diff --git a/.github/dependabot.yml b/.github/dependabot.yml
deleted file mode 100644
index 5b5616f1..00000000
--- a/.github/dependabot.yml
+++ /dev/null
@@ -1,16 +0,0 @@
-version: 2
-updates:
-  # Maintain dependencies for GitHub Actions
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "weekly"
-    groups:
-      actions:
-        patterns:
-          - "*"
-    labels:
-      - "github-actions"
-      - "dependencies"
-    reviewers:
-      - "asmeurer"
diff --git a/.github/workflows/array-api-tests-dask.yml b/.github/workflows/array-api-tests-dask.yml
deleted file mode 100644
index ef430d9c..00000000
--- a/.github/workflows/array-api-tests-dask.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-name: Array API Tests (Dask)
-
-on: [push, pull_request]
-
-jobs:
-  array-api-tests-dask:
-    uses: ./.github/workflows/array-api-tests.yml
-    with:
-      package-name: dask
-      module-name: dask.array
-      extra-requires: numpy
-      # Dask is substantially slower then other libraries on unit tests.
-      # Reduce the number of examples to speed up CI, even though this means that this
-      # workflow is barely more than a smoke test, and one should expect extreme 
-      # flakiness. Before changes to dask-xfails.txt or dask-skips.txt, please run
-      # the full test suite with at least 200 examples.
-      pytest-extra-args: --max-examples=200 -n 4
-      python-versions: '[''3.10'', ''3.13'']'
-      extra-env-vars: |
-        ARRAY_API_TESTS_XFAIL_MARK=skip
diff --git a/.github/workflows/array-api-tests-numpy-1-22.yml b/.github/workflows/array-api-tests-numpy-1-22.yml
deleted file mode 100644
index 83d4cf1d..00000000
--- a/.github/workflows/array-api-tests-numpy-1-22.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-name: Array API Tests (NumPy 1.22)
-
-on: [push, pull_request]
-
-jobs:
-  array-api-tests-numpy-1-22:
-    uses: ./.github/workflows/array-api-tests.yml
-    with:
-      package-name: numpy
-      package-version: '== 1.22.*'
-      xfails-file-extra: '-1-22'
-      python-versions: '[''3.10'']'
-      pytest-extra-args: -n 4
-      extra-env-vars: |
-        ARRAY_API_TESTS_XFAIL_MARK=skip
diff --git a/.github/workflows/array-api-tests-numpy-1-26.yml b/.github/workflows/array-api-tests-numpy-1-26.yml
deleted file mode 100644
index 13124644..00000000
--- a/.github/workflows/array-api-tests-numpy-1-26.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-name: Array API Tests (NumPy 1.26)
-
-on: [push, pull_request]
-
-jobs:
-  array-api-tests-numpy-latest:
-    uses: ./.github/workflows/array-api-tests.yml
-    with:
-      package-name: numpy
-      package-version: '== 1.26.*'
-      xfails-file-extra: '-1-26'
-      python-versions: '[''3.10'', ''3.12'']'
-      pytest-extra-args: -n 4
-      extra-env-vars: |
-        ARRAY_API_TESTS_XFAIL_MARK=skip
diff --git a/.github/workflows/array-api-tests-numpy-dev.yml b/.github/workflows/array-api-tests-numpy-dev.yml
deleted file mode 100644
index dec4c7ae..00000000
--- a/.github/workflows/array-api-tests-numpy-dev.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-name: Array API Tests (NumPy dev)
-
-on: [push, pull_request]
-
-jobs:
-  array-api-tests-numpy-dev:
-    uses: ./.github/workflows/array-api-tests.yml
-    with:
-      package-name: numpy
-      extra-requires: '--pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple'
-      xfails-file-extra: '-dev'
-      python-versions: '[''3.11'', ''3.13'']'
-      pytest-extra-args: -n 4
-      extra-env-vars: |
-        ARRAY_API_TESTS_XFAIL_MARK=skip
diff --git a/.github/workflows/array-api-tests-numpy-latest.yml b/.github/workflows/array-api-tests-numpy-latest.yml
deleted file mode 100644
index 65bbc9a2..00000000
--- a/.github/workflows/array-api-tests-numpy-latest.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-name: Array API Tests (NumPy latest)
-
-on: [push, pull_request]
-
-jobs:
-  array-api-tests-numpy-latest:
-    uses: ./.github/workflows/array-api-tests.yml
-    with:
-      package-name: numpy
-      python-versions: '[''3.10'', ''3.13'']'
-      pytest-extra-args: -n 4
-      extra-env-vars: |
-        ARRAY_API_TESTS_XFAIL_MARK=skip
diff --git a/.github/workflows/array-api-tests-torch.yml b/.github/workflows/array-api-tests-torch.yml
deleted file mode 100644
index 4b4b945e..00000000
--- a/.github/workflows/array-api-tests-torch.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-name: Array API Tests (PyTorch CPU)
-
-on: [push, pull_request]
-
-jobs:
-  array-api-tests-torch:
-    uses: ./.github/workflows/array-api-tests.yml
-    with:
-      package-name: torch
-      extra-requires: '--index-url https://download.pytorch.org/whl/cpu'
-      extra-env-vars: |
-        ARRAY_API_TESTS_SKIP_DTYPES=uint16,uint32,uint64
-        ARRAY_API_TESTS_XFAIL_MARK=skip
-      python-versions: '[''3.10'', ''3.13'']'
-      pytest-extra-args: -n 4
diff --git a/.github/workflows/array-api-tests.yml b/.github/workflows/array-api-tests.yml
deleted file mode 100644
index e832f870..00000000
--- a/.github/workflows/array-api-tests.yml
+++ /dev/null
@@ -1,94 +0,0 @@
-name: Array API Tests
-
-on:
-  workflow_call:
-    inputs:
-      package-name:
-        required: true
-        type: string
-      module-name:
-        required: false
-        type: string
-      extra-requires:
-        required: false
-        type: string
-      package-version:
-        required: false
-        type: string
-        default: '>= 0'
-      python-versions:
-        required: true
-        type: string
-        description: JSON array of Python versions to test against.
-      pytest-extra-args:
-        required: false
-        type: string
-      # This is not how I would prefer to implement this but it's the only way
-      # that seems possible with GitHub Actions' limited expressions syntax
-      xfails-file-extra:
-        required: false
-        type: string
-      skips-file-extra:
-        required: false
-        type: string
-      extra-env-vars:
-        required: false
-        type: string
-        description: Multiline string of environment variables to set for the test run.
-
-env:
-  PYTEST_ARGS: "--max-examples 1000 -v -rxXfE ${{ inputs.pytest-extra-args }} --hypothesis-disable-deadline --durations 20"
-
-jobs:
-  tests:
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ${{ fromJson(inputs.python-versions) }}
-
-    steps:
-    - name: Checkout array-api-compat
-      uses: actions/checkout@v4
-      with:
-        path: array-api-compat
-
-    - name: Checkout array-api-tests
-      uses: actions/checkout@v4
-      with:
-        repository: data-apis/array-api-tests
-        submodules: 'true'
-        path: array-api-tests
-
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v5
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Set Extra Environment Variables
-      # Set additional environment variables if provided
-      if: inputs.extra-env-vars
-      run: |
-        echo "${{ inputs.extra-env-vars }}" >> $GITHUB_ENV
-
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install '${{ inputs.package-name }} ${{ inputs.package-version }}' ${{ inputs.extra-requires }}
-        python -m pip install -r ${GITHUB_WORKSPACE}/array-api-tests/requirements.txt
-        python -m pip install pytest-xdist
-
-    - name: Dump pip environment
-      run: pip freeze
-
-    - name: Run the array API testsuite (${{ inputs.package-name }})
-      env:
-        ARRAY_API_TESTS_MODULE: array_api_compat.${{ inputs.module-name || inputs.package-name }}
-        ARRAY_API_TESTS_VERSION: 2024.12
-        # This enables the NEP 50 type promotion behavior (without it a lot of
-        # tests fail on bad scalar type promotion behavior)
-        NPY_PROMOTION_STATE: weak
-      run: |
-        export PYTHONPATH="${GITHUB_WORKSPACE}/array-api-compat"
-        cd ${GITHUB_WORKSPACE}/array-api-tests
-        pytest array_api_tests/ --xfails-file ${GITHUB_WORKSPACE}/array-api-compat/${{ inputs.package-name }}${{ inputs.xfails-file-extra }}-xfails.txt --skips-file ${GITHUB_WORKSPACE}/array-api-compat/${{ inputs.package-name }}${{ inputs.skips-file-extra}}-skips.txt ${PYTEST_ARGS}
diff --git a/.github/workflows/dependabot-auto-merge.yml b/.github/workflows/dependabot-auto-merge.yml
deleted file mode 100644
index bd29e25b..00000000
--- a/.github/workflows/dependabot-auto-merge.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-# https://docs.github.com/en/code-security/dependabot/working-with-dependabot/automating-dependabot-with-github-actions#approve-a-pull-request
-name: Dependabot auto-merge
-on: pull_request
-
-permissions:
-  contents: write
-  pull-requests: write
-
-jobs:
-  dependabot:
-    runs-on: ubuntu-latest
-    if: github.actor == 'dependabot[bot]'
-    steps:
-      - name: Dependabot metadata
-        id: metadata
-        uses: dependabot/fetch-metadata@v2
-        with:
-          github-token: "${{ secrets.GITHUB_TOKEN }}"
-      - name: Enable auto-merge for Dependabot PRs
-        run: gh pr merge --auto --merge "$PR_URL"
-        env:
-          PR_URL: ${{github.event.pull_request.html_url}}
-          GH_TOKEN: ${{secrets.GITHUB_TOKEN}}
diff --git a/.github/workflows/docs-build.yml b/.github/workflows/docs-build.yml
deleted file mode 100644
index 34b9cbc6..00000000
--- a/.github/workflows/docs-build.yml
+++ /dev/null
@@ -1,22 +0,0 @@
-name: Docs Build
-
-on: [push, pull_request]
-
-jobs:
-  docs-build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
-      - name: Install Dependencies
-        run: |
-          python -m pip install .[docs]
-      - name: Build Docs
-        run: |
-          cd docs
-          make html
-      - name: Upload Artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: docs-build
-          path: docs/_build/html
diff --git a/.github/workflows/docs-deploy.yml b/.github/workflows/docs-deploy.yml
deleted file mode 100644
index ed90b29d..00000000
--- a/.github/workflows/docs-deploy.yml
+++ /dev/null
@@ -1,30 +0,0 @@
-name: Docs Deploy
-
-on:
-  push:
-    branches:
-      - main
-
-jobs:
-  docs-deploy:
-    runs-on: ubuntu-latest
-    environment:
-      name: docs-deploy
-    steps:
-      - uses: actions/checkout@v4
-      - name: Download Artifact
-        uses: dawidd6/action-download-artifact@v11
-        with:
-          workflow: docs-build.yml
-          name: docs-build
-          path: docs/_build/html
-
-      # Note, the gh-pages deployment requires setting up a SSH deploy key.
-      # See
-      # https://github.com/JamesIves/github-pages-deploy-action/tree/dev#using-an-ssh-deploy-key-
-      - name: Deploy
-        uses: JamesIves/github-pages-deploy-action@v4
-        with:
-          folder: docs/_build/html
-          ssh-key: ${{ secrets.DEPLOY_KEY }}
-          force: no
diff --git a/.github/workflows/publish-package.yml b/.github/workflows/publish-package.yml
deleted file mode 100644
index 1e28689c..00000000
--- a/.github/workflows/publish-package.yml
+++ /dev/null
@@ -1,113 +0,0 @@
-name: publish distributions
-on:
-  push:
-    branches:
-    - main
-    tags:
-    - '[0-9]+.[0-9]+'
-    - '[0-9]+.[0-9]+.[0-9]+'
-  pull_request:
-    branches:
-    - main
-  release:
-    types: [published]
-  workflow_dispatch:
-    inputs:
-      publish:
-        type: choice
-        description: 'Publish to TestPyPI?'
-        options:
-        - false
-        - true
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  build:
-    name: Build Python distribution
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v4
-      with:
-        fetch-depth: 0
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version: '3.x'
-
-    - name: Install python-build and twine
-      run: |
-        python -m pip install --upgrade pip "setuptools<=67"
-        python -m pip install build twine
-        python -m pip list
-
-    - name: Build a wheel and a sdist
-      run: |
-         #PYTHONWARNINGS=error,default::DeprecationWarning python -m build .
-         python -m build .
-
-    - name: Verify the distribution
-      run: twine check --strict dist/*
-
-    - name: List contents of sdist
-      run: python -m tarfile --list dist/array_api_compat-*.tar.gz
-
-    - name: List contents of wheel
-      run: python -m zipfile --list dist/array_api_compat-*.whl
-
-    - name: Upload distribution artifact
-      uses: actions/upload-artifact@v4
-      with:
-        name: dist-artifact
-        path: dist
-
-  publish:
-    name: Publish Python distribution to (Test)PyPI
-    if: github.event_name != 'pull_request' && github.repository == 'data-apis/array-api-compat' && github.ref_type == 'tag'
-    needs: build
-    runs-on: ubuntu-latest
-    # Mandatory for publishing with a trusted publisher
-    # c.f. https://docs.pypi.org/trusted-publishers/using-a-publisher/
-    permissions:
-      id-token: write
-      contents: write
-    # Restrict to the environment set for the trusted publisher
-    environment:
-      name: publish-package
-
-    steps:
-    - name: Download distribution artifact
-      uses: actions/download-artifact@v5
-      with:
-        name: dist-artifact
-        path: dist
-
-    - name: List all files
-      run: ls -lh dist
-
-   # - name: Publish distribution 📦 to Test PyPI
-   #   # Publish to TestPyPI on tag events of if manually triggered
-   #   # Compare to 'true' string as booleans get turned into strings in the console
-   #   if: >-
-   #     (github.event_name == 'push' && startsWith(github.ref, 'refs/tags'))
-   #     || (github.event_name == 'workflow_dispatch' && github.event.inputs.publish == 'true')
-   #   uses: pypa/gh-action-pypi-publish@v1.12.4
-   #   with:
-   #     repository-url: https://test.pypi.org/legacy/
-   #     print-hash: true
-
-    - name: Publish distribution 📦 to PyPI
-      if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
-      uses: pypa/gh-action-pypi-publish@v1.12.4
-      with:
-        print-hash: true
-
-    - name: Create GitHub Release from a Tag
-      uses: softprops/action-gh-release@v2
-      if: startsWith(github.ref, 'refs/tags/')
-      with:
-          files: dist/*
diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
deleted file mode 100644
index a9f0fd4b..00000000
--- a/.github/workflows/ruff.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-name: CI
-on: [push, pull_request]
-jobs:
-  check-ruff:
-    runs-on: ubuntu-latest
-    continue-on-error: true
-    steps:
-      - uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install ruff
-      # Update output format to enable automatic inline annotations.
-      - name: Run Ruff
-        run: ruff check --output-format=github .
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
deleted file mode 100644
index c995b370..00000000
--- a/.github/workflows/tests.yml
+++ /dev/null
@@ -1,58 +0,0 @@
-name: Tests
-on: [push, pull_request]
-jobs:
-  tests:
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - numpy-version: '1.22'
-            python-version: '3.10'
-          - numpy-version: '1.26'
-            python-version: '3.10'
-          - numpy-version: '1.26'
-            python-version: '3.12'
-          - numpy-version: 'latest'
-            python-version: '3.10'
-          - numpy-version: 'latest'
-            python-version: '3.13'
-          - numpy-version: 'dev'
-            python-version: '3.11'
-          - numpy-version: 'dev'
-            python-version: '3.13'
-  
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-      - name: Install Dependencies
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install pytest
-
-          # Don't `pip install .[dev]` as it would pull in the whole torch cuda stack
-          python -m pip install array-api-strict
-          python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
-
-          if [ "${{ matrix.numpy-version }}" == "dev" ]; then
-            python -m pip install numpy --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple
-            python -m pip install dask[array] jax[cpu] sparse ndonnx
-          elif [ "${{ matrix.numpy-version }}" == "1.22" ]; then
-            python -m pip install 'numpy==1.22.*'
-          elif [ "${{ matrix.numpy-version }}" == "1.26" ]; then
-            python -m pip install 'numpy==1.26.*'
-          else
-            python -m pip install numpy
-            python -m pip install dask[array] jax[cpu] sparse ndonnx
-          fi
-
-      - name: Dump pip environment
-        run: pip freeze
-
-      - name: Test it installs
-        run: python -m pip install .
-
-      - name: Run Tests
-        run: pytest -v
diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 4b61f865..00000000
--- a/.gitignore
+++ /dev/null
@@ -1,132 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-pip-wheel-metadata/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-.python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# macOS specific iles
-.DS_Store
diff --git a/array_api_compat/dask/__init__.py b/.nojekyll
similarity index 100%
rename from array_api_compat/dask/__init__.py
rename to .nojekyll
diff --git a/CHANGELOG.md b/CHANGELOG.md
deleted file mode 120000
index 1bed66b3..00000000
--- a/CHANGELOG.md
+++ /dev/null
@@ -1 +0,0 @@
-docs/changelog.md
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
deleted file mode 100644
index d2469f4a..00000000
--- a/CONTRIBUTING.md
+++ /dev/null
@@ -1,7 +0,0 @@
-Contributions to array-api-compat are welcome, so long as they are [in
-scope](https://data-apis.org/array-api-compat/index.html#scope).
-
-Contributors are encouraged to read through the [development
-notes](https://data-apis.org/array-api-compat/dev/index.html) for the package
-to get full context on some of the design decisions and implementation
-details used in the codebase.
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index ca9f2fee..00000000
--- a/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 Consortium for Python Data API Standards
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/README.md b/README.md
deleted file mode 100644
index 4b0b0c9c..00000000
--- a/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Array API compatibility library
-
-This is a small wrapper around common array libraries that is compatible with
-the [Array API standard](https://data-apis.org/array-api/latest/). Currently,
-NumPy, CuPy, PyTorch, Dask, JAX, ndonnx and `sparse` are supported. If you want
-support for other array libraries, or if you encounter any issues, please [open
-an issue](https://github.com/data-apis/array-api-compat/issues).
-
-See the documentation for more details https://data-apis.org/array-api-compat/
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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new file mode 100644
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+  <path d="M5 12l5 5l10 -10" />
+</svg>
diff --git a/_static/clipboard.min.js b/_static/clipboard.min.js
new file mode 100644
index 00000000..54b3c463
--- /dev/null
+++ b/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},i="";return"string"==typeof t?(e=t,n="rtl"===document.documentElement.getAttribute("dir"),(o=document.createElement("textarea")).style.fontSize="12pt",o.style.border="0",o.style.padding="0",o.style.margin="0",o.style.position="absolute",o.style[n?"right":"left"]="-9999px",n=window.pageYOffset||document.documentElement.scrollTop,o.style.top="".concat(n,"px"),o.setAttribute("readonly",""),o.value=e,o=o,r.container.appendChild(o),i=c()(o),a("copy"),o.remove()):(i=c()(t),a("copy")),i};function r(t){return(r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==r(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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You can\'t cut text from elements with "readonly" or "disabled" attributes')}return t?l(t,{container:o}):e?"cut"===n?f(e):l(e,{container:o}):void 0};function d(t){return(d="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function p(t,e){for(var n=0;n<e.length;n++){var o=e[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(t,o.key,o)}}function y(t,e){return(y=Object.setPrototypeOf||function(t,e){return t.__proto__=e,t})(t,e)}function h(n){var o=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Date.prototype.toString.call(Reflect.construct(Date,[],function(){})),!0}catch(t){return!1}}();return function(){var t,e=m(n);return t=o?(t=m(this).constructor,Reflect.construct(e,arguments,t)):e.apply(this,arguments),e=this,!(t=t)||"object"!==d(t)&&"function"!=typeof t?function(t){if(void 0!==t)return t;throw new ReferenceError("this hasn't been initialised - super() hasn't been called")}(e):t}}function m(t){return(m=Object.setPrototypeOf?Object.getPrototypeOf:function(t){return t.__proto__||Object.getPrototypeOf(t)})(t)}function v(t,e){t="data-clipboard-".concat(t);if(e.hasAttribute(t))return e.getAttribute(t)}var o=function(){!function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),e&&y(t,e)}(r,i());var t,e,n,o=h(r);function r(t,e){var n;return function(t){if(!(t instanceof r))throw new TypeError("Cannot call a class as a function")}(this),(n=o.call(this)).resolveOptions(e),n.listenClick(t),n}return t=r,n=[{key:"copy",value:function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body};return l(t,e)}},{key:"cut",value:function(t){return f(t)}},{key:"isSupported",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:["copy","cut"],t="string"==typeof t?[t]:t,e=!!document.queryCommandSupported;return t.forEach(function(t){e=e&&!!document.queryCommandSupported(t)}),e}}],(e=[{key:"resolveOptions",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};this.action="function"==typeof t.action?t.action:this.defaultAction,this.target="function"==typeof t.target?t.target:this.defaultTarget,this.text="function"==typeof t.text?t.text:this.defaultText,this.container="object"===d(t.container)?t.container:document.body}},{key:"listenClick",value:function(t){var e=this;this.listener=u()(t,"click",function(t){return e.onClick(t)})}},{key:"onClick",value:function(t){var e=t.delegateTarget||t.currentTarget,t=s({action:this.action(e),container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:this.action,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&p(t.prototype,e),n&&p(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function 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f=n(879),l=n(438);t.exports=function(t,e,n){if(!t&&!e&&!n)throw new Error("Missing required arguments");if(!f.string(e))throw new TypeError("Second argument must be a String");if(!f.fn(n))throw new TypeError("Third argument must be a Function");if(f.node(t))return c=e,a=n,(u=t).addEventListener(c,a),{destroy:function(){u.removeEventListener(c,a)}};if(f.nodeList(t))return o=t,r=e,i=n,Array.prototype.forEach.call(o,function(t){t.addEventListener(r,i)}),{destroy:function(){Array.prototype.forEach.call(o,function(t){t.removeEventListener(r,i)})}};if(f.string(t))return t=t,e=e,n=n,l(document.body,t,e,n);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");var o,r,i,u,c,a}},817:function(t){t.exports=function(t){var e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/_static/copy-button.svg b/_static/copy-button.svg
new file mode 100644
index 00000000..9c074dae
--- /dev/null
+++ b/_static/copy-button.svg
@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>
diff --git a/_static/copybutton.css b/_static/copybutton.css
new file mode 100644
index 00000000..f1916ec7
--- /dev/null
+++ b/_static/copybutton.css
@@ -0,0 +1,94 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .3em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+    color: #22863a;
+}
+
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/_static/copybutton.js b/_static/copybutton.js
new file mode 100644
index 00000000..2ea7ff3e
--- /dev/null
+++ b/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_success']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_to_clipboard']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>`
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      ${iconCopy}
+    </button>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js
new file mode 100644
index 00000000..dbe1aaad
--- /dev/null
+++ b/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/docs/_static/custom.css b/_static/custom.css
similarity index 100%
rename from docs/_static/custom.css
rename to _static/custom.css
diff --git a/_static/debug.css b/_static/debug.css
new file mode 100644
index 00000000..74d4aec3
--- /dev/null
+++ b/_static/debug.css
@@ -0,0 +1,69 @@
+/*
+  This CSS file should be overridden by the theme authors. It's
+  meant for debugging and developing the skeleton that this theme provides.
+*/
+body {
+  font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif,
+    "Apple Color Emoji", "Segoe UI Emoji";
+  background: lavender;
+}
+.sb-announcement {
+  background: rgb(131, 131, 131);
+}
+.sb-announcement__inner {
+  background: black;
+  color: white;
+}
+.sb-header {
+  background: lightskyblue;
+}
+.sb-header__inner {
+  background: royalblue;
+  color: white;
+}
+.sb-header-secondary {
+  background: lightcyan;
+}
+.sb-header-secondary__inner {
+  background: cornflowerblue;
+  color: white;
+}
+.sb-sidebar-primary {
+  background: lightgreen;
+}
+.sb-main {
+  background: blanchedalmond;
+}
+.sb-main__inner {
+  background: antiquewhite;
+}
+.sb-header-article {
+  background: lightsteelblue;
+}
+.sb-article-container {
+  background: snow;
+}
+.sb-article-main {
+  background: white;
+}
+.sb-footer-article {
+  background: lightpink;
+}
+.sb-sidebar-secondary {
+  background: lightgoldenrodyellow;
+}
+.sb-footer-content {
+  background: plum;
+}
+.sb-footer-content__inner {
+  background: palevioletred;
+}
+.sb-footer {
+  background: pink;
+}
+.sb-footer__inner {
+  background: salmon;
+}
+.sb-article {
+  background: white;
+}
diff --git a/_static/doctools.js b/_static/doctools.js
new file mode 100644
index 00000000..0398ebb9
--- /dev/null
+++ b/_static/doctools.js
@@ -0,0 +1,149 @@
+/*
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
new file mode 100644
index 00000000..18c9cce6
--- /dev/null
+++ b/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '1.13.0.dev0',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/docs/_static/favicon.png b/_static/favicon.png
similarity index 100%
rename from docs/_static/favicon.png
rename to _static/favicon.png
diff --git a/_static/file.png b/_static/file.png
new file mode 100644
index 00000000..a858a410
Binary files /dev/null and b/_static/file.png differ
diff --git a/_static/language_data.js b/_static/language_data.js
new file mode 100644
index 00000000..c7fe6c6f
--- /dev/null
+++ b/_static/language_data.js
@@ -0,0 +1,192 @@
+/*
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/_static/minus.png b/_static/minus.png
new file mode 100644
index 00000000..d96755fd
Binary files /dev/null and b/_static/minus.png differ
diff --git a/_static/plus.png b/_static/plus.png
new file mode 100644
index 00000000..7107cec9
Binary files /dev/null and b/_static/plus.png differ
diff --git a/_static/pygments.css b/_static/pygments.css
new file mode 100644
index 00000000..c2e07c71
--- /dev/null
+++ b/_static/pygments.css
@@ -0,0 +1,258 @@
+.highlight pre { line-height: 125%; }
+.highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+.highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+.highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #8f5902; font-style: italic } /* Comment */
+.highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
+.highlight .g { color: #000000 } /* Generic */
+.highlight .k { color: #204a87; font-weight: bold } /* Keyword */
+.highlight .l { color: #000000 } /* Literal */
+.highlight .n { color: #000000 } /* Name */
+.highlight .o { color: #ce5c00; font-weight: bold } /* Operator */
+.highlight .x { color: #000000 } /* Other */
+.highlight .p { color: #000000; font-weight: bold } /* Punctuation */
+.highlight .ch { color: #8f5902; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #8f5902; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #8f5902; font-style: italic } /* Comment.Preproc */
+.highlight .cpf { color: #8f5902; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #8f5902; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #8f5902; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #a40000 } /* Generic.Deleted */
+.highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */
+.highlight .ges { color: #000000; font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #ef2929 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #000000; font-style: italic } /* Generic.Output */
+.highlight .gp { color: #8f5902 } /* Generic.Prompt */
+.highlight .gs { color: #000000; font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #a40000; font-weight: bold } /* Generic.Traceback */
+.highlight .kc { color: #204a87; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #204a87; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #204a87; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #204a87; font-weight: bold } /* Keyword.Pseudo */
+.highlight .kr { color: #204a87; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #204a87; font-weight: bold } /* Keyword.Type */
+.highlight .ld { color: #000000 } /* Literal.Date */
+.highlight .m { color: #0000cf; font-weight: bold } /* Literal.Number */
+.highlight .s { color: #4e9a06 } /* Literal.String */
+.highlight .na { color: #c4a000 } /* Name.Attribute */
+.highlight .nb { color: #204a87 } /* Name.Builtin */
+.highlight .nc { color: #000000 } /* Name.Class */
+.highlight .no { color: #000000 } /* Name.Constant */
+.highlight .nd { color: #5c35cc; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #ce5c00 } /* Name.Entity */
+.highlight .ne { color: #cc0000; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #000000 } /* Name.Function */
+.highlight .nl { color: #f57900 } /* Name.Label */
+.highlight .nn { color: #000000 } /* Name.Namespace */
+.highlight .nx { color: #000000 } /* Name.Other */
+.highlight .py { color: #000000 } /* Name.Property */
+.highlight .nt { color: #204a87; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #000000 } /* Name.Variable */
+.highlight .ow { color: #204a87; font-weight: bold } /* Operator.Word */
+.highlight .pm { color: #000000; font-weight: bold } /* Punctuation.Marker */
+.highlight .w { color: #f8f8f8 } /* Text.Whitespace */
+.highlight .mb { color: #0000cf; font-weight: bold } /* Literal.Number.Bin */
+.highlight .mf { color: #0000cf; font-weight: bold } /* Literal.Number.Float */
+.highlight .mh { color: #0000cf; font-weight: bold } /* Literal.Number.Hex */
+.highlight .mi { color: #0000cf; font-weight: bold } /* Literal.Number.Integer */
+.highlight .mo { color: #0000cf; font-weight: bold } /* Literal.Number.Oct */
+.highlight .sa { color: #4e9a06 } /* Literal.String.Affix */
+.highlight .sb { color: #4e9a06 } /* Literal.String.Backtick */
+.highlight .sc { color: #4e9a06 } /* Literal.String.Char */
+.highlight .dl { color: #4e9a06 } /* Literal.String.Delimiter */
+.highlight .sd { color: #8f5902; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4e9a06 } /* Literal.String.Double */
+.highlight .se { color: #4e9a06 } /* Literal.String.Escape */
+.highlight .sh { color: #4e9a06 } /* Literal.String.Heredoc */
+.highlight .si { color: #4e9a06 } /* Literal.String.Interpol */
+.highlight .sx { color: #4e9a06 } /* Literal.String.Other */
+.highlight .sr { color: #4e9a06 } /* Literal.String.Regex */
+.highlight .s1 { color: #4e9a06 } /* Literal.String.Single */
+.highlight .ss { color: #4e9a06 } /* Literal.String.Symbol */
+.highlight .bp { color: #3465a4 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #000000 } /* Name.Function.Magic */
+.highlight .vc { color: #000000 } /* Name.Variable.Class */
+.highlight .vg { color: #000000 } /* Name.Variable.Global */
+.highlight .vi { color: #000000 } /* Name.Variable.Instance */
+.highlight .vm { color: #000000 } /* Name.Variable.Magic */
+.highlight .il { color: #0000cf; font-weight: bold } /* Literal.Number.Integer.Long */
+@media not print {
+body[data-theme="dark"] .highlight pre { line-height: 125%; }
+body[data-theme="dark"] .highlight td.linenos .normal { color: #aaaaaa; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+body[data-theme="dark"] .highlight span.linenos { color: #aaaaaa; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+body[data-theme="dark"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+body[data-theme="dark"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+body[data-theme="dark"] .highlight .hll { background-color: #404040 }
+body[data-theme="dark"] .highlight { background: #202020; color: #d0d0d0 }
+body[data-theme="dark"] .highlight .c { color: #ababab; font-style: italic } /* Comment */
+body[data-theme="dark"] .highlight .err { color: #a61717; background-color: #e3d2d2 } /* Error */
+body[data-theme="dark"] .highlight .esc { color: #d0d0d0 } /* Escape */
+body[data-theme="dark"] .highlight .g { color: #d0d0d0 } /* Generic */
+body[data-theme="dark"] .highlight .k { color: #6ebf26; font-weight: bold } /* Keyword */
+body[data-theme="dark"] .highlight .l { color: #d0d0d0 } /* Literal */
+body[data-theme="dark"] .highlight .n { color: #d0d0d0 } /* Name */
+body[data-theme="dark"] .highlight .o { color: #d0d0d0 } /* Operator */
+body[data-theme="dark"] .highlight .x { color: #d0d0d0 } /* Other */
+body[data-theme="dark"] .highlight .p { color: #d0d0d0 } /* Punctuation */
+body[data-theme="dark"] .highlight .ch { color: #ababab; font-style: italic } /* Comment.Hashbang */
+body[data-theme="dark"] .highlight .cm { color: #ababab; font-style: italic } /* Comment.Multiline */
+body[data-theme="dark"] .highlight .cp { color: #ff3a3a; font-weight: bold } /* Comment.Preproc */
+body[data-theme="dark"] .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */
+body[data-theme="dark"] .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */
+body[data-theme="dark"] .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */
+body[data-theme="dark"] .highlight .gd { color: #d22323 } /* Generic.Deleted */
+body[data-theme="dark"] .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */
+body[data-theme="dark"] .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+body[data-theme="dark"] .highlight .gr { color: #d22323 } /* Generic.Error */
+body[data-theme="dark"] .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */
+body[data-theme="dark"] .highlight .gi { color: #589819 } /* Generic.Inserted */
+body[data-theme="dark"] .highlight .go { color: #cccccc } /* Generic.Output */
+body[data-theme="dark"] .highlight .gp { color: #aaaaaa } /* Generic.Prompt */
+body[data-theme="dark"] .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */
+body[data-theme="dark"] .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */
+body[data-theme="dark"] .highlight .gt { color: #d22323 } /* Generic.Traceback */
+body[data-theme="dark"] .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */
+body[data-theme="dark"] .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */
+body[data-theme="dark"] .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */
+body[data-theme="dark"] .highlight .kp { color: #6ebf26 } /* Keyword.Pseudo */
+body[data-theme="dark"] .highlight .kr { color: #6ebf26; font-weight: bold } /* Keyword.Reserved */
+body[data-theme="dark"] .highlight .kt { color: #6ebf26; font-weight: bold } /* Keyword.Type */
+body[data-theme="dark"] .highlight .ld { color: #d0d0d0 } /* Literal.Date */
+body[data-theme="dark"] .highlight .m { color: #51b2fd } /* Literal.Number */
+body[data-theme="dark"] .highlight .s { color: #ed9d13 } /* Literal.String */
+body[data-theme="dark"] .highlight .na { color: #bbbbbb } /* Name.Attribute */
+body[data-theme="dark"] .highlight .nb { color: #2fbccd } /* Name.Builtin */
+body[data-theme="dark"] .highlight .nc { color: #71adff; text-decoration: underline } /* Name.Class */
+body[data-theme="dark"] .highlight .no { color: #40ffff } /* Name.Constant */
+body[data-theme="dark"] .highlight .nd { color: #ffa500 } /* Name.Decorator */
+body[data-theme="dark"] .highlight .ni { color: #d0d0d0 } /* Name.Entity */
+body[data-theme="dark"] .highlight .ne { color: #bbbbbb } /* Name.Exception */
+body[data-theme="dark"] .highlight .nf { color: #71adff } /* Name.Function */
+body[data-theme="dark"] .highlight .nl { color: #d0d0d0 } /* Name.Label */
+body[data-theme="dark"] .highlight .nn { color: #71adff; text-decoration: underline } /* Name.Namespace */
+body[data-theme="dark"] .highlight .nx { color: #d0d0d0 } /* Name.Other */
+body[data-theme="dark"] .highlight .py { color: #d0d0d0 } /* Name.Property */
+body[data-theme="dark"] .highlight .nt { color: #6ebf26; font-weight: bold } /* Name.Tag */
+body[data-theme="dark"] .highlight .nv { color: #40ffff } /* Name.Variable */
+body[data-theme="dark"] .highlight .ow { color: #6ebf26; font-weight: bold } /* Operator.Word */
+body[data-theme="dark"] .highlight .pm { color: #d0d0d0 } /* Punctuation.Marker */
+body[data-theme="dark"] .highlight .w { color: #666666 } /* Text.Whitespace */
+body[data-theme="dark"] .highlight .mb { color: #51b2fd } /* Literal.Number.Bin */
+body[data-theme="dark"] .highlight .mf { color: #51b2fd } /* Literal.Number.Float */
+body[data-theme="dark"] .highlight .mh { color: #51b2fd } /* Literal.Number.Hex */
+body[data-theme="dark"] .highlight .mi { color: #51b2fd } /* Literal.Number.Integer */
+body[data-theme="dark"] .highlight .mo { color: #51b2fd } /* Literal.Number.Oct */
+body[data-theme="dark"] .highlight .sa { color: #ed9d13 } /* Literal.String.Affix */
+body[data-theme="dark"] .highlight .sb { color: #ed9d13 } /* Literal.String.Backtick */
+body[data-theme="dark"] .highlight .sc { color: #ed9d13 } /* Literal.String.Char */
+body[data-theme="dark"] .highlight .dl { color: #ed9d13 } /* Literal.String.Delimiter */
+body[data-theme="dark"] .highlight .sd { color: #ed9d13 } /* Literal.String.Doc */
+body[data-theme="dark"] .highlight .s2 { color: #ed9d13 } /* Literal.String.Double */
+body[data-theme="dark"] .highlight .se { color: #ed9d13 } /* Literal.String.Escape */
+body[data-theme="dark"] .highlight .sh { color: #ed9d13 } /* Literal.String.Heredoc */
+body[data-theme="dark"] .highlight .si { color: #ed9d13 } /* Literal.String.Interpol */
+body[data-theme="dark"] .highlight .sx { color: #ffa500 } /* Literal.String.Other */
+body[data-theme="dark"] .highlight .sr { color: #ed9d13 } /* Literal.String.Regex */
+body[data-theme="dark"] .highlight .s1 { color: #ed9d13 } /* Literal.String.Single */
+body[data-theme="dark"] .highlight .ss { color: #ed9d13 } /* Literal.String.Symbol */
+body[data-theme="dark"] .highlight .bp { color: #2fbccd } /* Name.Builtin.Pseudo */
+body[data-theme="dark"] .highlight .fm { color: #71adff } /* Name.Function.Magic */
+body[data-theme="dark"] .highlight .vc { color: #40ffff } /* Name.Variable.Class */
+body[data-theme="dark"] .highlight .vg { color: #40ffff } /* Name.Variable.Global */
+body[data-theme="dark"] .highlight .vi { color: #40ffff } /* Name.Variable.Instance */
+body[data-theme="dark"] .highlight .vm { color: #40ffff } /* Name.Variable.Magic */
+body[data-theme="dark"] .highlight .il { color: #51b2fd } /* Literal.Number.Integer.Long */
+@media (prefers-color-scheme: dark) {
+body:not([data-theme="light"]) .highlight pre { line-height: 125%; }
+body:not([data-theme="light"]) .highlight td.linenos .normal { color: #aaaaaa; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+body:not([data-theme="light"]) .highlight span.linenos { color: #aaaaaa; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+body:not([data-theme="light"]) .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+body:not([data-theme="light"]) .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+body:not([data-theme="light"]) .highlight .hll { background-color: #404040 }
+body:not([data-theme="light"]) .highlight { background: #202020; color: #d0d0d0 }
+body:not([data-theme="light"]) .highlight .c { color: #ababab; font-style: italic } /* Comment */
+body:not([data-theme="light"]) .highlight .err { color: #a61717; background-color: #e3d2d2 } /* Error */
+body:not([data-theme="light"]) .highlight .esc { color: #d0d0d0 } /* Escape */
+body:not([data-theme="light"]) .highlight .g { color: #d0d0d0 } /* Generic */
+body:not([data-theme="light"]) .highlight .k { color: #6ebf26; font-weight: bold } /* Keyword */
+body:not([data-theme="light"]) .highlight .l { color: #d0d0d0 } /* Literal */
+body:not([data-theme="light"]) .highlight .n { color: #d0d0d0 } /* Name */
+body:not([data-theme="light"]) .highlight .o { color: #d0d0d0 } /* Operator */
+body:not([data-theme="light"]) .highlight .x { color: #d0d0d0 } /* Other */
+body:not([data-theme="light"]) .highlight .p { color: #d0d0d0 } /* Punctuation */
+body:not([data-theme="light"]) .highlight .ch { color: #ababab; font-style: italic } /* Comment.Hashbang */
+body:not([data-theme="light"]) .highlight .cm { color: #ababab; font-style: italic } /* Comment.Multiline */
+body:not([data-theme="light"]) .highlight .cp { color: #ff3a3a; font-weight: bold } /* Comment.Preproc */
+body:not([data-theme="light"]) .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */
+body:not([data-theme="light"]) .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */
+body:not([data-theme="light"]) .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */
+body:not([data-theme="light"]) .highlight .gd { color: #d22323 } /* Generic.Deleted */
+body:not([data-theme="light"]) .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */
+body:not([data-theme="light"]) .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+body:not([data-theme="light"]) .highlight .gr { color: #d22323 } /* Generic.Error */
+body:not([data-theme="light"]) .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */
+body:not([data-theme="light"]) .highlight .gi { color: #589819 } /* Generic.Inserted */
+body:not([data-theme="light"]) .highlight .go { color: #cccccc } /* Generic.Output */
+body:not([data-theme="light"]) .highlight .gp { color: #aaaaaa } /* Generic.Prompt */
+body:not([data-theme="light"]) .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */
+body:not([data-theme="light"]) .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */
+body:not([data-theme="light"]) .highlight .gt { color: #d22323 } /* Generic.Traceback */
+body:not([data-theme="light"]) .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */
+body:not([data-theme="light"]) .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */
+body:not([data-theme="light"]) .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */
+body:not([data-theme="light"]) .highlight .kp { color: #6ebf26 } /* Keyword.Pseudo */
+body:not([data-theme="light"]) .highlight .kr { color: #6ebf26; font-weight: bold } /* Keyword.Reserved */
+body:not([data-theme="light"]) .highlight .kt { color: #6ebf26; font-weight: bold } /* Keyword.Type */
+body:not([data-theme="light"]) .highlight .ld { color: #d0d0d0 } /* Literal.Date */
+body:not([data-theme="light"]) .highlight .m { color: #51b2fd } /* Literal.Number */
+body:not([data-theme="light"]) .highlight .s { color: #ed9d13 } /* Literal.String */
+body:not([data-theme="light"]) .highlight .na { color: #bbbbbb } /* Name.Attribute */
+body:not([data-theme="light"]) .highlight .nb { color: #2fbccd } /* Name.Builtin */
+body:not([data-theme="light"]) .highlight .nc { color: #71adff; text-decoration: underline } /* Name.Class */
+body:not([data-theme="light"]) .highlight .no { color: #40ffff } /* Name.Constant */
+body:not([data-theme="light"]) .highlight .nd { color: #ffa500 } /* Name.Decorator */
+body:not([data-theme="light"]) .highlight .ni { color: #d0d0d0 } /* Name.Entity */
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+body:not([data-theme="light"]) .highlight .nf { color: #71adff } /* Name.Function */
+body:not([data-theme="light"]) .highlight .nl { color: #d0d0d0 } /* Name.Label */
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+body:not([data-theme="light"]) .highlight .nx { color: #d0d0d0 } /* Name.Other */
+body:not([data-theme="light"]) .highlight .py { color: #d0d0d0 } /* Name.Property */
+body:not([data-theme="light"]) .highlight .nt { color: #6ebf26; font-weight: bold } /* Name.Tag */
+body:not([data-theme="light"]) .highlight .nv { color: #40ffff } /* Name.Variable */
+body:not([data-theme="light"]) .highlight .ow { color: #6ebf26; font-weight: bold } /* Operator.Word */
+body:not([data-theme="light"]) .highlight .pm { color: #d0d0d0 } /* Punctuation.Marker */
+body:not([data-theme="light"]) .highlight .w { color: #666666 } /* Text.Whitespace */
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+body:not([data-theme="light"]) .highlight .mf { color: #51b2fd } /* Literal.Number.Float */
+body:not([data-theme="light"]) .highlight .mh { color: #51b2fd } /* Literal.Number.Hex */
+body:not([data-theme="light"]) .highlight .mi { color: #51b2fd } /* Literal.Number.Integer */
+body:not([data-theme="light"]) .highlight .mo { color: #51b2fd } /* Literal.Number.Oct */
+body:not([data-theme="light"]) .highlight .sa { color: #ed9d13 } /* Literal.String.Affix */
+body:not([data-theme="light"]) .highlight .sb { color: #ed9d13 } /* Literal.String.Backtick */
+body:not([data-theme="light"]) .highlight .sc { color: #ed9d13 } /* Literal.String.Char */
+body:not([data-theme="light"]) .highlight .dl { color: #ed9d13 } /* Literal.String.Delimiter */
+body:not([data-theme="light"]) .highlight .sd { color: #ed9d13 } /* Literal.String.Doc */
+body:not([data-theme="light"]) .highlight .s2 { color: #ed9d13 } /* Literal.String.Double */
+body:not([data-theme="light"]) .highlight .se { color: #ed9d13 } /* Literal.String.Escape */
+body:not([data-theme="light"]) .highlight .sh { color: #ed9d13 } /* Literal.String.Heredoc */
+body:not([data-theme="light"]) .highlight .si { color: #ed9d13 } /* Literal.String.Interpol */
+body:not([data-theme="light"]) .highlight .sx { color: #ffa500 } /* Literal.String.Other */
+body:not([data-theme="light"]) .highlight .sr { color: #ed9d13 } /* Literal.String.Regex */
+body:not([data-theme="light"]) .highlight .s1 { color: #ed9d13 } /* Literal.String.Single */
+body:not([data-theme="light"]) .highlight .ss { color: #ed9d13 } /* Literal.String.Symbol */
+body:not([data-theme="light"]) .highlight .bp { color: #2fbccd } /* Name.Builtin.Pseudo */
+body:not([data-theme="light"]) .highlight .fm { color: #71adff } /* Name.Function.Magic */
+body:not([data-theme="light"]) .highlight .vc { color: #40ffff } /* Name.Variable.Class */
+body:not([data-theme="light"]) .highlight .vg { color: #40ffff } /* Name.Variable.Global */
+body:not([data-theme="light"]) .highlight .vi { color: #40ffff } /* Name.Variable.Instance */
+body:not([data-theme="light"]) .highlight .vm { color: #40ffff } /* Name.Variable.Magic */
+body:not([data-theme="light"]) .highlight .il { color: #51b2fd } /* Literal.Number.Integer.Long */
+}
+}
\ No newline at end of file
diff --git a/array_api_compat/py.typed b/_static/scripts/furo-extensions.js
similarity index 100%
rename from array_api_compat/py.typed
rename to _static/scripts/furo-extensions.js
diff --git a/_static/scripts/furo.js b/_static/scripts/furo.js
new file mode 100644
index 00000000..0abb2afa
--- /dev/null
+++ b/_static/scripts/furo.js
@@ -0,0 +1,3 @@
+/*! For license information please see furo.js.LICENSE.txt */
+(()=>{var t={856:function(t,e,n){var o,r;r=void 0!==n.g?n.g:"undefined"!=typeof window?window:this,o=function(){return function(t){"use strict";var e={navClass:"active",contentClass:"active",nested:!1,nestedClass:"active",offset:0,reflow:!1,events:!0},n=function(t,e,n){if(n.settings.events){var o=new CustomEvent(t,{bubbles:!0,cancelable:!0,detail:n});e.dispatchEvent(o)}},o=function(t){var e=0;if(t.offsetParent)for(;t;)e+=t.offsetTop,t=t.offsetParent;return e>=0?e:0},r=function(t){t&&t.sort((function(t,e){return o(t.content)<o(e.content)?-1:1}))},c=function(e,n,o){var r=e.getBoundingClientRect(),c=function(t){return"function"==typeof t.offset?parseFloat(t.offset()):parseFloat(t.offset)}(n);return o?parseInt(r.bottom,10)<(t.innerHeight||document.documentElement.clientHeight):parseInt(r.top,10)<=c},s=function(){return Math.ceil(t.innerHeight+t.pageYOffset)>=Math.max(document.body.scrollHeight,document.documentElement.scrollHeight,document.body.offsetHeight,document.documentElement.offsetHeight,document.body.clientHeight,document.documentElement.clientHeight)},l=function(t,e){var n=t[t.length-1];if(function(t,e){return!(!s()||!c(t.content,e,!0))}(n,e))return n;for(var o=t.length-1;o>=0;o--)if(c(t[o].content,e))return t[o]},a=function(t,e){if(e.nested&&t.parentNode){var n=t.parentNode.closest("li");n&&(n.classList.remove(e.nestedClass),a(n,e))}},i=function(t,e){if(t){var o=t.nav.closest("li");o&&(o.classList.remove(e.navClass),t.content.classList.remove(e.contentClass),a(o,e),n("gumshoeDeactivate",o,{link:t.nav,content:t.content,settings:e}))}},u=function(t,e){if(e.nested){var n=t.parentNode.closest("li");n&&(n.classList.add(e.nestedClass),u(n,e))}};return function(o,c){var s,a,d,f,m,v={setup:function(){s=document.querySelectorAll(o),a=[],Array.prototype.forEach.call(s,(function(t){var e=document.getElementById(decodeURIComponent(t.hash.substr(1)));e&&a.push({nav:t,content:e})})),r(a)},detect:function(){var t=l(a,m);t?d&&t.content===d.content||(i(d,m),function(t,e){if(t){var o=t.nav.closest("li");o&&(o.classList.add(e.navClass),t.content.classList.add(e.contentClass),u(o,e),n("gumshoeActivate",o,{link:t.nav,content:t.content,settings:e}))}}(t,m),d=t):d&&(i(d,m),d=null)}},h=function(e){f&&t.cancelAnimationFrame(f),f=t.requestAnimationFrame(v.detect)},g=function(e){f&&t.cancelAnimationFrame(f),f=t.requestAnimationFrame((function(){r(a),v.detect()}))};return v.destroy=function(){d&&i(d,m),t.removeEventListener("scroll",h,!1),m.reflow&&t.removeEventListener("resize",g,!1),a=null,s=null,d=null,f=null,m=null},m=function(){var t={};return Array.prototype.forEach.call(arguments,(function(e){for(var n in e){if(!e.hasOwnProperty(n))return;t[n]=e[n]}})),t}(e,c||{}),v.setup(),v.detect(),t.addEventListener("scroll",h,!1),m.reflow&&t.addEventListener("resize",g,!1),v}}(r)}.apply(e,[]),void 0===o||(t.exports=o)}},e={};function n(o){var r=e[o];if(void 0!==r)return r.exports;var c=e[o]={exports:{}};return t[o].call(c.exports,c,c.exports,n),c.exports}n.n=t=>{var e=t&&t.__esModule?()=>t.default:()=>t;return n.d(e,{a:e}),e},n.d=(t,e)=>{for(var o in e)n.o(e,o)&&!n.o(t,o)&&Object.defineProperty(t,o,{enumerable:!0,get:e[o]})},n.g=function(){if("object"==typeof globalThis)return globalThis;try{return this||new Function("return this")()}catch(t){if("object"==typeof window)return window}}(),n.o=(t,e)=>Object.prototype.hasOwnProperty.call(t,e),(()=>{"use strict";var t=n(856),e=n.n(t),o=null,r=null,c=document.documentElement.scrollTop;const s=64;function l(){const t=localStorage.getItem("theme")||"auto";var e;"light"!==(e=window.matchMedia("(prefers-color-scheme: dark)").matches?"auto"===t?"light":"light"==t?"dark":"auto":"auto"===t?"dark":"dark"==t?"light":"auto")&&"dark"!==e&&"auto"!==e&&(console.error(`Got invalid theme mode: ${e}. 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+//# sourceMappingURL=furo.js.map
\ No newline at end of file
diff --git a/_static/scripts/furo.js.LICENSE.txt b/_static/scripts/furo.js.LICENSE.txt
new file mode 100644
index 00000000..1632189c
--- /dev/null
+++ b/_static/scripts/furo.js.LICENSE.txt
@@ -0,0 +1,7 @@
+/*!
+ * gumshoejs v5.1.2 (patched by @pradyunsg)
+ * A simple, framework-agnostic scrollspy script.
+ * (c) 2019 Chris Ferdinandi
+ * MIT License
+ * http://github.com/cferdinandi/gumshoe
+ */
diff --git a/_static/scripts/furo.js.map b/_static/scripts/furo.js.map
new file mode 100644
index 00000000..80ea12b8
--- /dev/null
+++ b/_static/scripts/furo.js.map
@@ -0,0 +1 @@
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get default export","webpack:///webpack/runtime/define property getters","webpack:///webpack/runtime/global","webpack:///webpack/runtime/hasOwnProperty shorthand","webpack:///./src/furo/assets/scripts/furo.js"],"sourcesContent":["/*!\n * gumshoejs v5.1.2 (patched by @pradyunsg)\n * A simple, framework-agnostic scrollspy script.\n * (c) 2019 Chris Ferdinandi\n * MIT License\n * http://github.com/cferdinandi/gumshoe\n */\n\n(function (root, factory) {\n  if (typeof define === \"function\" && define.amd) {\n    define([], function () {\n      return factory(root);\n    });\n  } else if (typeof exports === \"object\") {\n    module.exports = factory(root);\n  } else {\n    root.Gumshoe = factory(root);\n  }\n})(\n  typeof global !== \"undefined\"\n    ? global\n    : typeof window !== \"undefined\"\n      ? window\n      : this,\n  function (window) {\n    \"use strict\";\n\n    //\n    // Defaults\n    //\n\n    var defaults = {\n      // Active classes\n      navClass: \"active\",\n      contentClass: \"active\",\n\n      // Nested navigation\n      nested: false,\n      nestedClass: \"active\",\n\n      // Offset & reflow\n      offset: 0,\n      reflow: false,\n\n      // Event support\n      events: true,\n    };\n\n    //\n    // Methods\n    //\n\n    /**\n     * Merge two or more objects together.\n     * @param   {Object}   objects  The objects to merge together\n     * @returns {Object}            Merged values of defaults and options\n     */\n    var extend = function () {\n      var merged = {};\n      Array.prototype.forEach.call(arguments, function (obj) {\n        for (var key in obj) {\n          if (!obj.hasOwnProperty(key)) return;\n          merged[key] = obj[key];\n        }\n      });\n      return merged;\n    };\n\n    /**\n     * Emit a custom event\n     * @param  {String} type   The event type\n     * @param  {Node}   elem   The element to attach the event to\n     * @param  {Object} detail Any details to pass along with the event\n     */\n    var emitEvent = function (type, elem, detail) {\n      // Make sure events are enabled\n      if (!detail.settings.events) return;\n\n      // Create a new event\n      var event = new CustomEvent(type, {\n        bubbles: true,\n        cancelable: true,\n        detail: detail,\n      });\n\n      // Dispatch the event\n      elem.dispatchEvent(event);\n    };\n\n    /**\n     * Get an element's distance from the top of the Document.\n     * @param  {Node} elem The element\n     * @return {Number}    Distance from the top in pixels\n     */\n    var getOffsetTop = function (elem) {\n      var location = 0;\n      if (elem.offsetParent) {\n        while (elem) {\n          location += elem.offsetTop;\n          elem = elem.offsetParent;\n        }\n      }\n      return location >= 0 ? location : 0;\n    };\n\n    /**\n     * Sort content from first to last in the DOM\n     * @param  {Array} contents The content areas\n     */\n    var sortContents = function (contents) {\n      if (contents) {\n        contents.sort(function (item1, item2) {\n          var offset1 = getOffsetTop(item1.content);\n          var offset2 = getOffsetTop(item2.content);\n          if (offset1 < offset2) return -1;\n          return 1;\n        });\n      }\n    };\n\n    /**\n     * Get the offset to use for calculating position\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Float}           The number of pixels to offset the calculations\n     */\n    var getOffset = function (settings) {\n      // if the offset is a function run it\n      if (typeof settings.offset === \"function\") {\n        return parseFloat(settings.offset());\n      }\n\n      // Otherwise, return it as-is\n      return parseFloat(settings.offset);\n    };\n\n    /**\n     * Get the document element's height\n     * @private\n     * @returns {Number}\n     */\n    var getDocumentHeight = function () {\n      return Math.max(\n        document.body.scrollHeight,\n        document.documentElement.scrollHeight,\n        document.body.offsetHeight,\n        document.documentElement.offsetHeight,\n        document.body.clientHeight,\n        document.documentElement.clientHeight,\n      );\n    };\n\n    /**\n     * Determine if an element is in view\n     * @param  {Node}    elem     The element\n     * @param  {Object}  settings The settings for this instantiation\n     * @param  {Boolean} bottom   If true, check if element is above bottom of viewport instead\n     * @return {Boolean}          Returns true if element is in the viewport\n     */\n    var isInView = function (elem, settings, bottom) {\n      var bounds = elem.getBoundingClientRect();\n      var offset = getOffset(settings);\n      if (bottom) {\n        return (\n          parseInt(bounds.bottom, 10) <\n          (window.innerHeight || document.documentElement.clientHeight)\n        );\n      }\n      return parseInt(bounds.top, 10) <= offset;\n    };\n\n    /**\n     * Check if at the bottom of the viewport\n     * @return {Boolean} If true, page is at the bottom of the viewport\n     */\n    var isAtBottom = function () {\n      if (\n        Math.ceil(window.innerHeight + window.pageYOffset) >=\n        getDocumentHeight()\n      )\n        return true;\n      return false;\n    };\n\n    /**\n     * Check if the last item should be used (even if not at the top of the page)\n     * @param  {Object} item     The last item\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Boolean}         If true, use the last item\n     */\n    var useLastItem = function (item, settings) {\n      if (isAtBottom() && isInView(item.content, settings, true)) return true;\n      return false;\n    };\n\n    /**\n     * Get the active content\n     * @param  {Array}  contents The content areas\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Object}          The content area and matching navigation link\n     */\n    var getActive = function (contents, settings) {\n      var last = contents[contents.length - 1];\n      if (useLastItem(last, settings)) return last;\n      for (var i = contents.length - 1; i >= 0; i--) {\n        if (isInView(contents[i].content, settings)) return contents[i];\n      }\n    };\n\n    /**\n     * Deactivate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested || !nav.parentNode) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class\n      li.classList.remove(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      deactivateNested(li, settings);\n    };\n\n    /**\n     * Deactivate a nav and content area\n     * @param  {Object} items    The nav item and content to deactivate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivate = function (items, settings) {\n      // Make sure there are items to deactivate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class from the nav and content\n      li.classList.remove(settings.navClass);\n      items.content.classList.remove(settings.contentClass);\n\n      // Deactivate any parent navs in a nested navigation\n      deactivateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeDeactivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Activate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class\n      li.classList.add(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      activateNested(li, settings);\n    };\n\n    /**\n     * Activate a nav and content area\n     * @param  {Object} items    The nav item and content to activate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activate = function (items, settings) {\n      // Make sure there are items to activate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class to the nav and content\n      li.classList.add(settings.navClass);\n      items.content.classList.add(settings.contentClass);\n\n      // Activate any parent navs in a nested navigation\n      activateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeActivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Create the Constructor object\n     * @param {String} selector The selector to use for navigation items\n     * @param {Object} options  User options and settings\n     */\n    var Constructor = function (selector, options) {\n      //\n      // Variables\n      //\n\n      var publicAPIs = {};\n      var navItems, contents, current, timeout, settings;\n\n      //\n      // Methods\n      //\n\n      /**\n       * Set variables from DOM elements\n       */\n      publicAPIs.setup = function () {\n        // Get all nav items\n        navItems = document.querySelectorAll(selector);\n\n        // Create contents array\n        contents = [];\n\n        // Loop through each item, get it's matching content, and push to the array\n        Array.prototype.forEach.call(navItems, function (item) {\n          // Get the content for the nav item\n          var content = document.getElementById(\n            decodeURIComponent(item.hash.substr(1)),\n          );\n          if (!content) return;\n\n          // Push to the contents array\n          contents.push({\n            nav: item,\n            content: content,\n          });\n        });\n\n        // Sort contents by the order they appear in the DOM\n        sortContents(contents);\n      };\n\n      /**\n       * Detect which content is currently active\n       */\n      publicAPIs.detect = function () {\n        // Get the active content\n        var active = getActive(contents, settings);\n\n        // if there's no active content, deactivate and bail\n        if (!active) {\n          if (current) {\n            deactivate(current, settings);\n            current = null;\n          }\n          return;\n        }\n\n        // If the active content is the one currently active, do nothing\n        if (current && active.content === current.content) return;\n\n        // Deactivate the current content and activate the new content\n        deactivate(current, settings);\n        activate(active, settings);\n\n        // Update the currently active content\n        current = active;\n      };\n\n      /**\n       * Detect the active content on scroll\n       * Debounced for performance\n       */\n      var scrollHandler = function (event) {\n        // If there's a timer, cancel it\n        if (timeout) {\n          window.cancelAnimationFrame(timeout);\n        }\n\n        // Setup debounce callback\n        timeout = window.requestAnimationFrame(publicAPIs.detect);\n      };\n\n      /**\n       * Update content sorting on resize\n       * Debounced for performance\n       */\n      var resizeHandler = function (event) {\n        // If there's a timer, cancel it\n        if (timeout) {\n          window.cancelAnimationFrame(timeout);\n        }\n\n        // Setup debounce callback\n        timeout = window.requestAnimationFrame(function () {\n          sortContents(contents);\n          publicAPIs.detect();\n        });\n      };\n\n      /**\n       * Destroy the current instantiation\n       */\n      publicAPIs.destroy = function () {\n        // Undo DOM changes\n        if (current) {\n          deactivate(current, settings);\n        }\n\n        // Remove event listeners\n        window.removeEventListener(\"scroll\", scrollHandler, false);\n        if (settings.reflow) {\n          window.removeEventListener(\"resize\", resizeHandler, false);\n        }\n\n        // Reset variables\n        contents = null;\n        navItems = null;\n        current = null;\n        timeout = null;\n        settings = null;\n      };\n\n      /**\n       * Initialize the current instantiation\n       */\n      var init = function () {\n        // Merge user options into defaults\n        settings = extend(defaults, options || {});\n\n        // Setup variables based on the current DOM\n        publicAPIs.setup();\n\n        // Find the currently active content\n        publicAPIs.detect();\n\n        // Setup event listeners\n        window.addEventListener(\"scroll\", scrollHandler, false);\n        if (settings.reflow) {\n          window.addEventListener(\"resize\", resizeHandler, false);\n        }\n      };\n\n      //\n      // Initialize and return the public APIs\n      //\n\n      init();\n      return publicAPIs;\n    };\n\n    //\n    // Return the Constructor\n    //\n\n    return Constructor;\n  },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader(positionY) {\n  const headerTop = Math.floor(header.getBoundingClientRect().top);\n\n  console.log(`headerTop: ${headerTop}`);\n  if (headerTop == 0 && positionY != headerTop) {\n    header.classList.add(\"scrolled\");\n  } else {\n    header.classList.remove(\"scrolled\");\n  }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n  if (positionY < GO_TO_TOP_OFFSET) {\n    document.documentElement.classList.remove(\"show-back-to-top\");\n  } else {\n    if (positionY < lastScrollTop) {\n      document.documentElement.classList.add(\"show-back-to-top\");\n    } else if (positionY > lastScrollTop) {\n      document.documentElement.classList.remove(\"show-back-to-top\");\n    }\n  }\n  lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n  if (tocScroll === null) {\n    return;\n  }\n\n  // top of page.\n  if (positionY == 0) {\n    tocScroll.scrollTo(0, 0);\n  } else if (\n    // bottom of page.\n    Math.ceil(positionY) >=\n    Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n  ) {\n    tocScroll.scrollTo(0, tocScroll.scrollHeight);\n  } else {\n    // somewhere in the middle.\n    const current = document.querySelector(\".scroll-current\");\n    if (current == null) {\n      return;\n    }\n\n    // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n    // // scroll the currently \"active\" heading in toc, into view.\n    // const rect = current.getBoundingClientRect();\n    // if (0 > rect.top) {\n    //   current.scrollIntoView(true); // the argument is \"alignTop\"\n    // } else if (rect.bottom > window.innerHeight) {\n    //   current.scrollIntoView(false);\n    // }\n  }\n}\n\nfunction scrollHandler(positionY) {\n  scrollHandlerForHeader(positionY);\n  scrollHandlerForBackToTop(positionY);\n  scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  document.body.dataset.theme = mode;\n  localStorage.setItem(\"theme\", mode);\n  console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n  const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n  const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n  if (prefersDark) {\n    // Auto (dark) -> Light -> Dark\n    if (currentTheme === \"auto\") {\n      setTheme(\"light\");\n    } else if (currentTheme == \"light\") {\n      setTheme(\"dark\");\n    } else {\n      setTheme(\"auto\");\n    }\n  } else {\n    // Auto (light) -> Dark -> Light\n    if (currentTheme === \"auto\") {\n      setTheme(\"dark\");\n    } else if (currentTheme == \"dark\") {\n      setTheme(\"light\");\n    } else {\n      setTheme(\"auto\");\n    }\n  }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n  // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n  let last_known_scroll_position = 0;\n  let ticking = false;\n\n  window.addEventListener(\"scroll\", function (e) {\n    last_known_scroll_position = window.scrollY;\n\n    if (!ticking) {\n      window.requestAnimationFrame(function () {\n        scrollHandler(last_known_scroll_position);\n        ticking = false;\n      });\n\n      ticking = true;\n    }\n  });\n  window.scroll();\n}\n\nfunction setupScrollSpy() {\n  if (tocScroll === null) {\n    return;\n  }\n\n  // Scrollspy -- highlight table on contents, based on scroll\n  new Gumshoe(\".toc-tree a\", {\n    reflow: true,\n    recursive: true,\n    navClass: \"scroll-current\",\n    offset: () => {\n      let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n      return header.getBoundingClientRect().height + 2.5 * rem + 1;\n    },\n  });\n}\n\nfunction setupTheme() {\n  // Attach event handlers for toggling themes\n  const buttons = document.getElementsByClassName(\"theme-toggle\");\n  Array.from(buttons).forEach((btn) => {\n    btn.addEventListener(\"click\", cycleThemeOnce);\n  });\n}\n\nfunction setup() {\n  setupTheme();\n  setupScrollHandler();\n  setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n  document.body.parentNode.classList.remove(\"no-js\");\n\n  header = document.querySelector(\"header\");\n  tocScroll = document.querySelector(\".toc-scroll\");\n\n  setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","headerTop","floor","scrollHandlerForHeader","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""}
\ No newline at end of file
diff --git a/_static/searchtools.js b/_static/searchtools.js
new file mode 100644
index 00000000..91f4be57
--- /dev/null
+++ b/_static/searchtools.js
@@ -0,0 +1,635 @@
+/*
+ * Sphinx JavaScript utilities for the full-text search.
