diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 00000000..a9fe88f6 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,17 @@ +root = true + +[*] +insert_final_newline = true +end_of_line = lf + +[*.{ts,tsx,json}] +indent_style = space +indent_size = 2 + +[*.md] +indent_style = space +indent_size = 2 + +[*.{html,css}] +indent_style = space +indent_size = 4 \ No newline at end of file diff --git a/.github/workflows/build-test-publish.yml b/.github/workflows/build-test-publish.yml new file mode 100644 index 00000000..cbdaff42 --- /dev/null +++ b/.github/workflows/build-test-publish.yml @@ -0,0 +1,59 @@ +name: Build, Test, Publish + +on: [push] + +jobs: + test-publish: + name: Build, test, and publish package + runs-on: ubuntu-latest + permissions: + contents: read + timeout-minutes: 10 + steps: + - uses: actions/checkout@v6 + - run: corepack enable + - uses: actions/setup-node@v6 + with: + node-version: 24 + cache: yarn + + - run: yarn install --immutable + + - run: yarn test:coverage + - run: yarn build + - run: yarn test:build + + - name: Test in browser (If UMD tests are failing, check torch-bridge.js) + run: | + yarn serve & + npx wait-on tcp:8080 + npx mocha-headless-chrome -a no-sandbox -t 60000 -f http://127.0.0.1:8080/test/index.html + npx mocha-headless-chrome -a no-sandbox -t 60000 -f http://127.0.0.1:8080/test/umd.html + + - name: Build docs and prepare directory + run: | + yarn docs + mkdir -p docs/media + [ -d "build" ] && cp -r build docs/build + [ -d "build" ] && cp -r build docs/media/build + [ -d "examples" ] && cp -r examples docs/examples + [ -d "examples" ] && cp -r examples docs/media/examples + + - uses: actions/upload-pages-artifact@v4 + with: + path: './docs' + + - run: yarn pkg-pr-new publish + + deploy-docs: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + permissions: + pages: write + id-token: write + needs: test-publish + steps: + - uses: actions/deploy-pages@v5 + id: deployment diff --git a/.github/workflows/pyodide-test.yml b/.github/workflows/pyodide-test.yml new file mode 100644 index 00000000..99cf865f --- /dev/null +++ b/.github/workflows/pyodide-test.yml @@ -0,0 +1,36 @@ +name: Pyodide Test + +on: [push] + +jobs: + pyodide-test: + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - uses: actions/checkout@v6 + - run: corepack enable + - uses: actions/setup-node@v6 + with: + node-version: 24 + cache: yarn + + - run: yarn install --immutable + + - run: yarn build + + - name: Install Pyodide example dependencies + run: cd examples/pyodide && yarn install --immutable + + - uses: actions/setup-python@v6 + with: + python-version: '3.14' + cache: 'pip' + cache-dependency-path: scripts/requirements.txt + + - run: pip install -r scripts/requirements.txt + + - name: Generate PyTorch output + run: cd examples/pyodide && yarn gen-cache + + - name: Run Pyodide Tests + run: cd examples/pyodide && yarn test-pyodide diff --git a/.github/workflows/verify-generated-tests.yml b/.github/workflows/verify-generated-tests.yml new file mode 100644 index 00000000..459bb779 --- /dev/null +++ b/.github/workflows/verify-generated-tests.yml @@ -0,0 +1,37 @@ +name: Verify Generated Tests + +on: [push] + +jobs: + verify-sync: + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - uses: actions/checkout@v6 + - run: corepack enable + - uses: actions/setup-node@v6 + with: + node-version: 24 + cache: yarn + + - run: yarn install --immutable + + - uses: actions/setup-python@v6 + with: + python-version: '3.14' + cache: 'pip' + cache-dependency-path: scripts/requirements.txt + + - run: pip install -r scripts/requirements.txt + + - name: Sanity Check Generated File Length + run: | + COMMITTED_LINES=$(wc -l < test/testcases.gen.js) + yarn update-tests + GENERATED_LINES=$(wc -l < test/testcases.gen.js) + if [ "$COMMITTED_LINES" -ne "$GENERATED_LINES" ]; then + echo "❌ Line count mismatch! Committed: $COMMITTED_LINES, Generated: $GENERATED_LINES." + echo "Please run 'yarn update-tests' locally and commit the updated test file." + exit 1 + fi + echo "✅ Line counts match ($COMMITTED_LINES lines). Sanity check passed." diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..140c4045 --- /dev/null +++ b/.gitignore @@ -0,0 +1,61 @@ +# See https://help.github.com/ignore-files/ for more about ignoring files. +/docs + +.direnv +.envrc +/shell.nix +.idea +/test.js +/test.py + +# dependencies +/node_modules + +# testing +/coverage + +# production +/build +src/styles/*.css +/.env + +# deployment +terraform* + +# sourceror bundle +/public/externalLibs/sourceror + +# misc +.DS_Store +.env.local +.env.development.local +.env.test.local +.env.production.local +.idea/ + +npm-debug.log* +.eslintcache +yarn-error.log + +# emacs backup files + +*~ + +# yarn files + +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/sdks +!.yarn/versions + +# Swap the comments on the following lines if you wish to use zero-installs +# In that case, don't forget to run `yarn config set enableGlobalCache false`! +# Documentation here: https://yarnpkg.com/features/caching#zero-installs + +#!.yarn/cache +.pnp.* + +# We use .node-version +/.tool-versions diff --git a/.prettierrc b/.prettierrc new file mode 100644 index 00000000..00f0dc88 --- /dev/null +++ b/.prettierrc @@ -0,0 +1,8 @@ +{ + "semi": true, + "singleQuote": true, + "printWidth": 100, + "arrowParens": "avoid", + "trailingComma": "none", + "endOfLine": "auto" +} diff --git a/.yarn/sdks/eslint/bin/eslint.js b/.yarn/sdks/eslint/bin/eslint.js new file mode 100755 index 00000000..e6604ff5 --- /dev/null +++ b/.yarn/sdks/eslint/bin/eslint.js @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint/bin/eslint.js + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint/bin/eslint.js your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint/bin/eslint.js`)); diff --git a/.yarn/sdks/eslint/lib/api.js b/.yarn/sdks/eslint/lib/api.js new file mode 100644 index 00000000..8addf97f --- /dev/null +++ b/.yarn/sdks/eslint/lib/api.js @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint`)); diff --git a/.yarn/sdks/eslint/lib/config-api.js b/.yarn/sdks/eslint/lib/config-api.js new file mode 100644 index 00000000..e84435de --- /dev/null +++ b/.yarn/sdks/eslint/lib/config-api.js @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint/config + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint/config your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint/config`)); diff --git a/.yarn/sdks/eslint/lib/types/config-api.d.ts b/.yarn/sdks/eslint/lib/types/config-api.d.ts new file mode 100644 index 00000000..174070b0 --- /dev/null +++ b/.yarn/sdks/eslint/lib/types/config-api.d.ts @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint/config + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint/config your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint/config`)); diff --git a/.yarn/sdks/eslint/lib/types/index.d.ts b/.yarn/sdks/eslint/lib/types/index.d.ts new file mode 100644 index 00000000..19293d02 --- /dev/null +++ b/.yarn/sdks/eslint/lib/types/index.d.ts @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint`)); diff --git a/.yarn/sdks/eslint/lib/types/rules.d.ts b/.yarn/sdks/eslint/lib/types/rules.d.ts new file mode 100644 index 00000000..8d79c4cc --- /dev/null +++ b/.yarn/sdks/eslint/lib/types/rules.d.ts @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint/rules + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint/rules your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint/rules`)); diff --git a/.yarn/sdks/eslint/lib/types/universal.d.ts b/.yarn/sdks/eslint/lib/types/universal.d.ts new file mode 100644 index 00000000..662b3f4f --- /dev/null +++ b/.yarn/sdks/eslint/lib/types/universal.d.ts @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint/universal + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint/universal your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint/universal`)); diff --git a/.yarn/sdks/eslint/lib/types/use-at-your-own-risk.d.ts b/.yarn/sdks/eslint/lib/types/use-at-your-own-risk.d.ts new file mode 100644 index 00000000..2e2ccca2 --- /dev/null +++ b/.yarn/sdks/eslint/lib/types/use-at-your-own-risk.d.ts @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint/use-at-your-own-risk + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint/use-at-your-own-risk your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint/use-at-your-own-risk`)); diff --git a/.yarn/sdks/eslint/lib/universal.js b/.yarn/sdks/eslint/lib/universal.js new file mode 100644 index 00000000..85a8ccbc --- /dev/null +++ b/.yarn/sdks/eslint/lib/universal.js @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint/universal + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint/universal your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint/universal`)); diff --git a/.yarn/sdks/eslint/lib/unsupported-api.js b/.yarn/sdks/eslint/lib/unsupported-api.js new file mode 100644 index 00000000..c2b464ce --- /dev/null +++ b/.yarn/sdks/eslint/lib/unsupported-api.js @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require eslint/use-at-your-own-risk + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real eslint/use-at-your-own-risk your application uses +module.exports = wrapWithUserWrapper(absRequire(`eslint/use-at-your-own-risk`)); diff --git a/.yarn/sdks/eslint/package.json b/.yarn/sdks/eslint/package.json new file mode 100644 index 00000000..840be3b6 --- /dev/null +++ b/.yarn/sdks/eslint/package.json @@ -0,0 +1,31 @@ +{ + "name": "eslint", + "version": "9.37.0-sdk", + "main": "./lib/api.js", + "type": "commonjs", + "bin": { + "eslint": "./bin/eslint.js" + }, + "exports": { + ".": { + "types": "./lib/types/index.d.ts", + "default": "./lib/api.js" + }, + "./config": { + "types": "./lib/types/config-api.d.ts", + "default": "./lib/config-api.js" + }, + "./package.json": "./package.json", + "./use-at-your-own-risk": { + "types": "./lib/types/use-at-your-own-risk.d.ts", + "default": "./lib/unsupported-api.js" + }, + "./rules": { + "types": "./lib/types/rules.d.ts" + }, + "./universal": { + "types": "./lib/types/universal.d.ts", + "default": "./lib/universal.js" + } + } +} diff --git a/.yarn/sdks/integrations.yml b/.yarn/sdks/integrations.yml new file mode 100644 index 00000000..aa9d0d0a --- /dev/null +++ b/.yarn/sdks/integrations.yml @@ -0,0 +1,5 @@ +# This file is automatically generated by @yarnpkg/sdks. +# Manual changes might be lost! + +integrations: + - vscode diff --git a/.yarn/sdks/prettier/bin/prettier.cjs b/.yarn/sdks/prettier/bin/prettier.cjs new file mode 100755 index 00000000..9a4098f7 --- /dev/null +++ b/.yarn/sdks/prettier/bin/prettier.cjs @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require prettier/bin/prettier.cjs + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real prettier/bin/prettier.cjs your application uses +module.exports = wrapWithUserWrapper(absRequire(`prettier/bin/prettier.cjs`)); diff --git a/.yarn/sdks/prettier/index.cjs b/.yarn/sdks/prettier/index.cjs new file mode 100644 index 00000000..57cb2ab1 --- /dev/null +++ b/.yarn/sdks/prettier/index.cjs @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require prettier + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real prettier your application uses +module.exports = wrapWithUserWrapper(absRequire(`prettier`)); diff --git a/.yarn/sdks/prettier/package.json b/.yarn/sdks/prettier/package.json new file mode 100644 index 00000000..1488e98c --- /dev/null +++ b/.yarn/sdks/prettier/package.json @@ -0,0 +1,7 @@ +{ + "name": "prettier", + "version": "3.6.2-sdk", + "main": "./index.cjs", + "type": "commonjs", + "bin": "./bin/prettier.cjs" +} diff --git a/.yarn/sdks/typescript/bin/tsc b/.yarn/sdks/typescript/bin/tsc new file mode 100755 index 00000000..867a7bdf --- /dev/null +++ b/.yarn/sdks/typescript/bin/tsc @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require typescript/bin/tsc + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real typescript/bin/tsc your application uses +module.exports = wrapWithUserWrapper(absRequire(`typescript/bin/tsc`)); diff --git a/.yarn/sdks/typescript/bin/tsserver b/.yarn/sdks/typescript/bin/tsserver new file mode 100755 index 00000000..3fc5aa31 --- /dev/null +++ b/.yarn/sdks/typescript/bin/tsserver @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require typescript/bin/tsserver + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real typescript/bin/tsserver your application uses +module.exports = wrapWithUserWrapper(absRequire(`typescript/bin/tsserver`)); diff --git a/.yarn/sdks/typescript/lib/tsc.js b/.yarn/sdks/typescript/lib/tsc.js new file mode 100644 index 00000000..da411bdb --- /dev/null +++ b/.yarn/sdks/typescript/lib/tsc.js @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require typescript/lib/tsc.js + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real typescript/lib/tsc.js your application uses +module.exports = wrapWithUserWrapper(absRequire(`typescript/lib/tsc.js`)); diff --git a/.yarn/sdks/typescript/lib/tsserver.js b/.yarn/sdks/typescript/lib/tsserver.js new file mode 100644 index 00000000..6249c467 --- /dev/null +++ b/.yarn/sdks/typescript/lib/tsserver.js @@ -0,0 +1,248 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require typescript/lib/tsserver.js + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +const moduleWrapper = exports => { + return wrapWithUserWrapper(moduleWrapperFn(exports)); +}; + +const moduleWrapperFn = tsserver => { + if (!process.versions.pnp) { + return tsserver; + } + + const {isAbsolute} = require(`path`); + const pnpApi = require(`pnpapi`); + + const isVirtual = str => str.match(/\/(\$\$virtual|__virtual__)\//); + const isPortal = str => str.startsWith("portal:/"); + const normalize = str => str.replace(/\\/g, `/`).replace(/^\/?/, `/`); + + const dependencyTreeRoots = new Set(pnpApi.getDependencyTreeRoots().map(locator => { + return `${locator.name}@${locator.reference}`; + })); + + // VSCode sends the zip paths to TS using the "zip://" prefix, that TS + // doesn't understand. This layer makes sure to remove the protocol + // before forwarding it to TS, and to add it back on all returned paths. + + function toEditorPath(str) { + // We add the `zip:` prefix to both `.zip/` paths and virtual paths + if (isAbsolute(str) && !str.match(/^\^?(zip:|\/zip\/)/) && (str.match(/\.zip\//) || isVirtual(str))) { + // We also take the opportunity to turn virtual paths into physical ones; + // this makes it much easier to work with workspaces that list peer + // dependencies, since otherwise Ctrl+Click would bring us to the virtual + // file instances instead of the real ones. + // + // We only do this to modules owned by the the dependency tree roots. + // This avoids breaking the resolution when jumping inside a vendor + // with peer dep (otherwise jumping into react-dom would show resolution + // errors on react). + // + const resolved = isVirtual(str) ? pnpApi.resolveVirtual(str) : str; + if (resolved) { + const locator = pnpApi.findPackageLocator(resolved); + if (locator && (dependencyTreeRoots.has(`${locator.name}@${locator.reference}`) || isPortal(locator.reference))) { + str = resolved; + } + } + + str = normalize(str); + + if (str.match(/\.zip\//)) { + switch (hostInfo) { + // Absolute VSCode `Uri.fsPath`s need to start with a slash. + // VSCode only adds it automatically for supported schemes, + // so we have to do it manually for the `zip` scheme. + // The path needs to start with a caret otherwise VSCode doesn't handle the protocol + // + // Ref: https://github.com/microsoft/vscode/issues/105014#issuecomment-686760910 + // + // 2021-10-08: VSCode changed the format in 1.61. + // Before | ^zip:/c:/foo/bar.zip/package.json + // After | ^/zip//c:/foo/bar.zip/package.json + // + // 2022-04-06: VSCode changed the format in 1.66. + // Before | ^/zip//c:/foo/bar.zip/package.json + // After | ^/zip/c:/foo/bar.zip/package.json + // + // 2022-05-06: VSCode changed the format in 1.68 + // Before | ^/zip/c:/foo/bar.zip/package.json + // After | ^/zip//c:/foo/bar.zip/package.json + // + case `vscode <1.61`: { + str = `^zip:${str}`; + } break; + + case `vscode <1.66`: { + str = `^/zip/${str}`; + } break; + + case `vscode <1.68`: { + str = `^/zip${str}`; + } break; + + case `vscode`: { + str = `^/zip/${str}`; + } break; + + // To make "go to definition" work, + // We have to resolve the actual file system path from virtual path + // and convert scheme to supported by [vim-rzip](https://github.com/lbrayner/vim-rzip) + case `coc-nvim`: { + str = normalize(resolved).replace(/\.zip\//, `.zip::`); + str = resolve(`zipfile:${str}`); + } break; + + // Support neovim native LSP and [typescript-language-server](https://github.com/theia-ide/typescript-language-server) + // We have to resolve the actual file system path from virtual path, + // everything else is up to neovim + case `neovim`: { + str = normalize(resolved).replace(/\.zip\//, `.zip::`); + str = `zipfile://${str}`; + } break; + + default: { + str = `zip:${str}`; + } break; + } + } else { + str = str.replace(/^\/?/, process.platform === `win32` ? `` : `/`); + } + } + + return str; + } + + function fromEditorPath(str) { + switch (hostInfo) { + case `coc-nvim`: { + str = str.replace(/\.zip::/, `.zip/`); + // The path for coc-nvim is in format of //zipfile://.yarn/... + // So in order to convert it back, we use .* to match all the thing + // before `zipfile:` + return process.platform === `win32` + ? str.replace(/^.*zipfile:\//, ``) + : str.replace(/^.*zipfile:/, ``); + } break; + + case `neovim`: { + str = str.replace(/\.zip::/, `.zip/`); + // The path for neovim is in format of zipfile:////.yarn/... + return str.replace(/^zipfile:\/\//, ``); + } break; + + case `vscode`: + default: { + return str.replace(/^\^?(zip:|\/zip(\/ts-nul-authority)?)\/+/, process.platform === `win32` ? `` : `/`) + } break; + } + } + + // Force enable 'allowLocalPluginLoads' + // TypeScript tries to resolve plugins using a path relative to itself + // which doesn't work when using the global cache + // https://github.com/microsoft/TypeScript/blob/1b57a0395e0bff191581c9606aab92832001de62/src/server/project.ts#L2238 + // VSCode doesn't want to enable 'allowLocalPluginLoads' due to security concerns but + // TypeScript already does local loads and if this code is running the user trusts the workspace + // https://github.com/microsoft/vscode/issues/45856 + const ConfiguredProject = tsserver.server.ConfiguredProject; + const {enablePluginsWithOptions: originalEnablePluginsWithOptions} = ConfiguredProject.prototype; + ConfiguredProject.prototype.enablePluginsWithOptions = function() { + this.projectService.allowLocalPluginLoads = true; + return originalEnablePluginsWithOptions.apply(this, arguments); + }; + + // And here is the point where we hijack the VSCode <-> TS communications + // by adding ourselves in the middle. We locate everything that looks + // like an absolute path of ours and normalize it. + + const Session = tsserver.server.Session; + const {onMessage: originalOnMessage, send: originalSend} = Session.prototype; + let hostInfo = `unknown`; + + Object.assign(Session.prototype, { + onMessage(/** @type {string | object} */ message) { + const isStringMessage = typeof message === 'string'; + const parsedMessage = isStringMessage ? JSON.parse(message) : message; + + if ( + parsedMessage != null && + typeof parsedMessage === `object` && + parsedMessage.arguments && + typeof parsedMessage.arguments.hostInfo === `string` + ) { + hostInfo = parsedMessage.arguments.hostInfo; + if (hostInfo === `vscode` && process.env.VSCODE_IPC_HOOK) { + const [, major, minor] = (process.env.VSCODE_IPC_HOOK.match( + // The RegExp from https://semver.org/ but without the caret at the start + /(0|[1-9]\d*)\.(0|[1-9]\d*)\.(0|[1-9]\d*)(?:-((?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\.(?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\+([0-9a-zA-Z-]+(?:\.[0-9a-zA-Z-]+)*))?$/ + ) ?? []).map(Number) + + if (major === 1) { + if (minor < 61) { + hostInfo += ` <1.61`; + } else if (minor < 66) { + hostInfo += ` <1.66`; + } else if (minor < 68) { + hostInfo += ` <1.68`; + } + } + } + } + + const processedMessageJSON = JSON.stringify(parsedMessage, (key, value) => { + return typeof value === 'string' ? fromEditorPath(value) : value; + }); + + return originalOnMessage.call( + this, + isStringMessage ? processedMessageJSON : JSON.parse(processedMessageJSON) + ); + }, + + send(/** @type {any} */ msg) { + return originalSend.call(this, JSON.parse(JSON.stringify(msg, (key, value) => { + return typeof value === `string` ? toEditorPath(value) : value; + }))); + } + }); + + return tsserver; +}; + +const [major, minor] = absRequire(`typescript/package.json`).version.split(`.`, 2).map(value => parseInt(value, 10)); +// In TypeScript@>=5.5 the tsserver uses the public TypeScript API so that needs to be patched as well. +// Ref https://github.com/microsoft/TypeScript/pull/55326 +if (major > 5 || (major === 5 && minor >= 5)) { + moduleWrapper(absRequire(`typescript`)); +} + +// Defer to the real typescript/lib/tsserver.js your application uses +module.exports = moduleWrapper(absRequire(`typescript/lib/tsserver.js`)); diff --git a/.yarn/sdks/typescript/lib/tsserverlibrary.js b/.yarn/sdks/typescript/lib/tsserverlibrary.js new file mode 100644 index 00000000..0e50e0a2 --- /dev/null +++ b/.yarn/sdks/typescript/lib/tsserverlibrary.js @@ -0,0 +1,248 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require typescript/lib/tsserverlibrary.js + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +const moduleWrapper = exports => { + return wrapWithUserWrapper(moduleWrapperFn(exports)); +}; + +const moduleWrapperFn = tsserver => { + if (!process.versions.pnp) { + return tsserver; + } + + const {isAbsolute} = require(`path`); + const pnpApi = require(`pnpapi`); + + const isVirtual = str => str.match(/\/(\$\$virtual|__virtual__)\//); + const isPortal = str => str.startsWith("portal:/"); + const normalize = str => str.replace(/\\/g, `/`).replace(/^\/?/, `/`); + + const dependencyTreeRoots = new Set(pnpApi.getDependencyTreeRoots().map(locator => { + return `${locator.name}@${locator.reference}`; + })); + + // VSCode sends the zip paths to TS using the "zip://" prefix, that TS + // doesn't understand. This layer makes sure to remove the protocol + // before forwarding it to TS, and to add it back on all returned paths. + + function toEditorPath(str) { + // We add the `zip:` prefix to both `.zip/` paths and virtual paths + if (isAbsolute(str) && !str.match(/^\^?(zip:|\/zip\/)/) && (str.match(/\.zip\//) || isVirtual(str))) { + // We also take the opportunity to turn virtual paths into physical ones; + // this makes it much easier to work with workspaces that list peer + // dependencies, since otherwise Ctrl+Click would bring us to the virtual + // file instances instead of the real ones. + // + // We only do this to modules owned by the the dependency tree roots. + // This avoids breaking the resolution when jumping inside a vendor + // with peer dep (otherwise jumping into react-dom would show resolution + // errors on react). + // + const resolved = isVirtual(str) ? pnpApi.resolveVirtual(str) : str; + if (resolved) { + const locator = pnpApi.findPackageLocator(resolved); + if (locator && (dependencyTreeRoots.has(`${locator.name}@${locator.reference}`) || isPortal(locator.reference))) { + str = resolved; + } + } + + str = normalize(str); + + if (str.match(/\.zip\//)) { + switch (hostInfo) { + // Absolute VSCode `Uri.fsPath`s need to start with a slash. + // VSCode only adds it automatically for supported schemes, + // so we have to do it manually for the `zip` scheme. + // The path needs to start with a caret otherwise VSCode doesn't handle the protocol + // + // Ref: https://github.com/microsoft/vscode/issues/105014#issuecomment-686760910 + // + // 2021-10-08: VSCode changed the format in 1.61. + // Before | ^zip:/c:/foo/bar.zip/package.json + // After | ^/zip//c:/foo/bar.zip/package.json + // + // 2022-04-06: VSCode changed the format in 1.66. + // Before | ^/zip//c:/foo/bar.zip/package.json + // After | ^/zip/c:/foo/bar.zip/package.json + // + // 2022-05-06: VSCode changed the format in 1.68 + // Before | ^/zip/c:/foo/bar.zip/package.json + // After | ^/zip//c:/foo/bar.zip/package.json + // + case `vscode <1.61`: { + str = `^zip:${str}`; + } break; + + case `vscode <1.66`: { + str = `^/zip/${str}`; + } break; + + case `vscode <1.68`: { + str = `^/zip${str}`; + } break; + + case `vscode`: { + str = `^/zip/${str}`; + } break; + + // To make "go to definition" work, + // We have to resolve the actual file system path from virtual path + // and convert scheme to supported by [vim-rzip](https://github.com/lbrayner/vim-rzip) + case `coc-nvim`: { + str = normalize(resolved).replace(/\.zip\//, `.zip::`); + str = resolve(`zipfile:${str}`); + } break; + + // Support neovim native LSP and [typescript-language-server](https://github.com/theia-ide/typescript-language-server) + // We have to resolve the actual file system path from virtual path, + // everything else is up to neovim + case `neovim`: { + str = normalize(resolved).replace(/\.zip\//, `.zip::`); + str = `zipfile://${str}`; + } break; + + default: { + str = `zip:${str}`; + } break; + } + } else { + str = str.replace(/^\/?/, process.platform === `win32` ? `` : `/`); + } + } + + return str; + } + + function fromEditorPath(str) { + switch (hostInfo) { + case `coc-nvim`: { + str = str.replace(/\.zip::/, `.zip/`); + // The path for coc-nvim is in format of //zipfile://.yarn/... + // So in order to convert it back, we use .* to match all the thing + // before `zipfile:` + return process.platform === `win32` + ? str.replace(/^.*zipfile:\//, ``) + : str.replace(/^.*zipfile:/, ``); + } break; + + case `neovim`: { + str = str.replace(/\.zip::/, `.zip/`); + // The path for neovim is in format of zipfile:////.yarn/... + return str.replace(/^zipfile:\/\//, ``); + } break; + + case `vscode`: + default: { + return str.replace(/^\^?(zip:|\/zip(\/ts-nul-authority)?)\/+/, process.platform === `win32` ? `` : `/`) + } break; + } + } + + // Force enable 'allowLocalPluginLoads' + // TypeScript tries to resolve plugins using a path relative to itself + // which doesn't work when using the global cache + // https://github.com/microsoft/TypeScript/blob/1b57a0395e0bff191581c9606aab92832001de62/src/server/project.ts#L2238 + // VSCode doesn't want to enable 'allowLocalPluginLoads' due to security concerns but + // TypeScript already does local loads and if this code is running the user trusts the workspace + // https://github.com/microsoft/vscode/issues/45856 + const ConfiguredProject = tsserver.server.ConfiguredProject; + const {enablePluginsWithOptions: originalEnablePluginsWithOptions} = ConfiguredProject.prototype; + ConfiguredProject.prototype.enablePluginsWithOptions = function() { + this.projectService.allowLocalPluginLoads = true; + return originalEnablePluginsWithOptions.apply(this, arguments); + }; + + // And here is the point where we hijack the VSCode <-> TS communications + // by adding ourselves in the middle. We locate everything that looks + // like an absolute path of ours and normalize it. + + const Session = tsserver.server.Session; + const {onMessage: originalOnMessage, send: originalSend} = Session.prototype; + let hostInfo = `unknown`; + + Object.assign(Session.prototype, { + onMessage(/** @type {string | object} */ message) { + const isStringMessage = typeof message === 'string'; + const parsedMessage = isStringMessage ? JSON.parse(message) : message; + + if ( + parsedMessage != null && + typeof parsedMessage === `object` && + parsedMessage.arguments && + typeof parsedMessage.arguments.hostInfo === `string` + ) { + hostInfo = parsedMessage.arguments.hostInfo; + if (hostInfo === `vscode` && process.env.VSCODE_IPC_HOOK) { + const [, major, minor] = (process.env.VSCODE_IPC_HOOK.match( + // The RegExp from https://semver.org/ but without the caret at the start + /(0|[1-9]\d*)\.(0|[1-9]\d*)\.(0|[1-9]\d*)(?:-((?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\.(?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\+([0-9a-zA-Z-]+(?:\.[0-9a-zA-Z-]+)*))?$/ + ) ?? []).map(Number) + + if (major === 1) { + if (minor < 61) { + hostInfo += ` <1.61`; + } else if (minor < 66) { + hostInfo += ` <1.66`; + } else if (minor < 68) { + hostInfo += ` <1.68`; + } + } + } + } + + const processedMessageJSON = JSON.stringify(parsedMessage, (key, value) => { + return typeof value === 'string' ? fromEditorPath(value) : value; + }); + + return originalOnMessage.call( + this, + isStringMessage ? processedMessageJSON : JSON.parse(processedMessageJSON) + ); + }, + + send(/** @type {any} */ msg) { + return originalSend.call(this, JSON.parse(JSON.stringify(msg, (key, value) => { + return typeof value === `string` ? toEditorPath(value) : value; + }))); + } + }); + + return tsserver; +}; + +const [major, minor] = absRequire(`typescript/package.json`).version.split(`.`, 2).map(value => parseInt(value, 10)); +// In TypeScript@>=5.5 the tsserver uses the public TypeScript API so that needs to be patched as well. +// Ref https://github.com/microsoft/TypeScript/pull/55326 +if (major > 5 || (major === 5 && minor >= 5)) { + moduleWrapper(absRequire(`typescript`)); +} + +// Defer to the real typescript/lib/tsserverlibrary.js your application uses +module.exports = moduleWrapper(absRequire(`typescript/lib/tsserverlibrary.js`)); diff --git a/.yarn/sdks/typescript/lib/typescript.js b/.yarn/sdks/typescript/lib/typescript.js new file mode 100644 index 00000000..7b6cc220 --- /dev/null +++ b/.yarn/sdks/typescript/lib/typescript.js @@ -0,0 +1,32 @@ +#!/usr/bin/env node + +const {existsSync} = require(`fs`); +const {createRequire, register} = require(`module`); +const {resolve} = require(`path`); +const {pathToFileURL} = require(`url`); + +const relPnpApiPath = "../../../../.pnp.cjs"; + +const absPnpApiPath = resolve(__dirname, relPnpApiPath); +const absUserWrapperPath = resolve(__dirname, `./sdk.user.cjs`); +const absRequire = createRequire(absPnpApiPath); + +const absPnpLoaderPath = resolve(absPnpApiPath, `../.pnp.loader.mjs`); +const isPnpLoaderEnabled = existsSync(absPnpLoaderPath); + +if (existsSync(absPnpApiPath)) { + if (!process.versions.pnp) { + // Setup the environment to be able to require typescript + require(absPnpApiPath).setup(); + if (isPnpLoaderEnabled && register) { + register(pathToFileURL(absPnpLoaderPath)); + } + } +} + +const wrapWithUserWrapper = existsSync(absUserWrapperPath) + ? exports => absRequire(absUserWrapperPath)(exports) + : exports => exports; + +// Defer to the real typescript your application uses +module.exports = wrapWithUserWrapper(absRequire(`typescript`)); diff --git a/.yarn/sdks/typescript/package.json b/.yarn/sdks/typescript/package.json new file mode 100644 index 00000000..19a0b6f1 --- /dev/null +++ b/.yarn/sdks/typescript/package.json @@ -0,0 +1,10 @@ +{ + "name": "typescript", + "version": "5.9.3-sdk", + "main": "./lib/typescript.js", + "type": "commonjs", + "bin": { + "tsc": "./bin/tsc", + "tsserver": "./bin/tsserver" + } +} diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..cf153fe9 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,40 @@ +## Codebase Structure + +- [`src`](src) + - [`index.ts`](src/index.ts) is the entry point of the library. + - [`tensor.ts`](src/tensor.ts) is the main tensor class. + - [`function`](function) contains all functions that tensors can perform. + - [`nn`](nn) contains all neural network modules (for everything under `torch.nn`). + - [`optim`](optim) contains all optimizers (for everything under `torch.optim`). + - [`creation`](creation) contains all tensor creation functions (all functions that create a tensor not from scratch, including `zeros`, `randn`). +- [`examples`](examples) contains example usages of the library, including on node, on the browser, and using pyodide on the browser. +- [`test`](test) contains the test cases of the library, including on node and on the browser. See [Testing](#testing). + +### Development Scripts + +You can use `yarn watch` to automatically test after each edit. + +### Adding a new Function + +To add a new function, add it to [`src/functions/ops.ts`](src/functions/ops.ts). + +- To allow for `torch.(, )`, add it as well to [`src/functions/functional.ts`](src/functions/functional.ts). +- To allow for `.()`, add it as well to [`src/tensor.ts`](src/tensor.ts) as a `tensor` method. + +## Testing + +Tests are run using `mocha`. + +- Node: To test on node, run `yarn test`. +- Browser: To test on browser, run `yarn serve` (after `yarn build` if necessary) and navigate to http://localhost:8080/test/. + +To create a new test: + +1. Create a new `.test.js`/`.test.ts` file in [`test`](test) or write your new test in one of the existing files. +2. If you created a new file and would like to test it on the browser, add it to [`test/index.html`](test/index.html). + +## Documentation + +To see docs, run `yarn docs` to build, and run `yarn serve docs` to serve docs on http://localhost:8080/. + +To ensure familiarity with PyTorch, docs should be derived from the PyTorch docs, as mentioned in [`NOTICE`](NOTICE). diff --git a/NOTICE b/NOTICE new file mode 100644 index 00000000..5949bbc6 --- /dev/null +++ b/NOTICE @@ -0,0 +1,40 @@ +This project's licensing is Apache-2.0. It includes components +governed by other licenses, which are detailed below. + +================================================================================ +1. PyTorch Documentation (BSD-3-Clause license) + +https://github.com/pytorch/docs + +Portions of this project's documentation are derived from the PyTorch documentation. +The original PyTorch documentation license is included below: + +BSD 3-Clause License + +Copyright (c) 2018, Facebook Inc +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 08cb1b83..3d39c830 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,51 @@ # torch machine-learning libraries for Source Academy + +## Getting Started + +Install yarn: + +``` bash +corepack enable +# or +npm install -g yarn +``` + +Install dependencies: + +```bash +yarn install +``` + +## Demo Usage + +First, build it: + +```bash +yarn build +``` + +See [examples/](examples/) for examples. + +### Node + +See `examples/basic_backpropagation.js`. + +```bash +node examples/basic_backpropagation.js +``` + +### Browser + +You can run `http-server` and load `examples/browser/index.html` to see how it works. + +```bash +yarn serve +# and navigate to http://localhost:8080/examples/browser/index.html to run torch in js +# or http://localhost:8080/examples/pyodide/index.html to run in python +# or http://localhost:8080/test/ to run the tests +``` + +## Contributing + +For detailed information on the codebase and tests, see [CONTRIBUTING.md](CONTRIBUTING.md). diff --git a/eslint.config.mjs b/eslint.config.mjs new file mode 100644 index 00000000..784224be --- /dev/null +++ b/eslint.config.mjs @@ -0,0 +1,19 @@ +// @ts-check + +import eslint from '@eslint/js'; +import { defineConfig } from 'eslint/config'; +import tseslint from 'typescript-eslint'; + +export default defineConfig(eslint.configs.recommended, tseslint.configs.recommended, { + rules: { + '@typescript-eslint/no-unused-vars': [ + 'warn', + { + argsIgnorePattern: '^_', + varsIgnorePattern: '(^_)|(^[A-Z])', + caughtErrorsIgnorePattern: '^_', + ignoreRestSiblings: true + } + ] + } +}); diff --git a/examples/basic_backpropagation.js b/examples/basic_backpropagation.js new file mode 100644 index 00000000..8274ac2d --- /dev/null +++ b/examples/basic_backpropagation.js @@ -0,0 +1,9 @@ +import { Tensor } from '../build/node/torch.node.es.js'; + +const x = new Tensor([2.0], { requires_grad: true }); +const y = x.pow(new Tensor([2.0])); + +console.log(y.item()); +y.backward(); + +console.log(x.grad?.item()); diff --git a/examples/browser/index.html b/examples/browser/index.html new file mode 100644 index 00000000..fad64e69 --- /dev/null +++ b/examples/browser/index.html @@ -0,0 +1,32 @@ + + + + + + + torch.js browser backprop example + + + + + 

+
+    
+
+
+
diff --git a/examples/index.html b/examples/index.html
new file mode 100644
index 00000000..70308ffb
--- /dev/null
+++ b/examples/index.html
@@ -0,0 +1,18 @@
+
+
+
+
+    
+    
+    Examples
+
+
+
+    
Examples

+    
+
+
+
diff --git a/examples/pyodide/.gitignore b/examples/pyodide/.gitignore
new file mode 100644
index 00000000..2aa8c99a
--- /dev/null
+++ b/examples/pyodide/.gitignore
@@ -0,0 +1,144 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+lerna-debug.log*
+
+# Diagnostic reports (https://nodejs.org/api/report.html)
+report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
+
+# Runtime data
+pids
+*.pid
+*.seed
+*.pid.lock
+
+# Directory for instrumented libs generated by jscoverage/JSCover
+lib-cov
+
+# Coverage directory used by tools like istanbul
+coverage
+*.lcov
+
+# nyc test coverage
+.nyc_output
+
+# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
+.grunt
+
+# Bower dependency directory (https://bower.io/)
+bower_components
+
+# node-waf configuration
+.lock-wscript
+
+# Compiled binary addons (https://nodejs.org/api/addons.html)
+build/Release
+
+# Dependency directories
+node_modules/
+jspm_packages/
+
+# Snowpack dependency directory (https://snowpack.dev/)
+web_modules/
+
+# TypeScript cache
+*.tsbuildinfo
+
+# Optional npm cache directory
+.npm
+
+# Optional eslint cache
+.eslintcache
+
+# Optional stylelint cache
+.stylelintcache
+
+# Optional REPL history
+.node_repl_history
+
+# Output of 'npm pack'
+*.tgz
+
+# Yarn Integrity file
+.yarn-integrity
+
+# dotenv environment variable files
+.env
+.env.*
+!.env.example
+
+# parcel-bundler cache (https://parceljs.org/)
+.cache
+.parcel-cache
+
+# Next.js build output
+.next
+out
+
+# Nuxt.js build / generate output
+.nuxt
+dist
+.output
+
+# Gatsby files
+.cache/
+# Comment in the public line in if your project uses Gatsby and not Next.js
+# https://nextjs.org/blog/next-9-1#public-directory-support
+# public
+
+# vuepress build output
+.vuepress/dist
+
+# vuepress v2.x temp and cache directory
+.temp
+.cache
+
+# Sveltekit cache directory
+.svelte-kit/
+
+# vitepress build output
+**/.vitepress/dist
+
+# vitepress cache directory
+**/.vitepress/cache
+
+# Docusaurus cache and generated files
+.docusaurus
+
+# Serverless directories
+.serverless/
+
+# FuseBox cache
+.fusebox/
+
+# DynamoDB Local files
+.dynamodb/
+
+# Firebase cache directory
+.firebase/
+
+# TernJS port file
+.tern-port
+
+# Stores VSCode versions used for testing VSCode extensions
+.vscode-test
+
+# pnpm
+.pnpm-store
+
+# yarn v3
+.pnp.*
+.yarn/*
+!.yarn/patches
+!.yarn/plugins
+!.yarn/releases
+!.yarn/sdks
+!.yarn/versions
+
+# Vite files
+vite.config.js.timestamp-*
+vite.config.ts.timestamp-*
+.vite/
diff --git a/examples/pyodide/bridge.py b/examples/pyodide/bridge.py
new file mode 100644
index 00000000..872c2598
--- /dev/null
+++ b/examples/pyodide/bridge.py
@@ -0,0 +1,805 @@
+# bridge.py
+# Provides a PyTorch-compatible Python API over js_torch (the TypeScript torch library).
+#
+# Before loading this file, set the following globals in Pyodide:
+#   js_torch    - the torch module (window.torch from the UMD build)
+
+from pyodide.ffi import JsProxy, to_js
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+def _wrap_result(result):
+    """
+    Wrap a JS return value:
+      - JsProxy (JS object/Tensor) -> Python Tensor
+      - Python primitive (int, float, bool) -> return as-is
+    JS primitives are automatically converted to Python by Pyodide,
+    so they will NOT be JsProxy instances.
+    """
+    if isinstance(result, JsProxy):
+        return Tensor(result)
+    return result
+
+
+def _transform(obj):
+    """Convert Python objects to JS-compatible types before passing to JS."""
+    if isinstance(obj, Tensor):
+        return obj._js
+    if isinstance(obj, (list, tuple)):
+        return to_js([_transform(item) for item in obj])
+    return obj
+
+
+def _transform_args(args):
+    return [_transform(a) for a in args]
+
+
+# ---------------------------------------------------------------------------
+# Tensor
+# ---------------------------------------------------------------------------
+
+class Tensor:
+    """Python wrapper around a JS Tensor, mirroring the PyTorch Tensor API."""
+
+    # ------------------------------------------------------------------
+    # Construction
+    # ------------------------------------------------------------------
+
+    def __new__(cls, data, requires_grad=False):
+        # Return None for missing tensors so e.g. `tensor.grad` returns None
+        # when there is no gradient — matching PyTorch behaviour.
+        # Pyodide may represent JS null as a special JsNull type (not JsProxy, not None).
+        if data is None or type(data).__name__ in ('JsNull', 'JsUndefined'):
+            return None
+        return super().__new__(cls)
+
+    def __init__(self, data, requires_grad=False):
+        if isinstance(data, JsProxy):
+            self._js = data
+        else:
+            js_data = to_js(data) if isinstance(data, (list, tuple)) else data
+            self._js = js_torch.tensor(js_data, requires_grad)
+
+    # ------------------------------------------------------------------
+    # Representation
+    # ------------------------------------------------------------------
+
+    def __repr__(self):
+        extra = ", requires_grad=True" if self.requires_grad else ""
+        return f"tensor({self.tolist()}{extra})"
+
+    # ------------------------------------------------------------------
+    # Data access
+    # ------------------------------------------------------------------
+
+    def tolist(self):
+        """Return tensor data as a (nested) Python list, or a Python scalar for 0-d tensors."""
+        result = self._js.toArray()
+        if isinstance(result, JsProxy):
+            return result.to_py()
+        return result  # scalar
+
+    def item(self):
+        return self._js.item()
+
+    # ------------------------------------------------------------------
+    # Properties
+    # ------------------------------------------------------------------
+
+    @property
+    def shape(self):
+        return tuple(self._js.shape.to_py())
+
+    @property
+    def data(self):
+        """Detached view of the tensor data (no gradient)."""
+        return self.detach()
+
+    @property
+    def requires_grad(self):
+        return bool(self._js.requires_grad)
+
+    @requires_grad.setter
+    def requires_grad(self, value):
+        self._js.requires_grad = value
+
+    @property
+    def grad(self):
+        raw = self._js.grad
+        if raw is None or type(raw).__name__ in ('JsNull', 'JsUndefined'):
+            return None
+        return Tensor(raw)
+
+    @grad.setter
+    def grad(self, value):
+        self._js.grad = value._js if isinstance(value, Tensor) else None
+
+    @property
+    def T(self):
+        if len(self.shape) < 2:
+            return self
+        return Tensor(self._js.transpose(0, 1))
+
+    # ------------------------------------------------------------------
+    # Grad utilities
+    # ------------------------------------------------------------------
+
+    def backward(self, gradient=None):
+        if gradient is None:
+            self._js.backward()
+        else:
+            self._js.backward(gradient._js)
+
+    def detach(self):
+        return Tensor(self._js.detach())
+
+    def zero_(self):
+        self._js.zero_()
+        return self
+
+    def retain_grad(self):
+        self._js.retain_grad()
+
+    # ------------------------------------------------------------------
+    # Shape utilities
+    # ------------------------------------------------------------------
+
+    def size(self, dim=None):
+        s = self.shape
+        return s if dim is None else s[dim]
+
+    def dim(self):
+        return len(self.shape)
+
+    def numel(self):
+        n = 1
+        for s in self.shape:
+            n *= s
+        return n
+
+    def reshape(self, *args):
+        shape = list(args[0]) if len(args) == 1 and isinstance(args[0], (list, tuple)) else list(args)
+        return Tensor(self._js.reshape(to_js(shape)))
+
+    def view(self, *args):
+        return self.reshape(*args)
+
+    def squeeze(self, dim=None):
+        if dim is None:
+            new_shape = [s for s in self.shape if s != 1]
+            return Tensor(self._js.reshape(to_js(new_shape or [1])))
+        return Tensor(self._js.squeeze(dim))
+
+    def unsqueeze(self, dim):
+        return Tensor(self._js.unsqueeze(dim))
+
+    def expand(self, *args):
+        shape = list(args[0]) if len(args) == 1 and isinstance(args[0], (list, tuple)) else list(args)
+        return Tensor(self._js.expand(to_js(shape)))
+
+    def transpose(self, dim0, dim1):
+        return Tensor(self._js.transpose(dim0, dim1))
+
+    def flatten(self, start_dim=0, end_dim=-1):
+        return Tensor(self._js.flatten(start_dim, end_dim))
+
+    # ------------------------------------------------------------------
+    # Reductions — default (no dim) sums all elements, matching PyTorch
+    # ------------------------------------------------------------------
+
+    def sum(self, dim=None, keepdim=False):
+        return Tensor(self._js.sum() if dim is None else self._js.sum(dim, keepdim))
+
+    def mean(self, dim=None, keepdim=False):
+        return Tensor(self._js.mean() if dim is None else self._js.mean(dim, keepdim))
+
+    def max(self, dim=None, keepdim=False):
+        return Tensor(self._js.max() if dim is None else self._js.max(dim, keepdim))
+
+    def min(self, dim=None, keepdim=False):
+        return Tensor(self._js.min() if dim is None else self._js.min(dim, keepdim))
+
+    # ------------------------------------------------------------------
+    # Arithmetic — explicit methods
+    # ------------------------------------------------------------------
+
+    def _to_js(self, other):
+        return other._js if isinstance(other, Tensor) else other
+
+    def add(self, other):  return Tensor(self._js.add(self._to_js(other)))
+    def sub(self, other):  return Tensor(self._js.sub(self._to_js(other)))
+    def mul(self, other):  return Tensor(self._js.mul(self._to_js(other)))
+    def div(self, other):  return Tensor(self._js.div(self._to_js(other)))
+    def pow(self, other):  return Tensor(self._js.pow(self._to_js(other)))
+    def matmul(self, other): return Tensor(self._js.matmul(self._to_js(other)))
+
+    # ------------------------------------------------------------------
+    # Arithmetic operators
+    # ------------------------------------------------------------------
+
+    def __add__(self, other):  return self.add(other)
+    def __radd__(self, other): return self.add(other)  # add is commutative
+    def __sub__(self, other):  return self.sub(other)
+    def __rsub__(self, other):
+        o = other if isinstance(other, Tensor) else Tensor(other)
+        return o.sub(self)
+    def __mul__(self, other):  return self.mul(other)
+    def __rmul__(self, other): return self.mul(other)  # mul is commutative
+    def __truediv__(self, other):  return self.div(other)
+    def __rtruediv__(self, other):
+        o = other if isinstance(other, Tensor) else Tensor(other)
+        return o.div(self)
+    def __pow__(self, other):  return self.pow(other)
+    def __rpow__(self, other):
+        o = other if isinstance(other, Tensor) else Tensor(other)
+        return o.pow(self)
+    def __matmul__(self, other): return self.matmul(other)
+    def __neg__(self):  return Tensor(self._js.neg())
+    def __abs__(self):  return Tensor(self._js.abs())
+
+    # ------------------------------------------------------------------
+    # Unary operations
+    # ------------------------------------------------------------------
+
+    def neg(self):        return Tensor(self._js.neg())
+    def abs(self):        return Tensor(self._js.abs())
+    def log(self):        return Tensor(self._js.log())
+    def exp(self):        return Tensor(self._js.exp())
+    def sqrt(self):       return Tensor(self._js.sqrt())
+    def square(self):     return Tensor(self._js.square())
+    def sin(self):        return Tensor(self._js.sin())
+    def cos(self):        return Tensor(self._js.cos())
+    def tan(self):        return Tensor(self._js.tan())
+    def sigmoid(self):    return Tensor(self._js.sigmoid())
+    def relu(self):       return Tensor(js_torch.nn.functional.relu(self._js))
+    def softmax(self, dim): return Tensor(self._js.softmax(dim))
+    def clamp(self, min, max): return Tensor(self._js.clamp(min, max))
+    def sign(self):       return Tensor(self._js.sign())
+    def reciprocal(self): return Tensor(self._js.reciprocal())
+    def nan_to_num(self): return Tensor(self._js.nan_to_num())
+
+    # ------------------------------------------------------------------
+    # Comparison
+    # ------------------------------------------------------------------
+
+    def lt(self, other):  return Tensor(self._js.lt(self._to_js(other)))
+    def gt(self, other):  return Tensor(self._js.gt(self._to_js(other)))
+    def le(self, other):  return Tensor(self._js.le(self._to_js(other)))
+    def ge(self, other):  return Tensor(self._js.ge(self._to_js(other)))
+    def eq(self, other):  return Tensor(self._js.eq(self._to_js(other)))
+    def ne(self, other):  return Tensor(self._js.ne(self._to_js(other)))
+
+    def allclose(self, other, rtol=1e-5, atol=1e-8, equal_nan=False):
+        return bool(js_torch.allclose(self._js, other._js, rtol, atol, equal_nan))
+
+    # ------------------------------------------------------------------
+    # Type conversions
+    # ------------------------------------------------------------------
+
+    def __float__(self):          return float(self.item())
+    def __int__(self):            return int(self.item())
+    def __bool__(self):           return bool(self.item())
+    def __format__(self, fmt):    return format(self.item(), fmt)
+
+    # ------------------------------------------------------------------
+    # Indexing
+    # ------------------------------------------------------------------
+
+    def __getitem__(self, key):
+        if isinstance(key, int):
+            return Tensor(self._js.index(key))
+        if isinstance(key, tuple):
+            result = self._js
+            for k in key:
+                if isinstance(k, int):
+                    result = result.index(k)
+                else:
+                    raise NotImplementedError(
+                        "Only integer indexing is supported in multi-dimensional indexing"
+                    )
+            return Tensor(result)
+        if isinstance(key, slice):
+            start, stop, step = key.indices(self.shape[0])
+            data = [Tensor(self._js.index(i)).tolist() for i in range(start, stop, step)]
+            return Tensor(data)
+        raise TypeError(f"Invalid index type: {type(key).__name__}")
+
+    # ------------------------------------------------------------------
+    # Iteration and length
+    # ------------------------------------------------------------------
+
+    def __len__(self):
+        return self.shape[0]
+
+    def __iter__(self):
+        data = self.tolist()
+        if not isinstance(data, list):
+            raise TypeError("iteration over a 0-d tensor")
+        for item in data:
+            yield Tensor(item)
+
+    # ------------------------------------------------------------------
+    # Catch-all: delegate unknown attribute accesses to the JS tensor.
+    # Returned JsProxy objects are wrapped in Tensor; primitives pass through.
+    # ------------------------------------------------------------------
+
+    def __getattr__(self, name):
+        if name.startswith('_'):
+            raise AttributeError(name)
+        def method(*args, **kwargs):
+            js_args = _transform_args(args)
+            return _wrap_result(self._js.__getattribute__(name)(*js_args))
+        return method
+
+
+# ---------------------------------------------------------------------------
+# Typed tensor subclasses
+# ---------------------------------------------------------------------------
+
+def _trunc_nested(data):
+    """Truncate all numbers in a nested list toward zero (for LongTensor)."""
+    if isinstance(data, (int, float)):
+        return int(data)  # Python int() truncates toward zero
+    return [_trunc_nested(item) for item in data]
+
+
+class FloatTensor(Tensor):
+    """
+    A Tensor that stores floating-point values.
+    Equivalent to a regular Tensor; provided for PyTorch API compatibility.
+    """
+    def __init__(self, data, requires_grad=False):
+        if isinstance(data, JsProxy):
+            super().__init__(data)
+        else:
+            super().__init__(data, requires_grad)
+
+
+class LongTensor(Tensor):
+    """
+    A Tensor whose values are truncated to integers (toward zero).
+    LongTensor([-1.7]) -> tensor([-1]),  LongTensor([1.9]) -> tensor([1]).
+    """
+    def __init__(self, data, requires_grad=False):
+        if isinstance(data, JsProxy):
+            super().__init__(data)
+        else:
+            truncated = _trunc_nested(data) if isinstance(data, (list, tuple)) else int(data)
+            super().__init__(truncated, requires_grad)
+
+
+# ---------------------------------------------------------------------------
+# no_grad context manager — actually disables grad in the JS engine
+# ---------------------------------------------------------------------------
+
+class _NoGrad:
+    def __enter__(self):
+        self._prev = js_torch.enable_no_grad()
+        return self
+
+    def __exit__(self, *args):
+        js_torch.disable_no_grad(self._prev)
+
+
+# ---------------------------------------------------------------------------
+# Parameter
+# ---------------------------------------------------------------------------
+
+class Parameter(Tensor):
+    """A Tensor that is automatically registered as a parameter."""
+    def __init__(self, data, requires_grad=True):
+        if isinstance(data, Tensor):
+            self._js = js_torch.nn.Parameter.new(data._js)
+        elif isinstance(data, JsProxy):
+            self._js = js_torch.nn.Parameter.new(data)
+        else:
+            self._js = js_torch.nn.Parameter.new(js_torch.tensor(data))
+        if not requires_grad:
+            self._js.requires_grad = False
+
+
+# ---------------------------------------------------------------------------
+# Module — pure-Python base class for user-defined models
+# ---------------------------------------------------------------------------
+
+class Module:
+    """
+    Pure-Python nn.Module. Subclass this to build models using bridge Tensors.
+    Assign `Parameter` or `_NNModule` instances as attributes and they are
+    automatically tracked by `parameters()`.
+    """
+
+    def __init__(self):
+        object.__setattr__(self, '_parameters', {})
+        object.__setattr__(self, '_modules', {})
+        object.__setattr__(self, 'training', True)
+
+    def __setattr__(self, name, value):
+        try:
+            params  = object.__getattribute__(self, '_parameters')
+            modules = object.__getattribute__(self, '_modules')
+        except AttributeError:
+            object.__setattr__(self, name, value)
+            return
+
+        if isinstance(value, Parameter):
+            params[name] = value
+        elif isinstance(value, (Module, _NNModule)):
+            modules[name] = value
+        object.__setattr__(self, name, value)
+
+    def __call__(self, *args, **kwargs):
+        return self.forward(*args, **kwargs)
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError
+
+    def parameters(self):
+        params = list(object.__getattribute__(self, '_parameters').values())
+        for mod in object.__getattribute__(self, '_modules').values():
+            params.extend(mod.parameters())
+        return params
+
+    def named_parameters(self, prefix=''):
+        result = []
+        for name, p in object.__getattribute__(self, '_parameters').items():
+            full = f"{prefix}.{name}" if prefix else name
+            result.append((full, p))
+        for mod_name, mod in object.__getattribute__(self, '_modules').items():
+            full_mod = f"{prefix}.{mod_name}" if prefix else mod_name
+            result.extend(mod.named_parameters(full_mod))
+        return result
+
+    def train(self, mode=True):
+        object.__setattr__(self, 'training', mode)
+        for mod in object.__getattribute__(self, '_modules').values():
+            mod.train(mode)
+        return self
+
+    def eval(self):
+        return self.train(False)
+
+    def zero_grad(self):
+        for p in self.parameters():
+            p.grad = None
+
+
+# ---------------------------------------------------------------------------
+# _NNModule — wraps a JS nn.Module instance
+# ---------------------------------------------------------------------------
+
+class _NNModule:
+    """Wraps a JS nn.Module returned by the nn factory functions."""
+
+    def __init__(self, js_module):
+        self._module = js_module
+
+    def __call__(self, *args):
+        js_args = [a._js if isinstance(a, Tensor) else a for a in args]
+        return Tensor(self._module.call(*js_args))
+
+    def forward(self, *args):
+        js_args = [a._js if isinstance(a, Tensor) else a for a in args]
+        return Tensor(self._module.forward(*js_args))
+
+    def parameters(self):
+        return [Tensor(p) for p in self._module.parameters().to_py()]
+
+    def named_parameters(self, prefix=''):
+        raw = self._module.named_parameters(prefix).to_py()
+        return [(pair[0], Tensor(pair[1])) for pair in raw]
+
+    def train(self, mode=True):
+        self._module.train(mode)
+        return self
+
+    def eval(self):
+        return self.train(False)
+
+    def zero_grad(self):
+        for p in self.parameters():
+            p.grad = None
+
+
+# ---------------------------------------------------------------------------
+# nn.functional
+# ---------------------------------------------------------------------------
+
+class _NNFunctional:
+    def relu(self, input):
+        return Tensor(js_torch.nn.functional.relu(input._js))
+
+    def sigmoid(self, input):
+        return Tensor(js_torch.nn.functional.sigmoid(input._js))
+
+    def leaky_relu(self, input, negative_slope=0.01):
+        return Tensor(js_torch.nn.functional.leaky_relu(input._js, negative_slope))
+
+    def max_pool2d(self, input, kernel_size, stride=None, padding=0):
+        if stride is None:
+            return Tensor(js_torch.nn.functional.max_pool2d(input._js, kernel_size))
+        return Tensor(js_torch.nn.functional.max_pool2d(input._js, kernel_size, stride, padding))
+
+    def nll_loss(self, input, target, reduction='mean'):
+        return Tensor(js_torch.nn.functional.nll_loss(input._js, target._js, reduction))
+
+    def __getattr__(self, name):
+        if name.startswith('_'):
+            raise AttributeError(name)
+        def fn(*args, **kwargs):
+            return _wrap_result(js_torch.nn.functional.__getattribute__(name)(*_transform_args(args)))
+        return fn
+
+
+# ---------------------------------------------------------------------------
+# nn.parameter namespace
+# ---------------------------------------------------------------------------
+
+class _NNParameterNamespace:
+    def __init__(self):
+        self.Parameter = Parameter
+
+
+# ---------------------------------------------------------------------------
+# nn namespace
+# ---------------------------------------------------------------------------
+
+class _NNNamespace:
+    def __init__(self):
+        self.functional = _NNFunctional()
+        self.parameter = _NNParameterNamespace()
+        self.Module = Module
+        self.Parameter = Parameter
+
+    def Linear(self, in_features, out_features, bias=True):
+        return _NNModule(js_torch.nn.Linear.new(in_features, out_features, bias))
+
+    def ReLU(self):
+        return _NNModule(js_torch.nn.ReLU.new())
+
+    def Sigmoid(self):
+        return _NNModule(js_torch.nn.Sigmoid.new())
+
+    def Sequential(self, *modules):
+        js_mods = [m._module for m in modules]
+        return _NNModule(js_torch.nn.Sequential.new(*js_mods))
+
+    def MSELoss(self, reduction='mean'):
+        return _NNModule(js_torch.nn.MSELoss.new(reduction))
+
+    def L1Loss(self, reduction='mean'):
+        return _NNModule(js_torch.nn.L1Loss.new(reduction))
+
+    def BCELoss(self, weight=None, reduction='mean'):
+        js_weight = weight._js if isinstance(weight, Tensor) else None
+        return _NNModule(js_torch.nn.BCELoss.new(js_weight, reduction))
+
+    def CrossEntropyLoss(self, reduction='mean'):
+        return _NNModule(js_torch.nn.CrossEntropyLoss.new(reduction))
+
+    def Conv1d(self, in_channels, out_channels, kernel_size,
+               stride=1, padding=0, dilation=1, groups=1, bias=True):
+        return _NNModule(js_torch.nn.Conv1d.new(
+            in_channels, out_channels, kernel_size,
+            stride, padding, dilation, groups, bias
+        ))
+
+    def Conv2d(self, in_channels, out_channels, kernel_size,
+               stride=1, padding=0, dilation=1, groups=1, bias=True):
+        return _NNModule(js_torch.nn.Conv2d.new(
+            in_channels, out_channels, kernel_size,
+            stride, padding, dilation, groups, bias
+        ))
+
+    def Conv3d(self, in_channels, out_channels, kernel_size,
+               stride=1, padding=0, dilation=1, groups=1, bias=True):
+        return _NNModule(js_torch.nn.Conv3d.new(
+            in_channels, out_channels, kernel_size,
+            stride, padding, dilation, groups, bias
+        ))
+
+    def LeakyReLU(self, negative_slope=0.01):
+        return _NNModule(js_torch.nn.LeakyReLU.new(negative_slope))
+
+    def MaxPool2d(self, kernel_size, stride=None, padding=0):
+        if stride is None:
+            return _NNModule(js_torch.nn.MaxPool2d.new(kernel_size))
+        return _NNModule(js_torch.nn.MaxPool2d.new(kernel_size, stride, padding))
+
+    def Dropout(self, p=0.5):
+        return _NNModule(js_torch.nn.Dropout.new(p))
+
+    def Softmax(self, dim):
+        return _NNModule(js_torch.nn.Softmax.new(dim))
+
+    def Flatten(self, start_dim=1, end_dim=-1):
+        return _NNModule(js_torch.nn.Flatten.new(start_dim, end_dim))
+
+    def NLLLoss(self, reduction='mean'):
+        return _NNModule(js_torch.nn.NLLLoss.new(reduction))
+
+
+# ---------------------------------------------------------------------------
+# optim wrappers
+# ---------------------------------------------------------------------------
+
+class _Optimizer:
+    def __init__(self, js_optim):
+        self._optim = js_optim
+
+    def step(self):
+        self._optim.step()
+
+    def zero_grad(self):
+        self._optim.zero_grad()
+
+
+class _OptimNamespace:
+    def SGD(self, params, lr=0.001, momentum=0.0, dampening=0.0,
+            weight_decay=0.0, nesterov=False, maximize=False):
+        js_params = to_js([p._js for p in params])
+        return _Optimizer(js_torch.optim.SGD.new(
+            js_params, lr, momentum, dampening, weight_decay, nesterov, maximize
+        ))
+
+    def Adam(self, params, lr=0.001, betas=(0.9, 0.999), eps=1e-8,
+             weight_decay=0.0, amsgrad=False, maximize=False):
+        js_params = to_js([p._js for p in params])
+        js_betas = to_js(list(betas))
+        return _Optimizer(js_torch.optim.Adam.new(
+            js_params, lr, js_betas, eps, weight_decay, amsgrad, maximize
+        ))
+
+    def Adagrad(self, params, lr=0.01, lr_decay=0, weight_decay=0, eps=1e-10):
+        js_params = to_js([p._js for p in params])
+        return _Optimizer(js_torch.optim.Adagrad.new(
+            js_params, lr, lr_decay, weight_decay, eps
+        ))
+
+
+# ---------------------------------------------------------------------------
+# torch namespace
+# ---------------------------------------------------------------------------
+
+class _Torch:
+    def __init__(self):
+        self.nn    = _NNNamespace()
+        self.optim = _OptimNamespace()
+        self.no_grad = _NoGrad
+        self.Tensor = Tensor
+        self.FloatTensor = FloatTensor
+        self.LongTensor  = LongTensor
+
+    @property
+    def tensor(self):
+        return Tensor
+
+    # --- creation functions ---
+
+    def _shape_from_args(self, args):
+        return list(args[0]) if len(args) == 1 and isinstance(args[0], (list, tuple)) else list(args)
+
+    def zeros(self, *args, **kwargs):
+        return Tensor(js_torch.zeros(to_js(self._shape_from_args(args))))
+
+    def ones(self, *args, **kwargs):
+        return Tensor(js_torch.ones(to_js(self._shape_from_args(args))))
+
+    def zeros_like(self, input):
+        return Tensor(js_torch.zeros_like(input._js))
+
+    def ones_like(self, input):
+        return Tensor(js_torch.ones_like(input._js))
+
+    def randn(self, *args, **kwargs):
+        return Tensor(js_torch.randn(to_js(self._shape_from_args(args))))
+
+    def rand(self, *args, **kwargs):
+        return Tensor(js_torch.rand(to_js(self._shape_from_args(args))))
+
+    def arange(self, start, end=None, step=1):
+        if end is None:
+            end = start
+            start = 0
+        return Tensor(js_torch.arange(start, end, step))
+
+    def linspace(self, start, end, steps):
+        return Tensor(js_torch.linspace(start, end, steps))
+
+    def empty(self, *args, **kwargs):
+        return Tensor(js_torch.empty(to_js(self._shape_from_args(args))))
+
+    def empty_like(self, input):
+        return Tensor(js_torch.empty_like(input._js))
+
+    def full(self, shape, fill_value):
+        return Tensor(js_torch.full(to_js(list(shape)), fill_value))
+
+    def full_like(self, input, fill_value):
+        return Tensor(js_torch.full_like(input._js, fill_value))
+
+    def rand_like(self, input):
+        return Tensor(js_torch.rand_like(input._js))
+
+    def randn_like(self, input):
+        return Tensor(js_torch.randn_like(input._js))
+
+    def randint_like(self, input, low, high):
+        return Tensor(js_torch.randint_like(input._js, low, high))
+
+    # --- utility functions ---
+
+    def is_tensor(self, obj):
+        return isinstance(obj, Tensor)
+
+    def is_nonzero(self, input):
+        if input.numel() != 1:
+            raise RuntimeError(
+                "Boolean value of Tensor with more than one element is ambiguous"
+            )
+        return bool(input.item() != 0)
+
+    def numel(self, input):
+        return input.numel()
+
+    # --- functional wrappers ---
+
+    def sum(self, input, dim=None, keepdim=False):
+        return input.sum(dim, keepdim)
+
+    def mean(self, input, dim=None, keepdim=False):
+        return input.mean(dim, keepdim)
+
+    def sigmoid(self, input):
+        return input.sigmoid()
+
+    def relu(self, input):
+        return input.relu()
+
+    def softmax(self, input, dim):
+        return input.softmax(dim)
+
+    def clamp(self, input, min, max):
+        return input.clamp(min, max)
+
+    def clip(self, input, min, max):
+        return self.clamp(input, min, max)
+
+    def flatten(self, input, start_dim=0, end_dim=-1):
+        return input.flatten(start_dim, end_dim)
+
+    def allclose(self, a, b, rtol=1e-5, atol=1e-8, equal_nan=False):
+        return a.allclose(b, rtol, atol, equal_nan)
+
+    def is_grad_enabled(self):
+        return bool(js_torch.is_grad_enabled())
+
+    def cat(self, tensors, dim=0):
+        if isinstance(tensors, Tensor):
+            tensors = [tensors]
+        return Tensor(js_torch.cat(to_js([t._js for t in tensors]), dim))
+
+    def concatenate(self, tensors, dim=0):
+        return self.cat(tensors, dim)
+
+    def concat(self, tensors, dim=0):
+        return self.cat(tensors, dim)
+
+    def stack(self, tensors, dim=0):
+        return Tensor(js_torch.stack(to_js([t._js for t in tensors]), dim))
+
+    def Size(self, shape):
+        return list(shape)
+
+    def __getattr__(self, name):
+        if name.startswith('_'):
+            raise AttributeError(name)
+        def fn(*args, **kwargs):
+            return _wrap_result(js_torch.__getattribute__(name)(*_transform_args(args)))
+        return fn
+
+
+torch = _Torch()
diff --git a/examples/pyodide/index.html b/examples/pyodide/index.html
new file mode 100644
index 00000000..f3ea57b1
--- /dev/null
+++ b/examples/pyodide/index.html
@@ -0,0 +1,119 @@
+
+
+
+
+    
+    
+    torch+pyodide browser backprop example
+    
+
+
+
+    
Torch + Pyodide

