab8cd906d346005891048"},
+    {file = "torchvision-0.18.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:e5a24d620cea14a4bb89f24aa2b506230c0a16a3ada57fc53ad80cfd256a2128"},
+    {file = "torchvision-0.18.0-cp311-cp311-win_amd64.whl", hash = "sha256:6ad70ddfa879bda5ed886b2518fe562640e0059787cbd65cb2bffa7674541410"},
+    {file = "torchvision-0.18.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:eb9d83c0e1dbb54ecb0fb04c87f786333e3a6fb8b9c400aca7c31081f9aa5707"},
+    {file = "torchvision-0.18.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:b657d052d146f24cb3b2a78219bfc82ae70a9706671c50f632528907d10cccec"},
+    {file = "torchvision-0.18.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:a964afbc7ddf50a46b941477f6c35729b416deedd139756befd488245e2e226d"},
+    {file = "torchvision-0.18.0-cp312-cp312-win_amd64.whl", hash = "sha256:7c770f0f748e0b17f57c0297508d7254f686cdf03fc2e2949f422b20574f4c0f"},
+    {file = "torchvision-0.18.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2115a1906c015f5da9ceedc40a983313b0fd6e2c8a17108a92991706f51f6987"},
+    {file = "torchvision-0.18.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:6323f7e5423ff2594d5891863b919deb9d0de95f01c36bf26fbd879036b6ed08"},
+    {file = "torchvision-0.18.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:925d0a82cccf6f986c18b29b4392a942db65cbdb73c13a129c8493822eb9e36f"},
+    {file = "torchvision-0.18.0-cp38-cp38-win_amd64.whl", hash = "sha256:95b42d0dc599b47a01530c7439a5751e67e45b85e3a67113989cf7c7c70f2039"},
+    {file = "torchvision-0.18.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:75e22ecf44a13b8f95b8ad421c0261282d859c61816badaca1959e073ccdd691"},
+    {file = "torchvision-0.18.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:4c334b3e719ba0a9ba6e15d4aff1178f5e6d029174f346163fed525f0ccfffd3"},
+    {file = "torchvision-0.18.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:36efd87001c6bee2383e043e46a025affb03179747c8f4777b9918527ffce756"},
+    {file = "torchvision-0.18.0-cp39-cp39-win_amd64.whl", hash = "sha256:ccc292e093771d5baacf5535ac4416306b6b5f15676341cd4d010d8542eace25"},
 ]
 
 [package.dependencies]
@@ -4103,11 +4118,6 @@ torch = "2.3.0"
 [package.extras]
 scipy = ["scipy"]
 
-[package.source]
-type = "legacy"
-url = "https://download.pytorch.org/whl/cu121"
-reference = "torch_cuda121"
-
 [[package]]
 name = "tornado"
 version = "6.4"

From 087babeedb5476c75dff1a669d3847e0590ca295 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Thu, 16 May 2024 22:40:51 +0200
Subject: [PATCH 13/15] build: use Safe-DS >0.24.0

---
 poetry.lock    | 241 +------------------------------------------------
 pyproject.toml |   2 +-
 2 files changed, 5 insertions(+), 238 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index e572e07..1e2ed87 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -630,17 +630,6 @@ files = [
     {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
 ]
 
-[[package]]
-name = "et-xmlfile"
-version = "1.1.0"
-description = "An implementation of lxml.xmlfile for the standard library"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "et_xmlfile-1.1.0-py3-none-any.whl", hash = "sha256:a2ba85d1d6a74ef63837eed693bcb89c3f752169b0e3e7ae5b16ca5e1b3deada"},
-    {file = "et_xmlfile-1.1.0.tar.gz", hash = "sha256:8eb9e2bc2f8c97e37a2dc85a09ecdcdec9d8a396530a6d5a33b30b9a92da0c5c"},
-]
-
 [[package]]
 name = "executing"
 version = "2.0.1"
@@ -1537,108 +1526,6 @@ files = [
     {file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
 ]
 