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename, kind] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+// Global search result kind enum, used by themes to style search results.
+class SearchResultKind {
+    static get index() { return  "index"; }
+    static get object() { return "object"; }
+    static get text() { return "text"; }
+    static get title() { return "title"; }
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename, kind] = item;
+
+  let listItem = document.createElement("li");
+  // Add a class representing the item's type:
+  // can be used by a theme's CSS selector for styling
+  // See SearchResultKind for the class names.
+  listItem.classList.add(`kind-${kind}`);
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = Documentation.ngettext(
+      "Search finished, found one page matching the search query.",
+      "Search finished, found ${resultCount} pages matching the search query.",
+      resultCount,
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlink", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.setAttribute("role", "list");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename, kind].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score + boost,
+            filenames[file],
+            SearchResultKind.title,
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+            SearchResultKind.index,
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+        SearchResultKind.object,
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      // find documents, if any, containing the query word in their text/title term indices
+      // use Object.hasOwnProperty to avoid mismatching against prototype properties
+      const arr = [
+        { files: terms.hasOwnProperty(word) ? terms[word] : undefined, score: Scorer.term },
+        { files: titleTerms.hasOwnProperty(word) ? titleTerms[word] : undefined, score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, new Map());
+          const fileScores = scoreMap.get(file);
+          fileScores.set(word, record.score);
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file).get(w)));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+        SearchResultKind.text,
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/_static/skeleton.css b/_static/skeleton.css
new file mode 100644
index 00000000..467c878c
--- /dev/null
+++ b/_static/skeleton.css
@@ -0,0 +1,296 @@
+/* Some sane resets. */
+html {
+  height: 100%;
+}
+
+body {
+  margin: 0;
+  min-height: 100%;
+}
+
+/* All the flexbox magic! */
+body,
+.sb-announcement,
+.sb-content,
+.sb-main,
+.sb-container,
+.sb-container__inner,
+.sb-article-container,
+.sb-footer-content,
+.sb-header,
+.sb-header-secondary,
+.sb-footer {
+  display: flex;
+}
+
+/* These order things vertically */
+body,
+.sb-main,
+.sb-article-container {
+  flex-direction: column;
+}
+
+/* Put elements in the center */
+.sb-header,
+.sb-header-secondary,
+.sb-container,
+.sb-content,
+.sb-footer,
+.sb-footer-content {
+  justify-content: center;
+}
+/* Put elements at the ends */
+.sb-article-container {
+  justify-content: space-between;
+}
+
+/* These elements grow. */
+.sb-main,
+.sb-content,
+.sb-container,
+article {
+  flex-grow: 1;
+}
+
+/* Because padding making this wider is not fun */
+article {
+  box-sizing: border-box;
+}
+
+/* The announcements element should never be wider than the page. */
+.sb-announcement {
+  max-width: 100%;
+}
+
+.sb-sidebar-primary,
+.sb-sidebar-secondary {
+  flex-shrink: 0;
+  width: 17rem;
+}
+
+.sb-announcement__inner {
+  justify-content: center;
+
+  box-sizing: border-box;
+  height: 3rem;
+
+  overflow-x: auto;
+  white-space: nowrap;
+}
+
+/* Sidebars, with checkbox-based toggle */
+.sb-sidebar-primary,
+.sb-sidebar-secondary {
+  position: fixed;
+  height: 100%;
+  top: 0;
+}
+
+.sb-sidebar-primary {
+  left: -17rem;
+  transition: left 250ms ease-in-out;
+}
+.sb-sidebar-secondary {
+  right: -17rem;
+  transition: right 250ms ease-in-out;
+}
+
+.sb-sidebar-toggle {
+  display: none;
+}
+.sb-sidebar-overlay {
+  position: fixed;
+  top: 0;
+  width: 0;
+  height: 0;
+
+  transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease;
+
+  opacity: 0;
+  background-color: rgba(0, 0, 0, 0.54);
+}
+
+#sb-sidebar-toggle--primary:checked
+  ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"],
+#sb-sidebar-toggle--secondary:checked
+  ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] {
+  width: 100%;
+  height: 100%;
+  opacity: 1;
+  transition: width 0ms ease, height 0ms ease, opacity 250ms ease;
+}
+
+#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary {
+  left: 0;
+}
+#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary {
+  right: 0;
+}
+
+/* Full-width mode */
+.drop-secondary-sidebar-for-full-width-content
+  .hide-when-secondary-sidebar-shown {
+  display: none !important;
+}
+.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary {
+  display: none !important;
+}
+
+/* Mobile views */
+.sb-page-width {
+  width: 100%;
+}
+
+.sb-article-container,
+.sb-footer-content__inner,
+.drop-secondary-sidebar-for-full-width-content .sb-article,
+.drop-secondary-sidebar-for-full-width-content .match-content-width {
+  width: 100vw;
+}
+
+.sb-article,
+.match-content-width {
+  padding: 0 1rem;
+  box-sizing: border-box;
+}
+
+@media (min-width: 32rem) {
+  .sb-article,
+  .match-content-width {
+    padding: 0 2rem;
+  }
+}
+
+/* Tablet views */
+@media (min-width: 42rem) {
+  .sb-article-container {
+    width: auto;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 42rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 42rem;
+  }
+}
+@media (min-width: 46rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 46rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 46rem;
+  }
+}
+@media (min-width: 50rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 50rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 50rem;
+  }
+}
+
+/* Tablet views */
+@media (min-width: 59rem) {
+  .sb-sidebar-secondary {
+    position: static;
+  }
+  .hide-when-secondary-sidebar-shown {
+    display: none !important;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 59rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 42rem;
+  }
+}
+@media (min-width: 63rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 63rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 46rem;
+  }
+}
+@media (min-width: 67rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 67rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 50rem;
+  }
+}
+
+/* Desktop views */
+@media (min-width: 76rem) {
+  .sb-sidebar-primary {
+    position: static;
+  }
+  .hide-when-primary-sidebar-shown {
+    display: none !important;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 59rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 42rem;
+  }
+}
+
+/* Full desktop views */
+@media (min-width: 80rem) {
+  .sb-article,
+  .match-content-width {
+    width: 46rem;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 63rem;
+  }
+}
+
+@media (min-width: 84rem) {
+  .sb-article,
+  .match-content-width {
+    width: 50rem;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 67rem;
+  }
+}
+
+@media (min-width: 88rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 67rem;
+  }
+  .sb-page-width {
+    width: 88rem;
+  }
+}
diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js
new file mode 100644
index 00000000..8a96c69a
--- /dev/null
+++ b/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/_static/styles/furo-extensions.css b/_static/styles/furo-extensions.css
new file mode 100644
index 00000000..82295876
--- /dev/null
+++ b/_static/styles/furo-extensions.css
@@ -0,0 +1,2 @@
+#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout a{color:var(--color-foreground-secondary)!important}#furo-readthedocs-versions{background:transparent;display:block;position:static;width:100%}#furo-readthedocs-versions .rst-versions{background:#1a1c1e}#furo-readthedocs-versions .rst-current-version{background:var(--color-sidebar-item-background);cursor:unset}#furo-readthedocs-versions .rst-current-version:hover{background:var(--color-sidebar-item-background)}#furo-readthedocs-versions .rst-current-version 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button.copybtn.success:after{display:block}.highlight button.copybtn 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.card{background-color:var(--color-background-secondary);color:var(--color-foreground)}
+/*# sourceMappingURL=furo-extensions.css.map*/
\ No newline at end of file
diff --git a/_static/styles/furo-extensions.css.map b/_static/styles/furo-extensions.css.map
new file mode 100644
index 00000000..c26eac7f
--- /dev/null
+++ b/_static/styles/furo-extensions.css.map
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diff --git a/_static/styles/furo.css b/_static/styles/furo.css
new file mode 100644
index 00000000..05a56b17
--- /dev/null
+++ b/_static/styles/furo.css
@@ -0,0 +1,2 @@
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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n *    `fieldset` elements in all browsers.\n */\n\nlegend {\n  box-sizing: border-box; /* 1 */\n  color: inherit; /* 2 */\n  display: table; /* 1 */\n  max-width: 100%; /* 1 */\n  padding: 0; /* 3 */\n  white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n  vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n  overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n  box-sizing: border-box; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; /* 1 */\n  outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n  -webkit-appearance: button; /* 1 */\n  font: inherit; /* 2 */\n}\n\n/* Interactive\n   ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n  display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n  display: list-item;\n}\n\n/* Misc\n   ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n  display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n  display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Hide icon container.\n  .content-icon-container\n    display: none !important\n\n  // Hide showing header links if hovering over when printing.\n  .headerlink\n    display: none !important\n\n  // Hide mobile header.\n  .mobile-header\n    display: none !important\n\n  // Hide navigation links.\n  .related-pages\n    display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Apply a border around code which no longer have a color background.\n  .highlight\n    border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  ul, ol, dl, a, table, pre, blockquote, p\n    page-break-inside: avoid\n\n  h1, h2, h3, h4, h5, h6, img, figure, caption\n    page-break-inside: avoid\n    page-break-after: avoid\n\n  ul, ol, dl\n    page-break-before: avoid\n",".visually-hidden\n  position: absolute !important\n  width: 1px !important\n  height: 1px !important\n  padding: 0 !important\n  margin: -1px !important\n  overflow: hidden !important\n  clip: rect(0,0,0,0) !important\n  white-space: nowrap !important\n  border: 0 !important\n  color: var(--color-foreground-primary)\n  background: var(--color-background-primary)\n\n:-moz-focusring\n  outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n  @include fonts\n  @include spacing\n  @include icons\n  @include admonitions\n  @include default-admonition(#651fff, \"abstract\")\n  @include default-topic(#14B8A6, \"pencil\")\n\n  @include colors\n\n.only-light\n  display: block !important\nhtml body .only-dark\n  display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n  // Enable dark-mode, if requested.\n  body[data-theme=\"dark\"]\n    @include colors-dark\n\n    html & .only-light\n      display: none !important\n    .only-dark\n      display: block !important\n\n  // Enable dark mode, unless explicitly told to avoid.\n  @media (prefers-color-scheme: dark)\n    body:not([data-theme=\"light\"])\n      @include colors-dark\n\n      html & .only-light\n        display: none !important\n      .only-dark\n        display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n  .theme-toggle svg.theme-icon-when-auto-light\n    display: block\n\n  @media (prefers-color-scheme: dark)\n    .theme-toggle svg.theme-icon-when-auto-dark\n      display: block\n    .theme-toggle svg.theme-icon-when-auto-light\n      display: none\n\nbody[data-theme=\"dark\"]\n  .theme-toggle svg.theme-icon-when-dark\n    display: block\n\nbody[data-theme=\"light\"]\n  .theme-toggle svg.theme-icon-when-light\n    display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n  // These are adapted from https://systemfontstack.com/\n  --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n    sans-serif, Apple Color Emoji, Segoe UI Emoji;\n  --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n    Liberation Mono, Lucida Console, monospace;\n  --font-stack--headings: var(--font-stack);\n\n  --font-size--normal: 100%;\n  --font-size--small: 87.5%;\n  --font-size--small--2: 81.25%;\n  --font-size--small--3: 75%;\n  --font-size--small--4: 62.5%;\n\n  // Sidebar\n  --sidebar-caption-font-size: var(--font-size--small--2);\n  --sidebar-item-font-size: var(--font-size--small);\n  --sidebar-search-input-font-size: var(--font-size--small);\n\n  // Table of Contents\n  --toc-font-size: var(--font-size--small--3);\n  --toc-font-size--mobile: var(--font-size--normal);\n  --toc-title-font-size: var(--font-size--small--4);\n\n  // Admonitions\n  //\n  // These aren't defined in terms of %ages, since nesting these is permitted.\n  --admonition-font-size: 0.8125rem;\n  --admonition-title-font-size: 0.8125rem;\n\n  // Code\n  --code-font-size: var(--font-size--small--2);\n\n  // API\n  --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n  // Header!\n  --header-height: calc(\n    var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n  );\n  --header-padding: 0.5rem;\n\n  // Sidebar\n  --sidebar-tree-space-above: 1.5rem;\n  --sidebar-caption-space-above: 1rem;\n\n  --sidebar-item-line-height: 1rem;\n  --sidebar-item-spacing-vertical: 0.5rem;\n  --sidebar-item-spacing-horizontal: 1rem;\n  --sidebar-item-height: calc(\n    var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n  );\n\n  --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n  --sidebar-search-space-above: 0.5rem;\n  --sidebar-search-input-spacing-vertical: 0.5rem;\n  --sidebar-search-input-spacing-horizontal: 0.5rem;\n  --sidebar-search-input-height: 1rem;\n  --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n  // Table of Contents\n  --toc-title-padding: 0.25rem 0;\n  --toc-spacing-vertical: 1.5rem;\n  --toc-spacing-horizontal: 1.5rem;\n  --toc-item-spacing-vertical: 0.4rem;\n  --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n  // Adapted from tabler-icons\n  //    url: https://tablericons.com/\n  \"search\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"currentColor\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><circle cx=\"10\" cy=\"10\" r=\"7\" /><line x1=\"21\" y1=\"21\" x2=\"15\" y2=\"15\" /></svg>'),\n  // Factored out from mkdocs-material on 24-Aug-2020.\n  //    url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n  \"pencil\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z\"/></svg>'),\n  \"abstract\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M4 5h16v2H4V5m0 4h16v2H4V9m0 4h16v2H4v-2m0 4h10v2H4v-2z\"/></svg>'),\n  \"info\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10A10 10 0 0012 2z\"/></svg>'),\n  \"flame\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.83.83 0 01-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25l.05-.01m-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27h.01z\"/></svg>'),\n  \"question\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M15.07 11.25l-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 00-2-2 2 2 0 00-2 2H8a4 4 0 014-4 4 4 0 014 4 3.2 3.2 0 01-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10c0-5.53-4.5-10-10-10z\"/></svg>'),\n  \"warning\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 14h-2v-4h2m0 8h-2v-2h2M1 21h22L12 2 1 21z\"/></svg>'),\n  \"failure\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M12 2c5.53 0 10 4.47 10 10s-4.47 10-10 10S2 17.53 2 12 6.47 2 12 2m3.59 5L12 10.59 8.41 7 7 8.41 10.59 12 7 15.59 8.41 17 12 13.41 15.59 17 17 15.59 13.41 12 17 8.41 15.59 7z\"/></svg>'),\n  \"spark\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M11.5 20l4.86-9.73H13V4l-5 9.73h3.5V20M12 2c2.75 0 5.1 1 7.05 2.95C21 6.9 22 9.25 22 12s-1 5.1-2.95 7.05C17.1 21 14.75 22 12 22s-5.1-1-7.05-2.95C3 17.1 2 14.75 2 12s1-5.1 2.95-7.05C6.9 3 9.25 2 12 2z\"/></svg>')\n);\n\n@mixin icons {\n  @each $name, $glyph in $icons {\n    --icon-#{$name}: #{$glyph};\n  }\n}\n","// Admonitions\n\n// Structure of these is:\n//    admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n  // Each of these has an reST directives for it.\n  \"caution\":         #ff9100 \"spark\",\n  \"warning\":         #ff9100 \"warning\",\n  \"danger\":          #ff5252 \"spark\",\n  \"attention\":       #ff5252 \"warning\",\n  \"error\":           #ff5252 \"failure\",\n  \"hint\":            #00c852 \"question\",\n  \"tip\":             #00c852 \"info\",\n  \"important\":       #00bfa5 \"flame\",\n  \"note\":            #00b0ff \"pencil\",\n  \"seealso\":         #448aff \"info\",\n  \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n  --color-admonition-title: #{$color};\n  --color-admonition-title-background: #{rgba($color, 0.2)};\n\n  --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n  --color-topic-title: #{$color};\n  --color-topic-title-background: #{rgba($color, 0.2)};\n\n  --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n  @each $name, $values in $admonitions {\n    --color-admonition-title--#{$name}: #{nth($values, 1)};\n    --color-admonition-title-background--#{$name}: #{rgba(\n        nth($values, 1),\n        0.2\n      )};\n  }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n  --color-problematic: #b30000;\n\n  // Base Colors\n  --color-foreground-primary: black; // for main text and headings\n  --color-foreground-secondary: #5a5c63; // for secondary text\n  --color-foreground-muted: #6b6f76; // for muted text\n  --color-foreground-border: #878787; // for content borders\n\n  --color-background-primary: white; // for content\n  --color-background-secondary: #f8f9fb; // for navigation + ToC\n  --color-background-hover: #efeff4ff; // for navigation-item hover\n  --color-background-hover--transparent: #efeff400;\n  --color-background-border: #eeebee; // for UI borders\n  --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #0a4bff;\n  --color-brand-content: #2757dd;\n  --color-brand-visited: #872ee0;\n\n  // API documentation\n  --color-api-background: var(--color-background-hover--transparent);\n  --color-api-background-hover: var(--color-background-hover);\n  --color-api-overall: var(--color-foreground-secondary);\n  --color-api-name: var(--color-problematic);\n  --color-api-pre-name: var(--color-problematic);\n  --color-api-paren: var(--color-foreground-secondary);\n  --color-api-keyword: var(--color-foreground-primary);\n\n  --color-api-added: #21632c;\n  --color-api-added-border: #38a84d;\n  --color-api-changed: #046172;\n  --color-api-changed-border: #06a1bc;\n  --color-api-deprecated: #605706;\n  --color-api-deprecated-border: #f0d90f;\n  --color-api-removed: #b30000;\n  --color-api-removed-border: #ff5c5c;\n\n  --color-highlight-on-target: #ffffcc;\n\n  // Inline code background\n  --color-inline-code-background: var(--color-background-secondary);\n\n  // Highlighted text (search)\n  --color-highlighted-background: #ddeeff;\n  --color-highlighted-text: var(--color-foreground-primary);\n\n  // GUI Labels\n  --color-guilabel-background: #ddeeff80;\n  --color-guilabel-border: #bedaf580;\n  --color-guilabel-text: var(--color-foreground-primary);\n\n  // Admonitions!\n  --color-admonition-background: transparent;\n\n  //////////////////////////////////////////////////////////////////////////////\n  // Everything below this should be one of:\n  // - var(...)\n  // - *-gradient(...)\n  // - special literal values (eg: transparent, none)\n  //////////////////////////////////////////////////////////////////////////////\n\n  // Tables\n  --color-table-header-background: var(--color-background-secondary);\n  --color-table-border: var(--color-background-border);\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: transparent;\n  --color-card-marginals-background: var(--color-background-secondary);\n\n  // Header\n  --color-header-background: var(--color-background-primary);\n  --color-header-border: var(--color-background-border);\n  --color-header-text: var(--color-foreground-primary);\n\n  // Sidebar (left)\n  --color-sidebar-background: var(--color-background-secondary);\n  --color-sidebar-background-border: var(--color-background-border);\n\n  --color-sidebar-brand-text: var(--color-foreground-primary);\n  --color-sidebar-caption-text: var(--color-foreground-muted);\n  --color-sidebar-link-text: var(--color-foreground-secondary);\n  --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n  --color-sidebar-item-background: var(--color-sidebar-background);\n  --color-sidebar-item-background--current: var(\n    --color-sidebar-item-background\n  );\n  --color-sidebar-item-background--hover: linear-gradient(\n    90deg,\n    var(--color-background-hover--transparent) 0%,\n    var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n    var(--color-background-hover) 100%\n  );\n\n  --color-sidebar-item-expander-background: transparent;\n  --color-sidebar-item-expander-background--hover: var(\n    --color-background-hover\n  );\n\n  --color-sidebar-search-text: var(--color-foreground-primary);\n  --color-sidebar-search-background: var(--color-background-secondary);\n  --color-sidebar-search-background--focus: var(--color-background-primary);\n  --color-sidebar-search-border: var(--color-background-border);\n  --color-sidebar-search-icon: var(--color-foreground-muted);\n\n  // Table of Contents (right)\n  --color-toc-background: var(--color-background-primary);\n  --color-toc-title-text: var(--color-foreground-muted);\n  --color-toc-item-text: var(--color-foreground-secondary);\n  --color-toc-item-text--hover: var(--color-foreground-primary);\n  --color-toc-item-text--active: var(--color-brand-primary);\n\n  // Actual page contents\n  --color-content-foreground: var(--color-foreground-primary);\n  --color-content-background: transparent;\n\n  // Links\n  --color-link: var(--color-brand-content);\n  --color-link-underline: var(--color-background-border);\n  --color-link--hover: var(--color-brand-content);\n  --color-link-underline--hover: var(--color-foreground-border);\n\n  --color-link--visited: var(--color-brand-visited);\n  --color-link-underline--visited: var(--color-background-border);\n  --color-link--visited--hover: var(--color-brand-visited);\n  --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n  --color-problematic: #ee5151;\n\n  // Base Colors\n  --color-foreground-primary: #cfd0d0; // for main text and headings\n  --color-foreground-secondary: #9ca0a5; // for secondary text\n  --color-foreground-muted: #81868d; // for muted text\n  --color-foreground-border: #666666; // for content borders\n\n  --color-background-primary: #131416; // for content\n  --color-background-secondary: #1a1c1e; // for navigation + ToC\n  --color-background-hover: #1e2124ff; // for navigation-item hover\n  --color-background-hover--transparent: #1e212400;\n  --color-background-border: #303335; // for UI borders\n  --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #3d94ff;\n  --color-brand-content: #5ca5ff;\n  --color-brand-visited: #b27aeb;\n\n  // Highlighted text (search)\n  --color-highlighted-background: #083563;\n\n  // GUI Labels\n  --color-guilabel-background: #08356380;\n  --color-guilabel-border: #13395f80;\n\n  // API documentation\n  --color-api-keyword: var(--color-foreground-secondary);\n  --color-highlight-on-target: #333300;\n\n  --color-api-added: #3db854;\n  --color-api-added-border: #267334;\n  --color-api-changed: #09b0ce;\n  --color-api-changed-border: #056d80;\n  --color-api-deprecated: #b1a10b;\n  --color-api-deprecated-border: #6e6407;\n  --color-api-removed: #ff7575;\n  --color-api-removed-border: #b03b3b;\n\n  // Admonitions\n  --color-admonition-background: #18181a;\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: #18181a;\n  --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n  font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n  font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n  -webkit-font-smoothing: antialiased\n  -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n  line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n  line-height: 1.25\n  font-family: var(--font-stack--headings)\n  font-weight: bold\n\n  border-radius: 0.5rem\n  margin-top: 0.5rem\n  margin-bottom: 0.5rem\n  margin-left: -0.5rem\n  margin-right: -0.5rem\n  padding-left: 0.5rem\n  padding-right: 0.5rem\n\n  + p\n    margin-top: 0\n\nh1\n  font-size: 2.5em\n  margin-top: 1.75rem\n  margin-bottom: 1rem\nh2\n  font-size: 2em\n  margin-top: 1.75rem\nh3\n  font-size: 1.5em\nh4\n  font-size: 1.25em\nh5\n  font-size: 1.125em\nh6\n  font-size: 1em\n\nsmall\n  opacity: 75%\n  font-size: 80%\n\n// Paragraph\np\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n  height: 1px\n  padding: 0\n  margin: 2rem 0\n  background-color: var(--color-background-border)\n  border: 0\n\n.centered\n  text-align: center\n\n// Links\na\n  text-decoration: underline\n\n  color: var(--color-link)\n  text-decoration-color: var(--color-link-underline)\n\n  &:visited\n    color: var(--color-link--visited)\n    text-decoration-color: var(--color-link-underline--visited)\n    &:hover\n      color: var(--color-link--visited--hover)\n      text-decoration-color: var(--color-link-underline--visited--hover)\n\n  &:hover\n    color: var(--color-link--hover)\n    text-decoration-color: var(--color-link-underline--hover)\n  &.muted-link\n    color: inherit\n    &:hover\n      color: var(--color-link--hover)\n      text-decoration-color: var(--color-link-underline--hover)\n      &:visited\n        color: var(--color-link--visited--hover)\n        text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n  overflow-x: hidden\n  overflow-y: scroll\n  scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n  // Override Firefox scrollbar style\n  scrollbar-width: thin\n  scrollbar-color: var(--color-foreground-border) transparent\n\n  // Override Chrome scrollbar styles\n  &::-webkit-scrollbar\n    width: 0.25rem\n    height: 0.25rem\n  &::-webkit-scrollbar-thumb\n    background-color: var(--color-foreground-border)\n    border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n  height: 100%\n  color: var(--color-foreground-primary)\n  background: var(--color-background-primary)\n\n.skip-to-content\n  position: fixed\n  padding: 1rem\n  border-radius: 1rem\n  left: 0.25rem\n  top: 0.25rem\n  z-index: 40\n  background: var(--color-background-primary)\n  color: var(--color-foreground-primary)\n\n  transform: translateY(-200%)\n  transition: transform 300ms ease-in-out\n\n  &:focus-within\n    transform: translateY(0%)\n\narticle\n  color: var(--color-content-foreground)\n  background: var(--color-content-background)\n  overflow-wrap: break-word\n\n.page\n  display: flex\n  // fill the viewport for pages with little content.\n  min-height: 100%\n\n.mobile-header\n  width: 100%\n  height: var(--header-height)\n  background-color: var(--color-header-background)\n  color: var(--color-header-text)\n  border-bottom: 1px solid var(--color-header-border)\n\n  // Looks like sub-script/super-script have this, and we need this to\n  // be \"on top\" of those.\n  z-index: 10\n\n  // We don't show the header on large screens.\n  display: none\n\n  // Add shadow when scrolled\n  &.scrolled\n    border-bottom: none\n    box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n  .header-center\n    a\n      color: var(--color-header-text)\n      text-decoration: none\n\n.main\n  display: flex\n  flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n  box-sizing: border-box\n\n  border-right: 1px solid var(--color-sidebar-background-border)\n  background: var(--color-sidebar-background)\n\n  display: flex\n  justify-content: flex-end\n  // These next two lines took me two days to figure out.\n  width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n  min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n  box-sizing: border-box\n  width: $sidebar-width\n\n.toc-drawer\n  background: var(--color-toc-background)\n  // See HACK described on top of this document\n  padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n  position: sticky\n  top: 0\n  height: min(100%, 100vh)\n  height: 100vh\n\n  display: flex\n  flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n  flex-grow: 1\n  flex-shrink: 1\n\n  overflow: auto\n  scroll-behavior: smooth\n\n// Central items.\n.content\n  padding: 0 $content-padding\n  width: $content-width\n\n  display: flex\n  flex-direction: column\n  justify-content: space-between\n\n.icon\n  display: inline-block\n  height: 1rem\n  width: 1rem\n  svg\n    width: 100%\n    height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n  background-color: var(--color-announcement-background)\n  color: var(--color-announcement-text)\n\n  height: var(--header-height)\n  display: flex\n  align-items: center\n  overflow-x: auto\n  & + .page\n    min-height: calc(100% - var(--header-height))\n\n.announcement-content\n  box-sizing: border-box\n  padding: 0.5rem\n  min-width: 100%\n  white-space: nowrap\n  text-align: center\n\n  a\n    color: var(--color-announcement-text)\n    text-decoration-color: var(--color-announcement-text)\n\n    &:hover\n      color: var(--color-announcement-text)\n      text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container  // don't show theme toggle if there's no JS\n  display: none\n\n.theme-toggle-container\n  display: flex\n\n.theme-toggle\n  display: flex\n  cursor: pointer\n  border: none\n  padding: 0\n  background: transparent\n\n.theme-toggle svg\n  height: 1.25rem\n  width: 1.25rem\n  color: var(--color-foreground-primary)\n  display: none\n\n.theme-toggle-header\n  display: flex\n  align-items: center\n  justify-content: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n  display: none\n  cursor: pointer\n\n  .icon\n    color: var(--color-foreground-secondary)\n    height: 1.5rem\n    width: 1.5rem\n\n.toc-header-icon, .nav-overlay-icon\n  // for when we set display: flex\n  justify-content: center\n  align-items: center\n\n.toc-content-icon\n  height: 1.5rem\n  width: 1.5rem\n\n.content-icon-container\n  float: right\n  display: flex\n  margin-top: 1.5rem\n  margin-left: 1rem\n  margin-bottom: 1rem\n  gap: 0.5rem\n\n  .edit-this-page, .view-this-page\n    svg\n      color: inherit\n      height: 1.25rem\n      width: 1.25rem\n\n.sidebar-toggle\n  position: absolute\n  display: none\n// <debugging things>\n.sidebar-toggle[name=\"__toc\"]\n  left: 20px\n.sidebar-toggle:checked\n  left: 40px\n// </debugging things>\n\n.overlay\n  position: fixed\n  top: 0\n  width: 0\n  height: 0\n\n  transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n  opacity: 0\n  background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n  z-index: 20\n.toc-overlay\n  z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n  z-index: 30\n  transition: left 250ms ease-in-out\n.toc-drawer\n  z-index: 50\n  transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n  & ~ .sidebar-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .sidebar-drawer\n      top: 0\n      left: 0\n      // Show the toc sidebar\n#__toc:checked\n  & ~ .toc-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .toc-drawer\n      top: 0\n      right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n  text-decoration: none\n\n  display: none\n  position: fixed\n  left: 0\n  top: 1rem\n  padding: 0.5rem\n  padding-right: 0.75rem\n  border-radius: 1rem\n  font-size: 0.8125rem\n\n  background: var(--color-background-primary)\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n  z-index: 10\n\n  margin-left: 50%\n  transform: translateX(-50%)\n  svg\n    height: 1rem\n    width: 1rem\n    fill: currentColor\n    display: inline-block\n\n  span\n    margin-left: 0.25rem\n\n  .show-back-to-top &\n    display: flex\n    align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n  html\n    font-size: 110%\n\n@media (max-width: $full-width)\n  // Collapse \"toc\" into the icon.\n  .toc-content-icon\n    display: flex\n  .toc-drawer\n    position: fixed\n    height: 100vh\n    top: 0\n    right: -$sidebar-width\n    border-left: 1px solid var(--color-background-muted)\n  .toc-tree\n    border-left: none\n    font-size: var(--toc-font-size--mobile)\n\n  // Accomodate for a changed content width.\n  .sidebar-drawer\n    width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $content-padded-width + $sidebar-width)\n  // Center the page\n  .content\n    margin-left: auto\n    margin-right: auto\n    padding: 0 $content-padding--small\n\n@media (max-width: $content-padded-width--small + $sidebar-width)\n  // Collapse \"navigation\".\n  .nav-overlay-icon\n    display: flex\n  .sidebar-drawer\n    position: fixed\n    height: 100vh\n    width: $sidebar-width\n\n    top: 0\n    left: -$sidebar-width\n\n  // Swap which icon is visible.\n  .toc-header-icon, .theme-toggle-header\n    display: flex\n  .toc-content-icon, .theme-toggle-content\n    display: none\n\n  // Show the header.\n  .mobile-header\n    position: sticky\n    top: 0\n    display: flex\n    justify-content: space-between\n    align-items: center\n\n    .header-left,\n    .header-right\n      display: flex\n      height: var(--header-height)\n      padding: 0 var(--header-padding)\n      label\n        height: 100%\n        width: 100%\n        user-select: none\n\n  .nav-overlay-icon .icon,\n  .theme-toggle svg\n    height: 1.5rem\n    width: 1.5rem\n\n  // Add a scroll margin for the content\n  :target\n    scroll-margin-top: calc(var(--header-height) + 2.5rem)\n\n  // Show back-to-top below the header\n  .back-to-top\n    top: calc(var(--header-height) + 0.5rem)\n\n  // Accommodate for the header.\n  .page\n    flex-direction: column\n    justify-content: center\n\n@media (max-width: $content-width + 2* $content-padding--small)\n  // Content should respect window limits.\n  .content\n    width: 100%\n    overflow-x: auto\n\n@media (max-width: $content-width)\n  article[role=main] aside.sidebar\n    float: none\n    width: 100%\n    margin: 1rem 0\n","// Overall Layout Variables\n//\n// Because CSS variables can't be used in media queries. The fact that this\n// makes the layout non-user-configurable is a good thing.\n$content-padding: 3em;\n$content-padding--small: 1em;\n$content-width: 46em;\n$sidebar-width: 15em;\n$content-padded-width: $content-width + 2 * $content-padding;\n$content-padded-width--small: $content-width + 2 * $content-padding--small;\n$full-width: $content-padded-width + 2 * $sidebar-width;\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n  margin: 1rem auto\n  padding: 0 0.5rem 0.5rem 0.5rem\n\n  background: var(--color-admonition-background)\n\n  border-radius: 0.2rem\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  font-size: var(--admonition-font-size)\n\n  overflow: hidden\n  page-break-inside: avoid\n\n  // First element should have no margin, since the title has it.\n  > :nth-child(2)\n    margin-top: 0\n\n  // Last item should have no margin, since we'll control that w/ padding\n  > :last-child\n    margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n  position: relative\n  margin: 0 -0.5rem 0.5rem\n  padding-left: 2rem\n  padding-right: .5rem\n  padding-top: .4rem\n  padding-bottom: .4rem\n\n  font-weight: 500\n  font-size: var(--admonition-title-font-size)\n  line-height: 1.3\n\n    // Our fancy icon\n  &::before\n    content: \"\"\n    position: absolute\n    left: 0.5rem\n    width: 1rem\n    height: 1rem\n\n// Default styles\np.admonition-title\n  background-color: var(--color-admonition-title-background)\n  &::before\n    background-color: var(--color-admonition-title)\n    mask-image: var(--icon-admonition-default)\n    mask-repeat: no-repeat\n\np.topic-title\n  background-color: var(--color-topic-title-background)\n  &::before\n    background-color: var(--color-topic-title)\n    mask-image: var(--icon-topic-default)\n    mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n  border-left: 0.2rem solid var(--color-admonition-title)\n\n  @each $type, $value in $admonitions\n    &.#{$type}\n      border-left-color: var(--color-admonition-title--#{$type})\n      > .admonition-title\n        background-color: var(--color-admonition-title-background--#{$type})\n        &::before\n          background-color: var(--color-admonition-title--#{$type})\n          mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n  text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n  // Tweak the spacing of all the things!\n  dd\n    margin-left: 2rem\n    > :first-child\n      margin-top: 0.125rem\n    > :last-child\n      margin-bottom: 0.75rem\n\n  // This is used for the arguments\n  .field-list\n    margin-bottom: 0.75rem\n\n    // \"Headings\" (like \"Parameters\" and \"Return\")\n    > dt\n      text-transform: uppercase\n      font-size: var(--font-size--small)\n\n    dd:empty\n      margin-bottom: 0.5rem\n    dd > ul\n      margin-left: -1.2rem\n      > li\n        > p:nth-child(2)\n          margin-top: 0\n        // When the last-empty-paragraph follows a paragraph, it doesn't need\n        // to augument the existing spacing.\n        > p + p:last-child:empty\n          margin-top: 0\n          margin-bottom: 0\n\n  // Colorize the elements\n  > dt\n    color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n  font-weight: bold\n\n  font-size: var(--api-font-size)\n  font-family: var(--font-stack--monospace)\n\n  margin-left: -0.25rem\n  margin-right: -0.25rem\n  padding-top: 0.25rem\n  padding-bottom: 0.25rem\n  padding-right: 0.5rem\n\n  // These are intentionally em, to properly match the font size.\n  padding-left: 3em\n  text-indent: -2.5em\n\n  border-radius: 0.25rem\n\n  background: var(--color-api-background)\n  transition: background 100ms ease-out\n\n  &:hover\n    background: var(--color-api-background-hover)\n\n  // adjust the size of the [source] link on the right.\n  a.reference\n    .viewcode-link\n      font-weight: normal\n      width: 4.25rem\n\nem.property\n  font-style: normal\n  &:first-child\n    color: var(--color-api-keyword)\n.sig-name\n  color: var(--color-api-name)\n.sig-prename\n  font-weight: normal\n  color: var(--color-api-pre-name)\n.sig-paren\n  color: var(--color-api-paren)\n.sig-param\n  font-style: normal\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated,\ndiv.versionremoved\n  border-left: 0.1875rem solid\n  border-radius: 0.125rem\n\n  padding-left: 0.75rem\n\n  p\n    margin-top: 0.125rem\n    margin-bottom: 0.125rem\n\ndiv.versionadded\n  border-color: var(--color-api-added-border)\n  .versionmodified\n    color: var(--color-api-added)\n\ndiv.versionchanged\n  border-color: var(--color-api-changed-border)\n  .versionmodified\n    color: var(--color-api-changed)\n\ndiv.deprecated\n  border-color: var(--color-api-deprecated-border)\n  .versionmodified\n    color: var(--color-api-deprecated)\n\ndiv.versionremoved\n  border-color: var(--color-api-removed-border)\n  .versionmodified\n    color: var(--color-api-removed)\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n  float: right\n  text-align: right\n",".line-block\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n  .line-block\n    margin-top: 0rem\n    margin-bottom: 0rem\n    padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n  font-size: var(--font-size--small)\n  text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n\n    text-align: initial\n    margin-bottom: 0\n\n  > ul\n    margin-top: 0\n    margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n  background: var(--color-inline-code-background)\n  border-radius: 0.2em\n  // Make the font smaller, and use padding to recover.\n  font-size: var(--font-size--small--2)\n  padding: 0.1em 0.2em\n\n  pre.literal-block &\n    font-size: inherit\n    padding: 0\n\n  p &\n    border: 1px solid var(--color-background-border)\n\n.sig-inline\n  font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n  margin: 1em 0\n  display: flex\n\n  .table-wrapper\n    margin: 0\n    padding: 0\n\npre\n  margin: 0\n  padding: 0\n  overflow: auto\n\n  // Needed to have more specificity than pygments' \"pre\" selector. :(\n  article[role=\"main\"] .highlight &\n    line-height: 1.5\n\n  &.literal-block,\n  .highlight &\n    font-size: var(--code-font-size)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n\n  // Make it look like all the other blocks.\n  &.literal-block\n    margin-top: 1rem\n    margin-bottom: 1rem\n\n    border-radius: 0.2rem\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n  width: 100%\n  border-radius: 0.2rem\n\n  // Make line numbers and prompts un-selectable.\n  .gp, span.linenos\n    user-select: none\n    pointer-events: none\n\n  // Expand the line-highlighting.\n  .hll\n    display: block\n    margin-left: -$code-spacing-horizontal\n    margin-right: -$code-spacing-horizontal\n    padding-left: $code-spacing-horizontal\n    padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n  display: flex\n  padding: $code-spacing-vertical $code-spacing-horizontal\n\n  border-radius: 0.25rem\n  border-bottom-left-radius: 0\n  border-bottom-right-radius: 0\n  font-weight: 300\n  border-bottom: 1px solid\n\n  background-color: var(--color-code-background)\n  color: var(--color-code-foreground)\n  border-color: var(--color-background-border)\n\n  + div[class]\n    margin-top: 0\n    pre\n      border-top-left-radius: 0\n      border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n  width: 100%\n  display: block\n  tbody\n    display: block\n\n  tr\n    display: flex\n\n  // Line numbers\n  td.linenos\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n    padding-right: 0\n    border-top-left-radius: 0.2rem\n    border-bottom-left-radius: 0.2rem\n\n  .linenodiv\n    padding-right: $code-spacing-horizontal\n    font-size: var(--code-font-size)\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n  // Actual code\n  td.code\n    padding: 0\n    display: block\n    flex: 1\n    overflow: hidden\n\n    .highlight\n      border-top-left-radius: 0\n      border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n  span.linenos\n    display: inline-block\n    padding-left: 0\n    padding-right: $code-spacing-horizontal\n    margin-right: $code-spacing-horizontal\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n  font-size: var(--font-size--small--4)\n  vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\n  display: grid\n  grid-template-columns: max-content auto\n  dt\n    margin: 0\n    > .fn-backref\n      margin-left: 0.25rem\n\n    &:after\n      content: \":\"\n\n    .brackets\n      &:before\n        content: \"[\"\n      &:after\n        content: \"]\"\n\n  dd\n    margin: 0\n    padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n  float: left\n  font-weight: 500\n  padding-right: 0.25rem\n\naside.footnote > *:not(span),\ndiv.citation > p\n  margin-left: 2rem\n","//\n// Figures\n//\nimg\n  box-sizing: border-box\n  max-width: 100%\n  height: auto\n\narticle\n  figure, .figure\n    border-radius: 0.2rem\n\n    margin: 0\n    :last-child\n      margin-bottom: 0\n\n  .align-left\n    float: left\n    clear: left\n    margin: 0 1rem 1rem\n\n  .align-right\n    float: right\n    clear: right\n    margin: 0 1rem 1rem\n\n  .align-default,\n  .align-center\n    display: block\n    text-align: center\n    margin-left: auto\n    margin-right: auto\n\n  // WELL, table needs to be stylised like a table.\n  table.align-default\n    display: table\n    text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n  border-top: 1px solid var(--color-background-border)\n  border-bottom: 1px solid var(--color-background-border)\n  padding: 0.25rem\n\n.genindex-section, .domainindex-section\n  h2\n    margin-top: 0.75rem\n    margin-bottom: 0.5rem\n  ul\n    margin-top: 0\n    margin-bottom: 0\n","ul,\nol\n  padding-left: 1.2rem\n\n  // Space lists out like paragraphs\n  margin-top: 1rem\n  margin-bottom: 1rem\n  // reduce margins within li.\n  li\n    > p:first-child\n      margin-top: 0.25rem\n      margin-bottom: 0.25rem\n\n    > p:last-child\n      margin-top: 0.25rem\n\n    > ul,\n    > ol\n      margin-top: 0.5rem\n      margin-bottom: 0.5rem\n\nol\n  &.arabic\n    list-style: decimal\n  &.loweralpha\n    list-style: lower-alpha\n  &.upperalpha\n    list-style: upper-alpha\n  &.lowerroman\n    list-style: lower-roman\n  &.upperroman\n    list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n  li\n    > ul,\n    > ol\n      margin-top: 0\n      margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n  dt\n    font-weight: 500\n    margin-top: 0.25rem\n    + dt\n      margin-top: 0\n\n    .classifier::before\n      content: \":\"\n      margin-left: 0.2rem\n      margin-right: 0.2rem\n\n  dd\n    > p:first-child,\n    ul\n      margin-top: 0.125rem\n\n    ul\n      margin-bottom: 0.125rem\n",".math-wrapper\n  width: 100%\n  overflow-x: auto\n\ndiv.math\n  position: relative\n  text-align: center\n\n  .headerlink,\n  &:focus .headerlink\n    display: none\n\n  &:hover .headerlink\n    display: inline-block\n\n  span.eqno\n    position: absolute\n    right: 0.5rem\n    top: 50%\n    transform: translate(0, -50%)\n    z-index: 1\n","// Abbreviations\nabbr[title]\n  cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n  color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n  margin: 0 0.2rem\n  padding: 0 0.2rem\n  border-radius: 0.2rem\n  border: 1px solid var(--color-foreground-border)\n  color: var(--color-foreground-primary)\n  vertical-align: text-bottom\n\n  font-size: var(--font-size--small--3)\n  display: inline-block\n\n  box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n  background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n  border-left: 4px solid var(--color-background-border)\n  background: var(--color-background-secondary)\n\n  margin-left: 0\n  margin-right: 0\n  padding: 0.5rem 1rem\n\n  .attribution\n    font-weight: 600\n    text-align: right\n\n  &.pull-quote,\n  &.highlights\n    font-size: 1.25em\n\n  &.epigraph,\n  &.pull-quote\n    border-left-width: 0\n    border-radius: 0.5rem\n\n  &.highlights\n    border-left-width: 0\n    background: transparent\n\n// Center align embedded-in-text images\np .reference img\n  vertical-align: middle\n","p.rubric\n  line-height: 1.25\n  font-weight: bold\n  font-size: 1.125em\n\n  // For Numpy-style documentation that's got rubrics within it.\n  // https://github.com/pradyunsg/furo/discussions/505\n  dd &\n    line-height: inherit\n    font-weight: inherit\n\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n","article .sidebar\n  float: right\n  clear: right\n  width: 30%\n\n  margin-left: 1rem\n  margin-right: 0\n\n  border-radius: 0.2rem\n  background-color: var(--color-background-secondary)\n  border: var(--color-background-border) 1px solid\n\n  > *\n    padding-left: 1rem\n    padding-right: 1rem\n\n  > ul, > ol  // lists need additional padding, because bullets.\n    padding-left: 2.2rem\n\n  .sidebar-title\n    margin: 0\n    padding: 0.5rem 1rem\n    border-bottom: var(--color-background-border) 1px solid\n\n    font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n","[role=main] .table-wrapper.container\n  width: 100%\n  overflow-x: auto\n  margin-top: 1rem\n  margin-bottom: 0.5rem\n  padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n  border-radius: 0.2rem\n  border-spacing: 0\n  border-collapse: collapse\n\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  th\n    background: var(--color-table-header-background)\n\n  td,\n  th\n    // Space things out properly\n    padding: 0 0.25rem\n\n    // Get the borders looking just-right.\n    border-left: 1px solid var(--color-table-border)\n    border-right: 1px solid var(--color-table-border)\n    border-bottom: 1px solid var(--color-table-border)\n\n    p\n      margin: 0.25rem\n\n    &:first-child\n      border-left: none\n    &:last-child\n      border-right: none\n\n    // MyST-parser tables set these classes for control of column alignment\n    &.text-left\n      text-align: left\n    &.text-right\n      text-align: right\n    &.text-center\n      text-align: center\n",":target\n  scroll-margin-top: 2.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n  :target\n    scroll-margin-top: calc(2.5rem + var(--header-height))\n\n  // When a heading is selected\n  section > span:target\n    scroll-margin-top: calc(2.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n  font-weight: 100\n  user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n  > .headerlink\n    margin-left: 0.5rem\n    visibility: hidden\n  &:hover > .headerlink\n    visibility: visible\n\n  // Don't change to link-like, if someone adds the contents directive.\n  > .toc-backref\n    color: inherit\n    text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n  visibility: visible\n\n:target >,  // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n  h1,\n  h2,\n  h3,\n  h4,\n  h5,\n  h6\n    &:nth-of-type(1)\n      background-color: var(--color-highlight-on-target)\n      // .headerlink\n      //   visibility: visible\n      code.literal\n        background-color: transparent\n\ntable:target > caption,\nfigure:target\n  background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n  background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n  background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n//   There isn't really an alternative to !important here, due to the\n//   high-specificity of API documentation's selector.\ndt:target\n  background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n  background-color: var(--color-highlight-on-target)\n",".guilabel\n  background-color: var(--color-guilabel-background)\n  border: 1px solid var(--color-guilabel-border)\n  color: var(--color-guilabel-text)\n\n  padding: 0 0.3em\n  border-radius: 0.5em\n  font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n  font-size: var(--font-size--small)\n  display: flex\n  flex-direction: column\n\n  margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n  display: flex\n  align-items: center\n  justify-content: space-between\n\n  margin-top: 1rem\n  padding-top: 1rem\n  padding-bottom: 1rem\n\n  color: var(--color-foreground-secondary)\n  border-top: 1px solid var(--color-background-border)\n\n  line-height: 1.5\n\n  @media (max-width: $content-width)\n    text-align: center\n    flex-direction: column-reverse\n    gap: 0.25rem\n\n  .left-details\n    font-size: var(--font-size--small)\n\n  .right-details\n    display: flex\n    flex-direction: column\n    gap: 0.25rem\n    text-align: right\n\n  .icons\n    display: flex\n    justify-content: flex-end\n    gap: 0.25rem\n    font-size: 1rem\n\n    a\n      text-decoration: none\n\n    svg,\n    img\n      font-size: 1.125rem\n      height: 1em\n      width: 1em\n\n// Next/Prev page information\n.related-pages\n  a\n    display: flex\n    align-items: center\n\n    text-decoration: none\n    &:hover .page-info .title\n      text-decoration: underline\n      color: var(--color-link)\n      text-decoration-color: var(--color-link-underline)\n\n    svg.furo-related-icon,\n    svg.furo-related-icon > use\n      flex-shrink: 0\n\n      color: var(--color-foreground-border)\n\n      width: 0.75rem\n      height: 0.75rem\n      margin: 0 0.5rem\n\n    &.next-page\n      max-width: 50%\n\n      float: right\n      clear: right\n      text-align: right\n\n    &.prev-page\n      max-width: 50%\n\n      float: left\n      clear: left\n\n      svg\n        transform: rotate(180deg)\n\n.page-info\n  display: flex\n  flex-direction: column\n  overflow-wrap: anywhere\n\n  .next-page &\n    align-items: flex-end\n\n  .context\n    display: flex\n    align-items: center\n\n    padding-bottom: 0.1rem\n\n    color: var(--color-foreground-muted)\n    font-size: var(--font-size--small)\n    text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n  display: flex\n  flex-direction: column\n  flex-shrink: 0\n\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n  text-decoration: none\n\n.sidebar-brand-text\n  color: var(--color-sidebar-brand-text)\n  overflow-wrap: break-word\n  margin: var(--sidebar-item-spacing-vertical) 0\n  font-size: 1.5rem\n\n.sidebar-logo-container\n  margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n  margin: 0 auto\n  display: block\n  max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n  display: flex\n  align-items: center\n  margin-top: var(--sidebar-search-space-above)\n\n  position: relative\n\n  background: var(--color-sidebar-search-background)\n  &:hover,\n  &:focus-within\n    background: var(--color-sidebar-search-background--focus)\n\n  &::before\n    content: \"\"\n    position: absolute\n    left: var(--sidebar-item-spacing-horizontal)\n    width: var(--sidebar-search-icon-size)\n    height: var(--sidebar-search-icon-size)\n\n    background-color: var(--color-sidebar-search-icon)\n    mask-image: var(--icon-search)\n\n.sidebar-search\n  box-sizing: border-box\n\n  border: none\n  border-top: 1px solid var(--color-sidebar-search-border)\n  border-bottom: 1px solid var(--color-sidebar-search-border)\n\n  padding-top: var(--sidebar-search-input-spacing-vertical)\n  padding-bottom: var(--sidebar-search-input-spacing-vertical)\n  padding-right: var(--sidebar-search-input-spacing-horizontal)\n  padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n  width: 100%\n\n  color: var(--color-sidebar-search-foreground)\n  background: transparent\n  z-index: 10\n\n  &:focus\n    outline: none\n\n  &::placeholder\n    font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n  margin: 0\n  text-align: center\n\n  a\n    color: var(--color-sidebar-search-icon)\n    font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n  font-size: var(--sidebar-item-font-size)\n  margin-top: var(--sidebar-tree-space-above)\n  margin-bottom: var(--sidebar-item-spacing-vertical)\n\n  ul\n    padding: 0\n    margin-top: 0\n    margin-bottom: 0\n\n    display: flex\n    flex-direction: column\n\n    list-style: none\n\n  li\n    position: relative\n    margin: 0\n\n    > ul\n      margin-left: var(--sidebar-item-spacing-horizontal)\n\n  .icon\n    color: var(--color-sidebar-link-text)\n\n  .reference\n    box-sizing: border-box\n    color: var(--color-sidebar-link-text)\n\n    // Fill the parent.\n    display: inline-block\n    line-height: var(--sidebar-item-line-height)\n    text-decoration: none\n\n    // Don't allow long words to cause wrapping.\n    overflow-wrap: anywhere\n\n    height: 100%\n    width: 100%\n\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n    &:hover\n      color: var(--color-sidebar-link-text)\n      background: var(--color-sidebar-item-background--hover)\n\n    // Add a nice little \"external-link\" arrow here.\n    &.external::after\n      content: url('data:image/svg+xml,<svg width=\"12\" height=\"12\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"%23607D8B\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><path d=\"M11 7h-5a2 2 0 0 0 -2 2v9a2 2 0 0 0 2 2h9a2 2 0 0 0 2 -2v-5\" /><line x1=\"10\" y1=\"14\" x2=\"20\" y2=\"4\" /><polyline points=\"15 4 20 4 20 9\" /></svg>')\n      margin: 0 0.25rem\n      vertical-align: middle\n      color: var(--color-sidebar-link-text)\n\n  // Make the current page reference bold.\n  .current-page > .reference\n    font-weight: bold\n\n  label\n    position: absolute\n    top: 0\n    right: 0\n    height: var(--sidebar-item-height)\n    width: var(--sidebar-expander-width)\n\n    cursor: pointer\n    user-select: none\n\n    display: flex\n    justify-content: center\n    align-items: center\n\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--sidebar-caption-font-size)\n    color: var(--color-sidebar-caption-text)\n\n    font-weight: bold\n    text-transform: uppercase\n\n    margin: var(--sidebar-caption-space-above) 0 0 0\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n  // If it has children, add a bit more padding to wrap the content to avoid\n  // overlapping with the <label>\n  li.has-children\n    > .reference\n      padding-right: var(--sidebar-expander-width)\n\n  // Colorize the top-level list items and icon.\n  .toctree-l1\n    & > .reference,\n    & > label .icon\n      color: var(--color-sidebar-link-text--top-level)\n\n  // Color changes on hover\n  label\n    background: var(--color-sidebar-item-expander-background)\n    &:hover\n      background: var(--color-sidebar-item-expander-background--hover)\n\n  .current > .reference\n    background: var(--color-sidebar-item-background--current)\n    &:hover\n      background: var(--color-sidebar-item-background--hover)\n\n.toctree-checkbox\n  position: absolute\n  display: none\n\n////////////////////////////////////////////////////////////////////////////////\n// Togglable expand/collapse\n////////////////////////////////////////////////////////////////////////////////\n.toctree-checkbox\n  ~ ul\n    display: none\n\n  ~ label .icon svg\n    transform: rotate(90deg)\n\n.toctree-checkbox:checked\n  ~ ul\n    display: block\n\n  ~ label .icon svg\n    transform: rotate(-90deg)\n","// This file contains the styles for the contents of the right sidebar, which\n// contains the table of contents for the current page.\n.toc-title-container\n  padding: var(--toc-title-padding)\n  padding-top: var(--toc-spacing-vertical)\n\n.toc-title\n  color: var(--color-toc-title-text)\n  font-size: var(--toc-title-font-size)\n  padding-left: var(--toc-spacing-horizontal)\n  text-transform: uppercase\n\n// If the ToC is not present, hide these elements coz they're not relevant.\n.no-toc\n  display: none\n\n.toc-tree-container\n  padding-bottom: var(--toc-spacing-vertical)\n\n.toc-tree\n  font-size: var(--toc-font-size)\n  line-height: 1.3\n  border-left: 1px solid var(--color-background-border)\n\n  padding-left: calc(var(--toc-spacing-horizontal) - var(--toc-item-spacing-horizontal))\n\n  // Hide the first \"top level\" bullet.\n  > ul > li:first-child\n    padding-top: 0\n    & > ul\n      padding-left: 0\n    & > a\n      display: none\n\n  ul\n    list-style-type: none\n    margin-top: 0\n    margin-bottom: 0\n    padding-left: var(--toc-item-spacing-horizontal)\n  li\n    padding-top: var(--toc-item-spacing-vertical)\n\n    &.scroll-current > .reference\n      color: var(--color-toc-item-text--active)\n      font-weight: bold\n\n  a.reference\n    color: var(--color-toc-item-text)\n    text-decoration: none\n    overflow-wrap: anywhere\n\n.toc-scroll\n  max-height: 100vh\n  overflow-y: scroll\n\n// Be very annoying when someone includes the table of contents\n.contents:not(.this-will-duplicate-information-and-it-is-still-useful-here)\n  color: var(--color-problematic)\n  background: rgba(255, 0, 0, 0.25)\n  &::before\n    content: \"ERROR: Adding a table of contents in Furo-based documentation is unnecessary, and does not work well with existing styling. Add a 'this-will-duplicate-information-and-it-is-still-useful-here' class, if you want an escape hatch.\"\n","// Shameful hacks, to work around bugs.\n\n// MyST parser doesn't correctly generate classes, to align table contents.\n// https://github.com/executablebooks/MyST-Parser/issues/412\n.text-align\\:left > p\n  text-align: left\n\n.text-align\\:center > p\n  text-align: center\n\n.text-align\\:right > p\n  text-align: right\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/array_api_compat/__init__.py b/array_api_compat/__init__.py
deleted file mode 100644
index a00e8cbc..00000000
--- a/array_api_compat/__init__.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""
-NumPy Array API compatibility library
-
-This is a small wrapper around NumPy, CuPy, JAX, sparse and others that are
-compatible with the Array API standard https://data-apis.org/array-api/latest/.
-See also NEP 47 https://numpy.org/neps/nep-0047-array-api-standard.html.
-
-Unlike array_api_strict, this is not a strict minimal implementation of the
-Array API, but rather just an extension of the main NumPy namespace with
-changes needed to be compliant with the Array API. See
-https://numpy.org/doc/stable/reference/array_api.html for a full list of
-changes. In particular, unlike array_api_strict, this package does not use a
-separate Array object, but rather just uses numpy.ndarray directly.
-
-Library authors using the Array API may wish to test against array_api_strict
-to ensure they are not using functionality outside of the standard, but prefer
-this implementation for the default when working with NumPy arrays.
-
-"""
-__version__ = '1.13.0.dev0'
-
-from .common import *  # noqa: F401, F403
diff --git a/array_api_compat/_internal.py b/array_api_compat/_internal.py
deleted file mode 100644
index baa39ded..00000000
--- a/array_api_compat/_internal.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""
-Internal helpers
-"""
-
-import importlib
-from collections.abc import Callable
-from functools import wraps
-from inspect import signature
-from types import ModuleType
-from typing import TypeVar
-
-_T = TypeVar("_T")
-
-
-def get_xp(xp: ModuleType) -> Callable[[Callable[..., _T]], Callable[..., _T]]:
-    """
-    Decorator to automatically replace xp with the corresponding array module.
-
-    Use like
-
-    import numpy as np
-
-    @get_xp(np)
-    def func(x, /, xp, kwarg=None):
-        return xp.func(x, kwarg=kwarg)
-
-    Note that xp must be a keyword argument and come after all non-keyword
-    arguments.
-
-    """
-
-    def inner(f: Callable[..., _T], /) -> Callable[..., _T]:
-        @wraps(f)
-        def wrapped_f(*args: object, **kwargs: object) -> object:
-            return f(*args, xp=xp, **kwargs)
-
-        sig = signature(f)
-        new_sig = sig.replace(
-            parameters=[par for i, par in sig.parameters.items() if i != "xp"]
-        )
-
-        if wrapped_f.__doc__ is None:
-            wrapped_f.__doc__ = f"""\
-Array API compatibility wrapper for {f.__name__}.
-
-See the corresponding documentation in NumPy/CuPy and/or the array API
-specification for more details.
-
-"""
-        wrapped_f.__signature__ = new_sig  # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue]
-        return wrapped_f  # type: ignore[return-value] # pyright: ignore[reportReturnType]
-
-    return inner
-
-
-def clone_module(mod_name: str, globals_: dict[str, object]) -> list[str]:
-    """Import everything from module, updating globals().
-    Returns __all__.
-    """
-    mod = importlib.import_module(mod_name)
-    # Neither of these two methods is sufficient by itself,
-    # depending on various idiosyncrasies of the libraries we're wrapping.
-    objs = {}
-    exec(f"from {mod.__name__} import *", objs)
-
-    for n in dir(mod):
-        if not n.startswith("_") and hasattr(mod, n):
-            objs[n] = getattr(mod, n)
-
-    globals_.update(objs)
-    return list(objs)
-
-
-__all__ = ["get_xp", "clone_module"]
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/common/__init__.py b/array_api_compat/common/__init__.py
deleted file mode 100644
index 82360807..00000000
--- a/array_api_compat/common/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from ._helpers import *  # noqa: F403
diff --git a/array_api_compat/common/_aliases.py b/array_api_compat/common/_aliases.py
deleted file mode 100644
index 3587ef16..00000000
--- a/array_api_compat/common/_aliases.py
+++ /dev/null
@@ -1,702 +0,0 @@
-"""
-These are functions that are just aliases of existing functions in NumPy.
-"""
-
-from __future__ import annotations
-
-import inspect
-from collections.abc import Sequence
-from typing import TYPE_CHECKING, Any, NamedTuple, cast
-
-from ._helpers import _check_device, array_namespace
-from ._helpers import device as _get_device
-from ._helpers import is_cupy_namespace
-from ._typing import Array, Device, DType, Namespace
-
-if TYPE_CHECKING:
-    # TODO: import from typing (requires Python >=3.13)
-    from typing_extensions import TypeIs
-
-# These functions are modified from the NumPy versions.
-
-# Creation functions add the device keyword (which does nothing for NumPy and Dask)
-
-
-def arange(
-    start: float,
-    /,
-    stop: float | None = None,
-    step: float = 1,
-    *,
-    xp: Namespace,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.arange(start, stop=stop, step=step, dtype=dtype, **kwargs)
-
-
-def empty(
-    shape: int | tuple[int, ...],
-    xp: Namespace,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.empty(shape, dtype=dtype, **kwargs)
-
-
-def empty_like(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.empty_like(x, dtype=dtype, **kwargs)
-
-
-def eye(
-    n_rows: int,
-    n_cols: int | None = None,
-    /,
-    *,
-    xp: Namespace,
-    k: int = 0,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.eye(n_rows, M=n_cols, k=k, dtype=dtype, **kwargs)
-
-
-def full(
-    shape: int | tuple[int, ...],
-    fill_value: complex,
-    xp: Namespace,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.full(shape, fill_value, dtype=dtype, **kwargs)
-
-
-def full_like(
-    x: Array,
-    /,
-    fill_value: complex,
-    *,
-    xp: Namespace,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.full_like(x, fill_value, dtype=dtype, **kwargs)
-
-
-def linspace(
-    start: float,
-    stop: float,
-    /,
-    num: int,
-    *,
-    xp: Namespace,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    endpoint: bool = True,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.linspace(start, stop, num, dtype=dtype, endpoint=endpoint, **kwargs)
-
-
-def ones(
-    shape: int | tuple[int, ...],
-    xp: Namespace,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.ones(shape, dtype=dtype, **kwargs)
-
-
-def ones_like(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.ones_like(x, dtype=dtype, **kwargs)
-
-
-def zeros(
-    shape: int | tuple[int, ...],
-    xp: Namespace,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.zeros(shape, dtype=dtype, **kwargs)
-
-
-def zeros_like(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    _check_device(xp, device)
-    return xp.zeros_like(x, dtype=dtype, **kwargs)
-
-
-# np.unique() is split into four functions in the array API:
-# unique_all, unique_counts, unique_inverse, and unique_values (this is done
-# to remove polymorphic return types).
-
-# The functions here return namedtuples (np.unique() returns a normal
-# tuple).
-
-
-# Note that these named tuples aren't actually part of the standard namespace,
-# but I don't see any issue with exporting the names here regardless.
-class UniqueAllResult(NamedTuple):
-    values: Array
-    indices: Array
-    inverse_indices: Array
-    counts: Array
-
-
-class UniqueCountsResult(NamedTuple):
-    values: Array
-    counts: Array
-
-
-class UniqueInverseResult(NamedTuple):
-    values: Array
-    inverse_indices: Array
-
-
-def _unique_kwargs(xp: Namespace) -> dict[str, bool]:
-    # Older versions of NumPy and CuPy do not have equal_nan. Rather than
-    # trying to parse version numbers, just check if equal_nan is in the
-    # signature.