+    

+        
+    

+    

+        
+    

+
+    

+        
+        
+    

+    

+        
Output:

+        

+    

+
+    
+    
+
+
+
diff --git a/examples/pyodide/main.js b/examples/pyodide/main.js
new file mode 100644
index 00000000..ab17d477
--- /dev/null
+++ b/examples/pyodide/main.js
@@ -0,0 +1,120 @@
+import { loadPyodide } from 'pyodide';
+import * as torch from 'torch';
+import { readFileSync, writeFileSync, mkdirSync, readdirSync, existsSync } from 'fs';
+import { execSync } from 'child_process';
+import path from 'path';
+
+const PY_DIR = './py';
+const CACHE_DIR = './.cache';
+
+function getPyFiles() {
+  return readdirSync(PY_DIR).filter(f => f.endsWith('.py')).sort();
+}
+
+async function setupPyodide() {
+  const pyodide = await loadPyodide();
+  pyodide.globals.set('js_torch', torch);
+  pyodide.globals.set('_js_is_null', (x) => x == null);
+  pyodide.runPython(readFileSync('./bridge.py', 'utf8'));
+  return pyodide;
+}
+
+async function runWithPyodide(pyodide, code) {
+  // Reset grad state to enabled before each file.
+  // disable_no_grad(prev) restores a previous state; passing True enables grad.
+  pyodide.runPython('js_torch.disable_no_grad(True)');
+  pyodide.globals.set('_test_code', code);
+  pyodide.runPython(`
+import sys, builtins
+from io import StringIO
+_ns = {'torch': torch, '__builtins__': builtins}
+_saved = sys.stdout
+sys.stdout = StringIO()
+try:
+    exec(_test_code, _ns)
+    _output = sys.stdout.getvalue()
+finally:
+    sys.stdout = _saved
+`);
+  return pyodide.globals.get('_output');
+}
+
+async function cmdGen() {
+  if (!existsSync(CACHE_DIR)) mkdirSync(CACHE_DIR);
+  const files = getPyFiles();
+  let ok = 0, fail = 0;
+  for (const file of files) {
+    const name = path.basename(file, '.py');
+    try {
+      const output = execSync(
+        `python3 -c "import torch, builtins; exec(open('${PY_DIR}/${file}').read(), {'torch': torch, '__builtins__': builtins})"`,
+        { encoding: 'utf8' }
+      );
+      writeFileSync(path.join(CACHE_DIR, `${name}.out`), output);
+      console.log(`✓ ${name}`);
+      ok++;
+    } catch (e) {
+      console.error(`✗ ${name}: ${e.stderr?.trim() ?? e.message}`);
+      fail++;
+    }
+  }
+  console.log(`\n${ok} generated, ${fail} failed`);
+  if (fail > 0) process.exit(1);
+}
+
+async function cmdTest() {
+  const files = getPyFiles();
+  let passed = 0, failed = 0;
+
+  for (const file of files) {
+    const name = path.basename(file, '.py');
+    const cachePath = path.join(CACHE_DIR, `${name}.out`);
+    if (!existsSync(cachePath)) {
+      console.log(`✗ ${name}: no cache (run 'yarn gen-cache' first)`);
+      failed++;
+      continue;
+    }
+
+    const pyodide = await setupPyodide();
+    const code = readFileSync(path.join(PY_DIR, file), 'utf8');
+    let actual;
+    try {
+      actual = await runWithPyodide(pyodide, code);
+    } catch (e) {
+      console.log(`✗ ${name}: ${e.message}`);
+      failed++;
+      continue;
+    }
+
+    const expected = readFileSync(cachePath, 'utf8');
+    const aMarked = actual.split('\n').filter(l => l.startsWith('>'));
+    const eMarked = expected.split('\n').filter(l => l.startsWith('>'));
+    const mismatches = [];
+    const maxLen = Math.max(aMarked.length, eMarked.length);
+    for (let i = 0; i < maxLen; i++) {
+      if (aMarked[i] !== eMarked[i]) {
+        mismatches.push(`  [assertion ${i + 1}] expected: ${JSON.stringify(eMarked[i] ?? '(missing)')}`);
+        mismatches.push(`  [assertion ${i + 1}]   actual: ${JSON.stringify(aMarked[i] ?? '(missing)')}`);
+      }
+    }
+    if (mismatches.length === 0) {
+      console.log(`✓ ${name} (${aMarked.length} assertions)`);
+      passed++;
+    } else {
+      console.log(`✗ ${name}: ${mismatches.length / 2} assertion(s) failed`);
+      mismatches.forEach(l => console.log(l));
+      failed++;
+    }
+  }
+
+  console.log(`\n${passed} passed, ${failed} failed`);
+  if (failed > 0) process.exit(1);
+}
+
+const [mode] = process.argv.slice(2);
+if (mode === 'gen') cmdGen().catch(e => { console.error(e); process.exit(1); });
+else if (mode === 'test') cmdTest().catch(e => { console.error(e); process.exit(1); });
+else {
+  console.error('Usage: node main.js [gen|test]');
+  process.exit(1);
+}
diff --git a/examples/pyodide/package.json b/examples/pyodide/package.json
new file mode 100644
index 00000000..c7b84a73
--- /dev/null
+++ b/examples/pyodide/package.json
@@ -0,0 +1,14 @@
+{
+  "name": "example-pyodide",
+  "version": "0.1.0",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "gen-cache": "node main.js gen",
+    "test-pyodide": "node main.js test"
+  },
+  "dependencies": {
+    "pyodide": "^0.29.3",
+    "torch": "portal:../../"
+  }
+}
diff --git a/examples/pyodide/py/basic_ops.py b/examples/pyodide/py/basic_ops.py
new file mode 100644
index 00000000..7043b094
--- /dev/null
+++ b/examples/pyodide/py/basic_ops.py
@@ -0,0 +1,122 @@
+# basic_ops.py — Basic tensor operations, autograd, and backward pass.
+# Verifies: creation, arithmetic operators, reductions, backward, grad access.
+
+print("=== Basic tensor creation ===")
+x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
+y = torch.tensor([4.0, 5.0, 6.0], requires_grad=True)
+print("x:", x)
+print("y:", y)
+print("x.shape:", x.shape)
+print("x.requires_grad:", x.requires_grad)
+print("> x.requires_grad:", x.requires_grad)
+
+print("\n=== Arithmetic operators ===")
+print("x + y:", x + y)
+print("x - y:", x - y)
+print("x * y:", x * y)
+print("x / y:", x / y)
+print("x ** 2:", x ** 2)
+print("> x+y correct:", (x + y).allclose(torch.tensor([5.0, 7.0, 9.0])))
+print("> x*y correct:", (x * y).allclose(torch.tensor([4.0, 10.0, 18.0])))
+print("> x/y correct:", (x / y).allclose(torch.tensor([0.25, 0.4, 0.5])))
+
+print("\n=== Scalar arithmetic (reversed operators) ===")
+print("2 + x:", 2 + x)   # __radd__
+print("10 - x:", 10 - x) # __rsub__
+print("2 * x:", 2 * x)   # __rmul__
+print("1 / x:", 1 / x)   # __rtruediv__
+print("2 ** x:", 2 ** x) # __rpow__
+print("> 2+x correct:", (2 + x).allclose(torch.tensor([3.0, 4.0, 5.0])))
+print("> 2*x correct:", (2 * x).allclose(torch.tensor([2.0, 4.0, 6.0])))
+
+print("\n=== Unary operations ===")
+print("-x:", -x)
+print("x.neg():", x.neg())
+print("x.abs():", torch.tensor([-1.0, 2.0, -3.0]).abs())
+print("x.sqrt():", x.sqrt())
+print("x.exp():", x.exp())
+print("x.log():", x.log())
+print("x.sigmoid():", x.sigmoid())
+print("x.relu():", torch.tensor([-1.0, 0.0, 2.0]).relu())
+print("> neg correct:", (-x).allclose(torch.tensor([-1.0, -2.0, -3.0])))
+print("> sqrt correct:", x.sqrt().allclose(torch.tensor([1.0, 1.4142, 1.7321]), atol=1e-4))
+print("> relu correct:", torch.tensor([-1.0, 0.0, 2.0]).relu().allclose(torch.tensor([0.0, 0.0, 2.0])))
+
+print("\n=== Reductions ===")
+a = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+print("a:", a)
+print("a.sum():", a.sum())           # sum all
+print("a.sum(dim=0):", a.sum(dim=0)) # sum along rows
+print("a.sum(dim=1):", a.sum(dim=1)) # sum along cols
+print("a.mean():", a.mean())
+print("a.max():", a.max())
+print("a.min():", a.min())
+print("> a.sum() correct:", a.sum().allclose(torch.tensor(21.0)))
+print("> a.sum(dim=0) correct:", a.sum(dim=0).allclose(torch.tensor([5.0, 7.0, 9.0])))
+print("> a.mean() correct:", a.mean().allclose(torch.tensor(3.5)))
+
+print("\n=== Shape utilities ===")
+print("a.shape:", a.shape)
+print("a.size():", a.size())
+print("a.size(0):", a.size(0))
+print("a.dim():", a.dim())
+print("a.numel():", a.numel())
+print("a.reshape(3, 2):", a.reshape(3, 2))
+print("a.reshape([6]):", a.reshape([6]))
+print("a.T:", a.T)
+print("a.transpose(0, 1):", a.transpose(0, 1))
+print("> a.size(0):", a.size(0))
+print("> a.dim():", a.dim())
+print("> a.numel():", a.numel())
+
+print("\n=== Comparison operators ===")
+b = torch.tensor([1.0, 3.0, 3.0])
+c = torch.tensor([2.0, 2.0, 3.0])
+print("b:", b, " c:", c)
+print("b.lt(c):", b.lt(c))
+print("b.gt(c):", b.gt(c))
+print("b.eq(c):", b.eq(c))
+
+print("\n=== Backward pass (z = sum(x * y)) ===")
+x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
+y = torch.tensor([4.0, 5.0, 6.0], requires_grad=True)
+z = (x * y).sum()
+print("z:", z)
+z.backward()
+print("x.grad (expected [4,5,6]):", x.grad)
+print("y.grad (expected [1,2,3]):", y.grad)
+print("> x.grad correct:", x.grad is not None and x.grad.allclose(torch.tensor([4.0, 5.0, 6.0])))
+print("> y.grad correct:", y.grad is not None and y.grad.allclose(torch.tensor([1.0, 2.0, 3.0])))
+
+print("\n=== Backward with chain rule (z = sum((x+1)^2)) ===")
+x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
+z = ((x + 1) ** 2).sum()
+z.backward()
+# dz/dx = 2*(x+1) = [4, 6, 8]
+print("x.grad (expected [4,6,8]):", x.grad)
+print("> x.grad correct:", x.grad is not None and x.grad.allclose(torch.tensor([4.0, 6.0, 8.0])))
+
+print("\n=== allclose ===")
+a = torch.tensor([1.0, 2.0, 3.0])
+b = torch.tensor([1.0, 2.0, 3.0000001])
+print("allclose (expected True):", a.allclose(b))
+print("> allclose (expected True):", a.allclose(b))
+b2 = torch.tensor([1.0, 2.0, 4.0])
+print("allclose (expected False):", a.allclose(b2))
+print("> allclose (expected False):", a.allclose(b2))
+
+print("\n=== Iteration ===")
+t = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
+for row in t:
+    print("row:", row)
+print("> num rows:", sum(1 for _ in torch.tensor([[1.0, 2.0], [3.0, 4.0]])))
+
+print("\n=== item(), float(), int() ===")
+s = torch.tensor([42.0])
+print("s.item():", s.item())
+print("float(s):", float(s))
+print("int(s):", int(s))
+print("> float(s):", float(s))
+print("> int(s):", int(s))
+
+print("\nAll basic_ops checks passed.")
diff --git a/examples/pyodide/py/custom_module.py b/examples/pyodide/py/custom_module.py
new file mode 100644
index 00000000..ac6ed975
--- /dev/null
+++ b/examples/pyodide/py/custom_module.py
@@ -0,0 +1,88 @@
+# custom_module.py — User-defined Module subclass (pure Python), training loop.
+# Verifies: Module subclassing, parameter registration, forward(), parameters().
+
+class TwoLayerNet(torch.nn.Module):
+    def __init__(self, in_dim, hidden_dim, out_dim):
+        super().__init__()
+        self.fc1 = torch.nn.Linear(in_dim, hidden_dim)
+        self.fc2 = torch.nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = x.relu()
+        x = self.fc2(x)
+        return x
+
+
+print("=== Custom Module construction ===")
+model = TwoLayerNet(2, 4, 1)
+
+x = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
+out = model(x)
+print("input shape:", x.shape)
+print("output shape:", out.shape)
+print("output:", out)
+
+print("\n=== parameters() collects from all sub-modules ===")
+params = list(model.parameters())
+print("number of parameters:", len(params))
+# 2 layers × (weight + bias) = 4 parameters
+print("expected 4:", len(params) == 4)
+print("> n_params == 4:", len(params) == 4)
+
+print("\n=== named_parameters() ===")
+for name, p in model.named_parameters():
+    print(f"  {name}: shape={p.shape}")
+
+print("\n=== Backward through custom Module ===")
+loss = out.sum()
+loss.backward()
+print("All parameter grads computed:")
+for name, p in model.named_parameters():
+    print(f"  {name}.grad is not None:", p.grad is not None)
+print("> all param grads computed:", all(p.grad is not None for p in model.parameters()))
+
+print("\n=== Training loop with custom Module ===")
+
+# XOR-like dataset (linearly separable approximation)
+xs = torch.tensor([
+    [0.0, 0.0],
+    [0.0, 1.0],
+    [1.0, 0.0],
+    [1.0, 1.0],
+])
+ys = torch.tensor([[0.0], [1.0], [1.0], [0.0]])
+
+model   = TwoLayerNet(2, 8, 1)
+loss_fn = torch.nn.MSELoss()
+opt     = torch.optim.Adam(model.parameters(), lr=0.05)
+
+initial_loss = None
+for epoch in range(300):
+    opt.zero_grad()
+    pred = model(xs)
+    loss = loss_fn(pred, ys)
+    loss.backward()
+    opt.step()
+    if epoch == 0:
+        initial_loss = loss.item()
+    if epoch % 50 == 0:
+        print(f"  epoch {epoch:3d}  loss={loss:.4f}")
+
+print("Loss decreased:", loss.item() < initial_loss)
+print("> loss decreased:", loss.item() < initial_loss)
+
+print("\n=== Module.zero_grad() clears all parameter grads ===")
+# Run one backward pass
+opt.zero_grad()
+model(xs).sum().backward()
+grads_exist = all(p.grad is not None for p in model.parameters())
+print("Grads exist after backward:", grads_exist)
+print("> grads exist after backward:", grads_exist)
+
+model.zero_grad()
+grads_cleared = all(p.grad is None for p in model.parameters())
+print("Grads cleared after zero_grad:", grads_cleared)
+print("> grads cleared after zero_grad:", grads_cleared)
+
+print("\nAll custom_module checks passed.")
diff --git a/examples/pyodide/py/linear_model.py b/examples/pyodide/py/linear_model.py
new file mode 100644
index 00000000..4494a4b6
--- /dev/null
+++ b/examples/pyodide/py/linear_model.py
@@ -0,0 +1,83 @@
+# linear_model.py — torch.nn.Linear forward pass, loss computation, backward.
+# Verifies: nn.Linear, MSELoss, BCELoss, Sequential, nn.ReLU, gradient flow.
+
+print("=== nn.Linear forward pass ===")
+fc = torch.nn.Linear(3, 2)
+x = torch.tensor([[1.0, 2.0, 3.0]])   # batch of 1, 3 features
+out = fc(x)
+print("input shape:", x.shape)
+print("output shape:", out.shape)
+print("output:", out)
+
+print("\n=== nn.Linear parameters ===")
+params = list(fc.parameters())
+print("number of parameters:", len(params))
+print("param shapes:", [p.shape for p in params])
+print("all require grad:", all(p.requires_grad for p in params))
+print("> n_params:", len(params))
+print("> all require grad:", all(p.requires_grad for p in params))
+
+print("\n=== named_parameters ===")
+for name, p in fc.named_parameters():
+    print(f"  {name}: shape={p.shape}")
+
+print("\n=== MSELoss ===")
+pred   = torch.tensor([1.0, 2.0, 3.0])
+target = torch.tensor([1.5, 2.5, 2.0])
+loss_fn = torch.nn.MSELoss()
+loss = loss_fn(pred, target)
+print("MSELoss:", loss)
+# Expected: mean([(0.5)^2, (0.5)^2, (1)^2]) = mean([0.25, 0.25, 1.0]) = 0.5
+expected = ((1.0-1.5)**2 + (2.0-2.5)**2 + (3.0-2.0)**2) / 3
+print("Expected:", expected)
+print("Close:", abs(loss.item() - expected) < 1e-6)
+print("> MSELoss close:", abs(loss.item() - expected) < 1e-6)
+
+print("\n=== L1Loss ===")
+loss_fn = torch.nn.L1Loss()
+loss = loss_fn(pred, target)
+print("L1Loss:", loss)
+expected_l1 = (0.5 + 0.5 + 1.0) / 3
+print("Expected:", expected_l1)
+print("Close:", abs(loss.item() - expected_l1) < 1e-6)
+print("> L1Loss close:", abs(loss.item() - expected_l1) < 1e-6)
+
+print("\n=== Backward through Linear ===")
+fc = torch.nn.Linear(2, 1)
+x = torch.tensor([[1.0, 2.0]], requires_grad=True)
+out = fc(x)
+loss = out.sum()
+loss.backward()
+print("x.grad (should be non-None):", x.grad)
+for name, p in fc.named_parameters():
+    print(f"  {name}.grad is not None:", p.grad is not None)
+print("> x.grad is not None:", x.grad is not None)
+print("> all param grads computed:", all(p.grad is not None for p in fc.parameters()))
+
+print("\n=== Sequential ===")
+model = torch.nn.Sequential(
+    torch.nn.Linear(4, 8),
+    torch.nn.ReLU(),
+    torch.nn.Linear(8, 2),
+)
+x = torch.tensor([[1.0, 0.5, -1.0, 2.0]])
+out = model(x)
+print("Sequential output shape:", out.shape)
+print("Sequential output:", out)
+params = list(model.parameters())
+print("Total parameters:", len(params))
+print("> sequential n_params:", len(params))
+
+print("\n=== zero_grad clears gradients ===")
+fc = torch.nn.Linear(2, 1)
+x = torch.tensor([[1.0, 1.0]])
+out = fc(x).sum()
+out.backward()
+for name, p in fc.named_parameters():
+    print(f"  {name}.grad before zero_grad:", p.grad)
+fc.zero_grad()
+for name, p in fc.named_parameters():
+    print(f"  {name}.grad after zero_grad (expected None):", p.grad)
+print("> grads cleared:", all(p.grad is None for p in fc.parameters()))
+
+print("\nAll linear_model checks passed.")
diff --git a/examples/pyodide/py/nn_module.py b/examples/pyodide/py/nn_module.py
new file mode 100644
index 00000000..1b32eecb
--- /dev/null
+++ b/examples/pyodide/py/nn_module.py
@@ -0,0 +1,101 @@
+# nn_module.py — Tests for custom nn.Module subclasses, parameter registration,
+# call vs forward separation, and nested models.
+
+class MyLinearLayer(torch.nn.Module):
+    def __init__(self, in_features, out_features):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.rand(in_features, out_features))
+        self.bias = torch.nn.Parameter(torch.rand(out_features))
+
+    def forward(self, input):
+        return input @ self.weight + self.bias
+
+class MySmallModel(torch.nn.Module):
+    def __init__(self, in_features, intermediate_features, out_features):
+        super().__init__()
+        # Using our own defined layer
+        self.lin1 = MyLinearLayer(in_features, intermediate_features)
+        # Using pre-defined Linear Layer
+        self.lin2 = torch.nn.Linear(intermediate_features, out_features)
+
+    def forward(self, x):
+        x = self.lin1(x)
+        x = self.lin2(x)
+        return x
+
+
+print("=== MyLinearLayer: output shape ===")
+layer = MyLinearLayer(4, 3)
+x = torch.tensor([[1.0, 2.0, 3.0, 4.0]])
+out = layer(x)
+print("output shape:", list(out.shape))
+print("> output shape correct:", list(out.shape) == [1, 3])
+
+print("\n=== MyLinearLayer: parameter registration ===")
+params = list(layer.parameters())
+print("num parameters:", len(params))
+print("> num parameters:", len(params) == 2)  # weight + bias
+for name, p in layer.named_parameters():
+    print(f"  {name}: shape={list(p.shape)}")
+print("> weight shape:", list(layer.weight.shape) == [4, 3])
+print("> bias shape:", list(layer.bias.shape) == [3])
+
+print("\n=== MyLinearLayer: __call__ vs forward() ===")
+out_call    = layer(x)
+out_forward = layer.forward(x)
+print("> outputs match:", torch.allclose(out_call, out_forward))
+
+print("\n=== MyLinearLayer: backward ===")
+layer.zero_grad()
+layer(x).sum().backward()
+print("> weight.grad exists:", layer.weight.grad is not None)
+print("> bias.grad exists:", layer.bias.grad is not None)
+
+print("\n=== MySmallModel: output shape ===")
+model = MySmallModel(4, 8, 2)
+x = torch.tensor([[1.0, 2.0, 3.0, 4.0]])
+out = model(x)
+print("output shape:", list(out.shape))
+print("> output shape correct:", list(out.shape) == [1, 2])
+
+print("\n=== MySmallModel: parameters collected from both sub-modules ===")
+params = list(model.parameters())
+# lin1: weight (4x8) + bias (8) = 2 params
+# lin2: weight (8x2) + bias (2) = 2 params
+print("num parameters:", len(params))
+print("> num parameters:", len(params) == 4)
+for name, p in model.named_parameters():
+    print(f"  {name}: shape={list(p.shape)}")
+
+print("\n=== MySmallModel: __call__ vs forward() ===")
+out_call    = model(x)
+out_forward = model.forward(x)
+print("> outputs match:", torch.allclose(out_call, out_forward))
+
+print("\n=== MySmallModel: backward through nested modules ===")
+model.zero_grad()
+model(x).sum().backward()
+print("> all grads computed:", all(p.grad is not None for p in model.parameters()))
+
+print("\n=== __call__ vs forward() on a built-in module ===")
+fc = torch.nn.Linear(3, 2)
+x = torch.tensor([[1.0, 2.0, 3.0]])
+out_call    = fc(x)
+out_forward = fc.forward(x)
+print("> outputs match:", torch.allclose(out_call, out_forward))
+print("> shapes match:", out_call.shape == out_forward.shape)
+
+print("\n=== Sequential: submodules run via __call__ path ===")
+seq = torch.nn.Sequential(
+    torch.nn.Linear(2, 4),
+    torch.nn.ReLU(),
+    torch.nn.Linear(4, 1),
+)
+x = torch.tensor([[1.0, 2.0]])
+out_seq_call    = seq(x)
+out_seq_forward = seq.forward(x)
+print("output shape:", list(out_seq_call.shape))
+print("> output shape correct:", list(out_seq_call.shape) == [1, 1])
+print("> call and forward match:", torch.allclose(out_seq_call, out_seq_forward))
+
+print("\nAll nn_module checks passed.")
diff --git a/examples/pyodide/py/no_grad.py b/examples/pyodide/py/no_grad.py
new file mode 100644
index 00000000..682ff6ea
--- /dev/null
+++ b/examples/pyodide/py/no_grad.py
@@ -0,0 +1,53 @@
+# no_grad.py — Verify that torch.no_grad() actually disables gradient tracking.
+
+print("=== no_grad context manager ===")
+
+x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
+
+print("Grad enabled outside context:", torch.is_grad_enabled())
+print("> Grad enabled outside context:", torch.is_grad_enabled())
+
+with torch.no_grad():
+    print("Grad enabled inside context:", torch.is_grad_enabled())
+    print("> Grad enabled inside context:", torch.is_grad_enabled())
+    y = x * 2
+    print("y.requires_grad inside no_grad (expected False):", y.requires_grad)
+    print("> y.requires_grad inside no_grad:", y.requires_grad)
+    # y has no grad_fn, so backward would be a no-op
+
+print("Grad enabled after context:", torch.is_grad_enabled())
+print("> Grad enabled after context:", torch.is_grad_enabled())
+
+# Outside no_grad, requires_grad propagates normally
+z = x * 2
+print("z.requires_grad outside no_grad (expected True):", z.requires_grad)
+print("> z.requires_grad outside no_grad:", z.requires_grad)
+
+print("\n=== Gradient NOT computed inside no_grad ===")
+x = torch.tensor([3.0], requires_grad=True)
+with torch.no_grad():
+    y = x * x  # should not build computation graph
+    print("y:", y)
+    print("y.requires_grad (expected False):", y.requires_grad)
+    print("> y.requires_grad inside no_grad:", y.requires_grad)
+
+# x.grad should still be None since we didn't call backward
+print("x.grad after no_grad block (expected None):", x.grad)
+print("> x.grad after no_grad block is None:", x.grad is None)
+
+print("\n=== Gradient IS computed outside no_grad ===")
+x = torch.tensor([3.0], requires_grad=True)
+y = x * x
+y.backward()
+print("x.grad after backward (expected 6.0):", x.grad)
+print("> x.grad correct:", x.grad is not None and x.grad.allclose(torch.tensor([6.0])))
+
+print("\n=== Nested no_grad blocks restore state correctly ===")
+print("is_grad_enabled:", torch.is_grad_enabled())
+print("> is_grad_enabled before outer:", torch.is_grad_enabled())
+with torch.no_grad():
+    print("inside outer no_grad:", torch.is_grad_enabled())
+print("after outer no_grad (expected True):", torch.is_grad_enabled())
+print("> is_grad_enabled after outer:", torch.is_grad_enabled())
+
+print("\nAll no_grad checks passed.")
diff --git a/examples/pyodide/py/training_sgd.py b/examples/pyodide/py/training_sgd.py
new file mode 100644
index 00000000..ae7133be
--- /dev/null
+++ b/examples/pyodide/py/training_sgd.py
@@ -0,0 +1,81 @@
+# training_sgd.py — Full training loop: fit y = 2x + 1 with SGD and Adam.
+# Verifies: optimizer.step(), optimizer.zero_grad(), loss decreases.
+
+print("=== Training y = 2x + 1 with SGD ===")
+
+# Dataset: y = 2*x + 1
+xs = torch.tensor([[1.0], [2.0], [3.0], [4.0], [5.0]])
+ys = torch.tensor([[3.0], [5.0], [7.0], [9.0], [11.0]])
+
+model   = torch.nn.Linear(1, 1)
+loss_fn = torch.nn.MSELoss()
+opt     = torch.optim.SGD(model.parameters(), lr=0.01)
+
+losses = []
+for epoch in range(200):
+    opt.zero_grad()
+    pred = model(xs)
+    loss = loss_fn(pred, ys)
+    loss.backward()
+    opt.step()
+    if epoch % 40 == 0:
+        losses.append(loss.item())
+        print(f"  epoch {epoch:3d}  loss={loss:.6f}")
+
+print("Loss decreased:", losses[-1] < losses[0])
+print("> SGD loss decreased:", losses[-1] < losses[0])
+
+with torch.no_grad():
+    pred = model(xs)
+print("Final predictions:", pred)
+print("Expected:         ", ys)
+
+# Check weights are approximately correct (w≈2, b≈1)
+params = {name: p for name, p in model.named_parameters()}
+w = params['weight'].item()
+b = params['bias'].item()
+print(f"Learned weight={w:.3f} (expected ~2), bias={b:.3f} (expected ~1)")
+print("Weight close:", abs(w - 2.0) < 0.2)
+print("Bias close:  ", abs(b - 1.0) < 0.5)
+
+print("\n=== Training y = 2x + 1 with Adam ===")
+
+model   = torch.nn.Linear(1, 1)
+loss_fn = torch.nn.MSELoss()
+opt     = torch.optim.Adam(model.parameters(), lr=0.05)
+
+losses = []
+for epoch in range(200):
+    opt.zero_grad()
+    pred = model(xs)
+    loss = loss_fn(pred, ys)
+    loss.backward()
+    opt.step()
+    if epoch % 40 == 0:
+        losses.append(loss.item())
+        print(f"  epoch {epoch:3d}  loss={loss:.6f}")
+
+print("Loss decreased:", losses[-1] < losses[0])
+print("> Adam loss decreased:", losses[-1] < losses[0])
+params = {name: p for name, p in model.named_parameters()}
+w = params['weight'].item()
+b = params['bias'].item()
+print(f"Learned weight={w:.3f} (expected ~2), bias={b:.3f} (expected ~1)")
+
+print("\n=== Optimizer.zero_grad() resets gradients ===")
+fc  = torch.nn.Linear(2, 1)
+opt = torch.optim.SGD(fc.parameters(), lr=0.1)
+
+x = torch.tensor([[1.0, 2.0]])
+fc(x).sum().backward()
+
+grads_before = [p.grad.tolist() for p in fc.parameters()]
+print("Grads after first backward:", grads_before)
+print("> grads exist after backward:", all(p.grad is not None for p in fc.parameters()))
+
+opt.zero_grad()
+grads_after = [p.grad for p in fc.parameters()]
+print("Grads after zero_grad (expected all None):", grads_after)
+print("> grads None after zero_grad:", all(p.grad is None for p in fc.parameters()))
+
+print("\nAll training_sgd checks passed.")
diff --git a/examples/pyodide/yarn.lock b/examples/pyodide/yarn.lock
new file mode 100644
index 00000000..0c93a3a3
--- /dev/null
+++ b/examples/pyodide/yarn.lock
@@ -0,0 +1,53 @@
+# This file is generated by running "yarn install" inside your project.
+# Manual changes might be lost - proceed with caution!
+
+__metadata:
+  version: 8
+  cacheKey: 10c0
+
+"@types/emscripten@npm:^1.41.4":
+  version: 1.41.5
+  resolution: "@types/emscripten@npm:1.41.5"
+  checksum: 10c0/ae816da716f896434e59df7a71b67c71ae7e85ca067a32aef1616572fc4757459515d42ade6f5b8fd8d69733a9dbd0cf23010fec5b2f41ce52c09501aa350e45
+  languageName: node
+  linkType: hard
+
+"example-pyodide@workspace:.":
+  version: 0.0.0-use.local
+  resolution: "example-pyodide@workspace:."
+  dependencies:
+    pyodide: "npm:^0.29.3"
+    torch: "portal:../../"
+  languageName: unknown
+  linkType: soft
+
+"pyodide@npm:^0.29.3":
+  version: 0.29.3
+  resolution: "pyodide@npm:0.29.3"
+  dependencies:
+    "@types/emscripten": "npm:^1.41.4"
+    ws: "npm:^8.5.0"
+  checksum: 10c0/4c8108e9af7cd8997812507a01c3dd48789ab58973bdef3ac5a336c38837e7495146195be7fb8b0798bcc3c4f79e98877efaf0672d18e088488512eefbf1d3ca
+  languageName: node
+  linkType: hard
+
+"torch@portal:../../::locator=example-pyodide%40workspace%3A.":
+  version: 0.0.0-use.local
+  resolution: "torch@portal:../../::locator=example-pyodide%40workspace%3A."
+  languageName: node
+  linkType: soft
+
+"ws@npm:^8.5.0":
+  version: 8.19.0
+  resolution: "ws@npm:8.19.0"
+  peerDependencies:
+    bufferutil: ^4.0.1
+    utf-8-validate: ">=5.0.2"
+  peerDependenciesMeta:
+    bufferutil:
+      optional: true
+    utf-8-validate:
+      optional: true
+  checksum: 10c0/4741d9b9bc3f9c791880882414f96e36b8b254e34d4b503279d6400d9a4b87a033834856dbdd94ee4b637944df17ea8afc4bce0ff4a1560d2166be8855da5b04
+  languageName: node
+  linkType: hard
diff --git a/package.json b/package.json
new file mode 100644
index 00000000..bc0f1e53
--- /dev/null
+++ b/package.json
@@ -0,0 +1,84 @@
+{
+  "type": "module",
+  "name": "torch",
+  "packageManager": "yarn@4.10.3",
+  "version": "0.1.0",
+  "description": "machine-learning libraries for Source Academy",
+  "repository": {
+    "type": "git",
+    "url": "git+ssh://git@github.com/source-academy/torch.git"
+  },
+  "license": "Apache-2.0",
+  "files": [
+    "build",
+    "src",
+    "examples"
+  ],
+  "main": "./build/node/torch.node.cjs",
+  "module": "./build/node/torch.node.es.mjs",
+  "types": "./build/types/index.d.ts",
+  "unpkg": "./build/torch.min.js",
+  "jsdelivr": "./build/torch.min.js",
+  "exports": {
+    ".": {
+      "torch-src": "./src/index.ts",
+      "types": "./build/types/index.d.ts",
+      "browser": "./build/browser/torch.browser.es.js",
+      "import": "./build/node/torch.node.es.mjs",
+      "require": "./build/node/torch.node.cjs"
+    }
+  },
+  "mocha": {
+    "require": "tsx"
+  },
+  "scripts": {
+    "clean": "rimraf build",
+    "build:browser": "vite build --config vite.config.browser.ts",
+    "build:browser:watch": "vite build --config vite.config.browser.ts --watch",
+    "build:node": "vite build --config vite.config.node.ts",
+    "build:node:watch": "vite build --config vite.config.node.ts --watch",
+    "build:cdn": "vite build --config vite.config.cdn.ts",
+    "build:cdn:watch": "vite build --config vite.config.cdn.ts --watch",
+    "build:types": "tsc -p tsconfig.build.json",
+    "build": "yarn clean && concurrently \"yarn build:node\" \"yarn build:browser\" \"yarn build:cdn\" \"yarn build:types\"",
+    "dev": "concurrently \"yarn:build:browser:watch\" \"yarn:build:node:watch\" \"yarn:test:watch\" \"yarn:serve\"",
+    "docs": "typedoc --out docs src",
+    "lint": "eslint src",
+    "serve": "http-server -p 8080 -c-1",
+    "test": "mocha --node-option conditions=torch-src test/**/*.test.ts test/**/*.test.js",
+    "test:build": "mocha test/**/*.test.ts test/**/*.test.js",
+    "test:coverage": "c8 mocha --node-option conditions=torch-src test/**/*.test.ts test/**/*.test.js",
+    "test:watch": "mocha --node-option conditions=torch-src --watch test/**/*.test.ts test/**/*.test.js",
+    "update-tests": "python3 scripts/generate_tests.py > test/testcases.gen.js"
+  },
+  "scriptsComments": {
+    "build": "Builds the library for browser and node.",
+    "dev": "Starts a dev session. Watch and build, test, and serve on localhost:8080",
+    "test": "Tests on code in src/",
+    "test:build": "Tests on built library in build/",
+    "test:coverage": "Provide test coverage",
+    "update-tests": "Updates test using scripts/generate_tests.py to test/testcases.gen.js"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.37.0",
+    "@types/mocha": "^10.0.10",
+    "@types/node": "^24.6.2",
+    "c8": "^11.0.0",
+    "chai": "^6.2.0",
+    "concurrently": "^9.2.1",
+    "eslint": "^9.37.0",
+    "eslint-plugin-import": "^2.32.0",
+    "http-server": "^14.1.1",
+    "mocha": "^11.7.4",
+    "pkg-pr-new": "^0.0.62",
+    "prettier": "^3.6.2",
+    "rimraf": "^6.0.1",
+    "ts-node": "^10.9.2",
+    "tsx": "^4.20.6",
+    "typedoc": "^0.28.14",
+    "typescript": "^5.9.3",
+    "typescript-eslint": "^8.46.0",
+    "vite": "^7.1.9",
+    "vite-plugin-dts": "^4.5.4"
+  }
+}
diff --git a/scripts/.gitignore b/scripts/.gitignore
new file mode 100644
index 00000000..e15106e3
--- /dev/null
+++ b/scripts/.gitignore
@@ -0,0 +1,216 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+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/
+cover/
+
+# 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
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .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
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+# uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+# poetry.lock
+# poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+# pdm.lock
+# pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+# pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# Redis
+*.rdb
+*.aof
+*.pid
+
+# RabbitMQ
+mnesia/
+rabbitmq/
+rabbitmq-data/
+
+# ActiveMQ
+activemq-data/
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.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/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#   JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#   and can be added to the global gitignore or merged into this file.  For a more nuclear
+#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
+
+# Abstra
+#   Abstra is an AI-powered process automation framework.
+#   Ignore directories containing user credentials, local state, and settings.
+#   Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#   and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#   you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml
diff --git a/scripts/generate_tests.py b/scripts/generate_tests.py
new file mode 100644
index 00000000..2b9aaed4
--- /dev/null
+++ b/scripts/generate_tests.py
@@ -0,0 +1,47 @@
+import torch
+
+from generator.encoder import CompactJSONEncoder
+from generator.unary import generate_unary_tests
+from generator.binary import generate_binary_tests
+from generator.broadcasting import generate_broadcasting_tests
+from generator.matmul import generate_matmul_tests
+from generator.reduction import generate_reduction_tests
+from generator.linear import generate_linear_tests
+from generator.optimizer import generate_optimizer_tests
+from generator.expand import generate_expand_tests
+from generator.conv import generate_conv_tests
+from generator.export import generate_export_tests
+from generator.loss import generate_loss_tests
+from generator.activation import generate_activation_tests
+from generator.cat import generate_cat_tests
+from generator.softmax import generate_softmax_tests
+from generator.clamp import generate_clamp_tests
+from generator.maxpool import generate_maxpool_tests
+
+UNARY_OPS = ["log", "sqrt", "exp", "square", "abs", "sign", "neg", "reciprocal", "sin", "cos", "tan"]
+BINARY_OPS = ["add", "sub", "mul", "div", "pow", "maximum", "minimum"]
+
+torch.manual_seed(42)
+
+if __name__ == "__main__":
+    suite = {
+        "unary": {op: generate_unary_tests(op) for op in UNARY_OPS},
+        "binary": {op: generate_binary_tests(op) for op in BINARY_OPS},
+        "broadcasting": generate_broadcasting_tests(),
+        "matmul": generate_matmul_tests(),
+        "reductions": generate_reduction_tests(),
+        "linear": generate_linear_tests(),
+        "optimizers": generate_optimizer_tests(),
+        "expand": generate_expand_tests(),
+        "conv": generate_conv_tests(),
+        "export": generate_export_tests(),
+        "loss": generate_loss_tests(),
+        "activations": generate_activation_tests(),
+        "cat": generate_cat_tests(),
+        "softmax": generate_softmax_tests(),
+        "clamp": generate_clamp_tests(),
+        "maxpool": generate_maxpool_tests(),
+    }
+
+    print("export const testData = ", end="")
+    print(CompactJSONEncoder(indent=2).encode(suite), end=";\n")
diff --git a/scripts/generator/__init__.py b/scripts/generator/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/generator/activation.py b/scripts/generator/activation.py
new file mode 100644
index 00000000..a9492921
--- /dev/null
+++ b/scripts/generator/activation.py
@@ -0,0 +1,39 @@
+import torch
+import torch.nn as nn
+
+
+def generate_activation_tests():
+    tests = []
+
+    cases = [
+        # (ActivationClass, test_name, kwargs)
+        (nn.ReLU, "relu_1d", (5,), {}),
+        (nn.ReLU, "relu_2d", (3, 4), {}),
+        (nn.ReLU, "relu_3d", (2, 3, 4), {}),
+        (nn.Sigmoid, "sigmoid_1d", (5,), {}),
+        (nn.Sigmoid, "sigmoid_2d", (3, 4), {}),
+        (nn.Sigmoid, "sigmoid_3d", (2, 3, 4), {}),
+        (nn.LeakyReLU, "leaky_relu_default", (5,), {}),
+        (nn.LeakyReLU, "leaky_relu_slope_0_2", (3, 4), {"negative_slope": 0.2}),
+        (nn.LeakyReLU, "leaky_relu_3d", (2, 3, 4), {"negative_slope": 0.1}),
+    ]
+
+    for ActivationClass, desc, input_shape, kwargs in cases:
+        input = torch.randn(*input_shape, requires_grad=True)
+
+        activation = ActivationClass(**kwargs)
+        output = activation(input)
+        output.sum().backward()
+
+        entry = {
+            "test_name": desc,
+            "activation_type": ActivationClass.__name__,
+            "input": input.detach().numpy().tolist(),
+            "expected_output": output.detach().numpy().tolist(),
+            "expected_grad_input": input.grad.numpy().tolist(),
+        }
+        if kwargs:
+            entry["kwargs"] = kwargs
+        tests.append(entry)
+
+    return tests
diff --git a/scripts/generator/binary.py b/scripts/generator/binary.py
new file mode 100644
index 00000000..b742ef56
--- /dev/null
+++ b/scripts/generator/binary.py
@@ -0,0 +1,30 @@
+import torch
+
+
+def generate_binary_tests(op_name, num_tests=3):
+    tests = []
+    for i in range(num_tests):
+        x = torch.randn(4, 4, requires_grad=True)
+        y = torch.randn(4, 4, requires_grad=True)
+        with torch.no_grad():
+            x[0:2, 0] = 0
+            x[0:2, 1] = 1
+            x[0:2, 2] = -1
+            y[0, 0:2] = 0
+            y[1, 0:2] = 1
+            y[2, 0:2] = -1
+
+        torch_op = getattr(torch, op_name)
+        out = torch_op(x, y)
+        out.sum().backward()
+
+        tests.append(
+            {
+                "input_x": x.detach().numpy().tolist(),
+                "input_y": y.detach().numpy().tolist(),
+                "expected_output": out.detach().numpy().tolist(),
+                "expected_grad_x": x.grad.numpy().tolist(),
+                "expected_grad_y": y.grad.numpy().tolist(),
+            }
+        )
+    return tests
diff --git a/scripts/generator/broadcasting.py b/scripts/generator/broadcasting.py
new file mode 100644
index 00000000..da21d188
--- /dev/null
+++ b/scripts/generator/broadcasting.py
@@ -0,0 +1,41 @@
+import torch
+
+
+def generate_broadcasting_tests():
+    shape_pairs = [
+        ((2, 3), (2, 3)),
+        ((), (2, 2)),
+        ((2, 3, 4, 1), (3, 1, 1)),
+        ((1,), (3, 1, 2)),
+        ((5, 1, 4, 1), (3, 1, 1)),
+    ]
+
+    ops = ["add", "mul"]
+    tests = []
+
+    for shape_x, shape_y in shape_pairs:
+        for op_name in ops:
+            x = torch.randn(shape_x, requires_grad=True) if shape_x != () else torch.tensor(1.5, requires_grad=True)
+            y = torch.randn(shape_y, requires_grad=True) if shape_y != () else torch.tensor(-0.5, requires_grad=True)
+
+            torch_op = getattr(torch, op_name)
+            out = torch_op(x, y)
+
+            out.sum().backward()
+
+            sx_str = "scalar" if shape_x == () else str(shape_x)
+            sy_str = "scalar" if shape_y == () else str(shape_y)
+
+            tests.append(
+                {
+                    "test_name": f"broadcast_{op_name}_{sx_str}_and_{sy_str}",
+                    "op_name": op_name,
+                    "input_x": x.detach().numpy().tolist(),
+                    "input_y": y.detach().numpy().tolist(),
+                    "expected_output": out.detach().numpy().tolist(),
+                    "expected_grad_x": x.grad.numpy().tolist() if x.grad is not None else 0.0,
+                    "expected_grad_y": y.grad.numpy().tolist() if y.grad is not None else 0.0,
+                }
+            )
+
+    return tests
diff --git a/scripts/generator/cat.py b/scripts/generator/cat.py
new file mode 100644
index 00000000..cb29ccbf
--- /dev/null
+++ b/scripts/generator/cat.py
@@ -0,0 +1,34 @@
+import torch
+
+
+def generate_cat_tests():
+    tests = []
+
+    # (list_of_shapes, dim)
+    configs = [
+        ([(3,), (4,)], 0),                    # 1D, two tensors
+        ([(3,), (4,), (2,)], 0),               # 1D, three tensors
+        ([(2, 3), (4, 3)], 0),                 # 2D, along dim 0
+        ([(2, 3), (2, 4)], 1),                 # 2D, along dim 1
+        ([(2, 3, 4), (5, 3, 4)], 0),           # 3D, along dim 0
+        ([(2, 3, 4), (2, 1, 4)], 1),           # 3D, along dim 1
+        ([(2, 3, 4), (2, 3, 2)], 2),           # 3D, along dim 2
+        ([(2, 3, 4), (2, 3, 4)], -1),          # 3D, negative dim
+        ([(2, 3), (2, 3), (2, 3)], 0),         # three equal-shape tensors
+    ]
+
+    for shapes, dim in configs:
+        tensors = [torch.randn(*s, requires_grad=True) for s in shapes]
+        out = torch.cat(tensors, dim=dim)
+        out.sum().backward()
+
+        name_parts = "_".join("x".join(str(d) for d in s) for s in shapes)
+        tests.append({
+            "test_name": f"cat_dim{dim}_{name_parts}",
+            "inputs": [t.detach().numpy().tolist() for t in tensors],
+            "dim": dim,
+            "expected_output": out.detach().numpy().tolist(),
+            "expected_grads": [t.grad.numpy().tolist() for t in tensors],
+        })
+
+    return tests
diff --git a/scripts/generator/clamp.py b/scripts/generator/clamp.py
new file mode 100644
index 00000000..cea7611c
--- /dev/null
+++ b/scripts/generator/clamp.py
@@ -0,0 +1,30 @@
+import torch
+
+
+def generate_clamp_tests():
+    tests = []
+
+    cases = [
+        ("clamp_1d", (5,), -0.5, 0.5),
+        ("clamp_2d", (3, 4), 0.0, 1.0),
+        ("clamp_3d", (2, 3, 4), -1.0, 1.0),
+        ("clamp_negative_range", (4,), -2.0, -0.5),
+    ]
+
+    for desc, shape, min_val, max_val in cases:
+        x = torch.randn(*shape, requires_grad=True)
+        y = torch.clamp(x, min=min_val, max=max_val)
+        y.sum().backward()
+
+        tests.append(
+            {
+                "test_name": desc,
+                "min": min_val,
+                "max": max_val,
+                "input": x.detach().numpy().tolist(),
+                "expected_output": y.detach().numpy().tolist(),
+                "expected_grad": x.grad.numpy().tolist(),
+            }
+        )
+
+    return tests
diff --git a/scripts/generator/conv.py b/scripts/generator/conv.py
new file mode 100644
index 00000000..4e4951de
--- /dev/null
+++ b/scripts/generator/conv.py
@@ -0,0 +1,91 @@
+import torch
+
+
+def generate_conv_tests():
+    tests = []
+    cases = [
+        (
+            "Conv1d",
+            torch.nn.Conv1d,
+            [
+                (1, 1, 3, 1, 0, 1, 1, True, (1, 1, 5), "basic"),
+                (2, 3, 2, 2, 1, 1, 1, True, (2, 2, 6), "stride_padding"),
+                (2, 2, 2, 1, 0, 2, 1, False, (1, 2, 5), "dilation_no_bias"),
+                (4, 4, 3, 1, 1, 1, 2, True, (1, 4, 6), "groups"),
+            ],
+        ),
+        (
+            "Conv2d",
+            torch.nn.Conv2d,
+            [
+                (1, 1, 3, 1, 0, 1, 1, True, (1, 1, 5, 5), "basic"),
+                (2, 3, 2, 2, 1, 1, 1, True, (2, 2, 6, 6), "stride_padding"),
+                (2, 2, 2, 1, 0, 2, 1, False, (1, 2, 5, 5), "dilation_no_bias"),
+                (4, 4, 3, 1, 1, 1, 2, True, (1, 4, 4, 4), "groups"),
+            ],
+        ),
+        (
+            "Conv3d",
+            torch.nn.Conv3d,
+            [
+                (1, 1, 3, 1, 0, 1, 1, True, (1, 1, 5, 5, 5), "basic"),
+                (2, 3, 2, 2, 1, 1, 1, True, (2, 2, 4, 4, 4), "stride_padding"),
+                (2, 2, 2, 1, 0, 2, 1, False, (1, 2, 5, 5, 5), "dilation_no_bias"),
+                (4, 4, 3, 1, 1, 1, 2, True, (1, 4, 4, 4, 4), "groups"),
+            ],
+        ),
+    ]
+    for conv_type, conv_class, conv_cases in cases:
+        for (
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            input_shape,
+            desc,
+        ) in conv_cases:
+            layer = conv_class(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+                bias=bias,
+            )
+            x = torch.randn(*input_shape, requires_grad=True)
+
+            out = layer(x)
+            out.sum().backward()
+
+            test_data = {
+                "test_name": f"{conv_type.lower()}_{desc}",
+                "conv_type": conv_type,
+                "in_channels": in_channels,
+                "out_channels": out_channels,
+                "kernel_size": kernel_size,
+                "stride": stride,
+                "padding": padding,
+                "dilation": dilation,
+                "groups": groups,
+                "has_bias": bias,
+                "input": x.detach().numpy().tolist(),
+                "weight": layer.weight.detach().numpy().tolist(),
+                "expected_output": out.detach().numpy().tolist(),
+                "expected_grad_input": x.grad.numpy().tolist(),
+                "expected_grad_weight": layer.weight.grad.numpy().tolist(),
+            }
+            if bias:
+                test_data["bias"] = layer.bias.detach().numpy().tolist()
+                test_data["expected_grad_bias"] = layer.bias.grad.numpy().tolist()
+            else:
+                test_data["bias"] = None
+                test_data["expected_grad_bias"] = None
+
+            tests.append(test_data)
+    return tests
diff --git a/scripts/generator/encoder.py b/scripts/generator/encoder.py
new file mode 100644
index 00000000..482e1ff8
--- /dev/null
+++ b/scripts/generator/encoder.py
@@ -0,0 +1,39 @@
+import json
+
+
+class CompactJSONEncoder(json.JSONEncoder):
+    """JSON encoder that puts leaf arrays (arrays of non-list items) on single lines."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._indent = kwargs.get("indent", 2)
+
+    def encode(self, o):
+        return self._encode(o, 0)
+
+    def _encode(self, o, level):
+        indent_str = " " * (self._indent * level)
+        next_indent_str = " " * (self._indent * (level + 1))
+
+        if isinstance(o, dict):
+            if not o:
+                return "{}"
+            items = []
+            for k, v in o.items():
+                key = json.dumps(k)
+                val = self._encode(v, level + 1)
+                items.append(f"{next_indent_str}{key}: {val}")
+            return "{\n" + ",\n".join(items) + f"\n{indent_str}}}"
+
+        elif isinstance(o, list):
+            if not o:
+                return "[]"
+            # Leaf array: no nested lists inside
+            if not any(isinstance(item, (list, dict)) for item in o):
+                return "[" + ", ".join(self._encode(v, level) for v in o) + "]"
+            # Nested array: pretty print
+            items = [f"{next_indent_str}{self._encode(v, level + 1)}" for v in o]
+            return "[\n" + ",\n".join(items) + f"\n{indent_str}]"
+
+        else:
+            return json.dumps(o)
diff --git a/scripts/generator/expand.py b/scripts/generator/expand.py
new file mode 100644
index 00000000..459e3b25
--- /dev/null
+++ b/scripts/generator/expand.py
@@ -0,0 +1,32 @@
+import torch
+
+
+def generate_expand_tests():
+    tests = []
+
+    # Tuples of (initial_shape, expand_shape, description)
+    cases = [
+        ((1,), (3,), "1D_expand"),
+        ((3,), (2, 3), "prepend_2D"),
+        ((1, 3), (4, 3), "expand_dim_0"),
+        ((2, 1, 4), (2, 5, 4), "expand_middle_dim"),
+        ((1, 3, 1), (2, -1, 4), "preserve_with_negative_one"),
+    ]
+
+    for initial_shape, expand_shape, desc in cases:
+        x = torch.randn(initial_shape, requires_grad=True)
+
+        out = x.expand(*expand_shape)
+        out.sum().backward()
+
+        tests.append(
+            {
+                "test_name": f"expand_{desc}",
+                "input": x.detach().numpy().tolist(),
+                "expand_shape": list(expand_shape),
+                "expected_output": out.detach().numpy().tolist(),
+                "expected_grad": x.grad.numpy().tolist(),
+            }
+        )
+
+    return tests
diff --git a/scripts/generator/export.py b/scripts/generator/export.py
new file mode 100644
index 00000000..8e32f041
--- /dev/null
+++ b/scripts/generator/export.py
@@ -0,0 +1,93 @@
+import torch
+
+
+def generate_export_tests():
+    tests = []
+
+    def extract_nodes(ep):
+        nodes = []
+        for node in ep.graph.nodes:
+            nd = {
+                "op": node.op,
+                "name": node.name,
+                "target": str(node.target),
+            }
+            nd["args"] = []
+            for a in node.args:
+                if isinstance(a, torch.fx.Node):
+                    nd["args"].append(a.name)
+                elif isinstance(a, tuple):
+                    nd["args"].append([n.name if isinstance(n, torch.fx.Node) else str(n) for n in a])
+                else:
+                    nd["args"].append(str(a))
+            if "val" in node.meta:
+                v = node.meta["val"]
+                if isinstance(v, torch.Tensor):
+                    nd["val_shape"] = list(v.shape)
+            nodes.append(nd)
+        return nodes
+
+    def extract_specs(ep):
+        sig = ep.graph_signature
+        input_specs = [
+            {"kind": s.kind.name, "name": s.arg.name if hasattr(s.arg, "name") else str(s.arg)} for s in sig.input_specs
+        ]
+        output_specs = [
+            {"kind": s.kind.name, "name": s.arg.name if hasattr(s.arg, "name") else str(s.arg)}
+            for s in sig.output_specs
+        ]
+        return input_specs, output_specs
+
+    # Test 1: Simple Sequential(Linear, ReLU)
+    torch.manual_seed(42)
+    model1 = torch.nn.Sequential(torch.nn.Linear(3, 2), torch.nn.ReLU())
+    x1 = torch.randn(2, 3)
+    ep1 = torch.export.export(model1, (x1,))
+
+    nodes1 = extract_nodes(ep1)
+    input_specs1, output_specs1 = extract_specs(ep1)
+
+    tests.append(
+        {
+            "test_name": "linear_relu",
+            "model_type": "LinearReLU",
+            "input": x1.detach().numpy().tolist(),
+            "weight": model1[0].weight.detach().numpy().tolist(),
+            "bias": model1[0].bias.detach().numpy().tolist(),
+            "in_features": 3,
+            "out_features": 2,
+            "expected_nodes": nodes1,
+            "expected_input_specs": input_specs1,
+            "expected_output_specs": output_specs1,
+        }
+    )
+
+    # Test 2: Sequential(Linear, ReLU, Linear, Sigmoid)
+    torch.manual_seed(42)
+    model2 = torch.nn.Sequential(torch.nn.Linear(4, 3), torch.nn.ReLU(), torch.nn.Linear(3, 2), torch.nn.Sigmoid())
+    x2 = torch.randn(2, 4)
+    ep2 = torch.export.export(model2, (x2,))
+
+    nodes2 = extract_nodes(ep2)
+    input_specs2, output_specs2 = extract_specs(ep2)
+
+    tests.append(
+        {
+            "test_name": "two_layer",
+            "model_type": "TwoLayer",
+            "input": x2.detach().numpy().tolist(),
+            "linear1_weight": model2[0].weight.detach().numpy().tolist(),
+            "linear1_bias": model2[0].bias.detach().numpy().tolist(),
+            "linear2_weight": model2[2].weight.detach().numpy().tolist(),
+            "linear2_bias": model2[2].bias.detach().numpy().tolist(),
+            "linear1_in": 4,
+            "linear1_out": 3,
+            "linear2_in": 3,
+            "linear2_out": 2,
+            "expected_nodes": nodes2,
+            "expected_input_specs": input_specs2,
+            "expected_output_specs": output_specs2,
+        }
+    )
+
+    return tests
diff --git a/scripts/generator/linear.py b/scripts/generator/linear.py
new file mode 100644
index 00000000..10e494b3
--- /dev/null
+++ b/scripts/generator/linear.py
@@ -0,0 +1,30 @@
+import torch
+
+
+def generate_linear_tests():
+    tests = []
+    # Test standard 2D inputs and batched 3D inputs
+    cases = [(10, 5, (3, 10), "2D_input"), (4, 2, (2, 3, 4), "3D_batched_input")]
+
+    for in_features, out_features, input_shape, desc in cases:
+        layer = torch.nn.Linear(in_features, out_features)
+        x = torch.randn(*input_shape, requires_grad=True)
+
+        out = layer(x)
+        out.sum().backward()
+
+        tests.append(
+            {
+                "test_name": f"linear_{desc}",
+                "in_features": in_features,
+                "out_features": out_features,
+                "input": x.detach().numpy().tolist(),
+                "weight": layer.weight.detach().numpy().tolist(),
+                "bias": layer.bias.detach().numpy().tolist(),
+                "expected_output": out.detach().numpy().tolist(),
+                "expected_grad_input": x.grad.numpy().tolist(),
+                "expected_grad_weight": layer.weight.grad.numpy().tolist(),