-[[package]]
-name = "levenshtein"
-version = "0.25.1"
-description = "Python extension for computing string edit distances and similarities."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "Levenshtein-0.25.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:eb4d1ec9f2dcbde1757c4b7fb65b8682bc2de45b9552e201988f287548b7abdf"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b4d9fa3affef48a7e727cdbd0d9502cd060da86f34d8b3627edd769d347570e2"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c1b6cd186e58196ff8b402565317e9346b408d0c04fa0ed12ce4868c0fcb6d03"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:82637ef5428384dd1812849dd7328992819bf0c4a20bff0a3b3ee806821af7ed"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e73656da6cc3e32a6e4bcd48562fcb64599ef124997f2c91f5320d7f1532c069"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5abff796f92cdfba69b9cbf6527afae918d0e95cbfac000bd84017f74e0bd427"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:38827d82f2ca9cb755da6f03e686866f2f411280db005f4304272378412b4cba"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2b989df1e3231261a87d68dfa001a2070771e178b09650f9cf99a20e3d3abc28"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:2011d3b3897d438a2f88ef7aed7747f28739cae8538ec7c18c33dd989930c7a0"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6c375b33ec7acc1c6855e8ee8c7c8ac6262576ffed484ff5c556695527f49686"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:ce0cb9dd012ef1bf4d5b9d40603e7709b6581aec5acd32fcea9b371b294ca7aa"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:9da9ecb81bae67d784defed7274f894011259b038ec31f2339c4958157970115"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:3bd7be5dbe5f4a1b691f381e39512927b39d1e195bd0ad61f9bf217a25bf36c9"},
-    {file = "Levenshtein-0.25.1-cp310-cp310-win32.whl", hash = "sha256:f6abb9ced98261de67eb495b95e1d2325fa42b0344ed5763f7c0f36ee2e2bdba"},
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+    {file = "safe_ds-0.25.0.tar.gz", hash = "sha256:7dca8aed77ccffeada2fe995796387e9ccc4538f4015ee413c4fa1a0f6c64119"},
 ]
 
 [package.dependencies]
 apipkg = ">=3.0.2,<4.0.0"
-levenshtein = ">=0.21.1,<0.26.0"
 matplotlib = ">=3.6.3,<4.0.0"
-openpyxl = ">=3.1.2,<4.0.0"
-pandas = ">=2.0.0,<3.0.0"
 pillow = ">=9.5,<11.0"
 polars = {version = ">=0.20.25,<0.21.0", extras = ["numpy", "pyarrow"]}
 scikit-learn = ">=1.2.0,<2.0.0"
@@ -4465,4 +4232,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11,<3.13"
-content-hash = "f30dadb994c7fc8f7ce65009427c65c56b6f6483344ec825e3dfec452129bad6"
+content-hash = "86e130a4facb05292e83a161f655b05dd71963c4a561dade871660bbe3a30359"
diff --git a/pyproject.toml b/pyproject.toml
index 306c19b..3617b1d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,7 @@ packages = [
 
 [tool.poetry.dependencies]
 python = "^3.11,<3.13"
-safe-ds = "^0.24.0"
+safe-ds = ">0.24.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = ">=7.2.1,<9.0.0"

From 7726fa0f2859cc1853620207f12c0972963fcbf9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Thu, 13 Jun 2024 17:31:44 +0200
Subject: [PATCH 14/15] fix: tensor of mnist datasets now uses correct dtype
 uint8 build: mnist supports api changes made in Safe-DS 0.25

---
 poetry.lock                                   | 368 +++++++++++++++++-
 pyproject.toml                                |   2 +-
 src/safeds_datasets/image/_mnist/_mnist.py    |   2 +-
 .../image/_mnist/test_mnist.py                |  28 +-
 4 files changed, 388 insertions(+), 12 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 1e2ed87..430acf3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -872,6 +872,40 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 
+[[package]]
+name = "huggingface-hub"
+version = "0.23.3"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "huggingface_hub-0.23.3-py3-none-any.whl", hash = "sha256:22222c41223f1b7c209ae5511d2d82907325a0e3cdbce5f66949d43c598ff3bc"},
+    {file = "huggingface_hub-0.23.3.tar.gz", hash = "sha256:1a1118a0b3dea3bab6c325d71be16f5ffe441d32f3ac7c348d6875911b694b5b"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
 [[package]]
 name = "idna"
 version = "3.7"
@@ -3467,13 +3501,13 @@ files = [
 