-    s = inspect.signature(xp.unique)
-    if "equal_nan" in s.parameters:
-        return {"equal_nan": False}
-    return {}
-
-
-def unique_all(x: Array, /, xp: Namespace) -> UniqueAllResult:
-    kwargs = _unique_kwargs(xp)
-    values, indices, inverse_indices, counts = xp.unique(
-        x,
-        return_counts=True,
-        return_index=True,
-        return_inverse=True,
-        **kwargs,
-    )
-    # np.unique() flattens inverse indices, but they need to share x's shape
-    # See https://github.com/numpy/numpy/issues/20638
-    inverse_indices = inverse_indices.reshape(x.shape)
-    return UniqueAllResult(
-        values,
-        indices,
-        inverse_indices,
-        counts,
-    )
-
-
-def unique_counts(x: Array, /, xp: Namespace) -> UniqueCountsResult:
-    kwargs = _unique_kwargs(xp)
-    res = xp.unique(
-        x, return_counts=True, return_index=False, return_inverse=False, **kwargs
-    )
-
-    return UniqueCountsResult(*res)
-
-
-def unique_inverse(x: Array, /, xp: Namespace) -> UniqueInverseResult:
-    kwargs = _unique_kwargs(xp)
-    values, inverse_indices = xp.unique(
-        x,
-        return_counts=False,
-        return_index=False,
-        return_inverse=True,
-        **kwargs,
-    )
-    # xp.unique() flattens inverse indices, but they need to share x's shape
-    # See https://github.com/numpy/numpy/issues/20638
-    inverse_indices = inverse_indices.reshape(x.shape)
-    return UniqueInverseResult(values, inverse_indices)
-
-
-def unique_values(x: Array, /, xp: Namespace) -> Array:
-    kwargs = _unique_kwargs(xp)
-    return xp.unique(
-        x,
-        return_counts=False,
-        return_index=False,
-        return_inverse=False,
-        **kwargs,
-    )
-
-
-# These functions have different keyword argument names
-
-
-def std(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    axis: int | tuple[int, ...] | None = None,
-    correction: float = 0.0,  # correction instead of ddof
-    keepdims: bool = False,
-    **kwargs: object,
-) -> Array:
-    return xp.std(x, axis=axis, ddof=correction, keepdims=keepdims, **kwargs)
-
-
-def var(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    axis: int | tuple[int, ...] | None = None,
-    correction: float = 0.0,  # correction instead of ddof
-    keepdims: bool = False,
-    **kwargs: object,
-) -> Array:
-    return xp.var(x, axis=axis, ddof=correction, keepdims=keepdims, **kwargs)
-
-
-# cumulative_sum is renamed from cumsum, and adds the include_initial keyword
-# argument
-
-
-def cumulative_sum(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    axis: int | None = None,
-    dtype: DType | None = None,
-    include_initial: bool = False,
-    **kwargs: object,
-) -> Array:
-    wrapped_xp = array_namespace(x)
-
-    # TODO: The standard is not clear about what should happen when x.ndim == 0.
-    if axis is None:
-        if x.ndim > 1:
-            raise ValueError(
-                "axis must be specified in cumulative_sum for more than one dimension"
-            )
-        axis = 0
-
-    res = xp.cumsum(x, axis=axis, dtype=dtype, **kwargs)
-
-    # np.cumsum does not support include_initial
-    if include_initial:
-        initial_shape = list(x.shape)
-        initial_shape[axis] = 1
-        res = xp.concatenate(
-            [
-                wrapped_xp.zeros(
-                    shape=initial_shape, dtype=res.dtype, device=_get_device(res)
-                ),
-                res,
-            ],
-            axis=axis,
-        )
-    return res
-
-
-def cumulative_prod(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    axis: int | None = None,
-    dtype: DType | None = None,
-    include_initial: bool = False,
-    **kwargs: object,
-) -> Array:
-    wrapped_xp = array_namespace(x)
-
-    if axis is None:
-        if x.ndim > 1:
-            raise ValueError(
-                "axis must be specified in cumulative_prod for more than one dimension"
-            )
-        axis = 0
-
-    res = xp.cumprod(x, axis=axis, dtype=dtype, **kwargs)
-
-    # np.cumprod does not support include_initial
-    if include_initial:
-        initial_shape = list(x.shape)
-        initial_shape[axis] = 1
-        res = xp.concatenate(
-            [
-                wrapped_xp.ones(
-                    shape=initial_shape, dtype=res.dtype, device=_get_device(res)
-                ),
-                res,
-            ],
-            axis=axis,
-        )
-    return res
-
-
-# The min and max argument names in clip are different and not optional in numpy, and type
-# promotion behavior is different.
-def clip(
-    x: Array,
-    /,
-    min: float | Array | None = None,
-    max: float | Array | None = None,
-    *,
-    xp: Namespace,
-    # TODO: np.clip has other ufunc kwargs
-    out: Array | None = None,
-) -> Array:
-    def _isscalar(a: object) -> TypeIs[float | None]:
-        return isinstance(a, int | float) or a is None
-
-    min_shape = () if _isscalar(min) else min.shape
-    max_shape = () if _isscalar(max) else max.shape
-
-    wrapped_xp = array_namespace(x)
-
-    result_shape = xp.broadcast_shapes(x.shape, min_shape, max_shape)
-
-    # np.clip does type promotion but the array API clip requires that the
-    # output have the same dtype as x. We do this instead of just downcasting
-    # the result of xp.clip() to handle some corner cases better (e.g.,
-    # avoiding uint64 -> float64 promotion).
-
-    # Note: cases where min or max overflow (integer) or round (float) in the
-    # wrong direction when downcasting to x.dtype are unspecified. This code
-    # just does whatever NumPy does when it downcasts in the assignment, but
-    # other behavior could be preferred, especially for integers. For example,
-    # this code produces:
-
-    # >>> clip(asarray(0, dtype=int8), asarray(128, dtype=int16), None)
-    # -128
-
-    # but an answer of 0 might be preferred. See
-    # https://github.com/numpy/numpy/issues/24976 for more discussion on this issue.
-
-    # At least handle the case of Python integers correctly (see
-    # https://github.com/numpy/numpy/pull/26892).
-    if wrapped_xp.isdtype(x.dtype, "integral"):
-        if type(min) is int and min <= wrapped_xp.iinfo(x.dtype).min:
-            min = None
-        if type(max) is int and max >= wrapped_xp.iinfo(x.dtype).max:
-            max = None
-
-    dev = _get_device(x)
-    if out is None:
-        out = wrapped_xp.empty(result_shape, dtype=x.dtype, device=dev)
-    assert out is not None  # workaround for a type-narrowing issue in pyright
-    out[()] = x
-
-    if min is not None:
-        a = wrapped_xp.asarray(min, dtype=x.dtype, device=dev)
-        a = xp.broadcast_to(a, result_shape)
-        ia = (out < a) | xp.isnan(a)
-        out[ia] = a[ia]
-
-    if max is not None:
-        b = wrapped_xp.asarray(max, dtype=x.dtype, device=dev)
-        b = xp.broadcast_to(b, result_shape)
-        ib = (out > b) | xp.isnan(b)
-        out[ib] = b[ib]
-
-    # Return a scalar for 0-D
-    return out[()]
-
-
-# Unlike transpose(), the axes argument to permute_dims() is required.
-def permute_dims(x: Array, /, axes: tuple[int, ...], xp: Namespace) -> Array:
-    return xp.transpose(x, axes)
-
-
-# np.reshape calls the keyword argument 'newshape' instead of 'shape'
-def reshape(
-    x: Array,
-    /,
-    shape: tuple[int, ...],
-    xp: Namespace,
-    *,
-    copy: bool | None = None,
-    **kwargs: object,
-) -> Array:
-    if copy is True:
-        x = x.copy()
-    elif copy is False:
-        y = x.view()
-        y.shape = shape
-        return y
-    return xp.reshape(x, shape, **kwargs)
-
-
-# The descending keyword is new in sort and argsort, and 'kind' replaced with
-# 'stable'
-def argsort(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    axis: int = -1,
-    descending: bool = False,
-    stable: bool = True,
-    **kwargs: object,
-) -> Array:
-    # Note: this keyword argument is different, and the default is different.
-    # We set it in kwargs like this because numpy.sort uses kind='quicksort'
-    # as the default whereas cupy.sort uses kind=None.
-    if stable:
-        kwargs["kind"] = "stable"
-    if not descending:
-        res = xp.argsort(x, axis=axis, **kwargs)
-    else:
-        # As NumPy has no native descending sort, we imitate it here. Note that
-        # simply flipping the results of xp.argsort(x, ...) would not
-        # respect the relative order like it would in native descending sorts.
-        res = xp.flip(
-            xp.argsort(xp.flip(x, axis=axis), axis=axis, **kwargs),
-            axis=axis,
-        )
-        # Rely on flip()/argsort() to validate axis
-        normalised_axis = axis if axis >= 0 else x.ndim + axis
-        max_i = x.shape[normalised_axis] - 1
-        res = max_i - res
-    return res
-
-
-def sort(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    axis: int = -1,
-    descending: bool = False,
-    stable: bool = True,
-    **kwargs: object,
-) -> Array:
-    # Note: this keyword argument is different, and the default is different.
-    # We set it in kwargs like this because numpy.sort uses kind='quicksort'
-    # as the default whereas cupy.sort uses kind=None.
-    if stable:
-        kwargs["kind"] = "stable"
-    res = xp.sort(x, axis=axis, **kwargs)
-    if descending:
-        res = xp.flip(res, axis=axis)
-    return res
-
-
-# nonzero should error for zero-dimensional arrays
-def nonzero(x: Array, /, xp: Namespace, **kwargs: object) -> tuple[Array, ...]:
-    if x.ndim == 0:
-        raise ValueError("nonzero() does not support zero-dimensional arrays")
-    return xp.nonzero(x, **kwargs)
-
-
-# linear algebra functions
-
-
-def matmul(x1: Array, x2: Array, /, xp: Namespace, **kwargs: object) -> Array:
-    return xp.matmul(x1, x2, **kwargs)
-
-
-# Unlike transpose, matrix_transpose only transposes the last two axes.
-def matrix_transpose(x: Array, /, xp: Namespace) -> Array:
-    if x.ndim < 2:
-        raise ValueError("x must be at least 2-dimensional for matrix_transpose")
-    return xp.swapaxes(x, -1, -2)
-
-
-def tensordot(
-    x1: Array,
-    x2: Array,
-    /,
-    xp: Namespace,
-    *,
-    axes: int | tuple[Sequence[int], Sequence[int]] = 2,
-    **kwargs: object,
-) -> Array:
-    return xp.tensordot(x1, x2, axes=axes, **kwargs)
-
-
-def vecdot(x1: Array, x2: Array, /, xp: Namespace, *, axis: int = -1) -> Array:
-    if x1.shape[axis] != x2.shape[axis]:
-        raise ValueError("x1 and x2 must have the same size along the given axis")
-
-    if hasattr(xp, "broadcast_tensors"):
-        _broadcast = xp.broadcast_tensors
-    else:
-        _broadcast = xp.broadcast_arrays
-
-    x1_ = xp.moveaxis(x1, axis, -1)
-    x2_ = xp.moveaxis(x2, axis, -1)
-    x1_, x2_ = _broadcast(x1_, x2_)
-
-    res = xp.conj(x1_[..., None, :]) @ x2_[..., None]
-    return res[..., 0, 0]
-
-
-# isdtype is a new function in the 2022.12 array API specification.
-
-
-def isdtype(
-    dtype: DType,
-    kind: DType | str | tuple[DType | str, ...],
-    xp: Namespace,
-    *,
-    _tuple: bool = True,  # Disallow nested tuples
-) -> bool:
-    """
-    Returns a boolean indicating whether a provided dtype is of a specified data type ``kind``.
-
-    Note that outside of this function, this compat library does not yet fully
-    support complex numbers.
-
-    See
-    https://data-apis.org/array-api/latest/API_specification/generated/array_api.isdtype.html
-    for more details
-    """
-    if isinstance(kind, tuple) and _tuple:
-        return any(
-            isdtype(dtype, k, xp, _tuple=False)
-            for k in cast("tuple[DType | str, ...]", kind)
-        )
-    elif isinstance(kind, str):
-        if kind == "bool":
-            return dtype == xp.bool_
-        elif kind == "signed integer":
-            return xp.issubdtype(dtype, xp.signedinteger)
-        elif kind == "unsigned integer":
-            return xp.issubdtype(dtype, xp.unsignedinteger)
-        elif kind == "integral":
-            return xp.issubdtype(dtype, xp.integer)
-        elif kind == "real floating":
-            return xp.issubdtype(dtype, xp.floating)
-        elif kind == "complex floating":
-            return xp.issubdtype(dtype, xp.complexfloating)
-        elif kind == "numeric":
-            return xp.issubdtype(dtype, xp.number)
-        else:
-            raise ValueError(f"Unrecognized data type kind: {kind!r}")
-    else:
-        # This will allow things that aren't required by the spec, like
-        # isdtype(np.float64, float) or isdtype(np.int64, 'l'). Should we be
-        # more strict here to match the type annotation? Note that the
-        # array_api_strict implementation will be very strict.
-        return dtype == kind
-
-
-# unstack is a new function in the 2023.12 array API standard
-def unstack(x: Array, /, xp: Namespace, *, axis: int = 0) -> tuple[Array, ...]:
-    if x.ndim == 0:
-        raise ValueError("Input array must be at least 1-d.")
-    return tuple(xp.moveaxis(x, axis, 0))
-
-
-# numpy 1.26 does not use the standard definition for sign on complex numbers
-
-
-def sign(x: Array, /, xp: Namespace, **kwargs: object) -> Array:
-    if isdtype(x.dtype, "complex floating", xp=xp):
-        out = (x / xp.abs(x, **kwargs))[...]
-        # sign(0) = 0 but the above formula would give nan
-        out[x == 0j] = 0j
-    else:
-        out = xp.sign(x, **kwargs)
-    # CuPy sign() does not propagate nans. See
-    # https://github.com/data-apis/array-api-compat/issues/136
-    if is_cupy_namespace(xp) and isdtype(x.dtype, "real floating", xp=xp):
-        out[xp.isnan(x)] = xp.nan
-    return out[()]
-
-
-def finfo(type_: DType | Array, /, xp: Namespace) -> Any:
-    # It is surprisingly difficult to recognize a dtype apart from an array.
-    # np.int64 is not the same as np.asarray(1).dtype!
-    try:
-        return xp.finfo(type_)
-    except (ValueError, TypeError):
-        return xp.finfo(type_.dtype)
-
-
-def iinfo(type_: DType | Array, /, xp: Namespace) -> Any:
-    try:
-        return xp.iinfo(type_)
-    except (ValueError, TypeError):
-        return xp.iinfo(type_.dtype)
-
-
-__all__ = [
-    "arange",
-    "empty",
-    "empty_like",
-    "eye",
-    "full",
-    "full_like",
-    "linspace",
-    "ones",
-    "ones_like",
-    "zeros",
-    "zeros_like",
-    "UniqueAllResult",
-    "UniqueCountsResult",
-    "UniqueInverseResult",
-    "unique_all",
-    "unique_counts",
-    "unique_inverse",
-    "unique_values",
-    "std",
-    "var",
-    "cumulative_sum",
-    "cumulative_prod",
-    "clip",
-    "permute_dims",
-    "reshape",
-    "argsort",
-    "sort",
-    "nonzero",
-    "matmul",
-    "matrix_transpose",
-    "tensordot",
-    "vecdot",
-    "isdtype",
-    "unstack",
-    "sign",
-    "finfo",
-    "iinfo",
-]
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/common/_fft.py b/array_api_compat/common/_fft.py
deleted file mode 100644
index 18839d37..00000000
--- a/array_api_compat/common/_fft.py
+++ /dev/null
@@ -1,213 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Sequence
-from typing import Literal, TypeAlias
-
-from ._typing import Array, Device, DType, Namespace
-
-_Norm: TypeAlias = Literal["backward", "ortho", "forward"]
-
-# Note: NumPy fft functions improperly upcast float32 and complex64 to
-# complex128, which is why we require wrapping them all here.
-
-def fft(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    n: int | None = None,
-    axis: int = -1,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.fft(x, n=n, axis=axis, norm=norm)
-    if x.dtype in [xp.float32, xp.complex64]:
-        return res.astype(xp.complex64)
-    return res
-
-def ifft(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    n: int | None = None,
-    axis: int = -1,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.ifft(x, n=n, axis=axis, norm=norm)
-    if x.dtype in [xp.float32, xp.complex64]:
-        return res.astype(xp.complex64)
-    return res
-
-def fftn(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    s: Sequence[int] | None = None,
-    axes: Sequence[int] | None = None,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.fftn(x, s=s, axes=axes, norm=norm)
-    if x.dtype in [xp.float32, xp.complex64]:
-        return res.astype(xp.complex64)
-    return res
-
-def ifftn(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    s: Sequence[int] | None = None,
-    axes: Sequence[int] | None = None,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.ifftn(x, s=s, axes=axes, norm=norm)
-    if x.dtype in [xp.float32, xp.complex64]:
-        return res.astype(xp.complex64)
-    return res
-
-def rfft(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    n: int | None = None,
-    axis: int = -1,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.rfft(x, n=n, axis=axis, norm=norm)
-    if x.dtype == xp.float32:
-        return res.astype(xp.complex64)
-    return res
-
-def irfft(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    n: int | None = None,
-    axis: int = -1,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.irfft(x, n=n, axis=axis, norm=norm)
-    if x.dtype == xp.complex64:
-        return res.astype(xp.float32)
-    return res
-
-def rfftn(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    s: Sequence[int] | None = None,
-    axes: Sequence[int] | None = None,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.rfftn(x, s=s, axes=axes, norm=norm)
-    if x.dtype == xp.float32:
-        return res.astype(xp.complex64)
-    return res
-
-def irfftn(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    s: Sequence[int] | None = None,
-    axes: Sequence[int] | None = None,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.irfftn(x, s=s, axes=axes, norm=norm)
-    if x.dtype == xp.complex64:
-        return res.astype(xp.float32)
-    return res
-
-def hfft(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    n: int | None = None,
-    axis: int = -1,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.hfft(x, n=n, axis=axis, norm=norm)
-    if x.dtype in [xp.float32, xp.complex64]:
-        return res.astype(xp.float32)
-    return res
-
-def ihfft(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    n: int | None = None,
-    axis: int = -1,
-    norm: _Norm = "backward",
-) -> Array:
-    res = xp.fft.ihfft(x, n=n, axis=axis, norm=norm)
-    if x.dtype in [xp.float32, xp.complex64]:
-        return res.astype(xp.complex64)
-    return res
-
-def fftfreq(
-    n: int,
-    /,
-    xp: Namespace,
-    *,
-    d: float = 1.0,
-    dtype: DType | None = None,
-    device: Device | None = None,
-) -> Array:
-    if device not in ["cpu", None]:
-        raise ValueError(f"Unsupported device {device!r}")
-    res = xp.fft.fftfreq(n, d=d)
-    if dtype is not None:
-        return res.astype(dtype)
-    return res
-
-def rfftfreq(
-    n: int,
-    /,
-    xp: Namespace,
-    *,
-    d: float = 1.0,
-    dtype: DType | None = None,
-    device: Device | None = None,
-) -> Array:
-    if device not in ["cpu", None]:
-        raise ValueError(f"Unsupported device {device!r}")
-    res = xp.fft.rfftfreq(n, d=d)
-    if dtype is not None:
-        return res.astype(dtype)
-    return res
-
-def fftshift(
-    x: Array, /, xp: Namespace, *, axes: int | Sequence[int] | None = None
-) -> Array:
-    return xp.fft.fftshift(x, axes=axes)
-
-def ifftshift(
-    x: Array, /, xp: Namespace, *, axes: int | Sequence[int] | None = None
-) -> Array:
-    return xp.fft.ifftshift(x, axes=axes)
-
-__all__ = [
-    "fft",
-    "ifft",
-    "fftn",
-    "ifftn",
-    "rfft",
-    "irfft",
-    "rfftn",
-    "irfftn",
-    "hfft",
-    "ihfft",
-    "fftfreq",
-    "rfftfreq",
-    "fftshift",
-    "ifftshift",
-]
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/common/_helpers.py b/array_api_compat/common/_helpers.py
deleted file mode 100644
index 37f31ec2..00000000
--- a/array_api_compat/common/_helpers.py
+++ /dev/null
@@ -1,1066 +0,0 @@
-"""
-Various helper functions which are not part of the spec.
-
-Functions which start with an underscore are for internal use only but helpers
-that are in __all__ are intended as additional helper functions for use by end
-users of the compat library.
-"""
-
-from __future__ import annotations
-
-import enum
-import inspect
-import math
-import sys
-import warnings
-from collections.abc import Collection, Hashable
-from functools import lru_cache
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Final,
-    Literal,
-    SupportsIndex,
-    TypeAlias,
-    TypeGuard,
-    cast,
-    overload,
-)
-
-from ._typing import Array, Device, HasShape, Namespace, SupportsArrayNamespace
-
-if TYPE_CHECKING:
-    import cupy as cp
-    import dask.array as da
-    import jax
-    import ndonnx as ndx
-    import numpy as np
-    import numpy.typing as npt
-    import sparse
-    import torch
-
-    # TODO: import from typing (requires Python >=3.13)
-    from typing_extensions import TypeIs
-
-    _ZeroGradientArray: TypeAlias = npt.NDArray[np.void]
-
-    _ArrayApiObj: TypeAlias = (
-        npt.NDArray[Any]
-        | cp.ndarray
-        | da.Array
-        | jax.Array
-        | ndx.Array
-        | sparse.SparseArray
-        | torch.Tensor
-        | SupportsArrayNamespace[Any]
-    )
-
-_API_VERSIONS_OLD: Final = frozenset({"2021.12", "2022.12", "2023.12"})
-_API_VERSIONS: Final = _API_VERSIONS_OLD | frozenset({"2024.12"})
-
-
-@lru_cache(100)
-def _issubclass_fast(cls: type, modname: str, clsname: str) -> bool:
-    try:
-        mod = sys.modules[modname]
-    except KeyError:
-        return False
-    parent_cls = getattr(mod, clsname)
-    return issubclass(cls, parent_cls)
-
-
-def _is_jax_zero_gradient_array(x: object) -> TypeGuard[_ZeroGradientArray]:
-    """Return True if `x` is a zero-gradient array.
-
-    These arrays are a design quirk of Jax that may one day be removed.
-    See https://github.com/google/jax/issues/20620.
-    """
-    # Fast exit
-    try:
-        dtype = x.dtype  # type: ignore[attr-defined]
-    except AttributeError:
-        return False
-    cls = cast(Hashable, type(dtype))
-    if not _issubclass_fast(cls, "numpy.dtypes", "VoidDType"):
-        return False
-
-    if "jax" not in sys.modules:
-        return False
-
-    import jax
-    # jax.float0 is a np.dtype([('float0', 'V')])
-    return dtype == jax.float0
-
-
-def is_numpy_array(x: object) -> TypeIs[npt.NDArray[Any]]:
-    """
-    Return True if `x` is a NumPy array.
-
-    This function does not import NumPy if it has not already been imported
-    and is therefore cheap to use.
-
-    This also returns True for `ndarray` subclasses and NumPy scalar objects.
-
-    See Also
-    --------
-
-    array_namespace
-    is_array_api_obj
-    is_cupy_array
-    is_torch_array
-    is_ndonnx_array
-    is_dask_array
-    is_jax_array
-    is_pydata_sparse_array
-    """
-    # TODO: Should we reject ndarray subclasses?
-    cls = cast(Hashable, type(x))
-    return (
-        _issubclass_fast(cls, "numpy", "ndarray") 
-        or _issubclass_fast(cls, "numpy", "generic")
-    ) and not _is_jax_zero_gradient_array(x)
-
-
-def is_cupy_array(x: object) -> bool:
-    """
-    Return True if `x` is a CuPy array.
-
-    This function does not import CuPy if it has not already been imported
-    and is therefore cheap to use.
-
-    This also returns True for `cupy.ndarray` subclasses and CuPy scalar objects.
-
-    See Also
-    --------
-
-    array_namespace
-    is_array_api_obj
-    is_numpy_array
-    is_torch_array
-    is_ndonnx_array
-    is_dask_array
-    is_jax_array
-    is_pydata_sparse_array
-    """
-    cls = cast(Hashable, type(x))
-    return _issubclass_fast(cls, "cupy", "ndarray")
-
-
-def is_torch_array(x: object) -> TypeIs[torch.Tensor]:
-    """
-    Return True if `x` is a PyTorch tensor.
-
-    This function does not import PyTorch if it has not already been imported
-    and is therefore cheap to use.
-
-    See Also
-    --------
-
-    array_namespace
-    is_array_api_obj
-    is_numpy_array
-    is_cupy_array
-    is_dask_array
-    is_jax_array
-    is_pydata_sparse_array
-    """
-    cls = cast(Hashable, type(x))
-    return _issubclass_fast(cls, "torch", "Tensor")
-
-
-def is_ndonnx_array(x: object) -> TypeIs[ndx.Array]:
-    """
-    Return True if `x` is a ndonnx Array.
-
-    This function does not import ndonnx if it has not already been imported
-    and is therefore cheap to use.
-
-    See Also
-    --------
-
-    array_namespace
-    is_array_api_obj
-    is_numpy_array
-    is_cupy_array
-    is_ndonnx_array
-    is_dask_array
-    is_jax_array
-    is_pydata_sparse_array
-    """
-    cls = cast(Hashable, type(x))
-    return _issubclass_fast(cls, "ndonnx", "Array")
-
-
-def is_dask_array(x: object) -> TypeIs[da.Array]:
-    """
-    Return True if `x` is a dask.array Array.
-
-    This function does not import dask if it has not already been imported
-    and is therefore cheap to use.
-
-    See Also
-    --------
-
-    array_namespace
-    is_array_api_obj
-    is_numpy_array
-    is_cupy_array
-    is_torch_array
-    is_ndonnx_array
-    is_jax_array
-    is_pydata_sparse_array
-    """
-    cls = cast(Hashable, type(x))
-    return _issubclass_fast(cls, "dask.array", "Array")
-
-
-def is_jax_array(x: object) -> TypeIs[jax.Array]:
-    """
-    Return True if `x` is a JAX array.
-
-    This function does not import JAX if it has not already been imported
-    and is therefore cheap to use.
-
-
-    See Also
-    --------
-
-    array_namespace
-    is_array_api_obj
-    is_numpy_array
-    is_cupy_array
-    is_torch_array
-    is_ndonnx_array
-    is_dask_array
-    is_pydata_sparse_array
-    """
-    cls = cast(Hashable, type(x))
-    return _issubclass_fast(cls, "jax", "Array") or _is_jax_zero_gradient_array(x)
-
-
-def is_pydata_sparse_array(x: object) -> TypeIs[sparse.SparseArray]:
-    """
-    Return True if `x` is an array from the `sparse` package.
-
-    This function does not import `sparse` if it has not already been imported
-    and is therefore cheap to use.
-
-
-    See Also
-    --------
-
-    array_namespace
-    is_array_api_obj
-    is_numpy_array
-    is_cupy_array
-    is_torch_array
-    is_ndonnx_array
-    is_dask_array
-    is_jax_array
-    """
-    # TODO: Account for other backends.
-    cls = cast(Hashable, type(x))
-    return _issubclass_fast(cls, "sparse", "SparseArray")
-
-
-def is_array_api_obj(x: object) -> TypeGuard[_ArrayApiObj]:
-    """
-    Return True if `x` is an array API compatible array object.
-
-    See Also
-    --------
-
-    array_namespace
-    is_numpy_array
-    is_cupy_array
-    is_torch_array
-    is_ndonnx_array
-    is_dask_array
-    is_jax_array
-    """
-    return (
-        hasattr(x, '__array_namespace__') 
-        or _is_array_api_cls(cast(Hashable, type(x)))
-    )
-
-
-@lru_cache(100)
-def _is_array_api_cls(cls: type) -> bool:
-    return (
-        # TODO: drop support for numpy<2 which didn't have __array_namespace__
-        _issubclass_fast(cls, "numpy", "ndarray")
-        or _issubclass_fast(cls, "numpy", "generic")
-        or _issubclass_fast(cls, "cupy", "ndarray")
-        or _issubclass_fast(cls, "torch", "Tensor")
-        or _issubclass_fast(cls, "dask.array", "Array")
-        or _issubclass_fast(cls, "sparse", "SparseArray")
-        # TODO: drop support for jax<0.4.32 which didn't have __array_namespace__
-        or _issubclass_fast(cls, "jax", "Array")
-    )
-
-
-def _compat_module_name() -> str:
-    assert __name__.endswith(".common._helpers")
-    return __name__.removesuffix(".common._helpers")
-
-
-@lru_cache(100)
-def is_numpy_namespace(xp: Namespace) -> bool:
-    """
-    Returns True if `xp` is a NumPy namespace.
-
-    This includes both NumPy itself and the version wrapped by array-api-compat.
-
-    See Also
-    --------
-
-    array_namespace
-    is_cupy_namespace
-    is_torch_namespace
-    is_ndonnx_namespace
-    is_dask_namespace
-    is_jax_namespace
-    is_pydata_sparse_namespace
-    is_array_api_strict_namespace
-    """
-    return xp.__name__ in {"numpy", _compat_module_name() + ".numpy"}
-
-
-@lru_cache(100)
-def is_cupy_namespace(xp: Namespace) -> bool:
-    """
-    Returns True if `xp` is a CuPy namespace.
-
-    This includes both CuPy itself and the version wrapped by array-api-compat.
-
-    See Also
-    --------
-
-    array_namespace
-    is_numpy_namespace
-    is_torch_namespace
-    is_ndonnx_namespace
-    is_dask_namespace
-    is_jax_namespace
-    is_pydata_sparse_namespace
-    is_array_api_strict_namespace
-    """
-    return xp.__name__ in {"cupy", _compat_module_name() + ".cupy"}
-
-
-@lru_cache(100)
-def is_torch_namespace(xp: Namespace) -> bool:
-    """
-    Returns True if `xp` is a PyTorch namespace.
-
-    This includes both PyTorch itself and the version wrapped by array-api-compat.
-
-    See Also
-    --------
-
-    array_namespace
-    is_numpy_namespace
-    is_cupy_namespace
-    is_ndonnx_namespace
-    is_dask_namespace
-    is_jax_namespace
-    is_pydata_sparse_namespace
-    is_array_api_strict_namespace
-    """
-    return xp.__name__ in {"torch", _compat_module_name() + ".torch"}
-
-
-def is_ndonnx_namespace(xp: Namespace) -> bool:
-    """
-    Returns True if `xp` is an NDONNX namespace.
-
-    See Also
-    --------
-
-    array_namespace
-    is_numpy_namespace
-    is_cupy_namespace
-    is_torch_namespace
-    is_dask_namespace
-    is_jax_namespace
-    is_pydata_sparse_namespace
-    is_array_api_strict_namespace
-    """
-    return xp.__name__ == "ndonnx"
-
-
-@lru_cache(100)
-def is_dask_namespace(xp: Namespace) -> bool:
-    """
-    Returns True if `xp` is a Dask namespace.
-
-    This includes both ``dask.array`` itself and the version wrapped by array-api-compat.
-
-    See Also
-    --------
-
-    array_namespace
-    is_numpy_namespace
-    is_cupy_namespace
-    is_torch_namespace
-    is_ndonnx_namespace
-    is_jax_namespace
-    is_pydata_sparse_namespace
-    is_array_api_strict_namespace
-    """
-    return xp.__name__ in {"dask.array", _compat_module_name() + ".dask.array"}
-
-
-def is_jax_namespace(xp: Namespace) -> bool:
-    """
-    Returns True if `xp` is a JAX namespace.
-
-    This includes ``jax.numpy`` and ``jax.experimental.array_api`` which existed in
-    older versions of JAX.
-
-    See Also
-    --------
-
-    array_namespace
-    is_numpy_namespace
-    is_cupy_namespace
-    is_torch_namespace
-    is_ndonnx_namespace
-    is_dask_namespace
-    is_pydata_sparse_namespace
-    is_array_api_strict_namespace
-    """
-    return xp.__name__ in {"jax.numpy", "jax.experimental.array_api"}
-
-
-def is_pydata_sparse_namespace(xp: Namespace) -> bool:
-    """
-    Returns True if `xp` is a pydata/sparse namespace.
-
-    See Also
-    --------
-
-    array_namespace
-    is_numpy_namespace
-    is_cupy_namespace
-    is_torch_namespace
-    is_ndonnx_namespace
-    is_dask_namespace
-    is_jax_namespace
-    is_array_api_strict_namespace
-    """
-    return xp.__name__ == "sparse"
-
-
-def is_array_api_strict_namespace(xp: Namespace) -> bool:
-    """
-    Returns True if `xp` is an array-api-strict namespace.
-
-    See Also
-    --------
-
-    array_namespace
-    is_numpy_namespace
-    is_cupy_namespace
-    is_torch_namespace
-    is_ndonnx_namespace
-    is_dask_namespace
-    is_jax_namespace
-    is_pydata_sparse_namespace
-    """
-    return xp.__name__ == "array_api_strict"
-
-
-def _check_api_version(api_version: str | None) -> None:
-    if api_version in _API_VERSIONS_OLD:
-        warnings.warn(
-            f"The {api_version} version of the array API specification was requested but the returned namespace is actually version 2024.12"
-        )
-    elif api_version is not None and api_version not in _API_VERSIONS:
-        raise ValueError(
-            "Only the 2024.12 version of the array API specification is currently supported"
-        )
-
-
-class _ClsToXPInfo(enum.Enum):
-    SCALAR = 0
-    MAYBE_JAX_ZERO_GRADIENT = 1
-
-
-@lru_cache(100)
-def _cls_to_namespace(
-    cls: type,
-    api_version: str | None,
-    use_compat: bool | None,
-) -> tuple[Namespace | None, _ClsToXPInfo | None]:
-    if use_compat not in (None, True, False):
-        raise ValueError("use_compat must be None, True, or False")
-    _use_compat = use_compat in (None, True)
-    cls_ = cast(Hashable, cls)  # Make mypy happy
-
-    if (
-        _issubclass_fast(cls_, "numpy", "ndarray") 
-        or _issubclass_fast(cls_, "numpy", "generic")
-    ):
-        if use_compat is True:
-            _check_api_version(api_version)
-            from .. import numpy as xp
-        elif use_compat is False:
-            import numpy as xp  # type: ignore[no-redef]
-        else:
-            # NumPy 2.0+ have __array_namespace__; however they are not
-            # yet fully array API compatible.
-            from .. import numpy as xp  # type: ignore[no-redef]
-        return xp, _ClsToXPInfo.MAYBE_JAX_ZERO_GRADIENT
-
-    # Note: this must happen _after_ the test for np.generic,
-    # because np.float64 and np.complex128 are subclasses of float and complex.
-    if issubclass(cls, int | float | complex | type(None)):
-        return None, _ClsToXPInfo.SCALAR
-
-    if _issubclass_fast(cls_, "cupy", "ndarray"):
-        if _use_compat:
-            _check_api_version(api_version)
-            from .. import cupy as xp  # type: ignore[no-redef]
-        else:
-            import cupy as xp  # type: ignore[no-redef]
-        return xp, None
-
-    if _issubclass_fast(cls_, "torch", "Tensor"):
-        if _use_compat:
-            _check_api_version(api_version)
-            from .. import torch as xp  # type: ignore[no-redef]
-        else:
-            import torch as xp  # type: ignore[no-redef]
-        return xp, None
-
-    if _issubclass_fast(cls_, "dask.array", "Array"):
-        if _use_compat:
-            _check_api_version(api_version)
-            from ..dask import array as xp  # type: ignore[no-redef]
-        else:
-            import dask.array as xp  # type: ignore[no-redef]
-        return xp, None
-
-    # Backwards compatibility for jax<0.4.32
-    if _issubclass_fast(cls_, "jax", "Array"):
-        return _jax_namespace(api_version, use_compat), None
-
-    return None, None
-
-
-def _jax_namespace(api_version: str | None, use_compat: bool | None) -> Namespace:
-    if use_compat:
-        raise ValueError("JAX does not have an array-api-compat wrapper")
-    import jax.numpy as jnp
-    if not hasattr(jnp, "__array_namespace_info__"):
-        # JAX v0.4.32 and newer implements the array API directly in jax.numpy.
-        # For older JAX versions, it is available via jax.experimental.array_api.
-        # jnp.Array objects gain the __array_namespace__ method.
-        import jax.experimental.array_api  # noqa: F401
-    # Test api_version
-    return jnp.empty(0).__array_namespace__(api_version=api_version)
-
-
-def array_namespace(
-    *xs: Array | complex | None,
-    api_version: str | None = None,
-    use_compat: bool | None = None,
-) -> Namespace:
-    """
-    Get the array API compatible namespace for the arrays `xs`.
-
-    Parameters
-    ----------
-    xs: arrays
-        one or more arrays. xs can also be Python scalars (bool, int, float,
-        complex, or None), which are ignored.
-
-    api_version: str
-        The newest version of the spec that you need support for (currently
-        the compat library wrapped APIs support v2024.12).
-
-    use_compat: bool or None
-        If None (the default), the native namespace will be returned if it is
-        already array API compatible, otherwise a compat wrapper is used. If
-        True, the compat library wrapped library will be returned. If False,
-        the native library namespace is returned.
-
-    Returns
-    -------
-
-    out: namespace
-        The array API compatible namespace corresponding to the arrays in `xs`.
-
-    Raises
-    ------
-    TypeError
-        If `xs` contains arrays from different array libraries or contains a
-        non-array.
-
-
-    Typical usage is to pass the arguments of a function to
-    `array_namespace()` at the top of a function to get the corresponding
-    array API namespace:
-
-    .. code:: python
-
-       def your_function(x, y):
-           xp = array_api_compat.array_namespace(x, y)
-           # Now use xp as the array library namespace
-           return xp.mean(x, axis=0) + 2*xp.std(y, axis=0)
-
-
-    Wrapped array namespaces can also be imported directly. For example,
-    `array_namespace(np.array(...))` will return `array_api_compat.numpy`.
-    This function will also work for any array library not wrapped by
-    array-api-compat if it explicitly defines `__array_namespace__
-    <https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__array_namespace__.html>`__
-    (the wrapped namespace is always preferred if it exists).
-
-    See Also
-    --------
-
-    is_array_api_obj
-    is_numpy_array
-    is_cupy_array
-    is_torch_array
-    is_dask_array
-    is_jax_array
-    is_pydata_sparse_array
-
-    """
-    namespaces: set[Namespace] = set()
-    for x in xs:
-        xp, info = _cls_to_namespace(cast(Hashable, type(x)), api_version, use_compat)
-        if info is _ClsToXPInfo.SCALAR:
-            continue
-
-        if (
-            info is _ClsToXPInfo.MAYBE_JAX_ZERO_GRADIENT
-            and _is_jax_zero_gradient_array(x)
-        ):
-            xp = _jax_namespace(api_version, use_compat)
-
-        if xp is None:
-            get_ns = getattr(x, "__array_namespace__", None)
-            if get_ns is None:
-                raise TypeError(f"{type(x).__name__} is not a supported array type")
-            if use_compat:
-                raise ValueError(
-                    "The given array does not have an array-api-compat wrapper"
-                )
-            xp = get_ns(api_version=api_version)
-
-        namespaces.add(xp)
-
-    try:
-        (xp,) = namespaces
-        return xp
-    except ValueError:
-        if not namespaces:
-            raise TypeError(
-                "array_namespace requires at least one non-scalar array input"
-            )
-        raise TypeError(f"Multiple namespaces for array inputs: {namespaces}")
-
-
-# backwards compatibility alias
-get_namespace = array_namespace
-
-
-def _check_device(bare_xp: Namespace, device: Device) -> None:  # pyright: ignore[reportUnusedFunction]
-    """
-    Validate dummy device on device-less array backends.
-
-    Notes
-    -----
-    This function is also invoked by CuPy, which does have multiple devices
-    if there are multiple GPUs available.
-    However, CuPy multi-device support is currently impossible
-    without using the global device or a context manager:
-
-    https://github.com/data-apis/array-api-compat/pull/293
-    """
-    if bare_xp is sys.modules.get("numpy"):
-        if device not in ("cpu", None):
-            raise ValueError(f"Unsupported device for NumPy: {device!r}")
-
-    elif bare_xp is sys.modules.get("dask.array"):
-        if device not in ("cpu", _DASK_DEVICE, None):
-            raise ValueError(f"Unsupported device for Dask: {device!r}")
-
-
-# Placeholder object to represent the dask device
-# when the array backend is not the CPU.
-# (since it is not easy to tell which device a dask array is on)
-class _dask_device:
-    def __repr__(self) -> Literal["DASK_DEVICE"]:
-        return "DASK_DEVICE"
-
-
-_DASK_DEVICE = _dask_device()
-
-
-# device() is not on numpy.ndarray or dask.array and to_device() is not on numpy.ndarray
-# or cupy.ndarray. They are not included in array objects of this library
-# because this library just reuses the respective ndarray classes without
-# wrapping or subclassing them. These helper functions can be used instead of
-# the wrapper functions for libraries that need to support both NumPy/CuPy and
-# other libraries that use devices.
-def device(x: _ArrayApiObj, /) -> Device:
-    """
-    Hardware device the array data resides on.
-
-    This is equivalent to `x.device` according to the `standard
-    <https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.device.html>`__.
-    This helper is included because some array libraries either do not have
-    the `device` attribute or include it with an incompatible API.
-
-    Parameters
-    ----------
-    x: array
-        array instance from an array API compatible library.
-
-    Returns
-    -------
-    out: device
-        a ``device`` object (see the `Device Support <https://data-apis.org/array-api/latest/design_topics/device_support.html>`__
-        section of the array API specification).
-
-    Notes
-    -----
-
-    For NumPy the device is always `"cpu"`. For Dask, the device is always a
-    special `DASK_DEVICE` object.
-
-    See Also
-    --------
-
-    to_device : Move array data to a different device.
-
-    """
-    if is_numpy_array(x):
-        return "cpu"
-    elif is_dask_array(x):
-        # Peek at the metadata of the Dask array to determine type
-        if is_numpy_array(x._meta):
-            # Must be on CPU since backed by numpy
-            return "cpu"
-        return _DASK_DEVICE
-    elif is_jax_array(x):
-        # FIXME Jitted JAX arrays do not have a device attribute
-        #       https://github.com/jax-ml/jax/issues/26000
-        #       Return None in this case. Note that this workaround breaks
-        #       the standard and will result in new arrays being created on the
-        #       default device instead of the same device as the input array(s).
-        x_device = getattr(x, "device", None)
-        # Older JAX releases had .device() as a method, which has been replaced
-        # with a property in accordance with the standard.
-        if inspect.ismethod(x_device):
-            return x_device()
-        else:
-            return x_device
-    elif is_pydata_sparse_array(x):
-        # `sparse` will gain `.device`, so check for this first.
-        x_device = getattr(x, "device", None)
-        if x_device is not None:
-            return x_device
-        # Everything but DOK has this attr.
-        try:
-            inner = x.data  # pyright: ignore
-        except AttributeError:
-            return "cpu"
-        # Return the device of the constituent array
-        return device(inner)  # pyright: ignore
-    return x.device  # type: ignore  # pyright: ignore
-
-
-# Prevent shadowing, used below
-_device = device
-
-
-# Based on cupy.array_api.Array.to_device
-def _cupy_to_device(
-    x: cp.ndarray,
-    device: Device,
-    /,
-    stream: int | Any | None = None,
-) -> cp.ndarray:
-    import cupy as cp
-
-    if device == "cpu":
-        # allowing us to use `to_device(x, "cpu")`
-        # is useful for portable test swapping between
-        # host and device backends
-        return x.get()
-    if not isinstance(device, cp.cuda.Device):
-        raise TypeError(f"Unsupported device type {device!r}")
-
-    if stream is None:
-        with device:
-            return cp.asarray(x)
-
-    # stream can be an int as specified in __dlpack__, or a CuPy stream
-    if isinstance(stream, int):
-        stream = cp.cuda.ExternalStream(stream)
-    elif not isinstance(stream, cp.cuda.Stream):
-        raise TypeError(f"Unsupported stream type {stream!r}")
-
-    with device, stream:
-        return cp.asarray(x)
-
-
-def _torch_to_device(
-    x: torch.Tensor,
-    device: torch.device | str | int,
-    /,
-    stream: int | Any | None = None,
-) -> torch.Tensor:
-    if stream is not None:
-        raise NotImplementedError
-    return x.to(device)
-
-
-def to_device(x: Array, device: Device, /, *, stream: int | Any | None = None) -> Array:
-    """
-    Copy the array from the device on which it currently resides to the specified ``device``.
-
-    This is equivalent to `x.to_device(device, stream=stream)` according to
-    the `standard
-    <https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.to_device.html>`__.
-    This helper is included because some array libraries do not have the
-    `to_device` method.
-
-    Parameters
-    ----------
-
-    x: array
-        array instance from an array API compatible library.
-
-    device: device
-        a ``device`` object (see the `Device Support <https://data-apis.org/array-api/latest/design_topics/device_support.html>`__
-        section of the array API specification).
-
-    stream: int | Any | None
-        stream object to use during copy. In addition to the types supported
-        in ``array.__dlpack__``, implementations may choose to support any
-        library-specific stream object with the caveat that any code using
-        such an object would not be portable.
-
-    Returns
-    -------
-
-    out: array
-        an array with the same data and data type as ``x`` and located on the
-        specified ``device``.
-
-    Notes
-    -----
-
-    For NumPy, this function effectively does nothing since the only supported
-    device is the CPU. For CuPy, this method supports CuPy CUDA
-    :external+cupy:class:`Device <cupy.cuda.Device>` and
-    :external+cupy:class:`Stream <cupy.cuda.Stream>` objects. For PyTorch,
-    this is the same as :external+torch:meth:`x.to(device) <torch.Tensor.to>`
-    (the ``stream`` argument is not supported in PyTorch).
-
-    See Also
-    --------
-
-    device : Hardware device the array data resides on.
-
-    """
-    if is_numpy_array(x):
-        if stream is not None:
-            raise ValueError("The stream argument to to_device() is not supported")
-        if device == "cpu":
-            return x
-        raise ValueError(f"Unsupported device {device!r}")
-    elif is_cupy_array(x):
-        # cupy does not yet have to_device
-        return _cupy_to_device(x, device, stream=stream)
-    elif is_torch_array(x):
-        return _torch_to_device(x, device, stream=stream)
-    elif is_dask_array(x):
-        if stream is not None:
-            raise ValueError("The stream argument to to_device() is not supported")
-        # TODO: What if our array is on the GPU already?
-        if device == "cpu":
-            return x
-        raise ValueError(f"Unsupported device {device!r}")
-    elif is_jax_array(x):
-        if not hasattr(x, "__array_namespace__"):
-            # In JAX v0.4.31 and older, this import adds to_device method to x...
-            import jax.experimental.array_api  # noqa: F401  # pyright: ignore
-
-            # ... but only on eager JAX. It won't work inside jax.jit.
-            if not hasattr(x, "to_device"):
-                return x
-        return x.to_device(device, stream=stream)
-    elif is_pydata_sparse_array(x) and device == _device(x):
-        # Perform trivial check to return the same array if
-        # device is same instead of err-ing.
-        return x
-    return x.to_device(device, stream=stream)  # pyright: ignore
-
-
-@overload
-def size(x: HasShape[Collection[SupportsIndex]]) -> int: ...
-@overload
-def size(x: HasShape[Collection[SupportsIndex | None]]) -> int | None: ...
-def size(x: HasShape[Collection[SupportsIndex | None]]) -> int | None:
-    """
-    Return the total number of elements of x.
-
-    This is equivalent to `x.size` according to the `standard
-    <https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.size.html>`__.
-
-    This helper is included because PyTorch defines `size` in an
-    :external+torch:meth:`incompatible way <torch.Tensor.size>`.
-    It also fixes dask.array's behaviour which returns nan for unknown sizes, whereas
-    the standard requires None.
-    """
-    # Lazy API compliant arrays, such as ndonnx, can contain None in their shape
-    if None in x.shape:
-        return None
-    out = math.prod(cast("Collection[SupportsIndex]", x.shape))
-    # dask.array.Array.shape can contain NaN
-    return None if math.isnan(out) else out
-
-
-@lru_cache(100)
-def _is_writeable_cls(cls: type) -> bool | None:
-    if (
-        _issubclass_fast(cls, "numpy", "generic")
-        or _issubclass_fast(cls, "jax", "Array")
-        or _issubclass_fast(cls, "sparse", "SparseArray")
-    ):
-        return False
-    if _is_array_api_cls(cls):
-        return True
-    return None
-
-
-def is_writeable_array(x: object) -> TypeGuard[_ArrayApiObj]:
-    """
-    Return False if ``x.__setitem__`` is expected to raise; True otherwise.
-    Return False if `x` is not an array API compatible object.
-
-    Warning
-    -------
-    As there is no standard way to check if an array is writeable without actually
-    writing to it, this function blindly returns True for all unknown array types.
-    """
-    cls = cast(Hashable, type(x))
-    if _issubclass_fast(cls, "numpy", "ndarray"):
-        return cast("npt.NDArray", x).flags.writeable
-    res = _is_writeable_cls(cls)
-    if res is not None:
-        return res
-    return hasattr(x, '__array_namespace__')
-
-
-@lru_cache(100)
-def _is_lazy_cls(cls: type) -> bool | None:
-    if (
-        _issubclass_fast(cls, "numpy", "ndarray")
-        or _issubclass_fast(cls, "numpy", "generic")
-        or _issubclass_fast(cls, "cupy", "ndarray")
-        or _issubclass_fast(cls, "torch", "Tensor")
-        or _issubclass_fast(cls, "sparse", "SparseArray")
-    ):
-        return False
-    if (
-        _issubclass_fast(cls, "jax", "Array")
-        or _issubclass_fast(cls, "dask.array", "Array")
-        or _issubclass_fast(cls, "ndonnx", "Array")
-    ):
-        return True
-    return  None
-
-
-def is_lazy_array(x: object) -> TypeGuard[_ArrayApiObj]:
-    """Return True if x is potentially a future or it may be otherwise impossible or
-    expensive to eagerly read its contents, regardless of their size, e.g. by
-    calling ``bool(x)`` or ``float(x)``.
-
-    Return False otherwise; e.g. ``bool(x)`` etc. is guaranteed to succeed and to be
-    cheap as long as the array has the right dtype and size.
-
-    Note
-    ----
-    This function errs on the side of caution for array types that may or may not be
-    lazy, e.g. JAX arrays, by always returning True for them.
-    """
-    # **JAX note:** while it is possible to determine if you're inside or outside
-    # jax.jit by testing the subclass of a jax.Array object, as well as testing bool()
-    # as we do below for unknown arrays, this is not recommended by JAX best practices.
-
-    # **Dask note:** Dask eagerly computes the graph on __bool__, __float__, and so on.
-    # This behaviour, while impossible to change without breaking backwards
-    # compatibility, is highly detrimental to performance as the whole graph will end
-    # up being computed multiple times.
-
-    # Note: skipping reclassification of JAX zero gradient arrays, as one will
-    # exclusively get them once they leave a jax.grad JIT context.
-    cls = cast(Hashable, type(x))
-    res = _is_lazy_cls(cls)
-    if res is not None:
-        return res
-
-    if not hasattr(x, "__array_namespace__"):
-        return False
-
-    # Unknown Array API compatible object. Note that this test may have dire consequences
-    # in terms of performance, e.g. for a lazy object that eagerly computes the graph
-    # on __bool__ (dask is one such example, which however is special-cased above).
-
-    # Select a single point of the array
-    s = size(cast("HasShape[Collection[SupportsIndex | None]]", x))
-    if s is None:
-        return True
-    xp = array_namespace(x)
-    if s > 1:
-        x = xp.reshape(x, (-1,))[0]
-    # Cast to dtype=bool and deal with size 0 arrays
-    x = xp.any(x)
-
-    try:
-        bool(x)
-        return False
-    # The Array API standard dictactes that __bool__ should raise TypeError if the
-    # output cannot be defined.
-    # Here we allow for it to raise arbitrary exceptions, e.g. like Dask does.
-    except Exception:
-        return True
-
-
-__all__ = [
-    "array_namespace",
-    "device",
-    "get_namespace",
-    "is_array_api_obj",
-    "is_array_api_strict_namespace",
-    "is_cupy_array",
-    "is_cupy_namespace",
-    "is_dask_array",
-    "is_dask_namespace",
-    "is_jax_array",
-    "is_jax_namespace",
-    "is_numpy_array",
-    "is_numpy_namespace",
-    "is_torch_array",
-    "is_torch_namespace",
-    "is_ndonnx_array",
-    "is_ndonnx_namespace",
-    "is_pydata_sparse_array",
-    "is_pydata_sparse_namespace",
-    "is_writeable_array",
-    "is_lazy_array",
-    "size",
-    "to_device",
-]
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/common/_linalg.py b/array_api_compat/common/_linalg.py
deleted file mode 100644
index 69672af7..00000000
--- a/array_api_compat/common/_linalg.py
+++ /dev/null
@@ -1,230 +0,0 @@
-from __future__ import annotations
-
-import math
-from typing import Literal, NamedTuple, cast
-
-import numpy as np
-
-if np.__version__[0] == "2":
-    from numpy.lib.array_utils import normalize_axis_tuple
-else:
-    from numpy.core.numeric import normalize_axis_tuple  # type: ignore[no-redef]
-
-from .._internal import get_xp
-from ._aliases import isdtype, matmul, matrix_transpose, tensordot, vecdot
-from ._typing import Array, DType, JustFloat, JustInt, Namespace
-
-
-# These are in the main NumPy namespace but not in numpy.linalg
-def cross(
-    x1: Array,
-    x2: Array,
-    /,
-    xp: Namespace,
-    *,
-    axis: int = -1,
-    **kwargs: object,
-) -> Array:
-    return xp.cross(x1, x2, axis=axis, **kwargs)
-
-def outer(x1: Array, x2: Array, /, xp: Namespace, **kwargs: object) -> Array:
-    return xp.outer(x1, x2, **kwargs)
-
-class EighResult(NamedTuple):
-    eigenvalues: Array
-    eigenvectors: Array
-
-class QRResult(NamedTuple):
-    Q: Array
-    R: Array
-
-class SlogdetResult(NamedTuple):
-    sign: Array
-    logabsdet: Array
-
-class SVDResult(NamedTuple):
-    U: Array
-    S: Array
-    Vh: Array
-
-# These functions are the same as their NumPy counterparts except they return
-# a namedtuple.
-def eigh(x: Array, /, xp: Namespace, **kwargs: object) -> EighResult:
-    return EighResult(*xp.linalg.eigh(x, **kwargs))
-
-def qr(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    mode: Literal["reduced", "complete"] = "reduced",
-    **kwargs: object,
-) -> QRResult:
-    return QRResult(*xp.linalg.qr(x, mode=mode, **kwargs))
-
-def slogdet(x: Array, /, xp: Namespace, **kwargs: object) -> SlogdetResult:
-    return SlogdetResult(*xp.linalg.slogdet(x, **kwargs))
-
-def svd(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    full_matrices: bool = True,
-    **kwargs: object,
-) -> SVDResult:
-    return SVDResult(*xp.linalg.svd(x, full_matrices=full_matrices, **kwargs))
-
-# These functions have additional keyword arguments
-
-# The upper keyword argument is new from NumPy
-def cholesky(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    upper: bool = False,
-    **kwargs: object,
-) -> Array:
-    L = xp.linalg.cholesky(x, **kwargs)
-    if upper:
-        U = get_xp(xp)(matrix_transpose)(L)
-        if get_xp(xp)(isdtype)(U.dtype, 'complex floating'):
-            U = xp.conj(U)  # pyright: ignore[reportConstantRedefinition]
-        return U
-    return L
-
-# The rtol keyword argument of matrix_rank() and pinv() is new from NumPy.
-# Note that it has a different semantic meaning from tol and rcond.
-def matrix_rank(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    rtol: float | Array | None = None,
-    **kwargs: object,
-) -> Array:
-    # this is different from xp.linalg.matrix_rank, which supports 1
-    # dimensional arrays.
-    if x.ndim < 2:
-        raise xp.linalg.LinAlgError("1-dimensional array given. Array must be at least two-dimensional")
-    S: Array = get_xp(xp)(svdvals)(x, **kwargs)
-    if rtol is None:
-        tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * xp.finfo(S.dtype).eps
-    else:
-        # this is different from xp.linalg.matrix_rank, which does not
-        # multiply the tolerance by the largest singular value.
-        tol = S.max(axis=-1, keepdims=True)*xp.asarray(rtol)[..., xp.newaxis]
-    return xp.count_nonzero(S > tol, axis=-1)
-
-def pinv(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    rtol: float | Array | None = None,
-    **kwargs: object,
-) -> Array:
-    # this is different from xp.linalg.pinv, which does not multiply the
-    # default tolerance by max(M, N).
-    if rtol is None:
-        rtol = max(x.shape[-2:]) * xp.finfo(x.dtype).eps
-    return xp.linalg.pinv(x, rcond=rtol, **kwargs)
-
-# These functions are new in the array API spec
-
-def matrix_norm(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    keepdims: bool = False,
-    ord: Literal[1, 2, -1, -2] | JustFloat | Literal["fro", "nuc"] | None = "fro",
-) -> Array:
-    return xp.linalg.norm(x, axis=(-2, -1), keepdims=keepdims, ord=ord)
-
-# svdvals is not in NumPy (but it is in SciPy). It is equivalent to
-# xp.linalg.svd(compute_uv=False).
-def svdvals(x: Array, /, xp: Namespace) -> Array | tuple[Array, ...]:
-    return xp.linalg.svd(x, compute_uv=False)
-
-def vector_norm(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    axis: int | tuple[int, ...] | None = None,
-    keepdims: bool = False,
-    ord: JustInt | JustFloat = 2,
-) -> Array:
-    # xp.linalg.norm tries to do a matrix norm whenever axis is a 2-tuple or
-    # when axis=None and the input is 2-D, so to force a vector norm, we make
-    # it so the input is 1-D (for axis=None), or reshape so that norm is done
-    # on a single dimension.
-    if axis is None:
-        # Note: xp.linalg.norm() doesn't handle 0-D arrays
-        _x = x.ravel()
-        _axis = 0
-    elif isinstance(axis, tuple):
-        # Note: The axis argument supports any number of axes, whereas
-        # xp.linalg.norm() only supports a single axis for vector norm.
-        normalized_axis = cast(
-            "tuple[int, ...]",
-            normalize_axis_tuple(axis, x.ndim),  # pyright: ignore[reportCallIssue]
-        )
-        rest = tuple(i for i in range(x.ndim) if i not in normalized_axis)
-        newshape = axis + rest
-        _x = xp.transpose(x, newshape).reshape(
-            (math.prod([x.shape[i] for i in axis]), *[x.shape[i] for i in rest]))
-        _axis = 0
-    else:
-        _x = x
-        _axis = axis
-
-    res = xp.linalg.norm(_x, axis=_axis, ord=ord)
-
-    if keepdims:
-        # We can't reuse xp.linalg.norm(keepdims) because of the reshape hacks
-        # above to avoid matrix norm logic.
-        shape = list(x.shape)
-        axes = cast(
-            "tuple[int, ...]",
-            normalize_axis_tuple(  # pyright: ignore[reportCallIssue]
-                range(x.ndim) if axis is None else axis,
-                x.ndim,
-            ),
-        )
-        for i in axes:
-            shape[i] = 1
-        res = xp.reshape(res, tuple(shape))
-
-    return res
-
-# xp.diagonal and xp.trace operate on the first two axes whereas these
-# operates on the last two
-
-def diagonal(x: Array, /, xp: Namespace, *, offset: int = 0, **kwargs: object) -> Array:
-    return xp.diagonal(x, offset=offset, axis1=-2, axis2=-1, **kwargs)
-
-def trace(
-    x: Array,
-    /,
-    xp: Namespace,
-    *,
-    offset: int = 0,
-    dtype: DType | None = None,
-    **kwargs: object,
-) -> Array:
-    return xp.asarray(
-        xp.trace(x, offset=offset, dtype=dtype, axis1=-2, axis2=-1, **kwargs)
-    )
-
-__all__ = ['cross', 'matmul', 'outer', 'tensordot', 'EighResult',
-           'QRResult', 'SlogdetResult', 'SVDResult', 'eigh', 'qr', 'slogdet',
-           'svd', 'cholesky', 'matrix_rank', 'pinv', 'matrix_norm',
-           'matrix_transpose', 'svdvals', 'vecdot', 'vector_norm', 'diagonal',
-           'trace']
-
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/common/_typing.py b/array_api_compat/common/_typing.py
deleted file mode 100644
index 11b00bd1..00000000
--- a/array_api_compat/common/_typing.py
+++ /dev/null
@@ -1,189 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Mapping
-from types import ModuleType as Namespace
-from typing import (
-    TYPE_CHECKING,
-    Literal,
-    Protocol,
-    TypeAlias,
-    TypedDict,
-    TypeVar,
-    final,
-)
-
-if TYPE_CHECKING:
-    from _typeshed import Incomplete
-
-    SupportsBufferProtocol: TypeAlias = Incomplete
-    Array: TypeAlias = Incomplete
-    Device: TypeAlias = Incomplete
-    DType: TypeAlias = Incomplete
-else:
-    SupportsBufferProtocol = object
-    Array = object
-    Device = object
-    DType = object
-
-
-_T_co = TypeVar("_T_co", covariant=True)
-
-
-# These "Just" types are equivalent to the `Just` type from the `optype` library,
-# apart from them not being `@runtime_checkable`.
-# - docs: https://github.com/jorenham/optype/blob/master/README.md#just
-# - code: https://github.com/jorenham/optype/blob/master/optype/_core/_just.py
-@final
-class JustInt(Protocol):  # type: ignore[misc]
-    @property  # type: ignore[override]
-    def __class__(self, /) -> type[int]: ...
-    @__class__.setter
-    def __class__(self, value: type[int], /) -> None: ...  # pyright: ignore[reportIncompatibleMethodOverride]
-
-
-@final
-class JustFloat(Protocol):  # type: ignore[misc]
-    @property  # type: ignore[override]
-    def __class__(self, /) -> type[float]: ...
-    @__class__.setter
-    def __class__(self, value: type[float], /) -> None: ...  # pyright: ignore[reportIncompatibleMethodOverride]
-
-
-@final
-class JustComplex(Protocol):  # type: ignore[misc]
-    @property  # type: ignore[override]
-    def __class__(self, /) -> type[complex]: ...
-    @__class__.setter
-    def __class__(self, value: type[complex], /) -> None: ...  # pyright: ignore[reportIncompatibleMethodOverride]
-
-
-class NestedSequence(Protocol[_T_co]):
-    def __getitem__(self, key: int, /) -> _T_co | NestedSequence[_T_co]: ...
-    def __len__(self, /) -> int: ...
-
-
-class SupportsArrayNamespace(Protocol[_T_co]):
-    def __array_namespace__(self, /, *, api_version: str | None) -> _T_co: ...
-
-
-class HasShape(Protocol[_T_co]):
-    @property
-    def shape(self, /) -> _T_co: ...
-
-
-# Return type of `__array_namespace_info__.default_dtypes`
-Capabilities = TypedDict(
-    "Capabilities",
-    {
-        "boolean indexing": bool,
-        "data-dependent shapes": bool,
-        "max dimensions": int,
-    },
-)
-
-# Return type of `__array_namespace_info__.default_dtypes`
-DefaultDTypes = TypedDict(
-    "DefaultDTypes",
-    {
-        "real floating": DType,
-        "complex floating": DType,
-        "integral": DType,
-        "indexing": DType,
-    },
-)
-
-
-_DTypeKind: TypeAlias = Literal[
-    "bool",
-    "signed integer",
-    "unsigned integer",
-    "integral",
-    "real floating",
-    "complex floating",
-    "numeric",
-]
-# Type of the `kind` parameter in `__array_namespace_info__.dtypes`
-DTypeKind: TypeAlias = _DTypeKind | tuple[_DTypeKind, ...]