+                "expected_grad_bias": layer.bias.grad.numpy().tolist(),
+            }
+        )
+    return tests
diff --git a/scripts/generator/loss.py b/scripts/generator/loss.py
new file mode 100644
index 00000000..29995b82
--- /dev/null
+++ b/scripts/generator/loss.py
@@ -0,0 +1,100 @@
+import torch
+import torch.nn as nn
+
+
+def generate_loss_tests():
+    tests = []
+
+    cases = [
+        # (LossClass, input_shape, target_shape, test_name, needs_sigmoid)
+        (nn.MSELoss, (5,), (5,), "mse_1d", False),
+        (nn.MSELoss, (3, 4), (3, 4), "mse_2d", False),
+        (nn.MSELoss, (2, 3, 4), (2, 3, 4), "mse_3d", False),
+        (nn.L1Loss, (5,), (5,), "l1_1d", False),
+        (nn.L1Loss, (3, 4), (3, 4), "l1_2d", False),
+        (nn.L1Loss, (2, 3, 4), (2, 3, 4), "l1_3d", False),
+        # BCE requires inputs in (0, 1)
+        (nn.BCELoss, (5,), (5,), "bce_1d", True),
+        (nn.BCELoss, (3, 4), (3, 4), "bce_2d", True),
+    ]
+
+    for LossClass, input_shape, target_shape, desc, needs_sigmoid in cases:
+        if needs_sigmoid:
+            input = torch.sigmoid(torch.randn(*input_shape)).requires_grad_(True)
+            target = torch.rand(*target_shape)
+        else:
+            input = torch.randn(*input_shape, requires_grad=True)
+            target = torch.randn(*target_shape)
+
+        loss_fn = LossClass()
+        output = loss_fn(input, target)
+        output.backward()
+
+        tests.append(
+            {
+                "test_name": desc,
+                "loss_type": LossClass.__name__,
+                "input": input.detach().numpy().tolist(),
+                "target": target.detach().numpy().tolist(),
+                "expected_output": output.detach().numpy().tolist(),
+                "expected_grad_input": input.grad.numpy().tolist(),
+            }
+        )
+
+    # CrossEntropyLoss: input is (N, C) logits, target is (N,) class indices
+    ce_cases = [
+        ((3, 5), "ce_3x5"),
+        ((4, 3), "ce_4x3"),
+        ((2, 10), "ce_2x10"),
+    ]
+
+    for (N, C), desc in ce_cases:
+        input = torch.randn(N, C, requires_grad=True)
+        target = torch.randint(0, C, (N,))
+
+        loss_fn = nn.CrossEntropyLoss()
+        output = loss_fn(input, target)
+        output.backward()
+
+        tests.append(
+            {
+                "test_name": desc,
+                "loss_type": "CrossEntropyLoss",
+                "input": input.detach().numpy().tolist(),
+                "target": target.numpy().tolist(),
+                "expected_output": output.detach().numpy().tolist(),
+                "expected_grad_input": input.grad.numpy().tolist(),
+            }
+        )
+
+    # NLLLoss: input is (N, C) log-probs, target is (N,) class indices
+    nll_cases = [
+        ((3, 5), "nll_3x5"),
+        ((4, 3), "nll_4x3"),
+        ((2, 10), "nll_2x10"),
+    ]
+
+    for (N, C), desc in nll_cases:
+        # Produce proper log-probabilities via log_softmax
+        raw = torch.randn(N, C, requires_grad=True)
+        import torch.nn.functional as F
+        input = F.log_softmax(raw, dim=1)
+        input = input.detach().requires_grad_(True)
+        target = torch.randint(0, C, (N,))
+
+        loss_fn = nn.NLLLoss()
+        output = loss_fn(input, target)
+        output.backward()
+
+        tests.append(
+            {
+                "test_name": desc,
+                "loss_type": "NLLLoss",
+                "input": input.detach().numpy().tolist(),
+                "target": target.numpy().tolist(),
+                "expected_output": output.detach().numpy().tolist(),
+                "expected_grad_input": input.grad.numpy().tolist(),
+            }
+        )
+
+    return tests
diff --git a/scripts/generator/matmul.py b/scripts/generator/matmul.py
new file mode 100644
index 00000000..d9bfd739
--- /dev/null
+++ b/scripts/generator/matmul.py
@@ -0,0 +1,33 @@
+import torch
+
+
+def generate_matmul_tests():
+    matmul_cases = [
+        ((3,), (3,), "1D_dot_product"),
+        ((2, 3), (3, 4), "2D_matrix_multiply"),
+        ((3,), (3, 4), "1D_2D_prepend_remove"),
+        ((2, 3), (3,), "2D_1D_matrix_vector"),
+        ((2, 1, 2, 3), (3, 3, 2), "ND_batched_with_broadcast"),
+    ]
+
+    tests = []
+
+    for shape_x, shape_y, desc in matmul_cases:
+        x = torch.randn(shape_x, requires_grad=True)
+        y = torch.randn(shape_y, requires_grad=True)
+
+        out = torch.matmul(x, y)
+        out.sum().backward()
+
+        tests.append(
+            {
+                "test_name": f"matmul_{desc}",
+                "input_x": x.detach().numpy().tolist(),
+                "input_y": y.detach().numpy().tolist(),
+                "expected_output": out.detach().numpy().tolist(),
+                "expected_grad_x": x.grad.numpy().tolist(),
+                "expected_grad_y": y.grad.numpy().tolist(),
+            }
+        )
+
+    return tests
diff --git a/scripts/generator/maxpool.py b/scripts/generator/maxpool.py
new file mode 100644
index 00000000..b866b446
--- /dev/null
+++ b/scripts/generator/maxpool.py
@@ -0,0 +1,39 @@
+import torch
+import torch.nn as nn
+
+
+def generate_maxpool_tests():
+    tests = []
+
+    cases = [
+        ("maxpool_basic_2x2", (1, 1, 4, 4), 2, None, 0),
+        ("maxpool_stride_1", (1, 2, 4, 4), 2, 1, 0),
+        ("maxpool_padding_1", (1, 1, 4, 4), 3, 1, 1),
+        ("maxpool_multichannel", (2, 3, 6, 6), 2, 2, 0),
+        ("maxpool_3d_input", (1, 4, 4), 2, None, 0),
+    ]
+
+    for desc, shape, kernel_size, stride, padding in cases:
+        x = torch.randn(*shape, requires_grad=True)
+        if stride is None:
+            pool = nn.MaxPool2d(kernel_size, padding=padding)
+        else:
+            pool = nn.MaxPool2d(kernel_size, stride=stride, padding=padding)
+        y = pool(x)
+        y.sum().backward()
+
+        stride_val = stride if stride is not None else kernel_size
+
+        tests.append(
+            {
+                "test_name": desc,
+                "kernel_size": kernel_size,
+                "stride": stride_val,
+                "padding": padding,
+                "input": x.detach().numpy().tolist(),
+                "expected_output": y.detach().numpy().tolist(),
+                "expected_grad": x.grad.numpy().tolist(),
+            }
+        )
+
+    return tests
diff --git a/scripts/generator/optimizer.py b/scripts/generator/optimizer.py
new file mode 100644
index 00000000..0126fa54
--- /dev/null
+++ b/scripts/generator/optimizer.py
@@ -0,0 +1,54 @@
+import torch
+
+
+def generate_optimizer_tests():
+    tests = []
+    configs = [
+        ("SGD_basic", torch.optim.SGD, {"lr": 0.1}),
+        ("SGD_momentum", torch.optim.SGD, {"lr": 0.1, "momentum": 0.9}),
+        ("SGD_weight_decay", torch.optim.SGD, {"lr": 0.1, "weight_decay": 0.01}),
+        ("SGD_nesterov", torch.optim.SGD, {"lr": 0.1, "momentum": 0.9, "nesterov": True}),
+        ("Adam_basic", torch.optim.Adam, {"lr": 0.1}),
+        ("Adam_custom_betas", torch.optim.Adam, {"lr": 0.1, "betas": (0.95, 0.999)}),
+        ("Adam_weight_decay", torch.optim.Adam, {"lr": 0.1, "weight_decay": 0.01}),
+        ("Adam_amsgrad", torch.optim.Adam, {"lr": 0.1, "amsgrad": True}),
+        ("Adagrad_basic", torch.optim.Adagrad, {"lr": 0.1}),
+        ("Adagrad_lr_decay", torch.optim.Adagrad, {"lr": 0.1, "lr_decay": 0.01}),
+        ("Adagrad_weight_decay", torch.optim.Adagrad, {"lr": 0.1, "weight_decay": 0.01}),
+    ]
+
+    for test_name, optim_class, kwargs in configs:
+        # 1. Initialize a generic parameter tensor
+        w = torch.randn(3, 3, requires_grad=True)
+        x = torch.randn(3, 3)  # Dummy input to calculate a loss
+
+        # Save exact starting state
+        initial_w = w.detach().clone()
+
+        # 2. Setup Optimizer
+        optimizer = optim_class([w], **kwargs)
+        optimizer.zero_grad()
+
+        # 3. Compute loss and gradients
+        loss = (w * x).sum()
+        loss.backward()
+
+        # Save exact gradient computed by PyTorch
+        expected_grad = w.grad.detach().clone()
+
+        # 4. Step the optimizer
+        optimizer.step()
+
+        tests.append(
+            {
+                "test_name": test_name,
+                "optimizer": test_name.split("_")[0],  # "SGD" or "Adam"
+                "kwargs": kwargs,
+                "initial_weight": initial_w.numpy().tolist(),
+                "input_x": x.numpy().tolist(),
+                "expected_grad": expected_grad.numpy().tolist(),
+                "expected_updated_weight": w.detach().numpy().tolist(),
+            }
+        )
+
+    return tests
diff --git a/scripts/generator/reduction.py b/scripts/generator/reduction.py
new file mode 100644
index 00000000..3ff12ec8
--- /dev/null
+++ b/scripts/generator/reduction.py
@@ -0,0 +1,41 @@
+import torch
+
+
+def generate_reduction_tests():
+    tests = []
+    ops = ["sum", "mean", "max", "min"]
+    dims = [None, 0, 1, -1]
+    keepdims = [False, True]
+
+    for op in ops:
+        for dim in dims:
+            for keepdim in keepdims:
+                if dim is None and keepdim:
+                    continue
+
+                x = torch.randn(3, 4, 5, requires_grad=True)
+                torch_op = getattr(torch, op)
+
+                try:
+                    if dim is None:
+                        y = torch_op(x)
+                    else:
+                        out = torch_op(x, dim=dim, keepdim=keepdim)
+                        y = out.values if op in ["max", "min"] else out
+
+                    y.sum().backward()
+
+                    tests.append(
+                        {
+                            "test_name": f"{op}_dim_{dim}_keepdim_{keepdim}",
+                            "op_name": op,
+                            "dim": dim,
+                            "keepdim": keepdim,
+                            "input": x.detach().numpy().tolist(),
+                            "expected_output": y.detach().numpy().tolist(),
+                            "expected_grad": x.grad.numpy().tolist(),
+                        }
+                    )
+                except Exception as e:
+                    pass
+    return tests
diff --git a/scripts/generator/softmax.py b/scripts/generator/softmax.py
new file mode 100644
index 00000000..eed26091
--- /dev/null
+++ b/scripts/generator/softmax.py
@@ -0,0 +1,31 @@
+import torch
+import torch.nn.functional as F
+
+
+def generate_softmax_tests():
+    tests = []
+
+    cases = [
+        ("softmax_1d_dim0", (5,), 0),
+        ("softmax_2d_dim0", (3, 4), 0),
+        ("softmax_2d_dim1", (3, 4), 1),
+        ("softmax_3d_dim1", (2, 3, 4), 1),
+        ("softmax_3d_dim2", (2, 3, 4), 2),
+    ]
+
+    for desc, shape, dim in cases:
+        x = torch.randn(*shape, requires_grad=True)
+        y = F.softmax(x, dim=dim)
+        y.sum().backward()
+
+        tests.append(
+            {
+                "test_name": desc,
+                "dim": dim,
+                "input": x.detach().numpy().tolist(),
+                "expected_output": y.detach().numpy().tolist(),
+                "expected_grad": x.grad.numpy().tolist(),
+            }
+        )
+
+    return tests
diff --git a/scripts/generator/unary.py b/scripts/generator/unary.py
new file mode 100644
index 00000000..c43071b6
--- /dev/null
+++ b/scripts/generator/unary.py
@@ -0,0 +1,24 @@
+import torch
+
+
+def generate_unary_tests(op_name, num_tests=3):
+    tests = []
+    for i in range(num_tests):
+        x = torch.randn(3, 3, requires_grad=True)
+        with torch.no_grad():
+            x[0, 0] = 0
+            x[0, 1] = 1
+            x[0, 2] = -1
+
+        torch_op = getattr(torch, op_name)
+        y = torch_op(x)
+        y.sum().backward()
+
+        tests.append(
+            {
+                "input": x.detach().numpy().tolist(),
+                "expected_output": y.detach().numpy().tolist(),
+                "expected_grad": x.grad.numpy().tolist(),
+            }
+        )
+    return tests
diff --git a/scripts/pyproject.toml b/scripts/pyproject.toml
new file mode 100644
index 00000000..55ec8d78
--- /dev/null
+++ b/scripts/pyproject.toml
@@ -0,0 +1,2 @@
+[tool.black]
+line-length = 120
diff --git a/scripts/requirements.txt b/scripts/requirements.txt
new file mode 100644
index 00000000..5ecc8ff9
--- /dev/null
+++ b/scripts/requirements.txt
@@ -0,0 +1,4 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+
+torch==2.10.*
+numpy~=2.4.0
diff --git a/src/broadcasting.ts b/src/broadcasting.ts
new file mode 100644
index 00000000..f80a7ee1
--- /dev/null
+++ b/src/broadcasting.ts
@@ -0,0 +1,59 @@
+// https://docs.pytorch.org/docs/stable/notes/broadcasting.html
+export function _broadcast_shape(a_shape: number[], b_shape: number[]): number[] {
+  const result_length = Math.max(a_shape.length, b_shape.length);
+  const padded_a_shape = [...Array(result_length - a_shape.length).fill(1), ...a_shape];
+  const padded_b_shape = [...Array(result_length - b_shape.length).fill(1), ...b_shape];
+
+  const result_shape: number[] = [];
+
+  for (let i = 0; i < result_length; i++) {
+    if (
+      padded_a_shape[i] !== padded_b_shape[i] &&
+      padded_a_shape[i] !== 1 &&
+      padded_b_shape[i] !== 1
+    ) {
+      throw new Error(`Shape mismatch: ${a_shape} and ${b_shape}`);
+    }
+
+    result_shape.push(Math.max(padded_a_shape[i], padded_b_shape[i]));
+  }
+
+  return result_shape;
+}
+
+export function _unbroadcast(result_shape: number[], original_shape: number[], result: number[]) {
+  const this_shape = _pad_shape(original_shape, result_shape)
+  const unbroadcasted_result = new Array(original_shape.reduce((acc, cur) => acc * cur, 1)).fill(0);
+  for (let i = 0; i < result.length; i++) {
+    unbroadcasted_result[_get_original_index(this_shape, result_shape, i)] += result[i];
+  }
+  return unbroadcasted_result;
+}
+
+export function _pad_shape(shape: number[], broadcast_shape: number[]): number[] {
+  if (shape.length >= broadcast_shape.length) {
+    return shape;
+  }
+
+  return [...Array(broadcast_shape.length - shape.length).fill(1), ...shape];
+}
+
+export function _get_original_index(
+  original_shape: number[],
+  new_shape: number[],
+  index: number
+): number {
+  let original_index = 0;
+  let cur_stride = 1;
+  let temp_index = index;
+
+  for (let i = original_shape.length - 1; i >= 0; i--) {
+    if (original_shape[i] > 1) {
+      const dim_index = temp_index % new_shape[i];
+      original_index = original_index + dim_index * cur_stride;
+    }
+    cur_stride *= original_shape[i];
+    temp_index = Math.floor(temp_index / new_shape[i]);
+  }
+  return original_index;
+}
diff --git a/src/creation/index.ts b/src/creation/index.ts
new file mode 100644
index 00000000..8eb4eb86
--- /dev/null
+++ b/src/creation/index.ts
@@ -0,0 +1,3 @@
+export * from './rand';
+export * from './initializers';
+export * from './ranges';
diff --git a/src/creation/initializers.ts b/src/creation/initializers.ts
new file mode 100644
index 00000000..153f64c5
--- /dev/null
+++ b/src/creation/initializers.ts
@@ -0,0 +1,40 @@
+import { Tensor, NestedNumberArray } from '../tensor';
+import { _get_shape_from_args, _numel } from '../util';
+
+export function tensor(data: NestedNumberArray, requires_grad: boolean = false): Tensor {
+  return new Tensor(data, { requires_grad });
+}
+
+export function full(shape: number[], fill_value: number): Tensor {
+  const t = new Tensor(Array(_numel(shape)).fill(fill_value));
+  t.shape = shape;
+  return t;
+}
+
+export function zeros(...args: number[] | number[][]): Tensor {
+  return full(_get_shape_from_args(args), 0);
+}
+
+export function ones(...args: number[] | number[][]): Tensor {
+  return full(_get_shape_from_args(args), 1);
+}
+
+export function empty(...args: number[] | number[][]): Tensor {
+  return full(_get_shape_from_args(args), 0);
+}
+
+export function full_like(input: Tensor, fill_value: number): Tensor {
+  return full(input.shape, fill_value);
+}
+
+export function zeros_like(input: Tensor): Tensor {
+  return full(input.shape, 0);
+}
+
+export function ones_like(input: Tensor): Tensor {
+  return full(input.shape, 1);
+}
+
+export function empty_like(input: Tensor): Tensor {
+  return full(input.shape, 0);
+}
diff --git a/src/creation/rand.ts b/src/creation/rand.ts
new file mode 100644
index 00000000..98f14f3a
--- /dev/null
+++ b/src/creation/rand.ts
@@ -0,0 +1,47 @@
+import { Tensor } from '../tensor';
+import { _get_shape_from_args, _numel } from '../util';
+import { uniformDist, normalDist } from '../prng';
+
+export function randn(...args: number[] | number[][]): Tensor {
+  const shape = _get_shape_from_args(args);
+  const tensor = new Tensor(Array.from({ length: _numel(shape) }, normalDist()));
+  tensor.shape = shape;
+  return tensor;
+}
+
+export function rand(...args: number[] | number[][]): Tensor {
+  const shape = _get_shape_from_args(args);
+  const tensor = new Tensor(Array.from({ length: _numel(shape) }, uniformDist()));
+  tensor.shape = shape;
+  return tensor;
+}
+
+export function randint(low: number, high: number, shape: number[]): Tensor {
+  const tensor = new Tensor(
+    Array.from({ length: _numel(shape) }, () => Math.floor(uniformDist(low, high)()))
+  );
+  tensor.shape = shape;
+  return tensor;
+}
+
+export function randperm(n: number): Tensor {
+  const arr = Array.from({ length: n }, (_, i) => i);
+  for (let i = 0; i < n; i++) {
+    const j = Math.floor(uniformDist()() * (n - i)) + i;
+    [arr[i], arr[j]] = [arr[j], arr[i]];
+  }
+  const tensor = new Tensor(arr);
+  return tensor;
+}
+
+export function rand_like(input: Tensor): Tensor {
+  return rand(input.shape);
+}
+
+export function randn_like(input: Tensor): Tensor {
+  return randn(input.shape);
+}
+
+export function randint_like(input: Tensor, low: number, high: number): Tensor {
+  return randint(low, high, input.shape);
+}
diff --git a/src/creation/ranges.ts b/src/creation/ranges.ts
new file mode 100644
index 00000000..812c555a
--- /dev/null
+++ b/src/creation/ranges.ts
@@ -0,0 +1,29 @@
+import { Tensor } from '../tensor';
+
+export function linspace(start: number, end: number, steps: number) {
+  const data = [];
+  const step = (end - start) / (steps - 1);
+  for (let i = 0; i < steps - 1; i++) {
+    data.push(start + i * step);
+  }
+  data.push(end);
+  return new Tensor(data);
+}
+
+export function arange(start: number, end: number = undefined, step: number = 1) {
+  const data = [];
+  if (end === undefined) {
+    end = start;
+    start = 0;
+  }
+  if (step === 0) {
+    throw new Error('step must be nonzero');
+  }
+  if (Math.sign(end - start) !== Math.sign(step)) {
+    throw new Error('upper bound and lower bound inconsistent with step sign');
+  }
+  for (let i = start; i < end; i += step) {
+    data.push(i);
+  }
+  return new Tensor(data);
+}
diff --git a/src/export.ts b/src/export.ts
new file mode 100644
index 00000000..045a2982
--- /dev/null
+++ b/src/export.ts
@@ -0,0 +1,319 @@
+import { Tensor } from './tensor';
+import { TorchFunction } from './functions/base';
+import { Module } from './nn/base';
+import { no_grad } from './grad_mode';
+import { eventBus, events } from './util';
+
+/**
+ * A graph node in the exported program, matching PyTorch's FX node format.
+ */
+export interface GraphNode {
+  /** Node type: 'placeholder' for inputs/params, 'call_function' for ops, 'output' for result */
+  op: 'placeholder' | 'call_function' | 'output';
+  /** Unique name for this node (e.g. "add", "linear_1") */
+  name: string;
+  /** Operation target (e.g. "aten.add.default") */
+  target: string;
+  /** References to input node names */
+  args: (string | string[])[];
+  /** Output tensor shape, if available */
+  val_shape?: number[];
+}
+
+export interface InputSpec {
+  kind: 'PARAMETER' | 'USER_INPUT';
+  name: string;
+  target?: string;
+}
+
+export interface OutputSpec {
+  kind: 'USER_OUTPUT';
+  name: string;
+}
+
+export interface GraphSignature {
+  input_specs: InputSpec[];
+  output_specs: OutputSpec[];
+}
+
+/**
+ * Maps our internal op names to PyTorch's aten operator names.
+ * See: https://docs.pytorch.org/docs/2.10/user_guide/torch_compiler/torch.compiler_ir.html
+ */
+export const _atenMap: Record = {
+  'add': 'aten.add.Tensor',
+  'sub': 'aten.sub.Tensor',
+  'mul': 'aten.mul.Tensor',
+  'div': 'aten.div.Tensor',
+  'pow': 'aten.pow.Tensor_Tensor',
+  'powint': 'aten.pow.Tensor_Scalar',
+  'fmod': 'aten.fmod.Tensor',
+  'maximum': 'aten.maximum.default',
+  'minimum': 'aten.minimum.default',
+  'log': 'aten.log.default',
+  'sqrt': 'aten.sqrt.default',
+  'exp': 'aten.exp.default',
+  'square': 'aten.square.default',
+  'abs': 'aten.abs.default',
+  'sign': 'aten.sign.default',
+  'neg': 'aten.neg.default',
+  'reciprocal': 'aten.reciprocal.default',
+  'nan_to_num': 'aten.nan_to_num.default',
+  'reshape': 'aten.reshape.default',
+  'flatten': 'aten.flatten.using_ints',
+  'squeeze': 'aten.squeeze.dim',
+  'unsqueeze': 'aten.unsqueeze.default',
+  'expand': 'aten.expand.default',
+  'sin': 'aten.sin.default',
+  'cos': 'aten.cos.default',
+  'tan': 'aten.tan.default',
+  'tanh': 'aten.tanh.default',
+  'sum': 'aten.sum.default',
+  'mean': 'aten.mean.default',
+  'min': 'aten.min.default',
+  'max': 'aten.max.default',
+  'transpose': 'aten.transpose.int',
+  'matmul': 'aten.matmul.default',
+  'relu': 'aten.relu.default',
+  'sigmoid': 'aten.sigmoid.default',
+  'lt': 'aten.lt.Tensor',
+  'gt': 'aten.gt.Tensor',
+  'le': 'aten.le.Tensor',
+  'ge': 'aten.ge.Tensor',
+  'eq': 'aten.eq.Tensor',
+  'ne': 'aten.ne.Tensor',
+  'conv1d': 'aten.conv1d.default',
+  'conv2d': 'aten.conv2d.default',
+  'conv3d': 'aten.conv3d.default',
+  'linear': 'aten.linear.default',
+  'cross_entropy_loss': 'aten.cross_entropy_loss.default',
+  'nll_loss': 'aten.nll_loss_forward.default',
+  'cat': 'aten.cat.default',
+  'softmax': 'aten._softmax.default',
+  'clamp': 'aten.clamp.default',
+  'leaky_relu': 'aten.leaky_relu.default',
+  'max_pool2d': 'aten.max_pool2d.default',
+};
+
+/**
+ * Maps our internal op names to PyTorch's aten operator names with default value.
+ */
+function toAtenTarget(opName: string): string {
+  return _atenMap[opName] || `aten.${opName}.default`;
+}
+
+/**
+ * Manages unique node name generation with PyTorch-style deduplication.
+ * E.g. first "add" -> "add", second "add" -> "add_1"
+ */
+class NameGenerator {
+  private counts = new Map();
+
+  generate(baseName: string): string {
+    const count = this.counts.get(baseName) || 0;
+    this.counts.set(baseName, count + 1);
+    return count === 0 ? baseName : `${baseName}_${count}`;
+  }
+}
+
+/**
+ * An exported program, matching PyTorch's ExportedProgram structure.
+ */
+export class ExportedProgram {
+  constructor(
+    public graph: GraphNode[],
+    public graph_signature: GraphSignature,
+    public parameters: Map
+  ) { }
+
+  toString(): string {
+    const lines: string[] = ['ExportedProgram:'];
+
+    // Format forward signature
+    const inputArgs = this.graph
+      .filter(n => n.op === 'placeholder')
+      .map(n => {
+        const shape = n.val_shape ? JSON.stringify(n.val_shape) : '?';
+        return `${n.name}: "${shape}"`;
+      })
+      .join(', ');
+    lines.push(`    class GraphModule(torch.nn.Module):`);
+    lines.push(`        def forward(self, ${inputArgs}):`);
+
+    // Operations
+    for (const node of this.graph) {
+      if (node.op === 'call_function') {
+        const args = node.args.join(', ');
+        lines.push(`            ${node.name} = ${node.target}(${args})`);
+      } else if (node.op === 'output') {
+        lines.push(`            return (${node.args.join(', ')},)`);
+      }
+    }
+
+    lines.push('');
+    lines.push('Graph signature:');
+    lines.push('    # inputs');
+    for (const spec of this.graph_signature.input_specs) {
+      const target = spec.target ? ` target='${spec.target}'` : '';
+      lines.push(`    ${spec.name}: ${spec.kind}${target}`);
+    }
+    lines.push('    # outputs');
+    for (const spec of this.graph_signature.output_specs) {
+      lines.push(`    ${spec.name}: ${spec.kind}`);
+    }
+
+    return lines.join('\n');
+  }
+}
+
+/**
+ * Export a module's forward pass as an ExportedProgram.
+ *
+ * This traces the module's forward() with the given sample inputs
+ * and captures the computation graph. Similar to PyTorch's torch.export.export().
+ *
+ * Named `export_` to avoid conflict with the JavaScript `export` keyword.
+ *
+ * @param module The nn.Module to export
+ * @param sampleInputs Sample input tensors for tracing
+ * @returns An ExportedProgram containing the traced graph
+ */
+export function export_(
+  module: Module,
+  sampleInputs: Tensor[]
+): ExportedProgram {
+  const graph: GraphNode[] = [];
+  const nameGen = new NameGenerator();
+
+  // Map tensor IDs to their graph node names
+  const tensorIdToName = new Map();
+
+  // 1. Create placeholder nodes for parameters
+  const namedParams = module.named_parameters();
+  const paramTensorIds = new Set();
+  const inputSpecs: InputSpec[] = [];
+
+  for (const [paramPath, param] of namedParams) {
+    // Convert "linear.weight" -> "p_linear_weight" (PyTorch convention)
+    const placeholderName = 'p_' + paramPath.replace(/\./g, '_');
+    const nodeName = nameGen.generate(placeholderName);
+    tensorIdToName.set(param.id, nodeName);
+    paramTensorIds.add(param.id);
+
+    graph.push({
+      op: 'placeholder',
+      name: nodeName,
+      target: nodeName,
+      args: [],
+      val_shape: param.shape,
+    });
+
+    inputSpecs.push({
+      kind: 'PARAMETER',
+      name: nodeName,
+      target: paramPath,
+    });
+  }
+
+  // 2. Create placeholder nodes for user inputs
+  for (let i = 0; i < sampleInputs.length; i++) {
+    const baseName = 'input';
+    const nodeName = nameGen.generate(baseName);
+    tensorIdToName.set(sampleInputs[i].id, nodeName);
+
+    graph.push({
+      op: 'placeholder',
+      name: nodeName,
+      target: nodeName,
+      args: [],
+      val_shape: sampleInputs[i].shape,
+    });
+
+    inputSpecs.push({
+      kind: 'USER_INPUT',
+      name: nodeName,
+    });
+  }
+
+  // 3. Trace the forward pass, recording operations
+  const handler = (e: CustomEvent) => {
+    const { operation, args, result } = e.detail as {
+      operation: TorchFunction;
+      args: (Tensor | number | number[] | boolean)[];
+      result: Tensor;
+    };
+
+    const opName = operation.opName;
+    if (!opName) return; // Skip if no opName (shouldn't happen)
+
+    // Build arg references
+    const nodeArgs: (string | string[])[] = [];
+    for (const arg of args) {
+      if (arg instanceof Tensor) {
+        const name = tensorIdToName.get(arg.id);
+        if (name) {
+          nodeArgs.push(name);
+        }
+        // If not found, it's an intermediate constant — skip
+      }
+      // Numbers and arrays are non-tensor args; we don't include them
+      // in the graph node args to match PyTorch's behavior for simple cases
+    }
+
+    // Generate node name from opName
+    const nodeName = nameGen.generate(opName);
+    tensorIdToName.set(result.id, nodeName);
+
+    graph.push({
+      op: 'call_function',
+      name: nodeName,
+      target: toAtenTarget(opName),
+      args: nodeArgs,
+      val_shape: result.shape,
+    });
+  };
+
+  eventBus.addEventListener(
+    events.OPERATION_AFTER_FORWARD,
+    handler as EventListener
+  );
+
+  let output: Tensor;
+  try {
+    output = no_grad(() => module.forward(...sampleInputs));
+  } finally {
+    eventBus.removeEventListener(
+      events.OPERATION_AFTER_FORWARD,
+      handler as EventListener
+    );
+  }
+
+  // 4. Add output node
+  const outputName = tensorIdToName.get(output.id) || 'output';
+  graph.push({
+    op: 'output',
+    name: 'output',
+    target: 'output',
+    args: [outputName],
+  });
+
+  const outputSpecs: OutputSpec[] = [{
+    kind: 'USER_OUTPUT',
+    name: outputName,
+  }];
+
+  // 5. Collect parameters
+  const parameters = new Map();
+  for (const [paramPath, param] of namedParams) {
+    parameters.set(paramPath, {
+      data: [...param.data],
+      shape: [...param.shape],
+    });
+  }
+
+  return new ExportedProgram(
+    graph,
+    { input_specs: inputSpecs, output_specs: outputSpecs },
+    parameters
+  );
+}
diff --git a/src/functions/base.ts b/src/functions/base.ts
new file mode 100644
index 00000000..897d1741
--- /dev/null
+++ b/src/functions/base.ts
@@ -0,0 +1,125 @@
+import { _unbroadcast } from '../broadcasting';
+import { zeros_like } from '../creation';
+import { is_grad_enabled } from '../grad_mode';
+import { Tensor } from '../tensor';
+import { eventBus, getNextId, events } from '../util';
+
+export type ArgumentType = Tensor | Tensor[] | number | number[] | boolean | string;
+
+export function resultRequiresGrad(...args: ArgumentType[]): boolean {
+  if (!is_grad_enabled()) return false;
+  for (const arg of args) {
+    if (arg instanceof Tensor && arg.requires_grad) {
+      return true;
+    }
+  }
+  return false;
+}
+
+abstract class TorchFunction {
+  public id: number = getNextId();
+  public opName: string = '';
+  public next_functions: TorchFunction[] = [];
+  public saved_tensors: Tensor[] = [];
+  public _retained_tensors: Tensor[] = [];
+
+  protected abstract _forward(...args: ArgumentType[]): Tensor;
+  protected abstract _backward(dz: Tensor | number): void;
+
+  forward(...args: ArgumentType[]): Tensor {
+    const requires_grad = resultRequiresGrad(...args);
+    eventBus.dispatchEvent(
+      new CustomEvent(events.OPERATION_BEFORE_FORWARD, {
+        detail: {
+          operation: this,
+          requires_grad,
+          args
+        }
+      })
+    );
+    const result = this._forward(...args);
+    eventBus.dispatchEvent(
+      new CustomEvent(events.OPERATION_AFTER_FORWARD, {
+        detail: {
+          operation: this,
+          requires_grad,
+          args,
+          result
+        }
+      })
+    );
+    return result;
+  }
+
+  backward(dz: Tensor | number): void {
+    eventBus.dispatchEvent(
+      new CustomEvent(events.OPERATION_BEFORE_BACKWARD, { detail: { operation: this, dz } })
+    );
+    for (const x of this._retained_tensors) {
+      if (!x.grad) {
+        x.grad = new Tensor(new Array(x.dataLength()).fill(0));
+      }
+      x.grad = x.grad.add(dz);
+    }
+    this._backward(dz);
+    eventBus.dispatchEvent(
+      new CustomEvent(events.OPERATION_AFTER_BACKWARD, { detail: { operation: this, dz } })
+    );
+  }
+}
+
+class NullOp extends TorchFunction {
+  protected _forward(..._args: ArgumentType[]): Tensor {
+    throw new Error('NullOp should not be called');
+  }
+  protected _backward(_dz: Tensor): void {
+    return;
+  }
+}
+
+export const nullOp = new NullOp();
+
+abstract class UnaryFunction extends TorchFunction {
+  protected abstract _forward(a: Tensor): Tensor;
+  protected abstract _backward(dz: Tensor): void;
+}
+
+abstract class BinaryFunction extends TorchFunction {
+  protected abstract _forward(a: Tensor, b: Tensor): Tensor;
+  protected abstract _backward(dz: Tensor): void;
+}
+
+export type TorchFunctionConstructor = new () => TorchFunction;
+export type UnaryFunctionConstructor = new () => UnaryFunction;
+export type BinaryFunctionConstructor = new () => BinaryFunction;
+
+export { TorchFunction, UnaryFunction, BinaryFunction };
+
+export class AccumulateGrad extends UnaryFunction {
+  public variable: Tensor;
+
+  protected _forward(variable: Tensor): Tensor {
+    this.variable = variable;
+    return variable;
+  }
+
+  protected _backward(dz: Tensor | number): void {
+    if (!this.variable.grad) {
+      this.variable.grad = zeros_like(this.variable);
+    }
+    eventBus.dispatchEvent(
+      new CustomEvent(events.OPERATION_BEFORE_ACCUMULATE_GRAD, { detail: { operation: this, dz } })
+    );
+    if (typeof dz === 'number') {
+      this.variable.grad = this.variable.grad.add(dz);
+    } else {
+      const unbroadcasted_dz = _unbroadcast(dz.shape, this.variable.shape, dz.data);
+      this.variable.grad = this.variable.grad.add(
+        new Tensor(unbroadcasted_dz, {}, { shape: this.variable.shape })
+      );
+    }
+    eventBus.dispatchEvent(
+      new CustomEvent(events.OPERATION_AFTER_ACCUMULATE_GRAD, { detail: { operation: this, dz } })
+    );
+  }
+}
diff --git a/src/functions/functional.ts b/src/functions/functional.ts
new file mode 100644
index 00000000..305963c2
--- /dev/null
+++ b/src/functions/functional.ts
@@ -0,0 +1,315 @@
+import { Tensor } from '../tensor';
+import { createOperation } from './registry';
+import { ArgumentType } from './base';
+
+function generate_function(opname: string) {
+  return (...args: ArgumentType[]) => {
+    const operation = createOperation(opname);
+    return operation.forward(...args);
+  };
+}
+
+function generate_unary_function(opname: string) {
+  return (a: Tensor | number) => {
+    if (typeof a == 'number') {
+      a = new Tensor(a);
+    }
+
+    const operation = createOperation(opname);
+    return operation.forward(a);
+  };
+}
+
+function generate_binary_function(opname: string) {
+  return (a: Tensor | number, b: Tensor | number) => {
+    if (typeof a == 'number') {
+      a = new Tensor(a);
+    }
+
+    if (typeof b == 'number') {
+      b = new Tensor(b);
+    }
+
+    const operation = createOperation(opname);
+    return operation.forward(a, b);
+  };
+}
+
+// debug operations
+
+/**
+ * @ignore
+ * Get left index in a binary function
+ */
+export const __left_index__ = generate_binary_function('__left_index__');
+
+/**
+ * @ignore
+ * Get right index in a binary function
+ */
+export const __right_index__ = generate_binary_function('__right_index__');
+
+// binary pointwise
+
+/**
+ * Adds two tensors element-wise.
+ */
+export const add = generate_binary_function('add');
+
+/**
+ * Subtracts the second tensor from the first tensor element-wise.
+ */
+export const sub = generate_binary_function('sub');
+
+/**
+ * Multiplies two tensors element-wise.
+ */
+export const mul = generate_binary_function('mul');
+
+/**
+ * Divides the first tensor by the second tensor element-wise.
+ */
+export const div = generate_binary_function('div');
+
+/**
+ * Raises the first tensor to the power of the second tensor element-wise.
+ */
+export const pow = generate_binary_function('pow');
+
+/**
+ * Computes the element-wise remainder of the division of the first tensor by the second tensor.
+ */
+export const fmod = generate_binary_function('fmod');
+
+/**
+ * Returns the element-wise maximum of the two tensors.
+ */
+export const maximum = generate_binary_function('maximum');
+
+/**
+ * Returns the element-wise minimum of the two tensors.
+ */
+export const minimum = generate_binary_function('minimum');
+
+// unary pointwise
+
+/**
+ * Computes the natural logarithm of the input tensor element-wise.
+ */
+export const log = generate_unary_function('log');
+
+/**
+ * Computes the square root of the input tensor element-wise.
+ */
+export const sqrt = generate_unary_function('sqrt');
+
+/**
+ * Computes the exponential of the input tensor element-wise.
+ */
+export const exp = generate_unary_function('exp');
+
+/**
+ * Computes the square of the input tensor element-wise.
+ */
+export const square = generate_unary_function('square');
+
+/**
+ * Computes the absolute value of the input tensor element-wise.
+ */
+export const abs = generate_unary_function('abs');
+
+/**
+ * Computes the sign of the input tensor element-wise.
+ */
+export const sign = generate_unary_function('sign');
+
+/**
+ * Negates the input tensor element-wise.
+ */
+export const neg = generate_unary_function('neg');
+
+/**
+ * Computes the reciprocal of the input tensor element-wise.
+ */
+export const reciprocal = generate_unary_function('reciprocal');
+
+/**
+ * Replaces NaN values in the input tensor with 0, positive infinity with a large finite number, and negative infinity with a small finite number.
+ */
+export const nan_to_num = generate_unary_function('nan_to_num');
+
+/**
+ * Reshapes the input tensor to the given shape.
+ */
+export const reshape = generate_function('reshape');
+
+/**
+ * Removes all dimensions of size 1 from the input tensor.
+ */
+export const squeeze = generate_function('squeeze');
+
+/**
+ * Adds a dimension of size 1 to the input tensor at the given position.
+ */
+export const unsqueeze = generate_function('unsqueeze');
+
+/**
+ * Expands the input tensor to the given shape.
+ */
+export const expand = generate_function('expand');
+
+// trigonometric
+
+/**
+ * Computes the sine of the input tensor element-wise.
+ */
+export const sin = generate_unary_function('sin');
+
+/**
+ * Computes the cosine of the input tensor element-wise.
+ */
+export const cos = generate_unary_function('cos');
+
+/**
+ * Computes the tangent of the input tensor element-wise.
+ */
+export const tan = generate_unary_function('tan');
+
+// reduction
+
+/**
+ * Computes the sum of the elements of the input tensor.
+ */
+export const sum = generate_function('sum');
+
+/**
+ * Computes the mean of the elements of the input tensor.
+ */
+export const mean = generate_function('mean');
+
+/**
+ * Computes the minimum of the elements of the input tensor.
+ */
+export const min = generate_function('min');
+
+/**
+ * Computes the maximum of the elements of the input tensor.
+ */
+export const max = generate_function('max');
+
+// linalg
+
+/**
+ * Transposes the input tensor.
+ */
+export const transpose = generate_function('transpose');
+
+/**
+ * Computes the matrix product of the two input tensors.
+ */
+export const matmul = generate_binary_function('matmul');
+
+// comparison
+
+/**
+ * Checks if the first tensor is less than the second tensor element-wise.
+ */
+export const lt = generate_binary_function('lt');
+
+/**
+ * Checks if the first tensor is greater than the second tensor element-wise.
+ */
+export const gt = generate_binary_function('gt');
+
+/**
+ * Checks if the first tensor is less than or equal to the second tensor element-wise.
+ */
+export const le = generate_binary_function('le');
+
+/**
+ * Checks if the first tensor is greater than or equal to the second tensor element-wise.
+ */
+export const ge = generate_binary_function('ge');
+
+/**
+ * Checks if the first tensor is equal to the second tensor element-wise.
+ */
+export const eq = generate_binary_function('eq');
+
+/**
+ * Checks if the first tensor is not equal to the second tensor element-wise.
+ */
+export const ne = generate_binary_function('ne');
+
+/**
+ * Checks if the two tensors are equal element-wise within a given tolerance.
+ */
+export function allclose(
+  a: Tensor,
+  b: Tensor,
+  rtol: number = 1e-5,
+  atol: number = 1e-8,
+  equal_nan: boolean = false
+): boolean {
+  return a.allclose(b, rtol, atol, equal_nan);
+}
+
+/**
+ * Returns the number of elements in the input tensor.
+ */
+export function numel(a: Tensor): number {
+  return a.dataLength();
+}
+
+/**
+ * Flattens the input tensor.
+ */
+export function flatten(input: Tensor, start_dim: number = 0, end_dim: number = -1): Tensor {
+  return input.flatten(start_dim, end_dim);
+}
+
+/**
+ * Concatenates tensors along a given dimension.
+ */
+export function cat(tensors: Tensor[], dim: number = 0): Tensor {
+  const operation = createOperation('cat');
+  return operation.forward(tensors, dim);
+}
+
+/**
+ * Alias for {@link cat}.
+ */
+export const concatenate = cat;
+
+/**
+ * Alias for {@link cat}.
+ */
+export const concat = cat;
+
+/**
+ * Computes the softmax of the input tensor along the given dimension.
+ */
+export function softmax(input: Tensor, dim: number): Tensor {
+  const operation = createOperation('softmax');
+  return operation.forward(input, dim);
+}
+
+/**
+ * Clamps all elements in input tensor to the range [min, max].
+ */
+export function clamp(input: Tensor, min: number, max: number): Tensor {
+  const operation = createOperation('clamp');
+  return operation.forward(input, min, max);
+}
+
+/**
+ * Alias for {@link clamp}.
+ */
+export const clip = clamp;
+
+/**
+ * Stack tensors along a new dimension.
+ */
+export function stack(tensors: Tensor[], dim: number = 0): Tensor {
+  return cat(tensors.map(t => t.unsqueeze(dim)), dim);
+}
diff --git a/src/functions/mixin.ts b/src/functions/mixin.ts
new file mode 100644
index 00000000..da326fb6
--- /dev/null
+++ b/src/functions/mixin.ts
@@ -0,0 +1,229 @@
+import { Tensor } from '../tensor';
+import {
+  _broadcast_shape,
+  _get_original_index,
+  _pad_shape
+} from '../broadcasting';
+import { TorchFunction, BinaryFunction, UnaryFunction, nullOp, resultRequiresGrad } from './base';
+import { registerOperation } from './registry';
+import { _get_reduction_shape, _get_strides, _ravel_index, _unravel_index } from './util';
+
+export function BinaryFunctionMixin(
+  operation: (a: number[], b: number[], a_index: number, b_index: number) => number,
+  backward_operations: (a?: Tensor, b?: Tensor, aFn?: TorchFunction, bFn?: TorchFunction, dz?: Tensor) => void,
+  opName: string | null = null
+): typeof BinaryFunction {
+  const kernel = (
+    a: number[],
+    as: number[],
+    b: number[],
+    bs: number[],
+    bcs: number[],
+    output_size: number
+  ) => {
+    const res = Array(output_size);
+    for (let x = 0; x < output_size; x++) {
+      const a_index = _get_original_index(as, bcs, x);
+      const b_index = _get_original_index(bs, bcs, x);
+      res[x] = operation(a, b, a_index, b_index);
+    }
+    return res;
+  };
+
+  const forward_tensor = (a: Tensor, b: Tensor, operation: TorchFunction | null = null): Tensor => {
+    const broadcast_shape = _broadcast_shape(a.shape, b.shape);
+    const padded_a_shape = _pad_shape(a.shape, broadcast_shape);
+    const padded_b_shape = _pad_shape(b.shape, broadcast_shape);
+
+    const output_size = broadcast_shape.reduce((acc, val) => acc * val, 1);
+
+    return new Tensor(
+      kernel(
+        a.data,
+        padded_a_shape,
+        b.data,
+        padded_b_shape,
+        broadcast_shape,
+        output_size
+      ) as number[],
+      { requires_grad: resultRequiresGrad(a, b) },
+      { operation: operation, shape: broadcast_shape }
+    );
+  };
+
+  const result = {
+    [opName]: class extends BinaryFunction {
+      protected _forward(a: Tensor, b: Tensor): Tensor {
+        const rg = resultRequiresGrad(a, b);
+        if (rg) {
+          this.saved_tensors = [a, b];
+        }
+        this.next_functions.push(a.grad_fn ? a.grad_fn : nullOp);
+        this.next_functions.push(b.grad_fn ? b.grad_fn : nullOp);
+        return forward_tensor(a, b, rg ? this : null);
+      }
+
+      protected _backward(dz: Tensor): void {
+        const [a, b] = this.saved_tensors;
+        const [aFn, bFn] = this.next_functions;
+
+        backward_operations(a, b, aFn, bFn, dz);
+      }
+    }
+  }[opName];
+  if (opName) {
+    registerOperation(opName, result);
+  }
+  return result;
+}
+
+export function UnaryFunctionMixin(
+  operation: (a: number[], x: number) => number,
+  backward_operations: (a?: Tensor, aFn?: TorchFunction, dz?: Tensor) => void,
+  opName: string | null = null
+): typeof UnaryFunction {
+  const kernel = (a: number[], output_size: number) => {
+    const res = Array(output_size);
+    for (let x = 0; x < output_size; x++) {
+      res[x] = operation(a, x);
+    }
+    return res;
+  };
+  const forward_tensor = (a: Tensor, operation: TorchFunction | null = null): Tensor => {
+    const output_size = a.dataLength();
+
+    return new Tensor(
+      kernel(a.data, output_size) as number[],
+      { requires_grad: resultRequiresGrad(a) },
+      { operation: operation, shape: a.shape }
+    );
+  };
+
+  const result = {
+    [opName]: class extends UnaryFunction {
+      protected _forward(a: Tensor): Tensor {
+        const rg = resultRequiresGrad(a);
+        if (rg) {
+          this.saved_tensors = [a];
+        }
+        this.next_functions.push(a.grad_fn ? a.grad_fn : nullOp);
+        return forward_tensor(a, rg ? this : null);
+      }
+
+      protected _backward(dz: Tensor): void {
+        const [a] = this.saved_tensors;
+        const [aFn] = this.next_functions;
+
+        backward_operations(a, aFn, dz);
+      }
+    }
+  }[opName];
+  if (opName) {
+    registerOperation(opName, result);
+  }
+  return result;
+}
+
+export function ReductionFunctionMixin(
+  init_val: number,
+  reduce_op: (acc: number, val: number) => number,
+  backward_operations: (
+    a: Tensor,
+    restored_dz: Tensor,
+    dim: number | number[],
+    keepdim: boolean
+  ) => Tensor,
+  opName: string | null = null,
+  finalize_op?: (acc: number, count: number) => number
+): new () => TorchFunction {
+  const result = {
+    [opName]: class extends TorchFunction {
+      protected dim?: number | number[];
+      protected keepdim?: boolean;
+
+      protected _forward(a: Tensor, dim?: number | number[], keepdim: boolean = false): Tensor {
+        this.dim = dim;
+        this.keepdim = keepdim;
+
+        const rg = resultRequiresGrad(a);
+        if (rg) {
+          this.saved_tensors = [a];
+        }
+        this.next_functions.push(a.grad_fn ? a.grad_fn : nullOp);
+
+        const out_shape = _get_reduction_shape(a.shape, dim, keepdim);
+        const out_size = out_shape.reduce((acc, val) => acc * val, 1);
+
+        const res_data = new Array(out_size).fill(init_val);
+        const counts = new Array(out_size).fill(0); // Tracked specifically for mean()
+
+        const in_strides = _get_strides(a.shape);
+        const out_strides = _get_strides(out_shape);
+
+        const dims = dim === undefined ? [] : Array.isArray(dim) ? dim : [dim];
+        const normalized_dims = dims.map(d => (d < 0 ? d + a.shape.length : d));
+        const is_full_reduce = dim === undefined;
+
+        // Accumulate
+        const aData = a.data; // cache: avoids repeated allocations for view tensors
+        for (let i = 0; i < aData.length; i++) {
+          const in_coords = _unravel_index(i, in_strides);
+          let out_coords: number[];
+
+          if (is_full_reduce) {
+            out_coords = keepdim ? in_coords.map(() => 0) : [];
+          } else {
+            out_coords = [];
+            for (let j = 0; j < a.shape.length; j++) {
+              if (normalized_dims.includes(j)) {
+                if (keepdim) out_coords.push(0); // Collapse dimension to index 0
+              } else {
+                out_coords.push(in_coords[j]);
+              }
+            }
+          }
+
+          const out_idx = _ravel_index(out_coords, out_strides);
+          res_data[out_idx] = reduce_op(res_data[out_idx], aData[i]);
+          counts[out_idx]++;
+        }
+
+        // Finalize (e.g., divide by count for mean)
+        if (finalize_op) {
+          for (let i = 0; i < out_size; i++) {
+            res_data[i] = finalize_op(res_data[i], counts[i]);
+          }
+        }
+
+        return new Tensor(
+          res_data,
+          { requires_grad: rg },
+          { operation: rg ? this : null, shape: out_shape }
+        );
+      }
+
+      protected _backward(dz: Tensor): void {
+        const [a] = this.saved_tensors;
+        const [aFn] = this.next_functions;
+
+        let restored_dz = dz;
+
+        const target_shape = _get_reduction_shape(a.shape, this.dim, true);
+
+        if (dz.shape.length !== target_shape.length) {
+          restored_dz = dz.reshape(target_shape);
+        }
+
+        const expanded_dz = restored_dz.expand(a.shape);
+        const grad_a = backward_operations(a, expanded_dz, this.dim, this.keepdim);
+
+        aFn.backward(grad_a);
+      }
+    }
+  }[opName];
+
+  if (opName) {
+    registerOperation(opName, result);
+  }
+  return result;
+}
diff --git a/src/functions/ops.ts b/src/functions/ops.ts
new file mode 100644
index 00000000..982d6cc7
--- /dev/null
+++ b/src/functions/ops.ts
@@ -0,0 +1,1414 @@
+import { Tensor } from '../tensor';
+import {
+  _broadcast_shape,
+  _get_original_index,
+  _pad_shape,
+  _unbroadcast
+} from '../broadcasting';
+import { TorchFunction, BinaryFunction, nullOp, resultRequiresGrad } from './base';
+import * as functional from './functional';
+import { registerOperation } from './registry';
+import { ones } from '../creation';
+import { UnaryFunctionMixin, BinaryFunctionMixin, ReductionFunctionMixin } from './mixin';
+import { _get_reduction_shape } from './util';
+
+function unbroadcast(result: Tensor, original_shape: number[]): Tensor {
+  const unbroadcasted_result = _unbroadcast(result.shape, original_shape, result.data);
+  return new Tensor(unbroadcasted_result, { requires_grad: result.requires_grad }, { shape: original_shape });
+}
+
+function broadcast(tensor: Tensor, result_shape: number[]): Tensor {
+  return tensor.mul(ones(result_shape));
+}
+
+// debug operations
+
+const __Left_index__ = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, _b_index: number) => a_index,
+  () => { },
+  "__left_index__"
+);
+
+const __Right_index__ = BinaryFunctionMixin(
+  (a: number[], b: number[], _a_index: number, b_index: number) => b_index,
+  () => { },
+  "__right_index__"
+);
+
+// binary pointwise
+
+const Add = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => a[a_index] + b[b_index],
+  (_a, _b, aFn, bFn, dz) => {
+    aFn.backward(dz);
+    bFn.backward(dz);
+  },
+  "add"
+);
+
+const Sub = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => a[a_index] - b[b_index],
+  (_a, _b, aFn, bFn, dz) => {
+    aFn.backward(dz);
+    bFn.backward(dz.mul(new Tensor(-1)));
+  },
+  "sub"
+);
+
+const Mul = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => a[a_index] * b[b_index],
+  (a, b, aFn, bFn, dz) => {
+    aFn.backward(dz.mul(b));
+    bFn.backward(dz.mul(a));
+  },
+  "mul"
+);
+
+const Div = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => a[a_index] / b[b_index],
+  (a, b, aFn, bFn, dz) => {
+    aFn.backward(dz.div(b));
+    bFn.backward(dz.mul(a).mul(new Tensor(-1)).div(b).div(b));
+  },
+  "div"
+);
+
+function _where(mask: Tensor, x: Tensor, fallback: Tensor | number): Tensor {
+  const fb = typeof fallback === 'number' ? fallback : null;
+  const maskData = mask.data;
+  const xData = x.data;
+  const fbData = fb === null ? (fallback as Tensor).data : null;
+  const data = new Array(x.dataLength());
+  for (let i = 0; i < data.length; i++) {
+    data[i] = maskData[i] ? xData[i] : (fb !== null ? fb : fbData![i]);
+  }
+  return new Tensor(data, {}, { shape: x.shape });
+}
+
+const Pow = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => Math.pow(a[a_index], b[b_index]),
+  (a, b, aFn, bFn, dz) => {
+    const ga = dz.mul(b).mul(a.pow(b.sub(new Tensor(1))));
+    const gb = dz.mul(a.pow(b)).mul(a.log());
+    // When a==0, grads can produce NaN/Inf (from 0*Inf or log(0)); replace with 0
+    aFn.backward(_where(a.ne(0), ga, ga.nan_to_num()));
+    bFn.backward(_where(a.ne(0), gb, 0));
+  },
+  "pow"
+);
+
+const Fmod = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => a[a_index] % b[b_index],
+  (_a, _b, aFn, _bFn, dz) => {
+    aFn.backward(dz);
+  },
+  "fmod"
+);
+
+const Maximum = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => Math.max(a[a_index], b[b_index]),
+  (a, b, aFn, bFn, dz) => {
+    // When a == b, PyTorch splits gradient 0.5 each
+    const eq_mask = a.eq(b);
+    const a_mask = a.gt(b).add(eq_mask.mul(new Tensor(0.5)));
+    const b_mask = b.gt(a).add(eq_mask.mul(new Tensor(0.5)));