 [[package]]
 name = "safe-ds"
-version = "0.25.0"
+version = "0.26.0"
 description = "A user-friendly library for Data Science in Python."
 optional = false
 python-versions = "<3.13,>=3.11"
 files = [
-    {file = "safe_ds-0.25.0-py3-none-any.whl", hash = "sha256:238e4906518ed56e9855bc8d7f0a98a827d4cc3db55fefc6ed30fe668dc6d12b"},
-    {file = "safe_ds-0.25.0.tar.gz", hash = "sha256:7dca8aed77ccffeada2fe995796387e9ccc4538f4015ee413c4fa1a0f6c64119"},
+    {file = "safe_ds-0.26.0-py3-none-any.whl", hash = "sha256:d931612711e29f759537b7305e7037e0bd0900a1f0547cc6093d41a6083d11d7"},
+    {file = "safe_ds-0.26.0.tar.gz", hash = "sha256:2a954a838a36d8fb75036d8f07452f780009aeac19187a8db9d24259fb3d67f9"},
 ]
 
 [package.dependencies]
@@ -3486,8 +3520,131 @@ seaborn = ">=0.13.0,<0.14.0"
 statsmodels = ">=0.14.1,<0.15.0"
 torch = ">=2.3.0,<3.0.0"
 torchvision = ">=0.18.0,<0.19.0"
+transformers = ">=4.40.2,<5.0.0"
 xxhash = ">=3.4.1,<4.0.0"
 
+[[package]]
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 version = "0.10.2"
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+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.19,<0.20)"]
+torch = ["accelerate (>=0.21.0)", "torch"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
 [[package]]
 name = "triton"
 version = "2.3.0"
@@ -4232,4 +4592,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11,<3.13"
-content-hash = "86e130a4facb05292e83a161f655b05dd71963c4a561dade871660bbe3a30359"
+content-hash = "8b40612f64c49dd6b28ac1171dd4135aa792a993b4d254ae6bfd48ea5fd918a1"
diff --git a/pyproject.toml b/pyproject.toml
index 3617b1d..a1c5fa5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,7 @@ packages = [
 
 [tool.poetry.dependencies]
 python = "^3.11,<3.13"
-safe-ds = ">0.24.0"
+safe-ds = ">=0.25,<0.27"
 
 [tool.poetry.group.dev.dependencies]
 pytest = ">=7.2.1,<9.0.0"
diff --git a/src/safeds_datasets/image/_mnist/_mnist.py b/src/safeds_datasets/image/_mnist/_mnist.py
index 0c4c1d5..15419bd 100644
--- a/src/safeds_datasets/image/_mnist/_mnist.py
+++ b/src/safeds_datasets/image/_mnist/_mnist.py
@@ -214,7 +214,7 @@ def _load_mnist_like(
                 if magic != 2051:
                     raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2051.")  # pragma: no cover
                 image_data = array("B", image_file.read())
-                image_tensor = torch.empty(size, 1, rows, cols)
+                image_tensor = torch.empty(size, 1, rows, cols, dtype=torch.uint8)
                 for i in range(size):
                     image_tensor[i, 0] = torch.frombuffer(
                         image_data[i * rows * cols : (i + 1) * rows * cols],
diff --git a/tests/safeds_datasets/image/_mnist/test_mnist.py b/tests/safeds_datasets/image/_mnist/test_mnist.py
index b526bd8..5ada3db 100644
--- a/tests/safeds_datasets/image/_mnist/test_mnist.py
+++ b/tests/safeds_datasets/image/_mnist/test_mnist.py
@@ -3,6 +3,7 @@
 from pathlib import Path
 
 import pytest
+import torch
 from safeds.data.labeled.containers import ImageDataset
 from safeds_datasets.image import _mnist, load_fashion_mnist, load_kmnist, load_mnist
 
@@ -19,11 +20,16 @@ def test_should_download_and_return_mnist(self) -> None:
             assert isinstance(test, ImageDataset)
             assert len(train) == 60_000
             assert len(test) == 10_000
+            assert (
+                train.get_input()._as_single_size_image_list()._tensor.dtype
+                == test.get_input()._as_single_size_image_list()._tensor.dtype
+                == torch.uint8
+            )
             train_output = train.get_output()
             test_output = test.get_output()
             assert (
-                set(train_output.get_unique_values())
-                == set(test_output.get_unique_values())
+                set(train_output.get_distinct_values())
+                == set(test_output.get_distinct_values())
                 == set(_mnist._mnist._mnist_labels.values())
             )
 