-
-
-# `__array_namespace_info__.dtypes(kind="bool")`
-class DTypesBool(TypedDict):
-    bool: DType
-
-
-# `__array_namespace_info__.dtypes(kind="signed integer")`
-class DTypesSigned(TypedDict):
-    int8: DType
-    int16: DType
-    int32: DType
-    int64: DType
-
-
-# `__array_namespace_info__.dtypes(kind="unsigned integer")`
-class DTypesUnsigned(TypedDict):
-    uint8: DType
-    uint16: DType
-    uint32: DType
-    uint64: DType
-
-
-# `__array_namespace_info__.dtypes(kind="integral")`
-class DTypesIntegral(DTypesSigned, DTypesUnsigned):
-    pass
-
-
-# `__array_namespace_info__.dtypes(kind="real floating")`
-class DTypesReal(TypedDict):
-    float32: DType
-    float64: DType
-
-
-# `__array_namespace_info__.dtypes(kind="complex floating")`
-class DTypesComplex(TypedDict):
-    complex64: DType
-    complex128: DType
-
-
-# `__array_namespace_info__.dtypes(kind="numeric")`
-class DTypesNumeric(DTypesIntegral, DTypesReal, DTypesComplex):
-    pass
-
-
-# `__array_namespace_info__.dtypes(kind=None)` (default)
-class DTypesAll(DTypesBool, DTypesNumeric):
-    pass
-
-
-# `__array_namespace_info__.dtypes(kind=?)` (fallback)
-DTypesAny: TypeAlias = Mapping[str, DType]
-
-
-__all__ = [
-    "Array",
-    "Capabilities",
-    "DType",
-    "DTypeKind",
-    "DTypesAny",
-    "DTypesAll",
-    "DTypesBool",
-    "DTypesNumeric",
-    "DTypesIntegral",
-    "DTypesSigned",
-    "DTypesUnsigned",
-    "DTypesReal",
-    "DTypesComplex",
-    "DefaultDTypes",
-    "Device",
-    "HasShape",
-    "Namespace",
-    "JustInt",
-    "JustFloat",
-    "JustComplex",
-    "NestedSequence",
-    "SupportsArrayNamespace",
-    "SupportsBufferProtocol",
-]
-
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/cupy/__init__.py b/array_api_compat/cupy/__init__.py
deleted file mode 100644
index af003c5a..00000000
--- a/array_api_compat/cupy/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from typing import Final
-from cupy import * # noqa: F403
-
-# from cupy import * doesn't overwrite these builtin names
-from cupy import abs, max, min, round # noqa: F401
-
-# These imports may overwrite names from the import * above.
-from ._aliases import * # noqa: F403
-from ._info import __array_namespace_info__  # noqa: F401
-
-# See the comment in the numpy __init__.py
-__import__(__package__ + '.linalg')
-__import__(__package__ + '.fft')
-
-__array_api_version__: Final = '2024.12'
-
-__all__ = sorted(
-    {name for name in globals() if not name.startswith("__")}
-    - {"Final", "_aliases", "_info", "_typing"}
-    | {"__array_api_version__", "__array_namespace_info__", "linalg", "fft"}
-)
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/cupy/_aliases.py b/array_api_compat/cupy/_aliases.py
deleted file mode 100644
index 2e512fc8..00000000
--- a/array_api_compat/cupy/_aliases.py
+++ /dev/null
@@ -1,168 +0,0 @@
-from __future__ import annotations
-
-from builtins import bool as py_bool
-
-import cupy as cp
-
-from ..common import _aliases, _helpers
-from ..common._typing import NestedSequence, SupportsBufferProtocol
-from .._internal import get_xp
-from ._typing import Array, Device, DType
-
-bool = cp.bool_
-
-# Basic renames
-acos = cp.arccos
-acosh = cp.arccosh
-asin = cp.arcsin
-asinh = cp.arcsinh
-atan = cp.arctan
-atan2 = cp.arctan2
-atanh = cp.arctanh
-bitwise_left_shift = cp.left_shift
-bitwise_invert = cp.invert
-bitwise_right_shift = cp.right_shift
-concat = cp.concatenate
-pow = cp.power
-
-arange = get_xp(cp)(_aliases.arange)
-empty = get_xp(cp)(_aliases.empty)
-empty_like = get_xp(cp)(_aliases.empty_like)
-eye = get_xp(cp)(_aliases.eye)
-full = get_xp(cp)(_aliases.full)
-full_like = get_xp(cp)(_aliases.full_like)
-linspace = get_xp(cp)(_aliases.linspace)
-ones = get_xp(cp)(_aliases.ones)
-ones_like = get_xp(cp)(_aliases.ones_like)
-zeros = get_xp(cp)(_aliases.zeros)
-zeros_like = get_xp(cp)(_aliases.zeros_like)
-UniqueAllResult = get_xp(cp)(_aliases.UniqueAllResult)
-UniqueCountsResult = get_xp(cp)(_aliases.UniqueCountsResult)
-UniqueInverseResult = get_xp(cp)(_aliases.UniqueInverseResult)
-unique_all = get_xp(cp)(_aliases.unique_all)
-unique_counts = get_xp(cp)(_aliases.unique_counts)
-unique_inverse = get_xp(cp)(_aliases.unique_inverse)
-unique_values = get_xp(cp)(_aliases.unique_values)
-std = get_xp(cp)(_aliases.std)
-var = get_xp(cp)(_aliases.var)
-cumulative_sum = get_xp(cp)(_aliases.cumulative_sum)
-cumulative_prod = get_xp(cp)(_aliases.cumulative_prod)
-clip = get_xp(cp)(_aliases.clip)
-permute_dims = get_xp(cp)(_aliases.permute_dims)
-reshape = get_xp(cp)(_aliases.reshape)
-argsort = get_xp(cp)(_aliases.argsort)
-sort = get_xp(cp)(_aliases.sort)
-nonzero = get_xp(cp)(_aliases.nonzero)
-matmul = get_xp(cp)(_aliases.matmul)
-matrix_transpose = get_xp(cp)(_aliases.matrix_transpose)
-tensordot = get_xp(cp)(_aliases.tensordot)
-sign = get_xp(cp)(_aliases.sign)
-finfo = get_xp(cp)(_aliases.finfo)
-iinfo = get_xp(cp)(_aliases.iinfo)
-
-
-# asarray also adds the copy keyword, which is not present in numpy 1.0.
-def asarray(
-    obj: Array | complex | NestedSequence[complex] | SupportsBufferProtocol,
-    /,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    copy: py_bool | None = None,
-    **kwargs: object,
-) -> Array:
-    """
-    Array API compatibility wrapper for asarray().
-
-    See the corresponding documentation in the array library and/or the array API
-    specification for more details.
-    """
-    with cp.cuda.Device(device):
-        if copy is None:
-            return cp.asarray(obj, dtype=dtype, **kwargs)
-        else:
-            res = cp.array(obj, dtype=dtype, copy=copy, **kwargs)
-            if not copy and res is not obj:
-                raise ValueError("Unable to avoid copy while creating an array as requested")
-            return res
-
-
-def astype(
-    x: Array,
-    dtype: DType,
-    /,
-    *,
-    copy: py_bool = True,
-    device: Device | None = None,
-) -> Array:
-    if device is None:
-        return x.astype(dtype=dtype, copy=copy)
-    out = _helpers.to_device(x.astype(dtype=dtype, copy=False), device)
-    return out.copy() if copy and out is x else out
-
-
-# cupy.count_nonzero does not have keepdims
-def count_nonzero(
-    x: Array,
-    axis: int | tuple[int, ...] | None = None,
-    keepdims: py_bool = False,
-) -> Array:
-   result = cp.count_nonzero(x, axis)
-   if keepdims:
-       if axis is None:
-            return cp.reshape(result, [1]*x.ndim)
-       return cp.expand_dims(result, axis)
-   return result
-
-# ceil, floor, and trunc return integers for integer inputs
-
-def ceil(x: Array, /) -> Array:
-    if cp.issubdtype(x.dtype, cp.integer):
-        return x.copy()
-    return cp.ceil(x)
-
-
-def floor(x: Array, /) -> Array:
-    if cp.issubdtype(x.dtype, cp.integer):
-        return x.copy()
-    return cp.floor(x)
-
-
-def trunc(x: Array, /) -> Array:
-    if cp.issubdtype(x.dtype, cp.integer):
-        return x.copy()
-    return cp.trunc(x)
-
-
-# take_along_axis: axis defaults to -1 but in cupy (and numpy) axis is a required arg
-def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1) -> Array:
-    return cp.take_along_axis(x, indices, axis=axis)
-
-
-# These functions are completely new here. If the library already has them
-# (i.e., numpy 2.0), use the library version instead of our wrapper.
-if hasattr(cp, 'vecdot'):
-    vecdot = cp.vecdot
-else:
-    vecdot = get_xp(cp)(_aliases.vecdot)
-
-if hasattr(cp, 'isdtype'):
-    isdtype = cp.isdtype
-else:
-    isdtype = get_xp(cp)(_aliases.isdtype)
-
-if hasattr(cp, 'unstack'):
-    unstack = cp.unstack
-else:
-    unstack = get_xp(cp)(_aliases.unstack)
-
-__all__ = _aliases.__all__ + ['asarray', 'astype',
-                              'acos', 'acosh', 'asin', 'asinh', 'atan',
-                              'atan2', 'atanh', 'bitwise_left_shift',
-                              'bitwise_invert', 'bitwise_right_shift',
-                              'bool', 'concat', 'count_nonzero', 'pow', 'sign',
-                              'ceil', 'floor', 'trunc', 'take_along_axis']
-
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/cupy/_info.py b/array_api_compat/cupy/_info.py
deleted file mode 100644
index 78e48a33..00000000
--- a/array_api_compat/cupy/_info.py
+++ /dev/null
@@ -1,336 +0,0 @@
-"""
-Array API Inspection namespace
-
-This is the namespace for inspection functions as defined by the array API
-standard. See
-https://data-apis.org/array-api/latest/API_specification/inspection.html for
-more details.
-
-"""
-from cupy import (
-    dtype,
-    cuda,
-    bool_ as bool,
-    intp,
-    int8,
-    int16,
-    int32,
-    int64,
-    uint8,
-    uint16,
-    uint32,
-    uint64,
-    float32,
-    float64,
-    complex64,
-    complex128,
-)
-
-
-class __array_namespace_info__:
-    """
-    Get the array API inspection namespace for CuPy.
-
-    The array API inspection namespace defines the following functions:
-
-    - capabilities()
-    - default_device()
-    - default_dtypes()
-    - dtypes()
-    - devices()
-
-    See
-    https://data-apis.org/array-api/latest/API_specification/inspection.html
-    for more details.
-
-    Returns
-    -------
-    info : ModuleType
-        The array API inspection namespace for CuPy.
-
-    Examples
-    --------
-    >>> info = xp.__array_namespace_info__()
-    >>> info.default_dtypes()
-    {'real floating': cupy.float64,
-     'complex floating': cupy.complex128,
-     'integral': cupy.int64,
-     'indexing': cupy.int64}
-
-    """
-
-    __module__ = 'cupy'
-
-    def capabilities(self):
-        """
-        Return a dictionary of array API library capabilities.
-
-        The resulting dictionary has the following keys:
-
-        - **"boolean indexing"**: boolean indicating whether an array library
-          supports boolean indexing. Always ``True`` for CuPy.
-
-        - **"data-dependent shapes"**: boolean indicating whether an array
-          library supports data-dependent output shapes. Always ``True`` for
-          CuPy.
-
-        See
-        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
-        for more details.
-
-        See Also
-        --------
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Returns
-        -------
-        capabilities : dict
-            A dictionary of array API library capabilities.
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.capabilities()
-        {'boolean indexing': True,
-         'data-dependent shapes': True,
-         'max dimensions': 64}
-
-        """
-        return {
-            "boolean indexing": True,
-            "data-dependent shapes": True,
-            "max dimensions": 64,
-        }
-
-    def default_device(self):
-        """
-        The default device used for new CuPy arrays.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Returns
-        -------
-        device : Device
-            The default device used for new CuPy arrays.
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.default_device()
-        Device(0)
-
-        Notes
-        -----
-        This method returns the static default device when CuPy is initialized.
-        However, the *current* device used by creation functions (``empty`` etc.)
-        can be changed globally or with a context manager.
-
-        See Also
-        --------
-        https://github.com/data-apis/array-api/issues/835
-        """
-        return cuda.Device(0)
-
-    def default_dtypes(self, *, device=None):
-        """
-        The default data types used for new CuPy arrays.
-
-        For CuPy, this always returns the following dictionary:
-
-        - **"real floating"**: ``cupy.float64``
-        - **"complex floating"**: ``cupy.complex128``
-        - **"integral"**: ``cupy.intp``
-        - **"indexing"**: ``cupy.intp``
-
-        Parameters
-        ----------
-        device : str, optional
-            The device to get the default data types for.
-
-        Returns
-        -------
-        dtypes : dict
-            A dictionary describing the default data types used for new CuPy
-            arrays.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.default_dtypes()
-        {'real floating': cupy.float64,
-         'complex floating': cupy.complex128,
-         'integral': cupy.int64,
-         'indexing': cupy.int64}
-
-        """
-        # TODO: Does this depend on device?
-        return {
-            "real floating": dtype(float64),
-            "complex floating": dtype(complex128),
-            "integral": dtype(intp),
-            "indexing": dtype(intp),
-        }
-
-    def dtypes(self, *, device=None, kind=None):
-        """
-        The array API data types supported by CuPy.
-
-        Note that this function only returns data types that are defined by
-        the array API.
-
-        Parameters
-        ----------
-        device : str, optional
-            The device to get the data types for.
-        kind : str or tuple of str, optional
-            The kind of data types to return. If ``None``, all data types are
-            returned. If a string, only data types of that kind are returned.
-            If a tuple, a dictionary containing the union of the given kinds
-            is returned. The following kinds are supported:
-
-            - ``'bool'``: boolean data types (i.e., ``bool``).
-            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
-              ``int16``, ``int32``, ``int64``).
-            - ``'unsigned integer'``: unsigned integer data types (i.e.,
-              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
-            - ``'integral'``: integer data types. Shorthand for ``('signed
-              integer', 'unsigned integer')``.
-            - ``'real floating'``: real-valued floating-point data types
-              (i.e., ``float32``, ``float64``).
-            - ``'complex floating'``: complex floating-point data types (i.e.,
-              ``complex64``, ``complex128``).
-            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
-              'real floating', 'complex floating')``.
-
-        Returns
-        -------
-        dtypes : dict
-            A dictionary mapping the names of data types to the corresponding
-            CuPy data types.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.devices
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.dtypes(kind='signed integer')
-        {'int8': cupy.int8,
-         'int16': cupy.int16,
-         'int32': cupy.int32,
-         'int64': cupy.int64}
-
-        """
-        # TODO: Does this depend on device?
-        if kind is None:
-            return {
-                "bool": dtype(bool),
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-                "float32": dtype(float32),
-                "float64": dtype(float64),
-                "complex64": dtype(complex64),
-                "complex128": dtype(complex128),
-            }
-        if kind == "bool":
-            return {"bool": bool}
-        if kind == "signed integer":
-            return {
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-            }
-        if kind == "unsigned integer":
-            return {
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-            }
-        if kind == "integral":
-            return {
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-            }
-        if kind == "real floating":
-            return {
-                "float32": dtype(float32),
-                "float64": dtype(float64),
-            }
-        if kind == "complex floating":
-            return {
-                "complex64": dtype(complex64),
-                "complex128": dtype(complex128),
-            }
-        if kind == "numeric":
-            return {
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-                "float32": dtype(float32),
-                "float64": dtype(float64),
-                "complex64": dtype(complex64),
-                "complex128": dtype(complex128),
-            }
-        if isinstance(kind, tuple):
-            res = {}
-            for k in kind:
-                res.update(self.dtypes(kind=k))
-            return res
-        raise ValueError(f"unsupported kind: {kind!r}")
-
-    def devices(self):
-        """
-        The devices supported by CuPy.
-
-        Returns
-        -------
-        devices : list[Device]
-            The devices supported by CuPy.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes
-
-        """
-        return [cuda.Device(i) for i in range(cuda.runtime.getDeviceCount())]
diff --git a/array_api_compat/cupy/_typing.py b/array_api_compat/cupy/_typing.py
deleted file mode 100644
index e5c202dc..00000000
--- a/array_api_compat/cupy/_typing.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from __future__ import annotations
-
-__all__ = ["Array", "DType", "Device"]
-
-from typing import TYPE_CHECKING
-
-import cupy as cp
-from cupy import ndarray as Array
-from cupy.cuda.device import Device
-
-if TYPE_CHECKING:
-    # NumPy 1.x on Python 3.10 fails to parse np.dtype[]
-    DType = cp.dtype[
-        cp.intp
-        | cp.int8
-        | cp.int16
-        | cp.int32
-        | cp.int64
-        | cp.uint8
-        | cp.uint16
-        | cp.uint32
-        | cp.uint64
-        | cp.float32
-        | cp.float64
-        | cp.complex64
-        | cp.complex128
-        | cp.bool_
-    ]
-else:
-    DType = cp.dtype
diff --git a/array_api_compat/cupy/fft.py b/array_api_compat/cupy/fft.py
deleted file mode 100644
index 53a9a454..00000000
--- a/array_api_compat/cupy/fft.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from cupy.fft import *  # noqa: F403
-
-# cupy.fft doesn't have __all__. If it is added, replace this with
-#
-# from cupy.fft import __all__ as linalg_all
-_n: dict[str, object] = {}
-exec("from cupy.fft import *", _n)
-del _n["__builtins__"]
-fft_all = list(_n)
-del _n
-
-from ..common import _fft
-from .._internal import get_xp
-
-import cupy as cp
-
-fft = get_xp(cp)(_fft.fft)
-ifft = get_xp(cp)(_fft.ifft)
-fftn = get_xp(cp)(_fft.fftn)
-ifftn = get_xp(cp)(_fft.ifftn)
-rfft = get_xp(cp)(_fft.rfft)
-irfft = get_xp(cp)(_fft.irfft)
-rfftn = get_xp(cp)(_fft.rfftn)
-irfftn = get_xp(cp)(_fft.irfftn)
-hfft = get_xp(cp)(_fft.hfft)
-ihfft = get_xp(cp)(_fft.ihfft)
-fftfreq = get_xp(cp)(_fft.fftfreq)
-rfftfreq = get_xp(cp)(_fft.rfftfreq)
-fftshift = get_xp(cp)(_fft.fftshift)
-ifftshift = get_xp(cp)(_fft.ifftshift)
-
-__all__ = fft_all + _fft.__all__
-
-def __dir__() -> list[str]:
-    return __all__
-
diff --git a/array_api_compat/cupy/linalg.py b/array_api_compat/cupy/linalg.py
deleted file mode 100644
index da301574..00000000
--- a/array_api_compat/cupy/linalg.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from cupy.linalg import * # noqa: F403
-# cupy.linalg doesn't have __all__. If it is added, replace this with
-#
-# from cupy.linalg import __all__ as linalg_all
-_n: dict[str, object] = {}
-exec('from cupy.linalg import *', _n)
-del _n['__builtins__']
-linalg_all = list(_n)
-del _n
-
-from ..common import _linalg
-from .._internal import get_xp
-
-import cupy as cp
-
-# These functions are in both the main and linalg namespaces
-from ._aliases import matmul, matrix_transpose, tensordot, vecdot # noqa: F401
-
-cross = get_xp(cp)(_linalg.cross)
-outer = get_xp(cp)(_linalg.outer)
-EighResult = _linalg.EighResult
-QRResult = _linalg.QRResult
-SlogdetResult = _linalg.SlogdetResult
-SVDResult = _linalg.SVDResult
-eigh = get_xp(cp)(_linalg.eigh)
-qr = get_xp(cp)(_linalg.qr)
-slogdet = get_xp(cp)(_linalg.slogdet)
-svd = get_xp(cp)(_linalg.svd)
-cholesky = get_xp(cp)(_linalg.cholesky)
-matrix_rank = get_xp(cp)(_linalg.matrix_rank)
-pinv = get_xp(cp)(_linalg.pinv)
-matrix_norm = get_xp(cp)(_linalg.matrix_norm)
-svdvals = get_xp(cp)(_linalg.svdvals)
-diagonal = get_xp(cp)(_linalg.diagonal)
-trace = get_xp(cp)(_linalg.trace)
-
-# These functions are completely new here. If the library already has them
-# (i.e., numpy 2.0), use the library version instead of our wrapper.
-if hasattr(cp.linalg, 'vector_norm'):
-    vector_norm = cp.linalg.vector_norm
-else:
-    vector_norm = get_xp(cp)(_linalg.vector_norm)
-
-__all__ = linalg_all + _linalg.__all__
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/dask/array/__init__.py b/array_api_compat/dask/array/__init__.py
deleted file mode 100644
index f78aa8b3..00000000
--- a/array_api_compat/dask/array/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from typing import Final
-
-from ..._internal import clone_module
-
-__all__ = clone_module("dask.array", globals())
-
-# These imports may overwrite names from the import * above.
-from . import _aliases
-from ._aliases import *  # type: ignore[assignment] # noqa: F403
-from ._info import __array_namespace_info__  # noqa: F401
-
-__array_api_version__: Final = "2024.12"
-del Final
-
-# See the comment in the numpy __init__.py
-__import__(__package__ + '.linalg')
-__import__(__package__ + '.fft')
-
-__all__ = sorted(
-    set(__all__)
-    | set(_aliases.__all__)
-    | {"__array_api_version__", "__array_namespace_info__", "linalg", "fft"}
-)
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/dask/array/_aliases.py b/array_api_compat/dask/array/_aliases.py
deleted file mode 100644
index 54d323b2..00000000
--- a/array_api_compat/dask/array/_aliases.py
+++ /dev/null
@@ -1,369 +0,0 @@
-# pyright: reportPrivateUsage=false
-# pyright: reportUnknownArgumentType=false
-# pyright: reportUnknownMemberType=false
-# pyright: reportUnknownVariableType=false
-
-from __future__ import annotations
-
-from builtins import bool as py_bool
-from collections.abc import Callable
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
-    from typing_extensions import TypeIs
-
-import dask.array as da
-import numpy as np
-from numpy import bool_ as bool
-from numpy import (
-    can_cast,
-    complex64,
-    complex128,
-    float32,
-    float64,
-    int8,
-    int16,
-    int32,
-    int64,
-    result_type,
-    uint8,
-    uint16,
-    uint32,
-    uint64,
-)
-
-from ..._internal import get_xp
-from ...common import _aliases, _helpers, array_namespace
-from ...common._typing import (
-    Array,
-    Device,
-    DType,
-    NestedSequence,
-    SupportsBufferProtocol,
-)
-
-isdtype = get_xp(np)(_aliases.isdtype)
-unstack = get_xp(da)(_aliases.unstack)
-
-
-# da.astype doesn't respect copy=True
-def astype(
-    x: Array,
-    dtype: DType,
-    /,
-    *,
-    copy: py_bool = True,
-    device: Device | None = None,
-) -> Array:
-    """
-    Array API compatibility wrapper for astype().
-
-    See the corresponding documentation in the array library and/or the array API
-    specification for more details.
-    """
-    # TODO: respect device keyword?
-    _helpers._check_device(da, device)
-
-    if not copy and dtype == x.dtype:
-        return x
-    x = x.astype(dtype)
-    return x.copy() if copy else x
-
-
-# Common aliases
-
-
-# This arange func is modified from the common one to
-# not pass stop/step as keyword arguments, which will cause
-# an error with dask
-def arange(
-    start: float,
-    /,
-    stop: float | None = None,
-    step: float = 1,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    **kwargs: object,
-) -> Array:
-    """
-    Array API compatibility wrapper for arange().
-
-    See the corresponding documentation in the array library and/or the array API
-    specification for more details.
-    """
-    # TODO: respect device keyword?
-    _helpers._check_device(da, device)
-
-    args: list[Any] = [start]
-    if stop is not None:
-        args.append(stop)
-    else:
-        # stop is None, so start is actually stop
-        # prepend the default value for start which is 0
-        args.insert(0, 0)
-    args.append(step)
-
-    return da.arange(*args, dtype=dtype, **kwargs)
-
-
-eye = get_xp(da)(_aliases.eye)
-linspace = get_xp(da)(_aliases.linspace)
-UniqueAllResult = get_xp(da)(_aliases.UniqueAllResult)
-UniqueCountsResult = get_xp(da)(_aliases.UniqueCountsResult)
-UniqueInverseResult = get_xp(da)(_aliases.UniqueInverseResult)
-unique_all = get_xp(da)(_aliases.unique_all)
-unique_counts = get_xp(da)(_aliases.unique_counts)
-unique_inverse = get_xp(da)(_aliases.unique_inverse)
-unique_values = get_xp(da)(_aliases.unique_values)
-permute_dims = get_xp(da)(_aliases.permute_dims)
-std = get_xp(da)(_aliases.std)
-var = get_xp(da)(_aliases.var)
-cumulative_sum = get_xp(da)(_aliases.cumulative_sum)
-cumulative_prod = get_xp(da)(_aliases.cumulative_prod)
-empty = get_xp(da)(_aliases.empty)
-empty_like = get_xp(da)(_aliases.empty_like)
-full = get_xp(da)(_aliases.full)
-full_like = get_xp(da)(_aliases.full_like)
-ones = get_xp(da)(_aliases.ones)
-ones_like = get_xp(da)(_aliases.ones_like)
-zeros = get_xp(da)(_aliases.zeros)
-zeros_like = get_xp(da)(_aliases.zeros_like)
-reshape = get_xp(da)(_aliases.reshape)
-matrix_transpose = get_xp(da)(_aliases.matrix_transpose)
-vecdot = get_xp(da)(_aliases.vecdot)
-nonzero = get_xp(da)(_aliases.nonzero)
-matmul = get_xp(np)(_aliases.matmul)
-tensordot = get_xp(np)(_aliases.tensordot)
-sign = get_xp(np)(_aliases.sign)
-finfo = get_xp(np)(_aliases.finfo)
-iinfo = get_xp(np)(_aliases.iinfo)
-
-
-# asarray also adds the copy keyword, which is not present in numpy 1.0.
-def asarray(
-    obj: Array | complex | NestedSequence[complex] | SupportsBufferProtocol,
-    /,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    copy: py_bool | None = None,
-    **kwargs: object,
-) -> Array:
-    """
-    Array API compatibility wrapper for asarray().
-
-    See the corresponding documentation in the array library and/or the array API
-    specification for more details.
-    """
-    # TODO: respect device keyword?
-    _helpers._check_device(da, device)
-
-    if isinstance(obj, da.Array):
-        if dtype is not None and dtype != obj.dtype:
-            if copy is False:
-                raise ValueError("Unable to avoid copy when changing dtype")
-            obj = obj.astype(dtype)
-        return obj.copy() if copy else obj  # pyright: ignore[reportAttributeAccessIssue]
-
-    if copy is False:
-        raise ValueError(
-            "Unable to avoid copy when converting a non-dask object to dask"
-        )
-
-    # copy=None to be uniform across dask < 2024.12 and >= 2024.12
-    # see https://github.com/dask/dask/pull/11524/
-    obj = np.array(obj, dtype=dtype, copy=True)
-    return da.from_array(obj)
-
-
-# Element wise aliases
-from dask.array import arccos as acos
-from dask.array import arccosh as acosh
-from dask.array import arcsin as asin
-from dask.array import arcsinh as asinh
-from dask.array import arctan as atan
-from dask.array import arctan2 as atan2
-from dask.array import arctanh as atanh
-
-# Other
-from dask.array import concatenate as concat
-from dask.array import invert as bitwise_invert
-from dask.array import left_shift as bitwise_left_shift
-from dask.array import power as pow
-from dask.array import right_shift as bitwise_right_shift
-
-
-# dask.array.clip does not work unless all three arguments are provided.
-# Furthermore, the masking workaround in common._aliases.clip cannot work with
-# dask (meaning uint64 promoting to float64 is going to just be unfixed for
-# now).
-def clip(
-    x: Array,
-    /,
-    min: float | Array | None = None,
-    max: float | Array | None = None,
-) -> Array:
-    """
-    Array API compatibility wrapper for clip().
-
-    See the corresponding documentation in the array library and/or the array API
-    specification for more details.
-    """
-
-    def _isscalar(a: float | Array | None, /) -> TypeIs[float | None]:
-        return a is None or isinstance(a, (int, float))
-
-    min_shape = () if _isscalar(min) else min.shape
-    max_shape = () if _isscalar(max) else max.shape
-
-    # TODO: This won't handle dask unknown shapes
-    result_shape = np.broadcast_shapes(x.shape, min_shape, max_shape)
-
-    if min is not None:
-        min = da.broadcast_to(da.asarray(min), result_shape)
-    if max is not None:
-        max = da.broadcast_to(da.asarray(max), result_shape)
-
-    if min is None and max is None:
-        return da.positive(x)
-
-    if min is None:
-        return astype(da.minimum(x, max), x.dtype)
-    if max is None:
-        return astype(da.maximum(x, min), x.dtype)
-
-    return astype(da.minimum(da.maximum(x, min), max), x.dtype)
-
-
-def _ensure_single_chunk(x: Array, axis: int) -> tuple[Array, Callable[[Array], Array]]:
-    """
-    Make sure that Array is not broken into multiple chunks along axis.
-
-    Returns
-    -------
-    x : Array
-        The input Array with a single chunk along axis.
-    restore : Callable[Array, Array]
-        function to apply to the output to rechunk it back into reasonable chunks
-    """
-    if axis < 0:
-        axis += x.ndim
-    if x.numblocks[axis] < 2:
-        return x, lambda x: x
-
-    # Break chunks on other axes in an attempt to keep chunk size low
-    x = x.rechunk({i: -1 if i == axis else "auto" for i in range(x.ndim)})
-
-    # Rather than reconstructing the original chunks, which can be a
-    # very expensive affair, just break down oversized chunks without
-    # incurring in any transfers over the network.
-    # This has the downside of a risk of overchunking if the array is
-    # then used in operations against other arrays that match the
-    # original chunking pattern.
-    return x, lambda x: x.rechunk()
-
-
-def sort(
-    x: Array,
-    /,
-    *,
-    axis: int = -1,
-    descending: py_bool = False,
-    stable: py_bool = True,
-) -> Array:
-    """
-    Array API compatibility layer around the lack of sort() in Dask.
-
-    Warnings
-    --------
-    This function temporarily rechunks the array along `axis` to a single chunk.
-    This can be extremely inefficient and can lead to out-of-memory errors.
-
-    See the corresponding documentation in the array library and/or the array API
-    specification for more details.
-    """
-    x, restore = _ensure_single_chunk(x, axis)
-
-    meta_xp = array_namespace(x._meta)
-    x = da.map_blocks(
-        meta_xp.sort,
-        x,
-        axis=axis,
-        meta=x._meta,
-        dtype=x.dtype,
-        descending=descending,
-        stable=stable,
-    )
-
-    return restore(x)
-
-
-def argsort(
-    x: Array,
-    /,
-    *,
-    axis: int = -1,
-    descending: py_bool = False,
-    stable: py_bool = True,
-) -> Array:
-    """
-    Array API compatibility layer around the lack of argsort() in Dask.
-
-    See the corresponding documentation in the array library and/or the array API
-    specification for more details.
-
-    Warnings
-    --------
-    This function temporarily rechunks the array along `axis` into a single chunk.
-    This can be extremely inefficient and can lead to out-of-memory errors.
-    """
-    x, restore = _ensure_single_chunk(x, axis)
-
-    meta_xp = array_namespace(x._meta)
-    dtype = meta_xp.argsort(x._meta).dtype
-    meta = meta_xp.astype(x._meta, dtype)
-    x = da.map_blocks(
-        meta_xp.argsort,
-        x,
-        axis=axis,
-        meta=meta,
-        dtype=dtype,
-        descending=descending,
-        stable=stable,
-    )
-
-    return restore(x)
-
-
-# dask.array.count_nonzero does not have keepdims
-def count_nonzero(
-    x: Array,
-    axis: int | None = None,
-    keepdims: py_bool = False,
-) -> Array:
-    result = da.count_nonzero(x, axis)
-    if keepdims:
-        if axis is None:
-            return da.reshape(result, [1] * x.ndim)
-        return da.expand_dims(result, axis)
-    return result
-
-
-__all__ = [
-    "count_nonzero",
-    "bool",
-    "int8", "int16", "int32", "int64",
-    "uint8", "uint16", "uint32", "uint64",
-    "float32", "float64",
-    "complex64", "complex128",
-    "asarray", "astype", "can_cast", "result_type",
-    "pow",
-    "concat",
-    "acos", "acosh", "asin", "asinh", "atan", "atan2", "atanh",
-    "bitwise_left_shift", "bitwise_right_shift", "bitwise_invert",
-]  # fmt: skip
-__all__ += _aliases.__all__
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/dask/array/_info.py b/array_api_compat/dask/array/_info.py
deleted file mode 100644
index 2f39fc4b..00000000
--- a/array_api_compat/dask/array/_info.py
+++ /dev/null
@@ -1,407 +0,0 @@
-"""
-Array API Inspection namespace
-
-This is the namespace for inspection functions as defined by the array API
-standard. See
-https://data-apis.org/array-api/latest/API_specification/inspection.html for
-more details.
-
-"""
-
-# pyright: reportPrivateUsage=false
-
-from __future__ import annotations
-
-from typing import Literal, TypeAlias, overload
-
-import dask.array as da
-from numpy import bool_ as bool
-from numpy import (
-    complex64,
-    complex128,
-    dtype,
-    float32,
-    float64,
-    int8,
-    int16,
-    int32,
-    int64,
-    intp,
-    uint8,
-    uint16,
-    uint32,
-    uint64,
-)
-
-from ...common._helpers import _DASK_DEVICE, _check_device, _dask_device
-from ...common._typing import (
-    Capabilities,
-    DefaultDTypes,
-    DType,
-    DTypeKind,
-    DTypesAll,
-    DTypesAny,
-    DTypesBool,
-    DTypesComplex,
-    DTypesIntegral,
-    DTypesNumeric,
-    DTypesReal,
-    DTypesSigned,
-    DTypesUnsigned,
-)
-Device: TypeAlias = Literal["cpu"] | _dask_device
-
-
-class __array_namespace_info__:
-    """
-    Get the array API inspection namespace for Dask.
-
-    The array API inspection namespace defines the following functions:
-
-    - capabilities()
-    - default_device()
-    - default_dtypes()
-    - dtypes()
-    - devices()
-
-    See
-    https://data-apis.org/array-api/latest/API_specification/inspection.html
-    for more details.
-
-    Returns
-    -------
-    info : ModuleType
-        The array API inspection namespace for Dask.
-
-    Examples
-    --------
-    >>> info = xp.__array_namespace_info__()
-    >>> info.default_dtypes()
-    {'real floating': dask.float64,
-     'complex floating': dask.complex128,
-     'integral': dask.int64,
-     'indexing': dask.int64}
-
-    """
-
-    __module__ = "dask.array"
-
-    def capabilities(self) -> Capabilities:
-        """
-        Return a dictionary of array API library capabilities.
-
-        The resulting dictionary has the following keys:
-
-        - **"boolean indexing"**: boolean indicating whether an array library
-          supports boolean indexing.
-
-          Dask support boolean indexing as long as both the index
-          and the indexed arrays have known shapes.
-          Note however that the output .shape and .size properties
-          will contain a non-compliant math.nan instead of None.
-
-        - **"data-dependent shapes"**: boolean indicating whether an array
-          library supports data-dependent output shapes.
-
-          Dask implements unique_values et.al.
-          Note however that the output .shape and .size properties
-          will contain a non-compliant math.nan instead of None.
-
-        - **"max dimensions"**: integer indicating the maximum number of
-          dimensions supported by the array library.
-
-        See
-        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
-        for more details.
-
-        See Also
-        --------
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Returns
-        -------
-        capabilities : dict
-            A dictionary of array API library capabilities.
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.capabilities()
-        {'boolean indexing': True,
-         'data-dependent shapes': True,
-         'max dimensions': 64}
-
-        """
-        return {
-            "boolean indexing": True,
-            "data-dependent shapes": True,
-            "max dimensions": 64,
-        }
-
-    def default_device(self) -> Device:
-        """
-        The default device used for new Dask arrays.
-
-        For Dask, this always returns ``'cpu'``.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Returns
-        -------
-        device : Device
-            The default device used for new Dask arrays.
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.default_device()
-        'cpu'
-
-        """
-        return "cpu"
-
-    def default_dtypes(self, /, *, device: Device | None = None) -> DefaultDTypes:
-        """
-        The default data types used for new Dask arrays.
-
-        For Dask, this always returns the following dictionary:
-
-        - **"real floating"**: ``numpy.float64``
-        - **"complex floating"**: ``numpy.complex128``
-        - **"integral"**: ``numpy.intp``
-        - **"indexing"**: ``numpy.intp``
-
-        Parameters
-        ----------
-        device : str, optional
-            The device to get the default data types for.
-
-        Returns
-        -------
-        dtypes : dict
-            A dictionary describing the default data types used for new Dask
-            arrays.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.default_dtypes()
-        {'real floating': dask.float64,
-         'complex floating': dask.complex128,
-         'integral': dask.int64,
-         'indexing': dask.int64}
-
-        """
-        _check_device(da, device)
-        return {
-            "real floating": dtype(float64),
-            "complex floating": dtype(complex128),
-            "integral": dtype(intp),
-            "indexing": dtype(intp),
-        }
-
-    @overload
-    def dtypes(
-        self, /, *, device: Device | None = None, kind: None = None
-    ) -> DTypesAll: ...
-    @overload
-    def dtypes(
-        self, /, *, device: Device | None = None, kind: Literal["bool"]
-    ) -> DTypesBool: ...
-    @overload
-    def dtypes(
-        self, /, *, device: Device | None = None, kind: Literal["signed integer"]
-    ) -> DTypesSigned: ...
-    @overload
-    def dtypes(
-        self, /, *, device: Device | None = None, kind: Literal["unsigned integer"]
-    ) -> DTypesUnsigned: ...
-    @overload
-    def dtypes(
-        self, /, *, device: Device | None = None, kind: Literal["integral"]
-    ) -> DTypesIntegral: ...
-    @overload
-    def dtypes(
-        self, /, *, device: Device | None = None, kind: Literal["real floating"]
-    ) -> DTypesReal: ...
-    @overload
-    def dtypes(
-        self, /, *, device: Device | None = None, kind: Literal["complex floating"]
-    ) -> DTypesComplex: ...
-    @overload
-    def dtypes(
-        self, /, *, device: Device | None = None, kind: Literal["numeric"]
-    ) -> DTypesNumeric: ...
-    def dtypes(
-        self, /, *, device: Device | None = None, kind: DTypeKind | None = None
-    ) -> DTypesAny:
-        """
-        The array API data types supported by Dask.
-
-        Note that this function only returns data types that are defined by
-        the array API.
-
-        Parameters
-        ----------
-        device : str, optional
-            The device to get the data types for.
-        kind : str or tuple of str, optional
-            The kind of data types to return. If ``None``, all data types are
-            returned. If a string, only data types of that kind are returned.
-            If a tuple, a dictionary containing the union of the given kinds
-            is returned. The following kinds are supported:
-
-            - ``'bool'``: boolean data types (i.e., ``bool``).
-            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
-              ``int16``, ``int32``, ``int64``).
-            - ``'unsigned integer'``: unsigned integer data types (i.e.,
-              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
-            - ``'integral'``: integer data types. Shorthand for ``('signed
-              integer', 'unsigned integer')``.
-            - ``'real floating'``: real-valued floating-point data types
-              (i.e., ``float32``, ``float64``).
-            - ``'complex floating'``: complex floating-point data types (i.e.,
-              ``complex64``, ``complex128``).
-            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
-              'real floating', 'complex floating')``.
-
-        Returns
-        -------
-        dtypes : dict
-            A dictionary mapping the names of data types to the corresponding
-            Dask data types.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.devices
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.dtypes(kind='signed integer')
-        {'int8': dask.int8,
-         'int16': dask.int16,
-         'int32': dask.int32,
-         'int64': dask.int64}
-
-        """
-        _check_device(da, device)
-        if kind is None:
-            return {
-                "bool": dtype(bool),
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-                "float32": dtype(float32),
-                "float64": dtype(float64),
-                "complex64": dtype(complex64),
-                "complex128": dtype(complex128),
-            }
-        if kind == "bool":
-            return {"bool": bool}
-        if kind == "signed integer":
-            return {
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-            }
-        if kind == "unsigned integer":
-            return {
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-            }
-        if kind == "integral":
-            return {
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-            }
-        if kind == "real floating":
-            return {
-                "float32": dtype(float32),
-                "float64": dtype(float64),
-            }
-        if kind == "complex floating":
-            return {
-                "complex64": dtype(complex64),
-                "complex128": dtype(complex128),
-            }
-        if kind == "numeric":
-            return {
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-                "float32": dtype(float32),
-                "float64": dtype(float64),
-                "complex64": dtype(complex64),
-                "complex128": dtype(complex128),
-            }
-        if isinstance(kind, tuple):
-            res: dict[str, DType] = {}
-            for k in kind:
-                res.update(self.dtypes(kind=k))
-            return res
-        raise ValueError(f"unsupported kind: {kind!r}")
-
-    def devices(self) -> list[Device]:
-        """
-        The devices supported by Dask.
-
-        For Dask, this always returns ``['cpu', DASK_DEVICE]``.
-
-        Returns
-        -------
-        devices : list[Device]
-            The devices supported by Dask.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.devices()
-        ['cpu', DASK_DEVICE]
-
-        """
-        return ["cpu", _DASK_DEVICE]
diff --git a/array_api_compat/dask/array/fft.py b/array_api_compat/dask/array/fft.py
deleted file mode 100644
index 44b68e73..00000000
--- a/array_api_compat/dask/array/fft.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from ..._internal import clone_module
-
-__all__ = clone_module("dask.array.fft", globals())
-
-from ...common import _fft
-from ..._internal import get_xp
-
-import dask.array as da
-
-fftfreq = get_xp(da)(_fft.fftfreq)
-rfftfreq = get_xp(da)(_fft.rfftfreq)
-
-__all__ += ["fftfreq", "rfftfreq"]
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/dask/array/linalg.py b/array_api_compat/dask/array/linalg.py
deleted file mode 100644
index 6b3c1011..00000000
--- a/array_api_compat/dask/array/linalg.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from __future__ import annotations
-
-from typing import Literal
-
-import dask.array as da
-
-# The `matmul` and `tensordot` functions are in both the main and linalg namespaces
-from dask.array import matmul, outer, tensordot
-
-# Exports
-from ..._internal import clone_module, get_xp
-from ...common import _linalg
-from ...common._typing import Array
-
-__all__ = clone_module("dask.array.linalg", globals())
-
-from ._aliases import matrix_transpose, vecdot
-
-EighResult = _linalg.EighResult
-QRResult = _linalg.QRResult
-SlogdetResult = _linalg.SlogdetResult
-SVDResult = _linalg.SVDResult
-# TODO: use the QR wrapper once dask
-# supports the mode keyword on QR
-# https://github.com/dask/dask/issues/10388
-#qr = get_xp(da)(_linalg.qr)
-def qr(  # type: ignore[no-redef]
-    x: Array,
-    mode: Literal["reduced", "complete"] = "reduced",
-    **kwargs: object,
-) -> QRResult:
-    if mode != "reduced":
-        raise ValueError("dask arrays only support using mode='reduced'")
-    return QRResult(*da.linalg.qr(x, **kwargs))
-trace = get_xp(da)(_linalg.trace)
-cholesky = get_xp(da)(_linalg.cholesky)
-matrix_rank = get_xp(da)(_linalg.matrix_rank)
-matrix_norm = get_xp(da)(_linalg.matrix_norm)
-
-
-# Wrap the svd functions to not pass full_matrices to dask
-# when full_matrices=False (as that is the default behavior for dask),
-# and dask doesn't have the full_matrices keyword
-def svd(x: Array, full_matrices: bool = True, **kwargs: object) -> SVDResult:  # type: ignore[no-redef]
-    if full_matrices:
-        raise ValueError("full_matrics=True is not supported by dask.")
-    return da.linalg.svd(x, coerce_signs=False, **kwargs)
-
-def svdvals(x: Array) -> Array:
-    # TODO: can't avoid computing U or V for dask
-    _, s, _ =  svd(x)
-    return s
-
-vector_norm = get_xp(da)(_linalg.vector_norm)
-diagonal = get_xp(da)(_linalg.diagonal)
-
-__all__ += ["trace", "outer", "matmul", "tensordot",
-            "matrix_transpose", "vecdot", "EighResult",
-            "QRResult", "SlogdetResult", "SVDResult", "qr",
-            "cholesky", "matrix_rank", "matrix_norm", "svdvals",
-            "vector_norm", "diagonal"]
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/numpy/__init__.py b/array_api_compat/numpy/__init__.py
deleted file mode 100644
index 23379e44..00000000
--- a/array_api_compat/numpy/__init__.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# ruff: noqa: PLC0414
-from typing import Final
-
-from .._internal import clone_module
-
-# This needs to be loaded explicitly before cloning
-import numpy.typing  # noqa: F401
-
-__all__ = clone_module("numpy", globals())
-
-# These imports may overwrite names from the import * above.
-from . import _aliases
-from ._aliases import *  # type: ignore[assignment,no-redef] # noqa: F403
-from ._info import __array_namespace_info__  # noqa: F401
-
-# Don't know why, but we have to do an absolute import to import linalg. If we
-# instead do
-#
-# from . import linalg
-#
-# It doesn't overwrite np.linalg from above. The import is generated
-# dynamically so that the library can be vendored.
-__import__(__package__ + ".linalg")
-
-__import__(__package__ + ".fft")
-
-from .linalg import matrix_transpose, vecdot  # type: ignore[no-redef]  # noqa: F401
-
-__array_api_version__: Final = "2024.12"
-
-__all__ = sorted(
-    set(__all__) 
-    | set(_aliases.__all__) 
-    | {"__array_api_version__", "__array_namespace_info__", "linalg", "fft"}
-)
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/numpy/_aliases.py b/array_api_compat/numpy/_aliases.py
deleted file mode 100644
index 87b3c2f3..00000000
--- a/array_api_compat/numpy/_aliases.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# pyright: reportPrivateUsage=false
-from __future__ import annotations
-
-from builtins import bool as py_bool
-from typing import Any, cast
-
-import numpy as np
-
-from .._internal import get_xp
-from ..common import _aliases, _helpers
-from ..common._typing import NestedSequence, SupportsBufferProtocol
-from ._typing import Array, Device, DType
-
-bool = np.bool_
-
-# Basic renames
-acos = np.arccos
-acosh = np.arccosh
-asin = np.arcsin
-asinh = np.arcsinh
-atan = np.arctan
-atan2 = np.arctan2
-atanh = np.arctanh
-bitwise_left_shift = np.left_shift
-bitwise_invert = np.invert
-bitwise_right_shift = np.right_shift
-concat = np.concatenate
-pow = np.power
-
-arange = get_xp(np)(_aliases.arange)
-empty = get_xp(np)(_aliases.empty)
-empty_like = get_xp(np)(_aliases.empty_like)
-eye = get_xp(np)(_aliases.eye)
-full = get_xp(np)(_aliases.full)
-full_like = get_xp(np)(_aliases.full_like)
-linspace = get_xp(np)(_aliases.linspace)
-ones = get_xp(np)(_aliases.ones)
-ones_like = get_xp(np)(_aliases.ones_like)
-zeros = get_xp(np)(_aliases.zeros)
-zeros_like = get_xp(np)(_aliases.zeros_like)
-UniqueAllResult = get_xp(np)(_aliases.UniqueAllResult)
-UniqueCountsResult = get_xp(np)(_aliases.UniqueCountsResult)
-UniqueInverseResult = get_xp(np)(_aliases.UniqueInverseResult)
-unique_all = get_xp(np)(_aliases.unique_all)
-unique_counts = get_xp(np)(_aliases.unique_counts)
-unique_inverse = get_xp(np)(_aliases.unique_inverse)
-unique_values = get_xp(np)(_aliases.unique_values)
-std = get_xp(np)(_aliases.std)
-var = get_xp(np)(_aliases.var)
-cumulative_sum = get_xp(np)(_aliases.cumulative_sum)
-cumulative_prod = get_xp(np)(_aliases.cumulative_prod)
-clip = get_xp(np)(_aliases.clip)
-permute_dims = get_xp(np)(_aliases.permute_dims)
-reshape = get_xp(np)(_aliases.reshape)
-argsort = get_xp(np)(_aliases.argsort)
-sort = get_xp(np)(_aliases.sort)
-nonzero = get_xp(np)(_aliases.nonzero)
-matmul = get_xp(np)(_aliases.matmul)
-matrix_transpose = get_xp(np)(_aliases.matrix_transpose)
-tensordot = get_xp(np)(_aliases.tensordot)
-sign = get_xp(np)(_aliases.sign)
-finfo = get_xp(np)(_aliases.finfo)
-iinfo = get_xp(np)(_aliases.iinfo)
-
-
-# asarray also adds the copy keyword, which is not present in numpy 1.0.
-# asarray() is different enough between numpy, cupy, and dask, the logic
-# complicated enough that it's easier to define it separately for each module
-# rather than trying to combine everything into one function in common/
-def asarray(
-    obj: Array | complex | NestedSequence[complex] | SupportsBufferProtocol,
-    /,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    copy: py_bool | None = None,
-    **kwargs: Any,
-) -> Array:
-    """
-    Array API compatibility wrapper for asarray().
-
-    See the corresponding documentation in the array library and/or the array API
-    specification for more details.
-    """
-    _helpers._check_device(np, device)
-
-    # None is unsupported in NumPy 1.0, but we can use an internal enum
-    # False in NumPy 1.0 means None in NumPy 2.0 and in the Array API
-    if copy is None:
-        copy = np._CopyMode.IF_NEEDED  # type: ignore[assignment,attr-defined]
-    elif copy is False:
-        copy = np._CopyMode.NEVER  # type: ignore[assignment,attr-defined]
-
-    return np.array(obj, copy=copy, dtype=dtype, **kwargs)
-
-
-def astype(
-    x: Array,
-    dtype: DType,
-    /,
-    *,
-    copy: py_bool = True,
-    device: Device | None = None,
-) -> Array:
-    _helpers._check_device(np, device)
-    return x.astype(dtype=dtype, copy=copy)
-
-
-# count_nonzero returns a python int for axis=None and keepdims=False
-# https://github.com/numpy/numpy/issues/17562
-def count_nonzero(
-    x: Array,
-    axis: int | tuple[int, ...] | None = None,
-    keepdims: py_bool = False,
-) -> Array:
-    # NOTE: this is currently incorrectly typed in numpy, but will be fixed in
-    # numpy 2.2.5 and 2.3.0: https://github.com/numpy/numpy/pull/28750
-    result = cast("Any", np.count_nonzero(x, axis=axis, keepdims=keepdims))  # pyright: ignore[reportArgumentType, reportCallIssue]
-    if axis is None and not keepdims:
-        return np.asarray(result)
-    return result
-
-
-# take_along_axis: axis defaults to -1 but in numpy axis is a required arg
-def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1) -> Array:
-    return np.take_along_axis(x, indices, axis=axis)
-
-
-# ceil, floor, and trunc return integers for integer inputs in NumPy < 2
-
-def ceil(x: Array, /) -> Array:
-    if np.__version__ < '2' and np.issubdtype(x.dtype, np.integer):
-        return x.copy()
-    return np.ceil(x)
-
-
-def floor(x: Array, /) -> Array:
-    if np.__version__ < '2' and np.issubdtype(x.dtype, np.integer):
-        return x.copy()
-    return np.floor(x)
-
-
-def trunc(x: Array, /) -> Array:
-    if np.__version__ < '2' and np.issubdtype(x.dtype, np.integer):
-        return x.copy()
-    return np.trunc(x)
-
-
-# These functions are completely new here. If the library already has them
-# (i.e., numpy 2.0), use the library version instead of our wrapper.
-if hasattr(np, "vecdot"):
-    vecdot = np.vecdot
-else:
-    vecdot = get_xp(np)(_aliases.vecdot)  # type: ignore[assignment]
-
-if hasattr(np, "isdtype"):
-    isdtype = np.isdtype
-else:
-    isdtype = get_xp(np)(_aliases.isdtype)
-
-if hasattr(np, "unstack"):
-    unstack = np.unstack
-else:
-    unstack = get_xp(np)(_aliases.unstack)
-
-__all__ = _aliases.__all__ + [
-    "asarray",
-    "astype",
-    "acos",
-    "acosh",
-    "asin",
-    "asinh",
-    "atan",
-    "atan2",
-    "atanh",
-    "ceil",
-    "floor",
-    "trunc",
-    "bitwise_left_shift",
-    "bitwise_invert",
-    "bitwise_right_shift",
-    "bool",
-    "concat",
-    "count_nonzero",
-    "pow",
-    "take_along_axis"
-]
-
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/numpy/_info.py b/array_api_compat/numpy/_info.py
deleted file mode 100644
index c625c13e..00000000
--- a/array_api_compat/numpy/_info.py
+++ /dev/null
@@ -1,367 +0,0 @@
-"""
-Array API Inspection namespace
-
-This is the namespace for inspection functions as defined by the array API
-standard. See
-https://data-apis.org/array-api/latest/API_specification/inspection.html for
-more details.
-
-"""
-from __future__ import annotations
-
-from numpy import bool_ as bool
-from numpy import (
-    complex64,
-    complex128,
-    dtype,
-    float32,
-    float64,
-    int8,
-    int16,
-    int32,
-    int64,
-    intp,
-    uint8,
-    uint16,
-    uint32,
-    uint64,
-)
-
-from ..common._typing import DefaultDTypes
-from ._typing import Device, DType
-
-
-class __array_namespace_info__:
-    """
-    Get the array API inspection namespace for NumPy.
-
-    The array API inspection namespace defines the following functions:
-
-    - capabilities()
-    - default_device()
-    - default_dtypes()
-    - dtypes()
-    - devices()
-
-    See
-    https://data-apis.org/array-api/latest/API_specification/inspection.html
-    for more details.
-
-    Returns
-    -------
-    info : ModuleType
-        The array API inspection namespace for NumPy.
-
-    Examples
-    --------
-    >>> info = np.__array_namespace_info__()
-    >>> info.default_dtypes()
-    {'real floating': numpy.float64,
-     'complex floating': numpy.complex128,
-     'integral': numpy.int64,
-     'indexing': numpy.int64}
-
-    """
-
-    __module__ = 'numpy'
-
-    def capabilities(self):
-        """
-        Return a dictionary of array API library capabilities.
-
-        The resulting dictionary has the following keys:
-
-        - **"boolean indexing"**: boolean indicating whether an array library
-          supports boolean indexing. Always ``True`` for NumPy.
-
-        - **"data-dependent shapes"**: boolean indicating whether an array
-          library supports data-dependent output shapes. Always ``True`` for
-          NumPy.
-
-        See
-        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
-        for more details.
-
-        See Also
-        --------
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Returns
-        -------
-        capabilities : dict
-            A dictionary of array API library capabilities.
-
-        Examples
-        --------
-        >>> info = np.__array_namespace_info__()
-        >>> info.capabilities()
-        {'boolean indexing': True,
-         'data-dependent shapes': True,
-         'max dimensions': 64}
-
-        """
-        return {
-            "boolean indexing": True,
-            "data-dependent shapes": True,
-            "max dimensions": 64,
-        }
-
-    def default_device(self):
-        """
-        The default device used for new NumPy arrays.
-
-        For NumPy, this always returns ``'cpu'``.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Returns
-        -------
-        device : Device
-            The default device used for new NumPy arrays.
-
-        Examples
-        --------
-        >>> info = np.__array_namespace_info__()
-        >>> info.default_device()
-        'cpu'
-
-        """
-        return "cpu"
-
-    def default_dtypes(
-        self,
-        *,
-        device: Device | None = None,
-    ) -> DefaultDTypes:
-        """
-        The default data types used for new NumPy arrays.
-
-        For NumPy, this always returns the following dictionary:
-
-        - **"real floating"**: ``numpy.float64``
-        - **"complex floating"**: ``numpy.complex128``
-        - **"integral"**: ``numpy.intp``
-        - **"indexing"**: ``numpy.intp``
-
-        Parameters
-        ----------
-        device : str, optional
-            The device to get the default data types for. For NumPy, only
-            ``'cpu'`` is allowed.
-
-        Returns
-        -------
-        dtypes : dict
-            A dictionary describing the default data types used for new NumPy
-            arrays.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Examples
-        --------
-        >>> info = np.__array_namespace_info__()
-        >>> info.default_dtypes()
-        {'real floating': numpy.float64,
-         'complex floating': numpy.complex128,
-         'integral': numpy.int64,
-         'indexing': numpy.int64}
-
-        """
-        if device not in ["cpu", None]:
-            raise ValueError(
-                'Device not understood. Only "cpu" is allowed, but received:'
-                f' {device}'
-            )
-        return {
-            "real floating": dtype(float64),
-            "complex floating": dtype(complex128),
-            "integral": dtype(intp),
-            "indexing": dtype(intp),
-        }
-
-    def dtypes(
-        self,
-        *,
-        device: Device | None = None,
-        kind: str | tuple[str, ...] | None = None,
-    ) -> dict[str, DType]:
-        """
-        The array API data types supported by NumPy.
-
-        Note that this function only returns data types that are defined by
-        the array API.
-
-        Parameters
-        ----------
-        device : str, optional
-            The device to get the data types for. For NumPy, only ``'cpu'`` is
-            allowed.
-        kind : str or tuple of str, optional
-            The kind of data types to return. If ``None``, all data types are
-            returned. If a string, only data types of that kind are returned.
-            If a tuple, a dictionary containing the union of the given kinds
-            is returned. The following kinds are supported:
-
-            - ``'bool'``: boolean data types (i.e., ``bool``).
-            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
-              ``int16``, ``int32``, ``int64``).
-            - ``'unsigned integer'``: unsigned integer data types (i.e.,
-              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
-            - ``'integral'``: integer data types. Shorthand for ``('signed
-              integer', 'unsigned integer')``.
-            - ``'real floating'``: real-valued floating-point data types
-              (i.e., ``float32``, ``float64``).
-            - ``'complex floating'``: complex floating-point data types (i.e.,
-              ``complex64``, ``complex128``).
-            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
-              'real floating', 'complex floating')``.
-
-        Returns
-        -------
-        dtypes : dict
-            A dictionary mapping the names of data types to the corresponding
-            NumPy data types.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.devices
-
-        Examples
-        --------
-        >>> info = np.__array_namespace_info__()
-        >>> info.dtypes(kind='signed integer')
-        {'int8': numpy.int8,
-         'int16': numpy.int16,
-         'int32': numpy.int32,
-         'int64': numpy.int64}
-
-        """
-        if device not in ["cpu", None]:
-            raise ValueError(
-                'Device not understood. Only "cpu" is allowed, but received:'
-                f' {device}'
-            )
-        if kind is None:
-            return {
-                "bool": dtype(bool),
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-                "float32": dtype(float32),
-                "float64": dtype(float64),
-                "complex64": dtype(complex64),
-                "complex128": dtype(complex128),
-            }
-        if kind == "bool":
-            return {"bool": dtype(bool)}
-        if kind == "signed integer":
-            return {
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-            }
-        if kind == "unsigned integer":
-            return {
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-            }
-        if kind == "integral":
-            return {
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-            }
-        if kind == "real floating":
-            return {
-                "float32": dtype(float32),
-                "float64": dtype(float64),
-            }
-        if kind == "complex floating":
-            return {
-                "complex64": dtype(complex64),
-                "complex128": dtype(complex128),
-            }
-        if kind == "numeric":
-            return {
-                "int8": dtype(int8),
-                "int16": dtype(int16),
-                "int32": dtype(int32),
-                "int64": dtype(int64),
-                "uint8": dtype(uint8),
-                "uint16": dtype(uint16),
-                "uint32": dtype(uint32),
-                "uint64": dtype(uint64),
-                "float32": dtype(float32),
-                "float64": dtype(float64),
-                "complex64": dtype(complex64),
-                "complex128": dtype(complex128),
-            }
-        if isinstance(kind, tuple):
-            res: dict[str, DType] = {}
-            for k in kind:
-                res.update(self.dtypes(kind=k))
-            return res
-        raise ValueError(f"unsupported kind: {kind!r}")
-
-    def devices(self) -> list[Device]:
-        """
-        The devices supported by NumPy.
-
-        For NumPy, this always returns ``['cpu']``.
-
-        Returns
-        -------
-        devices : list[Device]
-            The devices supported by NumPy.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes
-
-        Examples
-        --------
-        >>> info = np.__array_namespace_info__()
-        >>> info.devices()
-        ['cpu']
-
-        """
-        return ["cpu"]
-
-
-__all__ = ["__array_namespace_info__"]
-
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/numpy/_typing.py b/array_api_compat/numpy/_typing.py
deleted file mode 100644
index b5fa188c..00000000
--- a/array_api_compat/numpy/_typing.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any, Literal, TypeAlias
-
-import numpy as np
-
-Device: TypeAlias = Literal["cpu"]
-
-if TYPE_CHECKING:
-
-    # NumPy 1.x on Python 3.10 fails to parse np.dtype[]
-    DType: TypeAlias = np.dtype[
-        np.bool_
-        | np.integer[Any]
-        | np.float32
-        | np.float64
-        | np.complex64
-        | np.complex128
-    ]
-    Array: TypeAlias = np.ndarray[Any, DType]
-else:
-    DType: TypeAlias = np.dtype
-    Array: TypeAlias = np.ndarray
-
-__all__ = ["Array", "DType", "Device"]
-
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/numpy/fft.py b/array_api_compat/numpy/fft.py
deleted file mode 100644
index a492feb8..00000000
--- a/array_api_compat/numpy/fft.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import numpy as np
-
-from .._internal import clone_module
-
-__all__ = clone_module("numpy.fft", globals())
-
-from .._internal import get_xp
-from ..common import _fft
-
-fft = get_xp(np)(_fft.fft)
-ifft = get_xp(np)(_fft.ifft)
-fftn = get_xp(np)(_fft.fftn)
-ifftn = get_xp(np)(_fft.ifftn)
-rfft = get_xp(np)(_fft.rfft)
-irfft = get_xp(np)(_fft.irfft)
-rfftn = get_xp(np)(_fft.rfftn)
-irfftn = get_xp(np)(_fft.irfftn)
-hfft = get_xp(np)(_fft.hfft)
-ihfft = get_xp(np)(_fft.ihfft)
-fftfreq = get_xp(np)(_fft.fftfreq)
-rfftfreq = get_xp(np)(_fft.rfftfreq)
-fftshift = get_xp(np)(_fft.fftshift)
-ifftshift = get_xp(np)(_fft.ifftshift)
-
-
-__all__ = sorted(set(__all__) | set(_fft.__all__))
-
-def __dir__() -> list[str]:
-    return __all__
-
diff --git a/array_api_compat/numpy/linalg.py b/array_api_compat/numpy/linalg.py
deleted file mode 100644
index 7168441c..00000000
--- a/array_api_compat/numpy/linalg.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# pyright: reportAttributeAccessIssue=false
-# pyright: reportUnknownArgumentType=false
-# pyright: reportUnknownMemberType=false
-# pyright: reportUnknownVariableType=false
-
-from __future__ import annotations
-
-import numpy as np
-
-from .._internal import clone_module, get_xp
-from ..common import _linalg
-
-__all__ = clone_module("numpy.linalg", globals())
-
-# These functions are in both the main and linalg namespaces
-from ._aliases import matmul, matrix_transpose, tensordot, vecdot  # noqa: F401
-from ._typing import Array
-
-cross = get_xp(np)(_linalg.cross)
-outer = get_xp(np)(_linalg.outer)
-EighResult = _linalg.EighResult
-QRResult = _linalg.QRResult
-SlogdetResult = _linalg.SlogdetResult
-SVDResult = _linalg.SVDResult
-eigh = get_xp(np)(_linalg.eigh)
-qr = get_xp(np)(_linalg.qr)
-slogdet = get_xp(np)(_linalg.slogdet)
-svd = get_xp(np)(_linalg.svd)
-cholesky = get_xp(np)(_linalg.cholesky)
-matrix_rank = get_xp(np)(_linalg.matrix_rank)
-pinv = get_xp(np)(_linalg.pinv)
-matrix_norm = get_xp(np)(_linalg.matrix_norm)
-svdvals = get_xp(np)(_linalg.svdvals)
-diagonal = get_xp(np)(_linalg.diagonal)
-trace = get_xp(np)(_linalg.trace)
-
-# Note: unlike np.linalg.solve, the array API solve() only accepts x2 as a
-# vector when it is exactly 1-dimensional. All other cases treat x2 as a stack
-# of matrices. The np.linalg.solve behavior of allowing stacks of both
-# matrices and vectors is ambiguous c.f.
-# https://github.com/numpy/numpy/issues/15349 and
-# https://github.com/data-apis/array-api/issues/285.
-
-# To workaround this, the below is the code from np.linalg.solve except
-# only calling solve1 in the exactly 1D case.
-
-
-# This code is here instead of in common because it is numpy specific. Also
-# note that CuPy's solve() does not currently support broadcasting (see
-# https://github.com/cupy/cupy/blob/main/cupy/cublas.py#L43).
-def solve(x1: Array, x2: Array, /) -> Array:
-    try:
-        from numpy.linalg._linalg import (  # type: ignore[attr-defined]
-            _assert_stacked_2d,
-            _assert_stacked_square,
-            _commonType,
-            _makearray,
-            _raise_linalgerror_singular,
-            isComplexType,
-        )
-    except ImportError:
-        from numpy.linalg.linalg import (  # type: ignore[attr-defined]
-            _assert_stacked_2d,
-            _assert_stacked_square,
-            _commonType,
-            _makearray,
-            _raise_linalgerror_singular,
-            isComplexType,
-        )
-    from numpy.linalg import _umath_linalg
-
-    x1, _ = _makearray(x1)
-    _assert_stacked_2d(x1)
-    _assert_stacked_square(x1)
-    x2, wrap = _makearray(x2)
-    t, result_t = _commonType(x1, x2)
-
-    # This part is different from np.linalg.solve
-    gufunc: np.ufunc
-    if x2.ndim == 1:
-        gufunc = _umath_linalg.solve1
-    else:
-        gufunc = _umath_linalg.solve
-
-    # This does nothing currently but is left in because it will be relevant
-    # when complex dtype support is added to the spec in 2022.