+    aFn.backward(dz.mul(a_mask));
+    bFn.backward(dz.mul(b_mask));
+  },
+  "maximum"
+);
+
+const Minimum = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => Math.min(a[a_index], b[b_index]),
+  (a, b, aFn, bFn, dz) => {
+    // When a == b, PyTorch splits gradient 0.5 each
+    const eq_mask = a.eq(b);
+    const a_mask = a.lt(b).add(eq_mask.mul(new Tensor(0.5)));
+    const b_mask = b.lt(a).add(eq_mask.mul(new Tensor(0.5)));
+    aFn.backward(dz.mul(a_mask));
+    bFn.backward(dz.mul(b_mask));
+  },
+  "minimum"
+);
+
+function _powint_tensor(a: Tensor, n: number, operation: TorchFunction | null = null): Tensor {
+  const aData = a.data;
+  const data = new Array(a.dataLength());
+  for (let i = 0; i < data.length; i++) {
+    data[i] = Math.pow(aData[i], n);
+  }
+  return new Tensor(
+    data,
+    { requires_grad: resultRequiresGrad(a) },
+    { operation: operation, shape: a.shape }
+  );
+}
+
+class PowInt extends TorchFunction {
+  private n: number;
+  protected _forward(a: Tensor, n: number): Tensor {
+    const rg = resultRequiresGrad(a);
+    if (rg) {
+      this.saved_tensors = [a];
+      this.n = n;
+    }
+
+    this.next_functions.push(a.grad_fn ? a.grad_fn : nullOp);
+    return _powint_tensor(a, n, rg ? this : null);
+  }
+  protected _backward(dz: Tensor): void {
+    const [a] = this.saved_tensors;
+    const n = this.n;
+    const [aFn] = this.next_functions;
+
+    // backward_operations:
+    aFn.backward(dz.mul(n).mul(a.pow(n - 1)));
+  }
+}
+registerOperation("powint", PowInt);
+
+// unary pointwise
+
+const Log = UnaryFunctionMixin(
+  (a: number[], a_index: number) => Math.log(a[a_index]),
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(new Tensor(1).div(a)));
+  },
+  "log"
+);
+
+const Sqrt = UnaryFunctionMixin(
+  (a: number[], x: number) => Math.sqrt(a[x]),
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(new Tensor(1).div(a.sqrt()).div(2)));
+  },
+  "sqrt"
+);
+
+const Exp = UnaryFunctionMixin(
+  (a: number[], x: number) => Math.exp(a[x]),
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(a.exp()));
+  },
+  "exp"
+);
+
+const Square = UnaryFunctionMixin(
+  (a: number[], x: number) => a[x] * a[x],
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(a).mul(new Tensor(2)));
+  },
+  "square"
+);
+
+const Abs = UnaryFunctionMixin(
+  (a: number[], x: number) => Math.abs(a[x]),
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(functional.sign(a)));
+  },
+  "abs"
+);
+
+const Sign = UnaryFunctionMixin(
+  (a: number[], x: number) => Math.sign(a[x]),
+  (_a, aFn) => {
+    aFn.backward(0);
+  },
+  "sign"
+);
+
+const Neg = UnaryFunctionMixin(
+  (a: number[], x: number) => -a[x],
+  (_a, aFn, dz) => {
+    aFn.backward(dz.mul(new Tensor(-1)));
+  },
+  "neg"
+);
+
+const Reciprocal = UnaryFunctionMixin(
+  (a: number[], x: number) => 1 / a[x],
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(a.pow(-2)).neg());
+  },
+  "reciprocal"
+);
+
+const NanToNum = UnaryFunctionMixin(
+  (a: number[], x: number) => {
+    const v = a[x];
+    if (Number.isNaN(v)) return 0;
+    if (v === Infinity) return 3.4028235e+38;
+    if (v === -Infinity) return -3.4028235e+38;
+    return v;
+  },
+  (a, aFn, dz) => {
+    aFn.backward(dz);
+  },
+  "nan_to_num"
+);
+
+class Reshape extends TorchFunction {
+  protected _forward(a: Tensor, shape: number[]) {
+    const previous_length = a.dataLength();
+
+    const negIdx = shape.indexOf(-1);
+    if (negIdx !== -1) {
+      if (shape.indexOf(-1, negIdx + 1) !== -1) {
+        throw new Error('Only one -1 is allowed in reshape shape');
+      }
+      const known = shape.reduce((acc, val, i) => i === negIdx ? acc : acc * val, 1);
+      if (previous_length % known !== 0) {
+        throw new Error('Shape mismatch: cannot infer -1 dimension for shape ' + a.shape + ' -> ' + shape);
+      }
+      shape = shape.slice();
+      shape[negIdx] = previous_length / known;
+    }
+
+    const target_length = shape.reduce((acc, val) => acc * val, 1);
+
+    if (previous_length !== target_length) {
+      throw new Error('Shape mismatch: ' + a.shape + ' and ' + shape);
+    }
+
+    const rg = resultRequiresGrad(a);
+    if (rg) {
+      this.saved_tensors = [a];
+    }
+    if (a.grad_fn) {
+      this.next_functions.push(a.grad_fn);
+    } else {
+      this.next_functions.push(nullOp);
+    }
+
+    return new Tensor(
+      a.data,
+      { requires_grad: rg },
+      { operation: rg ? this : null, shape }
+    );
+  }
+  protected _backward(dz: Tensor) {
+    const [a] = this.saved_tensors;
+    const [aFn] = this.next_functions;
+
+    aFn.backward(dz.reshape(a.shape));
+  }
+}
+registerOperation('reshape', Reshape);
+
+class Flatten extends TorchFunction {
+  protected _forward(a: Tensor, start_dim: number = 0, end_dim: number = -1) {
+    const ndim = a.shape.length;
+    const sd = start_dim < 0 ? start_dim + ndim : start_dim;
+    const ed = end_dim < 0 ? end_dim + ndim : end_dim;
+    const newShape = [
+      ...a.shape.slice(0, sd),
+      a.shape.slice(sd, ed + 1).reduce((acc, val) => acc * val, 1),
+      ...a.shape.slice(ed + 1),
+    ];
+
+    const rg = resultRequiresGrad(a);
+    if (rg) {
+      this.saved_tensors = [a];
+    }
+    if (a.grad_fn) {
+      this.next_functions.push(a.grad_fn);
+    } else {
+      this.next_functions.push(nullOp);
+    }
+
+    return new Tensor(
+      a.data,
+      { requires_grad: rg },
+      { operation: rg ? this : null, shape: newShape }
+    );
+  }
+  protected _backward(dz: Tensor) {
+    const [a] = this.saved_tensors;
+    const [aFn] = this.next_functions;
+
+    aFn.backward(dz.reshape(a.shape));
+  }
+}
+registerOperation('flatten', Flatten);
+
+class Squeeze extends TorchFunction {
+  protected _forward(a: Tensor, dim?: number) {
+    const rg = resultRequiresGrad(a);
+    if (rg) {
+      this.saved_tensors = [a];
+    }
+    if (a.grad_fn) {
+      this.next_functions.push(a.grad_fn);
+    } else {
+      this.next_functions.push(nullOp);
+    }
+
+    let shape = [...a.shape];
+
+    if (dim !== undefined) {
+      if (dim < 0) {
+        dim += a.shape.length;
+      }
+
+      // PyTorch only squeezes the specified dimension if its size is exactly 1
+      if (shape[dim] === 1) {
+        shape.splice(dim, 1);
+      }
+    } else {
+      // If no dim is provided, strip out all dimensions of size 1
+      shape = shape.filter((d) => d !== 1);
+    }
+
+    return new Tensor(
+      a.data,
+      { requires_grad: rg },
+      { operation: rg ? this : null, shape }
+    );
+  }
+
+  protected _backward(dz: Tensor) {
+    const [a] = this.saved_tensors;
+    const [aFn] = this.next_functions;
+
+    // The derivative of squeeze is just reshaping the gradient 
+    // back to the original unsqueezed shape.
+    aFn.backward(dz.reshape(a.shape));
+  }
+}
+registerOperation('squeeze', Squeeze);
+
+class Unsqueeze extends TorchFunction {
+  protected _forward(a: Tensor, dim: number) {
+    const rg = resultRequiresGrad(a);
+    if (rg) {
+      this.saved_tensors = [a];
+    }
+    if (a.grad_fn) {
+      this.next_functions.push(a.grad_fn);
+    } else {
+      this.next_functions.push(nullOp);
+    }
+
+    if (dim < 0) {
+      dim += a.shape.length + 1;
+    }
+
+    const shape = [...a.shape];
+    shape.splice(dim, 0, 1);
+
+    return new Tensor(
+      a.data,
+      { requires_grad: rg },
+      { operation: rg ? this : null, shape }
+    );
+  }
+  protected _backward(dz: Tensor) {
+    const [a] = this.saved_tensors;
+    const [aFn] = this.next_functions;
+
+    // backward_operations:
+    aFn.backward(dz.reshape(a.shape));
+  }
+}
+registerOperation('unsqueeze', Unsqueeze);
+
+class Expand extends TorchFunction {
+  protected _forward(a: Tensor, expanded_shape: number[]): Tensor {
+    const rg = resultRequiresGrad(a);
+    if (rg) {
+      this.saved_tensors = [a];
+    }
+    if (a.grad_fn) {
+      this.next_functions.push(a.grad_fn);
+    } else {
+      this.next_functions.push(nullOp);
+    }
+
+    const offset = expanded_shape.length - a.shape.length;
+    const target_shape = expanded_shape.map((dim, i) => {
+      if (dim === -1) {
+        const orig_i = i - offset;
+        return orig_i >= 0 ? a.shape[orig_i] : 1;
+      }
+      return dim;
+    });
+
+    // Steal data from just broadcasting
+    const outData = broadcast(a, target_shape).data;
+
+    return new Tensor(
+      outData,
+      { requires_grad: rg },
+      { operation: rg ? this : null, shape: target_shape }
+    );
+  }
+
+  protected _backward(dz: Tensor): void {
+    const [a] = this.saved_tensors;
+    const [aFn] = this.next_functions;
+
+    // Route the collapsed gradient upstream
+    aFn.backward(unbroadcast(dz, a.shape));
+  }
+}
+registerOperation('expand', Expand)
+
+// trigonometric
+
+const Sin = UnaryFunctionMixin(
+  (a: number[], x: number) => Math.sin(a[x]),
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(a.cos()));
+  },
+  "sin"
+);
+
+const Cos = UnaryFunctionMixin(
+  (a: number[], x: number) => Math.cos(a[x]),
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(a.sin().neg()));
+  },
+  "cos"
+);
+
+const Tan = UnaryFunctionMixin(
+  (a: number[], x: number) => Math.tan(a[x]),
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(a.cos().pow(-2)));
+  },
+  "tan"
+);
+
+// reduction
+
+export const Sum = ReductionFunctionMixin(
+  0,
+  (acc, val) => acc + val,
+  (a, expanded_dz) => expanded_dz,
+  'sum'
+);
+
+export const Mean = ReductionFunctionMixin(
+  0,
+  (acc, val) => acc + val,
+  (a, expanded_dz, dim) => {
+    const target_shape = _get_reduction_shape(a.shape, dim, false);
+    const out_size = target_shape.length > 0 ? target_shape.reduce((acc, v) => acc * v, 1) : 1;
+    const N = a.dataLength() / out_size;
+
+    return expanded_dz.mul(new Tensor([1 / N]));
+  },
+  'mean',
+  (acc, count) => acc / count
+);
+
+export const Max = ReductionFunctionMixin(
+  -Infinity,
+  (acc, val) => Math.max(acc, val),
+  (a, expanded_dz, dim) => {
+    const max_tensor = a.max(dim, true);
+    const max_expanded = max_tensor.expand(a.shape);
+    const mask = a.eq(max_expanded).detach();
+
+    return expanded_dz.mul(mask);
+  },
+  'max'
+);
+
+export const Min = ReductionFunctionMixin(
+  Infinity,
+  (acc, val) => Math.min(acc, val),
+  (a, expanded_dz, dim) => {
+    const min_tensor = a.min(dim, true);
+    const min_expanded = min_tensor.expand(a.shape);
+    const mask = a.eq(min_expanded).detach();
+
+    return expanded_dz.mul(mask);
+  },
+  'min'
+);
+
+// linalg
+
+function _transpose_tensor(
+  a: Tensor,
+  dim0: number,
+  dim1: number,
+  operation: TorchFunction | null = null
+): Tensor {
+  if (a.shape.length + dim0 < 0 || a.shape.length + dim1 < 0) {
+    throw new Error(`Transpose: Dimension out of range (${dim0} and ${dim1})`);
+  }
+  dim0 = dim0 < 0 ? a.shape.length + dim0 : dim0;
+  dim1 = dim1 < 0 ? a.shape.length + dim1 : dim1;
+
+  const output_shape = [...a.shape];
+  [output_shape[dim0], output_shape[dim1]] = [output_shape[dim1], output_shape[dim0]];
+  const size = a.dataLength();
+  const data = new Array(size);
+  const aData = a.data;
+
+  const a_strides = new Array(a.shape.length);
+  const out_strides = new Array(output_shape.length);
+  for (let i = a.shape.length - 1, s = 1; i >= 0; i--) {
+    a_strides[i] = s;
+    s *= a.shape[i];
+  }
+  for (let i = output_shape.length - 1, s = 1; i >= 0; i--) {
+    out_strides[i] = s;
+    s *= output_shape[i];
+  }
+
+  for (let i = 0; i < size; i++) {
+    let idx = i;
+    let input_idx = 0;
+    for (let d = 0; d < output_shape.length; d++) {
+      const stride = out_strides[d];
+      const coord = Math.floor(idx / stride);
+      idx %= stride;
+
+      let input_d = d;
+      if (d === dim0) input_d = dim1;
+      else if (d === dim1) input_d = dim0;
+
+      input_idx += coord * a_strides[input_d];
+    }
+    data[i] = aData[input_idx];
+  }
+
+  return new Tensor(
+    data,
+    { requires_grad: resultRequiresGrad(a) },
+    { operation: operation, shape: output_shape }
+  );
+}
+class Transpose extends TorchFunction {
+  private dim0: number;
+  private dim1: number;
+  protected _forward(a: Tensor, dim0: number, dim1: number): Tensor {
+    const rg = resultRequiresGrad(a);
+    if (rg) {
+      this.saved_tensors = [a];
+      this.dim0 = dim0;
+      this.dim1 = dim1;
+    }
+    this.next_functions.push(a.grad_fn ? a.grad_fn : nullOp);
+    return _transpose_tensor(a, dim0, dim1, rg ? this : null);
+  }
+  protected _backward(dz: Tensor): void {
+    // const [a] = this.saved_tensors;
+    const dim0 = this.dim0;
+    const dim1 = this.dim1;
+    const [aFn] = this.next_functions;
+
+    // backward_operations:
+    aFn.backward(dz.transpose(dim0, dim1));
+  }
+}
+registerOperation('transpose', Transpose);
+
+function _matmul_tensor(a: Tensor, b: Tensor, operation: TorchFunction | null = null): [Tensor, number[]] {
+  if (a.shape.length == 1 && b.shape.length == 1) {
+    return [a.mul(b).sum(), []];
+  }
+
+  const a_1d = a.shape.length == 1;
+  const b_1d = b.shape.length == 1;
+
+  const a_shape = a_1d ? [1, a.shape[0]] : a.shape;
+  const b_shape = b_1d ? [b.shape[0], 1] : b.shape;
+
+  if (a_shape[a_shape.length - 1] != b_shape[b_shape.length - 2]) {
+    throw new Error(`Shapes cannot be multiplied (${a_shape.join("x")} and ${b_shape.join("x")})`);
+  }
+
+  const broadcast_shape = _broadcast_shape(a_shape.slice(0, -2), b_shape.slice(0, -2)).concat([
+    a_shape[a_shape.length - 2],
+    b_shape[b_shape.length - 1]
+  ]);
+
+  const output_size = broadcast_shape.reduce((acc, val) => acc * val, 1);
+  const data = new Array(output_size).fill(0);
+
+  const padded_a_shape = _pad_shape(a_shape, broadcast_shape);
+  const padded_b_shape = _pad_shape(b_shape, broadcast_shape);
+
+  const dim_M = broadcast_shape[broadcast_shape.length - 2];
+  const dim_N = broadcast_shape[broadcast_shape.length - 1];
+  const dim_K = a_shape[a_shape.length - 1]; // or b_shape[b_shape.length - 2]
+
+  const aData = a.data;
+  const bData = b.data;
+  for (let i = 0; i < output_size; i++) {
+    const mn_idx = i % (dim_M * dim_N);
+    const m = Math.floor(mn_idx / dim_N);
+    const n = mn_idx % dim_N;
+
+    const base_a = _get_original_index(padded_a_shape, broadcast_shape, i - n);
+    const base_b = _get_original_index(padded_b_shape, broadcast_shape, i - m * dim_N);
+
+    let sum = 0;
+    for (let k = 0; k < dim_K; k++) {
+      sum += aData[base_a + k] * bData[base_b + k * dim_N];
+    }
+    data[i] = sum;
+  }
+
+  let shape_after_removing_extra_dims = [...broadcast_shape];
+
+  if (a_1d) {
+    shape_after_removing_extra_dims = shape_after_removing_extra_dims
+      .slice(0, -2)
+      .concat([broadcast_shape[broadcast_shape.length - 1]]);
+  }
+
+  if (b_1d) {
+    shape_after_removing_extra_dims = shape_after_removing_extra_dims.slice(0, -1);
+  }
+
+  return [new Tensor(
+    data,
+    { requires_grad: resultRequiresGrad(a, b) },
+    { operation: operation, shape: shape_after_removing_extra_dims }
+  ), shape_after_removing_extra_dims];
+}
+
+class Matmul extends BinaryFunction {
+  private shape: number[];
+
+  protected _forward(a: Tensor, b: Tensor): Tensor {
+    const rg = resultRequiresGrad(a, b);
+    if (rg) {
+      this.saved_tensors = [a, b];
+    }
+    this.next_functions.push(a.grad_fn ? a.grad_fn : nullOp);
+    this.next_functions.push(b.grad_fn ? b.grad_fn : nullOp);
+    const result = _matmul_tensor(a, b, rg ? this : null);
+    this.shape = result[1];
+    return result[0];
+  }
+  protected _backward(dz: Tensor): void {
+    const [a, b] = this.saved_tensors;
+    const [aFn, bFn] = this.next_functions;
+
+    // 1. 1D x 1D (Dot Product)
+    if (a.shape.length === 1 && b.shape.length === 1) {
+      aFn.backward(dz.mul(b));
+      bFn.backward(dz.mul(a));
+      return;
+    }
+
+    // 2. 1D x ND
+    if (a.shape.length === 1) {
+      const dz1 = dz.unsqueeze(-2);
+      const a1 = a.unsqueeze(-2);
+
+      let da = dz1.matmul(b.transpose(-2, -1));
+      let db = a1.transpose(-2, -1).matmul(dz1);
+
+      da = da.squeeze(-2);
+      db = unbroadcast(db, b.shape);
+
+      aFn.backward(da);
+      bFn.backward(db);
+      return;
+    }
+
+    // 3. ND x 1D
+    if (b.shape.length === 1) {
+      const dz1 = dz.unsqueeze(-1);
+      const b1 = b.unsqueeze(-1);
+
+      let da = dz1.matmul(b1.transpose(-2, -1));
+      let db = a.transpose(-2, -1).matmul(dz1);
+
+      da = unbroadcast(da, a.shape);
+      db = db.squeeze(-1);
+
+      aFn.backward(da);
+      bFn.backward(db);
+      return;
+    }
+
+    // 4. ND x ND (Batched or Standard)
+    let da = dz.matmul(b.transpose(-2, -1));
+    let db = a.transpose(-2, -1).matmul(dz);
+
+    da = unbroadcast(da, a.shape);
+    db = unbroadcast(db, b.shape);
+
+    aFn.backward(da);
+    bFn.backward(db);
+  }
+}
+registerOperation('matmul', Matmul);
+
+function _convNd_forward(
+  input: Tensor,
+  weight: Tensor,
+  bias: Tensor | null,
+  stride: number | number[],
+  padding: number | number[],
+  dilation: number | number[],
+  groups: number,
+  dims: number
+): Tensor {
+  const stride_arr = typeof stride === 'number' ? new Array(dims).fill(stride) : stride;
+  const padding_arr = typeof padding === 'number' ? new Array(dims).fill(padding) : padding;
+  const dilation_arr = typeof dilation === 'number' ? new Array(dims).fill(dilation) : dilation;
+
+  const batch_size = input.shape[0];
+  const in_channels = input.shape[1];
+  const out_channels = weight.shape[0];
+  const in_dims = input.shape.slice(2);
+  const kernel_dims = weight.shape.slice(2);
+
+  if (in_channels !== weight.shape[1] * groups) {
+    throw new Error(`in_channels (${in_channels}) must be divisible by groups (${groups}) and match weight.shape[1] * groups (${weight.shape[1] * groups})`);
+  }
+
+  const out_dims = in_dims.map((in_dim, i) => {
+    return Math.floor((in_dim + 2 * padding_arr[i] - dilation_arr[i] * (kernel_dims[i] - 1) - 1) / stride_arr[i] + 1);
+  });
+
+  const output_shape = [batch_size, out_channels, ...out_dims];
+  const output_size = output_shape.reduce((a, b) => a * b, 1);
+  const output_data = new Array(output_size).fill(0);
+
+  const get_strides = (shape: number[]) => {
+    const strides = new Array(shape.length);
+    let s = 1;
+    for (let i = shape.length - 1; i >= 0; i--) {
+      strides[i] = s;
+      s *= shape[i];
+    }
+    return strides;
+  };
+
+  const in_strides = get_strides(input.shape);
+  const w_strides = get_strides(weight.shape);
+  const out_strides = get_strides(output_shape);
+  const in_channels_per_group = in_channels / groups;
+  const out_channels_per_group = out_channels / groups;
+  const inputData = input.data;
+  const weightData = weight.data;
+  const biasData = bias ? bias.data : null;
+
+  for (let b = 0; b < batch_size; b++) {
+    for (let g = 0; g < groups; g++) {
+      for (let oc_g = 0; oc_g < out_channels_per_group; oc_g++) {
+        const oc = g * out_channels_per_group + oc_g;
+
+        // Iterate over output spatial dimensions
+        const out_spatial_size = out_dims.reduce((a, b) => a * b, 1);
+        for (let os_idx = 0; os_idx < out_spatial_size; os_idx++) {
+
+          // Decode output spatial index
+          const os_coords = new Array(dims);
+          let temp_os = os_idx;
+          for (let d = dims - 1; d >= 0; d--) {
+            os_coords[d] = temp_os % out_dims[d];
+            temp_os = Math.floor(temp_os / out_dims[d]);
+          }
+
+          let sum = biasData ? biasData[oc] : 0;
+
+          // Iterate over kernel spatial dimensions and in_channels
+          for (let ic_g = 0; ic_g < in_channels_per_group; ic_g++) {
+            const ic = g * in_channels_per_group + ic_g;
+
+            const kernel_spatial_size = kernel_dims.reduce((a, b) => a * b, 1);
+            for (let ks_idx = 0; ks_idx < kernel_spatial_size; ks_idx++) {
+              // Decode kernel spatial index
+              const ks_coords = new Array(dims);
+              let temp_ks = ks_idx;
+              for (let d = dims - 1; d >= 0; d--) {
+                ks_coords[d] = temp_ks % kernel_dims[d];
+                temp_ks = Math.floor(temp_ks / kernel_dims[d]);
+              }
+
+              // Calculate input spatial coordinates
+              let is_valid = true;
+              const is_coords = new Array(dims);
+              for (let d = 0; d < dims; d++) {
+                const in_coord = os_coords[d] * stride_arr[d] + ks_coords[d] * dilation_arr[d] - padding_arr[d];
+                if (in_coord < 0 || in_coord >= in_dims[d]) {
+                  is_valid = false;
+                  break;
+                }
+                is_coords[d] = in_coord;
+              }
+
+              if (is_valid) {
+                // Calculate flattened indices
+                let in_flat_idx = b * in_strides[0] + ic * in_strides[1];
+                for (let d = 0; d < dims; d++) in_flat_idx += is_coords[d] * in_strides[d + 2];
+
+                let w_flat_idx = oc * w_strides[0] + ic_g * w_strides[1];
+                for (let d = 0; d < dims; d++) w_flat_idx += ks_coords[d] * w_strides[d + 2];
+
+                sum += inputData[in_flat_idx] * weightData[w_flat_idx];
+              }
+            }
+          }
+
+          // Calculate output flattened index
+          let out_flat_idx = b * out_strides[0] + oc * out_strides[1];
+          for (let d = 0; d < dims; d++) out_flat_idx += os_coords[d] * out_strides[d + 2];
+
+          output_data[out_flat_idx] = sum;
+        }
+      }
+    }
+  }
+
+  return new Tensor(output_data, { requires_grad: false }, { shape: output_shape });
+}
+
+function _convNd_backward(
+  dz: Tensor,
+  input: Tensor,
+  weight: Tensor,
+  bias: Tensor | null,
+  stride: number | number[],
+  padding: number | number[],
+  dilation: number | number[],
+  groups: number,
+  dims: number,
+  input_requires_grad: boolean,
+  weight_requires_grad: boolean
+): [Tensor | null, Tensor | null, Tensor | null] {
+  const stride_arr = typeof stride === 'number' ? new Array(dims).fill(stride) : stride;
+  const padding_arr = typeof padding === 'number' ? new Array(dims).fill(padding) : padding;
+  const dilation_arr = typeof dilation === 'number' ? new Array(dims).fill(dilation) : dilation;
+
+  const batch_size = input.shape[0];
+  const in_channels = input.shape[1];
+  const out_channels = weight.shape[0];
+  const in_dims = input.shape.slice(2);
+  const kernel_dims = weight.shape.slice(2);
+  const out_dims = dz.shape.slice(2);
+
+  const get_strides = (shape: number[]) => {
+    const strides = new Array(shape.length);
+    let s = 1;
+    for (let i = shape.length - 1; i >= 0; i--) {
+      strides[i] = s;
+      s *= shape[i];
+    }
+    return strides;
+  };
+
+  const in_strides = get_strides(input.shape);
+  const w_strides = get_strides(weight.shape);
+  const dz_strides = get_strides(dz.shape);
+  const dzData = dz.data;
+  const weightDataBwd = weight.data;
+  const inputDataBwd = input.data;
+
+  let dInput: Tensor | null = null;
+  let dWeight: Tensor | null = null;
+  let dBias: Tensor | null = null;
+
+  let dInput_data: number[] | null = null;
+  let dWeight_data: number[] | null = null;
+
+  if (input_requires_grad) {
+    dInput_data = new Array(input.dataLength()).fill(0);
+  }
+  if (weight_requires_grad) {
+    dWeight_data = new Array(weight.dataLength()).fill(0);
+  }
+
+  const in_channels_per_group = in_channels / groups;
+  const out_channels_per_group = out_channels / groups;
+
+  for (let b = 0; b < batch_size; b++) {
+    for (let g = 0; g < groups; g++) {
+      for (let oc_g = 0; oc_g < out_channels_per_group; oc_g++) {
+        const oc = g * out_channels_per_group + oc_g;
+
+        const out_spatial_size = out_dims.reduce((a, b) => a * b, 1);
+        for (let os_idx = 0; os_idx < out_spatial_size; os_idx++) {
+
+          const os_coords = new Array(dims);
+          let temp_os = os_idx;
+          for (let d = dims - 1; d >= 0; d--) {
+            os_coords[d] = temp_os % out_dims[d];
+            temp_os = Math.floor(temp_os / out_dims[d]);
+          }
+
+          let dz_flat_idx = b * dz_strides[0] + oc * dz_strides[1];
+          for (let d = 0; d < dims; d++) dz_flat_idx += os_coords[d] * dz_strides[d + 2];
+          const dz_val = dzData[dz_flat_idx];
+
+          for (let ic_g = 0; ic_g < in_channels_per_group; ic_g++) {
+            const ic = g * in_channels_per_group + ic_g;
+
+            const kernel_spatial_size = kernel_dims.reduce((a, b) => a * b, 1);
+            for (let ks_idx = 0; ks_idx < kernel_spatial_size; ks_idx++) {
+              const ks_coords = new Array(dims);
+              let temp_ks = ks_idx;
+              for (let d = dims - 1; d >= 0; d--) {
+                ks_coords[d] = temp_ks % kernel_dims[d];
+                temp_ks = Math.floor(temp_ks / kernel_dims[d]);
+              }
+
+              let is_valid = true;
+              const is_coords = new Array(dims);
+              for (let d = 0; d < dims; d++) {
+                const in_coord = os_coords[d] * stride_arr[d] + ks_coords[d] * dilation_arr[d] - padding_arr[d];
+                if (in_coord < 0 || in_coord >= in_dims[d]) {
+                  is_valid = false;
+                  break;
+                }
+                is_coords[d] = in_coord;
+              }
+
+              if (is_valid) {
+                let in_flat_idx = b * in_strides[0] + ic * in_strides[1];
+                for (let d = 0; d < dims; d++) in_flat_idx += is_coords[d] * in_strides[d + 2];
+
+                let w_flat_idx = oc * w_strides[0] + ic_g * w_strides[1];
+                for (let d = 0; d < dims; d++) w_flat_idx += ks_coords[d] * w_strides[d + 2];
+
+                if (input_requires_grad) {
+                  dInput_data![in_flat_idx] += dz_val * weightDataBwd[w_flat_idx];
+                }
+                if (weight_requires_grad) {
+                  dWeight_data![w_flat_idx] += dz_val * inputDataBwd[in_flat_idx];
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  if (input_requires_grad) dInput = new Tensor(dInput_data!, { requires_grad: false }, { shape: input.shape });
+  if (weight_requires_grad) dWeight = new Tensor(dWeight_data!, { requires_grad: false }, { shape: weight.shape });
+  if (bias && bias.requires_grad) {
+    const sum_dims = [0];
+    for (let d = 2; d < dz.shape.length; d++) sum_dims.push(d);
+    dBias = dz.sum(sum_dims);
+  }
+
+  return [dInput, dWeight, dBias];
+}
+
+class Conv1dOp extends TorchFunction {
+  private stride: number | number[];
+  private padding: number | number[];
+  private dilation: number | number[];
+  private groups: number;
+
+  protected _forward(
+    input: Tensor,
+    weight: Tensor,
+    bias: Tensor | null,
+    stride: number | number[] = 1,
+    padding: number | number[] = 0,
+    dilation: number | number[] = 1,
+    groups: number = 1
+  ): Tensor {
+    const rg = resultRequiresGrad(input, weight, ...(bias ? [bias] : []));
+    if (rg) {
+      this.saved_tensors = [input, weight];
+      if (bias) this.saved_tensors.push(bias);
+    }
+    this.next_functions.push(input.grad_fn ? input.grad_fn : nullOp);
+    this.next_functions.push(weight.grad_fn ? weight.grad_fn : nullOp);
+    if (bias) this.next_functions.push(bias.grad_fn ? bias.grad_fn : nullOp);
+
+    this.stride = stride;
+    this.padding = padding;
+    this.dilation = dilation;
+    this.groups = groups;
+
+    const res = _convNd_forward(input, weight, bias, stride, padding, dilation, groups, 1);
+    res.requires_grad = rg;
+    res.grad_fn = rg ? this : null;
+    return res;
+  }
+
+  protected _backward(dz: Tensor): void {
+    const input = this.saved_tensors[0];
+    const weight = this.saved_tensors[1];
+    const bias = this.saved_tensors.length > 2 ? this.saved_tensors[2] : null;
+    const [inputFn, weightFn, biasFn] = this.next_functions;
+
+    const [dInput, dWeight, dBias] = _convNd_backward(
+      dz, input, weight, bias, this.stride, this.padding, this.dilation, this.groups, 1,
+      input.requires_grad, weight.requires_grad
+    );
+
+    if (input.requires_grad) inputFn.backward(dInput);
+    if (weight.requires_grad) weightFn.backward(dWeight);
+    if (bias && bias.requires_grad) biasFn.backward(dBias);
+  }
+}
+registerOperation('conv1d', Conv1dOp);
+
+class Conv2dOp extends TorchFunction {
+  private stride: number | number[];
+  private padding: number | number[];
+  private dilation: number | number[];
+  private groups: number;
+
+  protected _forward(
+    input: Tensor,
+    weight: Tensor,
+    bias: Tensor | null,
+    stride: number | number[] = 1,
+    padding: number | number[] = 0,
+    dilation: number | number[] = 1,
+    groups: number = 1
+  ): Tensor {
+    const rg = resultRequiresGrad(input, weight, ...(bias ? [bias] : []));
+    if (rg) {
+      this.saved_tensors = [input, weight];
+      if (bias) this.saved_tensors.push(bias);
+    }
+    this.next_functions.push(input.grad_fn ? input.grad_fn : nullOp);
+    this.next_functions.push(weight.grad_fn ? weight.grad_fn : nullOp);
+    if (bias) this.next_functions.push(bias.grad_fn ? bias.grad_fn : nullOp);
+
+    this.stride = stride;
+    this.padding = padding;
+    this.dilation = dilation;
+    this.groups = groups;
+
+    const res = _convNd_forward(input, weight, bias, stride, padding, dilation, groups, 2);
+    res.requires_grad = rg;
+    res.grad_fn = rg ? this : null;
+    return res;
+  }
+
+  protected _backward(dz: Tensor): void {
+    const input = this.saved_tensors[0];
+    const weight = this.saved_tensors[1];
+    const bias = this.saved_tensors.length > 2 ? this.saved_tensors[2] : null;
+    const [inputFn, weightFn, biasFn] = this.next_functions;
+
+    const [dInput, dWeight, dBias] = _convNd_backward(
+      dz, input, weight, bias, this.stride, this.padding, this.dilation, this.groups, 2,
+      input.requires_grad, weight.requires_grad
+    );
+
+    if (input.requires_grad) inputFn.backward(dInput);
+    if (weight.requires_grad) weightFn.backward(dWeight);
+    if (bias && bias.requires_grad) biasFn.backward(dBias);
+  }
+}
+registerOperation('conv2d', Conv2dOp);
+
+class Conv3dOp extends TorchFunction {
+  private stride: number | number[];
+  private padding: number | number[];
+  private dilation: number | number[];
+  private groups: number;
+
+  protected _forward(
+    input: Tensor,
+    weight: Tensor,
+    bias: Tensor | null,
+    stride: number | number[] = 1,
+    padding: number | number[] = 0,
+    dilation: number | number[] = 1,
+    groups: number = 1
+  ): Tensor {
+    const rg = resultRequiresGrad(input, weight, ...(bias ? [bias] : []));
+    if (rg) {
+      this.saved_tensors = [input, weight];
+      if (bias) this.saved_tensors.push(bias);
+    }
+    this.next_functions.push(input.grad_fn ? input.grad_fn : nullOp);
+    this.next_functions.push(weight.grad_fn ? weight.grad_fn : nullOp);
+    if (bias) this.next_functions.push(bias.grad_fn ? bias.grad_fn : nullOp);
+
+    this.stride = stride;
+    this.padding = padding;
+    this.dilation = dilation;
+    this.groups = groups;
+
+    const res = _convNd_forward(input, weight, bias, stride, padding, dilation, groups, 3);
+    res.requires_grad = rg;
+    res.grad_fn = rg ? this : null;
+    return res;
+  }
+
+  protected _backward(dz: Tensor): void {
+    const input = this.saved_tensors[0];
+    const weight = this.saved_tensors[1];
+    const bias = this.saved_tensors.length > 2 ? this.saved_tensors[2] : null;
+    const [inputFn, weightFn, biasFn] = this.next_functions;
+
+    const [dInput, dWeight, dBias] = _convNd_backward(
+      dz, input, weight, bias, this.stride, this.padding, this.dilation, this.groups, 3,
+      input.requires_grad, weight.requires_grad
+    );
+
+    if (input.requires_grad) inputFn.backward(dInput);
+    if (weight.requires_grad) weightFn.backward(dWeight);
+    if (bias && bias.requires_grad) biasFn.backward(dBias);
+  }
+}
+registerOperation('conv3d', Conv3dOp);
+
+
+// comparison
+
+const Lt = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => (a[a_index] < b[b_index]) ? 1 : 0,
+  () => { },
+  "lt"
+);
+
+const Gt = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => (a[a_index] > b[b_index]) ? 1 : 0,
+  () => { },
+  "gt"
+);
+
+const Le = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => (a[a_index] <= b[b_index]) ? 1 : 0,
+  () => { },
+  "le"
+);
+
+const Ge = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => (a[a_index] >= b[b_index]) ? 1 : 0,
+  () => { },
+  "ge"
+);
+
+const Eq = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => (a[a_index] == b[b_index]) ? 1 : 0,
+  () => { },
+  "eq"
+);
+
+const Ne = BinaryFunctionMixin(
+  (a: number[], b: number[], a_index: number, b_index: number) => (a[a_index] != b[b_index]) ? 1 : 0,
+  () => { },
+  "ne"
+);
+
+class Cat extends TorchFunction {
+  private _dim: number;
+  private _input_sizes: number[];
+
+  protected _forward(tensors: Tensor[], dim: number = 0): Tensor {
+    if (tensors.length === 0) {
+      throw new Error('torch.cat: expected a non-empty list of Tensors');
+    }
+
+    const ndim = tensors[0].shape.length;
+    if (ndim === 0) {
+      throw new Error('torch.cat: zero-dimensional tensors cannot be concatenated');
+    }
+
+    if (dim < 0) dim = dim + ndim;
+    if (dim < 0 || dim >= ndim) {
+      throw new Error(`torch.cat: dimension out of range`);
+    }
+
+    for (let i = 1; i < tensors.length; i++) {
+      if (tensors[i].shape.length !== ndim) {
+        throw new Error('torch.cat: all tensors must have the same number of dimensions');
+      }
+      for (let d = 0; d < ndim; d++) {
+        if (d !== dim && tensors[i].shape[d] !== tensors[0].shape[d]) {
+          throw new Error(`torch.cat: all tensors must have the same shape, except in dimension ${dim}`);
+        }
+      }
+    }
+
+    const rg = resultRequiresGrad(...tensors);
+    if (rg) {
+      this.saved_tensors = [...tensors];
+      this._dim = dim;
+      this._input_sizes = tensors.map(t => t.shape[dim]);
+    }
+
+    for (const t of tensors) {
+      this.next_functions.push(t.grad_fn ? t.grad_fn : nullOp);
+    }
+
+    const result_shape = [...tensors[0].shape];
+    result_shape[dim] = tensors.reduce((sum, t) => sum + t.shape[dim], 0);
+
+    const inner_size = result_shape.slice(dim + 1).reduce((a, b) => a * b, 1);
+    const outer_size = result_shape.slice(0, dim).reduce((a, b) => a * b, 1);
+    const total_size = result_shape.reduce((a, b) => a * b, 1);
+
+    const data = new Array(total_size);
+    let out_idx = 0;
+    for (let o = 0; o < outer_size; o++) {
+      for (const t of tensors) {
+        const block_size = t.shape[dim] * inner_size;
+        const tData = t.data;
+        const src_base = o * block_size;
+        for (let i = 0; i < block_size; i++) {
+          data[out_idx++] = tData[src_base + i];
+        }
+      }
+    }
+
+    return new Tensor(data, { requires_grad: rg }, { operation: rg ? this : null, shape: result_shape });
+  }
+
+  protected _backward(dz: Tensor): void {
+    const tensors = this.saved_tensors;
+    const dim = this._dim;
+    const input_sizes = this._input_sizes;
+
+    const inner_size = dz.shape.slice(dim + 1).reduce((a, b) => a * b, 1);
+    const outer_size = dz.shape.slice(0, dim).reduce((a, b) => a * b, 1);
+    const dzData = dz.data;
+
+    const grad_datas: number[][] = tensors.map(t => new Array(t.dataLength()));
+
+    let dz_idx = 0;
+    for (let o = 0; o < outer_size; o++) {
+      for (let i = 0; i < tensors.length; i++) {
+        const block_size = input_sizes[i] * inner_size;
+        const dst_base = o * block_size;
+        for (let j = 0; j < block_size; j++) {
+          grad_datas[i][dst_base + j] = dzData[dz_idx++];
+        }
+      }
+    }
+
+    for (let i = 0; i < tensors.length; i++) {
+      this.next_functions[i].backward(
+        new Tensor(grad_datas[i], {}, { shape: tensors[i].shape })
+      );
+    }
+  }
+}
+registerOperation('cat', Cat);
+
+// softmax
+
+class Softmax extends TorchFunction {
+  private dim: number;
+
+  protected _forward(input: Tensor, dim: number): Tensor {
+    this.dim = dim;
+    const rg = resultRequiresGrad(input);
+    this.next_functions.push(input.grad_fn ? input.grad_fn : nullOp);
+
+    const shape = input.shape;
+    const ndim = shape.length;
+    const d = dim < 0 ? dim + ndim : dim;
+
+    const inputData = input.data;
+    const outputData = new Array(input.dataLength());
+
+    const outer = shape.slice(0, d).reduce((a, b) => a * b, 1);
+    const dimSize = shape[d];
+    const inner = shape.slice(d + 1).reduce((a, b) => a * b, 1);
+
+    for (let o = 0; o < outer; o++) {
+      for (let i = 0; i < inner; i++) {
+        let maxVal = -Infinity;
+        for (let k = 0; k < dimSize; k++) {
+          const idx = o * dimSize * inner + k * inner + i;
+          if (inputData[idx] > maxVal) maxVal = inputData[idx];
+        }
+        let sumExp = 0;
+        for (let k = 0; k < dimSize; k++) {
+          const idx = o * dimSize * inner + k * inner + i;
+          sumExp += Math.exp(inputData[idx] - maxVal);
+        }
+        for (let k = 0; k < dimSize; k++) {
+          const idx = o * dimSize * inner + k * inner + i;
+          outputData[idx] = Math.exp(inputData[idx] - maxVal) / sumExp;
+        }
+      }
+    }
+
+    const result = new Tensor(outputData, { requires_grad: rg }, { operation: rg ? this : null, shape: [...shape] });
+    if (rg) {
+      this.saved_tensors = [result]; // save output for efficient backward
+    }
+    return result;
+  }
+
+  protected _backward(dz: Tensor): void {
+    const [softmaxOut] = this.saved_tensors;
+    const [inputFn] = this.next_functions;
+
+    const shape = softmaxOut.shape;
+    const ndim = shape.length;
+    const d = this.dim < 0 ? this.dim + ndim : this.dim;
+
+    const sData = softmaxOut.data;
+    const dzData = dz.data;
+
+    const outer = shape.slice(0, d).reduce((a, b) => a * b, 1);
+    const dimSize = shape[d];
+    const inner = shape.slice(d + 1).reduce((a, b) => a * b, 1);
+
+    const gradData = new Array(softmaxOut.dataLength());
+
+    for (let o = 0; o < outer; o++) {
+      for (let i = 0; i < inner; i++) {
+        // dot(dz, softmax) along this slice
+        let dotProd = 0;
+        for (let k = 0; k < dimSize; k++) {
+          const idx = o * dimSize * inner + k * inner + i;
+          dotProd += dzData[idx] * sData[idx];
+        }
+        // grad = softmax * (dz - dot)
+        for (let k = 0; k < dimSize; k++) {
+          const idx = o * dimSize * inner + k * inner + i;
+          gradData[idx] = sData[idx] * (dzData[idx] - dotProd);
+        }
+      }
+    }
+
+    inputFn.backward(new Tensor(gradData, {}, { shape: [...shape] }));
+  }
+}
+registerOperation('softmax', Softmax);
+
+// clamp
+
+class Clamp extends TorchFunction {
+  private min_val: number;
+  private max_val: number;
+
+  protected _forward(input: Tensor, min_val: number, max_val: number): Tensor {
+    this.min_val = min_val;
+    this.max_val = max_val;
+    const rg = resultRequiresGrad(input);
+    if (rg) {
+      this.saved_tensors = [input];
+    }
+    this.next_functions.push(input.grad_fn ? input.grad_fn : nullOp);
+
+    const inputData = input.data;
+    const outputData = inputData.map(v => Math.min(Math.max(v, min_val), max_val));
+
+    return new Tensor(outputData, { requires_grad: rg }, { operation: rg ? this : null, shape: [...input.shape] });
+  }
+
+  protected _backward(dz: Tensor): void {
+    const [input] = this.saved_tensors;
+    const [inputFn] = this.next_functions;
+    const min_val = this.min_val;
+    const max_val = this.max_val;
+
+    const inputData = input.data;
+    const dzData = dz.data;
+    const gradData = inputData.map((v, i) =>
+      (v > min_val && v < max_val) ? dzData[i] : 0
+    );
+
+    inputFn.backward(new Tensor(gradData, {}, { shape: [...input.shape] }));
+  }
+}
+registerOperation('clamp', Clamp);
diff --git a/src/functions/registry.ts b/src/functions/registry.ts
new file mode 100644
index 00000000..c4f82271
--- /dev/null
+++ b/src/functions/registry.ts
@@ -0,0 +1,41 @@
+import { TorchFunction, TorchFunctionConstructor } from './base';
+
+// Only allow registering concrete, constructible Operation classes
+const operations = new Map();
+const operations_cache = new Map();
+
+export function registerOperation(name: string, func: TorchFunctionConstructor) {
+  operations.set(name, func);
+}
+
+function getOperation(name: string): TorchFunctionConstructor {
+  const func = operations.get(name);
+  if (!func) {
+    throw new Error(`Operation '${name}' is not registered.`);
+  }
+  return func;
+}
+
+export function _getAllOperationNames(): Iterable {
+  return operations.keys();
+}
+
+export function getOperationCache(name: string): TorchFunction {
+  const operation = operations_cache.get(name);
+  if (!operation) {
+    const op = new (getOperation(name))();
+    op.opName = name;
+    operations_cache.set(name, op);
+    return op;
+  }
+  return operation;
+}
+
+/**
+ * Create a new operation instance with its opName set.
+ */
+export function createOperation(name: string): TorchFunction {
+  const op = new (getOperation(name))();
+  op.opName = name;
+  return op;
+}
diff --git a/src/functions/util.ts b/src/functions/util.ts
new file mode 100644
index 00000000..23ea6a99
--- /dev/null
+++ b/src/functions/util.ts
@@ -0,0 +1,36 @@
+export function _get_strides(shape: number[]): number[] {
+  const strides = new Array(shape.length).fill(1);
+  for (let i = shape.length - 2; i >= 0; i--) {
+    strides[i] = strides[i + 1] * shape[i + 1];
+  }
+  return strides;
+}
+
+export function _unravel_index(index: number, strides: number[]): number[] {
+  return strides.map((stride) => {
+    const coord = Math.floor(index / stride);
+    index %= stride;
+    return coord;
+  });
+}
+
+export function _ravel_index(coords: number[], strides: number[]): number {
+  return coords.reduce((acc, coord, i) => acc + coord * strides[i], 0);
+}
+
+export function _get_reduction_shape(
+  shape: number[],
+  dim?: number | number[],
+  keepdim: boolean = false
+): number[] {
+  if (dim === undefined) return keepdim ? shape.map(() => 1) : [];
+
+  const dims = Array.isArray(dim) ? dim : [dim];
+  const normalized_dims = dims.map((d) => (d < 0 ? d + shape.length : d));
+
+  if (keepdim) {
+    return shape.map((s, i) => (normalized_dims.includes(i) ? 1 : s));
+  } else {
+    return shape.filter((_, i) => !normalized_dims.includes(i));
+  }
+}
diff --git a/src/grad_mode.ts b/src/grad_mode.ts
new file mode 100644
index 00000000..3bf9b729
--- /dev/null
+++ b/src/grad_mode.ts
@@ -0,0 +1,42 @@
+/**
+ * Global gradient computation mode.
+ *
+ * When disabled, all operations behave as if no input requires grad,
+ * so no computation graph is built and no tensors are saved for backward.
+ */
+let _grad_enabled = true;
+
+export function is_grad_enabled(): boolean {
+  return _grad_enabled;
+}
+
+/**
+ * Disable gradient computation. Returns the previous state
+ * so it can be restored later with `disable_no_grad(prev)`.
+ */
+export function enable_no_grad(): boolean {
+  const prev = _grad_enabled;
+  _grad_enabled = false;
+  return prev;
+}
+
+/**
+ * Restore gradient computation to a previous state
+ * (as returned by `enable_no_grad`).
+ */
+export function disable_no_grad(prev: boolean): void {
+  _grad_enabled = prev;
+}
+
+/**
+ * Execute `fn` with gradient computation disabled.
+ * The previous grad mode is always restored, even if `fn` throws.
+ */
+export function no_grad(fn: () => T): T {
+  const prev = enable_no_grad();
+  try {
+    return fn();
+  } finally {
+    disable_no_grad(prev);
+  }
+}
diff --git a/src/index.ts b/src/index.ts
new file mode 100644
index 00000000..4c1b70ef
--- /dev/null
+++ b/src/index.ts
@@ -0,0 +1,38 @@
+export { Tensor, FloatTensor, LongTensor } from './tensor';
+export type { TypedArray, NestedNumberArray } from './tensor';
+export { no_grad, enable_no_grad, disable_no_grad, is_grad_enabled } from './grad_mode';
+
+export { TorchFunction, AccumulateGrad } from './functions/base';
+export * from './functions/ops';
+export * from './functions/functional';
+
+export * from './creation/index';
+
+export * as nn from './nn/index';
+
+// Allow for torch.relu -> torch.nn.functional.relu (and others)
+export * from './nn/functional';
+
+export * as optim from './optim/index';
+
+export { seed, manual_seed } from './prng';
+
+export { eventBus, events } from './util';
+
+export { export_, ExportedProgram } from './export';
+export type { GraphNode, InputSpec, OutputSpec, GraphSignature } from './export';
+
+import { Tensor } from './tensor';
+
+export function is_tensor(obj: unknown): boolean {
+  return obj instanceof Tensor;
+}
+
+export function is_nonzero(input: Tensor): boolean {
+  if (input.numel() !== 1) {
+    throw new Error(
+      `Boolean value of Tensor with more than one element is ambiguous`
+    );
+  }
+  return input.item() !== 0;
+}
diff --git a/src/nn/base.ts b/src/nn/base.ts
new file mode 100644
index 00000000..c2babbb7
--- /dev/null
+++ b/src/nn/base.ts
@@ -0,0 +1,116 @@
+import { Tensor } from '../tensor';
+import { Parameter } from './parameter';
+
+export { Parameter } from './parameter';
+
+export abstract class Module {
+  private _modules: { [key: string]: Module };
+  private _parameters: { [key: string]: Parameter };
+  public training: boolean = true;
+
+  constructor() {
+    this._parameters = {};
+    this._modules = {};
+  }
+
+  private register_parameter(parameter_name: string, parameter: Parameter) {
+    this._parameters[parameter_name] = parameter;
+  }
+
+  private register_module(module_name: string, module: Module) {
+    this._modules[module_name] = module;
+  }
+
+  protected register(name: string, value: Parameter | Module) {
+    if (value instanceof Parameter) {
+      this.register_parameter(name, value);
+    } else {
+      this.register_module(name, value);
+    }
+  }
+
+  public abstract forward(...args: Tensor[]): Tensor;
+
+  /**
+   * Entry point for running the module. Equivalent to `model(x)` in Python.
+   * In the future, this is where forward hooks will be triggered.
+   * Call `forward()` directly to bypass hooks.
+   */
+  public call(...args: Tensor[]): Tensor {
+    return this.forward(...args);
+  }
+
+  public train(mode: boolean = true): this {
+    this.training = mode;
+    for (const module of Object.values(this._modules)) {
+      module.train(mode);
+    }
+    return this;
+  }
+
+  public eval(): this {
+    return this.train(false);
+  }
+
+  public parameters(): Parameter[] {
+    let params: Parameter[] = Object.values(this._parameters);
+    for (const module of Object.values(this._modules)) {
+      params = params.concat(module.parameters());
+    }
+    return params;
+  }
+
+  public named_parameters(prefix: string = ''): [string, Parameter][] {
+    const result: [string, Parameter][] = [];
+    for (const [name, param] of Object.entries(this._parameters)) {
+      const fullName = prefix ? `${prefix}.${name}` : name;
+      result.push([fullName, param]);
+    }
+    for (const [name, module] of Object.entries(this._modules)) {
+      const fullName = prefix ? `${prefix}.${name}` : name;
+      result.push(...module.named_parameters(fullName));
+    }
+    return result;
+  }
+}
+
+export class Sequential extends Module {
+  private _modulesArr: Module[];
+
+  constructor(...modules: Module[]) {
+    super();
+    this._modulesArr = modules;
+    for (let i = 0; i < modules.length; i++) {
+      this.register(i.toString(), modules[i]);
+    }
+  }
+
+  append(module: Module): this {
+    this.register(this._modulesArr.length.toString(), module);
+    this._modulesArr.push(module);
+    return this;
+  }
+
+  extend(sequential: Sequential): this {
+    for (const module of sequential._modulesArr) {
+      this.append(module);
+    }
+    return this;
+  }
+
+  insert(index: number, module: Module): this {
+    this._modulesArr.splice(index, 0, module);
+    for (let i = index; i < this._modulesArr.length; i++) {
+      this.register(i.toString(), this._modulesArr[i]);
+    }
+    return this;
+  }
+
+  forward(input: Tensor) {
+    let x = input;
+    for (const module of this._modulesArr) {
+      x = module.call(x);
+    }
+    return x;
+  }
+}
diff --git a/src/nn/functional.ts b/src/nn/functional.ts
new file mode 100644
index 00000000..fe450a78
--- /dev/null
+++ b/src/nn/functional.ts
@@ -0,0 +1,34 @@
+import { Tensor } from '../tensor';
+import { createOperation } from '../functions/registry';
+import { ArgumentType } from '../functions/base';
+
+function generate_function(opname: string) {
+  return (...args: ArgumentType[]) => {
+    const operation = createOperation(opname);
+    return operation.forward(...args);
+  };
+}
+
+function generate_unary_function(opname: string) {
+  return (a: Tensor | number) => {
+    if (typeof a == 'number') {
+      a = new Tensor(a);
+    }
+
+    const operation = createOperation(opname);
+    return operation.forward(a);
+  };
+}
+
+export const relu = generate_unary_function('relu');
+export const sigmoid = generate_unary_function('sigmoid');
+export const tanh = generate_unary_function('tanh');
+
+export const leaky_relu = generate_function('leaky_relu');
+
+export const conv1d = generate_function('conv1d');
+export const conv2d = generate_function('conv2d');
+export const conv3d = generate_function('conv3d');
+export const cross_entropy = generate_function('cross_entropy_loss');
+export const nll_loss = generate_function('nll_loss');
+export const max_pool2d = generate_function('max_pool2d');
diff --git a/src/nn/index.ts b/src/nn/index.ts
new file mode 100644
index 00000000..9e30122a
--- /dev/null
+++ b/src/nn/index.ts
@@ -0,0 +1,7 @@
+import './ops';
+
+export * from './base';
+export * from './loss';
+export * from './module';
+export * as functional from './functional';
+export * as parameter from './parameter';
diff --git a/src/nn/loss.ts b/src/nn/loss.ts
new file mode 100644
index 00000000..8381df90
--- /dev/null
+++ b/src/nn/loss.ts
@@ -0,0 +1,92 @@
+import { Tensor } from "../tensor";
+import { Module } from "./base";
+import { createOperation } from "../functions/registry";
+
+export type Reduction = 'mean' | 'sum' | 'none';
+
+function applyReduction(loss: Tensor, reduction: Reduction): Tensor {
+  if (reduction === 'mean') return loss.mean();
+  if (reduction === 'sum') return loss.sum();
+  return loss;
+}
+
+abstract class Loss extends Module {
+  abstract forward(input: Tensor, target: Tensor): Tensor;
+}
+
+export class MSELoss extends Loss {
+  private reduction: Reduction;
+
+  constructor(reduction: Reduction = 'mean') {
+    super();
+    this.reduction = reduction;
+  }
+
+  forward(input: Tensor, target: Tensor) {
+    const unreduced = input.sub(target).pow(2);
+    return applyReduction(unreduced, this.reduction);
+  }
+}
+
+export class L1Loss extends Loss {
+  private reduction: Reduction;
+
+  constructor(reduction: Reduction = 'mean') {
+    super();
+    this.reduction = reduction;
+  }
+
+  forward(input: Tensor, target: Tensor) {
+    const unreduced = input.sub(target).abs();
+    return applyReduction(unreduced, this.reduction);
+  }
+}
+
+export class BCELoss extends Loss {