@@ -44,11 +50,16 @@ def test_should_download_and_return_mnist(self) -> None:
             assert isinstance(test, ImageDataset)
             assert len(train) == 60_000
             assert len(test) == 10_000
+            assert (
+                train.get_input()._as_single_size_image_list()._tensor.dtype
+                == test.get_input()._as_single_size_image_list()._tensor.dtype
+                == torch.uint8
+            )
             train_output = train.get_output()
             test_output = test.get_output()
             assert (
-                set(train_output.get_unique_values())
-                == set(test_output.get_unique_values())
+                set(train_output.get_distinct_values())
+                == set(test_output.get_distinct_values())
                 == set(_mnist._mnist._fashion_mnist_labels.values())
             )
 
@@ -69,11 +80,16 @@ def test_should_download_and_return_mnist(self) -> None:
             assert isinstance(test, ImageDataset)
             assert len(train) == 60_000
             assert len(test) == 10_000
+            assert (
+                train.get_input()._as_single_size_image_list()._tensor.dtype
+                == test.get_input()._as_single_size_image_list()._tensor.dtype
+                == torch.uint8
+            )
             train_output = train.get_output()
             test_output = test.get_output()
             assert (
-                set(train_output.get_unique_values())
-                == set(test_output.get_unique_values())
+                set(train_output.get_distinct_values())
+                == set(test_output.get_distinct_values())
                 == set(_mnist._mnist._kuzushiji_mnist_labels.values())
             )
 

From d410992ec844555cdbfa23e1be3f2d9dc1e5a460 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Gr=C3=A9us?= <alexgreus51@gmail.com>
Date: Fri, 12 Jul 2024 00:34:53 +0200
Subject: [PATCH 15/15] build: bump safe-ds to >=0.24

---
 poetry.lock | 340 ++++++++++++++++++++++++++--------------------------
 1 file changed, 171 insertions(+), 169 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 430acf3..711a876 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "anyio"
@@ -221,13 +221,13 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "certifi"
-version = "2024.2.2"
+version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
-    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
 ]
 
 [[package]]
@@ -804,13 +804,13 @@ dev = ["flake8", "markdown", "twine", "wheel"]
 
 [[package]]
 name = "griffe"
-version = "0.44.0"
+version = "0.47.0"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "griffe-0.44.0-py3-none-any.whl", hash = "sha256:8a4471c469ba980b87c843f1168850ce39d0c1d0c7be140dca2480f76c8e5446"},
-    {file = "griffe-0.44.0.tar.gz", hash = "sha256:34aee1571042f9bf00529bc715de4516fb6f482b164e90d030300601009e0223"},
+    {file = "griffe-0.47.0-py3-none-any.whl", hash = "sha256:07a2fd6a8c3d21d0bbb0decf701d62042ccc8a576645c7f8799fe1f10de2b2de"},
+    {file = "griffe-0.47.0.tar.gz", hash = "sha256:95119a440a3c932b13293538bdbc405bee4c36428547553dc6b327e7e7d35e5a"},
 ]
 
 [package.dependencies]
@@ -1873,13 +1873,13 @@ pyyaml = ">=5.1"
 
 [[package]]
 name = "mkdocs-glightbox"
-version = "0.3.7"
+version = "0.4.0"
 description = "MkDocs plugin supports image lightbox with GLightbox."
 optional = false
 python-versions = "*"
 files = [
-    {file = "mkdocs-glightbox-0.3.7.tar.gz", hash = "sha256:4e890140a97dd4ad128cb92174384bd0ac33adec3304bbd2b7c48d0847685c4f"},
-    {file = "mkdocs_glightbox-0.3.7-py3-none-any.whl", hash = "sha256:9659631a9829d93d8fb0ce3a20a10261c258605ba4dc87a3b7b5d847b93a276d"},
+    {file = "mkdocs-glightbox-0.4.0.tar.gz", hash = "sha256:392b34207bf95991071a16d5f8916d1d2f2cd5d5bb59ae2997485ccd778c70d9"},
+    {file = "mkdocs_glightbox-0.4.0-py3-none-any.whl", hash = "sha256:e0107beee75d3eb7380ac06ea2d6eac94c999eaa49f8c3cbab0e7be2ac006ccf"},
 ]
 