-    signature = "DD->D" if isComplexType(t) else "dd->d"
-    with np.errstate(
-        call=_raise_linalgerror_singular,
-        invalid="call",
-        over="ignore",
-        divide="ignore",
-        under="ignore",
-    ):
-        r: Array = gufunc(x1, x2, signature=signature)
-
-    return wrap(r.astype(result_t, copy=False))
-
-
-# These functions are completely new here. If the library already has them
-# (i.e., numpy 2.0), use the library version instead of our wrapper.
-if hasattr(np.linalg, "vector_norm"):
-    vector_norm = np.linalg.vector_norm
-else:
-    vector_norm = get_xp(np)(_linalg.vector_norm)
-
-
-_all = [
-    "LinAlgError",
-    "cond",
-    "det",
-    "eig",
-    "eigvals",
-    "eigvalsh",
-    "inv",
-    "lstsq",
-    "matrix_power",
-    "multi_dot",
-    "norm",
-    "solve", 
-    "tensorinv",
-    "tensorsolve",
-    "vector_norm",
-]
-__all__ = sorted(set(__all__) | set(_linalg.__all__) | set(_all))
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/torch/__init__.py b/array_api_compat/torch/__init__.py
deleted file mode 100644
index 6cbb6ec2..00000000
--- a/array_api_compat/torch/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from typing import Final
-
-from .._internal import clone_module
-
-__all__ = clone_module("torch", globals())
-
-# These imports may overwrite names from the import * above.
-from . import _aliases
-from ._aliases import * # noqa: F403
-from ._info import __array_namespace_info__  # noqa: F401
-
-# See the comment in the numpy __init__.py
-__import__(__package__ + '.linalg')
-__import__(__package__ + '.fft')
-
-__array_api_version__: Final = '2024.12'
-
-__all__ = sorted(
-    set(__all__)
-    | set(_aliases.__all__)
-    | {"__array_api_version__", "__array_namespace_info__", "linalg", "fft"}
-)
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/torch/_aliases.py b/array_api_compat/torch/_aliases.py
deleted file mode 100644
index 91161d24..00000000
--- a/array_api_compat/torch/_aliases.py
+++ /dev/null
@@ -1,848 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Sequence
-from functools import reduce as _reduce, wraps as _wraps
-from builtins import all as _builtin_all, any as _builtin_any
-from typing import Any, Literal
-
-import torch
-
-from .._internal import get_xp
-from ..common import _aliases
-from ..common._typing import NestedSequence, SupportsBufferProtocol
-from ._typing import Array, Device, DType
-
-_int_dtypes = {
-    torch.uint8,
-    torch.int8,
-    torch.int16,
-    torch.int32,
-    torch.int64,
-}
-try:
-    # torch >=2.3
-    _int_dtypes |= {torch.uint16, torch.uint32, torch.uint64}
-except AttributeError:
-    pass
-
-
-_array_api_dtypes = {
-    torch.bool,
-    *_int_dtypes,
-    torch.float32,
-    torch.float64,
-    torch.complex64,
-    torch.complex128,
-}
-
-_promotion_table = {
-    # ints
-    (torch.int8, torch.int16): torch.int16,
-    (torch.int8, torch.int32): torch.int32,
-    (torch.int8, torch.int64): torch.int64,
-    (torch.int16, torch.int32): torch.int32,
-    (torch.int16, torch.int64): torch.int64,
-    (torch.int32, torch.int64): torch.int64,
-    # ints and uints (mixed sign)
-    (torch.uint8, torch.int8): torch.int16,
-    (torch.uint8, torch.int16): torch.int16,
-    (torch.uint8, torch.int32): torch.int32,
-    (torch.uint8, torch.int64): torch.int64,
-    # floats
-    (torch.float32, torch.float64): torch.float64,
-    # complexes
-    (torch.complex64, torch.complex128): torch.complex128,
-    # Mixed float and complex
-    (torch.float32, torch.complex64): torch.complex64,
-    (torch.float32, torch.complex128): torch.complex128,
-    (torch.float64, torch.complex64): torch.complex128,
-    (torch.float64, torch.complex128): torch.complex128,
-}
-
-_promotion_table.update({(b, a): c for (a, b), c in _promotion_table.items()})
-_promotion_table.update({(a, a): a for a in _array_api_dtypes})
-
-
-def _two_arg(f):
-    @_wraps(f)
-    def _f(x1, x2, /, **kwargs):
-        x1, x2 = _fix_promotion(x1, x2)
-        return f(x1, x2, **kwargs)
-    if _f.__doc__ is None:
-        _f.__doc__ = f"""\
-Array API compatibility wrapper for torch.{f.__name__}.
-
-See the corresponding PyTorch documentation and/or the array API specification
-for more details.
-
-"""
-    return _f
-
-def _fix_promotion(x1, x2, only_scalar=True):
-    if not isinstance(x1, torch.Tensor) or not isinstance(x2, torch.Tensor):
-        return x1, x2
-    if x1.dtype not in _array_api_dtypes or x2.dtype not in _array_api_dtypes:
-        return x1, x2
-    # If an argument is 0-D pytorch downcasts the other argument
-    if not only_scalar or x1.shape == ():
-        dtype = result_type(x1, x2)
-        x2 = x2.to(dtype)
-    if not only_scalar or x2.shape == ():
-        dtype = result_type(x1, x2)
-        x1 = x1.to(dtype)
-    return x1, x2
-
-
-_py_scalars = (bool, int, float, complex)
-
-
-def result_type(*arrays_and_dtypes: Array | DType | complex) -> DType:
-    num = len(arrays_and_dtypes)
-
-    if num == 0:
-        raise ValueError("At least one array or dtype must be provided")
-
-    elif num == 1:
-        x = arrays_and_dtypes[0]
-        if isinstance(x, torch.dtype):
-            return x
-        return x.dtype
-
-    if num == 2:
-        x, y = arrays_and_dtypes
-        return _result_type(x, y)
-
-    else:
-        # sort scalars so that they are treated last
-        scalars, others = [], []
-        for x in arrays_and_dtypes:
-            if isinstance(x, _py_scalars):
-                scalars.append(x)
-            else:
-                others.append(x)
-        if not others:
-            raise ValueError("At least one array or dtype must be provided")
-
-        # combine left-to-right
-        return _reduce(_result_type, others + scalars)
-
-
-def _result_type(x: Array | DType | complex, y: Array | DType | complex) -> DType:
-    if not (isinstance(x, _py_scalars) or isinstance(y, _py_scalars)):
-        xdt = x if isinstance(x, torch.dtype) else x.dtype
-        ydt = y if isinstance(y, torch.dtype) else y.dtype
-
-        try:
-            return _promotion_table[xdt, ydt]
-        except KeyError:
-            pass
-
-    # This doesn't result_type(dtype, dtype) for non-array API dtypes
-    # because torch.result_type only accepts tensors. This does however, allow
-    # cross-kind promotion.
-    x = torch.tensor([], dtype=x) if isinstance(x, torch.dtype) else x
-    y = torch.tensor([], dtype=y) if isinstance(y, torch.dtype) else y
-    return torch.result_type(x, y)
-
-
-def can_cast(from_: DType | Array, to: DType, /) -> bool:
-    if not isinstance(from_, torch.dtype):
-        from_ = from_.dtype
-    return torch.can_cast(from_, to)
-
-# Basic renames
-bitwise_invert = torch.bitwise_not
-newaxis = None
-# torch.conj sets the conjugation bit, which breaks conversion to other
-# libraries. See https://github.com/data-apis/array-api-compat/issues/173
-conj = torch.conj_physical
-
-# Two-arg elementwise functions
-# These require a wrapper to do the correct type promotion on 0-D tensors
-add = _two_arg(torch.add)
-atan2 = _two_arg(torch.atan2)
-bitwise_and = _two_arg(torch.bitwise_and)
-bitwise_left_shift = _two_arg(torch.bitwise_left_shift)
-bitwise_or = _two_arg(torch.bitwise_or)
-bitwise_right_shift = _two_arg(torch.bitwise_right_shift)
-bitwise_xor = _two_arg(torch.bitwise_xor)
-copysign = _two_arg(torch.copysign)
-divide = _two_arg(torch.divide)
-# Also a rename. torch.equal does not broadcast
-equal = _two_arg(torch.eq)
-floor_divide = _two_arg(torch.floor_divide)
-greater = _two_arg(torch.greater)
-greater_equal = _two_arg(torch.greater_equal)
-hypot = _two_arg(torch.hypot)
-less = _two_arg(torch.less)
-less_equal = _two_arg(torch.less_equal)
-logaddexp = _two_arg(torch.logaddexp)
-# logical functions are not included here because they only accept bool in the
-# spec, so type promotion is irrelevant.
-maximum = _two_arg(torch.maximum)
-minimum = _two_arg(torch.minimum)
-multiply = _two_arg(torch.multiply)
-not_equal = _two_arg(torch.not_equal)
-pow = _two_arg(torch.pow)
-remainder = _two_arg(torch.remainder)
-subtract = _two_arg(torch.subtract)
-
-
-def asarray(
-    obj: Array | complex | NestedSequence[complex] | SupportsBufferProtocol,
-    /,
-    *,
-    dtype: DType | None = None,
-    device: Device | None = None,
-    copy: bool | None = None,
-    **kwargs: Any,
-) -> Array:
-    # torch.asarray does not respect input->output device propagation
-    # https://github.com/pytorch/pytorch/issues/150199
-    if device is None and isinstance(obj, torch.Tensor):
-        device = obj.device
-    return torch.asarray(obj, dtype=dtype, device=device, copy=copy, **kwargs)
-
-
-# These wrappers are mostly based on the fact that pytorch uses 'dim' instead
-# of 'axis'.
-
-# torch.min and torch.max return a tuple and don't support multiple axes https://github.com/pytorch/pytorch/issues/58745
-def max(x: Array, /, *, axis: int | tuple[int, ...] | None = None, keepdims: bool = False) -> Array:
-    # https://github.com/pytorch/pytorch/issues/29137
-    if axis == ():
-        return torch.clone(x)
-    return torch.amax(x, axis, keepdims=keepdims)
-
-def min(x: Array, /, *, axis: int | tuple[int, ...] |None = None, keepdims: bool = False) -> Array:
-    # https://github.com/pytorch/pytorch/issues/29137
-    if axis == ():
-        return torch.clone(x)
-    return torch.amin(x, axis, keepdims=keepdims)
-
-clip = get_xp(torch)(_aliases.clip)
-unstack = get_xp(torch)(_aliases.unstack)
-cumulative_sum = get_xp(torch)(_aliases.cumulative_sum)
-cumulative_prod = get_xp(torch)(_aliases.cumulative_prod)
-finfo = get_xp(torch)(_aliases.finfo)
-iinfo = get_xp(torch)(_aliases.iinfo)
-
-
-# torch.sort also returns a tuple
-# https://github.com/pytorch/pytorch/issues/70921
-def sort(
-    x: Array,
-    /,
-    *,
-    axis: int = -1,
-    descending: bool = False,
-    stable: bool = True,
-    **kwargs: object,
-) -> Array:
-    return torch.sort(x, dim=axis, descending=descending, stable=stable, **kwargs).values
-
-def _normalize_axes(axis, ndim):
-    axes = []
-    if ndim == 0 and axis:
-        # Better error message in this case
-        raise IndexError(f"Dimension out of range: {axis[0]}")
-    lower, upper = -ndim, ndim - 1
-    for a in axis:
-        if a < lower or a > upper:
-            # Match torch error message (e.g., from sum())
-            raise IndexError(f"Dimension out of range (expected to be in range of [{lower}, {upper}], but got {a}")
-        if a < 0:
-            a = a + ndim
-        if a in axes:
-            # Use IndexError instead of RuntimeError, and "axis" instead of "dim"
-            raise IndexError(f"Axis {a} appears multiple times in the list of axes")
-        axes.append(a)
-    return sorted(axes)
-
-def _axis_none_keepdims(x, ndim, keepdims):
-    # Apply keepdims when axis=None
-    # (https://github.com/pytorch/pytorch/issues/71209)
-    # Note that this is only valid for the axis=None case.
-    if keepdims:
-        for i in range(ndim):
-            x = torch.unsqueeze(x, 0)
-    return x
-
-def _reduce_multiple_axes(f, x, axis, keepdims=False, **kwargs):
-    # Some reductions don't support multiple axes
-    # (https://github.com/pytorch/pytorch/issues/56586).
-    axes = _normalize_axes(axis, x.ndim)
-    for a in reversed(axes):
-        x = torch.movedim(x, a, -1)
-    x = torch.flatten(x, -len(axes))
-
-    out = f(x, -1, **kwargs)
-
-    if keepdims:
-        for a in axes:
-            out = torch.unsqueeze(out, a)
-    return out
-
-
-def _sum_prod_no_axis(x: Array, dtype: DType | None) -> Array:
-    """
-    Implements `sum(..., axis=())` and `prod(..., axis=())`.
-    
-    Works around https://github.com/pytorch/pytorch/issues/29137
-    """
-    if dtype is not None:
-        return x.clone() if dtype == x.dtype else x.to(dtype)
-
-    # We can't upcast uint8 according to the spec because there is no
-    # torch.uint64, so at least upcast to int64 which is what prod does
-    # when axis=None.
-    if x.dtype in (torch.uint8, torch.int8, torch.int16, torch.int32):
-        return x.to(torch.int64)
-
-    return x.clone()
-
-
-def prod(x: Array,
-         /,
-         *,
-         axis: int | tuple[int, ...] | None = None,
-         dtype: DType | None = None,
-         keepdims: bool = False,
-         **kwargs: object) -> Array:
-
-    if axis == ():
-        return _sum_prod_no_axis(x, dtype)
-    # torch.prod doesn't support multiple axes
-    # (https://github.com/pytorch/pytorch/issues/56586).
-    if isinstance(axis, tuple):
-        return _reduce_multiple_axes(torch.prod, x, axis, keepdims=keepdims, dtype=dtype, **kwargs)
-    if axis is None:
-        # torch doesn't support keepdims with axis=None
-        # (https://github.com/pytorch/pytorch/issues/71209)
-        res = torch.prod(x, dtype=dtype, **kwargs)
-        res = _axis_none_keepdims(res, x.ndim, keepdims)
-        return res
-
-    return torch.prod(x, axis, dtype=dtype, keepdims=keepdims, **kwargs)
-
-
-def sum(x: Array,
-         /,
-         *,
-         axis: int | tuple[int, ...] | None = None,
-         dtype: DType | None = None,
-         keepdims: bool = False,
-         **kwargs: object) -> Array:
-
-    if axis == ():
-        return _sum_prod_no_axis(x, dtype)
-    if axis is None:
-        # torch doesn't support keepdims with axis=None
-        # (https://github.com/pytorch/pytorch/issues/71209)
-        res = torch.sum(x, dtype=dtype, **kwargs)
-        res = _axis_none_keepdims(res, x.ndim, keepdims)
-        return res
-
-    return torch.sum(x, axis, dtype=dtype, keepdims=keepdims, **kwargs)
-
-def any(x: Array,
-        /,
-        *,
-        axis: int | tuple[int, ...] | None = None,
-        keepdims: bool = False,
-        **kwargs: object) -> Array:
-
-    if axis == ():
-        return x.to(torch.bool)
-    # torch.any doesn't support multiple axes
-    # (https://github.com/pytorch/pytorch/issues/56586).
-    if isinstance(axis, tuple):
-        res = _reduce_multiple_axes(torch.any, x, axis, keepdims=keepdims, **kwargs)
-        return res.to(torch.bool)
-    if axis is None:
-        # torch doesn't support keepdims with axis=None
-        # (https://github.com/pytorch/pytorch/issues/71209)
-        res = torch.any(x, **kwargs)
-        res = _axis_none_keepdims(res, x.ndim, keepdims)
-        return res.to(torch.bool)
-
-    # torch.any doesn't return bool for uint8
-    return torch.any(x, axis, keepdims=keepdims).to(torch.bool)
-
-def all(x: Array,
-        /,
-        *,
-        axis: int | tuple[int, ...] | None = None,
-        keepdims: bool = False,
-        **kwargs: object) -> Array:
-
-    if axis == ():
-        return x.to(torch.bool)
-    # torch.all doesn't support multiple axes
-    # (https://github.com/pytorch/pytorch/issues/56586).
-    if isinstance(axis, tuple):
-        res = _reduce_multiple_axes(torch.all, x, axis, keepdims=keepdims, **kwargs)
-        return res.to(torch.bool)
-    if axis is None:
-        # torch doesn't support keepdims with axis=None
-        # (https://github.com/pytorch/pytorch/issues/71209)
-        res = torch.all(x, **kwargs)
-        res = _axis_none_keepdims(res, x.ndim, keepdims)
-        return res.to(torch.bool)
-
-    # torch.all doesn't return bool for uint8
-    return torch.all(x, axis, keepdims=keepdims).to(torch.bool)
-
-def mean(x: Array,
-         /,
-         *,
-         axis: int | tuple[int, ...] | None = None,
-         keepdims: bool = False,
-         **kwargs: object) -> Array:
-    # https://github.com/pytorch/pytorch/issues/29137
-    if axis == ():
-        return torch.clone(x)
-    if axis is None:
-        # torch doesn't support keepdims with axis=None
-        # (https://github.com/pytorch/pytorch/issues/71209)
-        res = torch.mean(x, **kwargs)
-        res = _axis_none_keepdims(res, x.ndim, keepdims)
-        return res
-    return torch.mean(x, axis, keepdims=keepdims, **kwargs)
-
-def std(x: Array,
-        /,
-        *,
-        axis: int | tuple[int, ...] | None = None,
-        correction: float = 0.0,
-        keepdims: bool = False,
-        **kwargs: object) -> Array:
-    # Note, float correction is not supported
-    # https://github.com/pytorch/pytorch/issues/61492. We don't try to
-    # implement it here for now.
-
-    if isinstance(correction, float):
-        _correction = int(correction)
-        if correction != _correction:
-            raise NotImplementedError("float correction in torch std() is not yet supported")
-    else:
-        _correction = correction
-
-    # https://github.com/pytorch/pytorch/issues/29137
-    if axis == ():
-        return torch.zeros_like(x)
-    if isinstance(axis, int):
-        axis = (axis,)
-    if axis is None:
-        # torch doesn't support keepdims with axis=None
-        # (https://github.com/pytorch/pytorch/issues/71209)
-        res = torch.std(x, tuple(range(x.ndim)), correction=_correction, **kwargs)
-        res = _axis_none_keepdims(res, x.ndim, keepdims)
-        return res
-    return torch.std(x, axis, correction=_correction, keepdims=keepdims, **kwargs)
-
-def var(x: Array,
-        /,
-        *,
-        axis: int | tuple[int, ...] | None = None,
-        correction: float = 0.0,
-        keepdims: bool = False,
-        **kwargs: object) -> Array:
-    # Note, float correction is not supported
-    # https://github.com/pytorch/pytorch/issues/61492. We don't try to
-    # implement it here for now.
-
-    # if isinstance(correction, float):
-    #     correction = int(correction)
-
-    # https://github.com/pytorch/pytorch/issues/29137
-    if axis == ():
-        return torch.zeros_like(x)
-    if isinstance(axis, int):
-        axis = (axis,)
-    if axis is None:
-        # torch doesn't support keepdims with axis=None
-        # (https://github.com/pytorch/pytorch/issues/71209)
-        res = torch.var(x, tuple(range(x.ndim)), correction=correction, **kwargs)
-        res = _axis_none_keepdims(res, x.ndim, keepdims)
-        return res
-    return torch.var(x, axis, correction=correction, keepdims=keepdims, **kwargs)
-
-# torch.concat doesn't support dim=None
-# https://github.com/pytorch/pytorch/issues/70925
-def concat(arrays: tuple[Array, ...] | list[Array],
-           /,
-           *,
-           axis: int | None = 0,
-           **kwargs: object) -> Array:
-    if axis is None:
-        arrays = tuple(ar.flatten() for ar in arrays)
-        axis = 0
-    return torch.concat(arrays, axis, **kwargs)
-
-# torch.squeeze only accepts int dim and doesn't require it
-# https://github.com/pytorch/pytorch/issues/70924. Support for tuple dim was
-# added at https://github.com/pytorch/pytorch/pull/89017.
-def squeeze(x: Array, /, axis: int | tuple[int, ...]) -> Array:
-    if isinstance(axis, int):
-        axis = (axis,)
-    for a in axis:
-        if x.shape[a] != 1:
-            raise ValueError("squeezed dimensions must be equal to 1")
-    axes = _normalize_axes(axis, x.ndim)
-    # Remove this once pytorch 1.14 is released with the above PR #89017.
-    sequence = [a - i for i, a in enumerate(axes)]
-    for a in sequence:
-        x = torch.squeeze(x, a)
-    return x
-
-# torch.broadcast_to uses size instead of shape
-def broadcast_to(x: Array, /, shape: tuple[int, ...], **kwargs: object) -> Array:
-    return torch.broadcast_to(x, shape, **kwargs)
-
-# torch.permute uses dims instead of axes
-def permute_dims(x: Array, /, axes: tuple[int, ...]) -> Array:
-    return torch.permute(x, axes)
-
-# The axis parameter doesn't work for flip() and roll()
-# https://github.com/pytorch/pytorch/issues/71210. Also torch.flip() doesn't
-# accept axis=None
-def flip(x: Array, /, *, axis: int | tuple[int, ...] | None = None, **kwargs: object) -> Array:
-    if axis is None:
-        axis = tuple(range(x.ndim))
-    # torch.flip doesn't accept dim as an int but the method does
-    # https://github.com/pytorch/pytorch/issues/18095
-    return x.flip(axis, **kwargs)
-
-def roll(x: Array, /, shift: int | tuple[int, ...], *, axis: int | tuple[int, ...] | None = None, **kwargs: object) -> Array:
-    return torch.roll(x, shift, axis, **kwargs)
-
-def nonzero(x: Array, /, **kwargs: object) -> tuple[Array, ...]:
-    if x.ndim == 0:
-        raise ValueError("nonzero() does not support zero-dimensional arrays")
-    return torch.nonzero(x, as_tuple=True, **kwargs)
-
-
-# torch uses `dim` instead of `axis`
-def diff(
-    x: Array,
-    /,
-    *,
-    axis: int = -1,
-    n: int = 1,
-    prepend: Array | None = None,
-    append: Array | None = None,
-) -> Array:
-    return torch.diff(x, dim=axis, n=n, prepend=prepend, append=append)
-
-
-# torch uses `dim` instead of `axis`, does not have keepdims
-def count_nonzero(
-    x: Array,
-    /,
-    *,
-    axis: int | tuple[int, ...] | None = None,
-    keepdims: bool = False,
-) -> Array:
-    result = torch.count_nonzero(x, dim=axis)
-    if keepdims:
-        if isinstance(axis, int):
-            return result.unsqueeze(axis)
-        elif isinstance(axis, tuple):
-            n_axis = [x.ndim + ax if ax < 0 else ax for ax in axis]
-            sh = [1 if i in n_axis else x.shape[i] for i in range(x.ndim)]
-            return torch.reshape(result, sh)
-        return _axis_none_keepdims(result, x.ndim, keepdims)
-    else:
-        return result
-
-
-# "repeat" is torch.repeat_interleave;  also the dim argument
-def repeat(x: Array, repeats: int | Array, /, *, axis: int | None = None) -> Array:
-    return torch.repeat_interleave(x, repeats, axis)
-
-
-def where(condition: Array, x1: Array | complex, x2: Array | complex, /) -> Array:
-    x1, x2 = _fix_promotion(x1, x2)
-    return torch.where(condition, x1, x2)
-
-
-# torch.reshape doesn't have the copy keyword
-def reshape(x: Array,
-            /,
-            shape: tuple[int, ...],
-            *,
-            copy: bool | None = None,
-            **kwargs: object) -> Array:
-    if copy is not None:
-        raise NotImplementedError("torch.reshape doesn't yet support the copy keyword")
-    return torch.reshape(x, shape, **kwargs)
-
-# torch.arange doesn't support returning empty arrays
-# (https://github.com/pytorch/pytorch/issues/70915), and doesn't support some
-# keyword argument combinations
-# (https://github.com/pytorch/pytorch/issues/70914)
-def arange(start: float,
-           /,
-           stop: float | None = None,
-           step: float = 1,
-           *,
-           dtype: DType | None = None,
-           device: Device | None = None,
-           **kwargs: object) -> Array:
-    if stop is None:
-        start, stop = 0, start
-    if step > 0 and stop <= start or step < 0 and stop >= start:
-        if dtype is None:
-            if _builtin_all(isinstance(i, int) for i in [start, stop, step]):
-                dtype = torch.int64
-            else:
-                dtype = torch.float32
-        return torch.empty(0, dtype=dtype, device=device, **kwargs)
-    return torch.arange(start, stop, step, dtype=dtype, device=device, **kwargs)
-
-# torch.eye does not accept None as a default for the second argument and
-# doesn't support off-diagonals (https://github.com/pytorch/pytorch/issues/70910)
-def eye(n_rows: int,
-        n_cols: int | None = None,
-        /,
-        *,
-        k: int = 0,
-        dtype: DType | None = None,
-        device: Device | None = None,
-        **kwargs: object) -> Array:
-    if n_cols is None:
-        n_cols = n_rows
-    z = torch.zeros(n_rows, n_cols, dtype=dtype, device=device, **kwargs)
-    if abs(k) <= n_rows + n_cols:
-        z.diagonal(k).fill_(1)
-    return z
-
-# torch.linspace doesn't have the endpoint parameter
-def linspace(start: float,
-             stop: float,
-             /,
-             num: int,
-             *,
-             dtype: DType | None = None,
-             device: Device | None = None,
-             endpoint: bool = True,
-             **kwargs: object) -> Array:
-    if not endpoint:
-        return torch.linspace(start, stop, num+1, dtype=dtype, device=device, **kwargs)[:-1]
-    return torch.linspace(start, stop, num, dtype=dtype, device=device, **kwargs)
-
-# torch.full does not accept an int size
-# https://github.com/pytorch/pytorch/issues/70906
-def full(shape: int | tuple[int, ...],
-         fill_value: complex,
-         *,
-         dtype: DType | None = None,
-         device: Device | None = None,
-         **kwargs: object) -> Array:
-    if isinstance(shape, int):
-        shape = (shape,)
-
-    return torch.full(shape, fill_value, dtype=dtype, device=device, **kwargs)
-
-# ones, zeros, and empty do not accept shape as a keyword argument
-def ones(shape: int | tuple[int, ...],
-         *,
-         dtype: DType | None = None,
-         device: Device | None = None,
-         **kwargs: object) -> Array:
-    return torch.ones(shape, dtype=dtype, device=device, **kwargs)
-
-def zeros(shape: int | tuple[int, ...],
-         *,
-         dtype: DType | None = None,
-         device: Device | None = None,
-         **kwargs: object) -> Array:
-    return torch.zeros(shape, dtype=dtype, device=device, **kwargs)
-
-def empty(shape: int | tuple[int, ...],
-         *,
-         dtype: DType | None = None,
-         device: Device | None = None,
-         **kwargs: object) -> Array:
-    return torch.empty(shape, dtype=dtype, device=device, **kwargs)
-
-# tril and triu do not call the keyword argument k
-
-def tril(x: Array, /, *, k: int = 0) -> Array:
-    return torch.tril(x, k)
-
-def triu(x: Array, /, *, k: int = 0) -> Array:
-    return torch.triu(x, k)
-
-# Functions that aren't in torch https://github.com/pytorch/pytorch/issues/58742
-def expand_dims(x: Array, /, *, axis: int = 0) -> Array:
-    return torch.unsqueeze(x, axis)
-
-
-def astype(
-    x: Array,
-    dtype: DType,
-    /,
-    *,
-    copy: bool = True,
-    device: Device | None = None,
-) -> Array:
-    if device is not None:
-        return x.to(device, dtype=dtype, copy=copy)
-    return x.to(dtype=dtype, copy=copy)
-
-
-def broadcast_arrays(*arrays: Array) -> list[Array]:
-    shape = torch.broadcast_shapes(*[a.shape for a in arrays])
-    return [torch.broadcast_to(a, shape) for a in arrays]
-
-# Note that these named tuples aren't actually part of the standard namespace,
-# but I don't see any issue with exporting the names here regardless.
-from ..common._aliases import (UniqueAllResult, UniqueCountsResult,
-                               UniqueInverseResult)
-
-# https://github.com/pytorch/pytorch/issues/70920
-def unique_all(x: Array) -> UniqueAllResult:
-    # torch.unique doesn't support returning indices.
-    # https://github.com/pytorch/pytorch/issues/36748. The workaround
-    # suggested in that issue doesn't actually function correctly (it relies
-    # on non-deterministic behavior of scatter()).
-    raise NotImplementedError("unique_all() not yet implemented for pytorch (see https://github.com/pytorch/pytorch/issues/36748)")
-
-    # values, inverse_indices, counts = torch.unique(x, return_counts=True, return_inverse=True)
-    # # torch.unique incorrectly gives a 0 count for nan values.
-    # # https://github.com/pytorch/pytorch/issues/94106
-    # counts[torch.isnan(values)] = 1
-    # return UniqueAllResult(values, indices, inverse_indices, counts)
-
-def unique_counts(x: Array) -> UniqueCountsResult:
-    values, counts = torch.unique(x, return_counts=True)
-
-    # torch.unique incorrectly gives a 0 count for nan values.
-    # https://github.com/pytorch/pytorch/issues/94106
-    counts[torch.isnan(values)] = 1
-    return UniqueCountsResult(values, counts)
-
-def unique_inverse(x: Array) -> UniqueInverseResult:
-    values, inverse = torch.unique(x, return_inverse=True)
-    return UniqueInverseResult(values, inverse)
-
-def unique_values(x: Array) -> Array:
-    return torch.unique(x)
-
-def matmul(x1: Array, x2: Array, /, **kwargs: object) -> Array:
-    # torch.matmul doesn't type promote (but differently from _fix_promotion)
-    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
-    return torch.matmul(x1, x2, **kwargs)
-
-matrix_transpose = get_xp(torch)(_aliases.matrix_transpose)
-_vecdot = get_xp(torch)(_aliases.vecdot)
-
-def vecdot(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
-    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
-    return _vecdot(x1, x2, axis=axis)
-
-# torch.tensordot uses dims instead of axes
-def tensordot(
-    x1: Array,
-    x2: Array,
-    /,
-    *, 
-    axes: int | tuple[Sequence[int], Sequence[int]] = 2, 
-    **kwargs: object,
-) -> Array:
-    # Note: torch.tensordot fails with integer dtypes when there is only 1
-    # element in the axis (https://github.com/pytorch/pytorch/issues/84530).
-    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
-    return torch.tensordot(x1, x2, dims=axes, **kwargs)
-
-
-def isdtype(
-    dtype: DType, 
-    kind: DType | str | tuple[DType | str, ...],
-    *,
-    _tuple: bool = True, # Disallow nested tuples
-) -> bool:
-    """
-    Returns a boolean indicating whether a provided dtype is of a specified data type ``kind``.
-
-    Note that outside of this function, this compat library does not yet fully
-    support complex numbers.
-
-    See
-    https://data-apis.org/array-api/latest/API_specification/generated/array_api.isdtype.html
-    for more details
-    """
-    if isinstance(kind, tuple) and _tuple:
-        return _builtin_any(isdtype(dtype, k, _tuple=False) for k in kind)
-    elif isinstance(kind, str):
-        if kind == 'bool':
-            return dtype == torch.bool
-        elif kind == 'signed integer':
-            return dtype in _int_dtypes and dtype.is_signed
-        elif kind == 'unsigned integer':
-            return dtype in _int_dtypes and not dtype.is_signed
-        elif kind == 'integral':
-            return dtype in _int_dtypes
-        elif kind == 'real floating':
-            return dtype.is_floating_point
-        elif kind == 'complex floating':
-            return dtype.is_complex
-        elif kind == 'numeric':
-            return isdtype(dtype, ('integral', 'real floating', 'complex floating'))
-        else:
-            raise ValueError(f"Unrecognized data type kind: {kind!r}")
-    else:
-        return dtype == kind
-
-def take(x: Array, indices: Array, /, *, axis: int | None = None, **kwargs: object) -> Array:
-    if axis is None:
-        if x.ndim != 1:
-            raise ValueError("axis must be specified when ndim > 1")
-        axis = 0
-    return torch.index_select(x, axis, indices, **kwargs)
-
-
-def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1) -> Array:
-    return torch.take_along_dim(x, indices, dim=axis)
-
-
-def sign(x: Array, /) -> Array:
-    # torch sign() does not support complex numbers and does not propagate
-    # nans. See https://github.com/data-apis/array-api-compat/issues/136
-    if x.dtype.is_complex:
-        out = x/torch.abs(x)
-        # sign(0) = 0 but the above formula would give nan
-        out[x == 0+0j] = 0+0j
-        return out
-    else:
-        out = torch.sign(x)
-        if x.dtype.is_floating_point:
-            out[torch.isnan(x)] = torch.nan
-        return out
-
-
-def meshgrid(*arrays: Array, indexing: Literal['xy', 'ij'] = 'xy') -> list[Array]:
-    # enforce the default of 'xy'
-    # TODO: is the return type a list or a tuple
-    return list(torch.meshgrid(*arrays, indexing='xy'))
-
-
-__all__ = ['asarray', 'result_type', 'can_cast',
-           'permute_dims', 'bitwise_invert', 'newaxis', 'conj', 'add',
-           'atan2', 'bitwise_and', 'bitwise_left_shift', 'bitwise_or',
-           'bitwise_right_shift', 'bitwise_xor', 'copysign', 'count_nonzero',
-           'diff', 'divide',
-           'equal', 'floor_divide', 'greater', 'greater_equal', 'hypot',
-           'less', 'less_equal', 'logaddexp', 'maximum', 'minimum',
-           'multiply', 'not_equal', 'pow', 'remainder', 'subtract', 'max',
-           'min', 'clip', 'unstack', 'cumulative_sum', 'cumulative_prod', 'sort', 'prod', 'sum',
-           'any', 'all', 'mean', 'std', 'var', 'concat', 'squeeze',
-           'broadcast_to', 'flip', 'roll', 'nonzero', 'where', 'reshape',
-           'arange', 'eye', 'linspace', 'full', 'ones', 'zeros', 'empty',
-           'tril', 'triu', 'expand_dims', 'astype', 'broadcast_arrays',
-           'UniqueAllResult', 'UniqueCountsResult', 'UniqueInverseResult',
-           'unique_all', 'unique_counts', 'unique_inverse', 'unique_values',
-           'matmul', 'matrix_transpose', 'vecdot', 'tensordot', 'isdtype',
-           'take', 'take_along_axis', 'sign', 'finfo', 'iinfo', 'repeat', 'meshgrid']
diff --git a/array_api_compat/torch/_info.py b/array_api_compat/torch/_info.py
deleted file mode 100644
index 818e5d37..00000000
--- a/array_api_compat/torch/_info.py
+++ /dev/null
@@ -1,369 +0,0 @@
-"""
-Array API Inspection namespace
-
-This is the namespace for inspection functions as defined by the array API
-standard. See
-https://data-apis.org/array-api/latest/API_specification/inspection.html for
-more details.
-
-"""
-import torch
-
-from functools import cache
-
-class __array_namespace_info__:
-    """
-    Get the array API inspection namespace for PyTorch.
-
-    The array API inspection namespace defines the following functions:
-
-    - capabilities()
-    - default_device()
-    - default_dtypes()
-    - dtypes()
-    - devices()
-
-    See
-    https://data-apis.org/array-api/latest/API_specification/inspection.html
-    for more details.
-
-    Returns
-    -------
-    info : ModuleType
-        The array API inspection namespace for PyTorch.
-
-    Examples
-    --------
-    >>> info = xp.__array_namespace_info__()
-    >>> info.default_dtypes()
-    {'real floating': numpy.float64,
-     'complex floating': numpy.complex128,
-     'integral': numpy.int64,
-     'indexing': numpy.int64}
-
-    """
-
-    __module__ = 'torch'
-
-    def capabilities(self):
-        """
-        Return a dictionary of array API library capabilities.
-
-        The resulting dictionary has the following keys:
-
-        - **"boolean indexing"**: boolean indicating whether an array library
-          supports boolean indexing. Always ``True`` for PyTorch.
-
-        - **"data-dependent shapes"**: boolean indicating whether an array
-          library supports data-dependent output shapes. Always ``True`` for
-          PyTorch.
-
-        See
-        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
-        for more details.
-
-        See Also
-        --------
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Returns
-        -------
-        capabilities : dict
-            A dictionary of array API library capabilities.
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.capabilities()
-        {'boolean indexing': True,
-         'data-dependent shapes': True,
-         'max dimensions': 64}
-
-        """
-        return {
-            "boolean indexing": True,
-            "data-dependent shapes": True,
-            "max dimensions": 64,
-        }
-
-    def default_device(self):
-        """
-        The default device used for new PyTorch arrays.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Returns
-        -------
-        device : Device
-            The default device used for new PyTorch arrays.
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.default_device()
-        device(type='cpu')
-
-        Notes
-        -----
-        This method returns the static default device when PyTorch is initialized.
-        However, the *current* device used by creation functions (``empty`` etc.)
-        can be changed at runtime.
-
-        See Also
-        --------
-        https://github.com/data-apis/array-api/issues/835
-        """
-        return torch.device("cpu")
-
-    def default_dtypes(self, *, device=None):
-        """
-        The default data types used for new PyTorch arrays.
-
-        Parameters
-        ----------
-        device : Device, optional
-            The device to get the default data types for.
-            Unused for PyTorch, as all devices use the same default dtypes.
-
-        Returns
-        -------
-        dtypes : dict
-            A dictionary describing the default data types used for new PyTorch
-            arrays.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.dtypes,
-        __array_namespace_info__.devices
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.default_dtypes()
-        {'real floating': torch.float32,
-         'complex floating': torch.complex64,
-         'integral': torch.int64,
-         'indexing': torch.int64}
-
-        """
-        # Note: if the default is set to float64, the devices like MPS that
-        # don't support float64 will error. We still return the default_dtype
-        # value here because this error doesn't represent a different default
-        # per-device.
-        default_floating = torch.get_default_dtype()
-        default_complex = torch.complex64 if default_floating == torch.float32 else torch.complex128
-        default_integral = torch.int64
-        return {
-            "real floating": default_floating,
-            "complex floating": default_complex,
-            "integral": default_integral,
-            "indexing": default_integral,
-        }
-
-
-    def _dtypes(self, kind):
-        bool = torch.bool
-        int8 = torch.int8
-        int16 = torch.int16
-        int32 = torch.int32
-        int64 = torch.int64
-        uint8 = torch.uint8
-        # uint16, uint32, and uint64 are present in newer versions of pytorch,
-        # but they aren't generally supported by the array API functions, so
-        # we omit them from this function.
-        float32 = torch.float32
-        float64 = torch.float64
-        complex64 = torch.complex64
-        complex128 = torch.complex128
-
-        if kind is None:
-            return {
-                "bool": bool,
-                "int8": int8,
-                "int16": int16,
-                "int32": int32,
-                "int64": int64,
-                "uint8": uint8,
-                "float32": float32,
-                "float64": float64,
-                "complex64": complex64,
-                "complex128": complex128,
-            }
-        if kind == "bool":
-            return {"bool": bool}
-        if kind == "signed integer":
-            return {
-                "int8": int8,
-                "int16": int16,
-                "int32": int32,
-                "int64": int64,
-            }
-        if kind == "unsigned integer":
-            return {
-                "uint8": uint8,
-            }
-        if kind == "integral":
-            return {
-                "int8": int8,
-                "int16": int16,
-                "int32": int32,
-                "int64": int64,
-                "uint8": uint8,
-            }
-        if kind == "real floating":
-            return {
-                "float32": float32,
-                "float64": float64,
-            }
-        if kind == "complex floating":
-            return {
-                "complex64": complex64,
-                "complex128": complex128,
-            }
-        if kind == "numeric":
-            return {
-                "int8": int8,
-                "int16": int16,
-                "int32": int32,
-                "int64": int64,
-                "uint8": uint8,
-                "float32": float32,
-                "float64": float64,
-                "complex64": complex64,
-                "complex128": complex128,
-            }
-        if isinstance(kind, tuple):
-            res = {}
-            for k in kind:
-                res.update(self.dtypes(kind=k))
-            return res
-        raise ValueError(f"unsupported kind: {kind!r}")
-
-    @cache
-    def dtypes(self, *, device=None, kind=None):
-        """
-        The array API data types supported by PyTorch.
-
-        Note that this function only returns data types that are defined by
-        the array API.
-
-        Parameters
-        ----------
-        device : Device, optional
-            The device to get the data types for.
-            Unused for PyTorch, as all devices use the same dtypes.
-        kind : str or tuple of str, optional
-            The kind of data types to return. If ``None``, all data types are
-            returned. If a string, only data types of that kind are returned.
-            If a tuple, a dictionary containing the union of the given kinds
-            is returned. The following kinds are supported:
-
-            - ``'bool'``: boolean data types (i.e., ``bool``).
-            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
-              ``int16``, ``int32``, ``int64``).
-            - ``'unsigned integer'``: unsigned integer data types (i.e.,
-              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
-            - ``'integral'``: integer data types. Shorthand for ``('signed
-              integer', 'unsigned integer')``.
-            - ``'real floating'``: real-valued floating-point data types
-              (i.e., ``float32``, ``float64``).
-            - ``'complex floating'``: complex floating-point data types (i.e.,
-              ``complex64``, ``complex128``).
-            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
-              'real floating', 'complex floating')``.
-
-        Returns
-        -------
-        dtypes : dict
-            A dictionary mapping the names of data types to the corresponding
-            PyTorch data types.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.devices
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.dtypes(kind='signed integer')
-        {'int8': numpy.int8,
-         'int16': numpy.int16,
-         'int32': numpy.int32,
-         'int64': numpy.int64}
-
-        """
-        res = self._dtypes(kind)
-        for k, v in res.copy().items():
-            try:
-                torch.empty((0,), dtype=v, device=device)
-            except:
-                del res[k]
-        return res
-
-    @cache
-    def devices(self):
-        """
-        The devices supported by PyTorch.
-
-        Returns
-        -------
-        devices : list[Device]
-            The devices supported by PyTorch.
-
-        See Also
-        --------
-        __array_namespace_info__.capabilities,
-        __array_namespace_info__.default_device,
-        __array_namespace_info__.default_dtypes,
-        __array_namespace_info__.dtypes
-
-        Examples
-        --------
-        >>> info = xp.__array_namespace_info__()
-        >>> info.devices()
-        [device(type='cpu'), device(type='mps', index=0), device(type='meta')]
-
-        """
-        # Torch doesn't have a straightforward way to get the list of all
-        # currently supported devices. To do this, we first parse the error
-        # message of torch.device to get the list of all possible types of
-        # device:
-        try:
-            torch.device('notadevice')
-            raise AssertionError("unreachable")  # pragma: nocover
-        except RuntimeError as e:
-            # The error message is something like:
-            # "Expected one of cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone device type at start of device string: notadevice"
-            devices_names = e.args[0].split('Expected one of ')[1].split(' device type')[0].split(', ')
-
-        # Next we need to check for different indices for different devices.
-        # device(device_name, index=index) doesn't actually check if the
-        # device name or index is valid. We have to try to create a tensor
-        # with it (which is why this function is cached).
-        devices = []
-        for device_name in devices_names:
-            i = 0
-            while True:
-                try:
-                    a = torch.empty((0,), device=torch.device(device_name, index=i))
-                    if a.device in devices:
-                        break
-                    devices.append(a.device)
-                except:
-                    break
-                i += 1
-
-        return devices
diff --git a/array_api_compat/torch/_typing.py b/array_api_compat/torch/_typing.py
deleted file mode 100644
index 52670871..00000000
--- a/array_api_compat/torch/_typing.py
+++ /dev/null
@@ -1,3 +0,0 @@
-__all__ = ["Array", "Device", "DType"]
-
-from torch import device as Device, dtype as DType, Tensor as Array
diff --git a/array_api_compat/torch/fft.py b/array_api_compat/torch/fft.py
deleted file mode 100644
index 76342980..00000000
--- a/array_api_compat/torch/fft.py
+++ /dev/null
@@ -1,82 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Sequence
-from typing import Literal
-
-import torch
-import torch.fft
-
-from ._typing import Array
-from .._internal import clone_module
-
-__all__ = clone_module("torch.fft", globals())
-
-# Several torch fft functions do not map axes to dim
-
-def fftn(
-    x: Array,
-    /,
-    *,
-    s: Sequence[int] = None,
-    axes: Sequence[int] = None,
-    norm: Literal["backward", "ortho", "forward"] = "backward",
-    **kwargs: object,
-) -> Array:
-    return torch.fft.fftn(x, s=s, dim=axes, norm=norm, **kwargs)
-
-def ifftn(
-    x: Array,
-    /,
-    *,
-    s: Sequence[int] = None,
-    axes: Sequence[int] = None,
-    norm: Literal["backward", "ortho", "forward"] = "backward",
-    **kwargs: object,
-) -> Array:
-    return torch.fft.ifftn(x, s=s, dim=axes, norm=norm, **kwargs)
-
-def rfftn(
-    x: Array,
-    /,
-    *,
-    s: Sequence[int] = None,
-    axes: Sequence[int] = None,
-    norm: Literal["backward", "ortho", "forward"] = "backward",
-    **kwargs: object,
-) -> Array:
-    return torch.fft.rfftn(x, s=s, dim=axes, norm=norm, **kwargs)
-
-def irfftn(
-    x: Array,
-    /,
-    *,
-    s: Sequence[int] = None,
-    axes: Sequence[int] = None,
-    norm: Literal["backward", "ortho", "forward"] = "backward",
-    **kwargs: object,
-) -> Array:
-    return torch.fft.irfftn(x, s=s, dim=axes, norm=norm, **kwargs)
-
-def fftshift(
-    x: Array,
-    /,
-    *,
-    axes: int | Sequence[int] = None,
-    **kwargs: object,
-) -> Array:
-    return torch.fft.fftshift(x, dim=axes, **kwargs)
-
-def ifftshift(
-    x: Array,
-    /,
-    *,
-    axes: int | Sequence[int] = None,
-    **kwargs: object,
-) -> Array:
-    return torch.fft.ifftshift(x, dim=axes, **kwargs)
-
-
-__all__ += ["fftn", "ifftn", "rfftn", "irfftn", "fftshift", "ifftshift"]
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/array_api_compat/torch/linalg.py b/array_api_compat/torch/linalg.py
deleted file mode 100644
index 08271d22..00000000
--- a/array_api_compat/torch/linalg.py
+++ /dev/null
@@ -1,114 +0,0 @@
-from __future__ import annotations
-
-import torch
-import torch.linalg
-
-from .._internal import clone_module
-
-__all__ = clone_module("torch.linalg", globals())
-
-# outer is implemented in torch but aren't in the linalg namespace
-from torch import outer
-from ._aliases import _fix_promotion, sum
-# These functions are in both the main and linalg namespaces
-from ._aliases import matmul, matrix_transpose, tensordot
-from ._typing import Array, DType
-from ..common._typing import JustInt, JustFloat
-
-# Note: torch.linalg.cross does not default to axis=-1 (it defaults to the
-# first axis with size 3), see https://github.com/pytorch/pytorch/issues/58743
-
-# torch.cross also does not support broadcasting when it would add new
-# dimensions https://github.com/pytorch/pytorch/issues/39656
-def cross(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
-    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
-    if not (-min(x1.ndim, x2.ndim) <= axis < max(x1.ndim, x2.ndim)):
-        raise ValueError(f"axis {axis} out of bounds for cross product of arrays with shapes {x1.shape} and {x2.shape}")
-    if not (x1.shape[axis] == x2.shape[axis] == 3):
-        raise ValueError(f"cross product axis must have size 3, got {x1.shape[axis]} and {x2.shape[axis]}")
-    x1, x2 = torch.broadcast_tensors(x1, x2)
-    return torch.linalg.cross(x1, x2, dim=axis)
-
-def vecdot(x1: Array, x2: Array, /, *, axis: int = -1, **kwargs: object) -> Array:
-    from ._aliases import isdtype
-
-    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
-
-    # torch.linalg.vecdot incorrectly allows broadcasting along the contracted dimension
-    if x1.shape[axis] != x2.shape[axis]:
-        raise ValueError("x1 and x2 must have the same size along the given axis")
-
-    # torch.linalg.vecdot doesn't support integer dtypes
-    if isdtype(x1.dtype, 'integral') or isdtype(x2.dtype, 'integral'):
-        if kwargs:
-            raise RuntimeError("vecdot kwargs not supported for integral dtypes")
-
-        x1_ = torch.moveaxis(x1, axis, -1)
-        x2_ = torch.moveaxis(x2, axis, -1)
-        x1_, x2_ = torch.broadcast_tensors(x1_, x2_)
-
-        res = x1_[..., None, :] @ x2_[..., None]
-        return res[..., 0, 0]
-    return torch.linalg.vecdot(x1, x2, dim=axis, **kwargs)
-
-def solve(x1: Array, x2: Array, /, **kwargs: object) -> Array:
-    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
-    # Torch tries to emulate NumPy 1 solve behavior by using batched 1-D solve
-    # whenever
-    # 1. x1.ndim - 1 == x2.ndim
-    # 2. x1.shape[:-1] == x2.shape
-    #
-    # See linalg_solve_is_vector_rhs in
-    # aten/src/ATen/native/LinearAlgebraUtils.h and
-    # TORCH_META_FUNC(_linalg_solve_ex) in
-    # aten/src/ATen/native/BatchLinearAlgebra.cpp in the PyTorch source code.
-    #
-    # The easiest way to work around this is to prepend a size 1 dimension to
-    # x2, since x2 is already one dimension less than x1.
-    #
-    # See https://github.com/pytorch/pytorch/issues/52915
-    if x2.ndim != 1 and x1.ndim - 1 == x2.ndim and x1.shape[:-1] == x2.shape:
-        x2 = x2[None]
-    return torch.linalg.solve(x1, x2, **kwargs)
-
-# torch.trace doesn't support the offset argument and doesn't support stacking
-def trace(x: Array, /, *, offset: int = 0, dtype: DType | None = None) -> Array:
-    # Use our wrapped sum to make sure it does upcasting correctly
-    return sum(torch.diagonal(x, offset=offset, dim1=-2, dim2=-1), axis=-1, dtype=dtype)
-
-def vector_norm(
-    x: Array,
-    /,
-    *,
-    axis: int | tuple[int, ...] | None = None,
-    keepdims: bool = False,
-    # JustFloat stands for inf | -inf, which are not valid for Literal
-    ord: JustInt | JustFloat = 2,
-    **kwargs: object,
-) -> Array:
-    # torch.vector_norm incorrectly treats axis=() the same as axis=None
-    if axis == ():
-        out = kwargs.get('out')
-        if out is None:
-            dtype = None
-            if x.dtype == torch.complex64:
-                dtype = torch.float32
-            elif x.dtype == torch.complex128:
-                dtype = torch.float64
-
-            out = torch.zeros_like(x, dtype=dtype)
-
-        # The norm of a single scalar works out to abs(x) in every case except
-        # for ord=0, which is x != 0.
-        if ord == 0:
-            out[:] = (x != 0)
-        else:
-            out[:] = torch.abs(x)
-        return out
-    return torch.linalg.vector_norm(x, ord=ord, axis=axis, keepdim=keepdims, **kwargs)
-
-__all__ += ['outer', 'matmul', 'matrix_transpose', 'tensordot',
-            'cross', 'vecdot', 'solve', 'trace', 'vector_norm']
-
-def __dir__() -> list[str]:
-    return __all__
diff --git a/changelog.html b/changelog.html
new file mode 100644
index 00000000..ec5876ea
--- /dev/null
+++ b/changelog.html
@@ -0,0 +1,918 @@
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+<li class="toctree-l1 current current-page"><a class="current reference internal" href="#">Changelog</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="dev/tests.html">Tests</a></li>
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+          </label>
+        </div>
+        <article role="main" id="furo-main-content">
+          <section class="tex2jax_ignore mathjax_ignore" id="changelog">
+<h1>Changelog<a class="headerlink" href="#changelog" title="Link to this heading">¶</a></h1>
+<section id="id1">
+<h2>1.12.0 (2025-05-13)<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h2>
+<section id="major-changes">
+<h3>Major changes<a class="headerlink" href="#major-changes" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>The build system has been updated to use <code class="docutils literal notranslate"><span class="pre">pyproject.toml</span></code> instead of <code class="docutils literal notranslate"><span class="pre">setup.py</span></code></p></li>
+<li><p>Support for Python 3.9 has been dropped. The minimum supported Python version is now
+3.10; the minimum supported NumPy version is 1.22.</p></li>
+<li><p>The <code class="docutils literal notranslate"><span class="pre">linalg</span></code> extension works correctly with <code class="docutils literal notranslate"><span class="pre">pytorch&gt;=2.7</span></code>.</p></li>
+<li><p>Multiple improvements to handling of devices in CuPy and PyTorch backends.
+Support for multiple devices in CuPy is still immature and you should use
+context managers rather than relying on input-output device propagation or
+on the <code class="docutils literal notranslate"><span class="pre">device</span></code> parameter.  Please report any issues you encounter.</p></li>
+</ul>
+</section>
+<section id="minor-changes">
+<h3>Minor changes<a class="headerlink" href="#minor-changes" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">finfo</span></code> and <code class="docutils literal notranslate"><span class="pre">iinfo</span></code> functions now accept array arguments, in accordance with the
+Array API spec;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch.asarray</span></code> function propagates the device of the input array. This works around
+the <a class="reference external" href="https://github.com/pytorch/pytorch/issues/150199">pytorch issue #150199</a>;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch.repeat</span></code> function is now available;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch.count_nonzero</span></code> function now correctly handles the case of a tuple <code class="docutils literal notranslate"><span class="pre">axis</span></code>
+arguments and <code class="docutils literal notranslate"><span class="pre">keepdims=True</span></code>;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch.meshgrid</span></code> wrapper defaults to <code class="docutils literal notranslate"><span class="pre">indexing=&quot;xy&quot;</span></code>, in accordance with the
+array API specification;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">cupy.asarray</span></code> function now implements the <code class="docutils literal notranslate"><span class="pre">copy=False</span></code> argument, albeit
+at the cost of risking to make a temporary copy.</p></li>
+<li><p>In <code class="docutils literal notranslate"><span class="pre">numpy.take_along_axis</span></code> and <code class="docutils literal notranslate"><span class="pre">cupy.take_along_axis</span></code> the <code class="docutils literal notranslate"><span class="pre">axis</span></code> parameter now
+defaults to -1, in accordance to the Array API spec.</p></li>
+</ul>
+<p>The following users contributed to this release:</p>
+<p>Evgeni Burovski,
+Lucas Colley,
+Neil Girdhar,
+Joren Hammudoglu,
+Guido Imperiale</p>
+</section>
+</section>
+<section id="id2">
+<h2>1.11.2 (2025-03-20)<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h2>
+<p>This is a bugfix release with no new features compared to version 1.11.</p>
+<ul class="simple">
+<li><p>fix the <code class="docutils literal notranslate"><span class="pre">result_type</span></code> wrapper for pytorch. Previously, <code class="docutils literal notranslate"><span class="pre">result_type</span></code> had multiple
+issues with scalar arguments.</p></li>
+<li><p>fix several issues with <code class="docutils literal notranslate"><span class="pre">clip</span></code> wrappers. Previously, <code class="docutils literal notranslate"><span class="pre">clip</span></code> was failing to allow
+behaviors which are unspecified by the 2024.12 standard but allowed by the array
+libraries.</p></li>
+</ul>
+<p>The following users contributed to this release:</p>
+<p>Evgeni Burovski
+Guido Imperiale
+Magnus Dalen Kvalevåg</p>
+</section>
+<section id="id3">
+<h2>1.11.1 (2025-03-04)<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h2>
+<p>This is a bugfix release with no new features compared to version 1.11.</p>
+<section id="id4">
+<h3>Major Changes<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>fix <code class="docutils literal notranslate"><span class="pre">count_nonzero</span></code> wrappers: work around the lack of the <code class="docutils literal notranslate"><span class="pre">keepdims</span></code> argument in
+several array libraries (torch, dask, cupy); work around numpy returning python
+ints in for some input combinations.</p></li>
+</ul>
+</section>
+<section id="id5">
+<h3>Minor Changes<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>runnings self-tests does not require all array libraries. Missing libraries are
+skipped.</p></li>
+</ul>
+<p>The following users contributed to this release:</p>
+<p>Evgeni Burovski
+Guido Imperiale</p>
+</section>
+</section>
+<section id="id6">
+<h2>1.11.0 (2025-02-27)<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h2>
+<section id="id7">
+<h3>Major Changes<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<p>This release targets the 2024.12 Array API revision. This includes</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">__array_api_version__</span></code> for the wrapped APIs is now set to <code class="docutils literal notranslate"><span class="pre">2024.12</span></code>;</p></li>
+<li><p>Wrappers for <code class="docutils literal notranslate"><span class="pre">count_nonzero</span></code>;</p></li>
+<li><p>Wrappers for <code class="docutils literal notranslate"><span class="pre">cumulative_prod</span></code>;</p></li>
+<li><p>Wrappers for <code class="docutils literal notranslate"><span class="pre">take_along_axis</span></code> (with the exception of Dask);</p></li>
+<li><p>Wrappers for <code class="docutils literal notranslate"><span class="pre">diff</span></code>;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">__capabilities__</span></code> dict contains a <code class="docutils literal notranslate"><span class="pre">max_dimensions</span></code> key;</p></li>
+<li><p>Python scalars are accepted as arguments to <code class="docutils literal notranslate"><span class="pre">result_type</span></code>;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fft.fftfreq</span></code> and <code class="docutils literal notranslate"><span class="pre">fft.rfftfreq</span></code> functions now accept an optional <code class="docutils literal notranslate"><span class="pre">dtype</span></code>
+argument to control the output data type.</p></li>
+</ul>
+<p>Note that these wrappers, as well as other 2024.12 features, are relatively undertested
+in this release, and may have rough edges. Please report any issues you encounter
+in <a class="reference external" href="https://github.com/data-apis/array-api-compat/issues">the issue tracker</a>.</p>
+<p>New functions to test properties of arrays:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">is_writeable_array</span></code> (benefits NumPy, JAX, Sparse)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">is_lazy_array</span></code> (benefits JAX, Dask, ndonnx)</p></li>
+</ul>
+<p>Improved support for JAX:</p>
+<ul class="simple">
+<li><p>Work arounds for <code class="docutils literal notranslate"><span class="pre">.device</span></code> attribute and <code class="docutils literal notranslate"><span class="pre">to_device</span></code> function
+not working correctly within <code class="docutils literal notranslate"><span class="pre">jax.jit</span></code></p></li>
+</ul>
+</section>
+<section id="id8">
+<h3>Minor Changes<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Several improvements to <code class="docutils literal notranslate"><span class="pre">dask.array</span></code> wrappers:</p>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">size</span></code> returns None for arrays of unknown shapes.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">astype(...,</span> <span class="pre">copy=True)</span></code> always copies, independently of the Dask version.</p></li>
+<li><p>implementations of <code class="docutils literal notranslate"><span class="pre">sort</span></code> and <code class="docutils literal notranslate"><span class="pre">argsort</span></code> are now available. Note that these
+implementations are relatively crude, and might be memory intensive.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">asarray</span></code> no longer accidentally materializes the Dask graph</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch</span></code> wrappers contain unsigned integer dtypes of widths &gt;8 bits, <code class="docutils literal notranslate"><span class="pre">uint16</span></code>,
+<code class="docutils literal notranslate"><span class="pre">uint32</span></code> and <code class="docutils literal notranslate"><span class="pre">uint64</span></code> if PyTorch version is at least 2.3. Note that the
+unsigned integer support is incomplete in PyTorch itself, see
+<a class="reference external" href="https://github.com/data-apis/array-api-compat/pull/253">gh-253</a>.</p></li>
+</ul>
+</li>
+</ul>
+</section>
+<section id="authors">
+<h3>Authors<a class="headerlink" href="#authors" title="Link to this heading">¶</a></h3>
+<p>The following users contributed to this release:</p>
+<p>Athan Reines
+Guido Imperiale
+Evgeni Burovski
+Guido Imperiale
+Lucas Colley
+Ralf Gommers
+Thomas Li</p>
+</section>
+</section>
+<section id="id9">
+<h2>1.10.0 (2024-12-25)<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h2>
+<section id="id10">
+<h3>Major Changes<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>New function <code class="docutils literal notranslate"><span class="pre">is_writeable_array</span></code> adds transparent support for readonly
+arrays, such as JAX arrays or numpy arrays with <code class="docutils literal notranslate"><span class="pre">.flags.writeable=False</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">asarray(...,</span> <span class="pre">copy=None)</span></code> with <code class="docutils literal notranslate"><span class="pre">dask</span></code> backend always copies, so that
+<code class="docutils literal notranslate"><span class="pre">copy=None</span></code> and <code class="docutils literal notranslate"><span class="pre">copy=True</span></code> are equivalent for the <code class="docutils literal notranslate"><span class="pre">dask</span></code> backend.
+This change is made to be forward compatible with the <code class="docutils literal notranslate"><span class="pre">dask==2024.12</span></code>
+release.</p></li>
+</ul>
+</section>
+<section id="id11">
+<h3>Minor Changes<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">array_namespace</span></code> accepts (and ignores) <code class="docutils literal notranslate"><span class="pre">None</span></code> and python scalars (int, float,
+complex, bool). This change is to simplify downstream adoption, for
+functions where arguments can be either arrays or scalars.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">vecdot</span></code> conjugates its first argument, as stipulated by the Array API spec.
+Previously, conjation if the first argument was missing.</p></li>
+</ul>
+</section>
+</section>
+<section id="id12">
+<h2>1.9.1 (2024-10-29)<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h2>
+<section id="id13">
+<h3>Major Changes<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">__array_api_version__</span></code> for the wrapped APIs is now set to <code class="docutils literal notranslate"><span class="pre">2023.12</span></code>.</p></li>
+</ul>
+</section>
+<section id="id14">
+<h3>Minor Changes<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Wrap <code class="docutils literal notranslate"><span class="pre">sign</span></code> so that it always uses the standard definition for complex
+numbers, and always propagates nans.</p></li>
+<li><p>Wrap dask.array.fft.</p></li>
+<li><p>Readd <code class="docutils literal notranslate"><span class="pre">python_requires</span></code> to the package metadata.</p></li>
+</ul>
+</section>
+</section>
+<section id="id15">
+<h2>1.9 (2024-10-??)<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h2>
+<section id="id16">
+<h3>Major Changes<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>New helper functions to determine if a namespace is from a given library
+(<a class="reference internal" href="helper-functions.html#array_api_compat.is_numpy_namespace" title="array_api_compat.is_numpy_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">is_numpy_namespace()</span></code></a>, <a class="reference internal" href="helper-functions.html#array_api_compat.is_torch_namespace" title="array_api_compat.is_torch_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">is_torch_namespace()</span></code></a>, etc.).</p></li>
+<li><p>More support for the <a class="reference external" href="https://data-apis.org/array-api/latest/changelog.html#v2023-12">2023.12 version of the
+standard</a>.