+  private weight: Tensor | null;
+  private reduction: Reduction;
+
+  constructor(weight: Tensor | null = null, reduction: Reduction = 'mean') {
+    super();
+    this.weight = weight;
+    this.reduction = reduction;
+  }
+
+  forward(input: Tensor, target: Tensor) {
+    const left = target.mul(input.log());
+    const right = target.neg().add(1).mul(input.neg().add(1).log());
+    let unreduced = left.add(right).neg();
+    if (this.weight) {
+      unreduced = unreduced.mul(this.weight);
+    }
+    return applyReduction(unreduced, this.reduction);
+  }
+}
+
+export class CrossEntropyLoss extends Loss {
+  private reduction: Reduction;
+
+  constructor(reduction: Reduction = 'mean') {
+    super();
+    this.reduction = reduction;
+  }
+
+  forward(input: Tensor, target: Tensor) {
+    const op = createOperation('cross_entropy_loss');
+    return op.forward(input, target, this.reduction);
+  }
+}
+
+export class NLLLoss extends Loss {
+  private reduction: Reduction;
+
+  constructor(reduction: Reduction = 'mean') {
+    super();
+    this.reduction = reduction;
+  }
+
+  forward(input: Tensor, target: Tensor) {
+    const op = createOperation('nll_loss');
+    return op.forward(input, target, this.reduction);
+  }
+}
diff --git a/src/nn/module.ts b/src/nn/module.ts
new file mode 100644
index 00000000..81ca53f5
--- /dev/null
+++ b/src/nn/module.ts
@@ -0,0 +1,304 @@
+import { Module, Parameter } from './base';
+import { rand } from '../creation';
+import * as functional from './functional';
+import { softmax as _softmax } from '../functions/functional';
+import { Tensor } from '../tensor';
+import { getRng } from '../prng';
+
+export class Linear extends Module {
+  public weight: Parameter;
+  public bias: Parameter | null;
+
+  constructor(in_features: number, out_features: number, bias: boolean = true) {
+    super();
+    const k = Math.sqrt(1 / in_features);
+
+    this.weight = new Parameter(
+      rand([out_features, in_features])
+        .mul(2 * k)
+        .sub(k)
+    );
+    this.register('weight', this.weight);
+
+    if (bias) {
+      this.bias = new Parameter(
+        rand([out_features])
+          .mul(2 * k)
+          .sub(k)
+      );
+      this.register('bias', this.bias);
+    } else {
+      this.bias = null;
+    }
+  }
+
+  forward(input: Tensor) {
+    const out = input.matmul(this.weight.transpose(0, 1));
+    return this.bias ? out.add(this.bias) : out;
+  }
+}
+
+export class ReLU extends Module {
+  constructor() {
+    super();
+  }
+
+  forward(input: Tensor) {
+    return functional.relu(input);
+  }
+}
+
+export class Sigmoid extends Module {
+  constructor() {
+    super();
+  }
+
+  forward(input: Tensor) {
+    return functional.sigmoid(input);
+  }
+}
+
+export class Tanh extends Module {
+  constructor() {
+    super();
+  }
+
+  forward(input: Tensor) {
+    return functional.tanh(input);
+  }
+}
+
+abstract class _ConvNd extends Module {
+  public weight: Parameter;
+  public bias: Parameter | null;
+
+  public in_channels: number;
+  public out_channels: number;
+  public kernel_size: number | number[];
+  public stride: number | number[];
+  public padding: number | number[];
+  public dilation: number | number[];
+  public groups: number;
+
+  constructor(
+    in_channels: number,
+    out_channels: number,
+    kernel_size: number | number[],
+    stride: number | number[],
+    padding: number | number[],
+    dilation: number | number[],
+    groups: number,
+    bias: boolean,
+    dims: number
+  ) {
+    super();
+
+    this.in_channels = in_channels;
+    this.out_channels = out_channels;
+    this.kernel_size = kernel_size;
+    this.stride = stride;
+    this.padding = padding;
+    this.dilation = dilation;
+    this.groups = groups;
+
+    if (in_channels % groups !== 0) {
+      throw new Error('in_channels must be divisible by groups');
+    }
+    if (out_channels % groups !== 0) {
+      throw new Error('out_channels must be divisible by groups');
+    }
+
+    const kernel_arr =
+      typeof kernel_size === 'number' ? new Array(dims).fill(kernel_size) : kernel_size;
+    const kernel_vol = kernel_arr.reduce((a: number, b: number) => a * b, 1);
+
+    const k = Math.sqrt(groups / (in_channels * kernel_vol));
+
+    this.weight = new Parameter(
+      rand([out_channels, in_channels / groups, ...kernel_arr])
+        .mul(2 * k)
+        .sub(k)
+    );
+
+    this.register('weight', this.weight);
+
+    if (bias) {
+      this.bias = new Parameter(
+        rand([out_channels])
+          .mul(2 * k)
+          .sub(k)
+      );
+      this.register('bias', this.bias);
+    } else {
+      this.bias = null;
+    }
+  }
+
+  abstract forward(input: Tensor): Tensor;
+}
+
+export class Conv1d extends _ConvNd {
+  constructor(
+    in_channels: number,
+    out_channels: number,
+    kernel_size: number | number[],
+    stride: number | number[] = 1,
+    padding: number | number[] = 0,
+    dilation: number | number[] = 1,
+    groups: number = 1,
+    bias: boolean = true
+  ) {
+    super(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, 1);
+  }
+
+  forward(input: Tensor) {
+    return functional.conv1d(
+      input,
+      this.weight,
+      this.bias,
+      this.stride,
+      this.padding,
+      this.dilation,
+      this.groups
+    );
+  }
+}
+
+export class Conv2d extends _ConvNd {
+  constructor(
+    in_channels: number,
+    out_channels: number,
+    kernel_size: number | number[],
+    stride: number | number[] = 1,
+    padding: number | number[] = 0,
+    dilation: number | number[] = 1,
+    groups: number = 1,
+    bias: boolean = true
+  ) {
+    super(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, 2);
+  }
+
+  forward(input: Tensor) {
+    return functional.conv2d(
+      input,
+      this.weight,
+      this.bias,
+      this.stride,
+      this.padding,
+      this.dilation,
+      this.groups
+    );
+  }
+}
+
+export class LeakyReLU extends Module {
+  private negative_slope: number;
+
+  constructor(negative_slope: number = 0.01) {
+    super();
+    this.negative_slope = negative_slope;
+  }
+
+  forward(input: Tensor) {
+    return functional.leaky_relu(input, this.negative_slope);
+  }
+}
+
+export class MaxPool2d extends Module {
+  private kernel_size: number | number[];
+  private stride: number | number[] | undefined;
+  private padding: number | number[];
+
+  constructor(
+    kernel_size: number | number[],
+    stride?: number | number[],
+    padding: number | number[] = 0
+  ) {
+    super();
+    this.kernel_size = kernel_size;
+    this.stride = stride;
+    this.padding = padding;
+  }
+
+  forward(input: Tensor) {
+    return functional.max_pool2d(input, this.kernel_size, this.stride, this.padding);
+  }
+}
+
+export class Dropout extends Module {
+  private p: number;
+
+  constructor(p: number = 0.5) {
+    super();
+    this.p = p;
+  }
+
+  forward(input: Tensor) {
+    if (!this.training || this.p === 0) {
+      return input;
+    }
+    if (this.p === 1) {
+      return input.mul(0);
+    }
+    const scale = 1 / (1 - this.p);
+    const prng = getRng();
+    const maskData = input.toFlatArray().map(() => (prng() > this.p ? scale : 0));
+    const mask = new Tensor(maskData, {}, { shape: [...input.shape] });
+    return input.mul(mask);
+  }
+}
+
+export class Softmax extends Module {
+  private dim: number;
+
+  constructor(dim: number) {
+    super();
+    this.dim = dim;
+  }
+
+  forward(input: Tensor) {
+    return _softmax(input, this.dim);
+  }
+}
+
+export class Flatten extends Module {
+  private start_dim: number;
+  private end_dim: number;
+
+  constructor(start_dim: number = 1, end_dim: number = -1) {
+    super();
+    this.start_dim = start_dim;
+    this.end_dim = end_dim;
+  }
+
+  forward(input: Tensor) {
+    return input.flatten(this.start_dim, this.end_dim);
+  }
+}
+
+export class Conv3d extends _ConvNd {
+  constructor(
+    in_channels: number,
+    out_channels: number,
+    kernel_size: number | number[],
+    stride: number | number[] = 1,
+    padding: number | number[] = 0,
+    dilation: number | number[] = 1,
+    groups: number = 1,
+    bias: boolean = true
+  ) {
+    super(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, 3);
+  }
+
+  forward(input: Tensor) {
+    return functional.conv3d(
+      input,
+      this.weight,
+      this.bias,
+      this.stride,
+      this.padding,
+      this.dilation,
+      this.groups
+    );
+  }
+}
diff --git a/src/nn/ops.ts b/src/nn/ops.ts
new file mode 100644
index 00000000..ae864471
--- /dev/null
+++ b/src/nn/ops.ts
@@ -0,0 +1,362 @@
+import { UnaryFunctionMixin } from '../functions/mixin';
+import { TorchFunction, resultRequiresGrad, nullOp } from '../functions/base';
+import { registerOperation } from '../functions/registry';
+import { Tensor } from '../tensor';
+
+const Relu = UnaryFunctionMixin(
+  (a: number[], x: number) => Math.max(a[x], 0),
+  (a, aFn, dz) => {
+    aFn.backward(dz.mul(a.gt(0)));
+  },
+  "relu"
+);
+
+const Sigmoid = UnaryFunctionMixin(
+  (a: number[], x: number) => 1 / (1 + Math.exp(-a[x])),
+  (a, aFn, dz) => {
+    const res = a.sigmoid();
+    aFn.backward(res.mul(res.mul(-1).add(1)).mul(dz));
+  },
+  "sigmoid"
+);
+
+const Tanh = UnaryFunctionMixin(
+  (a: number[], x: number) => Math.tanh(a[x]),
+  (a, aFn, dz) => {
+    const tanh_a = a.tanh();
+    aFn.backward(dz.mul(tanh_a.mul(tanh_a).mul(-1).add(1)));
+  },
+  "tanh"
+);
+
+/**
+ * CrossEntropyLoss operation.
+ *
+ * Forward:
+ *   input  – (N, C) logits (unnormalized scores)
+ *   target – (N,)   integer class indices in [0, C)
+ *   reduction – 'mean' | 'sum' | 'none'
+ *
+ * Backward:
+ *   d_input[i,j] = (softmax(input)[i,j] - 1{j == target[i]}) * scale
+ */
+class CrossEntropyLossOp extends TorchFunction {
+  private N: number = 0;
+  private C: number = 0;
+  private reduction: string = 'mean';
+
+  protected _forward(input: Tensor, target: Tensor, reduction: string = 'mean'): Tensor {
+    this.reduction = reduction;
+    const rg = resultRequiresGrad(input);
+    if (rg) {
+      this.saved_tensors = [input, target];
+    }
+    this.next_functions.push(input.grad_fn ? input.grad_fn : nullOp);
+
+    const shape = input.shape; // (N, C)
+    const N = shape[0];
+    const C = shape[1];
+    this.N = N;
+    this.C = C;
+
+    const inputData = input.data;
+    const targetData = target.data;
+
+    // Numerically stable log-softmax + gather
+    const perSampleLoss = new Array(N);
+    for (let i = 0; i < N; i++) {
+      const rowOffset = i * C;
+
+      // Find max for numerical stability
+      let maxVal = -Infinity;
+      for (let j = 0; j < C; j++) {
+        if (inputData[rowOffset + j] > maxVal) {
+          maxVal = inputData[rowOffset + j];
+        }
+      }
+
+      // Compute log(sum(exp(x - max)))
+      let sumExp = 0;
+      for (let j = 0; j < C; j++) {
+        sumExp += Math.exp(inputData[rowOffset + j] - maxVal);
+      }
+      const logSumExp = Math.log(sumExp);
+
+      // log_softmax for the target class
+      const t = targetData[i];
+      const logSoftmax = inputData[rowOffset + t] - maxVal - logSumExp;
+      perSampleLoss[i] = -logSoftmax;
+    }
+
+    let lossData: number[];
+    let resultShape: number[];
+    if (reduction === 'none') {
+      lossData = perSampleLoss;
+      resultShape = [N];
+    } else if (reduction === 'sum') {
+      lossData = [perSampleLoss.reduce((a: number, b: number) => a + b, 0)];
+      resultShape = [];
+    } else {
+      lossData = [perSampleLoss.reduce((a: number, b: number) => a + b, 0) / N];
+      resultShape = [];
+    }
+
+    const result = new Tensor(lossData, { requires_grad: rg }, { operation: rg ? this : null, shape: resultShape });
+    return result;
+  }
+
+  protected _backward(dz: Tensor | number): void {
+    const [input, target] = this.saved_tensors;
+    const [inputFn] = this.next_functions;
+    const N = this.N;
+    const C = this.C;
+    const reduction = this.reduction;
+
+    const inputData = input.data;
+    const targetData = target.data;
+
+    let dzData: number[];
+    if (typeof dz === 'number') {
+      dzData = new Array(reduction === 'none' ? N : 1).fill(dz);
+    } else {
+      dzData = [...dz.data];
+    }
+
+    const grad = new Array(N * C);
+    for (let i = 0; i < N; i++) {
+      const rowOffset = i * C;
+
+      // Compute softmax for this row
+      let maxVal = -Infinity;
+      for (let j = 0; j < C; j++) {
+        if (inputData[rowOffset + j] > maxVal) {
+          maxVal = inputData[rowOffset + j];
+        }
+      }
+      let sumExp = 0;
+      for (let j = 0; j < C; j++) {
+        sumExp += Math.exp(inputData[rowOffset + j] - maxVal);
+      }
+
+      const t = targetData[i];
+      const dzVal = reduction === 'none' ? dzData[i] : dzData[0];
+      const scale = reduction === 'mean' ? dzVal / N : dzVal;
+
+      for (let j = 0; j < C; j++) {
+        const softmax_j = Math.exp(inputData[rowOffset + j] - maxVal) / sumExp;
+        const oneHot = j === t ? 1 : 0;
+        grad[rowOffset + j] = (softmax_j - oneHot) * scale;
+      }
+    }
+
+    const gradTensor = new Tensor(grad, {}, { shape: [N, C] });
+    inputFn.backward(gradTensor);
+  }
+}
+
+registerOperation('cross_entropy_loss', CrossEntropyLossOp);
+
+class LeakyReluOp extends TorchFunction {
+  private negative_slope: number;
+
+  protected _forward(input: Tensor, negative_slope: number = 0.01): Tensor {
+    this.negative_slope = negative_slope;
+    const rg = resultRequiresGrad(input);
+    if (rg) {
+      this.saved_tensors = [input];
+    }
+    this.next_functions.push(input.grad_fn ? input.grad_fn : nullOp);
+
+    const inputData = input.data;
+    const outputData = inputData.map(v => v > 0 ? v : negative_slope * v);
+
+    return new Tensor(outputData, { requires_grad: rg }, { operation: rg ? this : null, shape: [...input.shape] });
+  }
+
+  protected _backward(dz: Tensor): void {
+    const [input] = this.saved_tensors;
+    const [inputFn] = this.next_functions;
+    const ns = this.negative_slope;
+
+    const inputData = input.data;
+    const dzData = dz.data;
+    const gradData = inputData.map((v, i) => v > 0 ? dzData[i] : ns * dzData[i]);
+
+    inputFn.backward(new Tensor(gradData, {}, { shape: [...input.shape] }));
+  }
+}
+registerOperation('leaky_relu', LeakyReluOp);
+
+class MaxPool2dOp extends TorchFunction {
+  private pool_h: number;
+  private pool_w: number;
+  private stride_h: number;
+  private stride_w: number;
+  private pad_h: number;
+  private pad_w: number;
+  private argmax_data: number[];
+  private input_ndim: number;
+
+  protected _forward(
+    input: Tensor,
+    kernel_size: number | number[],
+    stride?: number | number[],
+    padding: number | number[] = 0
+  ): Tensor {
+    const kArr = typeof kernel_size === 'number' ? [kernel_size, kernel_size] : kernel_size as number[];
+    const sArr = stride === undefined ? kArr : (typeof stride === 'number' ? [stride, stride] : stride as number[]);
+    const pArr = typeof padding === 'number' ? [padding, padding] : padding as number[];
+
+    this.pool_h = kArr[0];
+    this.pool_w = kArr[1];
+    this.stride_h = sArr[0];
+    this.stride_w = sArr[1];
+    this.pad_h = pArr[0];
+    this.pad_w = pArr[1];
+    this.input_ndim = input.shape.length;
+
+    const is4d = input.shape.length === 4;
+    let N: number, C: number, H: number, W: number;
+    if (is4d) {
+      [N, C, H, W] = input.shape;
+    } else {
+      N = 1;
+      [C, H, W] = input.shape;
+    }
+
+    const H_out = Math.floor((H + 2 * this.pad_h - this.pool_h) / this.stride_h) + 1;
+    const W_out = Math.floor((W + 2 * this.pad_w - this.pool_w) / this.stride_w) + 1;
+
+    const rg = resultRequiresGrad(input);
+    if (rg) {
+      this.saved_tensors = [input];
+    }
+    this.next_functions.push(input.grad_fn ? input.grad_fn : nullOp);
+
+    const inputData = input.data;
+    const totalOut = N * C * H_out * W_out;
+    const outputData = new Array(totalOut);
+    const argmax = new Array(totalOut);
+
+    for (let n = 0; n < N; n++) {
+      for (let c = 0; c < C; c++) {
+        for (let oh = 0; oh < H_out; oh++) {
+          for (let ow = 0; ow < W_out; ow++) {
+            let maxVal = -Infinity;
+            let maxIdx = -1;
+            for (let kh = 0; kh < this.pool_h; kh++) {
+              for (let kw = 0; kw < this.pool_w; kw++) {
+                const ih = oh * this.stride_h - this.pad_h + kh;
+                const iw = ow * this.stride_w - this.pad_w + kw;
+                if (ih >= 0 && ih < H && iw >= 0 && iw < W) {
+                  const inputIdx = ((n * C + c) * H + ih) * W + iw;
+                  if (inputData[inputIdx] > maxVal) {
+                    maxVal = inputData[inputIdx];
+                    maxIdx = inputIdx;
+                  }
+                }
+              }
+            }
+            const outIdx = ((n * C + c) * H_out + oh) * W_out + ow;
+            outputData[outIdx] = maxVal;
+            argmax[outIdx] = maxIdx;
+          }
+        }
+      }
+    }
+
+    this.argmax_data = argmax;
+    const outShape = is4d ? [N, C, H_out, W_out] : [C, H_out, W_out];
+
+    return new Tensor(outputData, { requires_grad: rg }, { operation: rg ? this : null, shape: outShape });
+  }
+
+  protected _backward(dz: Tensor): void {
+    const [input] = this.saved_tensors;
+    const [inputFn] = this.next_functions;
+
+    const gradData = new Array(input.dataLength()).fill(0);
+    const dzData = dz.data;
+
+    for (let i = 0; i < dzData.length; i++) {
+      if (this.argmax_data[i] >= 0) {
+        gradData[this.argmax_data[i]] += dzData[i];
+      }
+    }
+
+    inputFn.backward(new Tensor(gradData, {}, { shape: [...input.shape] }));
+  }
+}
+registerOperation('max_pool2d', MaxPool2dOp);
+
+class NLLLossOp extends TorchFunction {
+  private N: number;
+  private C: number;
+  private reduction: string;
+
+  protected _forward(input: Tensor, target: Tensor, reduction: string = 'mean'): Tensor {
+    this.reduction = reduction;
+    const rg = resultRequiresGrad(input);
+    if (rg) {
+      this.saved_tensors = [input, target];
+    }
+    this.next_functions.push(input.grad_fn ? input.grad_fn : nullOp);
+
+    const N = input.shape[0];
+    const C = input.shape[1];
+    this.N = N;
+    this.C = C;
+
+    const inputData = input.data;
+    const targetData = target.data;
+
+    const perSampleLoss = new Array(N);
+    for (let i = 0; i < N; i++) {
+      const t = Math.round(targetData[i]);
+      perSampleLoss[i] = -inputData[i * C + t];
+    }
+
+    let lossData: number[];
+    let resultShape: number[];
+    if (reduction === 'none') {
+      lossData = perSampleLoss;
+      resultShape = [N];
+    } else if (reduction === 'sum') {
+      lossData = [perSampleLoss.reduce((a, b) => a + b, 0)];
+      resultShape = [];
+    } else {
+      lossData = [perSampleLoss.reduce((a, b) => a + b, 0) / N];
+      resultShape = [];
+    }
+
+    return new Tensor(lossData, { requires_grad: rg }, { operation: rg ? this : null, shape: resultShape });
+  }
+
+  protected _backward(dz: Tensor | number): void {
+    const [input, target] = this.saved_tensors;
+    const [inputFn] = this.next_functions;
+    const N = this.N;
+    const C = this.C;
+    const reduction = this.reduction;
+
+    const targetData = target.data;
+
+    let dzData: number[];
+    if (typeof dz === 'number') {
+      dzData = new Array(reduction === 'none' ? N : 1).fill(dz);
+    } else {
+      dzData = [...dz.data];
+    }
+
+    const gradData = new Array(N * C).fill(0);
+    for (let i = 0; i < N; i++) {
+      const t = Math.round(targetData[i]);
+      const dzVal = reduction === 'none' ? dzData[i] : dzData[0];
+      const scale = reduction === 'mean' ? dzVal / N : dzVal;
+      gradData[i * C + t] = -scale;
+    }
+
+    inputFn.backward(new Tensor(gradData, {}, { shape: [N, C] }));
+  }
+}
+registerOperation('nll_loss', NLLLossOp);
diff --git a/src/nn/parameter.ts b/src/nn/parameter.ts
new file mode 100644
index 00000000..9d8791b0
--- /dev/null
+++ b/src/nn/parameter.ts
@@ -0,0 +1,20 @@
+import { Tensor } from '../tensor';
+import { NestedNumberArray } from '../tensor';
+import { TorchFunction } from '../functions/base';
+
+export class Parameter extends Tensor {
+  constructor(
+    data: NestedNumberArray | Tensor | Parameter,
+    // Default to requires_grad=true
+    options: { requires_grad?: boolean } = {
+      requires_grad: true
+    },
+    internal_options: { operation?: TorchFunction; shape?: number[] } = {}
+  ) {
+    if (data instanceof Tensor) {
+      super(data.data, { requires_grad: options.requires_grad }, { shape: data.shape });
+    } else {
+      super(data, options, internal_options);
+    }
+  }
+}
diff --git a/src/optim/base.ts b/src/optim/base.ts
new file mode 100644
index 00000000..99684b06
--- /dev/null
+++ b/src/optim/base.ts
@@ -0,0 +1,19 @@
+import { Parameter } from "../nn/base";
+
+export abstract class Optimizer {
+  params: Parameter[];
+  defaults: { [key: string]: any };
+
+  constructor(params: Parameter[], defaults: { [key: string]: any }) {
+    this.params = params;
+    this.defaults = defaults;
+  }
+
+  public zero_grad(): void {
+    for (const param of this.params) {
+      param.grad = null;
+    }
+  }
+
+  abstract step(): void;
+}
diff --git a/src/optim/index.ts b/src/optim/index.ts
new file mode 100644
index 00000000..3f38deda
--- /dev/null
+++ b/src/optim/index.ts
@@ -0,0 +1,2 @@
+export * from './base';
+export * from './optimizers';
diff --git a/src/optim/optimizers.ts b/src/optim/optimizers.ts
new file mode 100644
index 00000000..42381194
--- /dev/null
+++ b/src/optim/optimizers.ts
@@ -0,0 +1,196 @@
+import { Optimizer } from './base';
+import { Parameter } from '../nn/base';
+import { Tensor } from '../tensor';
+import { zeros_like } from '../creation';
+
+export class Adagrad extends Optimizer {
+  private state: Map = new Map();
+  private lr: number;
+  private lr_decay: number;
+  private weight_decay: number;
+  private eps: number;
+  private step_count: number = 0;
+
+  constructor(
+    params: Parameter[],
+    lr: number = 0.01,
+    lr_decay: number = 0,
+    weight_decay: number = 0,
+    eps: number = 1e-10
+  ) {
+    super(params, {});
+    this.lr = lr;
+    this.lr_decay = lr_decay;
+    this.weight_decay = weight_decay;
+    this.eps = eps;
+  }
+
+  step(): void {
+    this.step_count += 1;
+    const clr = this.lr / (1 + (this.step_count - 1) * this.lr_decay);
+
+    for (const param of this.params) {
+      let grad = param.grad;
+      if (grad === null) continue;
+
+      if (this.weight_decay !== 0) {
+        grad = grad.add(param.mul(this.weight_decay));
+      }
+
+      if (!this.state.has(param)) {
+        this.state.set(param, { sum: zeros_like(param) });
+      }
+
+      const state = this.state.get(param)!;
+      state.sum = state.sum.add(grad.mul(grad));
+
+      const update = grad.div(state.sum.sqrt().add(this.eps)).mul(clr);
+      param.data = param.sub(update).data;
+    }
+  }
+}
+
+export class SGD extends Optimizer {
+  private state: Map = new Map();
+  private lr: number;
+  private momentum: number;
+  private dampening: number;
+  private weight_decay: number;
+  private nesterov: boolean;
+  private maximize: boolean;
+
+  constructor(
+    params: Parameter[],
+    lr: number = 0.001,
+    momentum: number = 0.0,
+    dampening: number = 0.0,
+    weight_decay: number = 0.0,
+    nesterov: boolean = false,
+    maximize: boolean = false
+  ) {
+    super(params, {});
+    this.lr = lr;
+    this.momentum = momentum;
+    this.dampening = dampening;
+    this.weight_decay = weight_decay;
+    this.nesterov = nesterov;
+    this.maximize = maximize;
+  }
+
+  step(): void {
+    for (const param of this.params) {
+      if (param.grad === null) continue;
+      let g = this.maximize ? param.grad.mul(-1) : param.grad;
+      if (this.weight_decay !== 0) {
+        g = g.add(param.mul(this.weight_decay));
+      }
+
+      if (this.momentum !== 0) {
+        if (this.state.has(param)) {
+          let buf = this.state.get(param)!.velocity;
+          buf = buf.mul(this.momentum);
+          buf = buf.add(g.mul(1 - this.dampening));
+          this.state.set(param, { velocity: buf });
+        } else {
+          this.state.set(param, { velocity: g });
+        }
+
+        const buf = this.state.get(param)!.velocity;
+
+        if (this.nesterov) {
+          g = g.add(buf.mul(this.momentum));
+        } else {
+          g = buf;
+        }
+
+        this.state.set(param, { velocity: buf });
+      }
+
+      // potentially unsafe?
+      const newParam = param.sub(g.mul(this.lr));
+      param.data = newParam.data;
+    }
+  }
+}
+
+export class Adam extends Optimizer {
+  private state: Map<
+    Parameter,
+    {
+      m: Tensor;
+      v: Tensor;
+      vmax: Tensor;
+    }
+  > = new Map();
+
+  private step_count: number = 0;
+  private lr: number;
+  private beta1: number;
+  private beta2: number;
+  private eps: number;
+  private weight_decay: number;
+  private amsgrad: boolean;
+  private maximize: boolean;
+
+  constructor(
+    params: Parameter[],
+    lr: number = 0.001,
+    betas: [number, number] = [0.9, 0.999],
+    eps: number = 1e-8,
+    weight_decay: number = 0.0,
+    amsgrad: boolean = false,
+    maximize: boolean = false
+  ) {
+    super(params, {});
+    this.lr = lr;
+    this.beta1 = betas[0];
+    this.beta2 = betas[1];
+    this.eps = eps;
+    this.weight_decay = weight_decay;
+    this.amsgrad = amsgrad;
+    this.maximize = maximize;
+  }
+
+  step(): void {
+    this.step_count += 1;
+    for (const param of this.params) {
+      if (param.grad === null) continue;
+      let grad = this.maximize ? param.grad.mul(-1) : param.grad;
+
+      if (this.weight_decay !== 0) {
+        grad = grad.add(param.mul(this.weight_decay));
+      }
+
+      // Initialize
+      if (!this.state.has(param)) {
+        this.state.set(param, {
+          m: zeros_like(param),
+          v: zeros_like(param),
+          vmax: zeros_like(param)
+        });
+      }
+
+      const state = this.state.get(param)!;
+
+      state.m = state.m.mul(this.beta1).add(grad.mul(1 - this.beta1));
+      state.v = state.v.mul(this.beta2).add(grad.mul(grad).mul(1 - this.beta2));
+
+      const biasCorrection1 = 1 - Math.pow(this.beta1, this.step_count);
+      const biasCorrection2 = 1 - Math.pow(this.beta2, this.step_count);
+
+      let vhat: Tensor;
+      const mhat = state.m.div(biasCorrection1);
+      if (this.amsgrad) {
+        state.vmax = state.vmax.maximum(state.v);
+        vhat = state.vmax.div(biasCorrection2);
+      } else {
+        vhat = state.v.div(biasCorrection2);
+      }
+
+      const update = mhat.div(vhat.sqrt().add(this.eps)).mul(this.lr);
+
+      const newParam = param.sub(update);
+      param.data = newParam.data;
+    }
+  }
+}
diff --git a/src/prng.ts b/src/prng.ts
new file mode 100644
index 00000000..89cd18c0
--- /dev/null
+++ b/src/prng.ts
@@ -0,0 +1,42 @@
+// unseeded by default
+let _rng = () => Math.random();
+
+export function getRng(): () => number {
+  return _rng;
+}
+
+export function manual_seed(seed: number): number {
+  seed = seed >>> 0; // to uint32
+  _rng = mulberry32(seed);
+  return seed;
+}
+
+export function seed(): number {
+  const s = (Math.random() * 0xffffffff) >>> 0;
+  _rng = mulberry32(s);
+  return s;
+}
+
+// https://stackoverflow.com/a/47593316
+function mulberry32(seed: number): () => number {
+  return function () {
+    let t = (seed += 0x6d2b79f5);
+    t = Math.imul(t ^ (t >>> 15), t | 1);
+    t ^= t + Math.imul(t ^ (t >>> 7), t | 61);
+    return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+  };
+}
+
+export function uniformDist(min = 0, max = 1) {
+  return () => min + getRng()() * (max - min);
+}
+
+// https://stackoverflow.com/a/36481059
+export function normalDist(mean = 0, std = 1) {
+  return function () {
+    const u = 1 - getRng()(); // [0,1) -> (0,1]
+    const v = getRng()();
+    const z = Math.sqrt(-2.0 * Math.log(u)) * Math.cos(2.0 * Math.PI * v);
+    return z * std + mean;
+  };
+}
diff --git a/src/tensor.ts b/src/tensor.ts
new file mode 100644
index 00000000..9260f1b5
--- /dev/null
+++ b/src/tensor.ts
@@ -0,0 +1,625 @@
+import { AccumulateGrad, TorchFunction, resultRequiresGrad } from './functions/base';
+import { getOperationCache, createOperation } from './functions/registry';
+import { getNextId, eventBus, events } from './util';
+
+export type TypedArray =
+  | Int8Array
+  | Uint8Array
+  | Uint8ClampedArray
+  | Int16Array
+  | Uint16Array
+  | Int32Array
+  | Uint32Array
+  | Float32Array
+  | Float64Array;
+
+export type NestedNumberArray = number | TypedArray | NestedNumberArray[];
+
+function _get_shape(data: NestedNumberArray): number[] {
+  if (ArrayBuffer.isView(data)) {
+    return [data.length];
+  }
+
+  const shape = [];
+  while (Array.isArray(data)) {
+    shape.push(data.length);
+    data = data[0];
+  }
+  return shape;
+}
+
+function _assert_shape(data: NestedNumberArray, shape: number[]): void {
+  if (Array.isArray(data)) {
+    if (data.length !== shape[0]) {
+      throw new Error(
+        `Shape mismatch at dim ${shape.length}: expected ${shape[0]}, got ${data.length}`
+      );
+    }
+    for (let i = 0; i < data.length; i++) {
+      _assert_shape(data[i], shape.slice(1));
+    }
+  } else if (ArrayBuffer.isView(data)) {
+    if (shape.length !== 1) {
+      throw new Error(`Shape mismatch at dim ${shape.length}: expected 1D, got ${shape}`);
+    }
+    if (data.length !== shape[0]) {
+      throw new Error(
+        `Shape mismatch at dim ${shape.length}: expected ${shape[0]}, got ${data.length}`
+      );
+    }
+  } else {
+    if (shape.length !== 0) {
+      throw new Error(`Shape mismatch at dim ${shape.length}: expected scalar, got ${data}`);
+    }
+  }
+}
+
+function _get_and_assert_shape(data: NestedNumberArray): number[] {
+  const shape = _get_shape(data);
+  _assert_shape(data, shape);
+  return shape;
+}
+
+function _flatten(data: NestedNumberArray): number[] {
+  if (Array.isArray(data)) {
+    return data.flatMap(item => _flatten(item));
+  } else if (ArrayBuffer.isView(data)) {
+    return Array.from(data);
+  } else {
+    return [data];
+  }
+}
+
+/**
+ * A shared backing store for tensor data.
+ * Multiple tensors (views) may reference the same TensorStorage instance.
+ * Mutating `data` on the TensorStorage is visible to all sharing tensors.
+ */
+export class TensorStorage {
+  constructor(public data: number[]) {}
+}
+
+export class Tensor {
+  // Auto-generated ID
+  public id: number = getNextId();
+
+  // Optional user-defined name
+  public name: string | null = null;
+
+  // Shared backing storage and offset into it.
+  // Views share the same TensorStorage but differ in _offset and shape.
+  private _storage: TensorStorage = new TensorStorage([]);
+  private _offset: number = 0;
+
+  /**
+   * Returns the flat, contiguous data for this tensor.
+   *
+   * Fast path (non-view): returns the storage array directly — no allocation.
+   * View path: materialises a contiguous slice — one allocation per call,
+   * so callers inside tight loops should cache the result: `const d = t.data`.
+   */
+  get data(): number[] {
+    const n = this.dataLength();
+    if (this._offset === 0 && this._storage.data.length === n) {
+      return this._storage.data;
+    }
+    return this._storage.data.slice(this._offset, this._offset + n);
+  }
+
+  /**
+   * Sets the tensor's data.
+   *
+   * Non-view (offset=0, storage covers exactly this tensor's numel):
+   *   replaces the shared storage's data array in-place — all other views
+   *   sharing the same TensorStorage immediately see the new values.
+   *
+   * View (offset≠0 or storage is larger than this tensor):
+   *   copies `values` element-by-element into the shared storage at the
+   *   correct offset — the original tensor and sibling views are updated.
+   */
+  set data(values: number[]) {
+    const n = values.length;
+    if (this._offset === 0 && this._storage.data.length === n) {
+      // Full-storage owner: swap out the backing array.
+      this._storage.data = values;
+    } else {
+      // View: write into shared storage at the right offset.
+      for (let i = 0; i < n; i++) {
+        this._storage.data[this._offset + i] = values[i];
+      }
+    }
+  }
+
+  public shape: number[];
+  public grad_fn: TorchFunction | null = null;
+  public grad: Tensor | null = null;
+
+  public requires_grad: boolean;
+
+  constructor(
+    data: NestedNumberArray,
+    options: { requires_grad?: boolean; name?: string } = {},
+    internal_options: {
+      operation?: TorchFunction;
+      shape?: number[];
+      /** For internal view construction only — share an existing storage. */
+      _storage?: TensorStorage;
+      /** Byte offset into _storage (in elements). */
+      _offset?: number;
+    } = {}
+  ) {
+    if (internal_options._storage !== undefined) {
+      // View construction: share the provided storage.
+      this._storage = internal_options._storage;
+      this._offset = internal_options._offset ?? 0;
+      this.shape = internal_options.shape ?? [];
+    } else {
+      this._storage = new TensorStorage(_flatten(data));
+      this._offset = 0;
+      this.shape = internal_options.shape ?? _get_and_assert_shape(data);
+    }
+
+    this.requires_grad = options.requires_grad ?? false;
+
+    if (options.name) {
+      this.name = options.name;
+    }
+
+    this.grad_fn = internal_options.operation ?? null;
+
+    if (this.requires_grad && !this.grad_fn) {
+      const acc = new AccumulateGrad();
+      acc.variable = this;
+      this.grad_fn = acc;
+    }
+  }
+
+  size(dim?: number): number | number[] {
+    if (dim !== undefined) {
+      if (dim < 0) {
+        dim += this.shape.length;
+      }
+      if (dim < 0 || dim >= this.shape.length) {
+        throw new Error(
+          `Dimension out of range (expected to be in range of [${-this.shape.length}, ${this.shape.length - 1}], but got ${dim})`
+        );
+      }
+      return this.shape[dim];
+    }
+    return this.shape;
+  }
+
+  toArray_(): void {
+    return;
+  }
+
+  toFlatArray(): number[] {
+    return this.data;
+  }
+
+  toArray(): NestedNumberArray {
+    if (this.shape.length === 0) {
+      return this.data[0];
+    }
+
+    let flatIndex = 0;
+    const flatData = this.data;
+
+    const buildDimension = (currentDim: number): NestedNumberArray => {
+      const size = this.shape[currentDim];
+      const result = new Array(size);
+      const isLastDimension = currentDim === this.shape.length - 1;
+
+      for (let i = 0; i < size; i++) {
+        if (isLastDimension) {
+          result[i] = flatData[flatIndex++];
+        } else {
+          result[i] = buildDimension(currentDim + 1);
+        }
+      }
+
+      return result;
+    };
+
+    return buildDimension(0);
+  }
+
+  toString(): string {
+    let extra = '';
+    if (this.name) {
+      extra += `, name="${this.name}"`;
+    }
+    if (this.dataLength() == 0 && this.shape.length > 0) {
+      extra += `, size=(${this.shape.join(', ')})`;
+    }
+    if (this.requires_grad) {
+      extra += ', requires_grad=True';
+    }
+
+    function formatNum(val: number): string {
+      return String(Math.round(val * 1e4) / 1e4);
+    }
+
+    function formatArray(val: unknown): string {
+      if (Array.isArray(val)) {
+        return "[" + val.map(formatArray).join(", ") + "]";
+      }
+      if (typeof val === "number") {
+        return formatNum(val);
+      }
+      return String(val);
+    }
+
+    return `tensor(${formatArray(this.toArray())}${extra})`;
+  }
+
+  dataLength(): number {
+    if (this.shape.length === 0) return 1;
+    return this.shape.reduce((a, b) => a * b, 1);
+  }
+
+  private _executeUnaryOp(opName: string): Tensor {
+    const operation = resultRequiresGrad(this)
+      ? createOperation(opName)
+      : getOperationCache(opName);
+    return operation.forward(this);
+  }
+
+  private _executeBinaryOp(opName: string, other: Tensor | number): Tensor {
+    if (typeof other == 'number') {
+      other = new Tensor(other);
+    }
+    const operation = resultRequiresGrad(this, other)
+      ? createOperation(opName)
+      : getOperationCache(opName);
+    return operation.forward(this, other);
+  }
+
+  private _executeOpRaw(opName: string, ...args: any[]): Tensor {
+    const operation = createOperation(opName);
+    return operation.forward(this, ...args);
+  }
+
+  item(): number {
+    if (this.dataLength() !== 1) {
+      throw new Error('Tensor.item() is only valid for scalars');
+    }
+    return this.data[0];
+  }
+
+  detach(): Tensor {
+    return new Tensor(this.data, { requires_grad: false }, { shape: this.shape });
+  }
+
+  detach_(): void {
+    this.requires_grad = false;
+    this.grad = null;
+    this.grad_fn = null;
+  }
+
+  zero_(): void {
+    this.data = Array(this.dataLength()).fill(0);
+  }
+
+  private is_retain_grad: boolean = false;
+  retain_grad(): void {
+    // leaf node -> no-op
+    if (this.grad_fn instanceof AccumulateGrad) return;
+    if (this.is_retain_grad) return;
+    this.is_retain_grad = true;
+
+    this.grad_fn._retained_tensors.push(this);
+  }
+
+  backward(grad?: Tensor | null): void {
+    if (!this.requires_grad) {
+      return;
+    }
+
+    if (!grad) {
+      if (this.dataLength() !== 1) {
+        throw new Error('Gradient is required for non-scalar tensors');
+      }
+      grad = new Tensor(1);
+    } else {
+      grad.toArray_();
+    }
+
+    if (this.grad_fn) {
+      eventBus.dispatchEvent(
+        new CustomEvent(events.TENSOR_BEFORE_BACKWARD, { detail: { tensor: this } })
+      );
+      this.grad_fn.backward(grad);
+      eventBus.dispatchEvent(
+        new CustomEvent(events.TENSOR_AFTER_BACKWARD, { detail: { tensor: this } })
+      );
+    }
+  }
+
+  /**
+   * Returns a view of this tensor along dimension 0.
+   *
+   * The returned tensor shares the same underlying TensorStorage — mutations
+   * to either tensor (via zero_(), the data setter, or the optimizer) are
+   * immediately visible in the other.
+   *
+   * Supports negative indices (e.g. index(-1) is the last row).
+   *
+   * Note: the view does not carry a grad_fn; autograd does not propagate
+   * through index() at this time.
+   */
+  index(i: number): Tensor {
+    if (this.shape.length === 0) {
+      throw new Error('Cannot index a scalar tensor');
+    }
+    if (i < 0) {
+      i += this.shape[0];
+    }
+    if (i < 0 || i >= this.shape[0]) {
+      throw new Error(
+        `Index ${i} out of bounds for dimension 0 with size ${this.shape[0]}`
+      );
+    }
+    const newShape = this.shape.slice(1);
+    // Number of elements per row along dim 0.
+    const rowSize = newShape.length === 0 ? 1 : newShape.reduce((a, b) => a * b, 1);
+    const newOffset = this._offset + i * rowSize;
+    return new Tensor([], {}, { shape: newShape, _storage: this._storage, _offset: newOffset });
+  }
+
+  // operations
+
+  // binary pointwise
+
+  add(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('add', other);
+  }
+
+  sub(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('sub', other);
+  }
+
+  mul(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('mul', other);
+  }
+
+  div(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('div', other);
+  }
+
+  pow(other: Tensor | number): Tensor {
+    if (typeof other == 'number' && other % 1 === 0) {
+      return this._executeOpRaw('powint', other);
+    }
+    return this._executeBinaryOp('pow', other);
+  }
+
+  fmod(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('fmod', other);
+  }
+
+  maximum(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('maximum', other);
+  }
+
+  minimum(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('minimum', other);
+  }
+
+  // unary pointwise
+
+  log(): Tensor {
+    return this._executeUnaryOp('log');
+  }
+
+  sqrt(): Tensor {
+    return this._executeUnaryOp('sqrt');
+  }
+
+  exp(): Tensor {
+    return this._executeUnaryOp('exp');
+  }
+
+  square(): Tensor {
+    return this._executeUnaryOp('square');
+  }
+
+  abs(): Tensor {
+    return this._executeUnaryOp('abs');
+  }
+
+  sign(): Tensor {
+    return this._executeUnaryOp('sign');
+  }
+
+  neg(): Tensor {
+    return this._executeUnaryOp('neg');
+  }
+
+  reciprocal(): Tensor {
+    return this._executeUnaryOp('reciprocal');
+  }
+
+  nan_to_num(): Tensor {
+    return this._executeUnaryOp('nan_to_num');
+  }
+
+  reshape(shape: number[]): Tensor {
+    return this._executeOpRaw('reshape', shape);
+  }
+
+  flatten(start_dim: number = 0, end_dim: number = -1): Tensor {
+    return this._executeOpRaw('flatten', start_dim, end_dim);
+  }
+
+  squeeze(dim: number): Tensor {
+    return this._executeOpRaw('squeeze', dim);
+  }
+
+  unsqueeze(dim: number): Tensor {
+    return this._executeOpRaw('unsqueeze', dim);
+  }
+
+  expand(sizes: number[]): Tensor {
+    return this._executeOpRaw('expand', sizes);
+  }
+
+  // trigonometric
+
+  sin(): Tensor {
+    return this._executeUnaryOp('sin');
+  }
+
+  cos(): Tensor {
+    return this._executeUnaryOp('cos');
+  }
+
+  tan(): Tensor {
+    return this._executeUnaryOp('tan');
+  }
+
+  // reduction
+
+  sum(dim?: number | number[], keepdim: boolean = false): Tensor {
+    return this._executeOpRaw('sum', dim, keepdim);
+  }
+
+  mean(dim?: number | number[], keepdim: boolean = false): Tensor {
+    return this._executeOpRaw('mean', dim, keepdim);
+  }
+
+  max(dim?: number | number[], keepdim: boolean = false): Tensor {
+    return this._executeOpRaw('max', dim, keepdim);
+  }
+
+  min(dim?: number | number[], keepdim: boolean = false): Tensor {
+    return this._executeOpRaw('min', dim, keepdim);
+  }
+
+  // linalg
+
+  transpose(dim0: number, dim1: number): Tensor {
+    return this._executeOpRaw('transpose', dim0, dim1);
+  }
+
+  matmul(other: Tensor): Tensor {
+    return this._executeBinaryOp('matmul', other);
+  }
+
+  // comparison
+
+  lt(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('lt', other);
+  }
+
+  gt(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('gt', other);
+  }
+
+  le(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('le', other);
+  }
+
+  ge(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('ge', other);
+  }
+
+  eq(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('eq', other);
+  }
+
+  ne(other: Tensor | number): Tensor {
+    return this._executeBinaryOp('ne', other);
+  }
+
+  allclose(
+    other: Tensor,
+    rtol: number = 1e-5,
+    atol: number = 1e-8,
+    equal_nan: boolean = false
+  ): boolean {
+    const thisData = this.data;
+    const otherData = other.data;
+    if (thisData.length !== otherData.length) return false;
+    for (let i = 0; i < thisData.length; i++) {
+      const av = thisData[i],
+        bv = otherData[i];
+      if (equal_nan && isNaN(av) && isNaN(bv)) continue;
+      if (isNaN(av) || isNaN(bv)) return false;
+      if (Math.abs(av - bv) > atol + rtol * Math.abs(bv)) return false;
+    }
+    return true;
+  }
+
+  numel(): number {
+    return this.dataLength();
+  }
+
+  // other
+
+  sigmoid(): Tensor {
+    return this._executeUnaryOp('sigmoid');
+  }
+
+  tanh(): Tensor {
+    return this._executeUnaryOp('tanh');
+  }
+
+  relu(): Tensor {
+    return this._executeUnaryOp('relu');
+  }
+
+  softmax(dim: number): Tensor {
+    return this._executeOpRaw('softmax', dim);
+  }
+
+  clamp(min: number, max: number): Tensor {
+    return this._executeOpRaw('clamp', min, max);
+  }
+
+  cat(tensors: Tensor | Tensor[], dim: number = 0): Tensor {
+    const others = Array.isArray(tensors) ? tensors : [tensors];
+    return createOperation('cat').forward([this, ...others], dim);
+  }
+
+  concatenate(tensors: Tensor | Tensor[], dim: number = 0): Tensor {
+    return this.cat(tensors, dim);
+  }
+
+  concat(tensors: Tensor | Tensor[], dim: number = 0): Tensor {
+    return this.cat(tensors, dim);
+  }
+}
+
+// ---------------------------------------------------------------------------
+// Typed tensor constructors
+// ---------------------------------------------------------------------------
+
+function _truncate_nested(data: NestedNumberArray): NestedNumberArray {
+  if (typeof data === 'number') return Math.trunc(data);
+  if (Array.isArray(data)) return (data as NestedNumberArray[]).map(_truncate_nested);
+  // TypedArray
+  const out = new Float64Array((data as Float64Array).length);
+  for (let i = 0; i < out.length; i++) out[i] = Math.trunc((data as Float64Array)[i]);
+  return out;
+}
+
+/**
+ * A Tensor that stores 32-bit float values (same as the default Tensor).
+ * Provided for PyTorch API compatibility.
+ */
+export class FloatTensor extends Tensor {
+  constructor(data: NestedNumberArray, options: { requires_grad?: boolean } = {}) {
+    super(data, options);
+  }
+}
+
+/**
+ * A Tensor whose values are truncated to integers (64-bit integer semantics).
+ * Negative numbers are truncated toward zero: LongTensor([-1.7]) -> tensor([-1]).
+ */
+export class LongTensor extends Tensor {
+  constructor(data: NestedNumberArray, options: { requires_grad?: boolean } = {}) {
+    super(_truncate_nested(data), options);
+  }
+}
diff --git a/src/util.ts b/src/util.ts
new file mode 100644
index 00000000..4d350fdc
--- /dev/null
+++ b/src/util.ts
@@ -0,0 +1,29 @@
+let globalId = 0;
+
+export const getNextId = () => {
+  return globalId++;
+};
+
+export const eventBus = new EventTarget();
+export const events = {
+  TENSOR_BEFORE_BACKWARD: 'tensor.beforeBackward',
+  TENSOR_AFTER_BACKWARD: 'tensor.afterBackward',
+  OPERATION_BEFORE_FORWARD: 'operation.beforeForward',
+  OPERATION_AFTER_FORWARD: 'operation.afterForward',
+  OPERATION_BEFORE_BACKWARD: 'operation.beforeBackward',
+  OPERATION_AFTER_BACKWARD: 'operation.afterBackward',
+  OPERATION_BEFORE_ACCUMULATE_GRAD: 'operation.beforeAccumulateGrad',
+  OPERATION_AFTER_ACCUMULATE_GRAD: 'operation.afterAccumulateGrad'
+};
+
+export function _numel(shape: number[]): number {
+  return shape.reduce((a, b) => a * b, 1);
+}
+
+export function _get_shape_from_args(args: number[] | number[][]): number[] {
+  if (Array.isArray(args[0])) {
+    return args[0];
+  }
+
+  return args as number[];
+}
diff --git a/test/backward.test.js b/test/backward.test.js
new file mode 100644
index 00000000..d66171b3
--- /dev/null
+++ b/test/backward.test.js
@@ -0,0 +1,88 @@
+import { assert } from 'chai';
+import { Tensor } from 'torch';
+
+describe('Autograd', () => {
+  it('y = x**2, dy/dx at x=2 is 4', () => {
+    const x = new Tensor([2.0], { requires_grad: true });
+    const y = x.pow(new Tensor([2.0]));
+    assert.strictEqual(y.item(), 4.0);
+    y.backward();
+    assert.strictEqual(x.grad?.item(), 4.0);
+  });
+
+  it('y = 2*x, dy/dx at x=2 is 2', () => {
+    const x = new Tensor([2.0], { requires_grad: true });
+    const y = x.mul(new Tensor(2.0));
+    assert.strictEqual(y.item(), 4.0);
+    y.backward();
+    assert.strictEqual(x.grad?.item(), 2.0);
+  });
+
+  it('z = x + y, dz/dx at x=2, y=3 is 1', () => {
+    const x = new Tensor([2.0], { requires_grad: true });
+    const y = new Tensor([3.0], { requires_grad: true });
+    const z = x.add(y);
+    assert.strictEqual(z.item(), 5.0);
+    z.backward();
+    assert.strictEqual(x.grad?.item(), 1.0);
+    assert.strictEqual(y.grad?.item(), 1.0);
+  });
+
+  it('y = x**2 + 2*x (seperated operations), dy/dx at x=2 is 6', () => {
+    const x = new Tensor([2.0], { requires_grad: true });
+
+    const y1 = x.pow(new Tensor(2.0));
+    const y2 = x.mul(new Tensor(2.0));
+
+    const y = y1.add(y2);
+
+    assert.strictEqual(y.item(), 8.0);
+    assert.strictEqual(y1.item(), 4.0);
+    assert.strictEqual(y2.item(), 4.0);
+
+    y.backward();
+
+    assert.strictEqual(x.grad?.item(), 6.0);
+    assert.strictEqual(x.item(), 2.0);
+  });
+
+  it('y = x**2 + 2*x (combined operations), dy/dx at x=2 is 6', () => {
+    const x = new Tensor([2.0], { requires_grad: true });
+    const y = x.pow(new Tensor(2.0)).add(x.mul(new Tensor(2.0)));
+    assert.strictEqual(y.item(), 8.0);
+    y.backward();
+    assert.strictEqual(x.grad?.item(), 6.0);
+  });
+
+  it('Intermediate tensors do not store grad by default', () => {
+    const x = new Tensor([2.0], { requires_grad: true });
+
+    const y1 = x.pow(new Tensor(2.0));
+    const y2 = x.mul(new Tensor(2.0));
+
+    const y = y1.add(y2);
+
+    y.backward();
+
+    // Intermediate gradients are not retained by default
+    assert.strictEqual(y1.grad, null);
+    assert.strictEqual(y2.grad, null);
+  });
+
+  it('Intermediate tensors store grads after retain_grad', () => {
+    const x = new Tensor([2.0], { requires_grad: true });
+
+    const y1 = x.pow(new Tensor(2.0));
+    const y2 = x.mul(new Tensor(2.0));
+
+    const y = y1.add(y2);
+
+    y1.retain_grad();
+    y2.retain_grad();
+
+    y.backward();
+
+    assert.strictEqual(y1.grad.item(), 1.0);
+    assert.strictEqual(y2.grad.item(), 1.0);
+  });
+});
diff --git a/test/broadcast.test.js b/test/broadcast.test.js
new file mode 100644
index 00000000..524a2a81
--- /dev/null
+++ b/test/broadcast.test.js
@@ -0,0 +1,32 @@
+import { assert } from 'chai';
+import { Tensor, add, __left_index__, __right_index__ } from 'torch';
+
+describe('Broadcast Index', () => {
+    it('Broadcast indices should be correct in 2d array', () => {
+        const arr = new Tensor([
+            [0, 0],
+            [0, 0],
+            [0, 0]
+        ]);
+
+        assert.deepStrictEqual(__left_index__(arr, arr).toFlatArray(), [0, 1, 2, 3, 4, 5]);
+        assert.deepStrictEqual(__right_index__(arr, arr).toFlatArray(), [0, 1, 2, 3, 4, 5]);
+    });
+
+    it('Should get correct value of left and right index', () => {
+        const arr = new Tensor([
+            [1, 2],
+            [3, 4],
+            [5, 6]
+        ]);
+
+        const zeros = new Tensor([
+            [0, 0],
+            [0, 0],
+            [0, 0]
+        ])
+
+        assert.deepStrictEqual(add(arr, zeros).toFlatArray(), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(add(zeros, arr).toFlatArray(), [1, 2, 3, 4, 5, 6]);
+    });
+});
diff --git a/test/broadcast.test.ts b/test/broadcast.test.ts
new file mode 100644
index 00000000..0dae3a00
--- /dev/null
+++ b/test/broadcast.test.ts
@@ -0,0 +1,22 @@
+import { assert } from 'chai';
+import { _broadcast_shape } from '../src/broadcasting';
+
+describe('Broadcasting', () => {
+  it('Broadcast shape of same dimensions', () => {
+    assert.deepStrictEqual(_broadcast_shape([1, 3, 1], [4, 3, 6]), [4, 3, 6]);
+    assert.deepStrictEqual(_broadcast_shape([4, 3, 6], [1, 3, 1]), [4, 3, 6]);
+    assert.deepStrictEqual(_broadcast_shape([2, 2, 2], [2, 2, 2]), [2, 2, 2]);
+
+    assert.throws(() => _broadcast_shape([1, 2, 1], [1, 3, 1]), Error);
+  });
+
+  it('Broadcast shape of different dimensions', () => {
+    assert.deepStrictEqual(_broadcast_shape([2, 3, 1], [3, 1]), [2, 3, 1]);
+    assert.deepStrictEqual(_broadcast_shape([3, 1], [2, 3, 1]), [2, 3, 1]);
+    assert.deepStrictEqual(_broadcast_shape([1], [2, 2, 2]), [2, 2, 2]);
+    assert.deepStrictEqual(_broadcast_shape([2], []), [2]);
+    assert.deepStrictEqual(_broadcast_shape([1, 4], [3, 3, 1]), [3, 3, 4]);
+
+    assert.throws(() => _broadcast_shape([1, 2], [3, 3, 6]), Error);