 [[package]]
@@ -1916,13 +1916,13 @@ mkdocs = ">=1.0.3"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.20"
+version = "9.5.27"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.20-py3-none-any.whl", hash = "sha256:ad0094a7597bcb5d0cc3e8e543a10927c2581f7f647b9bb4861600f583180f9b"},
-    {file = "mkdocs_material-9.5.20.tar.gz", hash = "sha256:986eef0250d22f70fb06ce0f4eac64cc92bd797a589ec3892ce31fad976fe3da"},
+    {file = "mkdocs_material-9.5.27-py3-none-any.whl", hash = "sha256:af8cc263fafa98bb79e9e15a8c966204abf15164987569bd1175fd66a7705182"},
+    {file = "mkdocs_material-9.5.27.tar.gz", hash = "sha256:a7d4a35f6d4a62b0c43a0cfe7e987da0980c13587b5bc3c26e690ad494427ec0"},
 ]
 
 [package.dependencies]
@@ -1970,13 +1970,13 @@ mkdocs = ">=1.2"
 
 [[package]]
 name = "mkdocstrings"
-version = "0.25.0"
+version = "0.25.1"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings-0.25.0-py3-none-any.whl", hash = "sha256:df1b63f26675fcde8c1b77e7ea996cd2f93220b148e06455428f676f5dc838f1"},
-    {file = "mkdocstrings-0.25.0.tar.gz", hash = "sha256:066986b3fb5b9ef2d37c4417255a808f7e63b40ff8f67f6cab8054d903fbc91d"},
+    {file = "mkdocstrings-0.25.1-py3-none-any.whl", hash = "sha256:da01fcc2670ad61888e8fe5b60afe9fee5781017d67431996832d63e887c2e51"},
+    {file = "mkdocstrings-0.25.1.tar.gz", hash = "sha256:c3a2515f31577f311a9ee58d089e4c51fc6046dbd9e9b4c3de4c3194667fe9bf"},
 ]
 
 [package.dependencies]
@@ -1996,18 +1996,18 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.10.0"
+version = "1.10.5"
 description = "A Python handler for mkdocstrings."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings_python-1.10.0-py3-none-any.whl", hash = "sha256:ba833fbd9d178a4b9d5cb2553a4df06e51dc1f51e41559a4d2398c16a6f69ecc"},
-    {file = "mkdocstrings_python-1.10.0.tar.gz", hash = "sha256:71678fac657d4d2bb301eed4e4d2d91499c095fd1f8a90fa76422a87a5693828"},
+    {file = "mkdocstrings_python-1.10.5-py3-none-any.whl", hash = "sha256:92e3c588ef1b41151f55281d075de7558dd8092e422cb07a65b18ee2b0863ebb"},
+    {file = "mkdocstrings_python-1.10.5.tar.gz", hash = "sha256:acdc2a98cd9d46c7ece508193a16ca03ccabcb67520352b7449f84b57c162bdf"},
 ]
 
 [package.dependencies]
-griffe = ">=0.44"
-mkdocstrings = ">=0.24.2"
+griffe = ">=0.47"
+mkdocstrings = ">=0.25"
 
 [[package]]
 name = "mkl"
@@ -2672,17 +2672,17 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "polars"
-version = "0.20.25"
+version = "0.20.31"
 description = "Blazingly fast DataFrame library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "polars-0.20.25-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:126e3b7d9394e4b23b4cc48919b7188203feeeb35d861ad808f281eaa06d76e2"},
-    {file = "polars-0.20.25-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:3bda62b681726538714a1159638ab7c9eeca6b8633fd778d84810c3e13b9c7e3"},
-    {file = "polars-0.20.25-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:62c8826e81c759f07bf5c0ae00f57a537644ae05fe68737185666b8ad8430664"},
-    {file = "polars-0.20.25-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:0fb5e7a4a9831fba742f1c706e01656607089b6362a5e6f8d579b134a99795ce"},
-    {file = "polars-0.20.25-cp38-abi3-win_amd64.whl", hash = "sha256:9eaeb9080c853e11b207d191025e0ba8fd59ea06a36c22d410a48f2f124e18cd"},
-    {file = "polars-0.20.25.tar.gz", hash = "sha256:4308d63f956874bac9ae040bdd6d62b2992d0b1e1349301bc7a3b59458189108"},
+    {file = "polars-0.20.31-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:86454ade5ed302bbf87f145cfcb1b14f7a5765a9440e448659e1f3dba6ac4e79"},
+    {file = "polars-0.20.31-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:67f2fe842262b7e1b9371edad21b760f6734d28b74c78dda88dff1bf031b9499"},
+    {file = "polars-0.20.31-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:24b82441f93409e0e8abd6f427b029db102f02b8de328cee9a680f84b84e3736"},
+    {file = "polars-0.20.31-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:87f43bce4d41abf8c8c5658d881e4b8378e5c61010a696bfea8b4106b908e916"},
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 ]
 