+This includes</p>
+<ul>
+<li><p>Wrappers for <code class="docutils literal notranslate"><span class="pre">cumulative_sum()</span></code>.</p></li>
+<li><p>Wrappers for <code class="docutils literal notranslate"><span class="pre">unstack()</span></code>.</p></li>
+<li><p>Update floating-point type promotion in <code class="docutils literal notranslate"><span class="pre">sum()</span></code>, <code class="docutils literal notranslate"><span class="pre">prod()</span></code>, and <code class="docutils literal notranslate"><span class="pre">trace()</span></code>
+to be inline with the 2023.12 specification (32-bit types no longer
+promote to 64-bit when <code class="docutils literal notranslate"><span class="pre">dtype=None</span></code>).</p></li>
+<li><p>Add the <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/inspection.html">inspection
+APIs</a>
+to the wrapped namespaces. These can be accessed with
+<code class="docutils literal notranslate"><span class="pre">xp.__array_namespace_info__()</span></code>.</p></li>
+<li><p>Various fixes to the <code class="docutils literal notranslate"><span class="pre">clip()</span></code> wrappers.</p></li>
+</ul>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch.conj</span></code> now wrapps <code class="docutils literal notranslate"><span class="pre">torch.conj_physical</span></code>, which makes a copy rather
+than setting the conjugation bit, as arrays with the conjugation bit set do
+not support some APIs.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch.sign</span></code> is now wrapped to support complex numbers and propogate nans
+properly.</p></li>
+</ul>
+</section>
+<section id="id17">
+<h3>Minor Changes<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>NumPy 2.0 is now wrapped again. Previously it was unwrapped because it has
+full 2022.12 array API support but it now requires wrapping again for
+2023.12 support.</p></li>
+<li><p>Support for JAX 0.4.32 and newer which implements the array API directly in
+<code class="docutils literal notranslate"><span class="pre">jax.numpy</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">hypot</span></code>, <code class="docutils literal notranslate"><span class="pre">minimum</span></code>, and <code class="docutils literal notranslate"><span class="pre">maximum</span></code> (new in 2023.12) are wrapped in PyTorch to
+support proper scalar type promotion.</p></li>
+</ul>
+</section>
+</section>
+<section id="id18">
+<h2>1.8 (2024-07-24)<a class="headerlink" href="#id18" title="Link to this heading">¶</a></h2>
+<section id="id19">
+<h3>Major Changes<a class="headerlink" href="#id19" title="Link to this heading">¶</a></h3>
+<ul>
+<li><p>Add support for <a class="reference external" href="https://github.com/Quantco/ndonnx">ndonnx</a>. Array API
+support itself lives in the ndonnx library, but this adds the
+<a class="reference internal" href="helper-functions.html#array_api_compat.is_ndonnx_array" title="array_api_compat.is_ndonnx_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">is_ndonnx_array()</span></code></a> helper function.
+(<a class="reference external" href="https://github.com/adityagoel4512">&#64;adityagoel4512</a>).</p></li>
+<li><p>Partial support for the <a class="reference external" href="https://data-apis.org/array-api/latest/changelog.html#v2023-12">2023.12 version of the
+standard</a>.
+This includes</p>
+<ul class="simple">
+<li><p>Wrappers for <code class="docutils literal notranslate"><span class="pre">clip()</span></code>.</p></li>
+<li><p>torch wrapper for <code class="docutils literal notranslate"><span class="pre">copysign()</span></code> with correct type promotion.</p></li>
+</ul>
+<p>Note that many of the new functions in the 2023.12 version of the standard
+are already fully implemented in upstream libraries and will already work.</p>
+</li>
+</ul>
+</section>
+</section>
+<section id="id20">
+<h2>1.7.1 (2024-05-28)<a class="headerlink" href="#id20" title="Link to this heading">¶</a></h2>
+<section id="id21">
+<h3>Minor Changes<a class="headerlink" href="#id21" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Fix a typo in <a class="reference external" href="http://setup.py">setup.py</a> (<a class="reference external" href="https://github.com/sunpoet">&#64;sunpoet</a>).</p></li>
+</ul>
+</section>
+</section>
+<section id="id22">
+<h2>1.7 (2024-05-24)<a class="headerlink" href="#id22" title="Link to this heading">¶</a></h2>
+<section id="id23">
+<h3>Major Changes<a class="headerlink" href="#id23" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Add support for <code class="docutils literal notranslate"><span class="pre">sparse</span></code>. Note that unlike other array libraries,
+array-api-compat does not contain any wrappers for <code class="docutils literal notranslate"><span class="pre">sparse</span></code> functions. All
+<code class="docutils literal notranslate"><span class="pre">sparse</span></code> array API support is in <code class="docutils literal notranslate"><span class="pre">sparse</span></code> itself. Thus, there is no
+<code class="docutils literal notranslate"><span class="pre">array_api_compat.sparse</span></code> submodule, and
+<code class="docutils literal notranslate"><span class="pre">array_namespace(&lt;pydata/sparse</span> <span class="pre">array&gt;)</span></code> returns the <code class="docutils literal notranslate"><span class="pre">sparse</span></code> module.</p></li>
+<li><p>Added the function <code class="docutils literal notranslate"><span class="pre">is_pydata_sparse_array(x)</span></code>.</p></li>
+</ul>
+</section>
+<section id="id24">
+<h3>Minor Changes<a class="headerlink" href="#id24" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Fix JAX <code class="docutils literal notranslate"><span class="pre">float0</span></code> arrays. See <a class="reference external" href="https://github.com/google/jax/issues/20620">https://github.com/google/jax/issues/20620</a>.
+(<a class="reference external" href="https://github.com/NeilGirdhar">&#64;NeilGirdhar</a>)</p></li>
+<li><p>Fix <code class="docutils literal notranslate"><span class="pre">torch.linalg.vector_norm()</span></code> when <code class="docutils literal notranslate"><span class="pre">axis=()</span></code>.</p></li>
+<li><p>Fix <code class="docutils literal notranslate"><span class="pre">torch.linalg.solve()</span></code> to apply the array API standard rules for when
+<code class="docutils literal notranslate"><span class="pre">x2</span></code> should be treated as a vector vs. a matrix.</p></li>
+<li><p>Fix PyTorch test failures on CI by skipping uint16, uint32, uint64 tests.</p></li>
+</ul>
+</section>
+</section>
+<section id="id25">
+<h2>1.6 (2024-03-29)<a class="headerlink" href="#id25" title="Link to this heading">¶</a></h2>
+<section id="id26">
+<h3>Major Changes<a class="headerlink" href="#id26" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Drop support for Python 3.8.</p></li>
+<li><p>NumPy 2.0 is now left completely unwrapped.</p></li>
+<li><p>New flag <code class="docutils literal notranslate"><span class="pre">use_compat</span></code> to <a class="reference internal" href="helper-functions.html#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">array_namespace()</span></code></a> to force the use or
+non-use of the compat wrapper namespace. The default is to return a compat
+namespace when it is appropiate.</p></li>
+<li><p>Fix the <code class="docutils literal notranslate"><span class="pre">copy</span></code> flag to <code class="docutils literal notranslate"><span class="pre">asarray</span></code> for NumPy, CuPy, and Dask.</p></li>
+<li><p>Fix the <code class="docutils literal notranslate"><span class="pre">device</span></code> flag to <code class="docutils literal notranslate"><span class="pre">asarray</span></code> for CuPy.</p></li>
+<li><p>Fix various issues with <code class="docutils literal notranslate"><span class="pre">asarray</span></code> for Dask.</p></li>
+</ul>
+</section>
+<section id="id27">
+<h3>Minor Changes<a class="headerlink" href="#id27" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Test Python 3.12 on CI.</p></li>
+<li><p>Add more tests for <a class="reference internal" href="helper-functions.html#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">array_namespace()</span></code></a>.</p></li>
+<li><p>Add more tests for <code class="docutils literal notranslate"><span class="pre">asarray</span></code>.</p></li>
+<li><p>Add a test that there are no hard dependencies.</p></li>
+</ul>
+</section>
+</section>
+<section id="id28">
+<h2>1.5.1 (2024-03-20)<a class="headerlink" href="#id28" title="Link to this heading">¶</a></h2>
+<section id="id29">
+<h3>Minor Changes<a class="headerlink" href="#id29" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Add <a class="reference external" href="https://data-apis.org/array-api-compat/">HTML documentation</a>. Includes
+new documentation on the <a class="reference internal" href="index.html#scope"><span class="std std-ref">scope of the package</span></a> and new <a class="reference internal" href="dev/index.html"><span class="std std-doc">developer
+documentation</span></a>.</p></li>
+<li><p>Fix <code class="docutils literal notranslate"><span class="pre">array_api_compat.numpy.asarray(torch.Tensor)</span></code> to return a NumPy array.</p></li>
+<li><p>Allow Python scalars in torch functions.</p></li>
+<li><p>Fix the <code class="docutils literal notranslate"><span class="pre">torch.std</span></code> wrapper when correction is an <code class="docutils literal notranslate"><span class="pre">int</span></code>.</p></li>
+<li><p>Fix issues with <code class="docutils literal notranslate"><span class="pre">qr</span></code> and <code class="docutils literal notranslate"><span class="pre">svd</span></code> in the Dask wrappers.</p></li>
+</ul>
+</section>
+</section>
+<section id="id30">
+<h2>1.5 (2024-03-07)<a class="headerlink" href="#id30" title="Link to this heading">¶</a></h2>
+<section id="id31">
+<h3>Major Changes<a class="headerlink" href="#id31" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Add support for Dask (<a class="reference external" href="https://github.com/lithomas1">&#64;lithomas1</a>).</p></li>
+<li><p>Add support for JAX. Note that unlike other array libraries,
+array-api-compat does not contain any wrappers for JAX functions. All JAX
+array API support is in JAX itself. Thus, there is no <code class="docutils literal notranslate"><span class="pre">array_api_compat.jax</span></code>
+submodule, and <code class="docutils literal notranslate"><span class="pre">array_namespace(&lt;JAX</span> <span class="pre">array&gt;)</span></code> returns the
+<code class="docutils literal notranslate"><span class="pre">jax.experimental.array_api</span></code> module.</p></li>
+<li><p>The functions <code class="docutils literal notranslate"><span class="pre">is_numpy_array(x)</span></code>, <code class="docutils literal notranslate"><span class="pre">is_cupy_array(x)</span></code>, <code class="docutils literal notranslate"><span class="pre">is_torch_array(x)</span></code>,
+<code class="docutils literal notranslate"><span class="pre">is_dask_array(x)</span></code>, <code class="docutils literal notranslate"><span class="pre">is_jax_array(x)</span></code> are now part of the public
+<code class="docutils literal notranslate"><span class="pre">array_api_compat</span></code> API.</p></li>
+<li><p>Add wrappers for the <code class="docutils literal notranslate"><span class="pre">fft</span></code> extension module for NumPy, CuPy, and PyTorch.</p></li>
+</ul>
+</section>
+<section id="id32">
+<h3>Minor Changes<a class="headerlink" href="#id32" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Allow <code class="docutils literal notranslate"><span class="pre">'2022.12'</span></code> as the <code class="docutils literal notranslate"><span class="pre">api_version</span></code> in <a class="reference internal" href="helper-functions.html#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">array_namespace()</span></code></a>.
+<code class="docutils literal notranslate"><span class="pre">'2021.12'</span></code> is also supported but will issue a warning since the returned
+namespace will still be a 2022.12 compliant one.</p></li>
+<li><p>Add wrapper for numpy.linalg.solve, which broadcasts the inputs according to
+the standard.</p></li>
+<li><p>Add wrappers for various PyTorch linalg functions.</p></li>
+<li><p>Fix a bug with <code class="docutils literal notranslate"><span class="pre">numpy.linalg.vector_norm(keepdims=True)</span></code>.</p></li>
+<li><p>BREAKING: Update <code class="docutils literal notranslate"><span class="pre">vecdot</span></code> wrappers to apply <code class="docutils literal notranslate"><span class="pre">axes</span></code> before broadcasting, not
+after. This matches the updated 2023.12 standard wording, and also the
+behavior of the new <code class="docutils literal notranslate"><span class="pre">numpy.vecdot</span></code> gufunc in NumPy 2.0.</p></li>
+<li><p>Fix some linalg functions which were supposed to be in both the main
+namespace and the linalg extension namespace.</p></li>
+<li><p>Add Ruff to CI. (<a class="reference external" href="https://github.com/adonath">&#64;adonath</a>)</p></li>
+<li><p>Test that internal definitions of <code class="docutils literal notranslate"><span class="pre">__all__</span></code> are self-consistent, which
+should help to avoid issues where wrappers are accidentally not exported to
+the compat namespaces properly.</p></li>
+</ul>
+</section>
+</section>
+<section id="id33">
+<h2>1.4.1 (2024-01-18)<a class="headerlink" href="#id33" title="Link to this heading">¶</a></h2>
+<section id="id34">
+<h3>Minor Changes<a class="headerlink" href="#id34" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Add support for the upcoming NumPy 2.0 release.</p></li>
+<li><p>Added a torch wrapper for <code class="docutils literal notranslate"><span class="pre">trace</span></code> (<code class="docutils literal notranslate"><span class="pre">torch.trace</span></code> doesn’t support the
+<code class="docutils literal notranslate"><span class="pre">offset</span></code> argument or stacking)</p></li>
+<li><p>Wrap numpy, cupy, and torch <code class="docutils literal notranslate"><span class="pre">nonzero</span></code> to raise an error for zero-dimensional
+input arrays.</p></li>
+<li><p>Add torch wrapper for <code class="docutils literal notranslate"><span class="pre">newaxis</span></code>.</p></li>
+<li><p>Improve error message for <code class="docutils literal notranslate"><span class="pre">array_namespace</span></code></p></li>
+<li><p>Fix linalg.cholesky returning the conjugate of the expected upper
+decomposition for numpy and cupy.</p></li>
+</ul>
+</section>
+</section>
+<section id="id35">
+<h2>1.4 (2023-09-13)<a class="headerlink" href="#id35" title="Link to this heading">¶</a></h2>
+<section id="id36">
+<h3>Major Changes<a class="headerlink" href="#id36" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Releases are now made with GitHub Actions (thanks
+<a class="reference external" href="https://github.com/matthewfeickert">&#64;matthewfeickert</a>).</p></li>
+</ul>
+</section>
+<section id="id37">
+<h3>Minor Changes<a class="headerlink" href="#id37" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Fix <code class="docutils literal notranslate"><span class="pre">torch.result_type()</span></code> cross-kind promotion
+(<a class="reference external" href="https://github.com/lucascolley">&#64;lucascolley</a>).</p></li>
+<li><p>Fix the torch.take() wrapper to make axis optional for ndim = 1.</p></li>
+<li><p>Add requires-python metadata to the package
+(<a class="reference external" href="https://github.com/matthewfeickert">&#64;matthewfeickert</a>).</p></li>
+</ul>
+</section>
+</section>
+<section id="id38">
+<h2>1.3 (2023-06-20)<a class="headerlink" href="#id38" title="Link to this heading">¶</a></h2>
+<section id="id39">
+<h3>Major Changes<a class="headerlink" href="#id39" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Add <a class="reference external" href="https://data-apis.org/array-api/2022.12/">2022.12</a> standard support.
+This includes things like adding complex dtype support, adding the new
+<code class="docutils literal notranslate"><span class="pre">take</span></code> function, and various minor changes in the specification.</p></li>
+</ul>
+</section>
+<section id="id40">
+<h3>Minor Changes<a class="headerlink" href="#id40" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Support <code class="docutils literal notranslate"><span class="pre">&quot;cpu&quot;</span></code> in CuPy <code class="docutils literal notranslate"><span class="pre">to_device()</span></code>.</p></li>
+<li><p>Return a new array in NumPy/CuPy <code class="docutils literal notranslate"><span class="pre">reshape(copy=False)</span></code>.</p></li>
+<li><p>Fix signatures for PyTorch <code class="docutils literal notranslate"><span class="pre">broadcast_to</span></code> and <code class="docutils literal notranslate"><span class="pre">permute_dims</span></code>.</p></li>
+</ul>
+</section>
+</section>
+<section id="id41">
+<h2>1.2 (2023-04-03)<a class="headerlink" href="#id41" title="Link to this heading">¶</a></h2>
+<section id="id42">
+<h3>Major Changes<a class="headerlink" href="#id42" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Support the linalg extension in the <code class="docutils literal notranslate"><span class="pre">array_api_compat.torch</span></code> namespace.</p></li>
+<li><p>Add <code class="docutils literal notranslate"><span class="pre">isdtype()</span></code>.</p></li>
+</ul>
+</section>
+<section id="id43">
+<h3>Minor Changes<a class="headerlink" href="#id43" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Fix the <code class="docutils literal notranslate"><span class="pre">k</span></code> keyword argument to <code class="docutils literal notranslate"><span class="pre">tril</span></code> and <code class="docutils literal notranslate"><span class="pre">triu</span></code> in <code class="docutils literal notranslate"><span class="pre">torch</span></code>.</p></li>
+</ul>
+</section>
+</section>
+<section id="id44">
+<h2>1.1.1 (2023-03-10)<a class="headerlink" href="#id44" title="Link to this heading">¶</a></h2>
+<section id="id45">
+<h3>Major Changes<a class="headerlink" href="#id45" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Rename <code class="docutils literal notranslate"><span class="pre">get_namespace()</span></code> to <code class="docutils literal notranslate"><span class="pre">array_namespace()</span></code> (<code class="docutils literal notranslate"><span class="pre">get_namespace()</span></code> is
+maintained as a backwards compatible alias).</p></li>
+</ul>
+</section>
+<section id="id46">
+<h3>Minor Changes<a class="headerlink" href="#id46" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>The minimum supported NumPy version is now 1.21. Fixed a few issues with
+NumPy 1.21 (with <code class="docutils literal notranslate"><span class="pre">unique_*</span></code> and <code class="docutils literal notranslate"><span class="pre">asarray</span></code>), although there are also a few
+known issues with this version (see the README).</p></li>
+<li><p>Add <code class="docutils literal notranslate"><span class="pre">api_version</span></code> to <code class="docutils literal notranslate"><span class="pre">get_namespace()</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">array_namespace()</span></code> (<em>née</em> <code class="docutils literal notranslate"><span class="pre">get_namespace()</span></code>) now works correctly with
+<code class="docutils literal notranslate"><span class="pre">torch</span></code> tensors.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">array_namespace()</span></code> (<em>née</em> <code class="docutils literal notranslate"><span class="pre">get_namespace()</span></code>) now works correctly with
+<code class="docutils literal notranslate"><span class="pre">numpy.array_api</span></code> arrays.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">array_namespace()</span></code> (<em>née</em> <code class="docutils literal notranslate"><span class="pre">get_namespace()</span></code>) now raises <code class="docutils literal notranslate"><span class="pre">TypeError</span></code> instead
+of <code class="docutils literal notranslate"><span class="pre">ValueError</span></code>.</p></li>
+<li><p>Fix the <code class="docutils literal notranslate"><span class="pre">torch.std</span></code> wrapper.</p></li>
+<li><p>Add <code class="docutils literal notranslate"><span class="pre">torch</span></code> wrappers for <code class="docutils literal notranslate"><span class="pre">ones</span></code>, <code class="docutils literal notranslate"><span class="pre">empty</span></code>, and <code class="docutils literal notranslate"><span class="pre">zeros</span></code> so that <code class="docutils literal notranslate"><span class="pre">shape</span></code> can be
+passed as a keyword argument.</p></li>
+</ul>
+</section>
+</section>
+<section id="id47">
+<h2>1.1 (2023-02-24)<a class="headerlink" href="#id47" title="Link to this heading">¶</a></h2>
+<section id="id48">
+<h3>Major Changes<a class="headerlink" href="#id48" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Added support for PyTorch.</p></li>
+<li><p>Add helper function <code class="docutils literal notranslate"><span class="pre">size()</span></code> (required if torch is used as
+<code class="docutils literal notranslate"><span class="pre">torch.Tensor.size</span></code> is a method that is incompatible with the array API
+<a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.size.html#array_api.array.size"><code class="docutils literal notranslate"><span class="pre">.size</span></code></a>).</p></li>
+<li><p>All wrapper functions that wrap existing library functions now pass through
+arbitrary <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code>.</p></li>
+</ul>
+</section>
+<section id="id49">
+<h3>Minor Changes<a class="headerlink" href="#id49" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Added CI to run against the <a class="reference external" href="https://github.com/data-apis/array-api-tests">array API testsuite</a>.</p></li>
+<li><p>Fix <code class="docutils literal notranslate"><span class="pre">sort(stable=False)</span></code> and <code class="docutils literal notranslate"><span class="pre">argsort(stable=False)</span></code> with CuPy.</p></li>
+</ul>
+</section>
+</section>
+<section id="id50">
+<h2>1.0 (2022-12-05)<a class="headerlink" href="#id50" title="Link to this heading">¶</a></h2>
+<section id="id51">
+<h3>Major Changes<a class="headerlink" href="#id51" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Initial release. Includes support for NumPy and CuPy.</p></li>
+</ul>
+</section>
+</section>
+</section>
+
+        </article>
+      </div>
+      <footer>
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+          <a class="prev-page" href="supported-array-libraries.html">
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+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
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+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Changelog</a><ul>
+<li><a class="reference internal" href="#id1">1.12.0 (2025-05-13)</a><ul>
+<li><a class="reference internal" href="#major-changes">Major changes</a></li>
+<li><a class="reference internal" href="#minor-changes">Minor changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id2">1.11.2 (2025-03-20)</a></li>
+<li><a class="reference internal" href="#id3">1.11.1 (2025-03-04)</a><ul>
+<li><a class="reference internal" href="#id4">Major Changes</a></li>
+<li><a class="reference internal" href="#id5">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id6">1.11.0 (2025-02-27)</a><ul>
+<li><a class="reference internal" href="#id7">Major Changes</a></li>
+<li><a class="reference internal" href="#id8">Minor Changes</a></li>
+<li><a class="reference internal" href="#authors">Authors</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id9">1.10.0 (2024-12-25)</a><ul>
+<li><a class="reference internal" href="#id10">Major Changes</a></li>
+<li><a class="reference internal" href="#id11">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id12">1.9.1 (2024-10-29)</a><ul>
+<li><a class="reference internal" href="#id13">Major Changes</a></li>
+<li><a class="reference internal" href="#id14">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id15">1.9 (2024-10-??)</a><ul>
+<li><a class="reference internal" href="#id16">Major Changes</a></li>
+<li><a class="reference internal" href="#id17">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id18">1.8 (2024-07-24)</a><ul>
+<li><a class="reference internal" href="#id19">Major Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id20">1.7.1 (2024-05-28)</a><ul>
+<li><a class="reference internal" href="#id21">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id22">1.7 (2024-05-24)</a><ul>
+<li><a class="reference internal" href="#id23">Major Changes</a></li>
+<li><a class="reference internal" href="#id24">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id25">1.6 (2024-03-29)</a><ul>
+<li><a class="reference internal" href="#id26">Major Changes</a></li>
+<li><a class="reference internal" href="#id27">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id28">1.5.1 (2024-03-20)</a><ul>
+<li><a class="reference internal" href="#id29">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id30">1.5 (2024-03-07)</a><ul>
+<li><a class="reference internal" href="#id31">Major Changes</a></li>
+<li><a class="reference internal" href="#id32">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id33">1.4.1 (2024-01-18)</a><ul>
+<li><a class="reference internal" href="#id34">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id35">1.4 (2023-09-13)</a><ul>
+<li><a class="reference internal" href="#id36">Major Changes</a></li>
+<li><a class="reference internal" href="#id37">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id38">1.3 (2023-06-20)</a><ul>
+<li><a class="reference internal" href="#id39">Major Changes</a></li>
+<li><a class="reference internal" href="#id40">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id41">1.2 (2023-04-03)</a><ul>
+<li><a class="reference internal" href="#id42">Major Changes</a></li>
+<li><a class="reference internal" href="#id43">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id44">1.1.1 (2023-03-10)</a><ul>
+<li><a class="reference internal" href="#id45">Major Changes</a></li>
+<li><a class="reference internal" href="#id46">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id47">1.1 (2023-02-24)</a><ul>
+<li><a class="reference internal" href="#id48">Major Changes</a></li>
+<li><a class="reference internal" href="#id49">Minor Changes</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#id50">1.0 (2022-12-05)</a><ul>
+<li><a class="reference internal" href="#id51">Major Changes</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
+  </div>
+</div><script src="_static/documentation_options.js?v=1e6c7a3c"></script>
+    <script src="_static/doctools.js?v=9bcbadda"></script>
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+</html>
\ No newline at end of file
diff --git a/cupy-xfails.txt b/cupy-xfails.txt
deleted file mode 100644
index 0a91cafe..00000000
--- a/cupy-xfails.txt
+++ /dev/null
@@ -1,203 +0,0 @@
-# cupy doesn't have __index__ (and we cannot wrap the ndarray object)
-array_api_tests/test_array_object.py::test_scalar_casting[__index__(uint8)]
-array_api_tests/test_array_object.py::test_scalar_casting[__index__(uint16)]
-array_api_tests/test_array_object.py::test_scalar_casting[__index__(uint32)]
-array_api_tests/test_array_object.py::test_scalar_casting[__index__(uint64)]
-array_api_tests/test_array_object.py::test_scalar_casting[__index__(int8)]
-array_api_tests/test_array_object.py::test_scalar_casting[__index__(int16)]
-array_api_tests/test_array_object.py::test_scalar_casting[__index__(int32)]
-array_api_tests/test_array_object.py::test_scalar_casting[__index__(int64)]
-
-# testsuite bug (https://github.com/data-apis/array-api-tests/issues/172)
-array_api_tests/test_array_object.py::test_getitem
-
-# attributes are np.float32 instead of float
-# (see also https://github.com/data-apis/array-api/issues/405)
-array_api_tests/test_data_type_functions.py::test_finfo[float32]
-array_api_tests/test_data_type_functions.py::test_finfo[complex64]
-
-# Some array attributes are missing, and we do not wrap the array object
-array_api_tests/test_has_names.py::test_has_names[array_method-__array_namespace__]
-array_api_tests/test_has_names.py::test_has_names[array_method-__index__]
-array_api_tests/test_has_names.py::test_has_names[array_method-to_device]
-array_api_tests/test_has_names.py::test_has_names[array_attribute-mT]
-
-array_api_tests/test_linalg.py::test_solve
-
-# We cannot modify array methods
-array_api_tests/test_operators_and_elementwise_functions.py::test_divide[__truediv__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__floordiv__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__imod__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_subtract[__sub__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_add[__add__(x, s)]
-# floating point inaccuracy
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[remainder(x1, x2)]
-# incomplete NEP50 support in CuPy 13.x (fixed in 14.0.0a1)
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[pow]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[logaddexp]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[nextafter]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[hypot]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[atan2]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[divide]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[copysign]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[divide]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[maximum]
-
-# cupy (arg)min/max wrong with infinities
-# https://github.com/cupy/cupy/issues/7424
-array_api_tests/test_searching_functions.py::test_argmax
-array_api_tests/test_searching_functions.py::test_argmin
-array_api_tests/test_statistical_functions.py::test_min
-array_api_tests/test_statistical_functions.py::test_max
-
-# prod() sometimes doesn't give nan for 0*overflow
-array_api_tests/test_statistical_functions.py::test_prod
-
-# testsuite incorrectly thinks meshgrid doesn't have indexing argument
-# (https://github.com/data-apis/array-api-tests/issues/171)
-array_api_tests/test_signatures.py::test_func_signature[meshgrid]
-
-# We cannot add array attributes
-array_api_tests/test_signatures.py::test_array_method_signature[__array_namespace__]
-array_api_tests/test_signatures.py::test_array_method_signature[__index__]
-array_api_tests/test_signatures.py::test_array_method_signature[to_device]
-
-# We do not attempt to workaround special cases (and the operator method ones
-
-array_api_tests/test_special_cases.py::test_binary[__add__(isfinite(x1_i) and x1_i != 0 and x2_i == -x1_i) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__add__(x1_i is +0 and x2_i is -0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__add__(x1_i is -0 and x2_i is +0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__add__(x1_i is -0 and x2_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i < 0 and x2_i is -0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i > 0 and x2_i is -0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -0 and x2_i < 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -0 and x2_i > 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__mod__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> x2_i]
-array_api_tests/test_special_cases.py::test_binary[__mod__(isfinite(x1_i) and x1_i < 0 and x2_i is -infinity) -> x1_i]
-array_api_tests/test_special_cases.py::test_binary[__mod__(isfinite(x1_i) and x1_i > 0 and x2_i is +infinity) -> x1_i]
-array_api_tests/test_special_cases.py::test_binary[__mod__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> x2_i]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i < 0 and x2_i is -0) -> NaN]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i > 0 and x2_i is -0) -> NaN]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i is +0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i is +0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i is -0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i is -0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__pow__(x1_i is -0 and x2_i < 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__pow__(x1_i is -0 and x2_i < 0 and x2_i.is_integer() and x2_i % 2 == 1) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__pow__(x1_i is -0 and x2_i > 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__pow__(x1_i is -0 and x2_i > 0 and x2_i.is_integer() and x2_i % 2 == 1) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__pow__(x2_i is -0) -> 1]
-array_api_tests/test_special_cases.py::test_binary[__truediv__(x1_i < 0 and x2_i is -0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__truediv__(x1_i > 0 and x2_i is -0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__truediv__(x1_i is -0 and x2_i < 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__truediv__(x1_i is -0 and x2_i > 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[add(isfinite(x1_i) and x1_i != 0 and x2_i == -x1_i) -> +0]
-array_api_tests/test_special_cases.py::test_binary[add(x1_i is +0 and x2_i is -0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[add(x1_i is -0 and x2_i is +0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[add(x1_i is -0 and x2_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[atan2(x1_i < 0 and x2_i is -0) -> roughly -pi/2]
-array_api_tests/test_special_cases.py::test_binary[atan2(x1_i > 0 and x2_i is -0) -> roughly +pi/2]
-array_api_tests/test_special_cases.py::test_binary[atan2(x1_i is +0 and x2_i is -0) -> roughly +pi]
-array_api_tests/test_special_cases.py::test_binary[atan2(x1_i is -0 and x2_i < 0) -> roughly -pi]
-array_api_tests/test_special_cases.py::test_binary[atan2(x1_i is -0 and x2_i > 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[atan2(x1_i is -0 and x2_i is +0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[atan2(x1_i is -0 and x2_i is -0) -> roughly -pi]
-array_api_tests/test_special_cases.py::test_binary[divide(x1_i < 0 and x2_i is -0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[divide(x1_i > 0 and x2_i is -0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[divide(x1_i is -0 and x2_i < 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[divide(x1_i is -0 and x2_i > 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i < 0 and x2_i is -0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i > 0 and x2_i is -0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -0 and x2_i < 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -0 and x2_i > 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[pow(x1_i is -0 and x2_i < 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[pow(x1_i is -0 and x2_i < 0 and x2_i.is_integer() and x2_i % 2 == 1) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[pow(x1_i is -0 and x2_i > 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +0]
-array_api_tests/test_special_cases.py::test_binary[pow(x1_i is -0 and x2_i > 0 and x2_i.is_integer() and x2_i % 2 == 1) -> -0]
-array_api_tests/test_special_cases.py::test_binary[pow(x2_i is -0) -> 1]
-array_api_tests/test_special_cases.py::test_binary[remainder(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> x2_i]
-array_api_tests/test_special_cases.py::test_binary[remainder(isfinite(x1_i) and x1_i < 0 and x2_i is -infinity) -> x1_i]
-array_api_tests/test_special_cases.py::test_binary[remainder(isfinite(x1_i) and x1_i > 0 and x2_i is +infinity) -> x1_i]
-array_api_tests/test_special_cases.py::test_binary[remainder(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> x2_i]
-array_api_tests/test_special_cases.py::test_binary[remainder(x1_i < 0 and x2_i is -0) -> NaN]
-array_api_tests/test_special_cases.py::test_binary[remainder(x1_i > 0 and x2_i is -0) -> NaN]
-array_api_tests/test_special_cases.py::test_binary[remainder(x1_i is +0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[remainder(x1_i is +0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[remainder(x1_i is -0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[remainder(x1_i is -0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__iadd__(isfinite(x1_i) and x1_i != 0 and x2_i == -x1_i) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__iadd__(x1_i is +0 and x2_i is -0) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__iadd__(x1_i is -0 and x2_i is +0) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__iadd__(x1_i is -0 and x2_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i < 0 and x2_i is -0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i > 0 and x2_i is -0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -0 and x2_i < 0) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -0 and x2_i > 0) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__imod__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> x2_i]
-array_api_tests/test_special_cases.py::test_iop[__imod__(isfinite(x1_i) and x1_i < 0 and x2_i is -infinity) -> x1_i]
-array_api_tests/test_special_cases.py::test_iop[__imod__(isfinite(x1_i) and x1_i > 0 and x2_i is +infinity) -> x1_i]
-array_api_tests/test_special_cases.py::test_iop[__imod__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> x2_i]
-array_api_tests/test_special_cases.py::test_iop[__imod__(x1_i < 0 and x2_i is -0) -> NaN]
-array_api_tests/test_special_cases.py::test_iop[__imod__(x1_i > 0 and x2_i is -0) -> NaN]
-array_api_tests/test_special_cases.py::test_iop[__imod__(x1_i is +0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__imod__(x1_i is +0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__imod__(x1_i is -0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__imod__(x1_i is -0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__ipow__(x1_i is -0 and x2_i < 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ipow__(x1_i is -0 and x2_i < 0 and x2_i.is_integer() and x2_i % 2 == 1) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ipow__(x1_i is -0 and x2_i > 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__ipow__(x1_i is -0 and x2_i > 0 and x2_i.is_integer() and x2_i % 2 == 1) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ipow__(x2_i is -0) -> 1]
-array_api_tests/test_special_cases.py::test_iop[__itruediv__(x1_i < 0 and x2_i is -0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__itruediv__(x1_i > 0 and x2_i is -0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__itruediv__(x1_i is -0 and x2_i < 0) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__itruediv__(x1_i is -0 and x2_i > 0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[__abs__(x_i is -0) -> +0]
-array_api_tests/test_special_cases.py::test_unary[abs(x_i is -0) -> +0]
-array_api_tests/test_special_cases.py::test_unary[asin(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[asinh(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[atan(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[atanh(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[ceil(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[cos(x_i is -0) -> 1]
-array_api_tests/test_special_cases.py::test_unary[cosh(x_i is -0) -> 1]
-array_api_tests/test_special_cases.py::test_unary[exp(x_i is -0) -> 1]
-array_api_tests/test_special_cases.py::test_unary[expm1(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[floor(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[log1p(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[round(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[sin(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[signbit(x_i is -0) -> True]
-array_api_tests/test_special_cases.py::test_unary[sinh(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[sqrt(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[tan(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[tanh(x_i is -0) -> -0]
-array_api_tests/test_special_cases.py::test_unary[trunc(x_i is -0) -> -0]
-
-# CuPy gives the wrong shape for n-dim fft funcs. See
-# https://github.com/data-apis/array-api-compat/pull/78#issuecomment-1984527870
-array_api_tests/test_fft.py::test_fftn
-array_api_tests/test_fft.py::test_ifftn
-array_api_tests/test_fft.py::test_rfftn
- 
-# observed in the 1.10 release process, is likely related to xfails above
-array_api_tests/test_fft.py::test_irfftn
-
-# 2023.12 support
-# cupy.ndaray cannot be specified as `repeats` argument.
-array_api_tests/test_manipulation_functions.py::test_repeat
-array_api_tests/test_signatures.py::test_func_signature[from_dlpack]
-array_api_tests/test_signatures.py::test_array_method_signature[__dlpack__]
-
-# 2024.12 support
-array_api_tests/test_signatures.py::test_func_signature[bitwise_and]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_left_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_or]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_right_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_xor]
-array_api_tests/test_special_cases.py::test_binary[nextafter(x1_i is +0 and x2_i is -0) -> -0]
-
-# cupy 13.x follows numpy 1.x w/o weak promotion: result_type(int32, uint8, 1) != result_type(int32, uint8)
-array_api_tests/test_data_type_functions.py::TestResultType::test_with_scalars
diff --git a/dask-skips.txt b/dask-skips.txt
deleted file mode 100644
index a16a8588..00000000
--- a/dask-skips.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-# NOTE: dask tests run on a very small number of examples in CI due to
-# slowness. This causes very high flakiness in the tests.
-# Before changing this file, please run with at least 200 examples.
-
-# Passes, but extremely slow
-array_api_tests/test_linalg.py::test_outer
-
-# Hangs
-array_api_tests/test_creation_functions.py::test_eye
diff --git a/dask-xfails.txt b/dask-xfails.txt
deleted file mode 100644
index 3efb4f96..00000000
--- a/dask-xfails.txt
+++ /dev/null
@@ -1,150 +0,0 @@
-# NOTE: dask tests run on a very small number of examples in CI due to
-# slowness. This causes very high flakiness in the tests.
-# Before changing this file, please run with at least 200 examples.
-
-# Broken edge case with shape 0
-# https://github.com/dask/dask/issues/11800
-array_api_tests/test_array_object.py::test_setitem
-
-# Various indexing errors
-array_api_tests/test_array_object.py::test_getitem_masking
-
-# zero division error, and typeerror: tuple indices must be integers or slices not tuple
-array_api_tests/test_creation_functions.py::test_eye
-
-# attributes are np.float32 instead of float
-# (see also https://github.com/data-apis/array-api/issues/405)
-array_api_tests/test_data_type_functions.py::test_finfo[float32]
-array_api_tests/test_data_type_functions.py::test_finfo[complex64]
-
-# out[-1]=dask.array<getitem ...> but should be some floating number
-# (I think the test is not forcing the op to be computed?)
-array_api_tests/test_creation_functions.py::test_linspace
-
-# Shape mismatch
-array_api_tests/test_indexing_functions.py::test_take
-
-# missing `take_along_axis`, https://github.com/dask/dask/issues/3663
-array_api_tests/test_indexing_functions.py::test_take_along_axis
-
-# Array methods and attributes not already on da.Array cannot be wrapped
-array_api_tests/test_has_names.py::test_has_names[array_method-__array_namespace__]
-array_api_tests/test_has_names.py::test_has_names[array_method-to_device]
-array_api_tests/test_has_names.py::test_has_names[array_attribute-device]
-array_api_tests/test_has_names.py::test_has_names[array_attribute-mT]
-
-# Data-dependent output shape
-# These tests fail as array-api-tests doesn't cope with unknown shapes
-# Also, output shape is (math.nan, ) instead of (None, )
-# Also, da.unique() doesn't accept equals_nan which causes non-compliant
-# output when there are NaNs in the input.
-array_api_tests/test_searching_functions.py::test_nonzero
-array_api_tests/test_set_functions.py::test_unique_all
-array_api_tests/test_set_functions.py::test_unique_counts
-array_api_tests/test_set_functions.py::test_unique_inverse
-array_api_tests/test_set_functions.py::test_unique_values
-
-# Linalg failures (signature failures/missing methods)
-
-# fails for ndim > 2
-array_api_tests/test_linalg.py::test_svdvals
-
-# dtype mismatch got uint64, but should be uint8; NPY_PROMOTION_STATE=weak doesn't help
-array_api_tests/test_linalg.py::test_tensordot
-
-# AssertionError: out.dtype=uint64, but should be uint8 [tensordot(uint8, uint8)]
-array_api_tests/test_linalg.py::test_linalg_tensordot
-
-# ZeroDivisionError in dask's normalize_chunks/auto_chunks internals
-array_api_tests/test_linalg.py::test_inv
-array_api_tests/test_linalg.py::test_matrix_power
-
-# Linalg - these don't exist in dask
-array_api_tests/test_signatures.py::test_extension_func_signature[linalg.cross]
-array_api_tests/test_signatures.py::test_extension_func_signature[linalg.det]
-array_api_tests/test_signatures.py::test_extension_func_signature[linalg.eigh]
-array_api_tests/test_signatures.py::test_extension_func_signature[linalg.eigvalsh]
-array_api_tests/test_signatures.py::test_extension_func_signature[linalg.matrix_power]
-array_api_tests/test_signatures.py::test_extension_func_signature[linalg.pinv]
-array_api_tests/test_signatures.py::test_extension_func_signature[linalg.slogdet]
-array_api_tests/test_linalg.py::test_cross
-array_api_tests/test_linalg.py::test_det
-array_api_tests/test_linalg.py::test_eigh
-array_api_tests/test_linalg.py::test_eigvalsh
-array_api_tests/test_linalg.py::test_matrix_rank
-array_api_tests/test_linalg.py::test_pinv
-array_api_tests/test_linalg.py::test_slogdet
-array_api_tests/test_has_names.py::test_has_names[linalg-cross]
-array_api_tests/test_has_names.py::test_has_names[linalg-det]
-array_api_tests/test_has_names.py::test_has_names[linalg-eigh]
-array_api_tests/test_has_names.py::test_has_names[linalg-eigvalsh]
-array_api_tests/test_has_names.py::test_has_names[linalg-matrix_power]
-array_api_tests/test_has_names.py::test_has_names[linalg-pinv]
-array_api_tests/test_has_names.py::test_has_names[linalg-slogdet]
-
-# Constructing the input arrays fails to a weird shape error...
-array_api_tests/test_linalg.py::test_solve
-
-# missing full_matrices kw
-# https://github.com/dask/dask/issues/10389
-# also only supports 2-d inputs
-array_api_tests/test_linalg.py::test_svd
-
-# Missing dlpack stuff
-array_api_tests/test_signatures.py::test_func_signature[from_dlpack]
-array_api_tests/test_signatures.py::test_array_method_signature[__array_namespace__]
-array_api_tests/test_signatures.py::test_array_method_signature[__dlpack__]
-array_api_tests/test_signatures.py::test_array_method_signature[__dlpack_device__]
-array_api_tests/test_signatures.py::test_array_method_signature[to_device]
-array_api_tests/test_has_names.py::test_has_names[creation-from_dlpack]
-array_api_tests/test_has_names.py::test_has_names[array_method-__dlpack__]
-array_api_tests/test_has_names.py::test_has_names[array_method-__dlpack_device__]
-
-# No mT on dask array
-array_api_tests/meta/test_hypothesis_helpers.py::test_symmetric_matrices
-
-# Edge case of args near 2**63
-# https://github.com/dask/dask/issues/11706
-array_api_tests/test_creation_functions.py::test_arange
-
-# da.searchsorted with a sorter argument is not supported
-array_api_tests/test_searching_functions.py::test_searchsorted
-
-# 2023.12 support
-array_api_tests/test_manipulation_functions.py::test_repeat
-
-# 2024.12 support
-array_api_tests/test_array_object.py::test_getitem_arrays_and_ints_1[1]
-array_api_tests/test_array_object.py::test_getitem_arrays_and_ints_1[None]
-array_api_tests/test_array_object.py::test_getitem_arrays_and_ints_2[1]
-array_api_tests/test_array_object.py::test_getitem_arrays_and_ints_2[None]
-array_api_tests/test_has_names.py::test_has_names[indexing-take_along_axis]
-array_api_tests/test_signatures.py::test_func_signature[count_nonzero]
-array_api_tests/test_signatures.py::test_func_signature[take_along_axis]
-
-array_api_tests/test_linalg.py::test_cholesky
-array_api_tests/test_linalg.py::test_linalg_matmul
-array_api_tests/test_linalg.py::test_matmul
-array_api_tests/test_linalg.py::test_matrix_norm
-array_api_tests/test_linalg.py::test_qr
-array_api_tests/test_manipulation_functions.py::test_roll
-
-# Stubs have a comment: (**note**: libraries may return ``NaN`` to match Python behavior.)
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
diff --git a/dev/implementation-notes.html b/dev/implementation-notes.html
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+        <article role="main" id="furo-main-content">
+          <section class="tex2jax_ignore mathjax_ignore" id="implementation-notes">
+<h1>Implementation Notes<a class="headerlink" href="#implementation-notes" title="Link to this heading">¶</a></h1>
+<p>Since NumPy, CuPy, and to a degree, Dask, are nearly identical in behavior,
+most wrapping logic can be shared between them. Wrapped functions that have
+the same logic between multiple libraries are in <code class="docutils literal notranslate"><span class="pre">array_api_compat/common/</span></code>.
+These functions are defined like</p>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># In array_api_compat/common/_aliases.py</span>
+
+<span class="k">def</span> <span class="nf">acos</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">/</span><span class="p">,</span> <span class="n">xp</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">xp</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">xp</span></code> argument refers to the original array namespace (e.g., <code class="docutils literal notranslate"><span class="pre">numpy</span></code> or
+<code class="docutils literal notranslate"><span class="pre">cupy</span></code>). Then in the specific <code class="docutils literal notranslate"><span class="pre">array_api_compat/numpy/</span></code> and
+<code class="docutils literal notranslate"><span class="pre">array_api_compat/cupy/</span></code> namespaces, the <code class="docutils literal notranslate"><span class="pre">&#64;get_xp</span></code> decorator is applied to
+these functions, which automatically removes the <code class="docutils literal notranslate"><span class="pre">xp</span></code> argument from the
+function signature and replaces it with the corresponding array library, like</p>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># In array_api_compat/numpy/_aliases.py</span>
+
+<span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">_aliases</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">acos</span> <span class="o">=</span> <span class="n">get_xp</span><span class="p">(</span><span class="n">np</span><span class="p">)(</span><span class="n">_aliases</span><span class="o">.</span><span class="n">acos</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>This <code class="docutils literal notranslate"><span class="pre">acos</span></code> now has the signature <code class="docutils literal notranslate"><span class="pre">acos(x,</span> <span class="pre">/)</span></code> and calls <code class="docutils literal notranslate"><span class="pre">numpy.arccos</span></code>.</p>
+<p>Similarly, for CuPy:</p>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># In array_api_compat/cupy/_aliases.py</span>
+
+<span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">_aliases</span>
+
+<span class="kn">import</span> <span class="nn">cupy</span> <span class="k">as</span> <span class="nn">cp</span>
+
+<span class="n">acos</span> <span class="o">=</span> <span class="n">get_xp</span><span class="p">(</span><span class="n">cp</span><span class="p">)(</span><span class="n">_aliases</span><span class="o">.</span><span class="n">acos</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Most NumPy and CuPy are defined in this way, since their behaviors are nearly
+identical PyTorch uses a similar layout in <code class="docutils literal notranslate"><span class="pre">array_api_compat/torch/</span></code>, but it
+differs enough from NumPy/CuPy that very few common wrappers for those
+libraries are reused. Dask is close to NumPy in behavior and so most Dask
+functions also reuse the NumPy/CuPy common wrappers.</p>
+<p>Occasionally, a wrapper implementation will need to reference another wrapper
+implementation, rather than the base <code class="docutils literal notranslate"><span class="pre">xp</span></code> version. The easiest way to do this
+is to call <code class="docutils literal notranslate"><span class="pre">array_namespace</span></code>, like</p>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">wrapped_xp</span> <span class="o">=</span> <span class="n">array_namespace</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">wrapped_xp</span><span class="o">.</span><span class="n">wrapped_func</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Also, if there is a very minor difference required for wrapping, say, CuPy and
+NumPy, they can still use a common implementation in <code class="docutils literal notranslate"><span class="pre">common/_aliases.py</span></code> and
+use the <code class="docutils literal notranslate"><span class="pre">is_*_namespace()</span></code> or <code class="docutils literal notranslate"><span class="pre">is_*_function()</span></code> <a class="reference internal" href="../helper-functions.html"><span class="std std-doc">helper
+functions</span></a> to branch as necessary.</p>
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+<li class="toctree-l1"><a class="reference internal" href="../changelog.html">Changelog</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="tests.html">Tests</a></li>
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+          <section class="tex2jax_ignore mathjax_ignore" id="releasing">
+<h1>Releasing<a class="headerlink" href="#releasing" title="Link to this heading">¶</a></h1>
+<ul class="contains-task-list">
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Create a PR with a release branch</strong></p>
+<p>This makes it easy to verify that CI is passing, and also gives you a place
+to push up updates to the changelog and any last minute fixes for the
+release.</p>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Double check the release branch is fully merged with <code class="docutils literal notranslate"><span class="pre">main</span></code>.</strong></p>
+<p>(e.g., if the release branch is called <code class="docutils literal notranslate"><span class="pre">release</span></code>)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">checkout</span> <span class="n">main</span>
+<span class="n">git</span> <span class="n">pull</span>
+<span class="n">git</span> <span class="n">checkout</span> <span class="n">release</span>
+<span class="n">git</span> <span class="n">merge</span> <span class="n">main</span>
+</pre></div>
+</div>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Make sure that all CI tests are passing.</strong></p>
+<p>Note that the GitHub action that publishes to PyPI does not check if CI is
+passing before publishing. So you need to check this manually.</p>
+<p>This does mean you can ignore CI failures, but ideally you should fix any
+failures or update the <code class="docutils literal notranslate"><span class="pre">*-xfails.txt</span></code> files before tagging, so that CI and
+the CuPy tests fully pass. Otherwise it will be hard to tell what things are
+breaking in the future. It’s also a good idea to remove any xpasses from
+those files (but be aware that some xfails are from flaky failures, so
+unless you know the underlying issue has been fixed, an xpass test is
+probably still xfail).</p>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Test CuPy.</strong></p>
+<p>CuPy must be tested manually (it isn’t tested on CI, see
+<a class="reference external" href="https://github.com/data-apis/array-api-compat/issues/197">https://github.com/data-apis/array-api-compat/issues/197</a>). Use the script</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">test_cupy</span><span class="o">.</span><span class="n">sh</span>
+</pre></div>
+</div>
+<p>on a machine with a CUDA GPU.</p>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Update the version.</strong></p>
+<p>You must edit</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array_api_compat</span><span class="o">/</span><span class="fm">__init__</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+<p>and update the version (the version is not computed from the tag because
+that would break vendorability).</p>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Update the <a class="reference internal" href="../changelog.html"><span class="std std-doc">changelog</span></a>.</strong></p>
+<p>Edit</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">docs</span><span class="o">/</span><span class="n">changelog</span><span class="o">.</span><span class="n">md</span>
+</pre></div>
+</div>
+<p>with the changes for the release.</p>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Create the release tag.</strong></p>
+<p>Once everything is ready, create a tag</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">tag</span> <span class="o">-</span><span class="n">a</span> <span class="o">&lt;</span><span class="n">version</span><span class="o">&gt;</span>
+</pre></div>
+</div>
+<p>(note the tag names are not prefixed, for instance, the tag for version 1.5 is
+just <code class="docutils literal notranslate"><span class="pre">1.5</span></code>)</p>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Push the tag to GitHub.</strong></p>
+<p><em>This is the final step. Doing this will build and publish the release!</em></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">push</span> <span class="n">origin</span> <span class="o">&lt;</span><span class="n">version</span><span class="o">&gt;</span>
+</pre></div>
+</div>
+<p>This will trigger the <a class="reference external" href="https://github.com/data-apis/array-api-compat/actions/workflows/publish-package.yml"><code class="docutils literal notranslate"><span class="pre">publish</span> <span class="pre">distributions</span></code></a>
+GitHub Action that will build the release and push it to PyPI.</p>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Check that the <a class="reference external" href="https://github.com/data-apis/array-api-compat/actions/workflows/publish-package.yml"><code class="docutils literal notranslate"><span class="pre">publish</span> <span class="pre">distributions</span></code></a>
+action build on the tag worked.</strong> Note that this action will run even if the
+other CI fails, so you must make sure that CI is passing <em>before</em> tagging.</p>
+<p>If it failed for some reason, you may need to delete the tag and try again.</p>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Merge the release branch.</strong></p>
+<p>This way any changes you made in the branch, such as updates to the
+changelog or xfails files, are updated in <code class="docutils literal notranslate"><span class="pre">main</span></code>. This will also make the
+docs update (the docs are published automatically from the sources on
+<code class="docutils literal notranslate"><span class="pre">main</span></code>).</p>
+</li>
+<li class="task-list-item enabled"><p><input class="task-list-item-checkbox"  type="checkbox"> <strong>Update conda-forge.</strong></p>
+<p>After the PyPI package is published, the conda-forge bot should update the
+feedstock automatically after some time. The bot should automerge, so in
+most cases you don’t need to do anything here, unless some metadata on the
+feedstock needs to be updated.</p>
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diff --git a/dev/special-considerations.html b/dev/special-considerations.html
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+<li class="toctree-l1"><a class="reference internal" href="../helper-functions.html">Helper Functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../supported-array-libraries.html">Supported Array Libraries</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../changelog.html">Changelog</a></li>
+<li class="toctree-l1 current has-children"><a class="reference internal" href="index.html">Development Notes</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of Development Notes</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
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+          <section class="tex2jax_ignore mathjax_ignore" id="special-considerations">
+<h1>Special Considerations<a class="headerlink" href="#special-considerations" title="Link to this heading">¶</a></h1>
+<p>array-api-compat requires some special development considerations that are
+different from most other Python libraries. The goal of array-api-compat is to
+be a small library that packages can either vendor or add as a dependency to
+implement array API support. Consequently, certain design considerations
+should be taken into account:</p>
+<ul id="no-dependencies">
+<li><p><strong>No Hard Dependencies.</strong> Although array-api-compat “depends” on NumPy, CuPy,
+PyTorch, etc., it does not hard depend on them. These libraries are not
+imported unless either an array object is passed to
+<a class="reference internal" href="../helper-functions.html#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">array_namespace()</span></code></a>, or the specific <code class="docutils literal notranslate"><span class="pre">array_api_compat.&lt;namespace&gt;</span></code>
+sub-namespace is explicitly imported. This is tested (as best as possible)
+in <code class="docutils literal notranslate"><span class="pre">tests/test_no_dependencies.py</span></code>.</p></li>
+<li><p><strong>Vendorability.</strong> array-api-compat should be <a class="reference internal" href="../index.html#vendoring"><span class="std std-ref">vendorable</span></a>. This
+means that, for instance, all imports in the library are relative imports.
+No code in the package specifically references the name <code class="docutils literal notranslate"><span class="pre">array_api_compat</span></code>
+(we also support renaming the package to something else).
+Vendorability support is tested in <code class="docutils literal notranslate"><span class="pre">tests/test_vendoring.py</span></code>.</p></li>
+<li><p><strong>Pure Python.</strong> To make array-api-compat as easy as possible to add as a
+dependency, the code is all pure Python.</p></li>
+<li><p><strong>Minimal Wrapping Only.</strong> The wrapping functionality is minimal. This means
+that if something is difficult to wrap using pure Python, or if trying to
+support some array API behavior would require a significant amount of code,
+we prefer to leave the behavior as an upstream issue for the array library,
+and <a class="reference internal" href="../supported-array-libraries.html"><span class="std std-doc">document it as a known difference</span></a>.</p>
+<p>This also means that we do not at this point in time implement anything
+other than wrappers for functions in the standard, and basic <a class="reference internal" href="../helper-functions.html"><span class="std std-doc">helper
+functions</span></a> that would be useful for most users of
+array-api-compat. The addition of functions that are not part of the array
+API standard is currently out-of-scope for this package (see the
+<a class="reference internal" href="../index.html#scope"><span class="std std-ref">Scope</span></a> section of the documentation).</p>
+</li>
+<li><p><strong>No Side-Effects</strong>. array-api-compat behavior should be localized to only the
+specific code that imports and uses it. It should be invisible to end-users
+or users of dependent codes. This in particular implies to the next two
+points.</p></li>
+<li><p><strong>No Monkey Patching.</strong> <code class="docutils literal notranslate"><span class="pre">array-api-compat</span></code> should not attempt to modify
+anything about the underlying library. It is a <em>wrapper</em> library only.</p></li>
+<li><p><strong>No Modifying the Array Object.</strong> The array (or tensor) object of the array
+library cannot be modified. This also precludes the creation of array
+subclasses or wrapper classes.</p>
+<p>Any non-standard behavior that is built-in to the array object, such as the
+behavior of <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/array_object.html">array
+methods</a>,
+is therefore left unwrapped. Users can workaround issues by using
+corresponding <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/elementwise_functions.html">elementwise
+functions</a>
+instead of
+<a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/array_object.html#operators">operators</a>,
+and by using the <a class="reference internal" href="../helper-functions.html"><span class="std std-doc">helper functions</span></a> provided by
+array-api-compat instead of attributes or methods like <code class="docutils literal notranslate"><span class="pre">x.to_device()</span></code>.</p>
+</li>
+<li><p><strong>Avoid Restricting Behavior that is Outside the Scope of the Standard.</strong> All
+array libraries have functions and behaviors that are outside of the scope
+of what is specified by the standard. These behaviors should be left intact
+whenever possible, unless the standard explicitly disallows something. This
+means</p>
+<ul class="simple">
+<li><p>All namespaces are <em>extended</em> with wrapper functions. You may notice the
+extensive use of <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">*</span></code> in various files in <code class="docutils literal notranslate"><span class="pre">array_api_compat</span></code>. While
+this would normally be questionable, this is the <a class="reference external" href="https://peps.python.org/pep-0008/#imports">one actual legitimate
+use-case for <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">*</span></code></a>, to
+re-export names from an external namespace.</p></li>
+<li><p>All wrapper functions pass <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> through to the wrapped function.</p></li>
+<li><p>Input types not supported by the standard should work if they work in the
+underlying wrapped function (for instance, Python scalars or <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code>
+subclasses).</p></li>
+</ul>
+<p>By keeping underlying behaviors intact, it is easier for libraries to swap
+out NumPy or other array libraries for array-api-compat, and it is easier
+for libraries to write array library-specific code paths.</p>
+<p>The onus is on users of array-api-compat to ensure their array API code is
+portable, e.g., by testing against <a class="reference internal" href="../index.html#array-api-strict"><span class="std std-ref">array-api-strict</span></a>.</p>
+</li>
+</ul>
+</section>
+
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+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
+          </svg>
+          <span>Back to top</span>
+        </a>
+        <div class="content-icon-container">
+          <div class="view-this-page">
+  <a class="muted-link" href="../_sources/dev/tests.md.txt" title="View this page">
+    <svg><use href="#svg-eye"></use></svg>
+    <span class="visually-hidden">View this page</span>
+  </a>
+</div>
+<div class="theme-toggle-container theme-toggle-content">
+            <button class="theme-toggle">
+              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+              <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg>
+              <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg>
+              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+            </button>
+          </div>
+          <label class="toc-overlay-icon toc-content-icon no-toc" for="__toc">
+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main" id="furo-main-content">
+          <section class="tex2jax_ignore mathjax_ignore" id="tests">
+<h1>Tests<a class="headerlink" href="#tests" title="Link to this heading">¶</a></h1>
+<p>The majority of the behavior for array-api-compat is tested by the
+<a class="reference external" href="https://github.com/data-apis/array-api-tests">array-api-tests</a> test suite for
+the array API standard. There are also array-api-compat specific tests in
+<a class="reference external" href="https://github.com/data-apis/array-api-compat/tree/main/tests"><code class="docutils literal notranslate"><span class="pre">tests/</span></code></a>.
+These tests should be limited to things that are not tested by the test suite,
+e.g., tests for <a class="reference internal" href="../helper-functions.html"><span class="std std-doc">helper functions</span></a> or for behavior
+that is not strictly required by the standard. To run these tests, install the
+dependencies from the <code class="docutils literal notranslate"><span class="pre">dev</span></code> optional group (array-api-compat has <a class="reference internal" href="special-considerations.html#no-dependencies"><span class="std std-ref">no hard
+runtime dependencies</span></a>).</p>
+<p>array-api-tests is run against all supported libraries are tested on CI
+(<a class="reference internal" href="../supported-array-libraries.html#jax-support"><span class="std std-ref">except for JAX</span></a> and <a class="reference internal" href="../supported-array-libraries.html#sparse-support"><span class="std std-ref">Sparse</span></a>). This is achieved
+by a <a class="reference external" href="https://github.com/data-apis/array-api-compat/blob/main/.github/workflows/array-api-tests.yml">reusable GitHub Actions
+Workflow</a>.
+Most libraries have tests that must be xfailed or skipped for various reasons.
+These are defined in specific <code class="docutils literal notranslate"><span class="pre">&lt;library&gt;-xfails.txt</span></code> files and are
+automatically forwarded to array-api-tests.</p>
+<p>You may often need to update these xfail files, either to add new xfails
+(e.g., because of new test suite features, or because a test that was
+previously thought to be passing actually flaky fails). Try to keep the xfails
+files organized, with comments pointing to upstream issues whenever possible.</p>
+<p>From time to time, xpass tests should be removed from the xfail files, but be
+aware that many xfail tests are flaky, so an xpass should only be removed if
+you know that the underlying issue has been fixed.</p>
+<p>Array libraries that require a GPU to run (currently only CuPy) cannot be
+tested on CI. There is a helper script <code class="docutils literal notranslate"><span class="pre">test_cupy.sh</span></code> that can be used to
+manually test CuPy on a machine with a CUDA GPU.</p>
+</section>
+
+        </article>
+      </div>
+      <footer>
+        
+        <div class="related-pages">
+          <a class="next-page" href="releasing.html">
+              <div class="page-info">
+                <div class="context">
+                  <span>Next</span>
+                </div>
+                <div class="title">Releasing</div>
+              </div>
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+            </a>
+          <a class="prev-page" href="implementation-notes.html">
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+              <div class="page-info">
+                <div class="context">
+                  <span>Previous</span>
+                </div>
+                
+                <div class="title">Implementation Notes</div>
+                
+              </div>
+            </a>
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2024, Consortium for Python Data API Standards
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/data-apis/array-api-compat" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer no-toc">
+      
+      
+      
+    </aside>
+  </div>
+</div><script src="../_static/documentation_options.js?v=1e6c7a3c"></script>
+    <script src="../_static/doctools.js?v=9bcbadda"></script>
+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../_static/scripts/furo.js?v=5fa4622c"></script>
+    <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+    <script src="../_static/copybutton.js?v=f281be69"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/docs/Makefile b/docs/Makefile
deleted file mode 100644
index 11356c4f..00000000
--- a/docs/Makefile
+++ /dev/null
@@ -1,23 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = .
-BUILDDIR      = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-livehtml:
-	sphinx-autobuild --open-browser --watch .. --port 0 -b html $(SOURCEDIR) $(ALLSPHINXOPTS) $(BUILDDIR)/html
diff --git a/docs/conf.py b/docs/conf.py
deleted file mode 100644
index ac9e6dd7..00000000
--- a/docs/conf.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# For the full list of built-in configuration values, see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Project information -----------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
-
-import sys
-import os
-sys.path.insert(0, os.path.abspath('..'))
-
-project = 'array-api-compat'
-copyright = '2024, Consortium for Python Data API Standards'
-author = 'Consortium for Python Data API Standards'
-
-import array_api_compat
-release = array_api_compat.__version__
-
-# -- General configuration ---------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
-
-extensions = [
-    'myst_parser',
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.intersphinx',
-    'sphinx_copybutton',
-]
-
-intersphinx_mapping = {
-    'cupy': ('https://docs.cupy.dev/en/stable', None),
-    'torch': ('https://pytorch.org/docs/stable/', None),
-}
-# Require :external: to reference intersphinx.
-intersphinx_disabled_reftypes = ['*']
-
-templates_path = ['_templates']
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
-
-myst_enable_extensions = ["dollarmath", "linkify", "tasklist"]
-myst_enable_checkboxes = True
-
-napoleon_use_rtype = False
-napoleon_use_param = False
-
-# Make sphinx give errors for bad cross-references
-nitpicky = True
-# autodoc wants to make cross-references for every type hint. But a lot of
-# them don't actually refer to anything that we have a document for.