+  });
+});
diff --git a/test/chai-esm.js b/test/chai-esm.js
new file mode 100644
index 00000000..8104557e
--- /dev/null
+++ b/test/chai-esm.js
@@ -0,0 +1,4 @@
+// Wrap global UMD chai as an ES module export
+const { assert, expect, should, use, config } = window.chai;
+export { assert, expect, should, use, config };
+export default window.chai;
diff --git a/test/creation.test.ts b/test/creation.test.ts
new file mode 100644
index 00000000..3641f9f1
--- /dev/null
+++ b/test/creation.test.ts
@@ -0,0 +1,382 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+import { Tensor } from 'torch';
+
+describe('Creation Functions', () => {
+  describe('tensor', () => {
+    it('should create a scalar tensor', () => {
+      const t = torch.tensor(5);
+      assert.deepStrictEqual(t.toArray(), 5);
+      assert.deepStrictEqual(t.shape, []);
+    });
+
+    it('should create a tensor with 1D array', () => {
+      const t = torch.tensor([1, 2, 3]);
+      assert.deepStrictEqual(t.toArray(), [1, 2, 3]);
+      assert.deepStrictEqual(t.shape, [3]);
+    });
+
+    it('should create a tensor with nested 2D array', () => {
+      const t = torch.tensor([
+        [1, 2],
+        [3, 4]
+      ]);
+      assert.deepStrictEqual(t.toArray(), [
+        [1, 2],
+        [3, 4]
+      ]);
+      assert.deepStrictEqual(t.shape, [2, 2]);
+    });
+
+    it('should create a tensor with nested 3D array', () => {
+      const t = torch.tensor([
+        [
+          [1, 2],
+          [3, 4]
+        ],
+        [
+          [5, 6],
+          [7, 8]
+        ]
+      ]);
+      assert.deepStrictEqual(t.toArray(), [
+        [
+          [1, 2],
+          [3, 4]
+        ],
+        [
+          [5, 6],
+          [7, 8]
+        ]
+      ]);
+      assert.deepStrictEqual(t.shape, [2, 2, 2]);
+    });
+
+    it('should create empty tensors', () => {
+      const t = torch.tensor([]);
+      assert.deepStrictEqual(t.toArray(), []);
+      assert.deepStrictEqual(t.shape, [0]);
+
+      const t2 = torch.tensor([[]]);
+      assert.deepStrictEqual(t2.toArray(), [[]]);
+      assert.deepStrictEqual(t2.shape, [1, 0]);
+
+      const t3 = torch.tensor([[], []]);
+      assert.deepStrictEqual(t3.toArray(), [[], []]);
+      assert.deepStrictEqual(t3.shape, [2, 0]);
+    });
+
+    it('should not create tensor with inconsistent shape', () => {
+      assert.throws(() => torch.tensor([[1, 2], [3]]));
+      assert.throws(() => torch.tensor([1, [2, 3]]));
+      assert.throws(() =>
+        torch.tensor([
+          [1, 2],
+          [3, 4, 5]
+        ])
+      );
+      assert.throws(() =>
+        torch.tensor([
+          [
+            [1, 2],
+            [3, 4]
+          ],
+          [[5, 6]]
+        ])
+      );
+    });
+  });
+  describe('ones', () => {
+    it('should create a 1D tensor of ones', () => {
+      const t = torch.ones(5);
+      assert.deepStrictEqual(t.toArray(), [1, 1, 1, 1, 1]);
+      assert.deepStrictEqual(t.shape, [5]);
+    });
+
+    it('should create a 2D tensor of ones', () => {
+      const t = torch.ones(2, 3);
+      assert.deepStrictEqual(t.toArray(), [
+        [1, 1, 1],
+        [1, 1, 1]
+      ]);
+      assert.deepStrictEqual(t.shape, [2, 3]);
+    });
+
+    it('should create a 3D tensor of ones', () => {
+      const t = torch.ones(2, 3, 4);
+      assert.deepStrictEqual(t.shape, [2, 3, 4]);
+      assert.strictEqual(t.toFlatArray().length, 24);
+      assert.isTrue(t.toFlatArray().every((v: number) => v === 1));
+    });
+
+    it('should accept shape as an array', () => {
+      const t = torch.ones([3, 2]);
+      assert.deepStrictEqual(t.shape, [3, 2]);
+      assert.isTrue(t.toFlatArray().every((v: number) => v === 1));
+    });
+  });
+
+  describe('zeros', () => {
+    it('should create a 1D tensor of zeros', () => {
+      const t = torch.zeros(4);
+      assert.deepStrictEqual(t.toArray(), [0, 0, 0, 0]);
+      assert.deepStrictEqual(t.shape, [4]);
+    });
+
+    it('should create a 2D tensor of zeros', () => {
+      const t = torch.zeros(3, 2);
+      assert.deepStrictEqual(t.toArray(), [
+        [0, 0],
+        [0, 0],
+        [0, 0]
+      ]);
+      assert.deepStrictEqual(t.shape, [3, 2]);
+    });
+
+    it('should accept shape as an array', () => {
+      const t = torch.zeros([2, 4]);
+      assert.deepStrictEqual(t.shape, [2, 4]);
+      assert.isTrue(t.toFlatArray().every((v: number) => v === 0));
+    });
+  });
+
+  describe('ones_like', () => {
+    it('should create a tensor of ones with the same shape', () => {
+      const original = new Tensor([
+        [1, 2, 3],
+        [4, 5, 6]
+      ]);
+      const t = torch.ones_like(original);
+      assert.deepStrictEqual(t.shape, [2, 3]);
+      assert.isTrue(t.toFlatArray().every((v: number) => v === 1));
+    });
+  });
+
+  describe('zeros_like', () => {
+    it('should create a tensor of zeros with the same shape', () => {
+      const original = new Tensor([
+        [1, 2],
+        [3, 4],
+        [5, 6]
+      ]);
+      const t = torch.zeros_like(original);
+      assert.deepStrictEqual(t.shape, [3, 2]);
+      assert.isTrue(t.toFlatArray().every((v: number) => v === 0));
+    });
+  });
+
+  describe('rand', () => {
+    it('should create a tensor with the correct shape', () => {
+      const t = torch.rand(3, 4);
+      assert.deepStrictEqual(t.shape, [3, 4]);
+      assert.strictEqual(t.toFlatArray().length, 12);
+    });
+
+    it('should create values in [0, 1)', () => {
+      const t = torch.rand(10);
+      for (const v of t.toFlatArray()) {
+        assert.isAtLeast(v, 0);
+        assert.isBelow(v, 1);
+      }
+    });
+  });
+
+  describe('randn', () => {
+    it('should create a tensor with the correct shape', () => {
+      const t = torch.randn(2, 5);
+      assert.deepStrictEqual(t.shape, [2, 5]);
+      assert.strictEqual(t.toFlatArray().length, 10);
+    });
+  });
+
+  describe('randint', () => {
+    it('should create a tensor with the correct shape', () => {
+      const t = torch.randint(0, 10, [3, 3]);
+      assert.deepStrictEqual(t.shape, [3, 3]);
+      assert.strictEqual(t.toFlatArray().length, 9);
+    });
+
+    it('should create integer values in [low, high)', () => {
+      const t = torch.randint(5, 15, [20]);
+      for (const v of t.toFlatArray()) {
+        assert.isAtLeast(v, 5);
+        assert.isBelow(v, 15);
+        assert.strictEqual(v, Math.floor(v));
+      }
+    });
+  });
+
+  describe('linspace', () => {
+    it('should create evenly spaced values', () => {
+      const t = torch.linspace(0, 1, 5);
+      const expected = [0, 0.25, 0.5, 0.75, 1.0];
+      assert.deepStrictEqual(t.shape, [5]);
+      const arr = t.toFlatArray();
+      for (let i = 0; i < expected.length; i++) {
+        assert.closeTo(arr[i], expected[i], 1e-6);
+      }
+    });
+
+    it('should handle negative range', () => {
+      const t = torch.linspace(-1, 1, 3);
+      const arr = t.toFlatArray();
+      assert.closeTo(arr[0], -1, 1e-6);
+      assert.closeTo(arr[1], 0, 1e-6);
+      assert.closeTo(arr[2], 1, 1e-6);
+    });
+  });
+
+  describe('arange', () => {
+    it('should create a range of values', () => {
+      const t = torch.arange(0, 5);
+      assert.deepStrictEqual(t.toArray(), [0, 1, 2, 3, 4]);
+    });
+
+    it('should support custom step', () => {
+      const t = torch.arange(0, 10, 2);
+      assert.deepStrictEqual(t.toArray(), [0, 2, 4, 6, 8]);
+    });
+
+    it('should support negative step values with appropriate range', () => {
+      const t = torch.arange(1, 5, 0.5);
+      assert.deepStrictEqual(t.shape, [8]);
+      const arr = t.toFlatArray();
+      assert.closeTo(arr[0], 1.0, 1e-6);
+      assert.closeTo(arr[7], 4.5, 1e-6);
+    });
+  });
+
+  describe('empty', () => {
+    it('should create a tensor with the correct shape', () => {
+      const t = torch.empty(3, 4);
+      assert.deepStrictEqual(t.shape, [3, 4]);
+      assert.strictEqual(t.toFlatArray().length, 12);
+    });
+
+    it('should accept shape as an array', () => {
+      const t = torch.empty([2, 3]);
+      assert.deepStrictEqual(t.shape, [2, 3]);
+    });
+  });
+
+  describe('empty_like', () => {
+    it('should create a tensor with the same shape', () => {
+      const original = new Tensor([[1, 2, 3], [4, 5, 6]]);
+      const t = torch.empty_like(original);
+      assert.deepStrictEqual(t.shape, [2, 3]);
+    });
+  });
+
+  describe('full', () => {
+    it('should create a tensor filled with a value', () => {
+      const t = torch.full([2, 3], 7);
+      assert.deepStrictEqual(t.shape, [2, 3]);
+      assert.isTrue(t.toFlatArray().every((v: number) => v === 7));
+    });
+
+    it('should create a 1D tensor filled with a value', () => {
+      const t = torch.full([5], 3.14);
+      assert.deepStrictEqual(t.shape, [5]);
+      for (const v of t.toFlatArray()) {
+        assert.closeTo(v, 3.14, 1e-6);
+      }
+    });
+  });
+
+  describe('full_like', () => {
+    it('should create a tensor filled with a value with the same shape', () => {
+      const original = new Tensor([[1, 2], [3, 4]]);
+      const t = torch.full_like(original, 42);
+      assert.deepStrictEqual(t.shape, [2, 2]);
+      assert.isTrue(t.toFlatArray().every((v: number) => v === 42));
+    });
+  });
+
+  describe('rand_like', () => {
+    it('should create a tensor with the same shape and values in [0, 1)', () => {
+      const original = new Tensor([[1, 2, 3], [4, 5, 6]]);
+      const t = torch.rand_like(original);
+      assert.deepStrictEqual(t.shape, [2, 3]);
+      for (const v of t.toFlatArray()) {
+        assert.isAtLeast(v, 0);
+        assert.isBelow(v, 1);
+      }
+    });
+  });
+
+  describe('randn_like', () => {
+    it('should create a tensor with the same shape', () => {
+      const original = new Tensor([[1, 2], [3, 4]]);
+      const t = torch.randn_like(original);
+      assert.deepStrictEqual(t.shape, [2, 2]);
+      assert.strictEqual(t.toFlatArray().length, 4);
+    });
+  });
+
+  describe('randint_like', () => {
+    it('should create a tensor with the same shape and integer values in [low, high)', () => {
+      const original = new Tensor([[1, 2, 3], [4, 5, 6]]);
+      const t = torch.randint_like(original, 0, 10);
+      assert.deepStrictEqual(t.shape, [2, 3]);
+      for (const v of t.toFlatArray()) {
+        assert.isAtLeast(v, 0);
+        assert.isBelow(v, 10);
+        assert.strictEqual(v, Math.floor(v));
+      }
+    });
+  });
+
+  describe('is_tensor', () => {
+    it('should return true for tensors', () => {
+      const t = torch.tensor([1, 2, 3]);
+      assert.isTrue(torch.is_tensor(t));
+    });
+
+    it('should return false for non-tensors', () => {
+      assert.isFalse(torch.is_tensor(5));
+      assert.isFalse(torch.is_tensor([1, 2, 3]));
+      assert.isFalse(torch.is_tensor('hello'));
+      assert.isFalse(torch.is_tensor(null));
+    });
+  });
+
+  describe('is_nonzero', () => {
+    it('should return true for non-zero scalar tensor', () => {
+      assert.isTrue(torch.is_nonzero(torch.tensor(5)));
+      assert.isTrue(torch.is_nonzero(torch.tensor(-1)));
+      assert.isTrue(torch.is_nonzero(torch.tensor(0.001)));
+    });
+
+    it('should return false for zero scalar tensor', () => {
+      assert.isFalse(torch.is_nonzero(torch.tensor(0)));
+    });
+
+    it('should throw for multi-element tensor', () => {
+      assert.throws(() => torch.is_nonzero(torch.tensor([1, 2])));
+    });
+  });
+
+  describe('numel', () => {
+    it('should return the total number of elements', () => {
+      assert.strictEqual(torch.numel(torch.tensor([1, 2, 3])), 3);
+      assert.strictEqual(torch.numel(torch.tensor([[1, 2], [3, 4]])), 4);
+      assert.strictEqual(torch.numel(torch.tensor(5)), 1);
+    });
+  });
+
+  describe('seed', () => {
+    it('random numbers should be different', () => {
+      const t1 = torch.rand(5);
+      const t2 = torch.rand(5);
+      assert.notDeepEqual(t1.toArray(), t2.toArray());
+    });
+
+    it('manual_seed should seed the random number generator', () => {
+      torch.manual_seed(123);
+      const t1 = torch.rand(5);
+      torch.manual_seed(123);
+      const t2 = torch.rand(5);
+      assert.deepStrictEqual(t1.toArray(), t2.toArray());
+    });
+  });
+});
diff --git a/test/custom_operations.test.js b/test/custom_operations.test.js
new file mode 100644
index 00000000..12773947
--- /dev/null
+++ b/test/custom_operations.test.js
@@ -0,0 +1,118 @@
+import { assert } from 'chai';
+import { Tensor } from 'torch';
+
+describe('Custom Operations', () => {
+
+  describe('Matmul', () => {
+    it('should perform matrix multiplication on 2D tensors (1)', () => {
+      const t1 = new Tensor([
+        [1, 2, 3],
+        [4, 5, 6]
+      ]);
+      const t2 = new Tensor([
+        [7, 8],
+        [9, 1],
+        [2, 3]
+      ]);
+
+      const result = t1.matmul(t2);
+      assert.deepStrictEqual(result.shape, [2, 2]);
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [31, 19, 85, 55]);
+    });
+
+    it('should perform matrix multiplication on 2D tensors (2)', () => {
+      const t1 = new Tensor([
+        [1, 2, 3],
+        [4, 5, 6]
+      ]);
+
+      const t2 = new Tensor([
+        [9, 9, 1],
+        [6, 4, 3],
+        [5, 5, 6]
+      ]);
+
+      const result = t1.matmul(t2);
+
+      const expected = [36, 32, 25, 96, 86, 55];
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), expected);
+      assert.deepStrictEqual(result.shape, [2, 3]);
+    });
+
+    it('should perform dot product on 1D tensors', () => {
+      const t1 = new Tensor([1, 2, 3]);
+      const t2 = new Tensor([4, 5, 6]);
+
+      const result = t1.matmul(t2);
+      assert.deepStrictEqual(result.shape, []);
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [32]);
+    });
+
+    it('should handle batch matrix multiplication', () => {
+      const t1 = new Tensor([
+        [[62, 50], [7, 53]],
+        [[5, 48], [63, 94]]
+      ]);
+      const t2 = new Tensor([
+        [[98, 3], [59, 81]],
+        [[79, 74], [41, 98]]
+      ]);
+
+      const result = t1.matmul(t2);
+      assert.deepStrictEqual(result.shape, [2, 2, 2]);
+
+      const data = Array.from(result.toFlatArray());
+      assert.deepStrictEqual(data, [9026, 4236, 3813, 4314, 2363, 5074, 8831, 13874]);
+    });
+
+    it('should calculate backward correctly for matmul', () => {
+      const t1 = new Tensor([
+        [1, 2],
+        [3, 4]
+      ], { requires_grad: true });
+
+      const t2 = new Tensor([
+        [5, 6],
+        [7, 8]
+      ], { requires_grad: true });
+
+      const result = t1.matmul(t2);
+      result.sum().backward();
+
+      const t1Grad = Array.from(t1.grad.toFlatArray());
+      assert.deepStrictEqual(t1Grad, [11, 15, 11, 15]);
+
+      const t2Grad = Array.from(t2.grad.toFlatArray());
+      assert.deepStrictEqual(t2Grad, [4, 4, 6, 6]);
+    });
+  });
+
+  describe('Transpose', () => {
+    it('should transpose a 2D tensor', () => {
+      const t = new Tensor([
+        [1, 2, 3],
+        [4, 5, 6]
+      ]);
+
+      const result = t.transpose(0, 1);
+      assert.deepStrictEqual(result.shape, [3, 2]);
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 4, 2, 5, 3, 6]);
+    });
+
+    it('should transpose dimensions in a 3D tensor', () => {
+      const t = new Tensor([
+        [[1, 2], [3, 4]],
+        [[5, 6], [7, 8]]
+      ]);
+
+      const result = t.transpose(1, 2);
+      assert.deepStrictEqual(result.shape, [2, 2, 2]);
+      const data = Array.from(result.toFlatArray());
+      assert.deepStrictEqual(data, [1, 3, 2, 4, 5, 7, 6, 8]);
+    });
+
+
+  });
+
+});
diff --git a/test/event_listener.test.js b/test/event_listener.test.js
new file mode 100644
index 00000000..4a2aa2a1
--- /dev/null
+++ b/test/event_listener.test.js
@@ -0,0 +1,95 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+
+describe('Event Bus', () => {
+    const a = new torch.Tensor([1, 2, 3], { requires_grad: true });
+    const b = new torch.Tensor([4, 5, 6], { requires_grad: true });
+    const ab = a.add(b);
+    const sum = ab.sum();
+    
+    describe('tensor.beforeBackward', () => {
+        it('should dispatch event before backward', () => {
+            let eventDispatched = false;
+            torch.eventBus.addEventListener(torch.events.TENSOR_BEFORE_BACKWARD, () => {
+                eventDispatched = true;
+            });
+            sum.backward();
+            assert.isTrue(eventDispatched);
+        });
+    });
+
+    describe('tensor.afterBackward', () => {
+        it('should dispatch event after backward', () => {
+            let eventDispatched = false;
+            torch.eventBus.addEventListener(torch.events.TENSOR_AFTER_BACKWARD, () => {
+                eventDispatched = true;
+            });
+            sum.backward();
+            assert.isTrue(eventDispatched);
+        });
+    });
+
+    describe('operation.beforeForward', () => {
+        it('should dispatch event before forward', () => {
+            let eventDispatched = false;
+            torch.eventBus.addEventListener(torch.events.OPERATION_BEFORE_FORWARD, () => {
+                eventDispatched = true;
+            });
+            const c = a.add(b);
+            assert.isTrue(eventDispatched);
+        });
+    });
+
+    describe('operation.afterForward', () => {
+        it('should dispatch event after forward', () => {
+            let eventDispatched = false;
+            torch.eventBus.addEventListener(torch.events.OPERATION_AFTER_FORWARD, () => {
+                eventDispatched = true;
+            });
+            const c = a.add(b);
+            assert.isTrue(eventDispatched);
+        });
+    });
+
+    describe('operation.beforeBackward', () => {
+        it('should dispatch event before backward', () => {
+            let eventDispatched = false;
+            torch.eventBus.addEventListener(torch.events.OPERATION_BEFORE_BACKWARD, () => {
+                eventDispatched = true;
+            });
+            sum.backward();
+            assert.isTrue(eventDispatched);
+        });
+    });
+
+    describe('operation.afterBackward', () => {
+        it('should dispatch event after backward', () => {
+            let eventDispatched = false;
+            torch.eventBus.addEventListener(torch.events.OPERATION_AFTER_BACKWARD, () => {
+                eventDispatched = true;
+            });
+            sum.backward();
+            assert.isTrue(eventDispatched);
+        });
+    });
+
+    describe('operation.accumulateGrad', () => {
+        it('should dispatch event before accumulateGrad', () => {
+            let eventDispatched = false;
+            torch.eventBus.addEventListener(torch.events.OPERATION_BEFORE_ACCUMULATE_GRAD, () => {
+                eventDispatched = true;
+            });
+            sum.backward();
+            assert.isTrue(eventDispatched);
+        });
+
+        it('should dispatch event after accumulateGrad', () => {
+            let eventDispatched = false;
+            torch.eventBus.addEventListener(torch.events.OPERATION_AFTER_ACCUMULATE_GRAD, () => {
+                eventDispatched = true;
+            });
+            sum.backward();
+            assert.isTrue(eventDispatched);
+        });
+    });
+});
diff --git a/test/export.test.ts b/test/export.test.ts
new file mode 100644
index 00000000..167aba49
--- /dev/null
+++ b/test/export.test.ts
@@ -0,0 +1,23 @@
+import { assert } from 'chai';
+import { _atenMap } from '../src/export';
+import { _getAllOperationNames } from '../src/functions/registry';
+
+// Import torch to trigger operation registration side effects
+import 'torch';
+
+describe('Export', () => {
+  // List from https://docs.pytorch.org/docs/2.10/user_guide/torch_compiler/torch.compiler_ir.html
+
+  const EXCLUDED_OPERATIONS = new Set([
+    "__left_index__",
+    "__right_index__",
+  ]);
+
+  it('has all supported operations', () => {
+    for (const opName of _getAllOperationNames()) {
+      assert.isTrue(opName in _atenMap || EXCLUDED_OPERATIONS.has(opName),
+        `Missing aten mapping for operation: ${opName}. ` +
+        `Please add it in export.ts or exclude it in export.test.ts`);
+    }
+  });
+});
diff --git a/test/functional.test.js b/test/functional.test.js
new file mode 100644
index 00000000..d2091942
--- /dev/null
+++ b/test/functional.test.js
@@ -0,0 +1,239 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+import { Tensor } from 'torch';
+
+describe('Functional', () => {
+  describe('Addition', () => {
+    it('should add two tensors with same shape', () => {
+      const t1 = new Tensor([10]);
+      const t2 = new Tensor([20]);
+      const result = torch.add(t1, t2);
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [30]);
+      assert.deepStrictEqual(result.shape, [1]);
+    });
+
+    it('should add tensors with different shapes (broadcasting)', () => {
+      const t1 = new Tensor([10, 20, 30]);
+      const t2 = new Tensor([1]);
+      const result = torch.add(t1, t2);
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [11, 21, 31]);
+      assert.deepStrictEqual(result.shape, [3]);
+    });
+
+    it('should add two 1D tensors of same length', () => {
+      const t1 = new Tensor([1, 2, 3]);
+      const t2 = new Tensor([4, 5, 6]);
+      const result = torch.add(t1, t2);
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [5, 7, 9]);
+      assert.deepStrictEqual(result.shape, [3]);
+    });
+  });
+
+  describe('Multiplication', () => {
+    it('should multiply two tensors with same shape', () => {
+      const t1 = new Tensor([10]);
+      const t2 = new Tensor([20]);
+      const result = torch.mul(t1, t2);
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [200]);
+      assert.deepStrictEqual(result.shape, [1]);
+    });
+  });
+
+  describe('should multiply tensors with different shapes (broadcasting)', () => {
+    it('should multiply two tensors with different shapes (broadcasting)', () => {
+      const t1 = new Tensor([10, 20, 30]);
+      const t2 = new Tensor([1]);
+      const result = torch.mul(t1, t2);
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [10, 20, 30]);
+      assert.deepStrictEqual(result.shape, [3]);
+    });
+  });
+
+  describe('Matrix Multiplication', () => {
+    it('should multiply two tensors with shape (1)', () => {
+      const t1 = new Tensor([10]);
+      const t2 = new Tensor([20]);
+      const result = torch.matmul(t1, t2);
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [200]);
+      assert.deepStrictEqual(result.shape, []);
+    });
+
+    it('should multiply two tensors with 1 dim', () => {
+      const t1 = new Tensor([1, 2, 3, 4]);
+      const t2 = new Tensor([5, 6, 7, 8]);
+      const result = torch.matmul(t1, t2);
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [70]);
+      assert.deepStrictEqual(result.shape, []);
+    });
+
+    it('should multiply two tensors with shape (2, 3) and (3, 3) with the correct values', () => {
+      const t1 = new Tensor([
+        [1, 2, 3],
+        [4, 5, 6]
+      ]);
+
+      const t2 = new Tensor([
+        [9, 9, 1],
+        [6, 4, 3],
+        [5, 5, 6]
+      ]);
+
+      const result = torch.matmul(t1, t2);
+
+      const expected = [36, 32, 25, 96, 86, 55];
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), expected);
+      assert.deepStrictEqual(result.shape, [2, 3]);
+    });
+
+    it('should output correct shape', () => {
+      function shape_of(shape1, shape2) {
+        const tensor1 = torch.randn(shape1);
+        const tensor2 = torch.randn(shape2);
+        const result = torch.matmul(tensor1, tensor2);
+        return result.shape;
+      }
+
+      assert.deepStrictEqual(shape_of([3, 4], [4, 5]), [3, 5]);
+      assert.deepStrictEqual(shape_of([3, 4], [4]), [3]);
+      assert.deepStrictEqual(shape_of([10, 3, 4], [4]), [10, 3]);
+      assert.deepStrictEqual(shape_of([10, 3, 4], [10, 4, 5]), [10, 3, 5]);
+      assert.deepStrictEqual(shape_of([10, 3, 4], [4, 5]), [10, 3, 5]);
+    });
+  });
+
+  describe('Transpose', () => {
+    it('should transpose a tensor', () => {
+      const t = new Tensor([
+        [1, 2],
+        [3, 4]
+      ]);
+      const result = t.transpose(0, 1);
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 3, 2, 4]);
+      assert.deepStrictEqual(result.shape, [2, 2]);
+    });
+  });
+
+  describe('numel', () => {
+    it('should return the total number of elements', () => {
+      const t = new Tensor([[1, 2], [3, 4], [5, 6]]);
+      assert.strictEqual(torch.numel(t), 6);
+    });
+  });
+
+  describe('randperm', () => {
+    it('should return a permutation of 0..n-1', () => {
+      torch.manual_seed(42);
+      const t = torch.randperm(5);
+      assert.strictEqual(t.numel(), 5);
+      const sorted = Array.from(t.toFlatArray()).sort((a, b) => a - b);
+      assert.deepStrictEqual(sorted, [0, 1, 2, 3, 4]);
+    });
+  });
+
+  describe('seed', () => {
+    it('should return a seed and make rng deterministic', () => {
+      const s = torch.seed();
+      assert.isNumber(s);
+      const a = torch.rand(3);
+      // Re-seed with same seed should produce same values
+      torch.manual_seed(s);
+      const b = torch.rand(3);
+      assert.deepStrictEqual(Array.from(a.toFlatArray()), Array.from(b.toFlatArray()));
+    });
+  });
+
+  describe('allclose', () => {
+    it('returns true for identical tensors', () => {
+      const a = new Tensor([1, 2, 3]);
+      assert.strictEqual(torch.allclose(a, a), true);
+    });
+
+    it('returns true for tensors within default tolerance', () => {
+      const a = new Tensor([1.0, 2.0, 3.0]);
+      const b = new Tensor([1.0, 2.0, 3.0 + 1e-7]);
+      assert.strictEqual(torch.allclose(a, b), true);
+    });
+
+    it('returns false for tensors outside default tolerance', () => {
+      const a = new Tensor([1.0, 2.0, 3.0]);
+      const b = new Tensor([1.0, 2.0, 4.0]);
+      assert.strictEqual(torch.allclose(a, b), false);
+    });
+
+    it('returns false for tensors of different sizes', () => {
+      const a = new Tensor([1, 2, 3]);
+      const b = new Tensor([1, 2]);
+      assert.strictEqual(torch.allclose(a, b), false);
+    });
+
+    it('returns false for NaN when equal_nan=false', () => {
+      const a = new Tensor([1, NaN, 3]);
+      const b = new Tensor([1, NaN, 3]);
+      assert.strictEqual(torch.allclose(a, b, 1e-5, 1e-8, false), false);
+    });
+
+    it('returns true for NaN when equal_nan=true', () => {
+      const a = new Tensor([1, NaN, 3]);
+      const b = new Tensor([1, NaN, 3]);
+      assert.strictEqual(torch.allclose(a, b, 1e-5, 1e-8, true), true);
+    });
+
+    it('tensor.allclose() method matches torch.allclose()', () => {
+      const a = new Tensor([1.0, 2.0, 3.0]);
+      const b = new Tensor([1.0, 2.0, 3.0 + 1e-7]);
+      assert.strictEqual(a.allclose(b), torch.allclose(a, b));
+    });
+  });
+
+  describe('Softmax', () => {
+    it('outputs sum to 1 along specified dim', () => {
+      const x = torch.tensor([1.0, 2.0, 3.0]);
+      assert.closeTo(torch.softmax(x, 0).sum().item(), 1.0, 1e-6);
+    });
+
+    it('tensor.softmax() method works', () => {
+      const x = torch.tensor([[1.0, 2.0], [3.0, 4.0]]);
+      x.softmax(1).toArray().forEach(row => assert.closeTo(row.reduce((a, b) => a + b, 0), 1.0, 1e-6));
+    });
+
+    it('negative dim works', () => {
+      const x = torch.tensor([[1.0, 2.0], [3.0, 4.0]]);
+      assert.deepStrictEqual(torch.softmax(x, 1).toArray(), torch.softmax(x, -1).toArray());
+    });
+
+    it('gradient flows back through softmax (grad of sum = 0)', () => {
+      const x = torch.tensor([1.0, 2.0, 3.0], true);
+      torch.softmax(x, 0).sum().backward();
+      x.grad.toFlatArray().forEach(g => assert.closeTo(g, 0.0, 1e-6));
+    });
+  });
+
+  describe('Clamp / Clip', () => {
+    it('clamps values below min and above max', () => {
+      assert.deepStrictEqual(torch.clamp(torch.tensor([-2.0, 0.0, 2.0, 5.0]), 0, 3).toArray(), [0.0, 0.0, 2.0, 3.0]);
+    });
+
+    it('torch.clip is an alias for torch.clamp', () => {
+      const x = torch.tensor([-1.0, 0.5, 2.0]);
+      assert.deepStrictEqual(torch.clamp(x, 0, 1).toArray(), torch.clip(x, 0, 1).toArray());
+    });
+
+    it('tensor.clamp() method works', () => {
+      assert.deepStrictEqual(torch.tensor([-1.0, 0.5, 2.0]).clamp(0, 1).toArray(), [0.0, 0.5, 1.0]);
+    });
+
+    it('gradient is 1 inside range, 0 outside', () => {
+      const x = torch.tensor([-1.0, 0.5, 2.0], true);
+      torch.clamp(x, 0, 1).sum().backward();
+      assert.deepStrictEqual(x.grad.toArray(), [0.0, 1.0, 0.0]);
+    });
+  });
+});
diff --git a/test/generated.test.js b/test/generated.test.js
new file mode 100644
index 00000000..7d880d9d
--- /dev/null
+++ b/test/generated.test.js
@@ -0,0 +1,410 @@
+import * as torch from 'torch';
+import { Tensor } from 'torch';
+import { assert } from 'chai';
+import { testData } from './testcases.gen.js';
+
+function assertDeepCloseTo(actual, expected, name = null, delta = 1e-3, first_call = true) {
+  if (Array.isArray(expected)) {
+    assert.lengthOf(actual, expected.length, 'Array lengths do not match');
+    for (let i = 0; i < expected.length; i++) {
+      assertDeepCloseTo(actual[i], expected[i], name, delta, false);
+    }
+  } else {
+    if (Number.isNaN(expected)) {
+      assert.isTrue(Number.isNaN(actual), `${name}: Expected NaN but got ${actual}`);
+    } else if(!Number.isFinite(expected)) {
+      assert.equal(actual, expected, `${name}: Expected ${expected} but got ${actual}`);
+    } else {
+      assert.closeTo(actual, expected, delta, `${name}: Expected ${expected} but got ${actual}`);
+    }
+  }
+}
+
+describe('Automated Tests', () => {
+  describe('Unary Operations', () => {
+    for (const [opName, tests] of Object.entries(testData.unary)) {
+      describe(`.${opName}()`, () => {
+        tests.forEach((test, idx) => {
+          it(`case ${idx + 1}`, () => {
+            const x = new Tensor(test.input, { requires_grad: true });
+            const y = x[opName]();
+            assertDeepCloseTo(y.toArray(), test.expected_output, `${opName} output`);
+            y.sum().backward();
+            assertDeepCloseTo(x.grad.toArray(), test.expected_grad, `${opName} grad`);
+          });
+        });
+      });
+    }
+  });
+
+  describe('Binary Operations', () => {
+    for (const [opName, tests] of Object.entries(testData.binary)) {
+      describe(`.${opName}()`, () => {
+        tests.forEach((test, idx) => {
+          it(`case ${idx + 1}`, () => {
+            const x = new Tensor(test.input_x, { requires_grad: true });
+            const y = new Tensor(test.input_y, { requires_grad: true });
+            const out = x[opName](y);
+            assertDeepCloseTo(out.toArray(), test.expected_output, `${opName} output`);
+            out.sum().backward();
+            assertDeepCloseTo(x.grad.toArray(), test.expected_grad_x, `${opName} grad x`);
+            assertDeepCloseTo(y.grad.toArray(), test.expected_grad_y, `${opName} grad y`);
+          });
+        });
+      });
+    }
+  });
+
+  describe('Broadcasting Operations', () => {
+    testData.broadcasting?.forEach(test => {
+      it(test.test_name, () => {
+        const x = new Tensor(test.input_x, { requires_grad: true });
+        const y = new Tensor(test.input_y, { requires_grad: true });
+
+        const out = x[test.op_name](y);
+
+        assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+        out.sum().backward();
+        assertDeepCloseTo(x.grad.toArray(), test.expected_grad_x, `${test.test_name} grad x`);
+        assertDeepCloseTo(y.grad.toArray(), test.expected_grad_y, `${test.test_name} grad y`);
+      });
+    });
+  });
+
+  describe('Matmul Operations', () => {
+    testData.matmul?.forEach(test => {
+      it(test.test_name, () => {
+        const x = new Tensor(test.input_x, { requires_grad: true });
+        const y = new Tensor(test.input_y, { requires_grad: true });
+
+        const out = x.matmul(y);
+
+        assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+        out.sum().backward();
+        assertDeepCloseTo(x.grad.toArray(), test.expected_grad_x, `${test.test_name} grad x`);
+        assertDeepCloseTo(y.grad.toArray(), test.expected_grad_y, `${test.test_name} grad y`);
+      });
+    });
+  });
+
+  describe('Reduction Operations', () => {
+    testData.reductions?.forEach(test => {
+      it(test.test_name, () => {
+        const x = new Tensor(test.input, { requires_grad: true });
+        let out;
+        if (test.dim === null || test.dim === undefined) {
+          out = x[test.op_name]();
+        } else {
+          out = x[test.op_name](test.dim, test.keepdim);
+        }
+        assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+        out.sum().backward();
+        assertDeepCloseTo(x.grad.toArray(), test.expected_grad, `${test.test_name} grad`);
+      });
+    });
+  });
+
+  describe('Neural Network Modules', () => {
+    describe('nn.Linear', () => {
+      testData.linear?.forEach(test => {
+        it(test.test_name, () => {
+          const layer = new torch.nn.Linear(test.in_features, test.out_features);
+
+          // Overwrite the layer's internal parameters with Python's starting state
+          layer.weight = new Tensor(test.weight, { requires_grad: true });
+          layer.bias = new Tensor(test.bias, { requires_grad: true });
+
+          const x = new Tensor(test.input, { requires_grad: true });
+
+          const out = layer.forward(x);
+          assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+
+          out.sum().backward();
+
+          assertDeepCloseTo(x.grad.toArray(), test.expected_grad_input, `${test.test_name} grad input`);
+          assertDeepCloseTo(layer.weight.grad.toArray(), test.expected_grad_weight, `${test.test_name} grad weight`);
+          assertDeepCloseTo(layer.bias.grad.toArray(), test.expected_grad_bias, `${test.test_name} grad bias`);
+        });
+      });
+    });
+
+    describe('Convolutions', () => {
+      testData.conv?.forEach(test => {
+        it(test.test_name, () => {
+          const ConvClass = torch.nn[test.conv_type];
+          const layer = new ConvClass(
+            test.in_channels,
+            test.out_channels,
+            test.kernel_size,
+            test.stride,
+            test.padding,
+            test.dilation,
+            test.groups,
+            test.has_bias
+          );
+
+          layer.weight = new Tensor(test.weight, { requires_grad: true });
+          if (test.has_bias) {
+            layer.bias = new Tensor(test.bias, { requires_grad: true });
+          }
+
+          const x = new Tensor(test.input, { requires_grad: true });
+
+          const out = layer.forward(x);
+          assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+
+          out.sum().backward();
+
+          assertDeepCloseTo(x.grad.toArray(), test.expected_grad_input, `${test.test_name} grad input`);
+          assertDeepCloseTo(layer.weight.grad.toArray(), test.expected_grad_weight, `${test.test_name} grad weight`);
+          if (test.has_bias) {
+            assertDeepCloseTo(layer.bias.grad.toArray(), test.expected_grad_bias, `${test.test_name} grad bias`);
+          }
+        });
+      });
+    });
+
+    describe('Loss Functions', () => {
+      testData.loss?.forEach(test => {
+        it(test.test_name, () => {
+          const LossClass = torch.nn[test.loss_type];
+          const loss_fn = new LossClass();
+
+          const input = new Tensor(test.input, { requires_grad: true });
+          const target = new Tensor(test.target);
+
+          const output = loss_fn.forward(input, target);
+          assertDeepCloseTo(output.toArray(), test.expected_output, `${test.test_name} output`);
+
+          output.backward();
+          assertDeepCloseTo(input.grad.toArray(), test.expected_grad_input, `${test.test_name} grad input`);
+        });
+      });
+    });
+
+    describe('Activation Functions', () => {
+      testData.activations?.forEach(test => {
+        it(test.test_name, () => {
+          const ActivationClass = torch.nn[test.activation_type];
+          const kwargs = test.kwargs || {};
+          const activation = test.activation_type === 'LeakyReLU'
+            ? new ActivationClass(kwargs.negative_slope)
+            : new ActivationClass();
+
+          const input = new Tensor(test.input, { requires_grad: true });
+
+          const output = activation.forward(input);
+          assertDeepCloseTo(output.toArray(), test.expected_output, `${test.test_name} output`);
+
+          output.sum().backward();
+          assertDeepCloseTo(input.grad.toArray(), test.expected_grad_input, `${test.test_name} grad input`);
+        });
+      });
+    });
+  });
+
+  describe('Optimizers', () => {
+    testData.optimizers?.forEach(test => {
+      it(test.test_name, () => {
+        const w = new torch.nn.Parameter(test.initial_weight, { requires_grad: true });
+        const x = new Tensor(test.input_x);
+
+        const OptimizerClass = torch.optim[test.optimizer];
+        let optimizer;
+
+        if (test.optimizer === 'SGD') {
+          optimizer = new OptimizerClass(
+            [w],
+            test.kwargs.lr,
+            test.kwargs.momentum,
+            test.kwargs.dampening,
+            test.kwargs.weight_decay,
+            test.kwargs.nesterov,
+            test.kwargs.maximize
+          );
+        } else if (test.optimizer === 'Adam') {
+          optimizer = new OptimizerClass(
+            [w],
+            test.kwargs.lr,
+            test.kwargs.betas,
+            test.kwargs.eps,
+            test.kwargs.weight_decay,
+            test.kwargs.amsgrad,
+            test.kwargs.maximize
+          );
+        } else if (test.optimizer === 'Adagrad') {
+          optimizer = new OptimizerClass(
+            [w],
+            test.kwargs.lr,
+            test.kwargs.lr_decay,
+            test.kwargs.weight_decay,
+            test.kwargs.eps
+          );
+        }
+
+        optimizer.zero_grad();
+        const loss = w.mul(x).sum();
+        loss.backward();
+
+        assertDeepCloseTo(w.grad.toArray(), test.expected_grad, `${test.test_name} grad`);
+
+        optimizer.step();
+
+        assertDeepCloseTo(w.toArray(), test.expected_updated_weight, `${test.test_name} updated weight`);
+      });
+    });
+  });
+
+  describe('Cat Operations', () => {
+    testData.cat?.forEach(test => {
+      it(test.test_name, () => {
+        const tensors = test.inputs.map(inp => new Tensor(inp, { requires_grad: true }));
+        const out = torch.cat(tensors, test.dim);
+        assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+        out.sum().backward();
+        tensors.forEach((t, i) => {
+          assertDeepCloseTo(t.grad.toArray(), test.expected_grads[i], `${test.test_name} grad[${i}]`);
+        });
+      });
+    });
+  });
+
+  describe('Expand Operations', () => {
+    testData.expand?.forEach(test => {
+      it(test.test_name, () => {
+        const x = new Tensor(test.input, { requires_grad: true });
+        const out = x.expand(test.expand_shape);
+        assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+        out.sum().backward();
+        assertDeepCloseTo(x.grad.toArray(), test.expected_grad, `${test.test_name} grad`);
+      });
+    });
+  });
+  describe('Softmax', () => {
+    testData.softmax?.forEach(test => {
+      it(test.test_name, () => {
+        const x = new Tensor(test.input, { requires_grad: true });
+        const out = torch.softmax(x, test.dim);
+        assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+        out.sum().backward();
+        assertDeepCloseTo(x.grad.toArray(), test.expected_grad, `${test.test_name} grad`);
+      });
+    });
+  });
+
+  describe('Clamp', () => {
+    testData.clamp?.forEach(test => {
+      it(test.test_name, () => {
+        const x = new Tensor(test.input, { requires_grad: true });
+        const out = torch.clamp(x, test.min, test.max);
+        assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+        out.sum().backward();
+        assertDeepCloseTo(x.grad.toArray(), test.expected_grad, `${test.test_name} grad`);
+      });
+    });
+  });
+
+  describe('MaxPool2d', () => {
+    testData.maxpool?.forEach(test => {
+      it(test.test_name, () => {
+        const x = new Tensor(test.input, { requires_grad: true });
+        const pool = new torch.nn.MaxPool2d(test.kernel_size, test.stride, test.padding);
+        const out = pool.forward(x);
+        assertDeepCloseTo(out.toArray(), test.expected_output, `${test.test_name} output`);
+        out.sum().backward();
+        assertDeepCloseTo(x.grad.toArray(), test.expected_grad, `${test.test_name} grad`);
+      });
+    });
+  });
+
+  describe('Export', () => {
+    testData.export?.forEach(test => {
+      it(`export_${test.test_name}`, () => {
+        let model;
+
+        if (test.model_type === 'LinearReLU') {
+          const linear = new torch.nn.Linear(test.in_features, test.out_features);
+          linear.weight = new Tensor(test.weight, { requires_grad: true });
+          linear.bias = new Tensor(test.bias, { requires_grad: true });
+          const relu = new torch.nn.ReLU();
+          model = new torch.nn.Sequential(linear, relu);
+        } else if (test.model_type === 'TwoLayer') {
+          const linear1 = new torch.nn.Linear(test.linear1_in, test.linear1_out);
+          linear1.weight = new Tensor(test.linear1_weight, { requires_grad: true });
+          linear1.bias = new Tensor(test.linear1_bias, { requires_grad: true });
+          const relu = new torch.nn.ReLU();
+          const linear2 = new torch.nn.Linear(test.linear2_in, test.linear2_out);
+          linear2.weight = new Tensor(test.linear2_weight, { requires_grad: true });
+          linear2.bias = new Tensor(test.linear2_bias, { requires_grad: true });
+          const sigmoid = new torch.nn.Sigmoid();
+          model = new torch.nn.Sequential(linear1, relu, linear2, sigmoid);
+        }
+
+        const x = new Tensor(test.input);
+        const ep = torch.export_(model, [x]);
+
+        // Verify graph structure: must have placeholder, call_function, and output nodes
+        const placeholders = ep.graph.filter(n => n.op === 'placeholder');
+        const callFunctions = ep.graph.filter(n => n.op === 'call_function');
+        const outputs = ep.graph.filter(n => n.op === 'output');
+
+        assert.isAbove(placeholders.length, 0, 'Should have placeholder nodes');
+        assert.isAbove(callFunctions.length, 0, 'Should have call_function nodes');
+        assert.equal(outputs.length, 1, 'Should have exactly one output node');
+
+        // Verify all call_function nodes have aten.* targets
+        for (const node of callFunctions) {
+          assert.match(node.target, /^aten\./, `Target should start with aten.: ${node.target}`);
+        }
+
+        // Verify graph signature matches PyTorch's
+        // Input specs: parameters should be PARAMETER, user inputs should be USER_INPUT
+        const expectedParamSpecs = test.expected_input_specs.filter(s => s.kind === 'PARAMETER');
+        const expectedUserSpecs = test.expected_input_specs.filter(s => s.kind === 'USER_INPUT');
+
+        const actualParamSpecs = ep.graph_signature.input_specs.filter(s => s.kind === 'PARAMETER');
+        const actualUserSpecs = ep.graph_signature.input_specs.filter(s => s.kind === 'USER_INPUT');
+
+        assert.equal(actualParamSpecs.length, expectedParamSpecs.length, 'Number of parameter specs should match');
+        assert.equal(actualUserSpecs.length, expectedUserSpecs.length, 'Number of user input specs should match');
+
+        // Verify parameter placeholder naming matches PyTorch convention
+        for (let i = 0; i < expectedParamSpecs.length; i++) {
+          assert.equal(actualParamSpecs[i].name, expectedParamSpecs[i].name,
+            `Parameter spec name should match: expected ${expectedParamSpecs[i].name}`);
+          assert.equal(actualParamSpecs[i].kind, 'PARAMETER');
+        }
+
+        // Verify user input naming
+        for (let i = 0; i < expectedUserSpecs.length; i++) {
+          assert.equal(actualUserSpecs[i].name, expectedUserSpecs[i].name,
+            `User input spec name should match: expected ${expectedUserSpecs[i].name}`);
+        }
+
+        // Verify output spec
+        assert.equal(ep.graph_signature.output_specs.length, test.expected_output_specs.length);
+        assert.equal(ep.graph_signature.output_specs[0].kind, 'USER_OUTPUT');
+
+        // Verify placeholder node shapes match
+        const expectedPlaceholders = test.expected_nodes.filter(n => n.op === 'placeholder');
+        for (let i = 0; i < expectedPlaceholders.length; i++) {
+          assert.equal(placeholders[i].name, expectedPlaceholders[i].name,
+            `Placeholder name should match: expected ${expectedPlaceholders[i].name}`);
+          if (expectedPlaceholders[i].val_shape) {
+            assert.deepEqual(placeholders[i].val_shape, expectedPlaceholders[i].val_shape,
+              `Placeholder shape should match for ${expectedPlaceholders[i].name}`);
+          }
+        }
+
+        // Verify output node references a valid graph node
+        const outputArgs = outputs[0].args;
+        assert.isAbove(outputArgs.length, 0, 'Output should reference at least one node');
+
+        // All node names should be unique
+        const allNames = ep.graph.map(n => n.name);
+        const uniqueNames = new Set(allNames);
+        assert.equal(allNames.length, uniqueNames.size, 'All node names should be unique');
+      });
+    });
+  });
+});
diff --git a/test/grad_mode.test.js b/test/grad_mode.test.js
new file mode 100644
index 00000000..e6ca5e2d
--- /dev/null
+++ b/test/grad_mode.test.js
@@ -0,0 +1,164 @@
+import { assert } from 'chai';
+import { Tensor, no_grad, enable_no_grad, disable_no_grad, is_grad_enabled } from 'torch';
+
+describe('Grad Mode', () => {
+  afterEach(() => {
+    // Safety: ensure grad mode is always restored after each test
+    if (!is_grad_enabled()) disable_no_grad(true);
+  });
+
+  describe('is_grad_enabled', () => {
+    it('should be enabled by default', () => {
+      assert.isTrue(is_grad_enabled());
+    });
+  });
+
+  describe('no_grad', () => {
+    it('should disable gradient tracking inside callback', () => {
+      const x = new Tensor([2.0], { requires_grad: true });
+      const y = no_grad(() => x.mul(x));
+
+      assert.strictEqual(y.item(), 4.0);
+      assert.isFalse(y.requires_grad);
+      assert.isNull(y.grad_fn);
+    });
+
+    it('should re-enable gradient tracking after callback', () => {
+      no_grad(() => {});
+      assert.isTrue(is_grad_enabled());
+    });
+
+    it('should re-enable gradient tracking even if callback throws', () => {
+      try {
+        no_grad(() => { throw new Error('boom'); });
+      } catch (e) {
+        // expected
+      }
+      assert.isTrue(is_grad_enabled());
+    });
+
+    it('should return the value from the callback', () => {
+      const result = no_grad(() => 42);
+      assert.strictEqual(result, 42);
+    });
+
+    it('should nest correctly', () => {
+      assert.isTrue(is_grad_enabled());
+      no_grad(() => {
+        assert.isFalse(is_grad_enabled());
+        no_grad(() => {
+          assert.isFalse(is_grad_enabled());
+        });
+        // still disabled after inner no_grad returns
+        assert.isFalse(is_grad_enabled());
+      });
+      assert.isTrue(is_grad_enabled());
+    });
+
+    it('should not build computation graph for unary ops', () => {
+      const x = new Tensor([2.0], { requires_grad: true });
+      const y = no_grad(() => x.exp());
+
+      assert.isFalse(y.requires_grad);
+      assert.isNull(y.grad_fn);
+    });
+
+    it('should not build computation graph for chained ops', () => {
+      const x = new Tensor([3.0], { requires_grad: true });
+      const y = no_grad(() => x.mul(x).add(x));
+
+      assert.closeTo(y.item(), 12.0, 1e-6);
+      assert.isFalse(y.requires_grad);
+      assert.isNull(y.grad_fn);
+    });
+
+    it('should not accumulate gradients for operations inside no_grad', () => {
+      const x = new Tensor([2.0], { requires_grad: true });
+
+      // Normal forward + backward
+      const y = x.mul(x);
+      y.backward();
+      assert.strictEqual(x.grad.item(), 4.0);
+
+      // Operations inside no_grad should not affect grad
+      x.grad = null;
+      const z = no_grad(() => x.mul(new Tensor(100.0)));
+      // z has no graph, so backward would be a no-op
+      assert.isFalse(z.requires_grad);
+    });
+  });
+
+  describe('enable_no_grad / disable_no_grad', () => {
+    it('enable_no_grad should disable grad and return previous state', () => {
+      assert.isTrue(is_grad_enabled());
+      const prev = enable_no_grad();
+      assert.isTrue(prev); // was enabled
+      assert.isFalse(is_grad_enabled());
+      disable_no_grad(prev);
+      assert.isTrue(is_grad_enabled());
+    });
+
+    it('should work for pyodide-style usage pattern', () => {
+      // Simulate: python side calls enable_no_grad, does work, calls disable_no_grad
+      const prev = enable_no_grad();
+
+      const x = new Tensor([5.0], { requires_grad: true });
+      const y = x.mul(x);
+      assert.isFalse(y.requires_grad);
+      assert.isNull(y.grad_fn);
+
+      disable_no_grad(prev);
+      assert.isTrue(is_grad_enabled());
+
+      // After restoring, grad should work again
+      const a = new Tensor([3.0], { requires_grad: true });
+      const b = a.mul(a);
+      assert.isTrue(b.requires_grad);
+      assert.isNotNull(b.grad_fn);
+    });
+
+    it('should handle nested enable/disable correctly', () => {
+      const prev1 = enable_no_grad();
+      assert.isFalse(is_grad_enabled());
+
+      const prev2 = enable_no_grad();
+      assert.isFalse(is_grad_enabled());
+
+      disable_no_grad(prev2); // prev2 is false, still disabled
+      assert.isFalse(is_grad_enabled());
+
+      disable_no_grad(prev1); // prev1 is true, now enabled
+      assert.isTrue(is_grad_enabled());
+    });
+  });
+
+  describe('interaction with requires_grad', () => {
+    it('tensors created with requires_grad=true keep the flag, but ops do not track', () => {
+      const x = new Tensor([2.0], { requires_grad: true });
+      // x.requires_grad is an intrinsic property of the tensor, not affected by grad mode
+      assert.isTrue(x.requires_grad);
+
+      const y = no_grad(() => {
+        // x still says requires_grad=true, but the operation should not build a graph
+        return x.mul(x);
+      });
+
+      assert.isFalse(y.requires_grad);
+    });
+
+    it('backward still works on tensors created before no_grad', () => {
+      // Build graph normally
+      const x = new Tensor([3.0], { requires_grad: true });
+      const y = x.mul(x); // y = 9, dy/dx = 6
+
+      // Enter no_grad for some unrelated work
+      no_grad(() => {
+        const _ = x.add(new Tensor(100.0)); // no graph built
+      });
+
+      // Original graph is intact
+      y.backward();
+      assert.strictEqual(x.grad.item(), 6.0);
+    });
+  });
+});
diff --git a/test/index.html b/test/index.html
new file mode 100644
index 00000000..29e8f241
--- /dev/null
+++ b/test/index.html
@@ -0,0 +1,46 @@
+
+
+
+
+    
+    Mocha Tests
+    
+    
+
+
+
+    