 [package.dependencies]
@@ -2691,7 +2691,7 @@ pyarrow = {version = ">=7.0.0", optional = true, markers = "extra == \"pyarrow\"
 
 [package.extras]
 adbc = ["adbc-driver-manager", "adbc-driver-sqlite"]
-all = ["polars[adbc,async,cloudpickle,connectorx,deltalake,fastexcel,fsspec,gevent,numpy,pandas,plot,pyarrow,pydantic,pyiceberg,sqlalchemy,timezone,torch,xlsx2csv,xlsxwriter]"]
+all = ["polars[adbc,async,cloudpickle,connectorx,deltalake,fastexcel,fsspec,gevent,iceberg,numpy,pandas,plot,pyarrow,pydantic,sqlalchemy,timezone,xlsx2csv,xlsxwriter]"]
 async = ["nest-asyncio"]
 cloudpickle = ["cloudpickle"]
 connectorx = ["connectorx (>=0.3.2)"]
@@ -2699,6 +2699,7 @@ deltalake = ["deltalake (>=0.15.0)"]
 fastexcel = ["fastexcel (>=0.9)"]
 fsspec = ["fsspec"]
 gevent = ["gevent"]
+iceberg = ["pyiceberg (>=0.5.0)"]
 matplotlib = ["matplotlib"]
 numpy = ["numpy (>=1.16.0)"]
 openpyxl = ["openpyxl (>=3.0.0)"]
@@ -2706,11 +2707,9 @@ pandas = ["pandas", "pyarrow (>=7.0.0)"]
 plot = ["hvplot (>=0.9.1)"]
 pyarrow = ["pyarrow (>=7.0.0)"]
 pydantic = ["pydantic"]
-pyiceberg = ["pyiceberg (>=0.5.0)"]
 pyxlsb = ["pyxlsb (>=1.0)"]
 sqlalchemy = ["pandas", "sqlalchemy"]
 timezone = ["backports-zoneinfo", "tzdata"]
-torch = ["torch"]
 xlsx2csv = ["xlsx2csv (>=0.8.0)"]
 xlsxwriter = ["xlsxwriter"]
 
@@ -2797,47 +2796,47 @@ tests = ["pytest"]
 
 [[package]]
 name = "pyarrow"
-version = "16.0.0"
+version = "16.1.0"
 description = "Python library for Apache Arrow"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2903,13 +2902,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "8.2.0"
+version = "8.2.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3347,13 +3346,13 @@ files = [
 
 [[package]]
 name = "requests"
-version = "2.31.0"
+version = "2.32.0"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3647,45 +3646,48 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
 [[package]]
 name = "scikit-learn"
-version = "1.4.2"
+version = "1.5.0"
 description = "A set of python modules for machine learning and data mining"
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
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 nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
+triton = {version = "2.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -4124,59 +4126,59 @@ optree = ["optree (>=0.9.1)"]
 
 [[package]]
 name = "torchvision"
-version = "0.18.0"
+version = "0.18.1"
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 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 numpy = "*"
 pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
-torch = "2.3.0"
+torch = "2.3.1"
 
 [package.extras]
 scipy = ["scipy"]
 
 [[package]]
 name = "tornado"
-version = "6.4"
+version = "6.4.1"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">=3.8"
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 [[package]]
@@ -4282,17 +4284,17 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "triton"
-version = "2.3.0"
+version = "2.3.1"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -4352,13 +4354,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.2.1"
+version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -4592,4 +4594,4 @@ files = [
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 python-versions = "^3.11,<3.13"
-content-hash = "8b40612f64c49dd6b28ac1171dd4135aa792a993b4d254ae6bfd48ea5fd918a1"
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