-nitpick_ignore = [
-    ("py:class", "Array"),
-    ("py:class", "Device"),
-]
-
-# Lets us use single backticks for code in RST
-default_role = 'code'
-
-# -- Options for HTML output -------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
-
-html_theme = 'furo'
-html_static_path = ['_static']
-
-html_css_files = ['custom.css']
-
-html_theme_options = {
-    # See https://pradyunsg.me/furo/customisation/footer/
-    "footer_icons": [
-        {
-            "name": "GitHub",
-            "url": "https://github.com/data-apis/array-api-compat",
-            "html": """
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-            """,
-            "class": "",
-        },
-    ],
-}
-
-# Logo
-
-html_favicon = "_static/favicon.png"
-
-# html_logo = "_static/logo.svg"
diff --git a/docs/make.bat b/docs/make.bat
deleted file mode 100644
index 32bb2452..00000000
--- a/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.https://www.sphinx-doc.org/
-	exit /b 1
-)
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/genindex.html b/genindex.html
new file mode 100644
index 00000000..add5e75a
--- /dev/null
+++ b/genindex.html
@@ -0,0 +1,383 @@
+<!doctype html>
+<html class="no-js" lang="en" data-content_root="./">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="#" /><link rel="search" title="Search" href="search.html" />
+
+    <link rel="shortcut icon" href="_static/favicon.png"/><!-- Generated with Sphinx 8.2.3 and Furo 2024.08.06 --><title>Index - array-api-compat 1.13.0.dev0 documentation</title>
+<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=354aac6f" />
+    <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=302659d7" />
+    <link rel="stylesheet" type="text/css" href="_static/custom.css?v=dc4c535e" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #f8f8f8;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+      }
+    }
+  }
+</style></head>
+  <body>
+    
+    <script>
+      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
+    </script>
+    
+
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
+      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
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+  </symbol>
+  <symbol id="svg-menu" viewBox="0 0 24 24">
+    <title>Menu</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
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+  </symbol>
+  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
+    <title>Expand</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
+      <polyline points="9 18 15 12 9 6"></polyline>
+    </svg>
+  </symbol>
+  <symbol id="svg-sun" viewBox="0 0 24 24">
+    <title>Light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
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+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
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+      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
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+  </symbol>
+  <symbol id="svg-sun-with-moon" viewBox="0 0 24 24">
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+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
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+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="helper-functions.html#array_api_compat.array_namespace">array_namespace() (in module array_api_compat)</a>
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+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="helper-functions.html#array_api_compat.is_array_api_obj">is_array_api_obj() (in module array_api_compat)</a>
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+</li>
+        <li><a href="helper-functions.html#array_api_compat.is_dask_namespace">is_dask_namespace() (in module array_api_compat)</a>
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+</li>
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+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="helper-functions.html#array_api_compat.is_ndonnx_array">is_ndonnx_array() (in module array_api_compat)</a>
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+        <li><a href="helper-functions.html#array_api_compat.is_ndonnx_namespace">is_ndonnx_namespace() (in module array_api_compat)</a>
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+        <li><a href="helper-functions.html#array_api_compat.is_numpy_array">is_numpy_array() (in module array_api_compat)</a>
+</li>
+        <li><a href="helper-functions.html#array_api_compat.is_numpy_namespace">is_numpy_namespace() (in module array_api_compat)</a>
+</li>
+        <li><a href="helper-functions.html#array_api_compat.is_pydata_sparse_array">is_pydata_sparse_array() (in module array_api_compat)</a>
+</li>
+        <li><a href="helper-functions.html#array_api_compat.is_pydata_sparse_namespace">is_pydata_sparse_namespace() (in module array_api_compat)</a>
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+</li>
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+<section id="S" class="genindex-section">
+  <h2>S</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="helper-functions.html#array_api_compat.size">size() (in module array_api_compat)</a>
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+</section>
+
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+  <h2>T</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="helper-functions.html#array_api_compat.to_device">to_device() (in module array_api_compat)</a>
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+        <article role="main" id="furo-main-content">
+          <section id="helper-functions">
+<h1>Helper Functions<a class="headerlink" href="#helper-functions" title="Link to this heading">¶</a></h1>
+<p>In addition to the wrapped library namespaces and functions in the array API
+specification, there are several helper functions included here that aren’t
+part of the specification but which are useful for using the array API:</p>
+<section id="entry-point-helpers">
+<h2>Entry-point Helpers<a class="headerlink" href="#entry-point-helpers" title="Link to this heading">¶</a></h2>
+<p>The <code class="code docutils literal notranslate"><span class="pre">array_namespace()</span></code> function is the primary entry-point for array API
+consuming libraries.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.array_namespace">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">array_namespace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">xs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">complex</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">api_version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_compat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ModuleType</span></span></span><a class="headerlink" href="#array_api_compat.array_namespace" title="Link to this definition">¶</a></dt>
+<dd><p>Get the array API compatible namespace for the arrays <code class="code docutils literal notranslate"><span class="pre">xs</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>xs</strong> (<em>arrays</em>) – one or more arrays. xs can also be Python scalars (bool, int, float,
+complex, or None), which are ignored.</p></li>
+<li><p><strong>api_version</strong> (<em>str</em>) – The newest version of the spec that you need support for (currently
+the compat library wrapped APIs support v2024.12).</p></li>
+<li><p><strong>use_compat</strong> (<em>bool or None</em>) – If None (the default), the native namespace will be returned if it is
+already array API compatible, otherwise a compat wrapper is used. If
+True, the compat library wrapped library will be returned. If False,
+the native library namespace is returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> (<em>namespace</em>) – The array API compatible namespace corresponding to the arrays in <code class="code docutils literal notranslate"><span class="pre">xs</span></code>.</p>
+</dd>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>TypeError</strong> – If <code class="code docutils literal notranslate"><span class="pre">xs</span></code> contains arrays from different array libraries or contains a
+    non-array.</p>
+</dd>
+</dl>
+<p>Typical usage is to pass the arguments of a function to
+<code class="code docutils literal notranslate"><span class="pre">array_namespace()</span></code> at the top of a function to get the corresponding
+array API namespace:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">your_function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="n">xp</span> <span class="o">=</span> <span class="n">array_api_compat</span><span class="o">.</span><span class="n">array_namespace</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+    <span class="c1"># Now use xp as the array library namespace</span>
+    <span class="k">return</span> <span class="n">xp</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">xp</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Wrapped array namespaces can also be imported directly. For example,
+<code class="code docutils literal notranslate"><span class="pre">array_namespace(np.array(...))</span></code> will return <code class="code docutils literal notranslate"><span class="pre">array_api_compat.numpy</span></code>.
+This function will also work for any array library not wrapped by
+array-api-compat if it explicitly defines <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__array_namespace__.html">__array_namespace__</a>
+(the wrapped namespace is always preferred if it exists).</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.is_array_api_obj" title="array_api_compat.is_array_api_obj"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_obj</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_array" title="array_api_compat.is_numpy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_array" title="array_api_compat.is_cupy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_array" title="array_api_compat.is_torch_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_array" title="array_api_compat.is_dask_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_array" title="array_api_compat.is_jax_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_array" title="array_api_compat.is_pydata_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_array</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_array_api_obj">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_array_api_obj</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TypeGuard</span><span class="p"><span class="pre">[</span></span><span class="pre">_ArrayApiObj</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#array_api_compat.is_array_api_obj" title="Link to this definition">¶</a></dt>
+<dd><p>Return True if <code class="code docutils literal notranslate"><span class="pre">x</span></code> is an array API compatible array object.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_array" title="array_api_compat.is_numpy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_array" title="array_api_compat.is_cupy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_array" title="array_api_compat.is_torch_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_array" title="array_api_compat.is_ndonnx_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_array" title="array_api_compat.is_dask_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_array" title="array_api_compat.is_jax_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_array</span></code></a></p>
+</div>
+</dd></dl>
+
+</section>
+<section id="array-method-helpers">
+<h2>Array Method Helpers<a class="headerlink" href="#array-method-helpers" title="Link to this heading">¶</a></h2>
+<p>array-api-compat does not attempt to wrap or monkey patch the array object for
+any library. Consequently, any API differences for the <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/array_object.html">array object</a>
+cannot be directly wrapped. Some libraries do not define some of these methods
+or define them differently. For these, helper functions are provided which can
+be used instead.</p>
+<p>Note that if you have a compatibility issue with an operator method (like
+<code class="code docutils literal notranslate"><span class="pre">__add__</span></code>, i.e., <code class="code docutils literal notranslate"><span class="pre">+</span></code>) you can prefer to use the corresponding <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/elementwise_functions.html">elementwise
+function</a>
+instead, which would be wrapped.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.device">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">_ArrayApiObj</span></span></em>, <em class="sig-param"><span class="positional-only-separator o"><abbr title="Positional-only parameter separator (PEP 570)"><span class="pre">/</span></abbr></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Device</span></span></span><a class="headerlink" href="#array_api_compat.device" title="Link to this definition">¶</a></dt>
+<dd><p>Hardware device the array data resides on.</p>
+<p>This is equivalent to <code class="code docutils literal notranslate"><span class="pre">x.device</span></code> according to the <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.device.html">standard</a>.
+This helper is included because some array libraries either do not have
+the <code class="code docutils literal notranslate"><span class="pre">device</span></code> attribute or include it with an incompatible API.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>array</em>) – array instance from an array API compatible library.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> (<em>device</em>) – a <code class="docutils literal notranslate"><span class="pre">device</span></code> object (see the <a class="reference external" href="https://data-apis.org/array-api/latest/design_topics/device_support.html">Device Support</a>
+section of the array API specification).</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For NumPy the device is always <code class="code docutils literal notranslate"><span class="pre">&quot;cpu&quot;</span></code>. For Dask, the device is always a
+special <code class="code docutils literal notranslate"><span class="pre">DASK_DEVICE</span></code> object.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#array_api_compat.to_device" title="array_api_compat.to_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_device</span></code></a></dt><dd><p>Move array data to a different device.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.to_device">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">to_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em>, <em class="sig-param"><span class="positional-only-separator o"><abbr title="Positional-only parameter separator (PEP 570)"><span class="pre">/</span></abbr></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">object</span></span></span><a class="headerlink" href="#array_api_compat.to_device" title="Link to this definition">¶</a></dt>
+<dd><p>Copy the array from the device on which it currently resides to the specified <code class="docutils literal notranslate"><span class="pre">device</span></code>.</p>
+<p>This is equivalent to <code class="code docutils literal notranslate"><span class="pre">x.to_device(device,</span> <span class="pre">stream=stream)</span></code> according to
+the <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.to_device.html">standard</a>.
+This helper is included because some array libraries do not have the
+<code class="code docutils literal notranslate"><span class="pre">to_device</span></code> method.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array</em>) – array instance from an array API compatible library.</p></li>
+<li><p><strong>device</strong> (<em>device</em>) – a <code class="docutils literal notranslate"><span class="pre">device</span></code> object (see the <a class="reference external" href="https://data-apis.org/array-api/latest/design_topics/device_support.html">Device Support</a>
+section of the array API specification).</p></li>
+<li><p><strong>stream</strong> (<em>int | Any | None</em>) – stream object to use during copy. In addition to the types supported
+in <code class="docutils literal notranslate"><span class="pre">array.__dlpack__</span></code>, implementations may choose to support any
+library-specific stream object with the caveat that any code using
+such an object would not be portable.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> (<em>array</em>) – an array with the same data and data type as <code class="docutils literal notranslate"><span class="pre">x</span></code> and located on the
+specified <code class="docutils literal notranslate"><span class="pre">device</span></code>.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For NumPy, this function effectively does nothing since the only supported
+device is the CPU. For CuPy, this method supports CuPy CUDA
+<a class="reference external" href="https://docs.cupy.dev/en/stable/reference/generated/cupy.cuda.Device.html#cupy.cuda.Device" title="(in CuPy v13.4.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Device</span></code></a> and
+<a class="reference external" href="https://docs.cupy.dev/en/stable/reference/generated/cupy.cuda.Stream.html#cupy.cuda.Stream" title="(in CuPy v13.4.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> objects. For PyTorch,
+this is the same as <a class="reference external" href="https://docs.pytorch.org/docs/stable/generated/torch.Tensor.to.html#torch.Tensor.to" title="(in PyTorch v2.7)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">x.to(device)</span></code></a>
+(the <code class="docutils literal notranslate"><span class="pre">stream</span></code> argument is not supported in PyTorch).</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#array_api_compat.device" title="array_api_compat.device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></a></dt><dd><p>Hardware device the array data resides on.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.size">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">HasShape</span><span class="p"><span class="pre">[</span></span><span class="pre">Collection</span><span class="p"><span class="pre">[</span></span><span class="pre">SupportsIndex</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">int</span></span></span><a class="headerlink" href="#array_api_compat.size" title="Link to this definition">¶</a></dt>
+<dt class="sig sig-object py">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">HasShape</span><span class="p"><span class="pre">[</span></span><span class="pre">Collection</span><span class="p"><span class="pre">[</span></span><span class="pre">SupportsIndex</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></span></dt>
+<dd><p>Return the total number of elements of x.</p>
+<p>This is equivalent to <code class="code docutils literal notranslate"><span class="pre">x.size</span></code> according to the <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.size.html">standard</a>.</p>
+<p>This helper is included because PyTorch defines <code class="code docutils literal notranslate"><span class="pre">size</span></code> in an
+<a class="reference external" href="https://docs.pytorch.org/docs/stable/generated/torch.Tensor.size.html#torch.Tensor.size" title="(in PyTorch v2.7)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">incompatible</span> <span class="pre">way</span></code></a>.
+It also fixes dask.array’s behaviour which returns nan for unknown sizes, whereas
+the standard requires None.</p>
+</dd></dl>
+
+</section>
+<section id="inspection-helpers">
+<h2>Inspection Helpers<a class="headerlink" href="#inspection-helpers" title="Link to this heading">¶</a></h2>
+<p>These convenience functions can be used to test if an array or namespace comes from a
+specific library without importing that library if it hasn’t been imported
+yet.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_numpy_array">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_numpy_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TypeIs</span><span class="p"><span class="pre">[</span></span><span class="pre">npt.NDArray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#array_api_compat.is_numpy_array" title="Link to this definition">¶</a></dt>
+<dd><p>Return True if <code class="code docutils literal notranslate"><span class="pre">x</span></code> is a NumPy array.</p>
+<p>This function does not import NumPy if it has not already been imported
+and is therefore cheap to use.</p>
+<p>This also returns True for <code class="code docutils literal notranslate"><span class="pre">ndarray</span></code> subclasses and NumPy scalar objects.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_obj" title="array_api_compat.is_array_api_obj"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_obj</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_array" title="array_api_compat.is_cupy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_array" title="array_api_compat.is_torch_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_array" title="array_api_compat.is_ndonnx_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_array" title="array_api_compat.is_dask_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_array" title="array_api_compat.is_jax_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_array" title="array_api_compat.is_pydata_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_array</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_cupy_array">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_cupy_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#array_api_compat.is_cupy_array" title="Link to this definition">¶</a></dt>
+<dd><p>Return True if <code class="code docutils literal notranslate"><span class="pre">x</span></code> is a CuPy array.</p>
+<p>This function does not import CuPy if it has not already been imported
+and is therefore cheap to use.</p>
+<p>This also returns True for <code class="code docutils literal notranslate"><span class="pre">cupy.ndarray</span></code> subclasses and CuPy scalar objects.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_obj" title="array_api_compat.is_array_api_obj"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_obj</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_array" title="array_api_compat.is_numpy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_array" title="array_api_compat.is_torch_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_array" title="array_api_compat.is_ndonnx_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_array" title="array_api_compat.is_dask_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_array" title="array_api_compat.is_jax_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_array" title="array_api_compat.is_pydata_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_array</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_torch_array">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_torch_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TypeIs</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#array_api_compat.is_torch_array" title="Link to this definition">¶</a></dt>
+<dd><p>Return True if <code class="code docutils literal notranslate"><span class="pre">x</span></code> is a PyTorch tensor.</p>
+<p>This function does not import PyTorch if it has not already been imported
+and is therefore cheap to use.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_obj" title="array_api_compat.is_array_api_obj"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_obj</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_array" title="array_api_compat.is_numpy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_array" title="array_api_compat.is_cupy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_array" title="array_api_compat.is_dask_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_array" title="array_api_compat.is_jax_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_array" title="array_api_compat.is_pydata_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_array</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_dask_array">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_dask_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TypeIs</span><span class="p"><span class="pre">[</span></span><span class="pre">da.Array</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#array_api_compat.is_dask_array" title="Link to this definition">¶</a></dt>
+<dd><p>Return True if <code class="code docutils literal notranslate"><span class="pre">x</span></code> is a dask.array Array.</p>
+<p>This function does not import dask if it has not already been imported
+and is therefore cheap to use.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_obj" title="array_api_compat.is_array_api_obj"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_obj</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_array" title="array_api_compat.is_numpy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_array" title="array_api_compat.is_cupy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_array" title="array_api_compat.is_torch_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_array" title="array_api_compat.is_ndonnx_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_array" title="array_api_compat.is_jax_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_array" title="array_api_compat.is_pydata_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_array</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_jax_array">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_jax_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TypeIs</span><span class="p"><span class="pre">[</span></span><span class="pre">jax.Array</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#array_api_compat.is_jax_array" title="Link to this definition">¶</a></dt>
+<dd><p>Return True if <code class="code docutils literal notranslate"><span class="pre">x</span></code> is a JAX array.</p>
+<p>This function does not import JAX if it has not already been imported
+and is therefore cheap to use.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_obj" title="array_api_compat.is_array_api_obj"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_obj</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_array" title="array_api_compat.is_numpy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_array" title="array_api_compat.is_cupy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_array" title="array_api_compat.is_torch_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_array" title="array_api_compat.is_ndonnx_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_array" title="array_api_compat.is_dask_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_array" title="array_api_compat.is_pydata_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_array</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_pydata_sparse_array">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_pydata_sparse_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TypeIs</span><span class="p"><span class="pre">[</span></span><span class="pre">sparse.SparseArray</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#array_api_compat.is_pydata_sparse_array" title="Link to this definition">¶</a></dt>
+<dd><p>Return True if <code class="code docutils literal notranslate"><span class="pre">x</span></code> is an array from the <code class="code docutils literal notranslate"><span class="pre">sparse</span></code> package.</p>
+<p>This function does not import <code class="code docutils literal notranslate"><span class="pre">sparse</span></code> if it has not already been imported
+and is therefore cheap to use.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_obj" title="array_api_compat.is_array_api_obj"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_obj</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_array" title="array_api_compat.is_numpy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_array" title="array_api_compat.is_cupy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_array" title="array_api_compat.is_torch_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_array" title="array_api_compat.is_ndonnx_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_array" title="array_api_compat.is_dask_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_array" title="array_api_compat.is_jax_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_array</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_ndonnx_array">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_ndonnx_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TypeIs</span><span class="p"><span class="pre">[</span></span><span class="pre">ndx.Array</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#array_api_compat.is_ndonnx_array" title="Link to this definition">¶</a></dt>
+<dd><p>Return True if <code class="code docutils literal notranslate"><span class="pre">x</span></code> is a ndonnx Array.</p>
+<p>This function does not import ndonnx if it has not already been imported
+and is therefore cheap to use.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_obj" title="array_api_compat.is_array_api_obj"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_obj</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_array" title="array_api_compat.is_numpy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_array" title="array_api_compat.is_cupy_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_array" title="array_api_compat.is_ndonnx_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_array" title="array_api_compat.is_dask_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_array" title="array_api_compat.is_jax_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_array</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_array" title="array_api_compat.is_pydata_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_array</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_writeable_array">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_writeable_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TypeGuard</span><span class="p"><span class="pre">[</span></span><span class="pre">_ArrayApiObj</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#array_api_compat.is_writeable_array" title="Link to this definition">¶</a></dt>
+<dd><p>Return False if <code class="docutils literal notranslate"><span class="pre">x.__setitem__</span></code> is expected to raise; True otherwise.
+Return False if <code class="code docutils literal notranslate"><span class="pre">x</span></code> is not an array API compatible object.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>As there is no standard way to check if an array is writeable without actually
+writing to it, this function blindly returns True for all unknown array types.</p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_lazy_array">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_lazy_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">TypeGuard</span><span class="p"><span class="pre">[</span></span><span class="pre">_ArrayApiObj</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#array_api_compat.is_lazy_array" title="Link to this definition">¶</a></dt>
+<dd><p>Return True if x is potentially a future or it may be otherwise impossible or
+expensive to eagerly read its contents, regardless of their size, e.g. by
+calling <code class="docutils literal notranslate"><span class="pre">bool(x)</span></code> or <code class="docutils literal notranslate"><span class="pre">float(x)</span></code>.</p>
+<p>Return False otherwise; e.g. <code class="docutils literal notranslate"><span class="pre">bool(x)</span></code> etc. is guaranteed to succeed and to be
+cheap as long as the array has the right dtype and size.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This function errs on the side of caution for array types that may or may not be
+lazy, e.g. JAX arrays, by always returning True for them.</p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_numpy_namespace">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_numpy_namespace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xp</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ModuleType</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#array_api_compat.is_numpy_namespace" title="Link to this definition">¶</a></dt>
+<dd><p>Returns True if <code class="code docutils literal notranslate"><span class="pre">xp</span></code> is a NumPy namespace.</p>
+<p>This includes both NumPy itself and the version wrapped by array-api-compat.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_namespace" title="array_api_compat.is_cupy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_namespace" title="array_api_compat.is_torch_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_namespace" title="array_api_compat.is_ndonnx_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_namespace" title="array_api_compat.is_dask_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_namespace" title="array_api_compat.is_jax_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_namespace" title="array_api_compat.is_pydata_sparse_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_strict_namespace" title="array_api_compat.is_array_api_strict_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_strict_namespace</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_cupy_namespace">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_cupy_namespace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xp</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ModuleType</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#array_api_compat.is_cupy_namespace" title="Link to this definition">¶</a></dt>
+<dd><p>Returns True if <code class="code docutils literal notranslate"><span class="pre">xp</span></code> is a CuPy namespace.</p>
+<p>This includes both CuPy itself and the version wrapped by array-api-compat.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_namespace" title="array_api_compat.is_numpy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_namespace" title="array_api_compat.is_torch_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_namespace" title="array_api_compat.is_ndonnx_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_namespace" title="array_api_compat.is_dask_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_namespace" title="array_api_compat.is_jax_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_namespace" title="array_api_compat.is_pydata_sparse_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_strict_namespace" title="array_api_compat.is_array_api_strict_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_strict_namespace</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_torch_namespace">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_torch_namespace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xp</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ModuleType</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#array_api_compat.is_torch_namespace" title="Link to this definition">¶</a></dt>
+<dd><p>Returns True if <code class="code docutils literal notranslate"><span class="pre">xp</span></code> is a PyTorch namespace.</p>
+<p>This includes both PyTorch itself and the version wrapped by array-api-compat.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_namespace" title="array_api_compat.is_numpy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_namespace" title="array_api_compat.is_cupy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_namespace" title="array_api_compat.is_ndonnx_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_namespace" title="array_api_compat.is_dask_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_namespace" title="array_api_compat.is_jax_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_namespace" title="array_api_compat.is_pydata_sparse_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_strict_namespace" title="array_api_compat.is_array_api_strict_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_strict_namespace</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_dask_namespace">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_dask_namespace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xp</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ModuleType</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#array_api_compat.is_dask_namespace" title="Link to this definition">¶</a></dt>
+<dd><p>Returns True if <code class="code docutils literal notranslate"><span class="pre">xp</span></code> is a Dask namespace.</p>
+<p>This includes both <code class="docutils literal notranslate"><span class="pre">dask.array</span></code> itself and the version wrapped by array-api-compat.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_namespace" title="array_api_compat.is_numpy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_namespace" title="array_api_compat.is_cupy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_namespace" title="array_api_compat.is_torch_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_namespace" title="array_api_compat.is_ndonnx_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_namespace" title="array_api_compat.is_jax_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_namespace" title="array_api_compat.is_pydata_sparse_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_strict_namespace" title="array_api_compat.is_array_api_strict_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_strict_namespace</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_jax_namespace">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_jax_namespace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xp</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ModuleType</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#array_api_compat.is_jax_namespace" title="Link to this definition">¶</a></dt>
+<dd><p>Returns True if <code class="code docutils literal notranslate"><span class="pre">xp</span></code> is a JAX namespace.</p>
+<p>This includes <code class="docutils literal notranslate"><span class="pre">jax.numpy</span></code> and <code class="docutils literal notranslate"><span class="pre">jax.experimental.array_api</span></code> which existed in
+older versions of JAX.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_namespace" title="array_api_compat.is_numpy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_namespace" title="array_api_compat.is_cupy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_namespace" title="array_api_compat.is_torch_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_namespace" title="array_api_compat.is_ndonnx_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_namespace" title="array_api_compat.is_dask_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_namespace" title="array_api_compat.is_pydata_sparse_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_strict_namespace" title="array_api_compat.is_array_api_strict_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_strict_namespace</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_pydata_sparse_namespace">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_pydata_sparse_namespace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xp</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ModuleType</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#array_api_compat.is_pydata_sparse_namespace" title="Link to this definition">¶</a></dt>
+<dd><p>Returns True if <code class="code docutils literal notranslate"><span class="pre">xp</span></code> is a pydata/sparse namespace.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_namespace" title="array_api_compat.is_numpy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_namespace" title="array_api_compat.is_cupy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_namespace" title="array_api_compat.is_torch_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_namespace" title="array_api_compat.is_ndonnx_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_namespace" title="array_api_compat.is_dask_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_namespace" title="array_api_compat.is_jax_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_strict_namespace" title="array_api_compat.is_array_api_strict_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_strict_namespace</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_ndonnx_namespace">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_ndonnx_namespace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xp</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ModuleType</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#array_api_compat.is_ndonnx_namespace" title="Link to this definition">¶</a></dt>
+<dd><p>Returns True if <code class="code docutils literal notranslate"><span class="pre">xp</span></code> is an NDONNX namespace.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_namespace" title="array_api_compat.is_numpy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_namespace" title="array_api_compat.is_cupy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_namespace" title="array_api_compat.is_torch_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_namespace" title="array_api_compat.is_dask_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_namespace" title="array_api_compat.is_jax_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_namespace" title="array_api_compat.is_pydata_sparse_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_array_api_strict_namespace" title="array_api_compat.is_array_api_strict_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_array_api_strict_namespace</span></code></a></p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="array_api_compat.is_array_api_strict_namespace">
+<span class="sig-prename descclassname"><span class="pre">array_api_compat.</span></span><span class="sig-name descname"><span class="pre">is_array_api_strict_namespace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xp</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ModuleType</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#array_api_compat.is_array_api_strict_namespace" title="Link to this definition">¶</a></dt>
+<dd><p>Returns True if <code class="code docutils literal notranslate"><span class="pre">xp</span></code> is an array-api-strict namespace.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_numpy_namespace" title="array_api_compat.is_numpy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_numpy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_cupy_namespace" title="array_api_compat.is_cupy_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cupy_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_torch_namespace" title="array_api_compat.is_torch_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_ndonnx_namespace" title="array_api_compat.is_ndonnx_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ndonnx_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_dask_namespace" title="array_api_compat.is_dask_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_dask_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_jax_namespace" title="array_api_compat.is_jax_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_jax_namespace</span></code></a>, <a class="reference internal" href="#array_api_compat.is_pydata_sparse_namespace" title="array_api_compat.is_pydata_sparse_namespace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_pydata_sparse_namespace</span></code></a></p>
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+<li><a class="reference internal" href="#">Helper Functions</a><ul>
+<li><a class="reference internal" href="#entry-point-helpers">Entry-point Helpers</a><ul>
+<li><a class="reference internal" href="#array_api_compat.array_namespace"><code class="docutils literal notranslate"><span class="pre">array_namespace()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_array_api_obj"><code class="docutils literal notranslate"><span class="pre">is_array_api_obj()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#array-method-helpers">Array Method Helpers</a><ul>
+<li><a class="reference internal" href="#array_api_compat.device"><code class="docutils literal notranslate"><span class="pre">device()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.to_device"><code class="docutils literal notranslate"><span class="pre">to_device()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.size"><code class="docutils literal notranslate"><span class="pre">size()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#inspection-helpers">Inspection Helpers</a><ul>
+<li><a class="reference internal" href="#array_api_compat.is_numpy_array"><code class="docutils literal notranslate"><span class="pre">is_numpy_array()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_cupy_array"><code class="docutils literal notranslate"><span class="pre">is_cupy_array()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_torch_array"><code class="docutils literal notranslate"><span class="pre">is_torch_array()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_dask_array"><code class="docutils literal notranslate"><span class="pre">is_dask_array()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_jax_array"><code class="docutils literal notranslate"><span class="pre">is_jax_array()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_pydata_sparse_array"><code class="docutils literal notranslate"><span class="pre">is_pydata_sparse_array()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_ndonnx_array"><code class="docutils literal notranslate"><span class="pre">is_ndonnx_array()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_writeable_array"><code class="docutils literal notranslate"><span class="pre">is_writeable_array()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_lazy_array"><code class="docutils literal notranslate"><span class="pre">is_lazy_array()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_numpy_namespace"><code class="docutils literal notranslate"><span class="pre">is_numpy_namespace()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_cupy_namespace"><code class="docutils literal notranslate"><span class="pre">is_cupy_namespace()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_torch_namespace"><code class="docutils literal notranslate"><span class="pre">is_torch_namespace()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_dask_namespace"><code class="docutils literal notranslate"><span class="pre">is_dask_namespace()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_jax_namespace"><code class="docutils literal notranslate"><span class="pre">is_jax_namespace()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_pydata_sparse_namespace"><code class="docutils literal notranslate"><span class="pre">is_pydata_sparse_namespace()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_ndonnx_namespace"><code class="docutils literal notranslate"><span class="pre">is_ndonnx_namespace()</span></code></a></li>
+<li><a class="reference internal" href="#array_api_compat.is_array_api_strict_namespace"><code class="docutils literal notranslate"><span class="pre">is_array_api_strict_namespace()</span></code></a></li>
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+        <article role="main" id="furo-main-content">
+          <section class="tex2jax_ignore mathjax_ignore" id="array-api-compatibility-library">
+<h1>Array API compatibility library<a class="headerlink" href="#array-api-compatibility-library" title="Link to this heading">¶</a></h1>
+<p>This is a small wrapper around common array libraries that is compatible with
+the <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API standard</a>. Currently,
+NumPy, CuPy, PyTorch, Dask, JAX, ndonnx, and Sparse are supported. If you want
+support for other array libraries, or if you encounter any issues, please
+<a class="reference external" href="https://github.com/data-apis/array-api-compat/issues">open an issue</a>.</p>
+<p>Note that some of the functionality in this library is backwards incompatible
+with the corresponding wrapped libraries. The end-goal is to eventually make
+each array library itself fully compatible with the array API, but this
+requires making backwards incompatible changes in many cases, so this will
+take some time.</p>
+<p>Currently all libraries here are implemented against the <a class="reference external" href="https://data-apis.org/array-api/2024.12/">2024.12
+version</a> of the standard.</p>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">array-api-compat</span></code> is available on both <a class="reference external" href="https://pypi.org/project/array-api-compat/">PyPI</a></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="n">array</span><span class="o">-</span><span class="n">api</span><span class="o">-</span><span class="n">compat</span>
+</pre></div>
+</div>
+<p>and <a class="reference external" href="https://anaconda.org/conda-forge/array-api-compat">conda-forge</a></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="o">--</span><span class="n">channel</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span> <span class="n">array</span><span class="o">-</span><span class="n">api</span><span class="o">-</span><span class="n">compat</span>
+</pre></div>
+</div>
+</section>
+<section id="usage">
+<h2>Usage<a class="headerlink" href="#usage" title="Link to this heading">¶</a></h2>
+<p>The typical usage of this library will be to get the corresponding array API
+compliant namespace from the input arrays using <a class="reference internal" href="helper-functions.html#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">array_namespace()</span></code></a>, like</p>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">your_function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="n">xp</span> <span class="o">=</span> <span class="n">array_api_compat</span><span class="o">.</span><span class="n">array_namespace</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+    <span class="c1"># Now use xp as the array library namespace</span>
+    <span class="k">return</span> <span class="n">xp</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">xp</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>If you wish to have library-specific code-paths, you can import the
+corresponding wrapped namespace for each library, like</p>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">array_api_compat.numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">array_api_compat.cupy</span> <span class="k">as</span> <span class="nn">cp</span>
+</pre></div>
+</div>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">array_api_compat.torch</span> <span class="k">as</span> <span class="nn">torch</span>
+</pre></div>
+</div>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">array_api_compat.dask</span> <span class="k">as</span> <span class="nn">da</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>There are no <code class="docutils literal notranslate"><span class="pre">array_api_compat</span></code> submodules for JAX, sparse, or ndonnx. These
+support for these libraries is contained in the libraries themselves (JAX
+support is in the <code class="docutils literal notranslate"><span class="pre">jax.numpy</span></code> module in JAX v0.4.32 or newer, and in the
+<code class="docutils literal notranslate"><span class="pre">jax.experimental.array_api</span></code> module for older JAX versions). The
+array-api-compat support for these libraries consists of supporting them in
+the <a class="reference internal" href="helper-functions.html"><span class="doc std std-doc">helper functions</span></a>.</p>
+</div>
+<p>Each will include all the functions from the normal NumPy/CuPy/PyTorch/dask.array
+namespace, except that functions that are part of the array API are wrapped so
+that they have the correct array API behavior. In each case, the array object
+used will be the same array object from the wrapped library.</p>
+</section>
+<section id="difference-between-array-api-compat-and-array-api-strict">
+<span id="array-api-strict"></span><h2>Difference between <code class="docutils literal notranslate"><span class="pre">array_api_compat</span></code> and <code class="docutils literal notranslate"><span class="pre">array_api_strict</span></code><a class="headerlink" href="#difference-between-array-api-compat-and-array-api-strict" title="Link to this heading">¶</a></h2>
+<p><a class="reference external" href="https://data-apis.org/array-api-strict/"><code class="docutils literal notranslate"><span class="pre">array_api_strict</span></code></a> is a
+strict minimal implementation of the array API standard, formerly known as
+<code class="docutils literal notranslate"><span class="pre">numpy.array_api</span></code> (see <a class="reference external" href="https://numpy.org/neps/nep-0047-array-api-standard.html">NEP
+47</a>). For example,
+<code class="docutils literal notranslate"><span class="pre">array_api_strict</span></code> does not include any functions that are not part of the
+array API specification, and will explicitly disallow behaviors that are not
+required by the spec (e.g., <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/type_promotion.html">cross-kind type
+promotions</a>).
+(<code class="docutils literal notranslate"><span class="pre">cupy.array_api</span></code> is similar to <code class="docutils literal notranslate"><span class="pre">array_api_strict</span></code>)</p>
+<p><code class="docutils literal notranslate"><span class="pre">array_api_compat</span></code>, on the other hand, is just an extension of the
+corresponding array library namespaces with changes needed to be compliant
+with the array API. It includes all additional library functions not mentioned
+in the spec, and allows any library behaviors not explicitly disallowed by it,
+such as cross-kind casting.</p>
+<p>In particular, unlike <code class="docutils literal notranslate"><span class="pre">array_api_strict</span></code>, this package does not use a separate
+<code class="docutils literal notranslate"><span class="pre">Array</span></code> object, but rather just uses the corresponding array library array
+objects (<code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>, <code class="docutils literal notranslate"><span class="pre">cupy.ndarray</span></code>, <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, etc.) directly. This
+is because those are the objects that are going to be passed as inputs to
+functions by end users. This does mean that a few behaviors cannot be wrapped
+(see below), but most of the array API functional, so this does not affect
+most things.</p>
+<p>Array consuming library authors coding against the array API may wish to test
+against <code class="docutils literal notranslate"><span class="pre">array_api_strict</span></code> to ensure they are not using functionality outside
+of the standard, but prefer this implementation for the default behavior for
+end-users.</p>
+</section>
+<section id="vendoring">
+<span id="id1"></span><h2>Vendoring<a class="headerlink" href="#vendoring" title="Link to this heading">¶</a></h2>
+<p>This library supports vendoring as an installation method. To vendor the
+library, simply copy <code class="docutils literal notranslate"><span class="pre">array_api_compat</span></code> into the appropriate place in the
+library, like</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cp</span> <span class="o">-</span><span class="n">R</span> <span class="n">array_api_compat</span><span class="o">/</span> <span class="n">mylib</span><span class="o">/</span><span class="n">vendored</span><span class="o">/</span><span class="n">array_api_compat</span>
+</pre></div>
+</div>
+<p>You may also rename it to something else if you like (nowhere in the code
+references the name “array_api_compat”).</p>
+<p>Alternatively, the library may be installed as dependency from PyPI.</p>
+</section>
+<section id="scope">
+<span id="id2"></span><h2>Scope<a class="headerlink" href="#scope" title="Link to this heading">¶</a></h2>
+<p>At this time, the scope of array-api-compat is limited to wrapping array
+libraries so that they can comply with the <a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/index.html">array API
+standard</a>.
+This includes a small set of <a class="reference internal" href="helper-functions.html"><span class="std std-doc">helper functions</span></a> which may
+be useful to most users of array-api-compat, for instance, functions that
+provide meta-functionality to aid in supporting the array API, or functions
+that are necessary to work around wrapping limitations for certain libraries.</p>
+<p>Things that are out-of-scope include:</p>
+<ul class="simple">
+<li><p>functions that have not yet been
+standardized (although note that functions that are in a draft version of the
+standard are <em>in scope</em>),</p></li>
+<li><p>functions that are complicated to implement correctly/maintain,</p></li>
+<li><p>anything that requires the use of non-Python code.</p></li>
+</ul>
+<p>If you want a function that is not in array-api-compat that isn’t part of the
+standard, you should request it either for <a class="reference external" href="https://github.com/data-apis/array-api/issues">inclusion in the
+standard</a> or in specific array
+libraries.</p>
+<p>Why is the scope limited in this way? Firstly, we want to keep
+array-api-compat as primarily a
+<a class="reference external" href="https://en.wikipedia.org/wiki/Polyfill_(programming)">polyfill</a> compatibility
+shim. The goal is to let consuming libraries use the array API today, even
+with array libraries that do not yet fully support it. In an ideal world—one that we hope to eventually see in the future—array-api-compat would be
+unnecessary, because every array library would fully support the standard.</p>
+<p>The inclusion of non-standardized functions in array-api-compat would
+undermine this goal. But much more importantly, it would also undermine the
+goals of the <a class="reference external" href="https://data-apis.org/">Data APIs Consortium</a>. The Consortium
+creates the array API standard via the consensus of stakeholders from various
+array libraries and users. If a not-yet-standardized function were included in
+array-api-compat, it would become <em>de facto</em> standard, bypassing the decision
+making processes of the Consortium.</p>
+<p>Secondly, we want to keep array-api-compat as minimal as possible, so that it
+is easy for libraries to add as a (possibly vendored) dependency.</p>
+<p>Thirdly, array-api-compat has a relatively small development team. Pull
+requests to array-api-compat would not necessarily receive the same stringent
+level of scrutiny that changes to established array libraries like NumPy or
+PyTorch would. For wrapped standard functions, this is fine, since the
+wrappers typically just clean up a few small inconsistencies from the
+standard, leaving the complexity of the implementation to the base array
+library function. Furthermore, standard functions are tested by the rigorous
+<a class="reference external" href="https://github.com/data-apis/array-api-tests">array-api-tests</a> test suite.
+For this reason, functions that require complex implementations are generally
+out-of-scope and should be preferred to be implemented in upstream array
+libraries.</p>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+</section>
+
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+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer">
+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Array API compatibility library</a><ul>
+<li><a class="reference internal" href="#installation">Installation</a></li>
+<li><a class="reference internal" href="#usage">Usage</a></li>
+<li><a class="reference internal" href="#difference-between-array-api-compat-and-array-api-strict">Difference between <code class="docutils literal notranslate"><span class="pre">array_api_compat</span></code> and <code class="docutils literal notranslate"><span class="pre">array_api_strict</span></code></a></li>
+<li><a class="reference internal" href="#vendoring">Vendoring</a></li>
+<li><a class="reference internal" href="#scope">Scope</a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
+  </div>
+</div><script src="_static/documentation_options.js?v=1e6c7a3c"></script>
+    <script src="_static/doctools.js?v=9bcbadda"></script>
+    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
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+    <script src="_static/copybutton.js?v=f281be69"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/numpy-1-22-xfails.txt b/numpy-1-22-xfails.txt
deleted file mode 100644
index 5df1b6d7..00000000
--- a/numpy-1-22-xfails.txt
+++ /dev/null
@@ -1,175 +0,0 @@
-# attributes are np.float32 instead of float
-# (see also https://github.com/data-apis/array-api/issues/405)
-array_api_tests/test_data_type_functions.py::test_finfo[float32]
-array_api_tests/test_data_type_functions.py::test_finfo[complex64]
-
-# Array methods and attributes not already on np.ndarray cannot be wrapped
-array_api_tests/test_has_names.py::test_has_names[array_method-__array_namespace__]
-array_api_tests/test_has_names.py::test_has_names[array_method-to_device]
-array_api_tests/test_has_names.py::test_has_names[array_attribute-device]
-array_api_tests/test_has_names.py::test_has_names[array_attribute-mT]
-
-# Array methods and attributes not already on np.ndarray cannot be wrapped
-array_api_tests/test_signatures.py::test_array_method_signature[__array_namespace__]
-array_api_tests/test_signatures.py::test_array_method_signature[to_device]
-
-# NumPy deviates in some special cases for floordiv
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-
-# https://github.com/numpy/numpy/issues/21213
-array_api_tests/meta/test_hypothesis_helpers.py::test_symmetric_matrices
-
-# NumPy 1.22 specific XFAILS
-############################
-
-# cross has some promotion bug that is fixed in newer numpy versions
-array_api_tests/test_linalg.py::test_cross
-
-# linspace(-0.0, -1.0, num=1) returns +0.0 instead of -0.0.
-# Fixed in newer numpy versions.
-array_api_tests/test_creation_functions.py::test_linspace
-
-# vector_norm with ord=-1 which has since been fixed
-# https://github.com/numpy/numpy/issues/21083
-array_api_tests/test_linalg.py::test_vector_norm
-
-# NumPy 1.22 doesn't support NPY_PROMOTION_STATE=weak, so many tests fail with
-# type promotion issues
-# NOTE: some of these may not fail until one runs array-api-tests with
-# --max-examples 100000
-array_api_tests/test_manipulation_functions.py::test_concat
-array_api_tests/test_operators_and_elementwise_functions.py::test_add[__add__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_add[__add__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_add[__iadd__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_add[add(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_atan2
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_and[__and__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_and[__and__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_and[__iand__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_and[bitwise_and(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_left_shift[__ilshift__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_left_shift[__lshift__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_left_shift[__lshift__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_left_shift[bitwise_left_shift(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_or[__ior__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_or[__or__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_or[__or__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_or[bitwise_or(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_right_shift[__irshift__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_right_shift[__rshift__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_right_shift[__rshift__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_right_shift[bitwise_right_shift(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_xor[__ixor__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_xor[__xor__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_xor[__xor__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_xor[bitwise_xor(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_copysign
-array_api_tests/test_operators_and_elementwise_functions.py::test_divide[__truediv__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_divide[__truediv__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_divide[divide(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_equal[__eq__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_equal[equal(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__floordiv__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__floordiv__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__ifloordiv__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[floor_divide(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_greater[__gt__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_greater[greater(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_greater_equal[__ge__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_greater_equal[greater_equal(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_hypot
-array_api_tests/test_operators_and_elementwise_functions.py::test_less[__lt__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_less[less(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_less_equal[__le__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_less_equal[less_equal(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_logaddexp
-array_api_tests/test_operators_and_elementwise_functions.py::test_maximum
-array_api_tests/test_operators_and_elementwise_functions.py::test_minimum
-array_api_tests/test_operators_and_elementwise_functions.py::test_multiply[__imul__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_multiply[__mul__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_multiply[__mul__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_multiply[multiply(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_not_equal[__ne__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_not_equal[not_equal(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_pow[__ipow__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_pow[__pow__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_pow[__pow__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_pow[pow(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__imod__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[remainder(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_subtract[__isub__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_subtract[__sub__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_subtract[__sub__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_subtract[subtract(x1, x2)]
-array_api_tests/test_searching_functions.py::test_where
-array_api_tests/test_special_cases.py::test_iop[__iadd__(x1_i is -0 and x2_i is -0) -> -0]
-
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[add]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[divide]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[hypot]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[subtract]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[logaddexp]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[nextafter]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[multiply]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[maximum]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[minimum]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[copysign]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[pow]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[minimum]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[atan2]
-
-array_api_tests/test_operators_and_elementwise_functions.py::test_where_with_scalars
-
-# 2023.12 support
-array_api_tests/test_has_names.py::test_has_names[creation-from_dlpack]
-array_api_tests/test_signatures.py::test_func_signature[from_dlpack]
-array_api_tests/test_signatures.py::test_array_method_signature[__dlpack__]
-# uint64 repeats not supported
-array_api_tests/test_manipulation_functions.py::test_repeat
-
-# 2024.12 support
-array_api_tests/test_signatures.py::test_func_signature[bitwise_and]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_left_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_or]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_right_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_xor]
-array_api_tests/test_data_type_functions.py::TestResultType::test_with_scalars
-
-# Stubs have a comment: (**note**: libraries may return ``NaN`` to match Python behavior.); Apparently,NumPy does just that
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
diff --git a/numpy-1-26-xfails.txt b/numpy-1-26-xfails.txt
deleted file mode 100644
index 98cb9f6c..00000000
--- a/numpy-1-26-xfails.txt
+++ /dev/null
@@ -1,73 +0,0 @@
-# attributes are np.float32 instead of float
-# (see also https://github.com/data-apis/array-api/issues/405)
-array_api_tests/test_data_type_functions.py::test_finfo[float32]
-array_api_tests/test_data_type_functions.py::test_finfo[complex64]
-
-# Array methods and attributes not already on np.ndarray cannot be wrapped
-array_api_tests/test_has_names.py::test_has_names[array_method-__array_namespace__]
-array_api_tests/test_has_names.py::test_has_names[array_method-to_device]
-array_api_tests/test_has_names.py::test_has_names[array_attribute-device]
-array_api_tests/test_has_names.py::test_has_names[array_attribute-mT]
-
-# Array methods and attributes not already on np.ndarray cannot be wrapped
-array_api_tests/test_signatures.py::test_array_method_signature[__array_namespace__]
-array_api_tests/test_signatures.py::test_array_method_signature[to_device]
-
-# NumPy deviates in some special cases for floordiv
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-
-# https://github.com/numpy/numpy/issues/21213
-array_api_tests/meta/test_hypothesis_helpers.py::test_symmetric_matrices
-
-# 2023.12 support
-array_api_tests/test_signatures.py::test_func_signature[from_dlpack]
-array_api_tests/test_signatures.py::test_array_method_signature[__dlpack__]
-# uint64 repeats not supported
-array_api_tests/test_manipulation_functions.py::test_repeat
-
-# 2024.12 support
-array_api_tests/test_signatures.py::test_func_signature[bitwise_and]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_left_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_or]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_right_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_xor]
-array_api_tests/test_data_type_functions.py::TestResultType::test_with_scalars
-
-array_api_tests/test_operators_and_elementwise_functions.py::test_where_with_scalars
-
-# Stubs have a comment: (**note**: libraries may return ``NaN`` to match Python behavior.); Apparently, NumPy does just that
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
diff --git a/numpy-dev-xfails.txt b/numpy-dev-xfails.txt
deleted file mode 100644
index 972d2346..00000000
--- a/numpy-dev-xfails.txt
+++ /dev/null
@@ -1,40 +0,0 @@
-# attributes are np.float32 instead of float
-# (see also https://github.com/data-apis/array-api/issues/405)
-array_api_tests/test_data_type_functions.py::test_finfo[float32]
-array_api_tests/test_data_type_functions.py::test_finfo[complex64]
-
-# The test suite cannot properly get the signature for vecdot
-# https://github.com/numpy/numpy/pull/26237
-array_api_tests/test_signatures.py::test_func_signature[vecdot]
-array_api_tests/test_signatures.py::test_extension_func_signature[linalg.vecdot]
-
-# 2023.12 support
-# uint64 repeats not supported
-array_api_tests/test_manipulation_functions.py::test_repeat
-
-# 2024.12 support
-array_api_tests/test_signatures.py::test_func_signature[bitwise_and]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_left_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_or]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_right_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_xor]
-
-# Stubs have a comment: (**note**: libraries may return ``NaN`` to match Python behavior.); Apparently, NumPy does just that
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
diff --git a/numpy-skips.txt b/numpy-skips.txt
deleted file mode 100644
index e69de29b..00000000
diff --git a/numpy-xfails.txt b/numpy-xfails.txt
deleted file mode 100644
index 632b4ec3..00000000
--- a/numpy-xfails.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-# attributes are np.float32 instead of float
-# (see also https://github.com/data-apis/array-api/issues/405)
-array_api_tests/test_data_type_functions.py::test_finfo[float32]
-array_api_tests/test_data_type_functions.py::test_finfo[complex64]
-
-# The test suite cannot properly get the signature for vecdot
-# https://github.com/numpy/numpy/pull/26237
-array_api_tests/test_signatures.py::test_func_signature[vecdot]
-array_api_tests/test_signatures.py::test_extension_func_signature[linalg.vecdot]
-
-# 2023.12 support
-# uint64 repeats not supported
-array_api_tests/test_manipulation_functions.py::test_repeat
-
-# 2024.12 support
-array_api_tests/test_signatures.py::test_func_signature[bitwise_and]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_left_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_or]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_right_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_xor]
-
-# Stubs have a comment: (**note**: libraries may return ``NaN`` to match Python behavior.); Apparently, NumPy does just that
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-
diff --git a/objects.inv b/objects.inv
new file mode 100644
index 00000000..61819072
Binary files /dev/null and b/objects.inv differ
diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index ec054417..00000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,120 +0,0 @@
-[build-system]
-requires = ["setuptools", "setuptools-scm"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "array-api-compat"
-dynamic = ["version"]
-description = "A wrapper around NumPy and other array libraries to make them compatible with the Array API standard"
-readme = "README.md"
-requires-python = ">=3.10"
-license = "MIT"
-authors = [{name = "Consortium for Python Data API Standards"}]
-classifiers = [
-    "Operating System :: OS Independent",
-    "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
-    "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.12",
-    "Programming Language :: Python :: 3.13",
-    "Topic :: Software Development :: Libraries :: Python Modules",
-    "Typing :: Typed",
-]
-
-[project.optional-dependencies]
-cupy = ["cupy"]
-dask = ["dask>=2024.9.0"]
-jax = ["jax"]
-# Note: array-api-compat follows scikit-learn minimum dependencies, which support
-# much older versions of NumPy than what SPEC0 recommends.
-numpy = ["numpy>=1.22"]
-pytorch = ["torch"]
-sparse = ["sparse>=0.15.1"]
-ndonnx = ["ndonnx"]
-docs = [
-    "furo",
-    "linkify-it-py",
-    "myst-parser",
-    "sphinx",
-    "sphinx-copybutton",
-    "sphinx-autobuild",
-]
-dev = [
-    "array-api-strict",
-    "dask[array]>=2024.9.0",
-    "jax[cpu]",
-    "ndonnx",
-    "numpy>=1.22",
-    "pytest",
-    "torch",
-    "sparse>=0.15.1",
-]
-
-[project.urls]
-homepage = "https://data-apis.org/array-api-compat/"
-repository = "https://github.com/data-apis/array-api-compat/"
-
-[tool.setuptools.dynamic]
-version = {attr = "array_api_compat.__version__"}
-
-[tool.setuptools.packages.find]
-include = ["array_api_compat*"]
-namespaces = false
-
-[tool.ruff.lint]
-preview = true
-select = [
-# Defaults
-"E4", "E7", "E9", "F",
-# Undefined export
-"F822",
-# Useless import alias
-"PLC0414"
-]
-
-ignore = [
-  # Module import not at top of file
-  "E402",
-  # Do not use bare `except`
-  "E722"
-]
-
-
-[tool.mypy]
-files = ["array_api_compat"]
-disallow_incomplete_defs = true
-disallow_untyped_decorators = true
-disallow_untyped_defs = false  # TODO
-ignore_missing_imports = false
-no_implicit_optional = true
-show_error_codes = true
-warn_redundant_casts = true
-warn_unused_ignores = true
-warn_unreachable = true
-
-[[tool.mypy.overrides]]
-module = ["cupy.*", "cupy_backends.*", "dask.*", "jax.*", "ndonnx.*", "sparse.*", "torch.*"]
-ignore_missing_imports = true
-
-
-[tool.pyright]
-include = ["src", "tests"]
-pythonPlatform = "All"
-
-reportAny = false
-reportExplicitAny = false
-# missing type stubs
-reportAttributeAccessIssue = false
-reportUnknownMemberType = false
-reportUnknownVariableType = false
-# Redundant with mypy checks
-reportMissingImports = false
-reportMissingTypeStubs = false
-# false positives for input validation
-reportUnreachable = false
-# ruff handles this
-reportUnusedParameter = false
-
-executionEnvironments = [
-  { root = "array_api_compat" },
-]
diff --git a/search.html b/search.html
new file mode 100644
index 00000000..a0a9cb3e
--- /dev/null
+++ b/search.html
@@ -0,0 +1,308 @@
+<!doctype html>
+<html class="no-js" lang="en" data-content_root="./">
+  <head>
+<meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="#" />
+
+    <link rel="shortcut icon" href="_static/favicon.png"/><!-- Generated with Sphinx 8.2.3 and Furo 2024.08.06 -->
+<meta name="robots" content="noindex" />
+<title>Search - array-api-compat 1.13.0.dev0 documentation</title><link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=354aac6f" />
+    <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=302659d7" />
+    <link rel="stylesheet" type="text/css" href="_static/custom.css?v=dc4c535e" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #f8f8f8;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+      }
+    }
+  }
+</style></head>
+  <body>
+    
+    <script>
+      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
+    </script>
+    
+
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
+      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
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+    <title>Menu</title>
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+    <title>Light mode</title>
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+      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
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+          <section class="tex2jax_ignore mathjax_ignore" id="supported-array-libraries">
+<h1>Supported Array Libraries<a class="headerlink" href="#supported-array-libraries" title="Link to this heading">¶</a></h1>
+<p>The following array libraries are supported. This page outlines the known
+differences between this library and the array API specification for the
+supported packages.</p>
+<p>Note that the <a class="reference internal" href="helper-functions.html#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">array_namespace()</span></code></a> helper will also support any array
+library that explicitly supports the array API by defining
+<a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__array_namespace__.html"><code class="docutils literal notranslate"><span class="pre">__array_namespace__</span></code></a>.</p>
+<p>Any reasonably popular array library is in-scope for array-api-compat,
+assuming it is possible to wrap it to support the array API without too much
+complexity. If your favorite library is not supported, feel free to open an
+<a class="reference external" href="https://github.com/data-apis/array-api-compat/issues">issue or pull request</a>.</p>
+<section id="numpy-and-cupy">
+<h2><a class="reference external" href="https://numpy.org/">NumPy</a> and <a class="reference external" href="https://cupy.dev/">CuPy</a><a class="headerlink" href="#numpy-and-cupy" title="Link to this heading">¶</a></h2>
+<p>NumPy 2.0 has full array API compatibility. This package is not strictly
+necessary for NumPy 2.0 support, but may still be useful for the support of
+other libraries, as well as for the <a class="reference internal" href="helper-functions.html"><span class="std std-doc">helper functions</span></a>.</p>
+<p>For NumPy 1.26, as well as corresponding versions of CuPy, the following
+deviations from the standard should be noted:</p>
+<ul class="simple">
+<li><p>The array methods <code class="docutils literal notranslate"><span class="pre">__array_namespace__</span></code>, <code class="docutils literal notranslate"><span class="pre">device</span></code> (for NumPy), <code class="docutils literal notranslate"><span class="pre">to_device</span></code>,
+and <code class="docutils literal notranslate"><span class="pre">mT</span></code> are not defined. This reuses <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code> and <code class="docutils literal notranslate"><span class="pre">cp.ndarray</span></code> and we
+don’t want to monkey patch or wrap it. The <a class="reference internal" href="helper-functions.html"><span class="std std-doc">helper
+functions</span></a> <a class="reference internal" href="helper-functions.html#array_api_compat.device" title="array_api_compat.device"><code class="xref py py-func docutils literal notranslate"><span class="pre">device()</span></code></a> and <a class="reference internal" href="helper-functions.html#array_api_compat.to_device" title="array_api_compat.to_device"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_device()</span></code></a>
+are provided to work around these missing methods. <code class="docutils literal notranslate"><span class="pre">x.mT</span></code> can be replaced
+with <code class="docutils literal notranslate"><span class="pre">xp.linalg.matrix_transpose(x)</span></code>. <a class="reference internal" href="helper-functions.html#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">array_namespace()</span></code></a> should be
+used instead of <code class="docutils literal notranslate"><span class="pre">x.__array_namespace__</span></code>.</p></li>
+<li><p>Value-based casting for scalars will be in effect unless explicitly disabled
+with the environment variable <code class="docutils literal notranslate"><span class="pre">NPY_PROMOTION_STATE=weak</span></code> or
+<code class="docutils literal notranslate"><span class="pre">np._set_promotion_state('weak')</span></code> (requires NumPy 1.24 or newer, see <a class="reference external" href="https://numpy.org/neps/nep-0050-scalar-promotion.html">NEP
+50</a> and
+<a class="reference external" href="https://github.com/numpy/numpy/issues/22341">https://github.com/numpy/numpy/issues/22341</a>)</p></li>
+<li><p>Functions which are not wrapped may not have the same type annotations
+as the spec.</p></li>
+<li><p>Functions which are not wrapped may not use positional-only arguments.</p></li>
+</ul>
+<p>The minimum supported NumPy version is 1.22. However, this older version of
+NumPy has a few issues:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">unique_*</span></code> will not compare nans as unequal.</p></li>
+<li><p>No <code class="docutils literal notranslate"><span class="pre">from_dlpack</span></code> or <code class="docutils literal notranslate"><span class="pre">__dlpack__</span></code>.</p></li>
+<li><p>Type promotion behavior will be value based for 0-D arrays (and there is no
+<code class="docutils literal notranslate"><span class="pre">NPY_PROMOTION_STATE=weak</span></code> to disable this).</p></li>
+</ul>
+<p>If any of these are an issue, it is recommended to bump your minimum NumPy
+version.</p>
+</section>
+<section id="pytorch">
+<h2><a class="reference external" href="https://pytorch.org/">PyTorch</a><a class="headerlink" href="#pytorch" title="Link to this heading">¶</a></h2>
+<ul class="simple">
+<li><p>Like NumPy/CuPy, we do not wrap the <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> object. It is missing the
+<code class="docutils literal notranslate"><span class="pre">__array_namespace__</span></code> and <code class="docutils literal notranslate"><span class="pre">to_device</span></code> methods, so the corresponding helper
+functions <a class="reference internal" href="helper-functions.html#array_api_compat.array_namespace" title="array_api_compat.array_namespace"><code class="xref py py-func docutils literal notranslate"><span class="pre">array_namespace()</span></code></a> and <a class="reference internal" href="helper-functions.html#array_api_compat.to_device" title="array_api_compat.to_device"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_device()</span></code></a> in this
+library should be used instead.</p></li>
+<li><p>The <a class="reference external" href="https://docs.pytorch.org/docs/stable/generated/torch.Tensor.size.html#torch.Tensor.size" title="(in PyTorch v2.7)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">x.size()</span></code></a> attribute on
+<code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> is a method that behaves differently from the
+<a class="reference external" href="https://data-apis.org/array-api/draft/API_specification/generated/array_api.array.size.html"><code class="docutils literal notranslate"><span class="pre">x.size</span></code></a>
+attribute in the spec. Use the <a class="reference internal" href="helper-functions.html#array_api_compat.size" title="array_api_compat.size"><code class="xref py py-func docutils literal notranslate"><span class="pre">size()</span></code></a> helper function as a
+portable workaround.</p></li>
+<li><p>PyTorch has incomplete support for unsigned integer types other
+than <code class="docutils literal notranslate"><span class="pre">uint8</span></code>, and no attempt is made to implement them here.</p></li>
+<li><p>PyTorch has type promotion semantics that differ from the array API
+specification for 0-D tensor objects. The array functions in this wrapper
+library do work around this, but the operators on the Tensor object do not,
+as no operators or methods on the Tensor object are modified. If this is a
+concern, use the functional form instead of the operator form, e.g., <code class="docutils literal notranslate"><span class="pre">add(x,</span> <span class="pre">y)</span></code> instead of <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">+</span> <span class="pre">y</span></code>.</p></li>
+<li><p><a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/generated/array_api.unique_all.html#array_api.unique_all"><code class="docutils literal notranslate"><span class="pre">unique_all()</span></code></a>
+is not implemented, due to the fact that <code class="docutils literal notranslate"><span class="pre">torch.unique</span></code> does not support
+returning the <code class="docutils literal notranslate"><span class="pre">indices</span></code> array. The other
+<a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/set_functions.html"><code class="docutils literal notranslate"><span class="pre">unique_*</span></code></a>
+functions are implemented.</p></li>
+<li><p>Slices do not support negative steps.</p></li>
+<li><p><a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/generated/array_api.std.html#array_api.std"><code class="docutils literal notranslate"><span class="pre">std()</span></code></a>
+and
+<a class="reference external" href="https://data-apis.org/array-api/latest/API_specification/generated/array_api.var.html#array_api.var"><code class="docutils literal notranslate"><span class="pre">var()</span></code></a>
+do not support floating-point <code class="docutils literal notranslate"><span class="pre">correction</span></code>.</p></li>
+<li><p>The <code class="docutils literal notranslate"><span class="pre">stream</span></code> argument of the <a class="reference internal" href="helper-functions.html#array_api_compat.to_device" title="array_api_compat.to_device"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_device()</span></code></a> helper is not supported.</p></li>
+<li><p>As with NumPy, type annotations and positional-only arguments may not
+exactly match the spec for functions that are not wrapped at all.</p></li>
+</ul>
+</section>
+<section id="jax">
+<span id="jax-support"></span><h2><a class="reference external" href="https://jax.readthedocs.io/en/latest/">JAX</a><a class="headerlink" href="#jax" title="Link to this heading">¶</a></h2>
+<p>Unlike the other libraries supported here, JAX array API support is contained
+entirely in the JAX library. The JAX array API support is tracked at
+<a class="reference external" href="https://github.com/google/jax/issues/18353">https://github.com/google/jax/issues/18353</a>.</p>
+</section>
+<section id="dask">
+<h2><a class="reference external" href="https://www.dask.org/">Dask</a><a class="headerlink" href="#dask" title="Link to this heading">¶</a></h2>
+<p>If you’re using dask with numpy, many of the same limitations that apply to numpy
+will also apply to dask. Besides those differences, other limitations include missing
+sort functionality (no <code class="docutils literal notranslate"><span class="pre">sort</span></code> or <code class="docutils literal notranslate"><span class="pre">argsort</span></code>), and limited support for the optional <code class="docutils literal notranslate"><span class="pre">linalg</span></code>
+and <code class="docutils literal notranslate"><span class="pre">fft</span></code> extensions.</p>
+<p>In particular, the <code class="docutils literal notranslate"><span class="pre">fft</span></code> namespace is not compliant with the array API spec. Any functions
+that you find under the <code class="docutils literal notranslate"><span class="pre">fft</span></code> namespace are the original, unwrapped functions under <a class="reference external" href="https://docs.dask.org/en/latest/array-api.html#fast-fourier-transforms"><code class="docutils literal notranslate"><span class="pre">dask.array.fft</span></code></a>, which may or may not be Array API compliant. Use at your own risk!</p>
+<p>For <code class="docutils literal notranslate"><span class="pre">linalg</span></code>, several methods are missing, for example:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">cross</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">det</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">eigh</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">eigvalsh</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">matrix_power</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">pinv</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">slogdet</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">matrix_norm</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">matrix_rank</span></code>
+Other methods may only be partially implemented or return incorrect results at times.</p></li>
+</ul>
+</section>
+<section id="sparse">
+<span id="sparse-support"></span><h2><a class="reference external" href="https://sparse.pydata.org/en/stable/">Sparse</a><a class="headerlink" href="#sparse" title="Link to this heading">¶</a></h2>
+<p>Similar to JAX, <code class="docutils literal notranslate"><span class="pre">sparse</span></code> Array API support is contained directly in <code class="docutils literal notranslate"><span class="pre">sparse</span></code>.</p>
+</section>
+<section id="ndonnx">
+<span id="ndonnx-support"></span><h2><a class="reference external" href="https://github.com/quantco/ndonnx">ndonnx</a><a class="headerlink" href="#ndonnx" title="Link to this heading">¶</a></h2>
+<p>Similar to JAX, <code class="docutils literal notranslate"><span class="pre">ndonnx</span></code> Array API support is contained directly in <code class="docutils literal notranslate"><span class="pre">ndonnx</span></code>.</p>
+</section>
+<section id="array-api-strict">
+<span id="array-api-strict-support"></span><h2><a class="reference external" href="https://data-apis.org/array-api-strict/">array-api-strict</a><a class="headerlink" href="#array-api-strict" title="Link to this heading">¶</a></h2>
+<p>array-api-strict exists only to test support for the Array API, so it does not need any wrappers.</p>
+</section>
+</section>
+
+        </article>
+      </div>
+      <footer>
+        
+        <div class="related-pages">
+          <a class="next-page" href="changelog.html">
+              <div class="page-info">
+                <div class="context">
+                  <span>Next</span>
+                </div>
+                <div class="title">Changelog</div>
+              </div>
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+            </a>
+          <a class="prev-page" href="helper-functions.html">
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+              <div class="page-info">
+                <div class="context">
+                  <span>Previous</span>
+                </div>
+                
+                <div class="title">Helper Functions</div>
+                
+              </div>
+            </a>
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2024, Consortium for Python Data API Standards
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/data-apis/array-api-compat" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer">
+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Supported Array Libraries</a><ul>
+<li><a class="reference internal" href="#numpy-and-cupy">NumPy and CuPy</a></li>
+<li><a class="reference internal" href="#pytorch">PyTorch</a></li>
+<li><a class="reference internal" href="#jax">JAX</a></li>
+<li><a class="reference internal" href="#dask">Dask</a></li>
+<li><a class="reference internal" href="#sparse">Sparse</a></li>
+<li><a class="reference internal" href="#ndonnx">ndonnx</a></li>
+<li><a class="reference internal" href="#array-api-strict">array-api-strict</a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
+  </div>
+</div><script src="_static/documentation_options.js?v=1e6c7a3c"></script>
+    <script src="_static/doctools.js?v=9bcbadda"></script>
+    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="_static/scripts/furo.js?v=5fa4622c"></script>
+    <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+    <script src="_static/copybutton.js?v=f281be69"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/test_cupy.sh b/test_cupy.sh
deleted file mode 100755
index a6974333..00000000
--- a/test_cupy.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env bash
-# We cannot test cupy on CI so this script will test it manually. Assumes it
-# is being run in an environment that has cupy and the array-api-tests
-# dependencies installed
-set -x
-set -e
-
-# Run the vendoring tests in this repo
-pytest
-
-tmpdir=$(mktemp -d)
-SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
-export PYTHONPATH="$PYTHONPATH:$SCRIPT_DIR"
-
-PYTEST_ARGS="--max-examples 200 -v -rxXfE --ci --hypothesis-disable-deadline"
-
-cd $tmpdir
-git clone https://github.com/data-apis/array-api-tests
-cd array-api-tests
-
-git submodule update --init
-
-# store the hypothesis examples database in this directory, so that failures
-# will be remembered across runs
-mkdir -p $SCRIPT_DIR/.hypothesis
-ln -s $SCRIPT_DIR/.hypothesis .hypothesis
-
-export ARRAY_API_TESTS_MODULE=array_api_compat.cupy
-export ARRAY_API_TESTS_VERSION=2024.12
-pytest array_api_tests/ ${PYTEST_ARGS} --xfails-file $SCRIPT_DIR/cupy-xfails.txt "$@"
diff --git a/tests/__init__.py b/tests/__init__.py
deleted file mode 100644
index abc8f0f1..00000000
--- a/tests/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""
-Basic tests for the compat library
-
-This only tests basic things like that vendoring works. The extensive tests
-are done by the array API test suite https://github.com/data-apis/array-api-tests
-
-"""
diff --git a/tests/_helpers.py b/tests/_helpers.py
deleted file mode 100644
index 17865aa0..00000000
--- a/tests/_helpers.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from importlib import import_module
-
-import pytest
-
-wrapped_libraries = ["numpy", "cupy", "torch", "dask.array"]
-all_libraries = wrapped_libraries + [
-    "array_api_strict", "jax.numpy", "ndonnx", "sparse"
-]
-
-def import_(library, wrapper=False):
-    pytest.importorskip(library)
-    if wrapper:
-        if 'jax' in library:
-            # JAX v0.4.32 implements the array API directly in jax.numpy
-            # Older jax versions use jax.experimental.array_api
-            jax_numpy = import_module("jax.numpy")
-            if not hasattr(jax_numpy, "__array_api_version__"):
-                library = 'jax.experimental.array_api'
-        elif library in wrapped_libraries:
-            library = 'array_api_compat.' + library
-
-    return import_module(library)
-
-
-def xfail(request: pytest.FixtureRequest, reason: str) -> None:
-    """
-    XFAIL the currently running test.