+
+    
+    
+
+    
+    
+    
+
+
+
diff --git a/test/loss.test.js b/test/loss.test.js
new file mode 100644
index 00000000..c8a8b99a
--- /dev/null
+++ b/test/loss.test.js
@@ -0,0 +1,77 @@
+
+import * as torch from 'torch';
+import { Tensor } from 'torch';
+import { assert } from 'chai';
+
+describe('BCELoss', () => {
+    it('scalar', () => {
+        const input = new Tensor(0.123, { requires_grad: true });
+        const target = new Tensor(0.9876);
+
+        const loss = new torch.nn.BCELoss();
+        const result = loss.forward(input, target);
+
+        assert.closeTo(result.item(), 2.0712, 0.001);
+    })
+
+    it('should calculate binary cross entropy loss correctly', () => {
+        /* Python:
+         import torch
+         x, y
+         */
+        const m = new torch.nn.Sigmoid()
+        const input = new Tensor([[0.5424, 1.3919],
+                                [-1.0297, -0.6352],
+                                [0.5700, -1.0037]],
+                                { requires_grad: true });
+        const target = new Tensor([[0.8340, 0.4923],
+                                [0.7729, 0.7560],
+                                [0.5616, 0.0999]]);
+
+        const loss = new torch.nn.BCELoss();
+        const result = loss.forward(m.forward(input), target);
+
+        assert.closeTo(result.item(), 0.7657, 0.001);
+    });
+});
+
+describe('NLLLoss', () => {
+  it('basic mean reduction', () => {
+    const loss_fn = new torch.nn.NLLLoss();
+    const logProbs = torch.tensor([
+      [Math.log(0.1), Math.log(0.3), Math.log(0.6)],
+      [Math.log(0.7), Math.log(0.2), Math.log(0.1)],
+    ]);
+    const target = torch.tensor([2, 0]);
+    const out = loss_fn.forward(logProbs, target);
+    const expected = -(Math.log(0.6) + Math.log(0.7)) / 2;
+    assert.closeTo(out.item(), expected, 1e-5);
+  });
+
+  it('gradient flows back to input', () => {
+    const loss_fn = new torch.nn.NLLLoss();
+    const logProbs = torch.tensor([[-1.0, -2.0, -0.5], [-0.8, -1.2, -0.3]], true);
+    const target = torch.tensor([0, 2]);
+    loss_fn.forward(logProbs, target).backward();
+    assert.closeTo(logProbs.grad.toArray()[0][0], -0.5, 1e-6);
+    assert.closeTo(logProbs.grad.toArray()[1][2], -0.5, 1e-6);
+    assert.closeTo(logProbs.grad.toArray()[0][1],  0.0, 1e-6);
+  });
+
+  it('reduction=sum', () => {
+    const loss_fn = new torch.nn.NLLLoss('sum');
+    const logProbs = torch.tensor([[-1.0, -0.5], [-0.8, -0.3]]);
+    const target = torch.tensor([1, 0]);
+    assert.closeTo(loss_fn.forward(logProbs, target).item(), 1.3, 1e-5);
+  });
+
+  it('reduction=none returns per-sample losses', () => {
+    const loss_fn = new torch.nn.NLLLoss('none');
+    const logProbs = torch.tensor([[-1.0, -0.5], [-0.8, -0.3]]);
+    const target = torch.tensor([1, 0]);
+    const out = loss_fn.forward(logProbs, target);
+    assert.deepStrictEqual(out.shape, [2]);
+    assert.closeTo(out.toArray()[0], 0.5, 1e-5);
+    assert.closeTo(out.toArray()[1], 0.8, 1e-5);
+  });
+});
diff --git a/test/missing_args.test.ts b/test/missing_args.test.ts
new file mode 100644
index 00000000..4a4d6f6a
--- /dev/null
+++ b/test/missing_args.test.ts
@@ -0,0 +1,143 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+import { Tensor } from 'torch';
+
+describe('Linear bias parameter', () => {
+  it('should create Linear with bias=true by default', () => {
+    const linear = new torch.nn.Linear(4, 3);
+    assert.strictEqual(linear.parameters().length, 2);
+    assert.deepStrictEqual(linear.parameters()[0].shape, [3, 4]); // weight
+    assert.deepStrictEqual(linear.parameters()[1].shape, [3]);    // bias
+  });
+
+  it('should create Linear with bias=false', () => {
+    const linear = new torch.nn.Linear(4, 3, false);
+    assert.strictEqual(linear.parameters().length, 1);
+    assert.deepStrictEqual(linear.parameters()[0].shape, [3, 4]); // weight only
+    assert.isNull(linear.bias);
+  });
+
+  it('should forward without bias', () => {
+    const linear = new torch.nn.Linear(3, 2, false);
+    const input = new Tensor([1, 2, 3]);
+    const output = linear.forward(input);
+    assert.deepStrictEqual(output.shape, [2]);
+  });
+
+  it('should backward without bias', () => {
+    const linear = new torch.nn.Linear(3, 2, false);
+    const input = new Tensor([1, 2, 3]);
+    const output = linear.forward(input).sum();
+    output.backward();
+    assert.deepStrictEqual(linear.weight.grad.shape, [2, 3]);
+    assert.isNull(linear.bias);
+  });
+});
+
+describe('Loss reduction parameter', () => {
+  describe('MSELoss', () => {
+    const pred = new Tensor([1.0, 2.0, 3.0]);
+    const target = new Tensor([1.5, 2.5, 3.5]);
+
+    it('reduction=mean (default)', () => {
+      const loss = new torch.nn.MSELoss();
+      const result = loss.forward(pred, target);
+      // (0.25 + 0.25 + 0.25) / 3 = 0.25
+      assert.closeTo(result.item(), 0.25, 1e-5);
+    });
+
+    it('reduction=sum', () => {
+      const loss = new torch.nn.MSELoss('sum');
+      const result = loss.forward(pred, target);
+      // 0.25 + 0.25 + 0.25 = 0.75
+      assert.closeTo(result.item(), 0.75, 1e-5);
+    });
+
+    it('reduction=none', () => {
+      const loss = new torch.nn.MSELoss('none');
+      const result = loss.forward(pred, target);
+      assert.deepStrictEqual(result.shape, [3]);
+      const data = result.toFlatArray();
+      assert.closeTo(data[0], 0.25, 1e-5);
+      assert.closeTo(data[1], 0.25, 1e-5);
+      assert.closeTo(data[2], 0.25, 1e-5);
+    });
+  });
+
+  describe('L1Loss', () => {
+    const pred = new Tensor([1.0, 2.0, 3.0]);
+    const target = new Tensor([1.5, 2.5, 3.5]);
+
+    it('reduction=mean (default)', () => {
+      const loss = new torch.nn.L1Loss();
+      const result = loss.forward(pred, target);
+      assert.closeTo(result.item(), 0.5, 1e-5);
+    });
+
+    it('reduction=sum', () => {
+      const loss = new torch.nn.L1Loss('sum');
+      const result = loss.forward(pred, target);
+      assert.closeTo(result.item(), 1.5, 1e-5);
+    });
+
+    it('reduction=none', () => {
+      const loss = new torch.nn.L1Loss('none');
+      const result = loss.forward(pred, target);
+      assert.deepStrictEqual(result.shape, [3]);
+      const data = result.toFlatArray();
+      assert.closeTo(data[0], 0.5, 1e-5);
+      assert.closeTo(data[1], 0.5, 1e-5);
+      assert.closeTo(data[2], 0.5, 1e-5);
+    });
+  });
+
+  describe('CrossEntropyLoss', () => {
+    // Logits for batch of 3, 3 classes
+    const input = new Tensor([[2.0, 1.0, 0.1],
+                               [0.5, 2.5, 0.3],
+                               [0.1, 0.2, 3.0]], { requires_grad: true });
+    const target = new Tensor([0, 1, 2]);
+
+    it('reduction=mean (default)', () => {
+      const loss = new torch.nn.CrossEntropyLoss();
+      const result = loss.forward(input, target);
+      assert.deepStrictEqual(result.shape, []);
+      assert.closeTo(result.item(), 0.2489, 0.01);
+    });
+
+    it('reduction=sum', () => {
+      const loss = new torch.nn.CrossEntropyLoss('sum');
+      const result = loss.forward(input, target);
+      assert.deepStrictEqual(result.shape, []);
+      assert.closeTo(result.item(), 0.7467, 0.01);
+    });
+
+    it('reduction=none', () => {
+      const loss = new torch.nn.CrossEntropyLoss('none');
+      const result = loss.forward(input, target);
+      assert.deepStrictEqual(result.shape, [3]);
+    });
+
+    it('reduction=mean backward', () => {
+      const inp = new Tensor([[2.0, 1.0, 0.1],
+                               [0.5, 2.5, 0.3],
+                               [0.1, 0.2, 3.0]], { requires_grad: true });
+      const tgt = new Tensor([0, 1, 2]);
+      const loss = new torch.nn.CrossEntropyLoss();
+      const result = loss.forward(inp, tgt);
+      result.backward();
+      assert.deepStrictEqual(inp.grad.shape, [3, 3]);
+    });
+
+    it('reduction=sum backward', () => {
+      const inp = new Tensor([[2.0, 1.0, 0.1],
+                               [0.5, 2.5, 0.3],
+                               [0.1, 0.2, 3.0]], { requires_grad: true });
+      const tgt = new Tensor([0, 1, 2]);
+      const loss = new torch.nn.CrossEntropyLoss('sum');
+      const result = loss.forward(inp, tgt);
+      result.backward();
+      assert.deepStrictEqual(inp.grad.shape, [3, 3]);
+    });
+  });
+});
diff --git a/test/module.test.js b/test/module.test.js
new file mode 100644
index 00000000..092c4779
--- /dev/null
+++ b/test/module.test.js
@@ -0,0 +1,226 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+
+describe('Module', () => {
+  describe('Linear', () => {
+    it('should create a linear module', () => {
+      const linear = new torch.nn.Linear(10, 20);
+      assert.deepStrictEqual(linear.parameters().length, 2);
+      assert.deepStrictEqual(linear.parameters()[0].shape, [20, 10]);
+      assert.deepStrictEqual(linear.parameters()[1].shape, [20]);
+    });
+
+    it('should forward a tensor', () => {
+      const linear = new torch.nn.Linear(10, 20);
+      const input = new torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
+      const output = linear.forward(input);
+      assert.deepStrictEqual(output.shape, [20]);
+    });
+
+    it('should forward a tensor (2D)', () => {
+      // example from https://docs.pytorch.org/docs/stable/generated/torch.nn.Linear.html
+      const m = new torch.nn.Linear(20, 30);
+      const input = torch.randn(128, 20);
+      const output = m.forward(input);
+      assert.deepStrictEqual(output.shape, [128, 30]);
+    });
+
+    it('should backward a linear with correct shape', () => {
+      const linear = new torch.nn.Linear(10, 20);
+      const input = new torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
+      const output = linear.forward(input).sum();
+      output.backward();
+      assert.deepStrictEqual(linear.weight.grad.shape, [20, 10]);
+      assert.deepStrictEqual(linear.bias.grad.shape, [20]);
+    });
+
+    it('should backward a linear with correct values', () => {
+      const linear = new torch.nn.Linear(2, 3);
+      linear.weight.data = new torch.Tensor([[1, 2], [3, 4], [5, 6]]);
+      linear.bias.data = new torch.Tensor([1, 2, 3]);
+      const input = new torch.Tensor([1, 2]);
+      const output = linear.forward(input).sum();
+      output.backward();
+      assert.deepStrictEqual(linear.weight.grad.data, [1, 2, 1, 2, 1, 2]);
+      assert.deepStrictEqual(linear.bias.grad.data, [1, 1, 1]);
+    });
+  });
+
+  describe('Sequential', () => {
+    it('should forward the correct shape', () => {
+      const model = new torch.nn.Sequential(
+        new torch.nn.Linear(10, 20),
+        new torch.nn.ReLU(),
+        new torch.nn.Linear(20, 30)
+      );
+
+      const input = new torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
+      const output = model.forward(input);
+      assert.deepStrictEqual(output.shape, [30]);
+    });
+
+    it('should append a module', () => {
+      const model = new torch.nn.Sequential();
+      model.append(new torch.nn.Linear(10, 20));
+      model.append(new torch.nn.ReLU());
+      model.append(new torch.nn.Linear(20, 30));
+
+      const input = new torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
+      const output = model.forward(input);
+      assert.deepStrictEqual(output.shape, [30]);
+    });
+
+    it('should extend with modules', () => {
+      const model = new torch.nn.Sequential(new torch.nn.Linear(10, 20), new torch.nn.ReLU());
+      const model2 = new torch.nn.Sequential(new torch.nn.Linear(20, 30), new torch.nn.ReLU());
+      model.extend(model2);
+
+      const input = new torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
+      const output = model.forward(input);
+      assert.deepStrictEqual(output.shape, [30]);
+    });
+
+    it('should insert a module', () => {
+      const model = new torch.nn.Sequential(
+        new torch.nn.Linear(10, 20),
+        new torch.nn.Linear(30, 40)
+      );
+      model.insert(1, new torch.nn.Linear(20, 30));
+
+      const input = new torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
+      const output = model.forward(input);
+      assert.deepStrictEqual(output.shape, [40]);
+    });
+  });
+
+  describe('LeakyReLU', () => {
+    it('positive values pass through unchanged', () => {
+      const lr = new torch.nn.LeakyReLU();
+      const y = lr.forward(torch.tensor([1.0, 2.0, 3.0]));
+      assert.deepStrictEqual(y.toArray(), [1.0, 2.0, 3.0]);
+    });
+
+    it('negative values scaled by negative_slope (default 0.01)', () => {
+      const lr = new torch.nn.LeakyReLU();
+      const y = lr.forward(torch.tensor([-1.0, -2.0]));
+      assert.closeTo(y.toArray()[0], -0.01, 1e-6);
+      assert.closeTo(y.toArray()[1], -0.02, 1e-6);
+    });
+
+    it('custom negative_slope is respected', () => {
+      const lr = new torch.nn.LeakyReLU(0.2);
+      const y = lr.forward(torch.tensor([-1.0, -2.0]));
+      assert.closeTo(y.toArray()[0], -0.2, 1e-6);
+      assert.closeTo(y.toArray()[1], -0.4, 1e-6);
+    });
+
+    it('gradient for positive input is 1, negative is negative_slope', () => {
+      const lr = new torch.nn.LeakyReLU(0.1);
+      const x = torch.tensor([1.0, -1.0], true);
+      lr.forward(x).sum().backward();
+      assert.closeTo(x.grad.toArray()[0], 1.0, 1e-6);
+      assert.closeTo(x.grad.toArray()[1], 0.1, 1e-6);
+    });
+  });
+
+  describe('MaxPool2d', () => {
+    it('basic 2x2 pooling on 1x1x4x4 input', () => {
+      const pool = new torch.nn.MaxPool2d(2);
+      const x = torch.tensor([[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]]]);
+      const y = pool.forward(x);
+      assert.deepStrictEqual(y.shape, [1, 1, 2, 2]);
+      assert.strictEqual(y.toArray()[0][0][0][0], 6);
+      assert.strictEqual(y.toArray()[0][0][1][1], 16);
+    });
+
+    it('output shape is correct with stride', () => {
+      assert.deepStrictEqual(new torch.nn.MaxPool2d(2, 1).forward(torch.randn(1, 1, 4, 4)).shape, [1, 1, 3, 3]);
+    });
+
+    it('gradient accumulates at argmax positions', () => {
+      const pool = new torch.nn.MaxPool2d(2);
+      const x = torch.tensor([[[[1.0, 2.0], [3.0, 4.0]]]], true);
+      pool.forward(x).sum().backward();
+      assert.deepStrictEqual(x.grad.toArray(), [[[[0.0, 0.0], [0.0, 1.0]]]]);
+    });
+
+    it('accepts 3D input (no batch dim)', () => {
+      assert.deepStrictEqual(new torch.nn.MaxPool2d(2).forward(torch.randn(1, 4, 4)).shape, [1, 2, 2]);
+    });
+  });
+
+  describe('Dropout', () => {
+    it('in eval mode passes input through unchanged', () => {
+      const drop = new torch.nn.Dropout(0.5);
+      drop.eval();
+      const x = torch.tensor([1.0, 2.0, 3.0, 4.0]);
+      assert.deepStrictEqual(drop.forward(x).toArray(), x.toArray());
+    });
+
+    it('p=1 always zeros out in training mode', () => {
+      const drop = new torch.nn.Dropout(1.0);
+      assert.deepStrictEqual(drop.forward(torch.tensor([1.0, 2.0, 3.0])).toArray(), [0.0, 0.0, 0.0]);
+    });
+
+    it('train() / eval() toggle training attribute', () => {
+      const drop = new torch.nn.Dropout(0.5);
+      assert.isTrue(drop.training);
+      drop.eval();
+      assert.isFalse(drop.training);
+      drop.train();
+      assert.isTrue(drop.training);
+    });
+  });
+
+  describe('Softmax', () => {
+    it('output sums to 1 along specified dim', () => {
+      const sm = new torch.nn.Softmax(1);
+      const y = sm.forward(torch.tensor([[1.0, 2.0, 3.0]]));
+      assert.closeTo(y.toArray()[0].reduce((a, b) => a + b, 0), 1.0, 1e-6);
+    });
+
+    it('output values are in (0, 1)', () => {
+      const y = new torch.nn.Softmax(0).forward(torch.randn(5));
+      y.toFlatArray().forEach(v => { assert.isAbove(v, 0); assert.isBelow(v, 1); });
+    });
+  });
+
+  describe('Flatten', () => {
+    it('default flattens all dims except batch (start_dim=1)', () => {
+      const y = new torch.nn.Flatten().forward(torch.tensor([[[[1.0, 2.0], [3.0, 4.0]]]]));
+      assert.deepStrictEqual(y.shape, [1, 4]);
+    });
+
+    it('custom start_dim and end_dim', () => {
+      assert.deepStrictEqual(new torch.nn.Flatten(0, 1).forward(torch.randn(2, 3, 4)).shape, [6, 4]);
+    });
+  });
+
+  describe('training mode propagation', () => {
+    it('training=true by default', () => {
+      assert.isTrue(new torch.nn.Linear(3, 2).training);
+    });
+
+    it('eval() sets training=false', () => {
+      const linear = new torch.nn.Linear(3, 2);
+      linear.eval();
+      assert.isFalse(linear.training);
+    });
+
+    it('train() restores training=true', () => {
+      const linear = new torch.nn.Linear(3, 2);
+      linear.eval();
+      linear.train();
+      assert.isTrue(linear.training);
+    });
+
+    it('eval() propagates to submodules in Sequential', () => {
+      const drop = new torch.nn.Dropout(0.5);
+      const net = new torch.nn.Sequential(new torch.nn.Linear(2, 2), drop);
+      net.eval();
+      assert.isFalse(drop.training);
+      net.train();
+      assert.isTrue(drop.training);
+    });
+  });
+});
diff --git a/test/nn_functional.test.js b/test/nn_functional.test.js
new file mode 100644
index 00000000..e21d3c84
--- /dev/null
+++ b/test/nn_functional.test.js
@@ -0,0 +1,56 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+
+describe('NN Functional', () => {
+  describe('Relu', () => {
+    it('should forward a tensor', () => {
+      const x = new torch.Tensor([1, -2, 3, -4, 5]);
+      const result = torch.nn.functional.relu(x);
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 0, 3, 0, 5]);
+      assert.deepStrictEqual(result.shape, [5]);
+    });
+  });
+
+  describe('leaky_relu', () => {
+    it('passes positive values unchanged', () => {
+      const y = torch.nn.functional.leaky_relu(torch.tensor([1.0, 2.0]));
+      assert.deepStrictEqual(y.toArray(), [1.0, 2.0]);
+    });
+
+    it('scales negative values by negative_slope', () => {
+      const y = torch.nn.functional.leaky_relu(torch.tensor([-2.0]), 0.1);
+      assert.closeTo(y.toArray()[0], -0.2, 1e-6);
+    });
+  });
+
+  describe('max_pool2d', () => {
+    it('basic 2x2 pooling', () => {
+      const x = torch.tensor([[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]]]);
+      const y = torch.nn.functional.max_pool2d(x, 2);
+      assert.deepStrictEqual(y.shape, [1, 1, 2, 2]);
+      assert.strictEqual(y.toArray()[0][0][0][0], 6);
+      assert.strictEqual(y.toArray()[0][0][1][1], 16);
+    });
+
+    it('output shape is correct with explicit stride', () => {
+      assert.deepStrictEqual(torch.nn.functional.max_pool2d(torch.randn(1, 1, 4, 4), 2, 1).shape, [1, 1, 3, 3]);
+    });
+  });
+
+  describe('nll_loss', () => {
+    it('mean reduction matches expected value', () => {
+      const logProbs = torch.tensor([
+        [Math.log(0.1), Math.log(0.3), Math.log(0.6)],
+        [Math.log(0.7), Math.log(0.2), Math.log(0.1)],
+      ]);
+      const out = torch.nn.functional.nll_loss(logProbs, torch.tensor([2, 0]));
+      assert.closeTo(out.item(), -(Math.log(0.6) + Math.log(0.7)) / 2, 1e-5);
+    });
+
+    it('sum reduction', () => {
+      const logProbs = torch.tensor([[-1.0, -0.5], [-0.8, -0.3]]);
+      const out = torch.nn.functional.nll_loss(logProbs, torch.tensor([1, 0]), 'sum');
+      assert.closeTo(out.item(), 1.3, 1e-5);
+    });
+  });
+});
diff --git a/test/optimizers.test.js b/test/optimizers.test.js
new file mode 100644
index 00000000..c192e69e
--- /dev/null
+++ b/test/optimizers.test.js
@@ -0,0 +1,123 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+
+const EPS = 1e-6;
+
+describe('Optimizers', () => {
+  describe('SGD', () => {
+    it('should update parameters', () => {
+      const x = new torch.Tensor([1.0], { requires_grad: true });
+      const sgd = new torch.optim.SGD([x], 0.01);
+
+      const y = x.mul(new torch.Tensor(2.0));
+      y.backward();
+
+      /**
+       * x = 1, grad = 2
+       * x_new = x - lr * grad = 1 - 0.01 * 2 = 0.98
+       */
+      sgd.step();
+      const actual = sgd.params[0].data[0];
+      const expected = 0.98;
+      assert.closeTo(actual, expected, EPS);
+    });
+
+    it('should update parameters correctly over multiple steps (quadratic)', () => {
+      const x = new torch.Tensor([0.5], { requires_grad: true });
+      const sgd = new torch.optim.SGD([x], 0.1);
+
+      let y = x.pow(2);
+
+      /**
+       * x = 0.5, grad = 2x = 1
+       * x_new = x - lr * grad = 0.5 - 0.1 * 1 = 0.4
+       */
+      y.backward();
+      sgd.step();
+      sgd.zero_grad();
+
+      let actual = x.data[0];
+      assert.closeTo(actual, 0.4, EPS);
+
+      /**
+       * x = 0.4, grad = 2x = 0.8
+       * x_new = x - lr * grad = 0.4 - 0.1 * 0.8 = 0.32
+       */
+      y = x.pow(2);
+      y.backward();
+      sgd.step();
+
+      actual = x.data[0];
+      assert.closeTo(actual, 0.32, EPS);
+    });
+  });
+
+  describe('Adam', () => {
+    it('should handle bias correction with constant gradient', () => {
+      const x = new torch.Tensor([10.0], { requires_grad: true });
+      const adam = new torch.optim.Adam([x], 0.1);
+
+      let y = x.mul(2);
+      y.backward();
+      adam.step();
+      adam.zero_grad();
+
+      assert.closeTo(x.data[0], 9.9, EPS);
+
+      y = x.mul(2);
+      y.backward();
+      adam.step();
+
+      assert.closeTo(x.data[0], 9.8, EPS);
+    });
+  });
+
+  describe('Adagrad', () => {
+    it('parameters are updated', () => {
+      const w = new torch.nn.Parameter(torch.tensor([1.0, 2.0, 3.0]));
+      const opt = new torch.optim.Adagrad([w], 0.1);
+      opt.zero_grad();
+      w.mul(torch.tensor([1.0, 1.0, 1.0])).sum().backward();
+      const before = [...w.toFlatArray()];
+      opt.step();
+      w.toFlatArray().forEach((v, i) => assert.notEqual(v, before[i]));
+    });
+
+    it('step size decreases over time (accumulating squared gradients)', () => {
+      const w = new torch.nn.Parameter(torch.tensor([2.0]));
+      const opt = new torch.optim.Adagrad([w], 0.1);
+      const steps = [];
+      for (let i = 0; i < 3; i++) {
+        opt.zero_grad();
+        w.mul(torch.ones(1)).sum().backward();
+        const before = w.item();
+        opt.step();
+        steps.push(Math.abs(w.item() - before));
+      }
+      assert.isBelow(steps[1], steps[0]);
+      assert.isBelow(steps[2], steps[1]);
+    });
+
+    it('weight_decay adds L2 regularization', () => {
+      const w1 = new torch.nn.Parameter(torch.tensor([1.0, 2.0]));
+      const w2 = new torch.nn.Parameter(torch.tensor([1.0, 2.0]));
+      const opt1 = new torch.optim.Adagrad([w1], 0.1, 0.0, 0.0);
+      const opt2 = new torch.optim.Adagrad([w2], 0.1, 0.0, 0.01);
+      for (const [w, opt] of [[w1, opt1], [w2, opt2]]) {
+        opt.zero_grad();
+        w.mul(torch.ones(2)).sum().backward();
+        opt.step();
+      }
+      assert.notDeepEqual(w1.toArray(), w2.toArray());
+    });
+
+    it('zero_grad clears gradients', () => {
+      const w = new torch.nn.Parameter(torch.tensor([1.0, 2.0]));
+      const opt = new torch.optim.Adagrad([w], 0.1);
+      w.mul(torch.ones(2)).sum().backward();
+      assert.isNotNull(w.grad);
+      opt.zero_grad();
+      assert.isNull(w.grad);
+    });
+  });
+});
diff --git a/test/parameter.test.js b/test/parameter.test.js
new file mode 100644
index 00000000..a1776309
--- /dev/null
+++ b/test/parameter.test.js
@@ -0,0 +1,74 @@
+import { assert } from 'chai';
+import { Tensor, no_grad, enable_no_grad, disable_no_grad, is_grad_enabled, nn } from 'torch';
+
+const { Parameter } = nn;
+
+describe('Parameter', () => {
+  afterEach(() => {
+    // Safety: ensure grad mode is always restored after each test
+    if (!is_grad_enabled()) disable_no_grad(true);
+  });
+
+  describe('requires_grad default', () => {
+    it('should have requires_grad=true by default (from array)', () => {
+      const p = new Parameter([1.0, 2.0, 3.0]);
+      assert.isTrue(p.requires_grad);
+    });
+
+    it('should have requires_grad=true by default (from Tensor)', () => {
+      const t = new Tensor([1.0, 2.0, 3.0]);
+      const p = new Parameter(t);
+      assert.isTrue(p.requires_grad);
+    });
+
+    it('should have a grad_fn (AccumulateGrad) when requires_grad=true', () => {
+      const p = new Parameter([1.0, 2.0]);
+      assert.isNotNull(p.grad_fn);
+    });
+
+    it('should allow explicit requires_grad=false', () => {
+      const p = new Parameter([1.0, 2.0], { requires_grad: false });
+      assert.isFalse(p.requires_grad);
+    });
+  });
+
+  describe('no_grad does NOT affect Parameter creation', () => {
+    it('Parameter created inside no_grad still has requires_grad=true (from array)', () => {
+      const p = no_grad(() => new Parameter([1.0, 2.0, 3.0]));
+      assert.isTrue(p.requires_grad);
+    });
+
+    it('Parameter created inside no_grad still has requires_grad=true (from Tensor)', () => {
+      const t = new Tensor([1.0, 2.0]);
+      const p = no_grad(() => new Parameter(t));
+      assert.isTrue(p.requires_grad);
+    });
+
+    it('Parameter created inside no_grad still has a grad_fn', () => {
+      const p = no_grad(() => new Parameter([1.0, 2.0]));
+      assert.isNotNull(p.grad_fn);
+    });
+
+    it('Parameter created with enable_no_grad/disable_no_grad still has requires_grad=true', () => {
+      const prev = enable_no_grad();
+      const p = new Parameter([1.0, 2.0, 3.0]);
+      disable_no_grad(prev);
+      assert.isTrue(p.requires_grad);
+    });
+
+    it('grad mode is restored after no_grad block that creates a Parameter', () => {
+      no_grad(() => new Parameter([1.0]));
+      assert.isTrue(is_grad_enabled());
+    });
+
+    it('operations on Parameter inside no_grad do not build a graph, but Parameter retains requires_grad', () => {
+      const p = new Parameter([2.0]);
+      const result = no_grad(() => p.mul(p));
+      // Parameter itself is unaffected by no_grad
+      assert.isTrue(p.requires_grad);
+      // But the operation result has no grad
+      assert.isFalse(result.requires_grad);
+      assert.isNull(result.grad_fn);
+    });
+  });
+});
diff --git a/test/template.test.js b/test/template.test.js
new file mode 100644
index 00000000..22d1158f
--- /dev/null
+++ b/test/template.test.js
@@ -0,0 +1,12 @@
+import { assert } from 'chai';
+import { Tensor } from 'torch';
+
+describe('Tensor', () => {
+  describe('Constructor', () => {
+    it('should create a tensor with correct data and shape', () => {
+      const tensor = new Tensor([10, 20, 30]);
+      assert.deepStrictEqual(tensor.data, [10, 20, 30]);
+      assert.deepStrictEqual(tensor.shape, [3]);
+    });
+  });
+});
diff --git a/test/tensor.test.js b/test/tensor.test.js
new file mode 100644
index 00000000..e603e07f
--- /dev/null
+++ b/test/tensor.test.js
@@ -0,0 +1,522 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+import { Tensor } from 'torch';
+
+describe('Tensor', () => {
+  describe('Constructor', () => {
+    it('should create a tensor with correct data and shape', () => {
+      const tensor = new Tensor([10, 20, 30]);
+      assert.deepStrictEqual(tensor.data, [10, 20, 30]);
+      assert.deepStrictEqual(tensor.shape, [3]);
+    });
+
+    it('should create a tensor with nested array', () => {
+      const tensor = new Tensor([
+        [1, 2],
+        [3, 4]
+      ]);
+      assert.deepStrictEqual(tensor.data, [1, 2, 3, 4]);
+      assert.deepStrictEqual(tensor.shape, [2, 2]);
+    });
+  });
+
+  describe('Shape', () => {
+    it('should return the shape of the 1D tensor', () => {
+      const tensor = new Tensor([10, 20, 30]);
+      assert.deepStrictEqual(tensor.shape, [3]);
+    });
+
+    it('should return the shape of the 2D tensor', () => {
+      const tensor = new Tensor([
+        [1, 2, 5],
+        [3, 4, 6]
+      ]);
+      assert.deepStrictEqual(tensor.shape, [2, 3]);
+    });
+
+    describe('Reshape', () => {
+      it('should reshape a tensor', () => {
+        const t = new Tensor([1, 2, 3, 4]);
+        const result = t.reshape([2, 2]);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3, 4]);
+        assert.deepStrictEqual(result.shape, [2, 2]);
+      });
+
+      it('should not reshape a tensor if the shape is not compatible', () => {
+        const t = new Tensor([1, 2, 3, 4]);
+        assert.throws(() => t.reshape([2, 3]), Error);
+      });
+
+      it('should reshape a tensor with different dimensions', () => {
+        const t = new Tensor([1, 2, 3, 4, 5, 6]);
+        const result = t.reshape([2, 3]);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result.shape, [2, 3]);
+
+        const result2 = t.reshape([3, 2]);
+        assert.deepStrictEqual(Array.from(result2.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result2.shape, [3, 2]);
+
+        const result3 = t.reshape([6]);
+        assert.deepStrictEqual(Array.from(result3.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result3.shape, [6]);
+
+        const result4 = t.reshape([1, 2, 3]);
+        assert.deepStrictEqual(Array.from(result4.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result4.shape, [1, 2, 3]);
+      });
+    });
+
+    describe('Unsqueeze', () => {
+      it('should unsqueeze a tensor', () => {
+        const t = new Tensor([1, 2, 3]);
+        let result = t.unsqueeze(0);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3]);
+        assert.deepStrictEqual(result.shape, [1, 3]);
+
+        let s = new Tensor([1, 2, 3, 4, 5, 6]);
+        s = s.reshape([2, 3]);
+
+        result = s.unsqueeze(0);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result.shape, [1, 2, 3]);
+
+        result = s.unsqueeze(1);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result.shape, [2, 1, 3]);
+
+        result = s.unsqueeze(2);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result.shape, [2, 3, 1]);
+      });
+
+      it('should unsqueeze a tensor with negative dimension', () => {
+        const t = new Tensor([1, 2, 3]);
+        let result = t.unsqueeze(-1);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3]);
+        assert.deepStrictEqual(result.shape, [3, 1]);
+
+        let s = new Tensor([1, 2, 3, 4, 5, 6]);
+        s = s.reshape([2, 3]);
+
+        result = s.unsqueeze(-3);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result.shape, [1, 2, 3]);
+
+        result = s.unsqueeze(-2);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result.shape, [2, 1, 3]);
+
+        result = s.unsqueeze(-1);
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(result.shape, [2, 3, 1]);
+      });
+    });
+  });
+});
+
+describe('Operations', () => {
+  describe('Binary Pointwise Operations', () => {
+    describe('Addition', () => {
+      it('should add two tensors with same shape', () => {
+        const t1 = new Tensor([10]);
+        const t2 = new Tensor([20]);
+        const result = t1.add(t2);
+
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [30]);
+        assert.deepStrictEqual(result.shape, [1]);
+      });
+
+      it('should add two 1D tensors of same length', () => {
+        const t1 = new Tensor([1, 2, 3]);
+        const t2 = new Tensor([4, 5, 6]);
+        const result = t1.add(t2);
+
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [5, 7, 9]);
+        assert.deepStrictEqual(result.shape, [3]);
+      });
+    });
+
+    describe('Multiplication', () => {
+      it('should multiply two tensors with same shape, scalar', () => {
+        const t1 = new Tensor([10]);
+        const t2 = new Tensor([20]);
+        const result = t1.mul(t2);
+
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [200]);
+        assert.deepStrictEqual(result.shape, [1]);
+      });
+
+      it('should multiply two tensors with same shape, scalar 2', () => {
+        const t1 = new Tensor([-1.0604]);
+        const t2 = new Tensor([0.756]);
+        const result = t1.mul(t2);
+
+        assert.closeTo(Array.from(result.toFlatArray())[0], -0.8016, 0.001);
+        assert.deepStrictEqual(result.shape, [1]);
+      });
+
+      it('should multiply two tensors with the same shape, integers', () => {
+        const i1 = [
+          [2, 3],
+          [5, 7],
+          [11, 13]
+        ];
+        const t1 = new Tensor(i1);
+
+        const i2 = [
+          [1, 2],
+          [3, 4],
+          [5, 6]
+        ];
+        const t2 = new Tensor(i2);
+
+        const result = t1.mul(t2);
+        const expected = [
+          [2, 6],
+          [15, 28],
+          [55, 78]
+        ];
+
+        for (let i = 0; i < expected.length; i++) {
+          for (let j = 0; j < expected[i].length; j++) {
+            assert.closeTo(result.data[i * expected[i].length + j], expected[i][j], 0.001);
+          }
+        }
+      });
+
+      it('should multiply two tensors with the same shape', () => {
+        const i1 = [
+          [-0.4583, -0.222],
+          [-1.3351, -1.0604],
+          [-0.4482, -1.316]
+        ];
+        const t1 = new Tensor(i1);
+
+        const i2 = [
+          [0.834, 0.4923],
+          [0.7729, 0.756],
+          [0.5616, 0.0999]
+        ];
+        const t2 = new Tensor(i2);
+
+        const result = t1.mul(t2);
+        const expected = [
+          [-0.3822, -0.1093],
+          [-1.0319, -0.8016],
+          [-0.2517, -0.1315]
+        ];
+
+        for (let i = 0; i < expected.length; i++) {
+          for (let j = 0; j < expected[i].length; j++) {
+            assert.closeTo(result.data[i * expected[i].length + j], expected[i][j], 0.001);
+          }
+        }
+
+        assert.deepStrictEqual(result.shape, [3, 2]);
+      });
+    });
+  });
+
+  describe('Operations with Broadcasting', () => {
+    it('should multiply two tensors with different shapes (broadcasting)', () => {
+      const t1 = new Tensor([10, 20, 30]);
+      const t2 = new Tensor([1]);
+      const result = t1.mul(t2);
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [10, 20, 30]);
+      assert.deepStrictEqual(result.shape, [3]);
+    });
+    it('should add tensors with different shapes (broadcasting)', () => {
+      const t1 = new Tensor([10, 20, 30]);
+      const t2 = new Tensor([1]);
+      const result = t1.add(t2);
+
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [11, 21, 31]);
+      assert.deepStrictEqual(result.shape, [3]);
+    });
+  });
+
+  describe('Unary Operations', () => {
+    describe('Neg', () => {
+      it('should negate a tensor', () => {
+        const t = new Tensor([1, 2, 3, -4]);
+        const result = t.neg();
+        assert.deepStrictEqual(Array.from(result.toFlatArray()), [-1, -2, -3, 4]);
+        assert.deepStrictEqual(result.shape, [4]);
+      });
+    });
+
+    describe('Exp', () => {
+      const input = [1, 2, 3, -4, 2.5, -6.7];
+      const t = new Tensor(input);
+      const result = t.exp();
+      const expected = input.map(x => Math.exp(x));
+      for (let i = 0; i < expected.length; i++) {
+        assert.closeTo(result.data[i], expected[i], 0.0001);
+      }
+      assert.deepStrictEqual(result.shape, [6]);
+    });
+  });
+
+  describe('Comparison Operations', () => {
+    it('le should return 1 where a <= b', () => {
+      const a = new Tensor([1, 2, 3]);
+      const b = new Tensor([2, 2, 1]);
+      const result = a.le(b);
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, 1, 0]);
+    });
+
+    it('ge should return 1 where a >= b', () => {
+      const a = new Tensor([1, 2, 3]);
+      const b = new Tensor([2, 2, 1]);
+      const result = a.ge(b);
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [0, 1, 1]);
+    });
+
+    it('ne should return 1 where a != b', () => {
+      const a = new Tensor([1, 2, 3]);
+      const b = new Tensor([1, 0, 3]);
+      const result = a.ne(b);
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [0, 1, 0]);
+    });
+  });
+
+  describe('Fmod', () => {
+    it('should compute element-wise remainder', () => {
+      const a = new Tensor([7, -7, 5, 10]);
+      const b = new Tensor([3, 3, 2, 4]);
+      const result = a.fmod(b);
+      assert.deepStrictEqual(Array.from(result.toFlatArray()), [1, -1, 1, 2]);
+    });
+  });
+
+  describe('Numel', () => {
+    it('should return the total number of elements', () => {
+      const t = new Tensor([[1, 2, 3], [4, 5, 6]]);
+      assert.strictEqual(t.numel(), 6);
+    });
+  });
+
+  describe('cat', () => {
+    describe('1D tensors', () => {
+      it('concatenates two 1D tensors along dim 0', () => {
+        const a = new Tensor([1, 2, 3]);
+        const b = new Tensor([4, 5]);
+        const out = torch.cat([a, b]);
+        assert.deepStrictEqual(Array.from(out.toFlatArray()), [1, 2, 3, 4, 5]);
+        assert.deepStrictEqual(out.shape, [5]);
+      });
+
+      it('concatenates three 1D tensors', () => {
+        const a = new Tensor([1]);
+        const b = new Tensor([2, 3]);
+        const c = new Tensor([4, 5, 6]);
+        const out = torch.cat([a, b, c]);
+        assert.deepStrictEqual(Array.from(out.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(out.shape, [6]);
+      });
+    });
+
+    describe('2D tensors', () => {
+      it('concatenates along dim 0 (row-wise)', () => {
+        const a = new Tensor([[1, 2], [3, 4]]);
+        const b = new Tensor([[5, 6]]);
+        const out = torch.cat([a, b], 0);
+        assert.deepStrictEqual(Array.from(out.toFlatArray()), [1, 2, 3, 4, 5, 6]);
+        assert.deepStrictEqual(out.shape, [3, 2]);
+      });
+
+      it('concatenates along dim 1 (column-wise)', () => {
+        const a = new Tensor([[1, 2], [3, 4]]);
+        const b = new Tensor([[5], [6]]);
+        const out = torch.cat([a, b], 1);
+        assert.deepStrictEqual(Array.from(out.toFlatArray()), [1, 2, 5, 3, 4, 6]);
+        assert.deepStrictEqual(out.shape, [2, 3]);
+      });
+    });
+
+    describe('3D tensors', () => {
+      it('concatenates along dim 0', () => {
+        const out = torch.cat([torch.ones([2, 3, 4]), torch.zeros([1, 3, 4])], 0);
+        assert.deepStrictEqual(out.shape, [3, 3, 4]);
+      });
+
+      it('concatenates along dim 2', () => {
+        const out = torch.cat([torch.ones([2, 3, 4]), torch.ones([2, 3, 2])], 2);
+        assert.deepStrictEqual(out.shape, [2, 3, 6]);
+      });
+    });
+
+    describe('negative dim', () => {
+      it('dim=-1 is the last dimension', () => {
+        const a = new Tensor([[1, 2], [3, 4]]);
+        const b = new Tensor([[5], [6]]);
+        const pos = torch.cat([a, b], 1);
+        const neg = torch.cat([a, b], -1);
+        assert.deepStrictEqual(Array.from(neg.toFlatArray()), Array.from(pos.toFlatArray()));
+        assert.deepStrictEqual(neg.shape, [2, 3]);
+      });
+
+      it('dim=-2 on a 3D tensor', () => {
+        const a = torch.ones([2, 3, 4]);
+        const b = torch.ones([2, 1, 4]);
+        const pos = torch.cat([a, b], 1);
+        const neg = torch.cat([a, b], -2);
+        assert.deepStrictEqual(neg.shape, pos.shape);
+        assert.deepStrictEqual(Array.from(neg.toFlatArray()), Array.from(pos.toFlatArray()));
+      });
+    });
+
+    describe('autograd', () => {
+      it('grad flows back to each input (1D)', () => {
+        const x = new Tensor([1, 2, 3], { requires_grad: true });
+        const y = new Tensor([4, 5], { requires_grad: true });
+        torch.cat([x, y]).sum().backward();
+        assert.deepStrictEqual(Array.from(x.grad.toFlatArray()), [1, 1, 1]);
+        assert.deepStrictEqual(Array.from(y.grad.toFlatArray()), [1, 1]);
+      });
+
+      it('grad flows back to each input (2D, dim 0)', () => {
+        const x = new Tensor([[1, 2], [3, 4]], { requires_grad: true });
+        const y = new Tensor([[5, 6]], { requires_grad: true });
+        torch.cat([x, y], 0).sum().backward();
+        assert.deepStrictEqual(Array.from(x.grad.toFlatArray()), [1, 1, 1, 1]);
+        assert.deepStrictEqual(Array.from(y.grad.toFlatArray()), [1, 1]);
+      });
+
+      it('grad flows back to each input (2D, dim 1)', () => {
+        const x = new Tensor([[1, 2], [3, 4]], { requires_grad: true });
+        const y = new Tensor([[5], [6]], { requires_grad: true });
+        torch.cat([x, y], 1).sum().backward();
+        assert.deepStrictEqual(Array.from(x.grad.toFlatArray()), [1, 1, 1, 1]);
+        assert.deepStrictEqual(Array.from(y.grad.toFlatArray()), [1, 1]);
+      });
+
+      it('non-uniform upstream gradient is sliced correctly', () => {
+        const x = new Tensor([[1, 0], [0, 1]], { requires_grad: true });
+        const y = new Tensor([[2, 2]], { requires_grad: true });
+        const upstream = new Tensor([[1, 2], [3, 4], [5, 6]]);
+        torch.cat([x, y], 0).mul(upstream).sum().backward();
+        assert.deepStrictEqual(Array.from(x.grad.toFlatArray()), [1, 2, 3, 4]);
+        assert.deepStrictEqual(Array.from(y.grad.toFlatArray()), [5, 6]);
+      });
+
+      it('grad only flows to tensors with requires_grad=true', () => {
+        const x = new Tensor([1, 2, 3], { requires_grad: true });
+        const y = new Tensor([4, 5]);
+        torch.cat([x, y]).sum().backward();
+        assert.deepStrictEqual(Array.from(x.grad.toFlatArray()), [1, 1, 1]);
+        assert.isNull(y.grad);
+      });
+
+      it('grad flows through three inputs', () => {
+        const a = new Tensor([1, 2], { requires_grad: true });
+        const b = new Tensor([3], { requires_grad: true });
+        const c = new Tensor([4, 5, 6], { requires_grad: true });
+        torch.cat([a, b, c]).sum().backward();
+        assert.deepStrictEqual(Array.from(a.grad.toFlatArray()), [1, 1]);
+        assert.deepStrictEqual(Array.from(b.grad.toFlatArray()), [1]);
+        assert.deepStrictEqual(Array.from(c.grad.toFlatArray()), [1, 1, 1]);
+      });
+
+      it('cat result can be used in further computation', () => {
+        const x = new Tensor([2, 3], { requires_grad: true });
+        const y = new Tensor([4], { requires_grad: true });
+        torch.cat([x, y]).mul(new Tensor([1, 2, 3])).sum().backward();
+        assert.deepStrictEqual(Array.from(x.grad.toFlatArray()), [1, 2]);
+        assert.deepStrictEqual(Array.from(y.grad.toFlatArray()), [3]);
+      });
+    });
+
+    describe('tensor.cat method', () => {
+      it('tensor.cat(other) prepends self', () => {
+        const a = new Tensor([1, 2, 3]);
+        const b = new Tensor([4, 5]);
+        assert.deepStrictEqual(Array.from(a.cat(b).toFlatArray()), [1, 2, 3, 4, 5]);
+      });
+
+      it('tensor.cat([b, c]) prepends self before b and c', () => {
+        const a = new Tensor([1]);
+        const b = new Tensor([2, 3]);
+        const c = new Tensor([4]);
+        assert.deepStrictEqual(Array.from(a.cat([b, c]).toFlatArray()), [1, 2, 3, 4]);
+      });
+
+      it('tensor.cat with dim argument', () => {
+        const a = new Tensor([[1, 2], [3, 4]]);
+        const b = new Tensor([[5, 6]]);
+        assert.deepStrictEqual(a.cat(b, 0).shape, [3, 2]);
+      });
+
+      it('tensor.cat gradient flows back through self', () => {
+        const a = new Tensor([1, 2], { requires_grad: true });
+        const b = new Tensor([3, 4], { requires_grad: true });
+        a.cat(b).sum().backward();
+        assert.deepStrictEqual(Array.from(a.grad.toFlatArray()), [1, 1]);
+        assert.deepStrictEqual(Array.from(b.grad.toFlatArray()), [1, 1]);
+      });
+    });
+
+    describe('aliases (concatenate, concat)', () => {
+      it('torch.concatenate produces the same result as torch.cat', () => {
+        const a = new Tensor([1, 2, 3]);
+        const b = new Tensor([4, 5]);
+        assert.deepStrictEqual(
+          Array.from(torch.concatenate([a, b]).toFlatArray()),
+          Array.from(torch.cat([a, b]).toFlatArray())
+        );
+      });
+
+      it('torch.concat produces the same result as torch.cat', () => {
+        const a = new Tensor([1, 2, 3]);
+        const b = new Tensor([4, 5]);
+        assert.deepStrictEqual(
+          Array.from(torch.concat([a, b]).toFlatArray()),
+          Array.from(torch.cat([a, b]).toFlatArray())
+        );
+      });
+
+      it('tensor.concatenate is an alias for tensor.cat', () => {
+        const a = new Tensor([1, 2]);
+        const b = new Tensor([3]);
+        assert.deepStrictEqual(
+          Array.from(a.concatenate(b).toFlatArray()),
+          Array.from(a.cat(b).toFlatArray())
+        );
+      });
+
+      it('tensor.concat is an alias for tensor.cat', () => {
+        const a = new Tensor([1, 2]);
+        const b = new Tensor([3]);
+        assert.deepStrictEqual(
+          Array.from(a.concat(b).toFlatArray()),
+          Array.from(a.cat(b).toFlatArray())
+        );
+      });
+    });
+
+    describe('error handling', () => {
+      it('throws on empty tensor list', () => {
+        assert.throws(() => torch.cat([]), /non-empty/);
+      });
+
+      it('throws on zero-dimensional tensor', () => {
+        const a = new Tensor(1);
+        assert.throws(() => torch.cat([a, a]), /zero-dimensional/);
+      });
+
+      it('throws on mismatched ndim', () => {
+        const a = new Tensor([1, 2]);
+        const b = new Tensor([[1, 2]]);
+        assert.throws(() => torch.cat([a, b]), /dimensions/);
+      });
+
+      it('throws on mismatched non-cat dimension', () => {
+        const a = new Tensor([[1, 2, 3]]);
+        const b = new Tensor([[1, 2]]);
+        assert.throws(() => torch.cat([a, b], 0), /shape/);
+      });
+    });
+  });
+});
diff --git a/test/testcases.gen.js b/test/testcases.gen.js
new file mode 100644
index 00000000..ba3deaaa
--- /dev/null
+++ b/test/testcases.gen.js
@@ -0,0 +1,8351 @@
+export const testData = {
+  "unary": {
+    "log": [
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [0.23033303022384644, -1.1228563785552979, -0.18632829189300537],
+          [2.2082014083862305, -0.637997031211853, 0.46165722608566284]
+        ],
+        "expected_output": [
+          [-Infinity, 0.0, NaN],
+          [-1.468229055404663, NaN, NaN],
+          [0.7921783328056335, NaN, -0.7729325890541077]
+        ],
+        "expected_grad": [
+          [Infinity, 1.0, -1.0],
+          [4.3415398597717285, -0.8905858397483826, -5.366871356964111],
+          [0.45285725593566895, -1.5674054622650146, 2.166109323501587]
+        ]
+      },
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [1.110290288925171, -1.6897989511489868, -0.9889599084854126],
+          [0.9579718112945557, 1.322135090827942, 0.8171897530555725]
+        ],
+        "expected_output": [
+          [-Infinity, 0.0, NaN],
+          [0.10462149977684021, NaN, NaN],
+          [-0.04293692484498024, 0.279247909784317, -0.2018839567899704]
+        ],
+        "expected_grad": [
+          [Infinity, 1.0, -1.0],
+          [0.9006653428077698, -0.5917863845825195, -1.011163353919983],
+          [1.0438719987869263, 0.7563523650169373, 1.2237060070037842]
+        ]
+      },
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [0.6863219141960144, -0.32775864005088806, 0.7949687242507935],
+          [0.2815195620059967, 0.056163541972637177, 0.5227160453796387]
+        ],
+        "expected_output": [
+          [-Infinity, 0.0, NaN],
+          [-0.37640848755836487, NaN, -0.22945250570774078],
+          [-1.2675533294677734, -2.8794875144958496, -0.6487168669700623]
+        ],
+        "expected_grad": [
+          [Infinity, 1.0, -1.0],
+          [1.4570422172546387, -3.051025629043579, 1.2579110860824585],
+          [3.5521509647369385, 17.805145263671875, 1.913084626197815]
+        ]
+      }
+    ],
+    "sqrt": [
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [-0.008498823270201683, 0.7290605902671814, 0.13314196467399597],
+          [0.8639776706695557, -1.0156747102737427, -0.8887485265731812]
+        ],
+        "expected_output": [
+          [0.0, 1.0, NaN],
+          [NaN, 0.8538504242897034, 0.36488622426986694],
+          [0.9295039772987366, NaN, NaN]
+        ],
+        "expected_grad": [
+          [Infinity, 0.5, NaN],
+          [NaN, 0.5855826735496521, 1.3702901601791382],
+          [0.5379213094711304, NaN, NaN]
+        ]
+      },
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [0.9912377595901489, 0.4679030478000641, -0.20493191480636597],
+          [-0.7409071326255798, 0.3618396520614624, 1.9198992252349854]
+        ],
+        "expected_output": [
+          [0.0, 1.0, NaN],
+          [0.9956092238426208, 0.6840344071388245, NaN],
+          [NaN, 0.6015310883522034, 1.3856042623519897]
+        ],
+        "expected_grad": [
+          [Infinity, 0.5, NaN],
+          [0.5022050738334656, 0.7309573888778687, NaN],
+          [NaN, 0.8312122225761414, 0.3608534038066864]
+        ]
+      },
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [-0.6890136003494263, -1.1267184019088745, -0.2857550382614136],
+          [-1.0935109853744507, 1.1351022720336914, 0.7592452168464661]
+        ],
+        "expected_output": [
+          [0.0, 1.0, NaN],
+          [NaN, NaN, NaN],
+          [NaN, 1.0654118061065674, 0.8713467717170715]
+        ],
+        "expected_grad": [
+          [Infinity, 0.5, NaN],
+          [NaN, NaN, NaN],
+          [NaN, 0.46930208802223206, 0.5738243460655212]
+        ]
+      }
+    ],
+    "exp": [
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [0.960340142250061, -0.5671805739402771, -0.5706474184989929],
+          [1.5980384349822998, 0.11148621141910553, -0.03919669985771179]
+        ],
+        "expected_output": [
+          [1.0, 2.7182817459106445, 0.3678794503211975],
+          [2.6125850677490234, 0.5671221613883972, 0.5651594400405884],
+          [4.943326473236084, 1.1179382801055908, 0.9615615606307983]
+        ],
+        "expected_grad": [
+          [1.0, 2.7182817459106445, 0.3678794503211975],
+          [2.6125850677490234, 0.5671221613883972, 0.5651594400405884],
+          [4.943326473236084, 1.1179382801055908, 0.9615615606307983]
+        ]
+      },
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [-1.6270242929458618, -1.3951387405395508, -0.23872417211532593],
+          [-0.5049903988838196, -2.475163221359253, -0.931602954864502]
+        ],
+        "expected_output": [
+          [1.0, 2.7182817459106445, 0.3678794503211975],
+          [0.19651347398757935, 0.24779865145683289, 0.7876321077346802],
+          [0.603511393070221, 0.08414925634860992, 0.39392176270484924]
+        ],
+        "expected_grad": [
+          [1.0, 2.7182817459106445, 0.3678794503211975],
+          [0.19651347398757935, 0.24779865145683289, 0.7876321077346802],
+          [0.603511393070221, 0.08414925634860992, 0.39392176270484924]
+        ]
+      },
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [-0.09089037775993347, -1.3296922445297241, -0.542582631111145],
+          [0.5470984578132629, 0.6430637240409851, -0.7904810905456543]
+        ],
+        "expected_output": [
+          [1.0, 2.7182817459106445, 0.3678794503211975],
+          [0.9131178259849548, 0.26455867290496826, 0.5812451839447021],
+          [1.7282311916351318, 1.9023001194000244, 0.45362651348114014]
+        ],
+        "expected_grad": [
+          [1.0, 2.7182817459106445, 0.3678794503211975],
+          [0.9131178259849548, 0.26455867290496826, 0.5812451839447021],
+          [1.7282311916351318, 1.9023001194000244, 0.45362651348114014]
+        ]
+      }
+    ],
+    "square": [
+      {
+        "input": [
+          [0.0, 1.0, -1.0],
+          [2.117400646209717, -1.711801528930664, 0.16513441503047943],
+          [1.5818675756454468, 0.44846096634864807, 0.033029891550540924]
+        ],
+        "expected_output": [
+          [0.0, 1.0, 1.0],
+          [4.4833855628967285, 2.930264472961426, 0.027269374579191208],
+          [2.502305030822754, 0.20111723244190216, 0.0010909737320616841]
+        ],
+        "expected_grad": [
+          [0.0, 2.0, -2.0],
+          [4.234801292419434, -3.423603057861328, 0.33026883006095886],
+          [3.1637351512908936, 0.8969219326972961, 0.06605978310108185]
+        ]
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+          [1.0, 1.0, 0.0, 1.0]
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+  "broadcasting": [
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+        [0.31103116273880005, 1.7946441173553467, 3.19868803024292]
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+    },
+    {
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+      "input_x": 1.5,
+      "input_y": [
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+    },
+    {
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+      "input_x": 1.5,
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+  "matmul": [
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+      "op_name": "sum",
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+    {
+      "test_name": "sum_dim_0_keepdim_True",
+      "op_name": "sum",
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+      "input": [
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+          [1.0, 1.0, 1.0, 1.0, 1.0]
+        ]
+      ]
+    },
+    {
+      "test_name": "sum_dim_1_keepdim_False",
+      "op_name": "sum",
+      "dim": 1,
+      "keepdim": false,
+      "input": [
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+            [2.0, 0.0, 4.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0],
+            [0.0, 2.0, 0.0, 1.0]
+          ]
+        ]
+      ]
+    },
+    {
+      "test_name": "maxpool_padding_1",
+      "kernel_size": 3,
+      "stride": 1,
+      "padding": 1,
+      "input": [
+        [
+          [
+            [1.3967915773391724, 0.9178156852722168, 0.5120382905006409, -0.8405776023864746],
+            [-1.0445209741592407, 0.5547724366188049, -1.1883097887039185, -0.5416104793548584],
+            [-1.1297553777694702, -2.800556182861328, 1.2796905040740967, 0.2199985533952713],
+            [0.3249095380306244, 1.319007396697998, -0.5861101150512695, 0.7809817790985107]
+          ]
+        ]
+      ],
+      "expected_output": [
+        [
+          [
+            [1.3967915773391724, 1.3967915773391724, 0.9178156852722168, 0.5120382905006409],
+            [1.3967915773391724, 1.3967915773391724, 1.2796905040740967, 1.2796905040740967],
+            [1.319007396697998, 1.319007396697998, 1.319007396697998, 1.2796905040740967],
+            [1.319007396697998, 1.319007396697998, 1.319007396697998, 1.2796905040740967]
+          ]
+        ]
+      ],
+      "expected_grad": [
+        [
+          [
+            [4.0, 1.0, 1.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 4.0, 0.0],
+            [0.0, 6.0, 0.0, 0.0]
+          ]
+        ]
+      ]
+    },
+    {
+      "test_name": "maxpool_multichannel",
+      "kernel_size": 2,
+      "stride": 2,
+      "padding": 0,
+      "input": [
+        [
+          [
+            [-0.20516234636306763, -0.7811664342880249, 0.6872723698616028, 0.7835897207260132, -1.1108732223510742, -3.106328010559082],
+            [-0.5377766489982605, -0.4870067834854126, -0.7153372168540955, -0.467404842376709, 0.551419734954834, 2.654942035675049],
+            [1.0582451820373535, -0.1468161642551422, -1.318347454071045, -1.2894185781478882, 1.9681813716888428, -0.7403607368469238],
+            [-0.8665743470191956, -0.30639445781707764, -0.5359372496604919, -0.35750812292099, -1.4471426010131836, -1.6966946125030518],
+            [-0.8158796429634094, 1.1372771263122559, 0.21925963461399078, 0.4133651852607727, 0.0061524491757154465, -0.5672794580459595],
+            [0.10004822909832001, -0.2485211342573166, -1.2868070602416992, -1.3662828207015991, -0.04625223949551582, -0.6149584650993347]
+          ],
+          [
+            [1.2366944551467896, -0.8143561482429504, -0.7984835505485535, 0.5182347893714905, -0.036672789603471756, 0.6718097925186157],
+            [0.9242297410964966, 0.26971569657325745, 0.628537118434906, -0.7066188454627991, -0.03767240047454834, -1.4856157302856445],
+            [-0.5659265518188477, 0.3840969204902649, -0.7815778851509094, -0.15094131231307983, 0.4199317395687103, 1.4059346914291382],
+            [1.3185068368911743, 2.849227712431457e-05, -1.0952261686325073, 1.1855055093765259, 1.1880545616149902, 2.055619716644287],
+            [0.6602651476860046, -1.1078379154205322, 0.45688942074775696, -0.6034095287322998, -0.3147018849849701, -0.41328999400138855],
+            [0.3946205973625183, 1.1304746866226196, 0.8258382678031921, 0.9458276629447937, -1.1293034553527832, -1.6514161825180054]
+          ],
+          [
+            [-0.059470854699611664, -0.9928666353225708, 1.1634254455566406, 1.609459400177002, -0.29416555166244507, 1.0819309949874878],
+            [0.6690162420272827, 0.6591296792030334, -0.27264782786369324, 0.9804211258888245, -0.17533333599567413, -0.12276917695999146],
+            [0.7414069175720215, 0.35395190119743347, -1.4793612957000732, -0.7297910451889038, -2.9890658855438232, 0.18370741605758667],
+            [-0.4726978540420532, -0.958824634552002, -1.5123530626296997, 1.5067633390426636, 1.6462355852127075, -0.6936932802200317],
+            [0.21946865320205688, -0.439242959022522, -0.1391070932149887, -0.018692156299948692, 1.6560721397399902, 1.0661178827285767],
+            [-1.1016371250152588, 1.3902591466903687, 0.514220118522644, -0.15104348957538605, 0.13773603737354279, 1.2250828742980957]
+          ]
+        ],
+        [
+          [
+            [-0.7642602920532227, 0.9183834791183472, -0.5463470220565796, 0.5569576621055603, 0.1281542032957077, -0.19802889227867126],
+            [-1.478035569190979, -0.5910253524780273, 0.8357481956481934, -0.22924941778182983, 1.6714451313018799, -0.4941222369670868],
+            [-1.1415528059005737, 0.7821402549743652, 0.010817415080964565, 0.3816293179988861, -1.652677059173584, -0.38139888644218445],
+            [0.6544674038887024, -0.14682821929454803, 0.08301469683647156, 0.712009072303772, -0.9005926847457886, 0.8906894326210022],
+            [0.47655177116394043, -0.8396266102790833, -0.4006587862968445, -1.5424505472183228, -0.5745509266853333, -1.9059150218963623],
+            [-0.9665390253067017, 0.367727130651474, -0.5785751342773438, 1.2373405694961548, 0.5358533263206482, 0.5413376092910767]
+          ],
+          [
+            [1.2400078773498535, -0.905766487121582, 0.768028736114502, 1.62221360206604, 0.08158037811517715, 0.20281589031219482],
+            [-0.31887876987457275, -0.4908296763896942, 1.5734566450119019, 1.8697383403778076, -1.0638948678970337, -0.2272576242685318],
+            [0.25006136298179626, 1.161847710609436, -0.13073162734508514, -1.4062258005142212, 0.8497498631477356, -0.8599120378494263],
+            [-0.6105663776397705, 1.0629346370697021, 1.2221823930740356, 0.771891176700592, -1.01389479637146, -0.5315045118331909],
+            [-0.6020243763923645, 0.3213997781276703, -0.060615699738264084, -1.170371651649475, -2.773613929748535, -0.02982438914477825],
+            [1.2784193754196167, -1.5633939504623413, 1.8777929544448853, 0.5223742127418518, 0.051756951957941055, 0.4260155260562897]
+          ],
+          [
+            [0.9475129246711731, 0.4364280700683594, -1.022117257118225, 0.16795289516448975, 0.5066304802894592, 0.2779245674610138],
+            [1.351500153541565, -0.8949641585350037, -1.596110463142395, 0.673722505569458, -0.6978610754013062, 1.7838830947875977],
+            [0.2176828533411026, 1.1277714967727661, -1.5005147457122803, -0.2404831200838089, -0.4854878783226013, -0.06616523861885071],
+            [0.9090389609336853, 0.29768696427345276, 1.5183887481689453, -0.91494220495224, 2.251573324203491, -0.3680903911590576],
+            [-1.0800817012786865, 1.9881891012191772, 0.1034913882613182, 0.11661393195390701, 0.04335293173789978, 0.580488383769989],
+            [0.6679447293281555, 0.6449635624885559, -1.5224734544754028, -2.6157381534576416, -0.5713837146759033, -0.6671661734580994]
+          ]
+        ]
+      ],
+      "expected_output": [
+        [
+          [
+            [-0.20516234636306763, 0.7835897207260132, 2.654942035675049],
+            [1.0582451820373535, -0.35750812292099, 1.9681813716888428],
+            [1.1372771263122559, 0.4133651852607727, 0.0061524491757154465]
+          ],
+          [
+            [1.2366944551467896, 0.628537118434906, 0.6718097925186157],
+            [1.3185068368911743, 1.1855055093765259, 2.055619716644287],
+            [1.1304746866226196, 0.9458276629447937, -0.3147018849849701]
+          ],
+          [
+            [0.6690162420272827, 1.609459400177002, 1.0819309949874878],
+            [0.7414069175720215, 1.5067633390426636, 1.6462355852127075],
+            [1.3902591466903687, 0.514220118522644, 1.6560721397399902]
+          ]
+        ],
+        [
+          [
+            [0.9183834791183472, 0.8357481956481934, 1.6714451313018799],
+            [0.7821402549743652, 0.712009072303772, 0.8906894326210022],
+            [0.47655177116394043, 1.2373405694961548, 0.5413376092910767]
+          ],
+          [
+            [1.2400078773498535, 1.8697383403778076, 0.20281589031219482],
+            [1.161847710609436, 1.2221823930740356, 0.8497498631477356],
+            [1.2784193754196167, 1.8777929544448853, 0.4260155260562897]
+          ],
+          [
+            [1.351500153541565, 0.673722505569458, 1.7838830947875977],
+            [1.1277714967727661, 1.5183887481689453, 2.251573324203491],
+            [1.9881891012191772, 0.11661393195390701, 0.580488383769989]
+          ]
+        ]
+      ],
+      "expected_grad": [
+        [
+          [
+            [1.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
+            [1.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+            [0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+            [0.0, 1.0, 0.0, 1.0, 1.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+          ],
+          [
+            [1.0, 0.0, 0.0, 0.0, 0.0, 1.0],
+            [0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [1.0, 0.0, 0.0, 1.0, 0.0, 1.0],
+            [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+            [0.0, 1.0, 0.0, 1.0, 0.0, 0.0]
+          ],
+          [
+            [0.0, 0.0, 0.0, 1.0, 0.0, 1.0],
+            [1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 1.0, 1.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+            [0.0, 1.0, 1.0, 0.0, 0.0, 0.0]
+          ]
+        ],
+        [
+          [
+            [0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 1.0, 0.0, 1.0, 0.0],
+            [0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 1.0, 0.0, 1.0],
+            [1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 1.0, 0.0, 1.0]
+          ],
+          [
+            [1.0, 0.0, 0.0, 0.0, 0.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+            [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+            [0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [1.0, 0.0, 1.0, 0.0, 0.0, 1.0]
+          ],
+          [
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [1.0, 0.0, 0.0, 1.0, 0.0, 1.0],
+            [0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 1.0, 0.0, 1.0, 0.0],
+            [0.0, 1.0, 0.0, 1.0, 0.0, 1.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+          ]
+        ]
+      ]
+    },
+    {
+      "test_name": "maxpool_3d_input",
+      "kernel_size": 2,
+      "stride": 2,
+      "padding": 0,
+      "input": [
+        [
+          [-0.020498022437095642, 0.48795756697654724, 0.37779226899147034, -1.057392954826355],
+          [2.5331664085388184, -1.1566962003707886, -1.0765177011489868, -0.5477657318115234],
+          [1.5677039623260498, 0.4278451204299927, -0.2732718884944916, 0.5940448641777039],
+          [0.9717037081718445, -0.40528324246406555, -0.2497664988040924, 0.22397132217884064]
+        ]
+      ],
+      "expected_output": [
+        [
+          [2.5331664085388184, 0.37779226899147034],
+          [1.5677039623260498, 0.5940448641777039]
+        ]
+      ],
+      "expected_grad": [
+        [
+          [0.0, 0.0, 1.0, 0.0],
+          [1.0, 0.0, 0.0, 0.0],
+          [1.0, 0.0, 0.0, 1.0],
+          [0.0, 0.0, 0.0, 0.0]
+        ]
+      ]
+    }
+  ]
+};
diff --git a/test/torch-bridge.js b/test/torch-bridge.js
new file mode 100644
index 00000000..4c6c82c4
--- /dev/null
+++ b/test/torch-bridge.js
@@ -0,0 +1,42 @@
+const _torch = window.torch;
+
+export const {
+  Tensor,
+  tensor,
+  no_grad,
+  enable_no_grad,
+  disable_no_grad,
+  is_grad_enabled,
+  TorchFunction,
+  AccumulateGrad,
+  nn,
+  optim,
+  eventBus,
+  events,
+  export_,
+  ExportedProgram,
+  add,
+  cat,
+  concat,
+  concatenate,
+  matmul,
+  relu,
+  mul,
+  randn,
+  allclose,
+  numel,
+  softmax,
+  clip,
+  clamp,
+  seed,
+  manual_seed,
+  randperm,
+  rand,
+  ones,
+  ones_like,
+  zeros,
+  zeros_like,
+  __right_index__,
+  __left_index__,
+  // etc...
+} = window.torch;
diff --git a/test/typed_tensor.test.ts b/test/typed_tensor.test.ts
new file mode 100644
index 00000000..6b2683ae
--- /dev/null
+++ b/test/typed_tensor.test.ts
@@ -0,0 +1,70 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+import { Tensor, FloatTensor, LongTensor } from 'torch';
+
+describe('FloatTensor', () => {
+  it('is an instance of Tensor', () => {
+    assert.instanceOf(new FloatTensor([1.5, 2.5]), Tensor);
+  });
+
+  it('preserves float values unchanged', () => {
+    const data = new FloatTensor([1.1, 2.9, -3.7]).toArray() as number[];
+    assert.closeTo(data[0],  1.1, 1e-9);
+    assert.closeTo(data[1],  2.9, 1e-9);
+    assert.closeTo(data[2], -3.7, 1e-9);
+  });
+
+  it('works with nested (2D) data', () => {
+    assert.deepStrictEqual(new FloatTensor([[1.5, 2.5], [3.5, 4.5]]).shape, [2, 2]);
+  });
+
+  it('accepts requires_grad option', () => {
+    assert.isTrue(new FloatTensor([1.0, 2.0], { requires_grad: true }).requires_grad);
+  });
+
+  it('participates in autograd', () => {
+    const t = new FloatTensor([2.0, 3.0], { requires_grad: true });
+    t.sum().backward();
+    assert.deepStrictEqual((t.grad!.toArray() as number[]), [1, 1]);
+  });
+
+  it('also accessible as torch.FloatTensor', () => {
+    assert.instanceOf(new torch.FloatTensor([1.5, 2.5]), Tensor);
+  });
+});
+
+describe('LongTensor', () => {
+  it('is an instance of Tensor', () => {
+    assert.instanceOf(new LongTensor([1, 2, 3]), Tensor);
+  });
+
+  it('truncates positive floats toward zero', () => {
+    assert.deepStrictEqual(new LongTensor([1.1, 1.9, 2.0]).toArray(), [1, 1, 2]);
+  });
+
+  it('truncates negative floats toward zero (not floor)', () => {
+    assert.deepStrictEqual(new LongTensor([-1.1, -1.9, -2.0]).toArray(), [-1, -1, -2]);
+  });
+
+  it('works with nested (2D) data', () => {
+    const t = new LongTensor([[1.7, 2.3], [3.9, -4.1]]);
+    assert.deepStrictEqual(t.shape, [2, 2]);
+    assert.deepStrictEqual(t.toArray(), [[1, 2], [3, -4]]);
+  });
+
+  it('accepts integer data unchanged', () => {
+    assert.deepStrictEqual(new LongTensor([0, 1, 2, 3]).toArray(), [0, 1, 2, 3]);
+  });
+
+  it('accepts requires_grad option and truncates', () => {
+    const t = new LongTensor([1.5, 2.5], { requires_grad: true });
+    assert.isTrue(t.requires_grad);
+    assert.deepStrictEqual(t.toArray(), [1, 2]);
+  });
+
+  it('also accessible as torch.LongTensor', () => {
+    const t = new torch.LongTensor([1.9, -1.9]);
+    assert.instanceOf(t, Tensor);
+    assert.deepStrictEqual(t.toArray(), [1, -1]);
+  });
+});
diff --git a/test/umd.html b/test/umd.html
new file mode 100644
index 00000000..57dddfb6
--- /dev/null
+++ b/test/umd.html
@@ -0,0 +1,48 @@
+
+
+
+
+    
+    Mocha Tests
+    
+    
+
+
+
+    

+
+    
+    
+
+    
+
+    
+    
+    
+
+
+
diff --git a/test/view.test.ts b/test/view.test.ts
new file mode 100644
index 00000000..0671c3b3
--- /dev/null
+++ b/test/view.test.ts
@@ -0,0 +1,269 @@
+import { assert } from 'chai';
+import * as torch from 'torch';
+import { Tensor } from 'torch';
+
+describe('Tensor views via index()', () => {
+
+  // ─── shape and values ───────────────────────────────────────────────────────
+
+  describe('shape and values', () => {
+    it('2D → 1D: correct shape and values', () => {
+      const x = torch.tensor([[1, 2, 3], [4, 5, 6]]);
+      const y = x.index(0);
+      assert.deepStrictEqual(y.shape, [3]);
+      assert.deepStrictEqual(y.toArray(), [1, 2, 3]);
+    });
+
+    it('2D → 1D: second row', () => {
+      const x = torch.tensor([[1, 2, 3], [4, 5, 6]]);
+      const y = x.index(1);
+      assert.deepStrictEqual(y.shape, [3]);
+      assert.deepStrictEqual(y.toArray(), [4, 5, 6]);
+    });
+
+    it('3D → 2D: correct shape and values', () => {
+      const x = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]);
+      const y = x.index(1);
+      assert.deepStrictEqual(y.shape, [2, 2]);
+      assert.deepStrictEqual(y.toArray(), [[5, 6], [7, 8]]);
+    });
+
+    it('1D → scalar: shape [] and correct value', () => {
+      const x = torch.tensor([10, 20, 30]);
+      const s = x.index(1);
+      assert.deepStrictEqual(s.shape, []);
+      assert.strictEqual(s.item(), 20);
+    });
+
+    it('negative index: last row', () => {
+      const x = torch.tensor([[1, 2], [3, 4], [5, 6]]);
+      const y = x.index(-1);
+      assert.deepStrictEqual(y.shape, [2]);
+      assert.deepStrictEqual(y.toArray(), [5, 6]);
+    });
+
+    it('negative index: second-to-last row', () => {
+      const x = torch.tensor([[1, 2], [3, 4], [5, 6]]);
+      const y = x.index(-2);
+      assert.deepStrictEqual(y.shape, [2]);
+      assert.deepStrictEqual(y.toArray(), [3, 4]);
+    });
+  });
+
+  // ─── error cases ────────────────────────────────────────────────────────────
+
+  describe('error cases', () => {
+    it('throws on scalar tensor', () => {
+      const s = torch.tensor(5);
+      assert.throws(() => s.index(0), /scalar/i);
+    });
+
+    it('throws on out-of-bounds positive index', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      assert.throws(() => x.index(2), /out of bounds/i);
+    });
+
+    it('throws on out-of-bounds negative index', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      assert.throws(() => x.index(-3), /out of bounds/i);
+    });
+  });
+
+  // ─── data sharing: parent → view ────────────────────────────────────────────
+
+  describe('data sharing: parent mutations visible in view', () => {
+    it('zero_() on parent zeros out the view', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(0); // view of row 0
+      x.zero_();
+      assert.deepStrictEqual(y.toArray(), [0, 0]);
+    });
+
+    it('data setter on parent is visible in view', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(1); // view of row 1
+      x.data = [10, 20, 30, 40];
+      assert.deepStrictEqual(y.toArray(), [30, 40]);
+    });
+  });
+
+  // ─── data sharing: view → parent ────────────────────────────────────────────
+
+  describe('data sharing: view mutations visible in parent', () => {
+    it('zero_() on view zeros the corresponding row of parent', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(0);
+      y.zero_();
+      assert.deepStrictEqual(x.toArray(), [[0, 0], [3, 4]]);
+    });
+
+    it('zero_() on second-row view leaves first row intact', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(1);
+      y.zero_();
+      assert.deepStrictEqual(x.toArray(), [[1, 2], [0, 0]]);
+    });
+
+    it('data setter on view writes into parent storage', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(0);
+      y.data = [99, 88];
+      assert.deepStrictEqual(x.toArray(), [[99, 88], [3, 4]]);
+    });
+
+    it('multiple views are all linked to the same storage', () => {
+      const x = torch.tensor([[1, 2], [3, 4], [5, 6]]);
+      const row0 = x.index(0);
+      const row2 = x.index(2);
+      row0.zero_();
+      row2.data = [9, 9];
+      assert.deepStrictEqual(x.toArray(), [[0, 0], [3, 4], [9, 9]]);
+      assert.deepStrictEqual(row0.toArray(), [0, 0]);
+      assert.deepStrictEqual(row2.toArray(), [9, 9]);
+    });
+
+    it('3D → 2D view: zero_() on view updates parent', () => {
+      const x = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]);
+      const y = x.index(0); // [[1,2],[3,4]]
+      y.zero_();
+      assert.deepStrictEqual(x.toArray(), [[[0, 0], [0, 0]], [[5, 6], [7, 8]]]);
+    });
+  });
+
+  // ─── operations on views ─────────────────────────────────────────────────────
+
+  describe('operations on views produce correct results', () => {
+    it('add on a view gives correct values', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(1); // [3, 4]
+      const z = y.add(torch.tensor([10, 10]));
+      assert.deepStrictEqual(z.toArray(), [13, 14]);
+      // original unchanged
+      assert.deepStrictEqual(x.toArray(), [[1, 2], [3, 4]]);
+    });
+
+    it('mul on a view gives correct values', () => {
+      const x = torch.tensor([[2, 3], [4, 5]]);
+      const y = x.index(0); // [2, 3]
+      const z = y.mul(torch.tensor([2, 3]));
+      assert.deepStrictEqual(z.toArray(), [4, 9]);
+    });
+
+    it('sum on a view gives correct scalar', () => {
+      const x = torch.tensor([[1, 2, 3], [4, 5, 6]]);
+      const y = x.index(1); // [4, 5, 6]
+      assert.strictEqual(y.sum().item(), 15);
+    });
+
+    it('matmul on a 1D view gives correct dot product', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(0); // [1, 2]
+      const z = x.index(1); // [3, 4]
+      const dot = y.matmul(z); // 1*3 + 2*4 = 11
+      assert.strictEqual(dot.item(), 11);
+    });
+  });
+
+  // ─── detach() on a view ───────────────────────────────────────────────────────
+
+  describe('detach() on a view creates an independent copy', () => {
+    it('detached tensor has the same values', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(0);
+      const d = y.detach();
+      assert.deepStrictEqual(d.toArray(), [1, 2]);
+    });
+
+    it('mutating the detached tensor does not affect parent', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(0);
+      const d = y.detach();
+      d.zero_();
+      // x and y should be unchanged
+      assert.deepStrictEqual(x.toArray(), [[1, 2], [3, 4]]);
+      assert.deepStrictEqual(y.toArray(), [1, 2]);
+    });
+
+    it('mutating the parent does not affect the detached copy', () => {
+      const x = torch.tensor([[1, 2], [3, 4]]);
+      const y = x.index(0);
+      const d = y.detach();
+      x.zero_();
+      assert.deepStrictEqual(d.toArray(), [1, 2]);
+    });
+  });
+
+  // ─── optimizer step propagates through shared storage ────────────────────────
+
+  describe('optimizer step: views see updated parameter values', () => {
+    it('SGD step: view of param reflects new values', () => {
+      // param shape [2, 2], create view of first row before the step
+      const param = new torch.nn.Parameter(torch.tensor([[1, 2], [3, 4]]));
+      const row0_before = param.index(0).toArray() as number[];
+      assert.deepStrictEqual(row0_before, [1, 2]);
+
+      // Simulate what the optimizer does: param.data = newParam.data
+      const newValues = [10, 20, 30, 40];
+      param.data = newValues;
+
+      // A view created AFTER the update sees the new values
+      const row1_after = param.index(1);
+      assert.deepStrictEqual(row1_after.toArray(), [30, 40]);
+
+      // A view created BEFORE the update also sees the new values (shared storage)
+      const row0_view = param.index(0);
+      assert.deepStrictEqual(row0_view.toArray(), [10, 20]);
+    });
+
+    it('full SGD training step does not corrupt parameter shape', () => {
+      class Linear extends torch.nn.Module {
+        w: torch.nn.Parameter;
+        constructor() {
+          super();
+          this.w = new torch.nn.Parameter(torch.tensor([[1.0, 0.0], [0.0, 1.0]]));
+          this.register('w', this.w);
+        }
+        forward(x: Tensor): Tensor {
+          return x.matmul(this.w);
+        }
+      }
+
+      const model = new Linear();
+      const optim = new torch.optim.SGD(model.parameters(), 0.1);
+
+      const x = torch.tensor([[1.0, 2.0]]);
+      const y = model.forward(x);
+      const loss = y.sum();
+      loss.backward();
+
+      optim.step();
+
+      // After optimizer step, param shape must be preserved
+      assert.deepStrictEqual(model.w.shape, [2, 2]);
+      assert.strictEqual(model.w.dataLength(), 4);
+
+      // View of updated param must have correct shape and values
+      const row0 = model.w.index(0);
+      assert.deepStrictEqual(row0.shape, [2]);
+      assert.strictEqual(row0.dataLength(), 2);
+    });
+  });
+
+  // ─── chained index ────────────────────────────────────────────────────────────
+
+  describe('chained index()', () => {
+    it('index into a view gives correct scalar', () => {
+      const x = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]);
+      // x.index(1) → [[5,6],[7,8]], then .index(0) → [5,6], then .index(1) → 6
+      const val = x.index(1).index(0).index(1);
+      assert.deepStrictEqual(val.shape, []);
+      assert.strictEqual(val.item(), 6);
+    });
+
+    it('zero_() on doubly-chained view updates root storage', () => {
+      const x = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]);
+      x.index(0).index(1).zero_(); // zero out [3,4]
+      assert.deepStrictEqual(x.toArray(), [[[1, 2], [0, 0]], [[5, 6], [7, 8]]]);
+    });
+  });
+});
diff --git a/tsconfig.build.json b/tsconfig.build.json
new file mode 100644
index 00000000..23bf1dc0
--- /dev/null
+++ b/tsconfig.build.json
@@ -0,0 +1,10 @@
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "outDir": "build/types",
+    "declaration": true,
+    "emitDeclarationOnly": true,
+    "noEmit": false
+  },
+  "include": ["src"]
+}
diff --git a/tsconfig.json b/tsconfig.json
new file mode 100644
index 00000000..27bd2a93
--- /dev/null
+++ b/tsconfig.json
@@ -0,0 +1,17 @@
+{
+  "compilerOptions": {
+    "target": "ESNext",
+    "useDefineForClassFields": true,
+    "module": "ESNext",
+    "lib": ["ESNext", "DOM"],
+    "moduleResolution": "bundler",
+    "strict": false,
+    "noImplicitAny": false,
+    "resolveJsonModule": true,
+    "esModuleInterop": true,
+    "noEmit": true,
+    "skipLibCheck": true,
+    "types": ["node", "mocha"]
+  },
+  "include": ["src", "test", "vite.config.browser.ts", "vite.config.node.ts"]
+}
diff --git a/vite.config.browser.ts b/vite.config.browser.ts
new file mode 100644
index 00000000..9122ad90
--- /dev/null
+++ b/vite.config.browser.ts
@@ -0,0 +1,22 @@
+import { defineConfig } from 'vite';
+import path from 'path';
+
+export default defineConfig({
+  build: {
+    minify: false,
+    sourcemap: true,
+    lib: {
+      entry: path.resolve(__dirname, 'src/index.ts'),
+      name: 'torch',
+      fileName: (format) => `torch.browser.${format}.js`,
+      formats: ['es'] // umd -> see cdn build
+    },
+    outDir: 'build/browser',
+    rollupOptions: {
+      treeshake: true,
+    },
+  },
+  esbuild: {
+    keepNames: true,
+  }
+});
diff --git a/vite.config.cdn.ts b/vite.config.cdn.ts
new file mode 100644
index 00000000..dbfcb741
--- /dev/null
+++ b/vite.config.cdn.ts
@@ -0,0 +1,20 @@
+import { defineConfig } from 'vite';
+import path from 'path';
+
+export default defineConfig({
+  build: {
+    minify: 'esbuild',
+    sourcemap: true,
+    lib: {
+      entry: path.resolve(__dirname, 'src/index.ts'),
+      name: 'torch',
+      fileName: () => `torch.min.js`,
+      formats: ['umd']
+    },
+    emptyOutDir: false,
+    outDir: 'build',
+  },
+  esbuild: {
+    keepNames: true,
+  }
+});
diff --git a/vite.config.node.ts b/vite.config.node.ts
new file mode 100644
index 00000000..174682bc
--- /dev/null
+++ b/vite.config.node.ts
@@ -0,0 +1,16 @@
+import { defineConfig } from 'vite';
+import path from 'path';
+
+export default defineConfig({
+  build: {
+    minify: false, // don't minify for npm package
+    sourcemap: true,
+    lib: {
+      entry: path.resolve(__dirname, 'src/index.ts'),
+      fileName: (format) => format === 'es' ? 'torch.node.es.mjs' : 'torch.node.cjs',
+      formats: ['es', 'cjs']
+    },
+    outDir: 'build/node',
+    target: 'node20',
+  },
+});
diff --git a/yarn.lock b/yarn.lock
new file mode 100644
index 00000000..f10f0aaa
--- /dev/null
+++ b/yarn.lock
@@ -0,0 +1,5855 @@
+# This file is generated by running "yarn install" inside your project.
+# Manual changes might be lost - proceed with caution!
+
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+  version: 8
+  cacheKey: 10c0
+
+"@actions/core@npm:^1.11.1":
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