-
-    Unlike ``pytest.xfail``, allow rest of test to execute instead of immediately
-    halting it, so that it may result in a XPASS.
-    xref https://github.com/pandas-dev/pandas/issues/38902
-    """
-    request.node.add_marker(pytest.mark.xfail(reason=reason))
diff --git a/tests/test_all.py b/tests/test_all.py
deleted file mode 100644
index c36aef67..00000000
--- a/tests/test_all.py
+++ /dev/null
@@ -1,311 +0,0 @@
-"""Test exported names"""
-
-import builtins
-
-import numpy as np
-import pytest
-
-from array_api_compat._internal import clone_module
-
-from ._helpers import wrapped_libraries
-
-NAMES = {
-    "": [
-        # Inspection
-        "__array_api_version__",
-        "__array_namespace_info__",
-        # Submodules
-        "fft",
-        "linalg",
-        # Constants
-        "e",
-        "inf",
-        "nan",
-        "newaxis",
-        "pi",
-        # Creation Functions
-        "arange",
-        "asarray",
-        "empty",
-        "empty_like",
-        "eye",
-        "from_dlpack",
-        "full",
-        "full_like",
-        "linspace",
-        "meshgrid",
-        "ones",
-        "ones_like",
-        "tril",
-        "triu",
-        "zeros",
-        "zeros_like",
-        # Data Type Functions
-        "astype",
-        "can_cast",
-        "finfo",
-        "iinfo",
-        "isdtype",
-        "result_type",
-        # Data Types
-        "bool",
-        "int8",
-        "int16",
-        "int32",
-        "int64",
-        "uint8",
-        "uint16",
-        "uint32",
-        "uint64",
-        "float32",
-        "float64",
-        "complex64",
-        "complex128",
-        # Elementwise Functions
-        "abs",
-        "acos",
-        "acosh",
-        "add",
-        "asin",
-        "asinh",
-        "atan",
-        "atan2",
-        "atanh",
-        "bitwise_and",
-        "bitwise_left_shift",
-        "bitwise_invert",
-        "bitwise_or",
-        "bitwise_right_shift",
-        "bitwise_xor",
-        "ceil",
-        "clip",
-        "conj",
-        "copysign",
-        "cos",
-        "cosh",
-        "divide",
-        "equal",
-        "exp",
-        "expm1",
-        "floor",
-        "floor_divide",
-        "greater",
-        "greater_equal",
-        "hypot",
-        "imag",
-        "isfinite",
-        "isinf",
-        "isnan",
-        "less",
-        "less_equal",
-        "log",
-        "log1p",
-        "log2",
-        "log10",
-        "logaddexp",
-        "logical_and",
-        "logical_not",
-        "logical_or",
-        "logical_xor",
-        "maximum",
-        "minimum",
-        "multiply",
-        "negative",
-        "nextafter",
-        "not_equal",
-        "positive",
-        "pow",
-        "real",
-        "reciprocal",
-        "remainder",
-        "round",
-        "sign",
-        "signbit",
-        "sin",
-        "sinh",
-        "square",
-        "sqrt",
-        "subtract",
-        "tan",
-        "tanh",
-        "trunc",
-        # Indexing Functions
-        "take",
-        "take_along_axis",
-        # Linear Algebra Functions
-        "matmul",
-        "matrix_transpose",
-        "tensordot",
-        "vecdot",
-        # Manipulation Functions
-        "broadcast_arrays",
-        "broadcast_to",
-        "concat",
-        "expand_dims",
-        "flip",
-        "moveaxis",
-        "permute_dims",
-        "repeat",
-        "reshape",
-        "roll",
-        "squeeze",
-        "stack",
-        "tile",
-        "unstack",
-        # Searching Functions
-        "argmax",
-        "argmin",
-        "count_nonzero",
-        "nonzero",
-        "searchsorted",
-        "where",
-        # Set Functions
-        "unique_all",
-        "unique_counts",
-        "unique_inverse",
-        "unique_values",
-        # Sorting Functions
-        "argsort",
-        "sort",
-        # Statistical Functions
-        "cumulative_prod",
-        "cumulative_sum",
-        "max",
-        "mean",
-        "min",
-        "prod",
-        "std",
-        "sum",
-        "var",
-        # Utility Functions
-        "all",
-        "any",
-        "diff",
-    ],
-    "fft": [
-        "fft",
-        "ifft",
-        "fftn",
-        "ifftn",
-        "rfft",
-        "irfft",
-        "rfftn",
-        "irfftn",
-        "hfft",
-        "ihfft",
-        "fftfreq",
-        "rfftfreq",
-        "fftshift",
-        "ifftshift",
-    ],
-    "linalg": [
-        "cholesky",
-        "cross",
-        "det",
-        "diagonal",
-        "eigh",
-        "eigvalsh",
-        "inv",
-        "matmul",
-        "matrix_norm",
-        "matrix_power",
-        "matrix_rank",
-        "matrix_transpose",
-        "outer",
-        "pinv",
-        "qr",
-        "slogdet",
-        "solve",
-        "svd",
-        "svdvals",
-        "tensordot",
-        "trace",
-        "vecdot",
-        "vector_norm",
-    ],
-}
-
-XFAILS = {
-    ("numpy", ""): ["from_dlpack"] if np.__version__ < "1.23" else [],
-    ("dask.array", ""): ["from_dlpack", "take_along_axis"],
-    ("dask.array", "linalg"): [
-        "cross",
-        "det",
-        "eigh",
-        "eigvalsh",
-        "matrix_power",
-        "pinv",
-        "slogdet",
-    ],
-}
-
-
-def all_names(mod):
-    """Return all names available in a module."""
-    objs = {}
-    clone_module(mod.__name__, objs)
-    return set(objs)
-
-
-def get_mod(library, module, *, compat):
-    if compat:
-        library = f"array_api_compat.{library}"
-    xp = pytest.importorskip(library)
-    return getattr(xp, module) if module else xp
-
-
-@pytest.mark.parametrize("module", list(NAMES))
-@pytest.mark.parametrize("library", wrapped_libraries)
-def test_array_api_names(library, module):
-    """Test that __all__ isn't missing any exports
-    dictated by the Standard.
-    """
-    mod = get_mod(library, module, compat=True)
-    missing = set(NAMES[module]) - all_names(mod)
-    xfail = set(XFAILS.get((library, module), []))
-    xpass = xfail - missing
-    fails = missing - xfail
-    assert not xpass, f"Names in XFAILS are defined: {xpass}"
-    assert not fails, f"Missing exports: {fails}"
-
-
-@pytest.mark.parametrize("module", list(NAMES))
-@pytest.mark.parametrize("library", wrapped_libraries)
-def test_compat_doesnt_hide_names(library, module):
-    """The base namespace can have more names than the ones explicitly exported
-    by array-api-compat. Test that we're not suppressing them.
-    """
-    bare_mod = get_mod(library, module, compat=False)
-    compat_mod = get_mod(library, module, compat=True)
-
-    missing = all_names(bare_mod) - all_names(compat_mod)
-    missing = {name for name in missing if not name.startswith("_")}
-    assert not missing, f"Non-Array API names have been hidden: {missing}"
-
-
-@pytest.mark.parametrize("module", list(NAMES))
-@pytest.mark.parametrize("library", wrapped_libraries)
-def test_compat_doesnt_add_names(library, module):
-    """Test that array-api-compat isn't adding names to the namespace
-    besides those defined by the Array API Standard.
-    """
-    bare_mod = get_mod(library, module, compat=False)
-    compat_mod = get_mod(library, module, compat=True)
-
-    aapi_names = set(NAMES[module])
-    spurious = all_names(compat_mod) - all_names(bare_mod) - aapi_names
-    # Quietly ignore *Result dataclasses
-    spurious = {name for name in spurious if not name.endswith("Result")}
-    assert not spurious, (
-        f"array-api-compat is adding non-Array API names: {spurious}"
-    )
-
-
-@pytest.mark.parametrize(
-    "name", [name for name in NAMES[""] if hasattr(builtins, name)]
-)
-@pytest.mark.parametrize("library", wrapped_libraries)
-def test_builtins_collision(library, name):
-    """Test that xp.bool is not accidentally builtins.bool, etc."""
-    xp = pytest.importorskip(f"array_api_compat.{library}")
-    assert getattr(xp, name) is not getattr(builtins, name)
diff --git a/tests/test_array_namespace.py b/tests/test_array_namespace.py
deleted file mode 100644
index 311efc37..00000000
--- a/tests/test_array_namespace.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import subprocess
-import sys
-import warnings
-
-import numpy as np
-import pytest
-
-import array_api_compat
-from array_api_compat import array_namespace
-
-from ._helpers import all_libraries, wrapped_libraries, xfail
-
-
-@pytest.mark.parametrize("use_compat", [True, False, None])
-@pytest.mark.parametrize(
-    "api_version", [None, "2021.12", "2022.12", "2023.12", "2024.12"]
-)
-@pytest.mark.parametrize("library", all_libraries)
-def test_array_namespace(request, library, api_version, use_compat):
-    xp = pytest.importorskip(library)
-
-    array = xp.asarray([1.0, 2.0, 3.0])
-    if use_compat and library not in wrapped_libraries:
-        pytest.raises(ValueError, lambda: array_namespace(array, use_compat=use_compat))
-        return
-    if (library == "sparse" and api_version in ("2023.12", "2024.12")) or (
-        library == "jax.numpy" and api_version in ("2021.12", "2022.12", "2023.12")
-    ):
-        xfail(request, "Unsupported API version")
-
-    with warnings.catch_warnings():
-        warnings.simplefilter('ignore', UserWarning)
-        namespace = array_namespace(array, api_version=api_version, use_compat=use_compat)
-
-    if use_compat is False or use_compat is None and library not in wrapped_libraries:
-        if library == "jax.numpy" and not hasattr(xp, "__array_api_version__"):
-            # Backwards compatibility for JAX <0.4.32
-            import jax.experimental.array_api
-            assert namespace == jax.experimental.array_api
-        else:
-            assert namespace == xp
-    elif library == "dask.array":
-        assert namespace == array_api_compat.dask.array
-    else:
-        assert namespace == getattr(array_api_compat, library)
-
-    if library == "numpy":
-        # check that the same namespace is returned for NumPy scalars
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore', UserWarning)
-
-            scalar_namespace = array_namespace(
-                xp.float64(0.0), api_version=api_version, use_compat=use_compat
-            )
-            assert scalar_namespace == namespace
-
-
-def test_jax_backwards_compat():
-    """On JAX <0.4.32, test that array_namespace works even if
-    jax.experimental.array_api has not been imported yet.
-    """
-    pytest.importorskip("jax")
-    code = """\
-import sys
-import jax.numpy
-import array_api_compat
-
-array = jax.numpy.asarray([1.0, 2.0, 3.0])
-assert 'jax.experimental.array_api' not in sys.modules
-namespace = array_api_compat.array_namespace(array)
-
-if hasattr(jax.numpy, '__array_api_version__'):
-    assert namespace == jax.numpy
-else:
-    import jax.experimental.array_api
-    assert namespace == jax.experimental.array_api
-"""
-    subprocess.check_call([sys.executable, "-c", code])
-
-
-def test_jax_zero_gradient():
-    jax = pytest.importorskip("jax")
-    jx = jax.numpy.arange(4)
-    jax_zero = jax.vmap(jax.grad(jax.numpy.float32, allow_int=True))(jx)
-    assert array_namespace(jax_zero) is array_namespace(jx)
-
-
-def test_array_namespace_errors():
-    pytest.raises(TypeError, lambda: array_namespace([1]))
-    pytest.raises(TypeError, lambda: array_namespace())
-
-    x = np.asarray([1, 2])
-    pytest.raises(TypeError, lambda: array_namespace((x, x)))
-    pytest.raises(TypeError, lambda: array_namespace(x, (x, x)))
-
-
-@pytest.mark.parametrize("library", all_libraries)
-def test_array_namespace_many_args(library):
-    xp = pytest.importorskip(library)
-    a = xp.asarray(1)
-    b = xp.asarray(2)
-    assert array_namespace(a, b) is array_namespace(a)
-
-
-def test_array_namespace_mismatch():
-    xp = pytest.importorskip("array_api_strict")
-    with pytest.raises(TypeError, match="Multiple namespaces"):
-        array_namespace(np.asarray(1), xp.asarray(1))
-
-
-def test_get_namespace():
-    # Backwards compatible wrapper
-    assert array_api_compat.get_namespace is array_namespace
-
-
-@pytest.mark.parametrize("library", all_libraries)
-def test_python_scalars(library):
-    xp = pytest.importorskip(library)
-    a = xp.asarray([1, 2])
-    xp = array_namespace(a)
-
-    pytest.raises(TypeError, lambda: array_namespace(1))
-    pytest.raises(TypeError, lambda: array_namespace(1.0))
-    pytest.raises(TypeError, lambda: array_namespace(1j))
-    pytest.raises(TypeError, lambda: array_namespace(True))
-    pytest.raises(TypeError, lambda: array_namespace(None))
-
-    assert array_namespace(a, 1) is xp
-    assert array_namespace(a, 1.0) is xp
-    assert array_namespace(a, 1j) is xp
-    assert array_namespace(a, True) is xp
-    assert array_namespace(a, None) is xp
diff --git a/tests/test_common.py b/tests/test_common.py
deleted file mode 100644
index 85ed032e..00000000
--- a/tests/test_common.py
+++ /dev/null
@@ -1,378 +0,0 @@
-import math
-
-import pytest
-import numpy as np
-import array
-from numpy.testing import assert_equal
-
-from array_api_compat import (  # noqa: F401
-    is_numpy_array, is_cupy_array, is_torch_array,
-    is_dask_array, is_jax_array, is_pydata_sparse_array,
-    is_ndonnx_array,
-    is_numpy_namespace, is_cupy_namespace, is_torch_namespace,
-    is_dask_namespace, is_jax_namespace, is_pydata_sparse_namespace,
-    is_array_api_strict_namespace, is_ndonnx_namespace,
-)
-
-from array_api_compat import (
-    device, is_array_api_obj, is_lazy_array, is_writeable_array, size, to_device
-)
-from array_api_compat.common._helpers import _DASK_DEVICE
-from ._helpers import all_libraries, import_, wrapped_libraries, xfail
-
-
-is_array_functions = {
-    'numpy': 'is_numpy_array',
-    'cupy': 'is_cupy_array',
-    'torch': 'is_torch_array',
-    'dask.array': 'is_dask_array',
-    'jax.numpy': 'is_jax_array',
-    'sparse': 'is_pydata_sparse_array',
-    'ndonnx': 'is_ndonnx_array',
-}
-
-is_namespace_functions = {
-    'numpy': 'is_numpy_namespace',
-    'cupy': 'is_cupy_namespace',
-    'torch': 'is_torch_namespace',
-    'dask.array': 'is_dask_namespace',
-    'jax.numpy': 'is_jax_namespace',
-    'sparse': 'is_pydata_sparse_namespace',
-    'array_api_strict': 'is_array_api_strict_namespace',
-    'ndonnx': 'is_ndonnx_namespace',
-}
-
-
-@pytest.mark.parametrize('library', is_array_functions.keys())
-@pytest.mark.parametrize('func', is_array_functions.values())
-def test_is_xp_array(library, func):
-    lib = import_(library)
-    is_func = globals()[func]
-
-    x = lib.asarray([1, 2, 3])
-
-    assert is_func(x) == (func == is_array_functions[library])
-
-    assert is_array_api_obj(x)
-
-
-@pytest.mark.parametrize('library', is_namespace_functions.keys())
-@pytest.mark.parametrize('func', is_namespace_functions.values())
-def test_is_xp_namespace(library, func):
-    lib = import_(library)
-    is_func = globals()[func]
-
-    assert is_func(lib) == (func == is_namespace_functions[library])
-
-
-@pytest.mark.parametrize('library', all_libraries)
-def test_xp_is_array_generics(library):
-    """
-    Test that scalar selection on a xp.ndarray always returns
-    an object that matches with exactly one among the is_*_array
-    function of the same library and is_numpy_array.
-    """
-    lib = import_(library)
-    x = lib.asarray([1, 2, 3])
-    x0 = x[0]
-
-    matches = []
-    for library2, func in is_array_functions.items():
-        is_func = globals()[func]
-        if is_func(x0):
-            matches.append(library2)
-
-    if library == "array_api_strict":
-        # There is no is_array_api_strict_array() function
-        assert matches == []
-    else:
-        assert matches in ([library], ["numpy"])
-
-
-@pytest.mark.parametrize("library", all_libraries)
-def test_is_writeable_array(library):
-    lib = import_(library)
-    x = lib.asarray([1, 2, 3])
-    if is_writeable_array(x):
-        x[1] = 4
-    else:
-        with pytest.raises((TypeError, ValueError)):
-            x[1] = 4
-
-
-def test_is_writeable_array_numpy():
-    x = np.asarray([1, 2, 3])
-    assert is_writeable_array(x)
-    x.flags.writeable = False
-    assert not is_writeable_array(x)
-
-
-@pytest.mark.parametrize("library", all_libraries)
-def test_size(library):
-    xp = import_(library)
-    x = xp.asarray([1, 2, 3])
-    assert size(x) == 3
-
-
-@pytest.mark.parametrize("library", all_libraries)
-def test_size_none(library):
-    if library == "sparse":
-        pytest.skip("No arange(); no indexing by sparse arrays")
-
-    xp = import_(library)
-    x = xp.arange(10)
-    x = x[x < 5]
-
-    # dask.array now has shape=(nan, ) and size=nan
-    # ndonnx now has shape=(None, ) and size=None
-    # Eager libraries have shape=(5, ) and size=5
-    assert size(x) in (None, 5)
-
-
-@pytest.mark.parametrize("library", all_libraries)
-def test_is_lazy_array(library):
-    lib = import_(library)
-    x = lib.asarray([1, 2, 3])
-    assert isinstance(is_lazy_array(x), bool)
-
-
-@pytest.mark.parametrize("shape", [(math.nan,), (1, math.nan), (None, ), (1, None)])
-def test_is_lazy_array_nan_size(shape, monkeypatch):
-    """Test is_lazy_array() on an unknown Array API compliant object
-    with NaN (like Dask) or None (like ndonnx) in its shape
-    """
-    xp = import_("array_api_strict")
-    x = xp.asarray(1)
-    assert not is_lazy_array(x)
-    monkeypatch.setattr(type(x), "shape", shape)
-    assert is_lazy_array(x)
-
-
-@pytest.mark.parametrize("exc", [TypeError, AssertionError])
-def test_is_lazy_array_bool_raises(exc, monkeypatch):
-    """Test is_lazy_array() on an unknown Array API compliant object
-    where calling bool() raises:
-    - TypeError: e.g. like jitted JAX. This is the proper exception which
-      lazy arrays should raise as per the Array API specification
-    - something else: e.g. like Dask, where bool() triggers compute()
-      which can result in any kind of exception to be raised
-    """
-    xp = import_("array_api_strict")
-    x = xp.asarray(1)
-    assert not is_lazy_array(x)
-
-    def __bool__(self):
-        raise exc("Hello world")
-
-    monkeypatch.setattr(type(x), "__bool__", __bool__)
-    assert is_lazy_array(x)
-
-
-@pytest.mark.parametrize(
-    'func',
-    list(is_array_functions.values()) 
-    + ["is_array_api_obj", "is_lazy_array", "is_writeable_array"]
-)
-def test_is_array_any_object(func):
-    """Test that is_*_array functions return False and don't raise on non-array objects
-    """
-    func = globals()[func]
-
-    # These objects are missing attributes such as __name__
-    assert not func(object())
-    assert not func(None)
-    assert not func(1)
-
-    class C:
-        pass
-
-    assert not func(C())
-
-
-@pytest.mark.parametrize("library", all_libraries)
-def test_device_to_device(library, request):
-    if library == "ndonnx":
-        xfail(request, reason="Stub raises ValueError")
-    if library == "sparse":
-        xfail(request, reason="No __array_namespace_info__()")
-    if library == "array_api_strict":
-        if np.__version__ < "2":
-            xfail(request, reason="no copy argument of np.asarray")
-
-    xp = import_(library, wrapper=True)
-    devices = xp.__array_namespace_info__().devices()
-
-    # Default device
-    x = xp.asarray([1, 2, 3])
-    dev = device(x)
-
-    for dev in devices:
-        if dev is None:  # JAX >=0.5.3
-            continue
-        if dev is _DASK_DEVICE:  # TODO this needs a better design
-            continue
-        y = to_device(x, dev)
-        assert device(y) == dev
-
-
-@pytest.mark.parametrize("library", wrapped_libraries)
-def test_to_device_host(library):
-    # different libraries have different semantics
-    # for DtoH transfers; ensure that we support a portable
-    # shim for common array libs
-    # see: https://github.com/scipy/scipy/issues/18286#issuecomment-1527552919
-    xp = import_(library, wrapper=True)
-
-    expected = np.array([1, 2, 3])
-    x = xp.asarray([1, 2, 3])
-    x = to_device(x, "cpu")
-    # torch will return a genuine Device object, but
-    # the other libs will do something different with
-    # a `device(x)` query; however, what's really important
-    # here is that we can test portably after calling
-    # to_device(x, "cpu") to return to host
-    assert_equal(x, expected)
-
-
-@pytest.mark.parametrize("target_library", is_array_functions.keys())
-@pytest.mark.parametrize("source_library", is_array_functions.keys())
-def test_asarray_cross_library(source_library, target_library, request):
-    if source_library == "dask.array" and target_library == "torch":
-        # TODO: remove xfail once
-        # https://github.com/dask/dask/issues/8260 is resolved
-        xfail(request, reason="Bug in dask raising error on conversion")
-
-    elif (
-        source_library == "ndonnx" 
-        and target_library not in ("array_api_strict", "ndonnx", "numpy")
-    ):
-        xfail(request, reason="The truth value of lazy Array Array(dtype=Boolean) is unknown")
-    elif source_library == "ndonnx" and target_library == "numpy":
-        xfail(request, reason="produces numpy array of ndonnx scalar arrays")
-    elif target_library == "ndonnx" and source_library in ("torch", "dask.array", "jax.numpy"):
-        xfail(request, reason="unable to infer dtype")
-
-    elif source_library == "jax.numpy" and target_library == "torch":
-        xfail(request, reason="casts int to float")
-    elif source_library == "cupy" and target_library != "cupy":
-        # cupy explicitly disallows implicit conversions to CPU
-        pytest.skip(reason="cupy does not support implicit conversion to CPU")
-    elif source_library == "sparse" and target_library != "sparse":
-        pytest.skip(reason="`sparse` does not allow implicit densification")
-
-    src_lib = import_(source_library, wrapper=True)
-    tgt_lib = import_(target_library, wrapper=True)
-    is_tgt_type = globals()[is_array_functions[target_library]]
-
-    a = src_lib.asarray([1, 2, 3], dtype=src_lib.int32)
-    b = tgt_lib.asarray(a)
-
-    assert is_tgt_type(b), f"Expected {b} to be a {tgt_lib.ndarray}, but was {type(b)}"
-    assert b.dtype == tgt_lib.int32
-
-
-@pytest.mark.parametrize("library", wrapped_libraries)
-def test_asarray_copy(library):
-    # Note, we have this test here because the test suite currently doesn't
-    # test the copy flag to asarray() very rigorously. Once
-    # https://github.com/data-apis/array-api-tests/issues/241 is fixed we
-    # should be able to delete this.
-    xp = import_(library, wrapper=True)
-    asarray = xp.asarray
-    is_lib_func = globals()[is_array_functions[library]]
-
-    a = asarray([1])
-    b = asarray(a, copy=True)
-    assert is_lib_func(b)
-    a[0] = 0
-    assert b[0] == 1
-    assert a[0] == 0
-
-    a = asarray([1])
-
-    # Test copy=False within the same namespace
-    b = asarray(a, copy=False)
-    assert is_lib_func(b)
-    a[0] = 0
-    assert b[0] == 0
-    with pytest.raises(ValueError):
-        asarray(a, copy=False, dtype=xp.float64)
-
-    # copy=None defaults to False when possible
-    a = asarray([1])
-    b = asarray(a, copy=None)
-    assert is_lib_func(b)
-    a[0] = 0
-    assert b[0] == 0
-
-    # copy=None defaults to True when impossible
-    a = asarray([1.0], dtype=xp.float32)
-    assert a.dtype == xp.float32
-    b = asarray(a, dtype=xp.float64, copy=None)
-    assert is_lib_func(b)
-    assert b.dtype == xp.float64
-    a[0] = 0.0
-    assert b[0] == 1.0
-
-    # copy=None defaults to False when possible
-    a = asarray([1.0], dtype=xp.float64)
-    assert a.dtype == xp.float64
-    b = asarray(a, dtype=xp.float64, copy=None)
-    assert is_lib_func(b)
-    assert b.dtype == xp.float64
-    a[0] = 0.0
-    assert b[0] == 0.0
-
-    # Python built-in types
-    for obj in [True, 0, 0.0, 0j, [0], [[0]]]:
-        asarray(obj, copy=True)  # No error
-        asarray(obj, copy=None)  # No error
-
-        with pytest.raises(ValueError):
-            asarray(obj, copy=False)
-
-    # Use the standard library array to test the buffer protocol
-    a = array.array("f", [1.0])
-    b = asarray(a, copy=True)
-    assert is_lib_func(b)
-    a[0] = 0.0
-    assert b[0] == 1.0
-
-    a = array.array("f", [1.0])
-    if library in ("cupy", "dask.array"):
-        with pytest.raises(ValueError):
-            asarray(a, copy=False)
-    else:
-        b = asarray(a, copy=False)
-        assert is_lib_func(b)
-        a[0] = 0.0
-        assert b[0] == 0.0
-
-    a = array.array("f", [1.0])
-    b = asarray(a, copy=None)
-    assert is_lib_func(b)
-    a[0] = 0.0
-    if library in ("cupy", "dask.array"):
-        # A copy is required for libraries where the default device is not CPU
-        # dask changed behaviour of copy=None in 2024.12 to copy;
-        # this wrapper ensures the same behaviour in older versions too.
-        # https://github.com/dask/dask/pull/11524/
-        assert b[0] == 1.0
-    else:
-        # copy=None defaults to False when possible
-        assert b[0] == 0.0
-
-
-@pytest.mark.parametrize("library", ["numpy", "cupy", "torch"])
-def test_clip_out(library):
-    """Test non-standard out= parameter for clip()
-
-    (see "Avoid Restricting Behavior that is Outside the Scope of the Standard"
-    in https://data-apis.org/array-api-compat/dev/special-considerations.html)
-    """
-    xp = import_(library, wrapper=True)
-    x = xp.asarray([10, 20, 30])
-    out = xp.zeros_like(x)
-    xp.clip(x, 15, 25, out=out)
-    expect = xp.asarray([15, 20, 25])
-    assert xp.all(out == expect)
diff --git a/tests/test_copies_or_views.py b/tests/test_copies_or_views.py
deleted file mode 100644
index ec8995f7..00000000
--- a/tests/test_copies_or_views.py
+++ /dev/null
@@ -1,64 +0,0 @@
-"""
-A collection of tests to make sure that wrapped namespaces agree with the bare ones
-on whether to return a view or a copy of inputs.
-"""
-import pytest
-from ._helpers import import_, wrapped_libraries
-
-
-FUNC_INPUTS = [
-    # func_name, arr_input, dtype,  scalar_value
-    ('abs', [1, 2], 'int8', 3),
-    ('abs', [1, 2], 'float32', 3.),
-    ('ceil', [1, 2], 'int8', 3),
-    ('clip', [1, 2], 'int8', 3),
-    ('conj', [1, 2], 'int8', 3),
-    ('floor', [1, 2], 'int8', 3),
-    ('imag', [1j, 2j], 'complex64', 3),
-    ('positive', [1, 2], 'int8', 3),
-    ('real', [1., 2.], 'float32', 3.),
-    ('round', [1, 2], 'int8', 3),
-    ('sign', [0, 0], 'float32', 3),
-    ('trunc', [1, 2], 'int8', 3),
-    ('trunc', [1, 2], 'float32', 3),
-]
-
-
-def ensure_unary(func, arr):
-    """Make a trivial unary function from func."""
-    if func.__name__ == 'clip':
-        return lambda x: func(x, arr[0], arr[1])
-    return func
-
-
-def is_view(func, a, value):
-    """Apply `func`, mutate the output; does the input change?"""
-    b = func(a)
-    b[0] = value
-    return a[0] == value
-
-
-@pytest.mark.parametrize('xp_name', wrapped_libraries + ['array_api_strict'])
-@pytest.mark.parametrize('inputs', FUNC_INPUTS, ids=[inp[0] for inp in FUNC_INPUTS])
-def test_view_or_copy(inputs, xp_name):
-    bare_xp = import_(xp_name, wrapper=False)
-    wrapped_xp = import_(xp_name, wrapper=True)
-
-    func_name, arr_input, dtype_str, value = inputs
-    dtype = getattr(bare_xp, dtype_str)
-
-    bare_func = getattr(bare_xp, func_name)
-    bare_func = ensure_unary(bare_func, arr_input)
-
-    wrapped_func = getattr(wrapped_xp, func_name)
-    wrapped_func = ensure_unary(wrapped_func, arr_input)
-
-    # bare namespace: mutate the output, does the input change?
-    a = bare_xp.asarray(arr_input, dtype=dtype)
-    is_view_bare = is_view(bare_func, a, value)
-
-    # wrapped namespace: mutate the output, does the input change?
-    a1 = wrapped_xp.asarray(arr_input, dtype=dtype)
-    is_view_wrapped = is_view(wrapped_func, a1, value)
-
-    assert is_view_bare == is_view_wrapped
diff --git a/tests/test_cupy.py b/tests/test_cupy.py
deleted file mode 100644
index 4745b983..00000000
--- a/tests/test_cupy.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import pytest
-from array_api_compat import device, to_device
-
-xp = pytest.importorskip("array_api_compat.cupy")
-from cupy.cuda import Stream
-
-
-@pytest.mark.parametrize(
-    "make_stream",
-    [
-        lambda: Stream(),
-        lambda: Stream(non_blocking=True),
-        lambda: Stream(null=True),
-        lambda: Stream(ptds=True),
-    ],
-)
-def test_to_device_with_stream(make_stream):
-    devices = xp.__array_namespace_info__().devices()
-
-    a = xp.asarray([1, 2, 3])
-    for dev in devices:
-        # Streams are device-specific and must be created within
-        # the context of the device...
-        with dev:
-            stream = make_stream()
-        # ... however, to_device() does not need to be inside the
-        # device context.
-        b = to_device(a, dev, stream=stream)
-        assert device(b) == dev
-
-
-def test_to_device_with_dlpack_stream():
-    devices = xp.__array_namespace_info__().devices()
-
-    a = xp.asarray([1, 2, 3])
-    for dev in devices:
-        # Streams are device-specific and must be created within
-        # the context of the device...
-        with dev:
-            s1 = Stream()
-
-        # ... however, to_device() does not need to be inside the
-        # device context.
-        b = to_device(a, dev, stream=s1.ptr)
-        assert device(b) == dev
diff --git a/tests/test_dask.py b/tests/test_dask.py
deleted file mode 100644
index fb0a84d4..00000000
--- a/tests/test_dask.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from contextlib import contextmanager
-
-import numpy as np
-import pytest
-
-try:
-    import dask
-    import dask.array as da
-except ImportError:
-    pytestmark = pytest.skip(allow_module_level=True, reason="dask not found")
-
-from array_api_compat import array_namespace
-
-
-@pytest.fixture
-def xp():
-    """Fixture returning the wrapped dask namespace"""
-    return array_namespace(da.empty(0))
-
-
-@contextmanager
-def assert_no_compute():
-    """
-    Context manager that raises if at any point inside it anything calls compute()
-    or persist(), e.g. as it can be triggered implicitly by __bool__, __array__, etc.
-    """
-
-    def get(dsk, *args, **kwargs):
-        raise AssertionError("Called compute() or persist()")
-
-    with dask.config.set(scheduler=get):
-        yield
-
-
-def test_assert_no_compute():
-    """Test the assert_no_compute context manager"""
-    a = da.asarray(True)
-    with pytest.raises(AssertionError, match="Called compute"):
-        with assert_no_compute():
-            bool(a)
-
-    # Exiting the context manager restores the original scheduler
-    assert bool(a) is True
-
-
-# Test no_compute for functions that use generic _aliases with xp=np
-
-
-def test_unary_ops_no_compute(xp):
-    with assert_no_compute():
-        a = xp.asarray([1.5, -1.5])
-        xp.ceil(a)
-        xp.floor(a)
-        xp.trunc(a)
-        xp.sign(a)
-
-
-def test_matmul_tensordot_no_compute(xp):
-    A = da.ones((4, 4), chunks=2)
-    B = da.zeros((4, 4), chunks=2)
-    with assert_no_compute():
-        xp.matmul(A, B)
-        xp.tensordot(A, B)
-
-
-# Test no_compute for functions that are fully bespoke for dask
-
-
-def test_asarray_no_compute(xp):
-    with assert_no_compute():
-        a = xp.arange(10)
-        xp.asarray(a)
-        xp.asarray(a, dtype=np.int16)
-        xp.asarray(a, dtype=a.dtype)
-        xp.asarray(a, copy=True)
-        xp.asarray(a, copy=True, dtype=np.int16)
-        xp.asarray(a, copy=True, dtype=a.dtype)
-        xp.asarray(a, copy=False)
-        xp.asarray(a, copy=False, dtype=a.dtype)
-
-
-@pytest.mark.parametrize("copy", [True, False])
-def test_astype_no_compute(xp, copy):
-    with assert_no_compute():
-        a = xp.arange(10)
-        xp.astype(a, np.int16, copy=copy)
-        xp.astype(a, a.dtype, copy=copy)
-
-
-def test_clip_no_compute(xp):
-    with assert_no_compute():
-        a = xp.arange(10)
-        xp.clip(a)
-        xp.clip(a, 1)
-        xp.clip(a, 1, 8)
-
-
-@pytest.mark.parametrize("chunks", (5, 10))
-def test_sort_argsort_nocompute(xp, chunks):
-    with assert_no_compute():
-        a = xp.arange(10, chunks=chunks)
-        xp.sort(a)
-        xp.argsort(a)
-
-
-def test_generators_are_lazy(xp):
-    """
-    Test that generator functions are fully lazy, e.g. that
-    da.ones(n) is not implemented as da.asarray(np.ones(n))
-    """
-    size = 100_000_000_000  # 800 GB
-    chunks = size // 10  # 10x 80 GB chunks
-
-    with assert_no_compute():
-        xp.zeros(size, chunks=chunks)
-        xp.ones(size, chunks=chunks)
-        xp.empty(size, chunks=chunks)
-        xp.full(size, fill_value=123, chunks=chunks)
-        a = xp.arange(size, chunks=chunks)
-        xp.zeros_like(a)
-        xp.ones_like(a)
-        xp.empty_like(a)
-        xp.full_like(a, fill_value=123)
-
-
-@pytest.mark.parametrize("axis", [0, 1])
-@pytest.mark.parametrize("func", ["sort", "argsort"])
-def test_sort_argsort_chunks(xp, func, axis):
-    """Test that sort and argsort are functionally correct when
-    the array is chunked along the sort axis, e.g. the sort is
-    not just local to each chunk.
-    """
-    a = da.random.random((10, 10), chunks=(5, 5))
-    actual = getattr(xp, func)(a, axis=axis)
-    expect = getattr(np, func)(a.compute(), axis=axis)
-    np.testing.assert_array_equal(actual, expect)
-
-
-@pytest.mark.parametrize(
-    "shape,chunks",
-    [
-        # 3 GiB; 128 MiB per chunk; must rechunk before sorting.
-        # Sort chunks can be 128 MiB each; no need for final rechunk.
-        ((20_000, 20_000), "auto"),
-        # 3 GiB; 128 MiB per chunk; must rechunk before sorting.
-        # Must sort on two 1.5 GiB chunks; benefits from final rechunk.
-        ((2, 2**30 * 3 // 16), "auto"),
-        # 3 GiB; 1.5 GiB per chunk; no need to rechunk before sorting.
-        # Surely the user must know what they're doing, so don't
-        # perform the final rechunk.
-        ((2, 2**30 * 3 // 16), (1, -1)),
-    ],
-)
-@pytest.mark.parametrize("func", ["sort", "argsort"])
-def test_sort_argsort_chunk_size(xp, func, shape, chunks):
-    """
-    Test that sort and argsort produce reasonably-sized chunks
-    in the output array, even if they had to go through a singular
-    huge one to perform the operation.
-    """
-    a = da.random.random(shape, chunks=chunks)
-    b = getattr(xp, func)(a)
-    max_chunk_size = max(b.chunks[0]) * max(b.chunks[1]) * b.dtype.itemsize
-    assert (
-        max_chunk_size <= 128 * 1024 * 1024  # 128 MiB
-        or b.chunks == a.chunks
-    )
-
-
-@pytest.mark.parametrize("func", ["sort", "argsort"])
-def test_sort_argsort_meta(xp, func):
-    """Test meta-namespace other than numpy"""
-    mxp = pytest.importorskip("array_api_strict")
-    typ = type(mxp.asarray(0))
-    a = da.random.random(10)
-    b = a.map_blocks(mxp.asarray)
-    assert isinstance(b._meta, typ)
-    c = getattr(xp, func)(b)
-    assert isinstance(c._meta, typ)
-    d = c.compute()
-    # Note: np.sort(array_api_strict.asarray(0)) would return a numpy array
-    assert isinstance(d, typ)
-    np.testing.assert_array_equal(d, getattr(np, func)(a.compute()))
diff --git a/tests/test_isdtype.py b/tests/test_isdtype.py
deleted file mode 100644
index 6ad45d4c..00000000
--- a/tests/test_isdtype.py
+++ /dev/null
@@ -1,115 +0,0 @@
-"""
-isdtype is not yet tested in the test suite, and it should extend properly to
-non-spec dtypes
-"""
-
-import pytest
-
-from ._helpers import import_, wrapped_libraries
-
-# Check the known dtypes by their string names
-
-def _spec_dtypes(library):
-    if library == 'torch':
-        # torch does not have unsigned integer dtypes
-        return {
-            'bool',
-            'complex64',
-            'complex128',
-            'uint8',
-            'int8',
-            'int16',
-            'int32',
-            'int64',
-            'float32',
-            'float64',
-        }
-    else:
-        return {
-            'bool',
-            'complex64',
-            'complex128',
-            'float32',
-            'float64',
-            'int16',
-            'int32',
-            'int64',
-            'int8',
-            'uint16',
-            'uint32',
-            'uint64',
-            'uint8',
-        }
-
-dtype_categories = {
-    'bool': lambda d: d == 'bool',
-    'signed integer': lambda d: d.startswith('int'),
-    'unsigned integer': lambda d: d.startswith('uint'),
-    'integral': lambda d: dtype_categories['signed integer'](d) or
-                          dtype_categories['unsigned integer'](d),
-    'real floating': lambda d: 'float' in d,
-    'complex floating': lambda d: d.startswith('complex'),
-    'numeric': lambda d: dtype_categories['integral'](d) or
-                         dtype_categories['real floating'](d) or
-                         dtype_categories['complex floating'](d),
-}
-
-def isdtype_(dtype_, kind):
-    # Check a dtype_ string against kind. Note that 'bool' technically has two
-    # meanings here but they are both the same.
-    if kind in dtype_categories:
-        res = dtype_categories[kind](dtype_)
-    else:
-        res = dtype_ == kind
-    assert type(res) is bool #  noqa: E721
-    return res
-
-@pytest.mark.parametrize("library", wrapped_libraries)
-def test_isdtype_spec_dtypes(library):
-    xp = import_(library, wrapper=True)
-
-    isdtype = xp.isdtype
-
-    for dtype_ in _spec_dtypes(library):
-        for dtype2_ in _spec_dtypes(library):
-            dtype = getattr(xp, dtype_)
-            dtype2 = getattr(xp, dtype2_)
-            res = isdtype_(dtype_, dtype2_)
-            assert isdtype(dtype, dtype2) is res, (dtype_, dtype2_)
-
-        for cat in dtype_categories:
-            res = isdtype_(dtype_, cat)
-            assert isdtype(dtype, cat) == res, (dtype_, cat)
-
-        # Basic tuple testing (the array-api testsuite will be more complete here)
-        for kind1_ in [*_spec_dtypes(library), *dtype_categories]:
-            for kind2_ in [*_spec_dtypes(library), *dtype_categories]:
-                kind1 = kind1_ if kind1_ in dtype_categories else getattr(xp, kind1_)
-                kind2 = kind2_ if kind2_ in dtype_categories else getattr(xp, kind2_)
-                kind = (kind1, kind2)
-
-                res = isdtype_(dtype_, kind1_) or isdtype_(dtype_, kind2_)
-                assert isdtype(dtype, kind) == res, (dtype_, (kind1_, kind2_))
-
-additional_dtypes = [
-    'float16',
-    'float128',
-    'complex256',
-    'bfloat16',
-]
-
-@pytest.mark.parametrize("library", wrapped_libraries)
-@pytest.mark.parametrize("dtype_", additional_dtypes)
-def test_isdtype_additional_dtypes(library, dtype_):
-    xp = import_(library, wrapper=True)
-
-    isdtype = xp.isdtype
-
-    if not hasattr(xp, dtype_):
-        return
-        # pytest.skip(f"{library} doesn't have dtype {dtype_}")
-
-    dtype = getattr(xp, dtype_)
-    for cat in dtype_categories:
-        res = isdtype_(dtype_, cat)
-        assert isdtype(dtype, cat) == res, (dtype_, cat)
diff --git a/tests/test_jax.py b/tests/test_jax.py
deleted file mode 100644
index 285958d4..00000000
--- a/tests/test_jax.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from numpy.testing import assert_equal
-import pytest
-
-from array_api_compat import device, to_device
-
-try:
-    import jax
-    import jax.numpy as jnp
-except ImportError:
-    pytestmark = pytest.skip(allow_module_level=True, reason="jax not found")
-
-HAS_JAX_0_4_31 = jax.__version__ >= "0.4.31"
-
-
-@pytest.mark.parametrize(
-    "func", 
-    [
-        lambda x: jnp.zeros(1, device=device(x)),
-        lambda x: jnp.zeros_like(jnp.ones(1, device=device(x))),
-        lambda x: jnp.zeros_like(jnp.empty(1, device=device(x))),
-        lambda x: jnp.full(1, fill_value=0, device=device(x)),
-        pytest.param(
-            lambda x: jnp.asarray([0], device=device(x)),
-            marks=pytest.mark.skipif(
-                not HAS_JAX_0_4_31, reason="asarray() has no device= parameter"
-            ),
-        ),
-        lambda x: to_device(jnp.zeros(1), device(x)),
-    ]
-)
-def test_device_jit(func):
-    # Test work around to https://github.com/jax-ml/jax/issues/26000
-    # Also test missing to_device() method in JAX < 0.4.31
-    # when inside jax.jit, even after importing jax.experimental.array_api
-
-    x = jnp.ones(1)
-    assert_equal(func(x), jnp.asarray([0]))
-    assert_equal(jax.jit(func)(x), jnp.asarray([0]))
diff --git a/tests/test_no_dependencies.py b/tests/test_no_dependencies.py
deleted file mode 100644
index a1fdf731..00000000
--- a/tests/test_no_dependencies.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""
-Test that array_api_compat has no "hard" dependencies.
-
-Libraries like NumPy should only be imported if a numpy array is passed to
-array_namespace or if array_api_compat.numpy is explicitly imported.
-
-We have to test this in a subprocess because these libraries have already been
-imported from the other tests.
-"""
-
-import sys
-import subprocess
-
-import pytest
-
-class Array:
-    # Dummy array namespace that doesn't depend on any array library
-    def __array_namespace__(self, api_version=None):
-        class Namespace:
-            pass
-        return Namespace()
-
-def _test_dependency(mod):
-    assert mod not in sys.modules
-
-    # Run various functions that shouldn't depend on mod and check that they
-    # don't import it.
-
-    import array_api_compat
-    assert mod not in sys.modules
-
-    a = Array()
-
-    # array-api-strict is an example of an array API library that isn't
-    # wrapped by array-api-compat.
-    if "strict" not in mod and mod != "sparse":
-        is_mod_array = getattr(array_api_compat, f"is_{mod.split('.')[0]}_array")
-        assert not is_mod_array(a)
-        assert mod not in sys.modules
-
-    is_array_api_obj = getattr(array_api_compat, "is_array_api_obj")
-    assert is_array_api_obj(a)
-    assert mod not in sys.modules
-
-    array_namespace = getattr(array_api_compat, "array_namespace")
-    array_namespace(Array())
-    assert mod not in sys.modules
-
-# TODO: Test that wrapper for library X doesn't depend on wrappers for library
-# Y (except most array libraries actually do themselves depend on numpy).
-
-@pytest.mark.parametrize("library", ["cupy", "numpy", "torch", "dask.array",
-                                     "jax.numpy", "sparse", "array_api_strict"])
-def test_numpy_dependency(library):
-    # This import is here because it imports numpy
-    from ._helpers import import_
-
-    # This unfortunately won't go through any of the pytest machinery. We
-    # reraise the exception as an AssertionError so that pytest will show it
-    # in a semi-reasonable way
-
-    # Import (in this process) to make sure 'library' is actually installed and
-    # so that cupy can be skipped.
-    import_(library)
-
-    try:
-        subprocess.run([sys.executable, '-c', f'''\
-from tests.test_no_dependencies import _test_dependency
-
-_test_dependency({library!r})'''], check=True, capture_output=True, encoding='utf-8')
-    except subprocess.CalledProcessError as e:
-        print(e.stdout, end='')
-        raise AssertionError(e.stderr)
diff --git a/tests/test_torch.py b/tests/test_torch.py
deleted file mode 100644
index 7adb4ab3..00000000
--- a/tests/test_torch.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""Test "unspecified" behavior which we cannot easily test in the Array API test suite.
-"""
-import itertools
-
-import pytest
-
-try:
-    import torch
-except ImportError:
-    pytestmark = pytest.skip(allow_module_level=True, reason="pytorch not found")
-
-from array_api_compat import torch as xp
-
-
-class TestResultType:
-    def test_empty(self):
-        with pytest.raises(ValueError):
-            xp.result_type()
-
-    def test_one_arg(self):
-        for x in [1, 1.0, 1j, '...', None]:
-            with pytest.raises((ValueError, AttributeError)):
-                xp.result_type(x)
-
-        for x in [xp.float32, xp.int64, torch.complex64]:
-            assert xp.result_type(x) == x
-
-        for x in [xp.asarray(True, dtype=xp.bool), xp.asarray(1, dtype=xp.complex64)]:
-            assert xp.result_type(x) == x.dtype
-
-    def test_two_args(self):
-        # Only include here things "unspecified" in the spec
-
-        # scalar, tensor or tensor,tensor
-        for x, y in [
-            (1., 1j),
-            (1j, xp.arange(3)),
-            (True, xp.asarray(3.)),
-            (xp.ones(3) == 1, 1j*xp.ones(3)),
-        ]:
-            assert xp.result_type(x, y) == torch.result_type(x, y)
-
-        # dtype, scalar
-        for x, y in [
-            (1j, xp.int64),
-            (True, xp.float64),
-        ]:
-            assert xp.result_type(x, y) == torch.result_type(x, xp.empty([], dtype=y))
-
-        # dtype, dtype
-        for x, y in [
-            (xp.bool, xp.complex64)
-        ]:
-            xt, yt = xp.empty([], dtype=x), xp.empty([], dtype=y)
-            assert xp.result_type(x, y) == torch.result_type(xt, yt)
-
-    def test_multi_arg(self):
-        torch.set_default_dtype(torch.float32)
-
-        args = [1., 5, 3, torch.asarray([3], dtype=torch.float16), 5, 6, 1.]
-        assert xp.result_type(*args) == torch.float16
-
-        args = [1, 2, 3j, xp.arange(3, dtype=xp.float32), 4, 5, 6]
-        assert xp.result_type(*args) == xp.complex64
-
-        args = [1, 2, 3j, xp.float64, 4, 5, 6]
-        assert xp.result_type(*args) == xp.complex128
-
-        args = [1, 2, 3j, xp.float64, 4, xp.asarray(3, dtype=xp.int16), 5, 6, False]
-        assert xp.result_type(*args) == xp.complex128
-
-        i64 = xp.ones(1, dtype=xp.int64)
-        f16 = xp.ones(1, dtype=xp.float16)
-        for i in itertools.permutations([i64, f16, 1.0, 1.0]):
-            assert xp.result_type(*i) == xp.float16, f"{i}"
-
-        with pytest.raises(ValueError):
-            xp.result_type(1, 2, 3, 4)
-
-
-    @pytest.mark.parametrize("default_dt", ['float32', 'float64'])
-    @pytest.mark.parametrize("dtype_a",
-        (xp.int32, xp.int64, xp.float32, xp.float64, xp.complex64, xp.complex128)
-    )
-    @pytest.mark.parametrize("dtype_b", 
-        (xp.int32, xp.int64, xp.float32, xp.float64, xp.complex64, xp.complex128)
-    )
-    def test_gh_273(self, default_dt, dtype_a, dtype_b):
-        # Regression test for https://github.com/data-apis/array-api-compat/issues/273
-
-        try:
-            prev_default = torch.get_default_dtype()
-            default_dtype = getattr(torch, default_dt)
-            torch.set_default_dtype(default_dtype)
-
-            a = xp.asarray([2, 1], dtype=dtype_a)
-            b = xp.asarray([1, -1], dtype=dtype_b)
-            dtype_1 = xp.result_type(a, b, 1.0)
-            dtype_2 = xp.result_type(b, a, 1.0)
-            assert dtype_1 == dtype_2
-        finally:
-            torch.set_default_dtype(prev_default)
-
-
-def test_meshgrid():
-    """Verify that array_api_compat.torch.meshgrid defaults to indexing='xy'."""
-
-    x, y = xp.asarray([1, 2]), xp.asarray([4])
-
-    X, Y = xp.meshgrid(x, y)
-
-    # output of torch.meshgrid(x, y, indexing='xy') -- indexing='ij' is different
-    X_xy, Y_xy = xp.asarray([[1, 2]]), xp.asarray([[4, 4]])
-
-    assert X.shape == X_xy.shape
-    assert xp.all(X == X_xy)
-
-    assert Y.shape == Y_xy.shape
-    assert xp.all(Y == Y_xy)
diff --git a/tests/test_vendoring.py b/tests/test_vendoring.py
deleted file mode 100644
index 8b561551..00000000
--- a/tests/test_vendoring.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import pytest
-
-
-def test_vendoring_numpy():
-    from vendor_test import uses_numpy
-
-    uses_numpy._test_numpy()
-
-
-def test_vendoring_cupy():
-    pytest.importorskip("cupy")
-
-    from vendor_test import uses_cupy
-
-    uses_cupy._test_cupy()
-
-
-def test_vendoring_torch():
-    pytest.importorskip("torch")
-    from vendor_test import uses_torch
-
-    uses_torch._test_torch()
-
-
-def test_vendoring_dask():
-    pytest.importorskip("dask")
-    from vendor_test import uses_dask
-    uses_dask._test_dask()
diff --git a/torch-skips.txt b/torch-skips.txt
deleted file mode 100644
index e69de29b..00000000
diff --git a/torch-xfails.txt b/torch-xfails.txt
deleted file mode 100644
index 989df0c8..00000000
--- a/torch-xfails.txt
+++ /dev/null
@@ -1,166 +0,0 @@
-# Note: see array_api_compat/torch/_aliases.py for links to corresponding
-# pytorch issues
-
-# We cannot wrap the array object
-
-# Indexing does not support negative step
-array_api_tests/test_array_object.py::test_getitem
-array_api_tests/test_array_object.py::test_setitem
-# Masking doesn't suport 0 dimensions in the mask
-array_api_tests/test_array_object.py::test_getitem_masking
-
-# Overflow error from large inputs
-array_api_tests/test_creation_functions.py::test_arange
-# pytorch linspace bug (should be fixed in torch 2.0)
-
-# We cannot wrap the tensor object
-array_api_tests/test_has_names.py::test_has_names[array_method-__array_namespace__]
-array_api_tests/test_has_names.py::test_has_names[array_method-to_device]
-
-# We cannot wrap the tensor object
-array_api_tests/test_operators_and_elementwise_functions.py::test_add[__add__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_and[__and__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_left_shift[__lshift__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_or[__or__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_right_shift[__rshift__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_right_shift[__irshift__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_bitwise_xor[__xor__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_divide[__truediv__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_equal[__eq__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_not_equal[__ne__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__floordiv__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__floordiv__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__ifloordiv__(x, s)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[__ifloordiv__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_floor_divide[floor_divide(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_greater[__gt__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_greater_equal[__ge__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_less[__lt__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_less_equal[__le__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_multiply[__mul__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_pow[__pow__(x1, x2)]
-# This test is skipped instead of xfailed because it causes core dumps on CI
-# array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__imod__(x1, x2)]
-array_api_tests/test_operators_and_elementwise_functions.py::test_subtract[__sub__(x1, x2)]
-
-# inverse trig functions are too inaccurate on CPU
-array_api_tests/test_operators_and_elementwise_functions.py::test_acos
-array_api_tests/test_operators_and_elementwise_functions.py::test_atan
-array_api_tests/test_operators_and_elementwise_functions.py::test_asin
-
-# Torch bug for remainder in some cases with large values
-array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[remainder(x1, x2)]
-
-# unique_all cannot be implemented because torch's unique does not support
-# returning indices
-array_api_tests/test_set_functions.py::test_unique_all
-# unique_inverse incorrectly counts nan values
-# (https://github.com/pytorch/pytorch/issues/94106)
-array_api_tests/test_set_functions.py::test_unique_inverse
-
-
-# We cannot add attributes to the tensor object
-array_api_tests/test_signatures.py::test_array_method_signature[__array_namespace__]
-array_api_tests/test_signatures.py::test_array_method_signature[to_device]
-
-
-# We do not attempt to work around special-case differences (most are on
-# tensor methods which we couldn't fix anyway).
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is -infinity) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is +infinity) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i < 0 and x2_i is +0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i < 0 and x2_i is -0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i > 0 and x2_i is +0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i > 0 and x2_i is -0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -0 and x2_i < 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -0 and x2_i > 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[__floordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[__mod__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> x2_i]
-array_api_tests/test_special_cases.py::test_binary[__mod__(isfinite(x1_i) and x1_i < 0 and x2_i is -infinity) -> x1_i]
-array_api_tests/test_special_cases.py::test_binary[__mod__(isfinite(x1_i) and x1_i > 0 and x2_i is +infinity) -> x1_i]
-array_api_tests/test_special_cases.py::test_binary[__mod__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> x2_i]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i is +0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i is +0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i is -0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[__mod__(x1_i is -0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_binary[floor_divide(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_binary[remainder(x1_i is +0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_binary[remainder(x1_i is -0 and x2_i > 0) -> +0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i < 0 and x2_i is +infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(isfinite(x1_i) and x1_i > 0 and x2_i is -infinity) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i < 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is +infinity and isfinite(x2_i) and x2_i > 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i < 0) -> +infinity]
-array_api_tests/test_special_cases.py::test_iop[__ifloordiv__(x1_i is -infinity and isfinite(x2_i) and x2_i > 0) -> -infinity]
-array_api_tests/test_special_cases.py::test_iop[__imod__(x1_i is +0 and x2_i < 0) -> -0]
-array_api_tests/test_special_cases.py::test_iop[__imod__(x1_i is -0 and x2_i > 0) -> +0]
-
-# Float correction is not supported by pytorch
-# (https://github.com/data-apis/array-api-tests/issues/168)
-array_api_tests/test_statistical_functions.py::test_std
-array_api_tests/test_statistical_functions.py::test_var
-
-
-# These functions do not yet support complex numbers
-array_api_tests/test_operators_and_elementwise_functions.py::test_round
-array_api_tests/test_set_functions.py::test_unique_counts
-array_api_tests/test_set_functions.py::test_unique_values
-
-# finfo/iinfo.dtype is a string instead of a dtype
-array_api_tests/test_data_type_functions.py::test_finfo_dtype
-array_api_tests/test_data_type_functions.py::test_iinfo_dtype
-
-# 2023.12 support
-# https://github.com/pytorch/pytorch/issues/151311: torch.repeat_interleave rejects short integers
-array_api_tests/test_manipulation_functions.py::test_repeat
-# Argument 'device' missing from signature
-array_api_tests/test_signatures.py::test_func_signature[from_dlpack]
-# Argument 'max_version' missing from signature
-array_api_tests/test_signatures.py::test_array_method_signature[__dlpack__]
-
-# 2024.12 support
-array_api_tests/test_signatures.py::test_func_signature[bitwise_and]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_left_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_or]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_right_shift]
-array_api_tests/test_signatures.py::test_func_signature[bitwise_xor]
-array_api_tests/test_signatures.py::test_array_method_signature[__and__]
-array_api_tests/test_signatures.py::test_array_method_signature[__lshift__]
-array_api_tests/test_signatures.py::test_array_method_signature[__or__]
-array_api_tests/test_signatures.py::test_array_method_signature[__rshift__]
-array_api_tests/test_signatures.py::test_array_method_signature[__xor__]
-
-# 2024.12 support: binary functions reject python scalar arguments
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[atan2]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[copysign]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[divide]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[hypot]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[logaddexp]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[maximum]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[minimum]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[nextafter]
-
-# https://github.com/pytorch/pytorch/issues/149815
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[equal]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[not_equal]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[less]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[less_equal]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[greater]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_real[greater_equal]
-
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_bool[logical_and]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_bool[logical_or]
-array_api_tests/test_operators_and_elementwise_functions.py::test_binary_with_scalars_bool[logical_xor]
diff --git a/vendor_test/__init__.py b/vendor_test/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/vendor_test/uses_cupy.py b/vendor_test/uses_cupy.py
deleted file mode 100644
index e3bbdebe..00000000
--- a/vendor_test/uses_cupy.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Basic test that vendoring works
-
-from .vendored._compat import (
-    cupy as cp_compat,
-    is_cupy_array,
-    is_cupy_namespace,
-)
-
-import cupy as cp
-
-def _test_cupy():
-    a = cp_compat.asarray([1., 2., 3.])
-    b = cp_compat.arange(3, dtype=cp_compat.float32)
-
-    # cp.pow does not exist. Update this to use something else if it is added
-    res = cp_compat.pow(a, b)
-    assert res.dtype == cp_compat.float64 == cp.float64
-    assert isinstance(a, cp.ndarray)
-    assert isinstance(b, cp.ndarray)
-    assert isinstance(res, cp.ndarray)
-
-    cp.testing.assert_allclose(res, [1., 2., 9.])
-
-    assert is_cupy_array(res)
-    assert is_cupy_namespace(cp) and is_cupy_namespace(cp_compat)
diff --git a/vendor_test/uses_dask.py b/vendor_test/uses_dask.py
deleted file mode 100644
index 44fa8f2f..00000000
--- a/vendor_test/uses_dask.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Basic test that vendoring works
-
-from .vendored._compat.dask import array as dask_compat
-from .vendored._compat import is_dask_array, is_dask_namespace
-
-import dask.array as da
-import numpy as np
-
-def _test_dask():
-    a = dask_compat.asarray([1., 2., 3.])
-    b = dask_compat.arange(3, dtype=dask_compat.float32)
-
-    # np.pow does not exist. Update this to use something else if it is added
-    res = dask_compat.pow(a, b)
-    assert res.dtype == dask_compat.float64 == np.float64
-    assert isinstance(a, da.Array)
-    assert isinstance(b, da.Array)
-    assert isinstance(res, da.Array)
-
-    np.testing.assert_allclose(res, [1., 2., 9.])
-
-    assert is_dask_array(res)
-    assert is_dask_namespace(da) and is_dask_namespace(dask_compat)
diff --git a/vendor_test/uses_numpy.py b/vendor_test/uses_numpy.py
deleted file mode 100644
index d7a68248..00000000
--- a/vendor_test/uses_numpy.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Basic test that vendoring works
-
-from .vendored._compat import (
-    is_numpy_array,
-    is_numpy_namespace,
-    numpy as np_compat,
-)
-
-
-import numpy as np
-
-def _test_numpy():
-    a = np_compat.asarray([1., 2., 3.])
-    b = np_compat.arange(3, dtype=np_compat.float32)
-
-    # np.pow does not exist. Update this to use something else if it is added
-    res = np_compat.pow(a, b)
-    assert res.dtype == np_compat.float64 == np.float64
-    assert isinstance(a, np.ndarray)
-    assert isinstance(b, np.ndarray)
-    assert isinstance(res, np.ndarray)
-
-    np.testing.assert_allclose(res, [1., 2., 9.])
-
-    assert is_numpy_array(res)
-    assert is_numpy_namespace(np) and is_numpy_namespace(np_compat)
diff --git a/vendor_test/uses_torch.py b/vendor_test/uses_torch.py
deleted file mode 100644
index 747ecd51..00000000
--- a/vendor_test/uses_torch.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Basic test that vendoring works
-
-from .vendored._compat import (
-    is_torch_array,
-    is_torch_namespace,
-    torch as torch_compat,
-)
-
-import torch
-
-def _test_torch():
-    a = torch_compat.asarray([1., 2., 3.])
-    b = torch_compat.arange(3, dtype=torch_compat.float64)
-    assert a.dtype == torch_compat.float32 == torch.float32
-    assert b.dtype == torch_compat.float64 == torch.float64
-
-    # torch.expand_dims does not exist. Update this to use something else if it is added
-    res = torch_compat.expand_dims(a, axis=0)
-    assert res.dtype == torch_compat.float32 == torch.float32
-    assert res.shape == (1, 3)
-    assert isinstance(res.shape, torch.Size)
-    assert isinstance(a, torch.Tensor)
-    assert isinstance(b, torch.Tensor)
-    assert isinstance(res, torch.Tensor)
-
-    torch.testing.assert_close(res, torch.as_tensor([[1., 2., 3.]]))
-
-    assert is_torch_array(res)
-    assert is_torch_namespace(torch) and is_torch_namespace(torch_compat)
-
diff --git a/vendor_test/vendored/__init__.py b/vendor_test/vendored/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/vendor_test/vendored/_compat b/vendor_test/vendored/_compat
deleted file mode 120000
index ba484f91..00000000
--- a/vendor_test/vendored/_compat
+++ /dev/null
@@ -1 +0,0 @@
-../../array_api_compat/
\ No newline at end of file
